\\n\\n
IntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\\n\\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\\n\\nLaunching 2021
\\n\\nArtificial Intelligence, ISSN 2633-1403
\\n\\nVeterinary Medicine and Science, ISSN 2632-0517
\\n\\nBiochemistry, ISSN 2632-0983
\\n\\nBiomedical Engineering, ISSN 2631-5343
\\n\\nInfectious Diseases, ISSN 2631-6188
\\n\\nPhysiology (Coming Soon)
\\n\\nDentistry (Coming Soon)
\\n\\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\\n\\nNote: Edited in October 2021
\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/132"}},components:[{type:"htmlEditorComponent",content:'With the desire to make book publishing more relevant for the digital age and offer innovative Open Access publishing options, we are thrilled to announce the launch of our new publishing format: IntechOpen Book Series.
\n\nDesigned to cover fast-moving research fields in rapidly expanding areas, our Book Series feature a Topic structure allowing us to present the most relevant sub-disciplines. Book Series are headed by Series Editors, and a team of Topic Editors supported by international Editorial Board members. Topics are always open for submissions, with an Annual Volume published each calendar year.
\n\nAfter a robust peer-review process, accepted works are published quickly, thanks to Online First, ensuring research is made available to the scientific community without delay.
\n\nOur innovative Book Series format brings you:
\n\nIntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\n\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\n\nLaunching 2021
\n\nArtificial Intelligence, ISSN 2633-1403
\n\nVeterinary Medicine and Science, ISSN 2632-0517
\n\nBiochemistry, ISSN 2632-0983
\n\nBiomedical Engineering, ISSN 2631-5343
\n\nInfectious Diseases, ISSN 2631-6188
\n\nPhysiology (Coming Soon)
\n\nDentistry (Coming Soon)
\n\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\n\nNote: Edited in October 2021
\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:"5762",leadTitle:null,fullTitle:"Agricultural Value Chain",title:"Agricultural Value Chain",subtitle:null,reviewType:"peer-reviewed",abstract:"This book covers the agricultural value chain issues that occur in different parts of the world and aims to increase our understanding about the sustainable agricultural value chain paradigm. By reading through these chapters, the readers will witness various interesting, sometimes sad, commonalities among different regions of the world, where smallholder farmers and producers are severely affected by various agricultural policy deficiencies or mistakes and inexistences. The book consists of 14 chapters, which comprehensively cover over 20 agricultural products from more than 15 different regions of the world. Various qualitative and quantitative research methods are presented including surveys, case studies, interviews, price transmission, risk analysis, and multiagent system technology.",isbn:"978-1-78923-007-9",printIsbn:"978-1-78923-006-2",pdfIsbn:"978-1-83881-242-3",doi:"10.5772/65199",price:119,priceEur:129,priceUsd:155,slug:"agricultural-value-chain",numberOfPages:290,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"4b4b9668fe6fff8891429bfe61afc4af",bookSignature:"Gokhan Egilmez",publishedDate:"April 26th 2018",coverURL:"https://cdn.intechopen.com/books/images_new/5762.jpg",numberOfDownloads:22197,numberOfWosCitations:13,numberOfCrossrefCitations:21,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:28,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:62,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 15th 2016",dateEndSecondStepPublish:"October 6th 2016",dateEndThirdStepPublish:"September 18th 2017",dateEndFourthStepPublish:"October 18th 2017",dateEndFifthStepPublish:"December 18th 2017",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"111454",title:"Dr.",name:"Gokhan",middleName:null,surname:"Egilmez",slug:"gokhan-egilmez",fullName:"Gokhan Egilmez",profilePictureURL:"https://mts.intechopen.com/storage/users/111454/images/system/111454.jpg",biography:"Dr. Gokhan Egilmez is an associate professor of Industrial and Systems Engineering at the University of New Haven, West Haven, CT. He previously worked as an assistant professor of Industrial and Manufacturing Engineering at the North Dakota State University and a postdoctoral research associate at the Department of Civil, Environmental, and Construction Engineering, University of Central Florida. He obtained his PhD degree in Industrial and Systems Engineering and MS degree in Industrial and Systems Engineering and Civil Engineering from the Ohio University, USA. He also received his BS degree in Industrial Engineering from the Istanbul Technical University, Turkey. His research interests cover various topics that include social, economic, and environmental life cycle assessment; agriculture systems’ sustainability assessment; data analytics; engineering education; applied optimization; and simulation modeling. He has over 60 peer reviewed journal articles and conference proceedings, leading ASOS research lab, and 2022 president and board member of Institute of Industrial and Systems Engineers Sustainable Development Division.",institutionString:"University of New Haven",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"University of New Haven",institutionURL:null,country:{name:"United States of America"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"74",title:"Marketing",slug:"marketing"}],chapters:[{id:"56585",title:"Agriculture Value Chain as an Alternative to Increase Better Income’s Distribution: The Case of Indonesia",doi:"10.5772/intechopen.70141",slug:"agriculture-value-chain-as-an-alternative-to-increase-better-income-s-distribution-the-case-of-indon",totalDownloads:6231,totalCrossrefCites:0,totalDimensionsCites:3,hasAltmetrics:0,abstract:"Specific material handling and treatment for specific agriculture products is required. Enhancing the productivity, competitiveness and efficiency of agriculture value chain is a priority for Indonesia to achieve competitiveness. This chapter discusses the overview of agriculture value chain in Indonesia and provides case studies related to supply chain risk management and logistics cost. Then, the author may propose recommendations to optimize the agricultural value chain. Each agriculture commodity probably has different type of tier, type of supply chain risks, issues and activities which leads to the different proportion of logistics and distribution cost in each tier. The results showed differences in strategy either speculation or postponement for inventory management to improve the value of horticulture along supply chain, while in aquaculture research also shows the same differences. Moreover, the value chain analysis helps to identify the value created by each stakeholder. In the value chain of catchment fish, ship owner plays the dominant role in the whole income distribution, while in the aquaculture, spreader get the highest profit margin. Trader gives the highest value added during transportation but earns the lowest profit. The value chain analysis of fresh vegetables shows the highest portion of traders in the whole inventory cost.",signatures:"Adi Djoko