Mean squared error—S&P 500.
\\n\\n
These books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\\n\\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\\n\\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
\\n\\n\\n\\n\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
IntechOpen and Knowledge Unlatched formed a partnership to support researchers working in engineering sciences by enabling an easier approach to publishing Open Access content. Using the Knowledge Unlatched crowdfunding model to raise the publishing costs through libraries around the world, Open Access Publishing Fee (OAPF) was not required from the authors.
\n\nInitially, the partnership supported engineering research, but it soon grew to include physical and life sciences, attracting more researchers to the advantages of Open Access publishing.
\n\n\n\nThese books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\n\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\n\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
\n\n\n\n\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"4785",leadTitle:null,fullTitle:"Cellulose - Fundamental Aspects and Current Trends",title:"Cellulose",subtitle:"Fundamental Aspects and Current Trends",reviewType:"peer-reviewed",abstract:"Cellulose - Fundamental Aspects and Current Trends consists of 10 chapters written by international subject matter experts investigating the characteristics and current applications of this fascinating material. This book will help the reader to develop a deeper understanding about the concepts related to cellulose and the nanocellulose structure, modification, production, dissolution, and application. Biosynthesis mechanisms and medical applications of microbial cellulose are also discussed. This book will serve as the starting point for materials science researchers, engineers, and technologists from diverse backgrounds in physics, chemistry, biology, materials science, and engineering who want to know and better understand the unique characteristics of the most abundant biopolymer on earth.",isbn:null,printIsbn:"978-953-51-2229-6",pdfIsbn:"978-953-51-5059-6",doi:"10.5772/59889",price:119,priceEur:129,priceUsd:155,slug:"cellulose-fundamental-aspects-and-current-trends",numberOfPages:284,isOpenForSubmission:!1,isInWos:1,isInBkci:!1,hash:"e1dcc6cb2bc929641d711f320ac52970",bookSignature:"Matheus Poletto and Heitor Luiz Ornaghi Junior",publishedDate:"December 9th 2015",coverURL:"https://cdn.intechopen.com/books/images_new/4785.jpg",numberOfDownloads:43183,numberOfWosCitations:200,numberOfCrossrefCitations:113,numberOfCrossrefCitationsByBook:9,numberOfDimensionsCitations:269,numberOfDimensionsCitationsByBook:12,hasAltmetrics:1,numberOfTotalCitations:582,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 17th 2014",dateEndSecondStepPublish:"December 8th 2014",dateEndThirdStepPublish:"June 25th 2015",dateEndFourthStepPublish:"August 12th 2015",dateEndFifthStepPublish:"December 31st 2015",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"140017",title:"Dr.",name:"Matheus",middleName:null,surname:"Poletto",slug:"matheus-poletto",fullName:"Matheus Poletto",profilePictureURL:"https://mts.intechopen.com/storage/users/140017/images/4423_n.jpg",biography:"Matheus Poletto is professor and researcher at Universidade de Caxias do Sul. He is an expert in composite science, working with thermoplastic composites and cellulosic materials. He has published over 80 scientific articles and conference papers and several book chapters. He obtained his bachelor’s degree in Chemical Engineering and master’s degree in Materials Science and Engineering from Universidade de Caxias do Sul, Brazil, and his PhD in Materials Engineering from Universidade Federal do Rio Grande do Sul, Brazil. Professor Poletto currently works with cellulosic and lignocellulosic materials, studying the effect of fiber composition on the thermal and mechanical properties of polymer composite materials. He is also member of the Polymer Laboratory Research Group at Universidade de Caxias do Sul.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"3",institution:{name:"University of Caxias Do Sul",institutionURL:null,country:{name:"Brazil"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"494",title:"Bioorganic Chemistry",slug:"organic-chemistry-bioorganic-chemistry"}],chapters:[{id:"49327",title:"From Cellulose Dissolution and Regeneration to Added Value Applications — Synergism Between Molecular Understanding and Material Development",doi:"10.5772/61402",slug:"from-cellulose-dissolution-and-regeneration-to-added-value-applications-synergism-between-molecular-",totalDownloads:3414,totalCrossrefCites:4,totalDimensionsCites:18,hasAltmetrics:0,abstract:"Modern society is now demanding “greener” materials due to depleting fossil fuels and increasing environmental awareness. In the near future, industries will need to become more resource-conscious by making greater use of available renewable and sustainable raw materials. In this context, agro-forestry and related industries can indeed contribute to solve many resource challenges for society and suppliers in the near future. Thus, cellulose can be predicted to become an important resource for materials due to its abundance and versatility as a biopolymer. Cellulose is found in many different forms and applications. However, the dissolution and regeneration of cellulose are key (and challenging) aspects in many potential applications. This chapter is divided into two parts: (i) achievements in the field of dissolution and regeneration of cellulose including solvents and underlying mechanisms of dissolution; and (ii) state-of-the-art production of value-added materials and their applications including manmade textile fibers, hydrogels, aerogels, and all-cellulose composites, where the latter is given special attention.",signatures:"Poonam Singh, Hugo Duarte, Luís Alves, Filipe Antunes, Nicolas Le\nMoigne, Jan Dormanns, Benoît Duchemin, Mark P. Staiger and\nBruno Medronho",downloadPdfUrl:"/chapter/pdf-download/49327",previewPdfUrl:"/chapter/pdf-preview/49327",authors:[{id:"109477",title:"Dr.",name:"Filipe",surname:"Antunes",slug:"filipe-antunes",fullName:"Filipe Antunes"},{id:"175614",title:"Dr.",name:"Bruno",surname:"Medronho",slug:"bruno-medronho",fullName:"Bruno Medronho"},{id:"177162",title:"MSc.",name:"Poonam",surname:"Singh",slug:"poonam-singh",fullName:"Poonam Singh"},{id:"177163",title:"MSc.",name:"Hugo",surname:"Duarte",slug:"hugo-duarte",fullName:"Hugo Duarte"},{id:"177164",title:"Prof.",name:"Nicolas",surname:"Le Moigne",slug:"nicolas-le-moigne",fullName:"Nicolas Le Moigne"},{id:"177165",title:"MSc.",name:"Jan",surname:"Dormanns",slug:"jan-dormanns",fullName:"Jan Dormanns"},{id:"177166",title:"Dr.",name:"Benoît",surname:"Duchemin",slug:"benoit-duchemin",fullName:"Benoît Duchemin"},{id:"177167",title:"Prof.",name:"Mark",surname:"Staiger",slug:"mark-staiger",fullName:"Mark Staiger"},{id:"177168",title:"MSc.",name:"Luis",surname:"Alves",slug:"luis-alves",fullName:"Luis Alves"}],corrections:null},{id:"49231",title:"An Assessment of Surface Properties and Moisture Uptake of Nonwoven Fabrics from Ginning By-products",doi:"10.5772/61329",slug:"an-assessment-of-surface-properties-and-moisture-uptake-of-nonwoven-fabrics-from-ginning-by-products",totalDownloads:2640,totalCrossrefCites:3,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Greige (raw) cotton by-products resulting from cotton ginning and mill processes have long been bleached for use in absorbent nonwoven products. The potential to use greige cotton by-products as an economical source for absorbent nonwoven blends is explored. The nonwoven hydroentanglement of greige cotton lint with cotton gin motes and comber noils blends was analyzed for fiber surface polarity, swelling, and absorbance to assess properties with potential usefulness in absorbent nonwovens. The electrokinetic analysis of the fabric surface gives a composite picture of the relative hydrophilic/hydrophobic polarity absorbency and swelling properties. Nonwoven fabrics made with cleaned greige cotton lint separately blended with comber noils and ginning motes at 40:60 and 60:40 blend ratios demonstrated charge, swell, and percent moisture uptake profiles that are characteristic of the fabrics’ crystalline/amorphous cellulosic content with some variance in swelling properties. However, cellulose crystallite size varied. X-ray diffraction patterns of the three different cotton constituents displayed similar crystalline cellulose compositions. An electrochemical double-layer analysis of charge based on a pH titration (ζplateau) was employed to measure the relative fiber and fabric surface polarity which varied slightly between -21 and -29 mV. A relationship of fiber swelling (∆ζ) and percent moisture content is apparent when greige cotton lint and other fibers are blended. The blended nonwoven materials possess absorbent properties characterized by similar moisture uptake (7.1-9.5 %) and fiber polarity, but some variation in swelling is based on the by-product additive and its percent content. The crystallinity, electrokinetic, and water binding properties of the nonwoven by-product materials are discussed in the context of the molecular features water, cellulose, and greige cotton components that enhance potential uses as absorbent nonwoven end-use products.",signatures:"Vince Edwards, Paul Sawhney, Alvin Bopp, Alfred French, Ryan\nSlopek, Michael Reynolds, Chuck Allen, Brian Condon and Joseph\nMontalvo",downloadPdfUrl:"/chapter/pdf-download/49231",previewPdfUrl:"/chapter/pdf-preview/49231",authors:[{id:"147109",title:"Dr.",name:"J. Vincent",surname:"Edwards",slug:"j.