ADF stationarity tests.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"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:"10548",leadTitle:null,fullTitle:"Lean Manufacturing",title:"Lean Manufacturing",subtitle:null,reviewType:"peer-reviewed",abstract:"Lean manufacturing is a process used in production to maximize efficiency and minimize waste by considering sustainability and the environment. This book presents a comprehensive overview of lean manufacturing in various enterprises, including manufacturing, construction, and the fabric and textile industry, among others. Chapters cover such topics as barriers to lean manufacturing, enterprise modeling, lean practices and circular economies, and more.",isbn:"978-1-83969-150-8",printIsbn:"978-1-83969-149-2",pdfIsbn:"978-1-83969-151-5",doi:"10.5772/intechopen.92922",price:119,priceEur:129,priceUsd:155,slug:"lean-manufacturing",numberOfPages:244,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"7409b2acd5150a93004300800918b736",bookSignature:"Karmen Pažek",publishedDate:"November 3rd 2021",coverURL:"https://cdn.intechopen.com/books/images_new/10548.jpg",numberOfDownloads:3491,numberOfWosCitations:0,numberOfCrossrefCitations:2,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:2,numberOfDimensionsCitationsByBook:0,hasAltmetrics:0,numberOfTotalCitations:4,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 20th 2020",dateEndSecondStepPublish:"November 17th 2020",dateEndThirdStepPublish:"January 16th 2021",dateEndFourthStepPublish:"April 6th 2021",dateEndFifthStepPublish:"June 5th 2021",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"179642",title:"Prof.",name:"Karmen",middleName:null,surname:"Pažek",slug:"karmen-pazek",fullName:"Karmen Pažek",profilePictureURL:"https://mts.intechopen.com/storage/users/179642/images/system/179642.jpg",biography:"Karmen Pažek was born in 1976. She graduated from the Faculty of Agriculture, University of Maribor, Slovenia, in 2000. In 2001 she was employed at the same faculty as an assistant for the field of grassland management. Between 2000 and 2003, she enrolled in the master\\'s study program in Agriculture Economics at the same faculty, and in 2003 she received her master\\'s degree. In the same year, she enrolled in a doctoral study in Agriculture Economics at the Faculty of Agriculture and obtained the status of a research assistant. In 2006 she successfully completed her Ph.D. in Agriculture Economics.\n\n\n\nSince 2006 she has been habilitated at the University of Maribor, and Life Sciences (she has been a full professor since 2016) for the field of Farm management. She holds several courses at all levels of study. She is currently the head of the 1st-degree study Agriculture Economics and Rural Development and the Vice Dean for Education.\n\n\n\nHer research includes the development of decision support tools and systems for farm management (simulation modeling, multicriteria decision analysis, option models, risk management), the economics of agricultural production, and other modern methods of operational research.",institutionString:"University of Maribor",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"University of Maribor",institutionURL:null,country:{name:"Slovenia"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1360",title:"Production Engineering",slug:"technology-industrial-engineering-production-engineering"}],chapters:[{id:"76130",title:"Application of Lean in a Small and Medium Enterprise",doi:"10.5772/intechopen.97059",slug:"application-of-lean-in-a-small-and-medium-enterprise",totalDownloads:199,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Application of lean principles in manufacturing as well as services has been revolutionizing the operations for more than five decades. Many large as well as small enterprises have implemented lean and reported benefits in both direct and indirect activities of business. Due to advent of digital technologies and better understanding of process improvement approaches made lean much more effective across many sectors. In this chapter, we highlight various elements of lean and its application to a small enterprise in food processing sector in India. We draw some useful insights based on the implementation of lean and challenges faced by SMEs.",signatures:"Venkataramanaiah Saddikuti, Saketh Saddikuti Venkat and Ganesh Babu Shanmugam",downloadPdfUrl:"/chapter/pdf-download/76130",previewPdfUrl:"/chapter/pdf-preview/76130",authors:[{id:"292211",title:"Associate Prof.",name:"Venkataramanaiah",surname:"Saddikuti",slug:"venkataramanaiah-saddikuti",fullName:"Venkataramanaiah Saddikuti"},{id:"337430",title:"Mr.",name:"Saketh",surname:"Saddikuti Venkat",slug:"saketh-saddikuti-venkat",fullName:"Saketh Saddikuti Venkat"},{id:"349737",title:"Mr.",name:"Ganesh Babu",surname:"Shanmugam",slug:"ganesh-babu-shanmugam",fullName:"Ganesh