Guritno",downloadPdfUrl:"/chapter/pdf-download/56585",previewPdfUrl:"/chapter/pdf-preview/56585",authors:[{id:"195827",title:"Dr.",name:"Adi Djoko",surname:"Guritno",slug:"adi-djoko-guritno",fullName:"Adi Djoko Guritno"}],corrections:null},{id:"56393",title:"SamenMarkt®, a Proposal for Restoring Trust in the Horticultural Fresh Food Market by Using Multi-Agent System Technology",doi:"10.5772/intechopen.70140",slug:"samenmarkt-a-proposal-for-restoring-trust-in-the-horticultural-fresh-food-market-by-using-multi-agen",totalDownloads:1011,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In the horticultural fresh food supply chain network in the Netherlands, a crisis is emerging. The market is out of balance and many growers are facing bankruptcy, in the period of 2011–2013, 50% of the growers were not able to pay interest and redemption. Trust between participants in the supply chain network has decreased. This chapter presents the currently not established and identifies design requirements for new systems to address this challenge and provide directions for possible improvement. As a result, this chapter introduces the concept of SamenMarkt®, a participatory system in which multi-agent system technology enables distributed price negotiation, distribution and communication between producers, retailers and consumers. A SWOT analysis of the concept of SamenMarkt® is provided together with a research and development plan in which simulation and emulation create the basis for stakeholder- and participant involvement in the design process of a distributed digital market place. Further research aims to study how SamenMarkt® can provide a solution space for the emerging global food crises. At present, we are using agent-based modelling to simulate the present market and scenarios. The next step will be to build the actual agent-based platform for real-time negotiations and business intelligence.",signatures:"Olaf van Kooten, Caroline Nevejan, Frances Brazier, Michel Oey and\nCoen Hubers",downloadPdfUrl:"/chapter/pdf-download/56393",previewPdfUrl:"/chapter/pdf-preview/56393",authors:[{id:"126250",title:"Prof.",name:"Michel",surname:"Oey",slug:"michel-oey",fullName:"Michel Oey"},{id:"195836",title:"Prof.",name:"Olaf",surname:"Van Kooten",slug:"olaf-van-kooten",fullName:"Olaf Van Kooten"},{id:"195837",title:"Dr.",name:"Caroline",surname:"Nevejan",slug:"caroline-nevejan",fullName:"Caroline Nevejan"},{id:"195838",title:"Prof.",name:"Frances",surname:"Brazier",slug:"frances-brazier",fullName:"Frances Brazier"},{id:"195839",title:"MSc.",name:"Coen",surname:"Hubers",slug:"coen-hubers",fullName:"Coen Hubers"}],corrections:null},{id:"56485",title:"Citrus Value Chain(s): A Survey of Pakistan Citrus Industry",doi:"10.5772/intechopen.70161",slug:"citrus-value-chain-s-a-survey-of-pakistan-citrus-industry",totalDownloads:2192,totalCrossrefCites:6,totalDimensionsCites:8,hasAltmetrics:0,abstract:"Pakistan is producing more than 30 types of different fruits of which citrus fruit is leading among all fruit and constitutes about 30% of total fruit production in the country. Above 90% of citrus fruits are produced in Punjab province and distributed through different value chains in domestic as well as in international markets. A large part of citrus fruit produced in Pakistan is mostly consumed locally without much value addition; however, 10–12% of total production is exported after value addition. The value chains are very diverse, and a number of different players actively participate in these chains, which ultimately decide the destination of citrus fruit in these supply chain(s). Knowing all these facts, the main aim of this research is to identify different value chains of citrus fruit (Kinnow) in Pakistan and also to identify and discuss the role and function of different value chain players in the citrus industry in Pakistan. A survey involving of different players of Pakistan’s citrus industry was conducted in 2013–2014 to better understand the citrus value chain(s). Using a convenience sampling technique, a total of 245 respondents were interviewed during a period of 4–5 months from three leading citrus-producing districts. It was found that citrus value chains can be classified into two major types: unprocessed citrus value chain and processed citrus value chains. It was also found that in the past, a large number of citrus growers were involved in preharvest contracting for their orchards and only a small number of citrus growers sold their orchards directly into local and foreign markets. The proportion has been gradually changed now and growers are becoming progressive and more market oriented.",signatures:"Muhammad Imran Siddique and Elena Garnevska",downloadPdfUrl:"/chapter/pdf-download/56485",previewPdfUrl:"/chapter/pdf-preview/56485",authors:[{id:"181547",title:"Dr.",name:"Elena",surname:"Garnevska",slug:"elena-garnevska",fullName:"Elena Garnevska"},{id:"196724",title:"Dr.",name:"Muhammad Imran",surname:"Siddique",slug:"muhammad-imran-siddique",fullName:"Muhammad Imran Siddique"}],corrections:null},{id:"55095",title:"The Value Aspect of Reallocating Seafood Freight from Road to Sea Transport",doi:"10.5772/intechopen.68779",slug:"the-value-aspect-of-reallocating-seafood-freight-from-road-to-sea-transport",totalDownloads:1041,totalCrossrefCites:1,totalDimensionsCites:0,hasAltmetrics:0,abstract:"A case study elaborates on the project organization promoting change of transport mode in a food chain from a value perspective. This project organization may perspectively be viewed as a supply chain with value conceptions different from the everyday seafood supply chain it is meant to develop. Value is in this project context revealed as an intersubjective complex phenomenon, founded in that value conceptions by actors located at different locations in the supply chain. This renders customer value as one of many dynamic value components in this project organization. Value embedded in a supply chain is therefore always a source of uncertainty, a subjective perspective; it cannot be considered as a clear functional purpose in projects aimed at developing food supply. The route to reallocate seafood freight should therefore focus on organizing interconnectivity to support networking and the project members accepting that the project outcome is emergent.",signatures:"Per Engelseth, Irina V. Karlsen, Shulin Huang and Arild Hoff",downloadPdfUrl:"/chapter/pdf-download/55095",previewPdfUrl:"/chapter/pdf-preview/55095",authors:[{id:"197127",title:"Dr.",name:"Per",surname:"Engelseth",slug:"per-engelseth",fullName:"Per Engelseth"},{id:"197138",title:"Ms.",name:"Irina V.",surname:"Karlsen",slug:"irina-v.