-vincent-edwards",fullName:"J. Vincent Edwards"}],corrections:null},{id:"49508",title:"Cellulose Grafting by Atom Transfer Radical Polymerization Method",doi:"10.5772/61707",slug:"cellulose-grafting-by-atom-transfer-radical-polymerization-method",totalDownloads:2884,totalCrossrefCites:3,totalDimensionsCites:5,hasAltmetrics:0,abstract:"Increased public awareness on environmental issues, fluctuations in raw material prices, and global difficulties in raw material supplies have made it necessary to find new, sustainable, environment-friendly, and inexpensive natural polymer sources. Cellulose and its derivatives, at this point, have become an important research matter once again, because of its easy accessibility, abundance, price, minimal effect on the environment, and new properties discovered with the help of technology.",signatures:"Nevin Çankaya",downloadPdfUrl:"/chapter/pdf-download/49508",previewPdfUrl:"/chapter/pdf-preview/49508",authors:[{id:"175645",title:"Associate Prof.",name:"Nevin",surname:"Çankaya",slug:"nevin-cankaya",fullName:"Nevin Çankaya"}],corrections:null},{id:"49168",title:"Chemical Functionalization of Cellulosic Materials — Main Reactions and Applications in the Contaminants Removal of Aqueous Medium",doi:"10.5772/61431",slug:"chemical-functionalization-of-cellulosic-materials-main-reactions-and-applications-in-the-contaminan",totalDownloads:3435,totalCrossrefCites:2,totalDimensionsCites:7,hasAltmetrics:0,abstract:"The cellulose is the most abundant biopolymer in the world and presents a higher chemical variability for presence of several hydroxyl groups. These hydroxyl groups allow surface modification of biomaterials, with insertion of several chemical groups which change cellulose characteristics. This natural biopolymer and its derivatives have been used a lot as adsorbent, from several contaminants of aqueous medium due to biocompatibility, chemical degradability, and variability. Therefore, this chapter has the objective to review the literature about several cellulose surfaces or cellulosic material (incorporation of carboxymethyl, phosphorus, carboxyl, amines, and sulfur), presenting the main characteristics of reactions and showing its adsorption in application of aqueous medium (metals, dyes, and drugs), locating the main interactions between biomaterial/contaminant.",signatures:"Roosevelt D.S. Bezerra, Paulo R.S. Teixeira, Ana S.N.M. Teixeira, Carla\nEiras, Josy A. Osajima and Edson C. Silva Filho",downloadPdfUrl:"/chapter/pdf-download/49168",previewPdfUrl:"/chapter/pdf-preview/49168",authors:[{id:"84472",title:"Dr.",name:"Carla",surname:"Eiras",slug:"carla-eiras",fullName:"Carla Eiras"},{id:"175808",title:"Prof.",name:"Roosevelt",surname:"Delano",slug:"roosevelt-delano",fullName:"Roosevelt Delano"},{id:"175809",title:"Prof.",name:"Paulo",surname:"Ronaldo",slug:"paulo-ronaldo",fullName:"Paulo Ronaldo"},{id:"175810",title:"Prof.",name:"Ana",surname:"Siqueira",slug:"ana-siqueira",fullName:"Ana Siqueira"},{id:"175811",title:"Dr.",name:"Josy",surname:"Anteveli",slug:"josy-anteveli",fullName:"Josy Anteveli"},{id:"175812",title:"Prof.",name:"Edson",surname:"Cavalcanti",slug:"edson-cavalcanti",fullName:"Edson Cavalcanti"}],corrections:null},{id:"49450",title:"Use of Cellulosic Materials as Dye Adsorbents — A Prospective Study",doi:"10.5772/61343",slug:"use-of-cellulosic-materials-as-dye-adsorbents-a-prospective-study",totalDownloads:3339,totalCrossrefCites:7,totalDimensionsCites:10,hasAltmetrics:0,abstract:"Cellulose is the most abundant biopolymer of nature, and it is widely used in the synthesis of new materials as well as in the adsorption of dye. This study reports a literature review (articles) and technology review (patents) about publications and product invention, which contain information on the use of cellulose on the adsorption of dyes in the period 2004–2014. For this work, research database and keywords were used to find articles and patents related to the subject under review. Specific words were used to find articles and patents related to the subject under review. After a demanding research, 1 patent and 23 articles that contain the words “cellulose,” “dye,” and “adsorption or sorption” in their titles were assessed, and annual evolution studies were performed for publications and countries that publish more.",signatures:"Fabrícia C. Silva, Luciano C.B. Lima, Roosevelt D.S. Bezerra, Josy A.\nOsajima and Edson C. Silva Filho",downloadPdfUrl:"/chapter/pdf-download/49450",previewPdfUrl:"/chapter/pdf-preview/49450",authors:[{id:"175812",title:"Prof.",name:"Edson",surname:"Cavalcanti",slug:"edson-cavalcanti",fullName:"Edson Cavalcanti"},{id:"175866",title:"Prof.",name:"Josy",surname:"Osajima",slug:"josy-osajima",fullName:"Josy Osajima"},{id:"175867",title:"MSc.",name:"Fabricia",surname:"Silva",slug:"fabricia-silva",fullName:"Fabricia Silva"},{id:"175868",title:"MSc.",name:"Luciano",surname:"Lima",slug:"luciano-lima",fullName:"Luciano Lima"},{id:"177406",title:"Prof.",name:"Edson",surname:"Cavalcanti",slug:"edson-cavalcanti",fullName:"Edson Cavalcanti"}],corrections:null},{id:"49485",title:"Microbial Cellulose — Biosynthesis Mechanisms and Medical Applications",doi:"10.5772/61797",slug:"microbial-cellulose-biosynthesis-mechanisms-and-medical-applications",totalDownloads:3834,totalCrossrefCites:9,totalDimensionsCites:18,hasAltmetrics:1,abstract:"Currently some principles of sustainability, eco-efficiency and green chemistry are guiding the development of a new generation of materials as an alternative to conventional polymers based on petroleum. Then, in the field of biodegradable polymers one of the most promising investigations is focused on the use of microbial cellulose (MC), biocellulose or bacterial cellulose. MC has received substantial interest since it is synthesized from the bacterium Gluconacetobacter genus from a variety of carbon sources such as glucose, fructose, galactose, etc. MC is an interesting emerging biomaterial, with no toxicity, and since its discovery has shown tremendous potential in various fields, because the structural aspect of MC is far superior to those of plant cellulose. Thus, the main focus of the chapter review involves detailed aspects about the biosynthesis and recent advances on microbial production, including mechanism for the biochemistry of the cellulose synthesis, new sources for culture medium, main aspects about static and air-reactor productions and genetic modifications. We also revised microbial cellulose devices for biomedical applications: artificial skin, artificial blood vessels and microvessels, wound dressing of second- or third-degree burn ulcers, scaffolds for tissue engineering, drug delivery systems, dental implants, among others.",signatures:"Wilton R. Lustri, Hélida Gomes de Oliveira Barud, Hernane da Silva\nBarud, Maristela F. S. Peres, Junkal Gutierrez, Agnieszka Tercjak,\nOsmir Batista de Oliveira Junior and Sidney José Lima Ribeiro",downloadPdfUrl:"/chapter/pdf-download/49485",previewPdfUrl:"/chapter/pdf-preview/49485",authors:[{id:"6487",title:"Prof.",name:"Hernane",surname:"Barud",slug:"hernane-barud",fullName:"Hernane Barud"},{id:"41366",title:"Dr.",name:"Agnieszka",surname:"Tercjak",slug:"agnieszka-tercjak",fullName:"Agnieszka Tercjak"},{id:"177587",title:"Prof.",name:"Wilton R",surname:"Lustri",slug:"wilton-r-lustri",fullName:"Wilton R Lustri"},{id:"177589",title:"MSc.",name:"Helida G",surname:"Oliveira Barud",slug:"helida-g-oliveira-barud",fullName:"Helida G Oliveira Barud"},{id:"177590",title:"Dr.",name:"Maristela Fs",surname:"Peres",slug:"maristela-fs-peres",fullName:"Maristela Fs Peres"},{id:"177591",title:"Dr.",name:"Junkal",surname:"Gutierrez",slug:"junkal-gutierrez",fullName:"Junkal Gutierrez"},{id:"177592",title:"Prof.",name:"Osmir B",surname:"Oliveira Junior",slug:"osmir-b-oliveira-junior",fullName:"Osmir B Oliveira Junior"},{id:"177593",title:"Prof.",name:"Sidney Jl",surname:"Ribeiro",slug:"sidney-jl-ribeiro",fullName:"Sidney Jl Ribeiro"}],corrections:null},{id:"49666",title:"Crystalline Nanocellulose — Preparation, Modification, and Properties",doi:"10.5772/61899",slug:"crystalline-nanocellulose-preparation-modification-and-properties",totalDownloads:10526,totalCrossrefCites:50,totalDimensionsCites:119,hasAltmetrics:1,abstract:"Cellulose is a linear biopolymer found naturally in plant cells such as wood and cotton. It is the worlds most abundant polymer in nature and possesses properties such as good biocompatibility, low cost, low density, high