Babu Shanmugam"}],corrections:null},{id:"75200",title:"Lean and Kaizen: The Past and the Future of the Methodologies",doi:"10.5772/intechopen.96169",slug:"lean-and-kaizen-the-past-and-the-future-of-the-methodologies",totalDownloads:360,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Lean and Kaizen improvement methodologies have been in the entrepreneurship spotlight for a long time. They can be adopted by any kind of enterprise, and they succeed in producing better long-term results, improving their performance, but most important, influencing the philosophy of the organizations implemented. In this research, many case studies and success stories of companies implementing Kaizen or/and Lean methodologies, or even the new Lean Kaizen methodology, will be introduced. We attempt to evaluate the performance of Lean and Kaizen implemented companies and distinguish the elements that made the difference. Maybe, it is some specific tool, or an aspect in the culture that was enhanced, since the implementation of these business process improvement methodologies. Finally, thoughts and estimations will be presented, regarding the future of these methodologies, in the unstable and rapidly changing economic environment.",signatures:"Vasileios Ismyrlis",downloadPdfUrl:"/chapter/pdf-download/75200",previewPdfUrl:"/chapter/pdf-preview/75200",authors:[{id:"190036",title:"Dr.",name:"Vasileios",surname:"Ismyrlis",slug:"vasileios-ismyrlis",fullName:"Vasileios Ismyrlis"}],corrections:null},{id:"76432",title:"Introduction to Lean Waste and Lean Tools",doi:"10.5772/intechopen.97573",slug:"introduction-to-lean-waste-and-lean-tools",totalDownloads:277,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In the turbulent and complex business environments, many Indian SMEs are facing stiff competition in the domestic as well as in the global market from their multinational counterpart. The concept of lean has gained prominence due to the fact that the resource based competitive advantages are no longer sufficient in this economy. Hence, lean is no longer merely an option but rather a core necessity for engineering industries situated in any part of the globe, if they have to compete successfully. Lean Manufacturing (LM) which provides new opportunities to create and retain greater value from the employee of the industry based on their core business competencies. The challenge of capturing, organizing, and disseminating throughout the aggregate business unit is a huge responsibility of the top management. The success of any industry depends on how well it can manage its resources and translate in to action. The adoption of lean manufacturing through effective lean practices depends on interpretations of past experiences and present information resides in the industry. Generally, in an industry, some tangible and intangible factors exist in the form of non-value adding activities which hinder the smooth lean implementation are known as lean manufacturing barriers (LMBs).",signatures:"Shyam Sunder Sharma and Rahul Khatri",downloadPdfUrl:"/chapter/pdf-download/76432",previewPdfUrl:"/chapter/pdf-preview/76432",authors:[{id:"311981",title:"Dr.",name:"Shyam Sunder",surname:"Sharma",slug:"shyam-sunder-sharma",fullName:"Shyam Sunder Sharma"},{id:"338090",title:"Mr.",name:"Rahul",surname:"Khatri",slug:"rahul-khatri",fullName:"Rahul Khatri"}],corrections:null},{id:"76093",title:"Effect of Lean Practices on Organizational Performance",doi:"10.5772/intechopen.96482",slug:"effect-of-lean-practices-on-organizational-performance",totalDownloads:129,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The study focuses on the analysis of the direct effect of Lean Manufacturing (LM) practices on operational performance in manufacturing industry. A model for evaluating the effect of LM is developed taking into consideration as a fundamental variable that affects the causal relationship between LM practices and operational performance. A structural equation model was proposed and investigated across the manufacturing industry in India. A structured survey questionnaire was used to collect empirical data from 400 Indian companies. A total of 203 usable responses were obtained giving a response rate of 53%. The data was analyzed using SPSS- AMOS software. The results revealed that LM practices directly and positively affected operational performance. The results indicated that the structural equation model remained invariant across the Industry. The study provides further evidence to managers and practitioner on the effect of LM practices on operational performance in developing countries like India.",signatures:"Lokpriya Mohanrao Gaikwad and Vivek K. Sunnapwar",downloadPdfUrl:"/chapter/pdf-download/76093",previewPdfUrl:"/chapter/pdf-preview/76093",authors:[{id:"246830",title:"Prof.",name:"Lokpriya Mohanrao",surname:"Gaikwad",slug:"lokpriya-mohanrao-gaikwad",fullName:"Lokpriya Mohanrao Gaikwad"},{id:"251857",title:"Dr.",name:"Vivek K.",surname:"Sunnapwar",slug:"vivek-k.