-karlsen",fullName:"Irina V. Karlsen"},{id:"197139",title:"Ms.",name:"Shulin",surname:"Huang",slug:"shulin-huang",fullName:"Shulin Huang"},{id:"197140",title:"Dr.",name:"Arild",surname:"Hoff",slug:"arild-hoff",fullName:"Arild Hoff"}],corrections:null},{id:"58906",title:"Agricultural Diversification in Japan",doi:"10.5772/intechopen.73192",slug:"agricultural-diversification-in-japan",totalDownloads:1037,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In recent few decades, Japanese agriculture has been facing many problems such as low profit rate, lack of labor force, abandonment of farmland, losing competitive competence for low-price, imported products, and so forth. One of the trials for encouraging Japanese agriculture by the government is agricultural diversification, which is a kind of restructuring value chain in order to gain profitability of farmers. The strategy is that, by integrating some elements of value chain of food industries including primary industry (agricultural production), secondary industry (processed food manufacturing), and tertiary industry (food retails and restaurants), and re-allocating farmers’ business resources, farmers could be much more activated and their profit would be gained. In the past several decades, Japanese agricultural supply chain has strongly depended on Japan Agricultural Cooperatives (JA), and this caused some issues such as mismatching of demand and supply and low profitability of farmers. The policy of agricultural diversification was proposed to induce new integration of value chain and restructuring supply chain for solving these issues. This chapter presents some successful cases of agricultural diversification in Japan and infers the Key Factor of Success (KFS) of such trials.",signatures:"Makoto Hirano",downloadPdfUrl:"/chapter/pdf-download/58906",previewPdfUrl:"/chapter/pdf-preview/58906",authors:[{id:"220116",title:"Prof.",name:"Makoto",surname:"Hirano",slug:"makoto-hirano",fullName:"Makoto Hirano"}],corrections:null},{id:"56732",title:"Dairy Value Chain In Vietnam: Evidences from Bavi Area",doi:"10.5772/intechopen.69450",slug:"dairy-value-chain-in-vietnam-evidences-from-bavi-area",totalDownloads:1242,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Dairy farming, in Vietnam, existed in the early twentieth century thanks to the favorable natural advantage. During many difficult periods, the Vietnam’s dairy industry has developed constantly and contributed significantly to the food needs ensuring. However, Vietnam’s dairy industry still could not satisfy the domestic milk demand. Retail milk prices in Vietnam are very high, whereas the price of milk sold by the dairy farmers is very low. The cause stems from the control of dairy companies in the quantity and quality of milk. Moreover, that control caused an imbalance in the profits and benefits of each actor in the dairy value chain. This study, hence, finds out the distribution of benefits, costs, value-added among the actors, and problems in the practical management in dairy milk value chain with specific focus on Bavi as the case study.",signatures:"Nguyen Viet Khoi, Hoang Thi Hai Yen, Tong Van Khai, Nguyen Tien\nDuc and Dang Thi Phuong Hoa",downloadPdfUrl:"/chapter/pdf-download/56732",previewPdfUrl:"/chapter/pdf-preview/56732",authors:[{id:"195845",title:"Prof.",name:"Nguyen",surname:"Viet Khoi",slug:"nguyen-viet-khoi",fullName:"Nguyen Viet Khoi"},{id:"204479",title:"Ms.",name:"Hoang",surname:"Thi Hai Yen",slug:"hoang-thi-hai-yen",fullName:"Hoang Thi Hai Yen"},{id:"204480",title:"Mr.",name:"Tong",surname:"Van Khai",slug:"tong-van-khai",fullName:"Tong Van Khai"},{id:"204494",title:"MSc.",name:"Nguyen",surname:"Tien Duc",slug:"nguyen-tien-duc",fullName:"Nguyen Tien Duc"},{id:"214643",title:"Dr.",name:"Dang",surname:"Thi Phuong Hoa",slug:"dang-thi-phuong-hoa",fullName:"Dang Thi Phuong Hoa"}],corrections:null},{id:"56719",title:"Soybean Agribusiness in Argentina (1990–2015): Socio- Economic, Territorial, Environmental, and Political Implications",doi:"10.5772/intechopen.70463",slug:"soybean-agribusiness-in-argentina-1990-2015-socio-economic-territorial-environmental-and-political-i",totalDownloads:959,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Nowadays, soybean value chain is both the major expression of agribusiness and one of the most troublesome uses of territory of Argentina. This chapter is aimed to analyzing the worrying socio-economic, territorial, environmental, and political implications unchained by the expansion of the soybean’s pattern during the last 25 years. On the basis of scholarly literature and both official and unofficial sources of data, we have studied the restructuration of the rural sector, the concentration of both the rural property and the agro-industrial chain, the new territorial enclosures, the socio-ecological and health consequences of the soybean’s advance, and the influence of the transnational seed industry on the farmers’ subordination. Our results show a substantial reduction of both the amount of rural units and the traditional production areas, the emergence of new leasing practices, the accumulation chain’s vertical integration, the growth of the land’s concentration, the expulsion of aborigines and peasants, the increase of deforestation and environmental degradation, the loss of legal and food sovereignty, and the serious impacts on the population’s health due to the massive fumigations with agrochemicals. The chapter’s findings suggest that soybean agribusiness should be considered as an irrational use of territory for most of the national society.",signatures:"Sebastián Gómez Lende and Guillermo Velázquez",downloadPdfUrl:"/chapter/pdf-download/56719",previewPdfUrl:"/chapter/pdf-preview/56719",authors:[{id:"195930",title:"Dr.",name:"Sebastián",surname:"Gómez Lende",slug:"sebastian-gomez-lende",fullName:"Sebastián Gómez Lende"},{id:"197286",title:"Dr.",name:"Guillermo",surname:"Velázquez",slug:"guillermo-velazquez",fullName:"Guillermo Velázquez"},{id:"197350",title:"Dr.",name:"Sebastián",surname:"Gómez Lende",slug:"sebastian-gomez-lende",fullName:"Sebastián Gómez Lende"}],corrections:null},{id:"56263",title:"Agricultural Market Integration in the Commonwealth of Independent States: What Are the Main Driving Forces and Challenges?",doi:"10.5772/intechopen.69869",slug:"agricultural-market-integration-in-the-commonwealth-of-independent-states-what-are-the-main-driving-",totalDownloads:1025,totalCrossrefCites:3,totalDimensionsCites:3,hasAltmetrics:0,abstract:"Utilizing a price transmission approach, we focus on price relationships between the countries of the Commonwealth of Independent States (CIS) and how price changes on the world agricultural market are transmitted to the domestic CIS markets. In this study, we establish a unique price data set on five different agricultural products (i.e. wheat, pork, beef, poultry and whole milk powder) observed in eight different CIS countries. The results of the price transmission analysis indicate that regional integration within the CIS is strongest for pork and beef, followed by poultry and whole milk powder. The integration of CIS markets in world agricultural markets is strongest for wheat and beef, whereas it is relatively low for pork and poultry. Furthermore, beef markets in the CIS countries are the strongest integrated within the region, with the EU and the world market. Overall, our results indicate that domestic market support and trade policies, physical trade flows between countries, infrastructure, and bilateral or multilateral trade agreements play a key role in market integration of the CIS countries. These determinants should be particularly considered when designing recommendations for improving agri-food supply chain competitiveness in the CIS countries.",signatures:"Ivan Djuric, Linde Götz, Miranda Svanidze and Thomas