strength, and good mechanical properties. By mechanical or chemical treatment, the cellulose fibers can be converted into cellulose nanofibers (CNFs) or cellulose nanocrystals (CNCs) that possess outstanding properties compared with the original cellulosic fiber but also when compared with other materials normally used as reinforcements in composite materials such as Kevlar or steel wires. This review will describe the nanocellulose materials preparation techniques and cellulose sources, chemical modification both on the crystalline surface and during hydrolysis and its many properties and its use in biocomposite materials. Nanocellulose in its different forms shows an increasing interest in application areas such as packaging, paper and paperboard, food industry, medical and hygiene products, paints, cosmetics, and optical sensors",signatures:"Mikaela Börjesson and Gunnar Westman",downloadPdfUrl:"/chapter/pdf-download/49666",previewPdfUrl:"/chapter/pdf-preview/49666",authors:[{id:"142372",title:"Prof.",name:"Gunnar",surname:"Westman",slug:"gunnar-westman",fullName:"Gunnar Westman"},{id:"174946",title:"MSc.",name:"Mikaela",surname:"Borjesson",slug:"mikaela-borjesson",fullName:"Mikaela Borjesson"}],corrections:null},{id:"49195",title:"Current Trends in the Production of Cellulose Nanoparticles and Nanocomposites for Biomedical Applications",doi:"10.5772/61334",slug:"current-trends-in-the-production-of-cellulose-nanoparticles-and-nanocomposites-for-biomedical-applic",totalDownloads:7455,totalCrossrefCites:28,totalDimensionsCites:71,hasAltmetrics:0,abstract:"The goal of this chapter is to review the most recent trends to produce cellulose nanoparticles and nanocomposites with biomedical applications. These particles could be named as bacterial cellulose, cellulose nanofibers, and cellulose nanocrystals. The production of these nanoparticles with diameters below 100 nm is challenging because of the strong agglomeration tendency which occur upon drying aqueous cellulose suspensions or during the compounding process with hydrophobic polymers. Typically, the physical and mechanical properties of these nanoparticles depend on the source of cellulose and the extraction process employed. Cellulose nanoparticles are obtained by mechanical, chemical, or enzymatic process treatments to open the structure of the cellulose source and facilitate accessibility to its microstructure. Usually, a combination of these processes makes the extraction more efficient.",signatures:"John Rojas, Mauricio Bedoya and Yhors Ciro",downloadPdfUrl:"/chapter/pdf-download/49195",previewPdfUrl:"/chapter/pdf-preview/49195",authors:[{id:"143552",title:"Dr",name:null,surname:"John Rojas",slug:"john-rojas",fullName:"John Rojas"}],corrections:null},{id:"49410",title:"Cellulose - Chitosan Nanocomposites - Evaluation of Physical, Mechanical and Biological Properties",doi:"10.5772/61727",slug:"cellulose-chitosan-nanocomposites-evaluation-of-physical-mechanical-and-biological-properties",totalDownloads:2998,totalCrossrefCites:3,totalDimensionsCites:5,hasAltmetrics:0,abstract:"This research describes the preparation of membranes with chitosan (CS) as the polymeric matrix and cellulose nanocrystals (CNC) as reinforcement. The aim was to evaluate their physical, mechanical and biological properties, and to determine their potential for biomedical use. Membranes were prepared via casting CNC suspensions in CS solution, at CNC concentrations of 0.5%, 1.0% and 2.0% (w/w) with pure chitosan as a reference. Analysis of membrane properties was performed using several techniques, such as ATR – FTIR, SEM, swelling test, maximum water absorption, dynamical mechanical analysis and in vivo (Winstar rats) biocompatibility and biodegradability assays for biological evaluation. Experimental results established that CNC reduced swelling rates and increased the maximum water absorption when CNC concentration was higher. Therefore, the presence of CNC in the matrix reduced Young’s modulus by approximately 50% in comparison with pure chitosan. All formulations demonstrated biocompatibility and biodegradability values ranged between 4% and 21% in the 30 days after implantation. Based on these results, these membranes may be of use for biomedical applications.",signatures:"Guillermo H. Riva, Joaquín García-Estrada, Brenda Vega, Fernando\nLópez-Dellamary, María E. Hérnandez and José A. Silva",downloadPdfUrl:"/chapter/pdf-download/49410",previewPdfUrl:"/chapter/pdf-preview/49410",authors:[{id:"175606",title:"M.Sc.",name:"Guillermo",surname:"Riva",slug:"guillermo-riva",fullName:"Guillermo Riva"},{id:"179991",title:"Dr.",name:"Jose",surname:"A. Silva",slug:"jose-a.-silva",fullName:"Jose A. Silva"},{id:"179992",title:"Dr.",name:"Fernando",surname:"Lopez-Dellamary",slug:"fernando-lopez-dellamary",fullName:"Fernando Lopez-Dellamary"},{id:"179993",title:"Dr.",name:"Maria",surname:"E. Hernandez",slug:"maria-e.-hernandez",fullName:"Maria E. Hernandez"},{id:"179994",title:"Dr.",name:"Joaquin",surname:"García-Estrada",slug:"joaquin-garcia-estrada",fullName:"Joaquin García-Estrada"},{id:"179995",title:"Dr.",name:"Brenda",surname:"Vega",slug:"brenda-vega",fullName:"Brenda Vega"}],corrections:null},{id:"49155",title:"Nanocellulosic Materials in Tissue Engineering Applications",doi:"10.5772/61344",slug:"nanocellulosic-materials-in-tissue-engineering-applications",totalDownloads:2660,totalCrossrefCites:4,totalDimensionsCites:14,hasAltmetrics:0,abstract:"This chapter deals with an overview of design and fabrication of three-dimensional (3D) scaffolds for tissue engineering (TE) applications using the electrospinning technique. A general introduction to cellulose, a short overview of sources and methodology for the production of cellulose nanocrystals (CNCs), and principles of tissue engineering and the electrospinning technique will be given. Applications for CNCs are manifold and range from super water absorbent, drug delivery, packaging, personal care to pharmaceuticals. 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\r\n\tIn recent years, neurorehabilitation (neuroscience-based rehabilitation), which utilizes the brain's plasticity, has made remarkable progress and has attracted much attention. The term "neurorehabilitation" is defined as "concepts, evaluation methods, therapies, and devices that apply the knowledge of brain theory revealed by neuroscience and related research to rehabilitation medicine. The concept of neurorehabilitation is widely accepted in physical therapy, and evaluation and treatment based on this concept are being practiced. What is required in neurorehabilitation research is to analyze the changes and improvements in motor behavior and cognitive and learning abilities and the changes in brain functions that bring about these changes. This will allow us to get closer to the neural mechanisms of rehabilitation effects and is expected to develop effective methods that are more suitable for the subject.
\r\n\r\n\tThe purpose of this book is to provide a broad introduction to neurorehabilitation, from basic research to advanced treatment and science and technology, which is also being developed in the field of physical therapy. This book hopes to cover three topics related to neurorehabilitation and physical therapy (basic research, applied research, and advanced technology).
\r\n\t
The future has five faces: innovation, digitalization, urbanization, community, and humanity. The scientific sector should develop each of them, but one that occupies a leadership position is definitely digitalization. It strives for the future every day and is struggling to overcome professional challenges, but in fact it is already the present. Modern technologies surround all of us, and they are our most reliable partners for the future. Through good-quality work and determination, clients will share with you their business needs and requirements, certain that you will find the right solutions for them.
Nowadays, many companies and organizations are involved in collecting data in large scale, in order to discover the necessary knowledge from them to help managers gain a competitive advantage. Timely and accurate analysis of such data is a difficult task, and it is not always possible to do it using conventional methods. Considering the effect that could be obtained, new horizons are opening, and challenges are created for researchers in order to extract useful information [1].
The concept that is very important and where more companies are investing in development is data science in order to find new ways to discover the real needs, behaviors, and intentions of the users, as well as their detailed analysis. The analysis, improved by the methods of machine learning and, in general, training the data, gives a complete experience as a mix of business and technology. The main purpose is a good mechanism in order to meet the increasing demands of users and even overcome its challenges, because this is the biggest competitive advantage of the companies of every modern business. Neural networks are certainly an indispensable part of it.