-sunnapwar",fullName:"Vivek K. Sunnapwar"}],corrections:null},{id:"75353",title:"Enhancement of Textile Supply Chain Performance through Optimal Capacity Planning",doi:"10.5772/intechopen.96292",slug:"enhancement-of-textile-supply-chain-performance-through-optimal-capacity-planning",totalDownloads:233,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Manufacturing companies in the textile and apparel field face stiff competition due to the globalization of trade between suppliers, producers and customers. To meet this challenge, they need to be efficient by adopting new lean manufacturing approaches and new analysis and management tools leading to more flexible and agile production and distribution processes. For the textile and apparel industry, where products’ life cycle is short due to fashion changes, a new integrated approach of production and distribution planning is needed. Based on linear programming techniques and integrating subcontracting activities, our approach takes into account the characteristics of demand, including its short life cycle, seasonality and fashion effect. For these reasons, a sequential approach is adopted, combining tactical and operational decision levels for production and distribution activities, in order to satisfy customer needs at lower cost by reacting quickly to changes and delivering on time. The deployed approach is structured according to the DMAIC lean tool. Validated on real instances, this approach proves its efficiency by achieving cost reduction when internal production capacity is adequately and efficiently planned.",signatures:"Imen Safra and Kaouther Ghachem",downloadPdfUrl:"/chapter/pdf-download/75353",previewPdfUrl:"/chapter/pdf-preview/75353",authors:[{id:"336845",title:"Assistant Prof.",name:"safra",surname:"Imen",slug:"safra-imen",fullName:"safra Imen"},{id:"345868",title:"Dr.",name:"Kaouther",surname:"Ghachem",slug:"kaouther-ghachem",fullName:"Kaouther Ghachem"}],corrections:null},{id:"75657",title:"From Lean Manufacturing to Lean Construction: How Principles, Tools, and Techniques Evolved",doi:"10.5772/intechopen.96191",slug:"from-lean-manufacturing-to-lean-construction-how-principles-tools-and-techniques-evolved",totalDownloads:309,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Lean manufacturing first emerged in the automotive industry. However, low productivity and low efficiency in production are major problems for the majority of industries relying on a heavy workforce. Being one of these, the construction industry suffers from low productivity rates along with inefficient work practices. To prevent those, the industry has shifted its focus from the traditional approach to a more innovative one, which is called Lean construction. Lean construction aims to maximize value while minimizing waste. Therefore, it intends to create safer, smoother, and more efficient processes to eliminate waste. This chapter focuses on Lean construction and highlights the generic Lean tools and techniques practiced in the construction industry indicating its historical journey from Lean manufacturing. The chapter aims to raise awareness towards the efficiency of Lean methods in the construction industry with respect to practices observed in manufacturing.",signatures:"Sevilay Demirkesen",downloadPdfUrl:"/chapter/pdf-download/75657",previewPdfUrl:"/chapter/pdf-preview/75657",authors:[{id:"338001",title:"Assistant Prof.",name:"Sevilay",surname:"Demirkesen",slug:"sevilay-demirkesen",fullName:"Sevilay Demirkesen"}],corrections:null},{id:"75939",title:"Model-Based Enterprise Continuous Improvement",doi:"10.5772/intechopen.96856",slug:"model-based-enterprise-continuous-improvement",totalDownloads:224,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The enterprise reengineering based on enterprise modelling is usually carried out within the framework of conventional projects. This leads to relatively long projects that are not compatible with a highly variable economic environment. The objective of the evolution management presented here is to use enterprise modelling and all the benefits it brings in a framework that allows for more continuous improvement than is generally observed. The proposed architecture is made up of three levels: a strategic level based on performance measurement, a tactical level that manages system migration and is based on enterprise models, and an operational level consisting of managing a portfolio of evolution projects. Together, these allow a shorter set of projects to be carried out, while remaining coherent and aligned with the company’s strategy. This approach puts enterprise modelling methods and continuous improvement/Lean