Glauben",downloadPdfUrl:"/chapter/pdf-download/56263",previewPdfUrl:"/chapter/pdf-preview/56263",authors:[{id:"197133",title:"Dr.",name:"Ivan",surname:"Djuric",slug:"ivan-djuric",fullName:"Ivan Djuric"},{id:"197272",title:"Dr.",name:"Linde",surname:"Götz",slug:"linde-gotz",fullName:"Linde Götz"},{id:"197273",title:"MSc.",name:"Miranda",surname:"Svanidze",slug:"miranda-svanidze",fullName:"Miranda Svanidze"},{id:"197275",title:"Prof.",name:"Thomas",surname:"Glauben",slug:"thomas-glauben",fullName:"Thomas Glauben"}],corrections:null},{id:"60463",title:"The Struggles of Smallholder Farmers: A Cause of Modern Agricultural Value Chains in South Africa",doi:"10.5772/intechopen.75710",slug:"the-struggles-of-smallholder-farmers-a-cause-of-modern-agricultural-value-chains-in-south-africa",totalDownloads:1631,totalCrossrefCites:2,totalDimensionsCites:5,hasAltmetrics:1,abstract:"The potential of sustaining smallholder farmers (SHFs), for long-term food security remains, within the context of rising modern food value chains, particularly in Africa, a threat. Support for a greener, lower carbon economy that creates jobs and improves human well-being as part of a sustainable and socially inclusive stable economic development should be driven, at least in part, by SHF.",signatures:"Wolfgang Johann von Loeper, Scott Drimie and James Blignaut",downloadPdfUrl:"/chapter/pdf-download/60463",previewPdfUrl:"/chapter/pdf-preview/60463",authors:[{id:"219859",title:"Mr.",name:"Wolfgang Johann",surname:"von Loeper",slug:"wolfgang-johann-von-loeper",fullName:"Wolfgang Johann von Loeper"},{id:"222873",title:"Prof.",name:"James",surname:"Blignaut",slug:"james-blignaut",fullName:"James Blignaut"},{id:"222874",title:"Dr.",name:"Scott",surname:"Drimie",slug:"scott-drimie",fullName:"Scott Drimie"}],corrections:null},{id:"58833",title:"Integration of Small Farmers into Value Chains: Evidence from Eastern Europe and Central Asia",doi:"10.5772/intechopen.73191",slug:"integration-of-small-farmers-into-value-chains-evidence-from-eastern-europe-and-central-asia",totalDownloads:1589,totalCrossrefCites:7,totalDimensionsCites:6,hasAltmetrics:0,abstract:"The economic breakdown of the early transition process weighed heavily on food supply relationships in the Eastern European and Central Asian (EECA) countries. Small and medium-sized farm suppliers and processors suffered from lack of necessary production inputs whereas processors and retailers faced problems of insufficient quantity and quality of supplies. At the same time, changes in consumer demand as well as the accompanying entry of foreign investors in the retail and processing sectors necessitated significant and lengthy reforms and adjustments in the structure of food commodity chains to overcome these problems. Based on an extensive literature overview and a synthesis of five case studies conducted upon the assignment of Food and Agriculture Organization (FAO) of the United Nations, the current chapter demonstrates how small and medium-sized food processors manage to install effective procurement systems in weak institutional environments of EECA. The chapter also identifies the factors that drive small farmer-processor business linkages and their integration into national and international value chains in order to develop options for support and assistance.",signatures:"Jon H. Hanf and Taras Gagalyuk",downloadPdfUrl:"/chapter/pdf-download/58833",previewPdfUrl:"/chapter/pdf-preview/58833",authors:[{id:"20211",title:"Dr.",name:"Taras",surname:"Gagalyuk",slug:"taras-gagalyuk",fullName:"Taras Gagalyuk"},{id:"220215",title:"Prof.",name:"Jon",surname:"Hanf",slug:"jon-hanf",fullName:"Jon Hanf"}],corrections:null},{id:"58871",title:"Economic Synergies from Tighter Agri-Business and Coal Seam Gas Integration",doi:"10.5772/intechopen.73195",slug:"economic-synergies-from-tighter-agri-business-and-coal-seam-gas-integration",totalDownloads:1073,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In addition to government royalties, Australia’s coal seam gas (CSG) development has been beneficial in terms of facilitating regional economic development and growth, expansion of remote populations and facilities, increased employment opportunities and improved regional infrastructure, mainly in regional Queensland. There is substantial revenue potential for the Australian economy from the export of the resource to international energy markets. Many current CSG operations in Australia are located in prime agricultural-cattle grazing regions. Failure to identify potential coexistence opportunities between agribusiness promoting industries (API’s) and the CSG industry could limit the agriculture value chain and consequently restrict Australia’s food security and agricultural export potential. The economic benefits of the CSG industry combined with the importance of a sustained agricultural industry lay the foundation for investigating coexistence opportunities between these industries. Emphasis has been placed on potential synergies exhibited by the CSG industry (namely from CSG by-products) and the local agricultural industry which is typically dominated by API’s.",signatures:"Syeda U. Mehreen and Jim R. Underschultz",downloadPdfUrl:"/chapter/pdf-download/58871",previewPdfUrl:"/chapter/pdf-preview/58871",authors:[{id:"220025",title:"Ph.D. Student",name:"Syeda",surname:"Mehreen",slug:"syeda-mehreen",fullName:"Syeda Mehreen"},{id:"221813",title:"Prof.",name:"Jim",surname:"Underschultz",slug:"jim-underschultz",fullName:"Jim Underschultz"}],corrections:null},{id:"56493",title:"Collaboration in Agri-Value Chains: Building Supplier Production Capabilities for Productivity Gains",doi:"10.5772/intechopen.70132",slug:"collaboration-in-agri-value-chains-building-supplier-production-capabilities-for-productivity-gains",totalDownloads:918,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"This research employed an explanatory case study to compare supplier production capabilities for enhancing productivity gains between Uganda commercial forestry and sugarcane sector value chains. Key study results indicated that only 18% of the sugarcane farmers achieved the desired industry productivity output of at least 100 t/ha from their fields, with majority (82%) of the cane growers producing below expected industry productivity output. In the forestry sector, 41.3% of the farmers achieved the desired industry performance targets, with 58.7% of the growers performing below expected performance targets. The major buyers’ supplier development behaviour as seen in the diffusion of knowledge, skills and appropriate technology along vertical and horizontal collaborative value chain relationships, explains this paradox. Millers in the sugarcane sector used contractors to diffuse knowledge and skills, which weakened the supplier production capabilities. In the forestry sector, with the support of development partner agencies, productivity was higher due to effective diffusion of knowledge, skills and appropriate technology to primary producers. This finding strongly points to the need to implement deliberate supplier development strategies by the development partner agencies and governments, if productivity gains are to be improved within the agri-business value chains in developing countries.",signatures:"Michael