One of the modern directions of the development of information technologies, which is a perspective and which has found an application in practice, is undoubtedly the development of artificial neural networks. Neural networks represent one of the learning models based on the work of biological neural networks such as the human brain. From such a learning model, a system that adapts to changes, which are very common on market, can be made and therefore would have more success. This stems from the desire to create an artificial system capable of performing sophisticated and intelligent calculations and represents a perspective in the future.
The aim of this chapter is to predict the financial time series using a neural network that has been trained and tested both in the foreign exchange market and the stock market. Historical data has been collected and analyzed to create a model that would establish a link between the corresponding variables.
The development of the neural network is currently oriented in two directions. The first is to increase the availability of modern computers and develop software tools for easy use, which enables the rapid development of neural networks by the individuals and the groups that has only basic knowledge about these areas. Other direction is the notable success of neural networks in areas where traditional computer systems have many problems and disadvantages. Nevertheless, there are many other methods that deal with the same or similar problems, so some of them will be listed.
A method that is increasingly used in predicting financial time series is support vector machines (SVM). There are many scientific papers comparing this method with neural networks in that which is more precise, which corresponds better to the set goals and its advantages in relation to the others [2, 3].
As a commonly used method in solving this type of problem, there is also a random walk method. It is used as a financial theory that describes changes in the stock market as accidentally and unpredictably. Changes have a statistical distribution, and an appropriate model is developed. Then statistical testing of the hypothesis is performed, and a certain conclusion is made, whether price changes depend on one another or are completely independent.
In finance, the main problem is unstable nature of observed time series and its heteroscedasticity, making it impossible to apply certain time series models. This study empirically investigates the forecasting performance of generalized autoregressive conditional heteroscedastic (GARCH) model for NASDAQ-100 return over the period of 6 years, which prove to be a financial time series characterized by heteroscedasticity. Volatility performance is found to be significantly improved. Generally, ARCH and GARCH model along with their extensions provide a statistical stage on which many theories of asset pricing, portfolio analysis, value at risk, or index volatility can be exhibited or tested. Volatility has been the subject of many researches in financial markets, especially as an essential input to many financial decision-making models. Investment decisions strongly depend on the forecast of expected returns and volatilities of the assets. The introduction of ARCH model has created a new approach and has application for financial econometricians, becoming a popular tool for volatility modeling and forecasting [4].
Also known as econometric models for time series are generalized autoregressive conditional heteroscedastic and exponential generalized autoregressive conditional heteroscedastic (EGARCH), but in other papers, in comparative analysis they have proved less effective than NARX, so in this paper, they will not be considered or compared to the network [5].
Traditionally, Box-Jenkins or autoregressive integrated moving-average (ARIMA) model has been dominating over time series for forecasting the time series and includes the identification, evaluation, and checking of the suitability of the selected time series model. Although it is rather flexible and can be used for a large number of time series, the main limitation is the assumption of the linearity of the model, and it is used to model nonstationary time series. The model cannot explain nonlinear behavior, which is at the core of financial time series. The connection between conventional statistical approaches and neural networks for this use is complementary. The neural network is not transparent and has the corresponding stochastic part. It should be trained several times, after which the average value is taken to see how stable the solution is obtained afterwards. Also, statistical predictive techniques have reached their limitations when it comes to nonlinearity in data, while neural networks increasingly (except in the prediction) are applied in the classification and pattern recognition [6, 7].
Neural networks are computer simulations programmed to learn on the basis of available data. They are used to solve a wide range of problems related to clustering, classification, pattern recognition, optimization, function approximation, and prediction. They are characterized by the layers—the input layer, the hidden layer, the output layer from the network, and the connections between all of them. The number of these connections along with the weight coefficients represents the real power of the neural network. Input neurons accept information, while output neurons generate signals for specific actions [8].
The types of networks are grouped into five main classes:
Single-layer feedforward networks
Multilayer feedforward networks
Simple recurrent networks, such as the Elman simple recurrent neural networks
Radial basis function networks
Self-organizing maps
Depending on the algorithm, it determined what kind of network propagation will be in relation to the type of network. The most important thing in this paper is the hidden layer whose number of nodes determines the complexity for which a prediction model is made. The activation function as an indispensable part is necessary for the neural network to be able to learn nonlinear functions, especially because of their importance to the network. Without nonlinearity, the network would be able to model only linear data dependencies.
By combining linear functions, a linear function is obtained, so it is advisable to choose a nonlinear function for the activation function. The network compares the obtained and expected results and, based on this, if there are differences, modifies the neural connections in order to reduce the difference between the current and the desired output. During the learning process, the existing synaptic weights are corrected in order to get a better and more reliable output. The net is trained continuously, until the samples do not lead to a change in coefficients. As a good and highly efficient predictor of time series, NARX neural networks are used very often. The structure of NARX neural network is shown in Figure 1.
The structure of the NARX model (
Previously, for predicting time series, linear parametric models such as autoregressive (AR), moving-average (MA), or autoregressive integrated moving-average model were used. They were not able to solve problems related to nonstationary signals and signals whose mathematical model is not linear. On the other hand, neural network is a powerful tool when applying to problems whose solutions require knowledge that is difficult to specify and express, but there is sufficient representation in examples and practices.
Nonlinear autoregressive exogenous neural network is a dynamic neural architecture that is used to model nonlinear dynamic systems. The nonlinear autoregressive (NAR) network differs in that it has, besides the standard input, another additional time series with external data, which gives an increased accuracy of the prediction. For applications related to the prediction of time series, it is designed as a feedforward neural network with time delay (TDNN). The equation represented by the NARX model [8] is
where 𝑦 is the output of the NARX neural network with delays (2 legs) and 𝑥 is input of the NARX neural network with delays (2 legs).
In the NARX neural network model, multilayer perceptron (MLP) is used. The task of the program is to learn how to assign to the new, unmarked data the accurate output. When the variables that need to be predicted are continuous, then the problem is defined as regression. If the predicted values can only contain a limited set of discrete values, then the problem is defined as a classification. Each time the data is trained, the results can give a different solution considering the initial weight w and the value of the bias b.
The methods based on Fourier transform have a great application in all areas of science and engineering. Fourier transform is used in signal processing, for solving differential equations, or in analyzing the dynamics of the market and stock market with the same possibilities. In addition to many other tools, the frequency used along with transformation is convolution, which is often applied in the same areas. It is known that it is not possible to define the product of two random distributions, and there it finds its application, especially in the field of finance (securities) when performing the necessary formulas.
Fourier series represents a periodic function as an infinite sum of the sinus and cosine functions in the domain of frequency expressed below (Eq. (2)). The application of the price system of options, which is uniquely determined by the characteristic functions within the Fourier analysis, is shown. To describe, the random stochastic Levi processes are often mentioned in the fields of insurance and finance, as well as the assumption of the Black-Scholes model that the price of the substrate is followed by the geometric Braun motion model. This is precisely one of the disadvantages with the assumption of constant volatility over time. It is difficult to determine whether these are really disadvantages or simply the market is ineffective, which is significant to investors as information about the risk protection they are trying to achieve:
However, Fourier transform is rarely suitable for the processing of nonstationary signals or those whose frequency content changes over time, where the periodic signal should be centered around the integer multiplicity of selection frequencies. Then this signal is divided into smaller time segments and analyzes the frequency content of each individual part. Because of that, there is wavelet transformation with the possibility of dilatation and translation of waves as the basic function of transformation [9].
The six Forex major traded currency pairs are EUR/USD, GBP/USD, AUD/USD, USD/CAD, USD/JPY, and USD/CHF. In this chapter for the time series analysis, a pair of EUR/USD was selected considering its share in the total trading volume (27%). Often, cross currency pairs, which do not include the US dollar, have a smaller trading volume and larger spreads than the major currency pairs, so they are less suitable for analysis.
Unlike Forex, which is characterized by large oscillations, it may be better to notice a certain trend that changes slowly over time. Based on this, it might be assumed that the S&P 500 index will show better features related to the prediction of the series.
Relevant historical currency pair data for more than 10 years have been downloaded from the website of Fusion Media Limited [10]. In the analysis of time series from the stock exchange, a representative index S&P 500 was used with the historical data downloaded from the website of Yahoo! Finance [11].
The collected data are related to the prices (high, low, open, close) in the period from 2003 to September 2018, for each day four prices, but the close price will be used in the analysis. The graph of the time series for the S&P 500 stock index in the time domain, returns based on 3950 observations in the period 31/12/2002–07/09/2018 is shown in Figure 2.
Time series S&P500 in the time domain.
After determining the returns and application of FFT (fast Fourier transform), the graph shown in Figure 3 is plotted.