management approaches into perspective, allowing complementarities and opening up interesting perspectives concerning enterprise re-engineering methods.",signatures:"Bruno Vallespir and Anne Zouggar-Amrani",downloadPdfUrl:"/chapter/pdf-download/75939",previewPdfUrl:"/chapter/pdf-preview/75939",authors:[{id:"345188",title:"Prof.",name:"Bruno",surname:"Vallespir",slug:"bruno-vallespir",fullName:"Bruno Vallespir"},{id:"348617",title:"Dr.",name:"Anne",surname:"Zougar-Amrani",slug:"anne-zougar-amrani",fullName:"Anne Zougar-Amrani"}],corrections:null},{id:"75617",title:"Single Minute Exchange of Dies: Classical Tool of Lean Manufacturing",doi:"10.5772/intechopen.96665",slug:"single-minute-exchange-of-dies-classical-tool-of-lean-manufacturing",totalDownloads:251,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Effective utilization of the resources is the need of an hour particularly when it comes to the manufacturing industry. It is having a paramount importance to have a proper utilization of the resources, on the same line in any manufacturing industries to reduce the setup time is also one of the ways to do so. Single Minute Exchange of Dies (SMED) is one of the classical method which is normally used to reduce the setup time. In this technique complete videography of the existing changeover is done and then by analyzing it waste activities identified and other improvement plant has been done in each iteration. The chapter also showcases the SMED technique applications in a gear industry. Remarkable resources and results have been achieved through the implementation of classical tool of Lean manufacturing is made.",signatures:"Yash Dave",downloadPdfUrl:"/chapter/pdf-download/75617",previewPdfUrl:"/chapter/pdf-preview/75617",authors:[{id:"338618",title:"Dr.",name:"Yash",surname:"Dave",slug:"yash-dave",fullName:"Yash Dave"}],corrections:null},{id:"75408",title:"Lean Manufacturing as a Strategy for Continuous Improvement in Organizations",doi:"10.5772/intechopen.96427",slug:"lean-manufacturing-as-a-strategy-for-continuous-improvement-in-organizations",totalDownloads:324,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"The implementation of lean manufacturing is one of the most discussed and studied topics in management; references are at the business, educational and public levels. However, the changes faced in the fourth industrial revolution generate challenges that will only encounter solution through innovative efforts and industrial improvements as well as a radical change in our way of interacting. In the current revolution, there are digital changes that cause ruptures in social, economic and political aspects, and the administrative process is part of it, this chapter proposes to analyze the implementation of lean manufacturing in the process of continuous improvement in business organizations through a literary review of the implementation of tools.",signatures:"María Marcela Solís-Quinteros, Carolina Zayas-Márquez, Luis Alfredo Ávila-López and Teresa Carrillo-Gutirrez",downloadPdfUrl:"/chapter/pdf-download/75408",previewPdfUrl:"/chapter/pdf-preview/75408",authors:[{id:"281004",title:"Dr.",name:"María Marcela",surname:"Solís-Quinteros",slug:"maria-marcela-solis-quinteros",fullName:"María Marcela Solís-Quinteros"},{id:"343581",title:"Dr.",name:"Luis Alfredo",surname:"Avila-Lopez",slug:"luis-alfredo-avila-lopez",fullName:"Luis Alfredo Avila-Lopez"},{id:"343697",title:"Dr.",name:"Carolina",surname:"Zayas-Márquez",slug:"carolina-zayas-marquez",fullName:"Carolina Zayas-Márquez"},{id:"343698",title:"Dr.",name:"Teresa",surname:"Carrillo-Gutiérrez",slug:"teresa-carrillo-gutierrez",fullName:"Teresa Carrillo-Gutiérrez"}],corrections:null},{id:"74769",title:"Development of Integrated Lean Six Sigma-Baldrige Framework for Manufacturing Waste Minimization: A Case of NAS Foods Plc",doi:"10.5772/intechopen.95279",slug:"development-of-integrated-lean-six-sigma-baldrige-framework-for-manufacturing-waste-minimization-a-c",totalDownloads:329,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,abstract:"The aim of this study objective is to develop an integrated constant quality improvement model so as to minimize unwanted biscuit processing industry wastes. The method used was lean- six- sigma elements to define measure and improve unwanted process company wastes. In other word, Baldrige with six-sigma were created to define, measure and improve management perspectives. The tasks were integrated using both quantitative and qualitative analyzing tools implementing mixed strategies. The result was improved by using FMEA analysis was carried out at each stage of the existing process used to determine the failure of the process and to analyses and improve the production quality. The SPSS software was also used. In the finding section, the correlation and regression analysis