Mugabira and Richard Chivaka",downloadPdfUrl:"/chapter/pdf-download/56493",previewPdfUrl:"/chapter/pdf-preview/56493",authors:[{id:"195879",title:"Mr.",name:"Michael",surname:"Mugabira",slug:"michael-mugabira",fullName:"Michael Mugabira"},{id:"195881",title:"Dr.",name:"Richard",surname:"Chivaka",slug:"richard-chivaka",fullName:"Richard Chivaka"}],corrections:null},{id:"55801",title:"A Review of Supply Chain Prices Analyses with Emphasis on Perishable Markets",doi:"10.5772/intechopen.69451",slug:"a-review-of-supply-chain-prices-analyses-with-emphasis-on-perishable-markets",totalDownloads:1137,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:1,abstract:"Prices at different levels of the supply chain are linked through long-run relationships and tend to differ by the marketing costs. However, several aspects intervene in making price dynamics along the supply chain quite complicated and erratic. In particular, several issues on how marketing margins evolve over time and across commodities, as well as how prices are transmitted along the supply chain are still debated. The implications for the understanding of the economy, the management of the firms, and the regulations of the markets are important and pushed scholars to dedicate particular attention to these topics. In particular, how prices evolve in the supply chain of perishable products is an intriguing challenge that has stimulated a hot debate. 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A concentration on improving efficiency and profitability within value chains for key local commodities has, through the creation of women’s cooperatives, also led to positive outcomes in female empowerment. Through qualitative and participatory research methods, our analysis of gendered aspects of value chains for argan, rose, cactus, and saffron in southwestern Morocco suggests that economic empowerment, fostered through existing women’s cooperatives, is fragile and subject to significant threats. In large part, this is the result of a state-driven approach that has not effectively considered the inequities inherent within value chains for key local commodities; and the meshing of existing social and cultural norms with the tenets of a national drive toward ‘modernization’ of the agricultural sector. We suggest that the MGP is gender blind in this respect. 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Let us first consider the generic real systems which take the form
where
where
Suppose that
The first equation in Eq. (3) is called the restriction of system (2) to its center manifold at the origin. The local center manifold, which is tangent to the
If
If
In this section, we consider Hopf bifurcation from the nondegenerate origin of system (1) restricted to the center manifold, in which the Jacobian matrix
Considering the Jacobian matrix
where
Here, we recall first the calculation method of the singular point quantities on center manifold for the above real three‐dimensional nonlinear dynamical systems. By means of transformation
system (4) is also transformed into the following complex system:
where
Based on the previous work in Ref. [16], we have developed the calculation method of the focal values on the center manifold for real four‐dimensional nonlinear dynamical systems in Ref. [35]. In fact, here Theorem 1 can be generalized in the
where
By means of transformation of Eq. (5), system (10) can be transformed into the following complex system
where the subscript “
Recently, the study of chaos has become a hot research topic, and the attention of many researchers is turning to 4D systems from 3D dynamical systems, for example, the authors of Ref. [36] investigated Hopf bifurcation of a 4D‐hyoerchaotic system by applying the normal form theory in 2012, but its multiple Hopf bifurcation on the center manifold have not been considered. Here, we will investigate the system further by computing the singular point quantities of its equilibrium point, which takes the following form
where
with the characteristic equation:
To guarantee that
Thus, we obtain the critical condition of Hopf bifurcation at
where
such that
Namely, we can use the nondegenerate transformation and the time rescaling:
where
where
According to Theorem 3, we obtain the recursive formulas of
By applying the above formulas in the Mathematica symbolic computation system, we figure out easily the first two singular point quantities of the origin of system (18):
where
and the above expression of
From Remark 1 and the singular point quantities (21), we have
Considering its Hopf bifurcation form of Theorem 6, we have the following:
The rigorous proof of the above theorem is very similar to the previous ones in [14, 16], namely, by calculating the Jacobian determinant with respect to the functions
Up till now, study on bifurcation of limit cycles from the degenerate singularity of higher dimensional nonlinear systems (1) is hardly seen in published references. Here, we will investigate the Hopf bifurcation problem from the high‐order critical point on the center manifold.
Let us consider the real
where the subscript “
In order to discuss the calculation method of the focal values on center manifold of the system (23), from the center manifold theorem [1], we take an approximation to the center manifold:
where
where
For system (25), some significant works have been done in Refs. [26] and [27]. Let us recall the related notions and results.
By means of transformation (5)
system (25) is transformed into following system:
where
For any positive integer
a homogeneous polynomial of degree
such that
Similar to Theorem 2, there also exists a equivalence between the
Now we consider an example for system (23) with
where
namely,
where
Hence,
Applying the powerful symbolic computation function of the Mathematica system and the recursive formulas in Theorem 10, and from Remark 5, we obtain the first three singular point quantities as follows
In the above expression of each
Thus, from Theorem 9 and Eqs. (35) and (31), we have
Now we consider Hopf bifurcation of limit cycles from the origin for perturbed system (30).
Considering the conditions (37) of Theorem 12 and substituting the group of critical values of Eq. (38) into Eq. (39), we obtain
and
hold, one must obtain that the succession function on the center manifold has three small real positive roots, just the system (30) has at least three limit cycles in the neighborhood of the origin. We can refer to references [16, 26, 27] for more details about the construction of limit cycles.
The two classes of methods for computing the nondegenerate and degenerate singular point quantities on center manifold of the three‐, four‐, and more higher dimensional polynomial systems are discussed here, and more as the applications of them, the multiple limit cycles or Hopf cyclicity of two typical nonlinear dynamic systems restricted to the corresponding center manifolds are investigated.
where
This work was supported by Natural Science Foundation of China grants (11461021, 11261013), Nature Science Foundation of Guangxi (2015GXNSFAA139011), Research Foundation of Hezhou University (No.HZUBS201302), and Guangxi Education Department Key Laboratory of Symbolic Computation and Engineering Data Processing.