Time series S&P500 in the frequency domain.
The time series graph for the EUR/USD currency pair in the time domain by observing the returns based on 4093 observations in the period 01/01/2003–07/09/2018 is shown in Figure 4. After determining the returns and application of FFT (fast Fourier transform), the graph shown in Figure 5 is plotted.
Time series of the EUR/USD currency pair in the time domain.
Time series of the EUR/USD currency pair in the frequency domain.
From Figures 2 to 4, the conclusion is that the time series of the prices is not stationary, while the returns are a stationary time series, as can be seen in Figures 3 and 5. It is also concluded that prices don’t have the normal distribution and deviate significantly from it, but returns have significantly better statistical characteristics.
In this case, the time series of the returns are much closer to the normal distribution, and the normal distribution with thick tails occurs. This shows that unexpected events occur more often than in the normal distribution, which is characteristic of the analysis of financial data and forecasts.
Linear dependence, which is very important for observation during the analysis of time series, is autocorrelation. In general, there is doubt whether the explanatory variables are determined by a stochastic member or there is an exact linear dependence between the explanatory variables. The absence of autocorrelation means that random errors are uncorrelated and that the covariance between them is equal to 0. This would mean that there is no any pattern in the correlation structure of random errors. Otherwise if there is autocorrelation and covariance is different from 0, then accidental errors are correlated and followed by a recognizable pattern in movement. In this case the results of the statistical tests are biased, the confidence intervals are imprecise, and the prediction is unreliable. Autocorrelation can also be accurate if it is a consequence of the nature of the data and false if the model is incorrectly set.
The Ljung-Box Q statistical test is significant for analyzing those time series in which autocorrelation is different from 0. Ideally, a series of errors should be a process with an independent random variable from the same distribution, and there is a white noise; however, often in the series of errors, there is a dependence. The greater absence of autocorrelation or its complete absence indicates that the market is mature.
The autocorrelation function of S&P 500 index and EUR/USD currency is shown in Figures 6 and 7, respectively.
Autocorrelation function of returns for time series S&P 500.
Autocorrelation function of returns for time series EUR/USD.
Figure 6 shows the deviation of the autocorrelation value beyond the confidence interval for the first 2 legs, and therefore, in the network architecture, the default value 2 should be used as a time delay. Due to the lack of statistically significant autocorrelation in the data, the NARX neural network will be used for analyzing the time series.
Observing variances of random errors and their differentiation by individual observations, there is the phenomenon of heteroscedasticity. The cause of this phenomenon may be specification errors, exclusion of an important regressor whose influence will be covered by the error or the existence of extreme values in the sample. As a method of elimination, the method of the least squares is applied. The idea is that in the process of minimizing the sum of the quadrate of the residual, a smaller weight is given to those residues that are greater by absolute value and vice versa.
Engle’s ARCH test allows to see if there is heteroscedasticity or not. For the obtained value 1 as a result of the test, it was established for both time series that the zero hypothesis is rejected (the residual series does not show heteroscedasticity), so it can be concluded that it exists in both time series.
In this section, a brief review of well-known and useful mathematical tools from the field of machine learning is presented. For predicting indexes and prices on Forex and stock exchanges, NARX neural network architecture is developed. The input data for the analysis both in the time domain and in the frequency domain are obtained after applying the Fourier transform to the historical data [12, 13].
The tool used is MATLAB® with a special set of functions known as the Neural Network Toolbox applicable to finance. With the help of the functions, a training, evaluation, and test set can be generated from the original set with the corresponding percentile division. Then, several NARX networks are generated that are trained on train data. Subsequently, networks are evaluated on the evaluation data in order to determine the network with appropriate behavior and predict this behavior on the test set of data.
The NARX model can be implemented in many ways, but the simpler is developed by using a feedforward neural network with the embedded memory plus a delayed connection from the output of the second layer to input. In practice it was observed that forecasting of a time series will be enhanced by analyzing related time series. A two-layered feedforward network is used, where the sigmoid function is in a hidden layer and that is the most common form of a transmission function, which is nondecreasing and nonlinear. The linear transfer function is in the output layer. The neural network is shown in Figure 8.
The structure of two-layered feedforward network (
The prediction method in the given experiment applies to changes in the exchange rate or changes in the stock exchange index over a certain period of time. The goal is to go beyond the assumption and to notice the specific pattern of observations along with the usual fluctuations. These fluctuations would mean that a certain inheritance or some kind of random variation occurred over a period of time. Finally, based on the data, a series with damped random fluctuations should be obtained, which indicates exactly the long-term trend or trend present in the time series, and then it is used to predict the future values of the time series.
Levenberg-Marquardt (LMA), a combination of gradient descent and Gauss-Newton algorithm, is used as an algorithm for learning, as opposed to Elman’s recurrent networks, using gradient discent with a momentum. It is known as the advanced and fast algorithm for nonlinear optimization, whereby, unlike the Quasi-Newton algorithm, LMA does not need to compute Hessian matrix, so it has significantly better performance. The Jacobian matrix, which contains the first network error, is used, and it is expressed by a backpropagation algorithm, which is easier than calculation of the Hessian matrix. It is necessary to reach the proximity of the minimal error function and get closer as soon as possible [14].
The data for analysis are divided in the following way: 70% training, 15% evaluation, and 15% test.
After training the network, the results are shown in Figures 9–11. The epoch represents the number of iterations during the training in which it was attempted to minimize the error function.
Mean squared error with best validation performance.
Histogram of time series errors for time series S&P 500.
Histogram of time series errors for time series EUR/USD.
The network architecture is such that the initial number of hidden neurons is set to 10 with 2 time delays. The network will be applied to returns instead of prices for both time series that are observed in the time and frequency domain. The smallest mean squared error occurred in the third epoch and is 1.11455 × 10−4. It represents a deviation of the predicted value in relation to the actual value. If the number is closer to 0, it means that the results obtained are more accurate.
The training error is significantly higher than the error during testing, which means that the model did not overfitting as shown in Figures 10 and 11.
After ten consecutive training of the network, the smallest mean squared error after appeared in the seventh epoch and is 1.11092 × 10−4. As in the analysis of the previous time series, the same training algorithm was used, and the subsets for training, validation, and testing were obtained for the same percentile values. The network architecture is identical with sigmoid function in the hidden and linear function in the output layer. In the analysis of this time series, the smallest mean squared error occurred in the ninth epoch and is 3.71 × 10−5. It represented the deviation of the predicted values in relation to the actual value.
The first network for the stock exchange index S&P 500 was tested as a feedforward network. The smallest MSE for training was 1.23081 × 10−4; for validation, 1.0336 × 10−4; and for testing, 1.1380 × 10−4. The network for the currency pair EUR/USD was tested also as a feedforward network. The smallest MSE was smaller than for the first network: 3.6199 × 10−5 for training, 3.4246 × 10−5 for validation, and 3.4792 × 10−5 for testing.
The algorithm is also trained at 70% of the data, evaluated at 15%, and tested at 15%. Each network consists of two hidden layers. The first hidden layer has ten neurons with a sigmoid transfer function, and the other one is a neuron with a linear transfer function. In the second network, a smaller average mean squared error was detected than in the first one. Also, the standard deviation of the secondary squared error for the other network is lower than for the first one for all three stages of training, validation, and testing, respectively. The results for each iteration and summary of mean squared error are presented in Tables 1 and 2 for S&P 500.
Iterations | Mean squared error | ||
---|---|---|---|
Train | Validation | Test | |
1 | 1.3568 × 10−4 | 1.1455 × 10−4 | 1.1280 × 10−4 |
2 | 1.3680 × 10−4 | 1.1922 × 10−4 | 8.7396 × 10−4 |
3 | 1.3512 × 10−4 | 1.1848 × 10−4 | 1.1948 × 10−4 |
4 | 1.2437 × 10−4 | 1.0698 × 10−4 | 1.6513 × 10−4 |
5 | 1.2820 × 10−4 | 1.0336 × 10−4 | 1.5894 × 10−4 |
6 | 1.2941 × 10−4 | 1.5599 × 10−4 | 1.2687 × 10−4 |
7 | 1.2601 × 10−4 | 1.3396 × 10−4 | 1.3046 × 10−4 |
8 | 1.2619 × 10−4 | 1.0994 × 10−4 | 1.5612 × 10−4 |
9 | 1.2308 × 10−4 | 1.1070 × 10−4 | 1.7836 × 10−4 |
10 | 1.2748 × 10−4 | 1.1092 × 10−4 | 1.3480 × 10−4 |
Mean squared error—S&P 500.