has shown that there is strong relationship between each variance. There are different wastes that identified in six sigma (DMAIC) on NAS food Plc as a result; the value of waste ratio indicated is 36.7%. This show non-lean of the food industry is practiced. The defect of the company also calculated and defect per million are 67,308. This shows that the biscuit production has a production capability with a failure of 67,308 every 1000,000 productions it high failure rate. The contribution of the paper has indicated that there are limited studies were conducted so far to implement waste minimization tools like six-sigma, lean and MBNQA framework approach integration for food processing industry.",signatures:"Kassu Jilcha Sileyew and Selamawit Gebreyohanis",downloadPdfUrl:"/chapter/pdf-download/74769",previewPdfUrl:"/chapter/pdf-preview/74769",authors:[{id:"292841",title:"Ph.D.",name:"Kassu",surname:"Jilcha Sileyew",slug:"kassu-jilcha-sileyew",fullName:"Kassu Jilcha Sileyew"},{id:"338417",title:"Ms.",name:"Selamawit",surname:"Gebreyohanis",slug:"selamawit-gebreyohanis",fullName:"Selamawit Gebreyohanis"}],corrections:null},{id:"75149",title:"Analysis, an Anathema: Is That a Fervent Diatribe of Lean?",doi:"10.5772/intechopen.96166",slug:"analysis-an-anathema-is-that-a-fervent-diatribe-of-lean-",totalDownloads:221,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Should there be an understanding that rigor in analysis must be out-of-bounds for Lean initiatives? Will this rigor not facilitate a benchmarking of Lean initiatives? Why not a Lean initiative cause-consequence assessment not performed for building future fault tolerance? The effectiveness of a company’s strategy is critical to its success or failure. Lean strategy seems to be claimed as a widely recognized factor for business success and competitive advantage. However, empirical evidences do not promote the idea that Lean has delivered results every time. Study results indicate that success or failure of lean initiatives strongly depends on how companies approach it and on whether company has created their own curated philosophy towards Lean. Then, success is not dependent alone on a strategy, but on how daily operations are aligned to strategy. This chapter aims to address the above questions and a greater number of questions that we experience on a day-to-day basis with regard to Lean applications in the real world. Chapter Learning Objectives: Understanding Lean, Lean failure modes, and Lean initiative precautions.",signatures:"Sajit Jacob and Krishnamurthy Kothandaraman",downloadPdfUrl:"/chapter/pdf-download/75149",previewPdfUrl:"/chapter/pdf-preview/75149",authors:[{id:"299036",title:"Mr.",name:"Sajit",surname:"Jacob",slug:"sajit-jacob",fullName:"Sajit Jacob"},{id:"299213",title:"Dr.",name:"Krishnamurthy",surname:"Kothandaraman",slug:"krishnamurthy-kothandaraman",fullName:"Krishnamurthy Kothandaraman"}],corrections:null},{id:"76883",title:"Lean Manufacturing towards Green Manufacturing Practices and Its Implementation in SME’s",doi:"10.5772/intechopen.97389",slug:"lean-manufacturing-towards-green-manufacturing-practices-and-its-implementation-in-sme-s",totalDownloads:171,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The manufacturing SMEs are facing the burden of non-equilibrium of the supply–demand chain along with the global change in the climate. Several SMEs are looking for a substitute that can create a balance between performance and the environment. In spite of numerous studies related to green and lean that has been evolved, none of them is able to clearly define the spheres of green and lean. Here in this chapter, there is an exploration of advancement of lean and green manufacturing and its impact on other sectors. It also highlights the methodology adopted in implementing the same. This chapter recognizes the commonalities between lean and green approaches, the collaboration and impact, techniques involved. Also, the impediments and perplexities confronted by the manufacturing sector are examined. Further, this gives a better understanding of the challenges before implementing lean with green. This chapter also recognizes possible gaps in the literature that will help to eliminate the barrier toward this Neo manufacturing.",signatures:"J.P. Rishi",downloadPdfUrl:"/chapter/pdf-download/76883",previewPdfUrl:"/chapter/pdf-preview/76883",authors:[{id:"339553",title:"Dr.",name:"J.P.",surname:"Rishi",slug:"j.p.