The Fourth Industrial Revolution, or Industry 4.0, aims at automating traditional manufacturing and industrial practices exploiting the most recent technologies depicted in Figure 1. Integrating artificial intelligence (AI) and robotics with traditional practices, the world of manufacturing processes is undergoing a transformation from activities that rely on human experience and skills into flexible environments, including objective decisional systems fully integrated within the industrial process. Advanced robotics is meant to develop autonomous and intelligent systems that could reduce the intervention of human workers [1] in many of the crucial and repetitive tasks that represent the core business of companies. Augmented and virtual reality can give operators more information about their tasks [2] and help them to alleviate mental stress during some jobs. Additive manufacturing [3] can speed up the production process. Internet of things (IoT) [4] allows new forms of communication between machines, giving rise to smart devices that can help humans achieve their objectives. Radiofrequency identification (RFID) technologies are used for efficient logistics and inventory warehouse management [5] reducing costs while increasing quality and competitiveness.
Industry 4.0 pillar technologies.
Among all the aforementioned technologies, AI is perhaps the one that received more interest during the years. Indeed, nowadays, the industrial interest in AI applications in various sectors is undeniable. However, for industries, artificial intelligence is both a source of enthusiasm and skepticism. One reason is that deep learning (DL) is a technology based on data, and problems solved using AI are as good or as bad as the data they are trained on. In addition, companies perceive AI as a black box and would prefer understandable and explainable processes [6]. Both these aspects should be taken into consideration when developing industrial AI solutions.
Current automation-assisted production is mostly open-loop and relies on specific checkpoints to perform product quality analysis. Early systems based on vision date back to the nineties. Such an approach suits best when critical issues can be formally expressed by taking advantage of geometrical measurements or well-known features on the inspected objects. Unfortunately, these techniques cannot perform many quality-control activities because they need a predefined sequence of actions where quality checks should be designed carefully to meet the precise production requirements. Moreover, human nature shows formidable efficiency in learning simple checks even if it would be difficult to formalize such operations with a sequence of rules. Indeed, experience plays a relevant role in human evaluation for products quality assessment. Similarly, vision inspection processes performed by automated machines will require the development of novel algorithms that should be trained and improved with time and experience.
The introduction of automation systems in the production lines that exploit AI techniques has reduced the need for human intervention in the manufacturing process of many products. This innovation had a major impact on many industrial applications, and visual inspection is by far the activity that has profited most. Thanks to deep neural networks (DNNs), difficult computer vision tasks, such as object classification or detection and image segmentation, have been addressed recently using an adequate number of training data. DNNs are scalable, experience-based, and have similar performance to human workers. Since the development of AlexNet [7], solutions based on deep learning have been encouraged, and convolutional neural networks (CNN) also have been extensively utilized for automating optical quality inspections. However, since such networks need a huge amount of labeled data for training their parameters, it is difficult to have an adequately large set of faulty samples with well-optimized industrial processes for creating a
Welding is a fundamental activity in many industrial manufacturing processes, such as automotive, shipbuilding, aerospace, and electronics. It is a crucial operation for the overall quality of the production line because a defect not detected in the early stages can determine the rejection of the entire product. This chapter introduces deep neural networks in the context of welding defect detection, starting by analyzing common problems in the industrial applications of such technologies and presenting in detail a solution for quality checks in fuel injectors welding during the production stage.
Inspection analysis can be classified into one of the following categories [8]—
As of today, different methods have been proposed for inspecting the welding process online [9]. Their design is suited to diverse defects types and differ in the data processed during the evaluation. Among the sensing technologies employed in literature, optical detectors [10], acoustic measurements [11], and vision analysis [12] are surely the most utilized. While, for classification applications, artificial neural networks [13, 14, 15] and fuzzy inference systems [16, 17] are usually preferred thanks to the wide range of problems and diversity of defects they could cope with as in the case of classification of steel strip defects [18, 19].
However, the focus of these works is on defects classification and not on their detection. Therefore, they could not cope with feature understanding problems such as discriminating between good samples and defective ones. A different approach is proposed by Ak et al. [20] where X-ray images are used to detect defects in metal castings.
Recent literature is plenty of research addressing the problem of welding localization employing off-the-shelf DL architectures or introducing slight modifications on the tail of popular networks. These approaches are mostly based on the R-CNN [21], Faster R-CNN [22], and YOLO [23] architectures. The reason behind their adoption is that these architectures usually require little fine-tuning procedures for efficiently localizing welding areas and spots. Such efficiency is strictly related to the presence of plain metal surfaces in the surrounding area of the welding by enabling simple and accurate segmentation of the feature under inspection. This is the case of resistance spot welding (RSW) processes typically employed to connect metal sheets at a low cost and in a short time.
Concerning detection approaches, early methods based on traditional computer vision techniques [24] require hand-crafted features and complex threshold settings to adapt to environmental conditions. However, approaches based on deep learning allow increasing the robustness of the detection coping with environmental noise and the sensitivity of the welding processes.
The majority of approaches are built upon the above-mentioned architectures for welding spots localization. Fast R-CNN [25] is a region proposal network that computes the region of interest (ROI) on the feature map, thus improving upon the R-CNN architecture. Faster R-CNN integrates convolutional layers for object classification, feature extraction, bounding box regression, and region proposals into a network, further improving the detection performance but still not reaching real-time capabilities. Unlike the R-CNN family, which has a two-stage detection architecture, YOLO implements a regression network with a grid of bounding boxes and associated class probabilities, thus enabling real-time detection with recent hardware. In the race for timing performance, YOLOv2 [26] borrowed the anchor mechanism from SSD [27] and Faster R-CNN, which also enhanced the network
Considering the reduced dimension of small spot welds, low-resolution feature maps in the backbone, and convolution strides dimension could cause an information leak. To face this issue, the work proposed by Dai et al. [34] introduces a modified MobileNEtV3 [35] architecture obtaining a good tradeoff between
Focusing on the classification and detection of defects over the welding area or joint, off-the-shelf solutions are no more efficient by themselves, and some issues need to be faced to enable the use of DNNs. Clustering and segmentation become difficult because the feature to be recognized are not easily separable. This chapter introduces some of the most common issues in the employment of DL for industrial quality inspection discussing the practical case of detection of welding defects in diesel injectors heads.