Summary | Mean squared error | ||
---|---|---|---|
Train | Validation | Test | |
Min | 1.2308 × 10−4 | 1.0336 × 10−4 | 1.1380 × 10−4 |
Max | 1.3680 × 10−4 | 1.5599 × 10−4 | 8.7369 × 10−4 |
Average | 1.2923 × 10−4 | 1.1841 × 10−4 | 2.1569 × 10−4 |
Standard deviation | 4.9307 × 10−6 | 1.5685 × 10−5 | 2.3228 × 10−4 |
Summary—S&P 500.
The results for each iteration and summary of mean squared error are presented in Tables 3 and 4 for EUR/USD currency pair, respectively.
Iterations | Mean squared error | ||
---|---|---|---|
Train | Validation | Test | |
1 | 3.6199 × 10−5 | 3.7105 × 10−5 | 4.1646 × 10−5 |
2 | 3.7100 × 10−5 | 3.7924 × 10−5 | 3.8488 × 10−5 |
3 | 3.8090 × 10−5 | 3.6691 × 10−5 | 3.7361 × 10−5 |
4 | 3.7694 × 10−5 | 3.4246 × 10−5 | 3.8251 × 10−5 |
5 | 3.6808 × 10−5 | 3.7144 × 10−5 | 3.8759 × 10−5 |
6 | 3.8302 × 10−5 | 3.5430 × 10−5 | 3.4792 × 10−5 |
7 | 3.7862 × 10−5 | 3.4881 × 10−5 | 3.7759 × 10−5 |
8 | 3.6938 × 10−5 | 3.7867 × 10−5 | 3.7924 × 10−5 |
9 | 3.8322 × 10−5 | 3.7484 × 10−5 | 3.6947 × 10−5 |
10 | 3.8169 × 10−5 | 3.5506 × 10−5 | 3.5472 × 10−5 |
Mean squared error—EUR/USD.
Summary | Mean squared error | ||
---|---|---|---|
Train | Validation | Test | |
Min | 3.6199 × 10−5 | 3.4246 × 10−5 | 3.4792 × 10−5 |
Max | 3.8302 × 10−5 | 3.7924 × 10−5 | 4.1646 × 10−5 |
Average | 3.7548 × 10−5 | 3.6427 × 10−5 | 3.7739 × 10−5 |
Standard deviation | 7.3840 × 10−7 | 1.3108 × 10−6 | 1.8784 × 10−6 |
Summary—EUR/USD.
Unlike the analysis of time series in the time domain, in the frequency domain, it is interesting to consider the spectrum of the amplitude (relative share of a certain frequency component relative to the other) of the historical price for the stock index S&P 500 and the currency pair EUR/USD in several different aspects. These analyses include the spectral analysis of time series, which are usually used for stationary time series. This is a good assumption for adjusted stock prices in the frequency domain statistics [15].
For converting to the frequency fk, it should be emphasized that, if daily prices are used as an input signal, the sampling frequency is equal to 1 [1/day], which means that the frequencies must be reallocated.
The unit of a new set of discrete frequencies is [1/day] and has the form of the real frequencies required in this analysis. Also, according to the sampling theorem, it is known that only those signal components who having a frequency less than or equal to Fs/2 = 0.5 days−1, without aliasing effect, will be measured. Considering these facts, it is necessary to limit the frequency coordinates to the range from 0 to 0.5.
In order to better understand the shape of the spectrum, a log-log scale is used, and logarithm of the amplitude values obtained after application of FFT is used. Observing the slope of such a curve could be observed if the spectrum of the amplitude is close to the special power-law form 1/f. Using a logarithmic format is a good way to avoid overestimating high-frequency components.
After applying FFT on prices and returns, equivalent time series in the frequency domain are obtained. As in the above procedure, in order to better detect the spectrum, a modulus representing the amplitude was found, and then the result was logarithmic. The obtained values of the S&P 500 index and EUR/USD currency pair were used to train the NARX neural network. The average mean squared error obtained after ten consecutive training is 1.5738 × 10−1 and 4.8713 × 10−1, respectively, which represents a significantly higher number than the one obtained in the time domain. The conclusion is that, regardless of the time series being analyzed, the results are significantly worse and the prediction is less reliable.
The simulation performed with the input that represents the logarithmic value of the amplitude and the frequency as an exogenous input did not show the possibility of good training and convergence even after the maximum possible 1000 iterations or the corresponding statistical characteristics, and hence, its analysis would make no sense.
Due to its wide practical application in various fields, Fourier transform is increasingly in the focus of international scientific meetings, as well as numerous publications (scientific monographs, journals, chapters, etc.), whether it is economics, biomedicine, chemical engineering, electronics, or art [16].
Considering the domain in which one of the methods of computational intelligence is applied in this chapter, other methods are often applied. Bankruptcy prediction is one of the main issues threatening many companies and governments and a complex process that consists of numerous inseparable factors. Financial distress begins when an organization is unable to meet its scheduled payments or when the projection of future cash flows points to an inability to meet the payments in the near future. The causes leading to business failure and subsequent bankruptcy can be divided into economic, financial, fraud, disaster, and others. With more accurate bankruptcy detection techniques, companies could take some preventive measures in order to minimize the risk of falling to bankruptcy [17].
There are two dominant approaches when it comes to predicting bankruptcy: one that used multi-discriminant analysis, univariate approach (net income to total debt has highest predictive ability), and developing stochastic model such as logit and probit. The other one approach refers to using artificial intelligence and adapts it for predicting bankruptcy (decision tree, fuzzy set theory, genetic algorithm, and support vector machine). Also neural networks such as BPNN (backpropagation-trained neural network), PNN (probabilistic neural networks), or SOM (self-organizing map) could be developed. In this paper, three LC models are tested whether they are able to improve Altman Z-score as a benchmark model for bankruptcy prediction. Even though LC method shows more accurate results, Altman model behaves slightly better for gray-zone companies, where it is important to reduce number of bankrupt firms identified as an active.
In modern approaches it is necessary to introduce different approaches to modeling similarity specially using IBA with two main steps to perform it. First thing is data preprocessing (data normalization, detection of attribute nature, and their potential interaction), where normalization functions may be adapted depending on data range and distribution. Also, it is recommended to use correlation to detect similar nature between attribute data, because the existence of significant correlation in attribute data could overemphasize certain attributes and cause incoherent model results. IBA similarity modeling (attribute-by-attribute comparison, comparison on the level of the object and general approach) show what kind of aggregation is appropriate for similarity modeling.
In this case it is proven that IBA-based similarity framework has a solid mathematical background and can also be expanded to model nonmonotonic inference. The practical advantage is evaluated on two numerical examples. The first example confirms motivation and reasoning behind the novel OL comparison with importance of when one object’s attributes is logically dependent or can be compensated by another attribute. In the second example the proposed similarity framework is applied for predicting corporate bankruptcy with different KNN classifiers [18].
Analysis of time series is a specific topic, which is indispensable in dealing with the data science and statistical analysis. By combining an analysis with a tool such as a neural network, especially in an increasingly important area such as finance, it is certain that in the future it can conquer new territories and have a global impact. Looking for the financial protection from losses and safe investments without risky investment, it is necessary to apply modern methods with continuous upgrading and improvement. In cooperation with existing platform with varied parameters and transactional data, this tool would be a good prerequisite for successful forecasting of trends and secure business.
The obtained results of the time series analysis confirmed the possibility of a good prediction. Better forecasting can be done for time series in Forex (EUR/USD), in the time domain without applying Fourier transform to input data. In this sense, NARX proved to be a good method for solving the given type of problem in the time domain, but in the frequency domain, it is recommended that the analysis be carried out by a classical feedforward neural network with the backpropagation algorithm. The results of the research indicated that NARX is capable of providing a certain amount of security to those entities that invest their funds, as well as to point out future expectations. On the other hand, the results of this paper give only a proposal and advice on how to behave on the market during trading. It should always be cautious, given the already mentioned market variability. Timeliness is also important, because when a particular news arrives on the market, then it reacts to certain changes. The news is then incorporated into the price and the market returns to the previous state where it was before the news arrived.
Proposals for the improvement of the neural network are:
Include new input parameters that can be reached by new research, or do a different preparation of data for the training to make sure of the credibility of this network in a dynamic environment.
Change the number of neurons in the hidden layer, time delay, or activation function in the hidden and output layer.
Use network results as entering the new network together with a change in the time period, which can give a broader picture of the trend of the observed currency pair or stock exchange index.
The author declares that there are no conflicting interests.
India is a fast-growing economy and agrarian country. Almost 70 percent of the Indian population depends on agriculture and its allied sectors to obtain employment and sustain livelihood. The seed is considered as a basic and key input in agriculture. High-quality seed production was the major concern in the Indian subcontinent till the 1960s. Before that India was mostly dependent on the USA for food grain (PL480) to mitigate its hunger [1] and feed large human population. In order to reduce the dependence of food on foreign countries and to meet the food and nutritional demand of burgeoning population and to become self-reliant in food grain production, Indian Government established All India Coordinated Crop Research Projects (AICCRPs) and other institutes in a systemic manner to produce a large number of varieties with assured seed quality in all major crops. The production of high-quality seeds was one of the pillars to change the position of Indian agriculture into the new world order. The ultimate intention was to introduce the newly evolved high yielding cultivars to the resource-poor farmers for broad-spectrum cultivation in the area of their adoption.