-rishi",fullName:"J.P. Rishi"}],corrections:null},{id:"75839",title:"Lean Manufacturing Practices and Environmental Performance",doi:"10.5772/intechopen.96973",slug:"lean-manufacturing-practices-and-environmental-performance",totalDownloads:300,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Lean manufacturing is considered a rewarding production strategy due to its positive effects on organizational and economic efficiency in various industries. Given the growing ecological consciousness, environmental achievements of lean manufacturing also incorporate a strong economic relevance. The main objective of this chapter is, therefore, to investigate the impact of lean manufacturing practices on environmental performance and the existing coherences between Lean and ecologically oriented variables such as resource usage, energy consumption, and air pollution. The methodology is literature review evaluating the findings of research in this sphere. Besides the discussion of its principles and methods, current trends and challenges regarding lean production as a business model that supports eco-efficiency are presented. The implications of this study will allow executives to better recognize and simultaneously solve both the economic and environmental problems posed by their companies.",signatures:"Ruhet Genç",downloadPdfUrl:"/chapter/pdf-download/75839",previewPdfUrl:"/chapter/pdf-preview/75839",authors:[{id:"340234",title:"Prof.",name:"Ruhet",surname:"Genç",slug:"ruhet-genc",fullName:"Ruhet Genç"}],corrections:null},{id:"78284",title:"Circular and Lean Food Supply Chains",doi:"10.5772/intechopen.99769",slug:"circular-and-lean-food-supply-chains",totalDownloads:164,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Circular economy (CE) refers to the industrial economy that aims to achieve enriched sustainability through restorative objects and supply chain design. Many governments have put in place different initiatives in line with the CE. On the other hand, the term Lean operations refers to the reduction of the non-value adding activities and waste in a supply chain. The food sector has been criticized for its sustainability and circularity due to the high levels of food and packaging waste and at the same time the increasing costs. Although food supply chain entities have started to implement circular economy and lean practices, the current efforts do not seem to be sufficient to achieve a circular and lean food system. 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Because of the need for this product, the oil market is subjected to the market forces of demand and supply, which do lead to the fluctuation in the pricing. Hamilton [1], Blanchard and Gali [2], viewed, changes in the price of oil as an imperative source of economic fluctuations, in which the resultant effect led to global shock, capable of affecting many economic activities instantaneously. This shock is perceived generally to have a similar impact due to events like fall in growth rate, high unemployment rate, and high inflation rate, while the magnitude and the causes of the effect of these shocks may differ. For import-based economy, hike in the oil price will lead to shock in the economy, vice versa for the export-based economy [1, 3].
There are many established empirical analyses on the macroeconomic consequence of oil price shocks to net exporting countries, this is based on the dependency between oil price and the business cycle which can be explained through the impact of the oil price shocks on aggregate demand. Practitioners opined that an increase in oil price reduces aggregate supply since high energy prices mean that firms will purchase less energy. As a consequence, the productivity of any given volume of capital and labor will decline and leads to potential output loss. This invariably will lead to a decline in factors of production and real wages ([4, 5], p. 23; [6, 7]).
To expatiate further the influence of the oil price shocks on aggregate demand, Riaz et al. [5] submitted that oil is one of the basic inputs in manufacturing industries, any positive oil price shock increases the cost of manufacturing. As the cost of manufacturing rises the profit margins on investments fall will influence investors to postpone their irrevocable investments. Reductions in investment causes cuts in production level, consequently exports of the country are negatively affected and economy has to face adverse balance of trade. So also the effect permeates into households, oil price fluctuation induces the consumers to reschedule their expenditures on durable goods. This suggested that oil price shocks have serious concerns for all types of economies as aggregate demand is reduced from both consumption and investment sides. Increase in both oil prices and uncertainty in oil prices is detrimental for the economy (p. 24).
The negative effects of oil price shocks are more on the net-exporters of oil of the developing economies, the effect could be attributed to over-dependence on oil revenue, importation of basic necessity and susceptibility of their tradable lagging sectors to Dutch disease syndrome, the consequences of externalities, and economic pass-through (inflation) [8, 9, 10, 11, 12].