Quality inspection systems based on vision techniques in most cases follow the workflow depicted in Figure 2. The process starts by collecting the sample images using a set of cameras or sensors exploiting an adequate source of illumination. Such samples are then processed to improve images quality. Therefore, once the features are extrapolated, the evaluation of the quality and the classification of the defect are performed. Measurement and classification could either be implemented with traditional computer vision algorithms, with modern DNN architectures, or with a fusion of both of them, as in the case presented in the following. Usually, the inspection system also provides an actuation step that triggers actions, depending on the analysis result, to the production lines that directly communicate with the control unit (commonly based on programmable logic controllers (PLCs)).
Typical visual inspection workflow.
The work discussed in the study by Sassi et al. [36] originated from industrial demands with the specific target of detecting welding defects on diesel injectors in the production line. Such a project focused on realizing the most effective combination of traditional computer vision methods and deep neural network architecture for identifying the defects in the welding. In particular, the aim was to substitute the existing vision inspection system extending the classes of detectable defects in the analysis phase.
Welding joint defects may appear in different typologies: some are related to anomalies on the surface of the joint, while others are related to its geometrical properties, such as its thickness and position. Four categories have been defined for the analysis of the welding joint, as depicted graphically in Figure 3 showing an example from each category:
D1 (
D2
D3 (
D4 (
Examples of defect classes. IN D3, green and red circles show the detected inner and outer edges of the welding joint. In D4, the red arrows highlight thin welding, while the green ones are standard ones.
Defects D3 and D4 are quantitative measurable and are examined employing an algorithm based on traditional computer vision techniques (similar to the existing commercial solution). On the contrary, the others (D1 and D2) are more qualitative and are recognized through a method based on deep learning.
Furthermore, the analysis of the defects must be performed within a time slot that depends on the actual production line (1.8 seconds cycle time in the depicted scenario) to avoid interferences with the manufacturing process. This amount of time is required for the actuation system and the welding stage to process a new injector as input to the system.
During dataset preparation, the ideal case is the one in which several samples (in the order of thousands or more) are available for each class to be detected, the classes have balanced data, and they are well separated from each other. In such an ideal case, it is possible to give the network a representative set of samples of the whole input space for the training and avoid confusing the network with an uneven distribution of the inputs or the similarities between the classes.
Unfortunately, industrial production lines having well-optimized processes are usually present with few defective products and much more good samples. Therefore, it is often unfeasible to get sets of defective samples large enough to train CNNs for classification purposes. In the majority of the cases, the objective of the training moves from defect classification to anomaly detection. The worst-case scenario is the one presenting an
Different sampling strategies could be implemented to deal with
An alternative approach that is often used to increase the robustness of the classification is
Other ways for enlarging the dataset have been experimented like passing the input data through an encoder-decoder network that applies different transformations featured with random noise [42]. Another approach worth mentioning is the generation of
Virtual data generation could be obtained by producing synthetic images with the intent to cover the whole input feature space. Generative adversarial network (GAN) [44] or the most recent conditional GAN (cGAN) [45] could be alternatively used for this purpose. However, this is computationally expensive and requires taking into account all possible configurations and boundary conditions for generating samples as close as possible to real ones. Domain randomization techniques [46] could be applied to synthetically generated data for improving the generalization capabilities and the robustness of the network.
Similar to humans, when learning new concepts or rules, if not clearly defined, the training can lead to fuzzy assumptions, possibly resulting in wrong outcomes. Additionally, when dealing with data obtained by a sensing apparatus, it is important to check the correctness of the acquired data samples to avoid possible causes of classification errors. A cleaning process should remove outliers (wrong data association of a sample with a class) and spurious samples that could confuse the learning process. Industrial processes often rely on qualitative evaluation, and unfortunately, different quality experts in the same industrial process classify the same product as belonging to different classes. If the same confusion is transferred to the DL architecture, the learning process will probably worsen the decision process. For this reason, a preprocessing stage on the data is essential. In most cases, the help of professionals of the sector for interpreting, filtering, and preprocessing the data is welcome.
A last and quite important aspect is the adoption of correct performance metrics and
It has been seen that the first layers of CNNs learn kernels acting as color blob detectors or Gabor filters. Such a property seems to be very general and the features learned do not appear to be strictly dependent on the particular training set that has been adopted. As humans can learn from experience and transfer the notion learned in diverse application domains, similarly, a DL architecture can transfer the features learned on a particular dataset to another CNN, which will be trained on a different one [51]. Such a technique is called
Following the
The work combines a traditional computer vision pipeline together with a DL architecture. This pipeline was necessary to maintain the compatibility with classical production lines and provide a correct input to the welding defect detection phase. The algorithm receives the raw image as input, converts it from Bayer format to grayscale, and improves the edge detection by equalizing the levels and applying a Gaussian blur. In a successive step, since different kinds of injectors can be analyzed by the same system, the type of injector is identified, and the position of its center is obtained. The algorithm proceeds to detect the outer shell of the injector head by estimating an external radius that approximates the detected blob. Then, using the extracted information, the algorithm performs an area search for welding points and estimates a welding circle on the joint. Subsequently, the algorithm collects statistics about the number of welding points found and their positions. In traditional industrial systems, a set of thresholds decided by the manufacturing company is used to evaluate the welding quality from the measured quantities.
A schematic overview of the algorithm is shown in Figure 4. The algorithm’s output gives quantitative information about the welding and produces a processed image to be given as input to the second analysis stage. The extracted information allows evaluating the continuity of the welding in a certain area on the injector’s head, verifying the centering of the inner part of the injector with respect to the outer one, and eventually the welding thickness. This information is also beneficial to clean the image from unnecessary data for the subsequent analysis and to center the injector images to obtain more controlled conditions on the input of the successive stage.
Schematics of the components of the geometrical analysis pipeline.
The DL architecture chosen in that work is the DenseNet-121. Figure 5 depicts the structure of the network. DenseNet efficiently simplifies the connectivity pattern between layers guaranteeing maximum information flow by reusing the features through the network. Concerning the training phase, every layer has direct access to the gradients from the original input image and the
Schematic representation of the layers and blocks in the DenseNet-121 deep learning architecture.
In the approach presented by Sassi et al. [36], the
Unfortunately, the MINC dataset is highly unbalanced. Therefore, three classes, that is,
Sometimes, during production lines maintenance or innovations, the replacement of a machine, the change of a supplier, or the change in a manufacturing process, could lead to a significant variation on the usual production procedure in terms of the visual quality of the products. Such situations could vanish the capacity of a machine computation to return the expected results.