By seeing this scenario, the Government of India acknowledged seed an essential commodity under the Essential Commodities Act, 1955. On October 1964 Varietal Release System (VRS) came into existence with the formation of the Central Variety Release Committee (CVRC) at the national level, and State Variety Release Committees (SVRCs) at each state level. A Central Seed Committee (CSC) was established under the Ministry of Agriculture, Cooperation and Farmers Welfare provided in the Seeds Act, 1966. The functions of the CVRC were taken over by the CSC in 1969 to ensure the quality of seeds on sale and notification of the kinds/varieties. To perform the function at central level to release/notification, provisional notification and de-notification of cultivars, CSC constituted a Central Sub-Committee on Crop Standards, Notification & Release of Varieties for Agricultural Crops and Horticultural Crops, while to perform similar functions at state level, State Seed Sub-Committee (SSSC) was constituted [2].
Entries (pure lines/open pollinated varieties/composites/synthetics/hybrids etc.) are developed by the concerned plant breeders/agencies through scientific temperament and extensive breeding programs for the benefit (food and nutritional security) of humankind. Different conventional (Introduction, selection, hybridization followed by selection etc.) and advanced (tissue culture-based techniques like somaclonal variation, anther and pollen culture; mutation, marker assisted breeding, transgenic or genome editing techniques) breeding methods are being used by the different agencies (ICAR or non-ICAR national institutes, SAUs, private national or multinational companies etc.) to generate elite material for high yield potential, nutritional quality and other associated traits. Developed elite materials are being tested by the concerned plant breeder/s at their research station for three to four years in replications for stability and selected superior cultivars enter into the All India coordinated crop improvement projects (AICCIPs) trials for further testing in multi-environments across the country.
First AICCIP was started in way back of 1957 by ICAR on maize crop for systemic testing of entries and for release of high yielding new maize varieties. In general, the three-tier system of multi-location evaluation is used for three years except perennial fodder crops (requires four years-one for crop establishment and three for evaluation) in India. Multilocational trials are conducted by the Project Coordinator (PC)/Project Director (PD) of AICCIPs [3] with the help of concerned principle investigators. The AICCIPs have been developed for all the major crops including forage crops. The AICCIPs come under the umbrella of ICAR, has great role in the development of improved crop varieties and generation of production and protection technologies that directly benefit farmers for their economic amelioration. All AICCIP trials are well organized, systemic and conducted through a uniform testing procedure across the centers as per crop standard. It is a powerful system to screen large number of entries and recommend well-tested, superior, and adapted new cultivars to the end users. The flow chart of varietal release and notification in India is illustrated in Figure 1.
Flow chart of plant varietal release and notification system in India.
Newly entered material/entries into the three-tier system must have the following requirements.
Station trial or preliminary yield trial-Concerned plant breeder must perform station or regional trial and proposed entry must have undergone censorious evaluation process or screening (insect pests and diseases). Crop based quality parameters and tolerance to key abiotic stresses are also to be screened as per the requirement. Pre-coordinated trial data on yield, trait stability and other related agronomic traits must be available to the PC/PD in support of the relevance of his/her entry [4].
The entry must have a high degree of genotypic stability, phenotypic uniformity, germination percentage and physical purity (as per the minimum seed certification standards).
The entry must have few distinct diagnostic traits which make it different to all remaining varieties. These distinct traits help to identification of variety during legal infringement (DUS testing) [5].
All the information related to the development of entry
Private companies can enter their material into the coordinated trial system as similar to other agencies but have to pay the prescribed fee for their entries as per guideline of the Government of India.
All the material (product of selection, hybridization followed by selection, synthetics, composites, and hybrids, etc.) shall be subjected to the same system.
The AICCIP centers for various crops are located at ICAR institutes or State Agricultural Universities (SAUs) or other volunteer centers recommended by AICCIP workshop based on covered crop area, adaptability, and agro-climatic condition etc. It involves various steps [6].
The time duration of the initial varietal trial (IVT) is one year. All the entries, which were superior to their respective station trials, would be introduced into the IVT. These entries would be used for multi-location trials along with checks. In general, three checks (national, zonal and local checks) are being used for efficient evaluation of entries across the centers. The national check (a crop variety which had been released previously for whole country) would be used for a long period but zonal (a crop variety which had been released previously for specific zone) and local checks (high yielding local variety) can be replaced based on the requirement. These checks cannot be replaced after the IVT. Maintenance of genetic purity, germination and physical purity of new material are the prime objectives of the concern plant breeder/agency. The IVT trials are conducted in such a manner that minimum difference of yield (5–10%) and other ancillary traits can be measured. Experimental layout (experimental design, number of replications and treatments) is the prime responsibility of the PC/PD through concerned principle investigator. The cultural practices
Based on superiority (5–10%) over the best performing check, superior entries will enter into the AVT-I from IVT. The number of tested entries in the AVT-I will be less than IVT. The plot size is large in AVT-I as compared to IVT, therefore data generated on yield and other ancillary traits will be more realistic, accurate and minimal chances of error. The number of testing locations should be more as compare to IVT in a given zone for more realistic data on yield and other economically important traits, varietal adaptation, biotic and abiotic tolerance, quality parameters, etc. National, zonal and local checks (which were used in IVT) shall be used for critical analysis along with the entries. During AVT-I, additional data on disease and or insect pest tolerance under artificial epiphytotic condition must be generated by the experts. Same as IVT, monitoring team would be deputed by the PC/PD at different growth stages of crop and observed data shall be submitted to the concerned PC/PD. Based on the performance of entry over the best performing check-in the respective zone, the superior entries would enter into the AVT-II.
All the requirements shall be fulfilled as similar to AVT-I. However, few additional data will be generated at AVT-II stage
Based on three year performance, best performing test entries shall be identified in the annual crop workshop or national group meet at the pre-defined institute/university. The Zonal Coordinators and Principal Investigators attend the national group meet to provide wider aspects of information on the varieties. After the approval from Deputy Director General (Crop Science) of Indian Council of Agricultural Research (ICAR), a “Varietal Identification committee (VIC)” constituted in advance of annual workshop or national group meet. All the committee members (Table 1) shall be informed well in advance by the PC or PD [3]. Principal investigators (PIs) of different disciplines can assist in the process of discussion but they do not have the right to vote. Only committee members have the right to cast vote. The VIC provides detailed information on recommended entries to the Central Sub-Committee on Crop Standards, Notification, and Release. This committee has sole right to release and notify the best-performing entry into national wise or zonal wise based on the recommendations of the VIC.
S.N. | Representative | Organizational position |
---|---|---|
1 | DDG (Crop Science)/ his or her nominee | Chairman |
2 | Project Coordinator/Project Director of AICCIP | Member Secretary |
3 | Director of Research of institute/SAUs of that region where the meeting is held | Member |
4 | Agricultural Commissioner (Department of Agriculture) | Member |
5 | One nominee of Seed organization (NSC, SSC) | Member |
6 | One representative of private seed agencies | Member |
7 | One representative of crop-based industries | Member |
8 | Project coordinator (seed technology) | Member |
9 | Two eminent scientists of that institute | Member |
Organizational setup of varietal identification committee (VIC). The committee comprises one chairman and nine members.
Source: [3] Tandon et al., 2015.
The candidate variety must have a minimum of three years of yield and other ancillary trait data from multi-location coordinated trials.
At least two-year data on disease and pest reaction at a hot spot or artificial epiphytotic condition.
The candidate variety must have at least one-year data on agronomic performance like seed rate, dates of sowing, planting density, irrigation, and fertilization. In forage crops, three year rigorous evaluation must be done for annual crops (seed yield data for third year only) and four year for perennial crops (one year for crop establishment and other three years for evaluation).
The concerned breeder must have at least a minimum requirement of nucleus seed so that breeder seed can be generated easily.
The concerned plant breeder should have pure seed for planting of 5 ha area. If he or she did not match the requirement, then identification can only be postponed for one year.
All these issues shall be discussed by the project coordinator in the annual workshop itself. The candidate variety must be phenotypically uniform (plant height, maturity, etc.) and stable in performance throughout the years.
Central Sub-Committee on Crop Standards, Notification, and Release of Varieties appointed by Central Seed Committee under Section 3 of the seed act, 1966 during 1994. The committee comprised one chairman and 17 members (Table 2).