In the submissions of Abeng [8], opined that theoretically, an increase in oil price should reflect more revenue dividend for oil-exporting countries as it is expected to enhance foreign exchange earnings and build reserve in the short-run. Conversely, for net-importers of refined petroleum products for instance Nigeria with domestic regulation of oil prices (subsidies), oil price increase may not transform to the anticipated economic advantage, due to fiscal difficulties, restraining government’s ability to finance import in addition to meeting other international obligations (p.3). Nigerian has a deficit of ₦7114.49 and ₦8324.76 billion Naira for 2017 and 2018 periods for importation of non-oil products and spent about ₦2618.97 and ₦3833.82 billion on importation of refined petroleum product for the period of 2017 and 2018 [13]. These figures stress the vulnerability of the economy to the impulses of international oil price. The consequences may be unfavorable to economic growth arising from increased domestic production cost and decline in aggregate demand (p. 23).
In Ibrahim [14] remarks in studying the responses of non-oil productive sectors that is agriculture, manufacturing and service to shocks in change in oil price in Nigeria. In his submissions, the results obtained reveal that oil price impacted positively on aggregate output but negatively on agricultural, manufacturing and service sector suggesting that at the aggregate level, oil price is incline to increase aggregate output whereas an increase in oil price impacted negatively on the outputs of productive sectors as oil serves as an input factor in the production process of these sectors. This specifies that fluctuation in oil price creates uncertainty in the production capacity of the productive sectors and it also destabilizes the effectiveness of the government fiscal management of crude oil revenue.
Also Ayadi [15] posited that the forecast errors in industrial production are credited to volatility in real exchange rates and that changes in oil prices are only slightly important in influencing industrial production in Nigeria. Moreover, oil price changes affect real exchange rates, which, in turn, affect industrial production. He remarked that it should be noted that the indirect effect of oil prices on industrial production is not statistically significant. Therefore, the implication of the results presented in his paper is that an increase in oil prices does not cause an increase in industrial production in Nigeria.
According to [16, 17], the economy of Nigeria was affected by the decline in the revenue due to a fall in the price of crude oil alongside production. They cited that in about twenty months, the oil price has nosedived rapidly from as high as about one hundred and thirty dollars per barrel to as low as twenty-eight dollars and quantity also dropped from 2.15 Mbpd to 1.81 Mbpd in the earlier months of 2016, this resulted to a recession.
The crude petroleum industry is among the largest contributors to the economic growth, before the recession experienced by the country, in 2016 the growth rate shrank by −13.65%, a more substantial decline than that in 2015 of −5.45%. This reduced the oil sectors share of real GDP to 8.42% in 2016, compared to 9.61 per cent in 2015, (NBS, Q4 [18]). Aside from the contribution to the growth rate, the industry affects monetary variable and high unemployment rate [2]. According to Nweze and Edeme [19], as quoted by Adedokun [16], CBN [20] opined that on average, 75% of government revenues and on average 93% of foreign earnings from trade in goods and services, in the last ten years come from oil export, which informs part of the major sources used in financing the country’s imports.
Fluctuate in the price of natural resources is a term more related to the oil shocks because the majority of the problems encountered concerning recession is aggravated by a change in oil price. Hamilton [1], in his abstract, he opined that historical oil price shocks were principally caused by physical disruptions of supply, the price hike of 2007–2008 was caused by supply not meeting the excessive world demand. The consequences of recession are very similar with significant effects on consumption. According to Hamilton (1983) as cited by Sabiu [21], opined that ten out of eleven economic recessions were preceded by a sharp increase in oil prices in the United States.
Although, In a more recent development in the investigation of the causes of oil price shocks, many practitioners do not see supply as the sole cause of oil price shocks. The neo-monetarist, the likes of Bernanke et al. [22] sees oil and energy costs as insignificant relative to total production costs to account for the entire decline in output that, at least some events, has followed increases in the price of oil, they foresee that the monetary policy taken during spikes in the price of oil as the major contributing factors to the economic shocks.
Kilian [23] opined that historically, the decompositions of fluctuations in the real price of oil shows that oil price shocks have been driven mainly by a combination of global aggregate demand shocks and precautionary demand shocks, rather than oil supply shocks.