In this context, continuing on the problem of detecting welding defects on injectors heads, the work presented by Tripicchio et al. [48] proposes possible solutions to this issue without requiring an architectural change in the learning architecture. The new case had to handle some modifications concerning the parameters associated with the welding process, producing input samples with specific artifacts that the previously designed and trained network did never encounter. In particular, such new inputs were correlated to a variation in the substance used for the soldering that generated gold-violet spots on the injector head in random positions. Such noise introduces a novel complexity in the detection of the defects because the spots can hide or visually resemble the presence of bumps and holes in the welding layer. The followed approach was to make fewer changes as possible in the architecture of the network, operating a smart preprocessing and applying filtering techniques.
The results show the ability to train a network with almost 7 million parameters on just 306 training images belonging to the new alteration, achieving a
Such a result has been achieved leveraging on two important aspects. The first is the design of a custom preprocessing and filtering stage, while the second is the adoption of a novel data balancing strategy.
A preprocessing stage is needed on the input images with the aim of erasing or smoothing the chromatic nuances that could confuse the feature learning process. In particular, three filtering approaches have been proposed and tested (Figure 6). The first filter (
Different filters applied on a sector of the same injector contour image. (a) No filter. (b) Median fill filter. (c) Patch filter.
Different analyses have been done to assess the performance improvement given by such filters. As a result, a
Concerning data imbalance, an exploration of different unbalanced splits has been performed. To prevent overfitting and lead the learning process toward generalization, the authors propose to compute the performance metrics at each evaluation step considering the input imbalance. In particular, metrics like
Defective injectors were chosen as positive samples and
Cross-validation has been applied to improve generalization concerning the stochastic gradient descent optimization. The network has been trained multiple times by combining different variations of the proportions between defective and good samples and changing the numbers of epochs. During the training phase, each epoch is compared with all previous epochs for obtaining the one with the highest performance in terms of
The
This chapter highlights the importance of the employment of deep learning architectures in the context of future industrial applications with a focus on welding and welding defects detection. The industrial sector and especially the manufacturing industry pose several challenges to the design of efficient and robust quality inspection processes. The most common issues are discussed in detail, and possible countermeasures are suggested to overcome such issues. In particular, the problem of data imbalance, scarcity of examples, environmental noises, change in the nominal conditions of the process, or the presence of artifacts are discussed. Application examples from previous works of the authors are proposed to clarify how the suggested countermeasures can be put into practice. Although many industries are still scared of adopting deep learning approaches due to a lack of knowledge of their internal processes or reasoning, extensive use of artificial intelligence applications is envisaged for the near future.
AI | Artificial Intelligence |
CNN | Convolutional Neural Network |
DL | Deep Learning |
DNN | Deep Neural Network |
GAN | Generative Adversarial Network |
ICT | Information and Communication Technologies |
IoT | Internet of Things |
PLC | Programmable Logic Controller |
ROI | Region of Interest |
RSW | Resistance Spot Welding |
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\n\nOur mission is to support Authors in publishing their research and making an impact within the scientific community. Currently, 14% of Authors receive full waivers and 6% receive partial waivers.
\n\nWhile providing support and advice to all our international Authors, waiver priority will be given to those Authors who reside in countries that are classified by the World Bank as low-income economies. In this way, we can help ensure that the scientific work being carried out can make an impact within the worldwide scientific community, no matter where an Author might live.
\n\nThe application process is open after your submitted manuscript has been accepted for publication. To apply, please fill out a Waiver Request Form and send it to your Author Service Manager. If you have an official letter from your university or institution showing that funds for your OA publication are unavailable, please attach that as well. The Waiver Request will normally be addressed within one week from the application date. All chapters that receive waivers or partial waivers will be designated as such online.
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\n\nNote: All data represented above was collected by IntechOpen from 2013 to 2017.
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Here, we discuss two mechanisms to explain the deviation of the lunar COM to the East from the mean direction to Earth. The first mechanism considers the secular evolution of the Moon’s orbit, using the effect of the preferred orientation of the satellite with synchronous rotation to the second (empty) orbital focus. It is established that only the scenario with an increase in the orbital eccentricity e leads to the required displacement of the lunar COM to the East. It is important that high-precision calculations confirm an increase e in our era. In order to fully explain the shift of the lunar COM to the East, a second mechanism was developed that takes into account the influence of tidal changes in the shape of the Moon at its gradual removal from the Earth. The second mechanism predicts that the elongation of the lunar figure in the early era was significant. 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Growing experience in educational aspect of the training as well as continuous development of the habitat and its professional space science laboratory equipment correspond to increased interest of educational organizations, universities, and individual students. We serve unique practical platform for space engineering, space master, and even space doctoral theses. In addition to a wide range of training courses offered for future astronauts, for example, diving, skydiving, rocket workshops, and stratospheric missions, AATC provides a private laboratory to simulate the space environment. It carries out scientific experiments focused on biology and space medicine, as well as addressing several multidisciplinary issues related to the Moon and Mars exploration, including space mining. 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She obtained her Ph.D. in Veterinary Sciences from the University of Trás-os-Montes e Alto Douro, Portugal. After almost 32 years of teaching at the University of Trás-os-Montes and Alto Douro, she recently moved to the University of Évora, Department of Veterinary Medicine, where she teaches in the field of Animal Reproduction and Clinics. Her primary research areas include the molecular markers of the endometrial cycle and the embryo–maternal interaction, including oxidative stress and the reproductive physiology and disorders of sexual development, besides the molecular determinants of male and female fertility. She often supervises students preparing their master's or doctoral theses. 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She obtained a BSc from the University of Derby, England, a master’s degree from Technische Universität München, Germany, and a Ph.D. from the University of Nottingham. She undertook a post-doctoral research fellowship in the School of Medicine before accepting tenure in Veterinary Medicine and Science. Dr. Rutland also obtained an MMedSci (Medical Education) and a Postgraduate Certificate in Higher Education (PGCHE). She is the author of more than sixty peer-reviewed journal articles, twelve books/book chapters, and more than 100 research abstracts in cardiovascular biology and oncology. She is a board member of the European Association of Veterinary Anatomists, Fellow of the Anatomical Society, and Senior Fellow of the Higher Education Academy. 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Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}}]}},subseries:{item:{id:"95",type:"subseries",title:"Urban Planning and Environmental Management",keywords:"Circular economy, Contingency planning and response to disasters, Ecosystem services, Integrated urban water management, Nature-based solutions, Sustainable urban development, Urban green spaces",scope:"