S.N. | Representative | Organizational position |
---|---|---|
1 | Deputy Director General (Crop Sciences), ICAR | Chairman |
2 | Deputy Commissioner (QC) DAC & FW, GOI | Member Secretary |
3 | Directors of State Seed Certification Agencies, or their representatives | Member |
4 | Project Directors of Departments of Agriculture of all states, or their representatives | Member |
5 | Project Coordinators/Directors of AICCIPs | Member |
6 | Agricultural Commissioner, GOI | Member |
7 | Representatives of the seed industry, NSC, State Seed Corporations, private seed companies | Member |
8 | Representatives of ICAR, ICAR institutes, NGOs | Member |
9 | Progressive farmers | Member |
Organizational setup of central sub-committee on seed standards, release, and notification of variety (agricultural crops). The committee comprised one chairman and 17 members.
Source: [7] SeedNet India Portal; QC-Quality Control, DAC & FW-Department of Agriculture, Cooperation and Farmers Welfare, GOI-Government of India.
Central Sub-Committee releases varieties as per the benefit of the stakeholders and need of regional, zonal or national importance, and the State Seed Sub-Committee releases varieties beneficial for particular state. Notification of variety is compulsory on regulating the seed quality under the provision of Seed Act, 1966. Notification usually authorizes certified seed production throughout the country, by private or public seed multiplication organizations. Once the Central Sub-Committee accepts the proposal, the varieties/hybrids will be released for the concerned agro-climatic zone/s (may cover one or more number of states or nationally). Simultaneously, it must be notified for seed certification purpose in the country. During the release, the concerned breeder must have a minimum amount of seed which can be sown at least ten-hectare area [3]. Later on, seed multiplication is the responsibility of various seed agencies (NSC, SSC, private seed companies and progressive farmers, etc.). The significant differences between released and notified varieties are illustrated in Table 3.
S.N. | Released variety | Notified variety |
---|---|---|
1 | It is not a statutory function under the Seed Act, 1966 | Statutory function and variety will be registered under Section 5 of seed act 1966. |
2 | It cannot be used for seed certification | Only notified varieties to come under seed certification |
3 | No guarantee on seed quality for farmers | Assured seed quality |
4 | Seed law enforcement agencies (seed inspector etc.) cannot draw and test seed samples | They have the right to draw and test seed samples |
5 | These are not assets of Govt. of India | Notified varieties are assets of Govt. of India |
6 | Its main purpose is to make available the information of cultivar to the public and its area of adoption | The main purpose is seed quality regulation |
7 | Difficult to trace out the genesis | The notification of the varieties will help to trace out its genesis. |
Critical differences between released and notified varieties.
It is a legal body constituted by the Department of Agriculture, Cooperation and Farmers’ Welfare (DAC&FW), Ministry of Agriculture and Farmers’ Welfare (MoA&FW), Government of India to advise central and state government on matters related to the implementation of seed act, 1966 and other related functions. The core committee includes one chairman and eight members to be nominated by the central government and one person to be nominated by the Governments of each State (Table 4). State Seed Committee (SSC) has a similar role at the state level. The CSC and SSC are empowered to release varieties but only CSC can notify those [8].
S.N. | Representative | Organizational position |
---|---|---|
1 | Secretary, DAC&FW, MoA& FW, GOI | Chairman |
2 | Additional Secretary (In charge Seeds), MoA& FW, GOI | Member |
3 | Agricultural Commissioner, MoA& FW, GOI | Member |
4 | Deputy Director General (Crop Sciences), ICAR | Member |
5 | Joint Secretary (In charge Seeds), MoA& FW, GOI | Member |
6 | Progressive farmers/ seed growers (4) nominated by the Central Government | Member |
7 | One representative from each State Govt. | Member |
8 | Director of National Seeds Project, MoA& FW, GOI | Member Secretary |
Organizational setup of central seed committee (CSC). The committee comprises one chairman and nine other members by the central government.
Source: [9] SeedNet India Portal; MoA & FW- Ministry of Agriculture and Farmers Welfare.
The CSC has authority to release varieties (pure lines/hybrids/composites/synthetics) developed by central research institutes (ICAR/non-ICAR), AICCIPs, private or corporate sector, and other organization as per the scientific data authenticity for zonal basis (which may include more than one state) or at national level.
The CSC has authority to approve proposals received from the State Variety Release Committees/State Seed Sub-Committees for varieties developed by the State Research Institutes but is considered suitable for areas outside the state (based on their performance).
The CSC can delimit the regions or tracts for the cultivation of varieties approved for release.
The CSC can advise the ICAR regarding the manner in which the National Register of Approved Varieties may be maintained, and to suggest the standard description of crop varieties.
The CSC can notify kinds/varieties for the purpose of the Seeds Act and the areas of their notification.
The CSC specifies minimum limits of germination percentage and purity for the notified kinds/varieties of seeds as per minimum seed certification standards.
The CSC specifies the “mark” or “label” in respect of notified kinds/varieties.
The State Seed Sub-Committees are constituted by Central Seed Committee and are authorized to set up a State Seed Laboratory, State Seed Certification Agency (SSCA) and an Appeals Authority, and to appoint seed inspectors and seed analysts. The differences between Central Sub-Committee and State Sub-Committee are given in Table 5.
S.N. | Central sub-committee | State sub-committee |
---|---|---|
1 | Authorized by Central Seed Committee | Authorized by State Seed Committee |
2 | Releases varieties for regional/zonal/national level | Only for concerned state/regions within the state |
3 | Statutory body for varietal notification | It cannot notify varieties |
4 | Notification followed by seed certification | Certified seed cannot be produced without notification |
5 | Members in the committee are appointed by the Central Govt. | Members in the committee are appointed by the State Govt. |
Critical differences between central sub-committee and state sub-committee.
There are some rights which have been provided by the Central Seed Committee for proper functioning of seed chain in respective state in India. These empowerments are-
The State Seed Sub Committee will advise the state government on all matters related to the execution of the Seeds Act, 1966.
Reviewing the implementation of the Seeds Act in the state and send periodic reports to the state government and the Central Seed Committee.
Inspect, analyze and report on the State Seed Testing Laboratory.
Advise on educational and promotional measures for proper enforcement and understanding of the Seed Act.
Planning for different crop varieties to be grown in different regions of the state, and to review the assessment of seed requirements.
Considering the release of new varieties for the state and recommend their notification to the Central Seed Committee.
Monitoring the performance of newly released varieties in the state.
Being agriculture as a state subject in India, centrally released varieties are not directly accepted by all the states for which they have been released. Each state has its own regulatory system which they have to follow for varietal release in the state. They have to pass through all the steps of the concerned state release procedure before they approve for cultivation in the state
Since only notified varieties will be under the purview of Seed Law Enforcement, hence it is necessary to bring the seed of a particular crop variety under notification system. The seed inspector can only draw a sample from notified variety for analysis and ensure the seed quality [10, 11]. A released variety cannot come under seed chain without notification by the Gazette of India. Therefore, these issues will make the notification as necessary requirement for other things to act on it. The notification is made by the Central Government on the recommendation of the Central Seed Committee. Thus, notification is prerequisite for production of certified seed which ensures high quality of seeds to the farmers. After notification, variety becomes asset of government of India. The breeder seed can only be produced after the notification of variety and notified varieties enter into seed chain. Notification also helps in the genesis of original variety based on its pedigree and also regulates any kind of infringement in the later stages of varietal promotion.
Released varieties can be denotified if they are not performing well in the area of their adoption or have been in cultivation for more than 15 years or are not much in demand. Denotification can be done based on the recommendation of central seed committee by the government of India.
There are several ways and means to increase the crop production and productivity, however using genetically pure and high-quality seed is first and prime objective in agriculture. Therefore the variety which will be used by farmers must have undergone several evaluations in order to ensure its stable yield potential, tolerance to biotic and abiotic stresses and these criteria are being fulfilled by a legal varietal release system. The main objective of the varietal release system in India is to introduce newly developed, high yielding varieties to the farmers for broad-spectrum cultivation in the area of their adoption and only those varieties will be notified which are superior to existing one. It provides choice to the farmers to cultivate a specific variety, based on their need for crop diversification. In India, the systemic framework has helped farmers to get high quality of seed from market and production has increased many folds since the inception of AICCPs. Notification is mandatory to release a variety, though the release process itself does not have legal cover.
Authors acknowledge Director, ICAR-Indian Grassland and Fodder Research Institute and Project Coordinator, ICAR-All India Coordinated Research Project on Forage Crops and Utilization for their assistance, guidance and funding.
The authors declare no conflict of interest.
The Edited Volume, also known as the IntechOpen Book, is an IntechOpen pioneered publishing product. Edited Volumes make up the core of our business - and as pioneers and developers of this Open Access book publishing format, we have helped change the way scholars and scientists publish their scientific papers - as scientific chapters.
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