In furtherance to clear the air on the causes of oil price fluctuations, which was generally believed to have outgrown the traditional demand and supply factors, Humbatova and Hajiyev [24] made references, to the Er-Riad summit of 2007 where conclusions where reached on the oil market trend that, it is not related to OPEC decisions. They concluded that the current trend is due to financialisation factors, lack of production capacities in oil production, reduction in the world oil reserves, natural disasters, political events and processes.
The financialisation of oil market made oil a speculative commodity in the financial market contrary to the real commodity. This has been one among the major sources of oil price volatility [25, 26].
The exposure of the oil market to commodity market brought about the issue of speculation, that is investors’ expectations about future oil supply and demand. This breeds in the issue of inventory, either below or above the ground since oil can be stored. Others factors are the price of dollars, for net oil importers appreciation of dollar mean lower consumption of oil whereas the net exporters mean more revenue from the sales of oil, the reverse is the case when dollar price depreciate [26, 27].
The most recent factor in the front burner affecting fluctuation of oil price is the improvement of shale-oil technology (the shale revolution in the United States). The technological innovations that decreased the liquid fuel consumption and influenced the global energy markets to the point that many countries that are solely dependent on the oil resource plunged into economic crisis in 2016 due to falling in oil demand [26, 28]. Davig et al. [29] added that the fall in demand led to shifts in precautionary demand in the mid-2014 to mid-2015, this played a fundamental role in driving oil prices lower due to market glut and exacerbate the oil crisis to net exporters in 2016.
Fluctuation in the price of oil as a result of the aforesaid causes create the effect of uncertainty in the outputs of industries, not only to the manufacturing sector but also to the energy management sectors in process industries, that is oil and gas industries. According to Elder and Serletis [30] they posited that the theories of investment under uncertainty and real options predict that uncertainty about oil prices will tend to depress current investment. This uncertainty can be due to rise or fall in the oil prices.
Higher oil prices do come with a glade tidings for some industries. Apparently, they benefit oil and gas industries, but have both positive and negative multiplier effects to other components of an economy [31]. According to Hayes upstream firms face more hitches when oil prices fall since market forces is the determining factor at which oil is sold, and their costs of production are largely fixed. The higher the cost of production the higher the losses incurred by the producer. Downstream companies suffer a lesser consequences since they profit by purchasing crude oil and selling the refined products at a premium. Their earnings and profit margins always remain fairly stable even with fluctuating in oil prices. The submissions of Hayes is line with the suggestions of Jobert et al. [32] they posited that rise in the prices of oil are much desirable to the oil industries because they will make higher turnover, simultaneously, the rise in the oil prices correlate with waning outcomes for large capital expenditure projects for oil recovery. Large and capital-intensive drilling operations are hit harder in contrast to the smaller rigs, which can decide to shut down pending on when prices rise again.
Energy and the development of the shale oil is among the current drivers of US economy, new jobs opportunities has sprang up due to economy of scale (internal and external) for the Americans. Persistence, fall in oil price, could lead to folding up of operations for many onshore fracking wells that lack the working capital to continue drilling. Although the hydraulic fracturing is more expensive than typical drilling, so shale gas companies will be among the first hit if the cost of production prevail over profits [33].
According to Adesina [34], he made references to the local key oil and gas corporation having a rough time due to the fall in oil price in the recent time with prices lower than local production in Nigeria. The local oil firms are fighting hard to survive as Crude and remains at the $20, which means Nigeria’s crude is being sold at a loss, coupled with the fact that oil demand has plummeted to the lowest level in more than a generation.
While on the other side Deloitte [35] views was on the impact of the oil price collapse on company accounts, fall in oil price tends to increase risk of loss of assets. They opined that lower oil price forecasts mean lower future profits from an asset. These leads to reduction in the present value of the asset, and the asset values on balance sheets cannot be fully recovered, this results in write-off, and tendencies of knock-on effect connected to deferring taxes and holding company investment balances.
In Nigeria one of the major contributing factors for 2016 recession was fall in the price of oil coupled with decreased in quantity of production, the recession was accompanied by high inflation rate on basic commodities (cost-push) [16]. Monetary policy on inflation is always been informed by the general price level. Before the recession, the inflation rate was at a single digit of 8.0% and 9.55% per cent for 2014 and 2015 [36]. During the recession, the inflation rate was about 18.55% per cent that is in 2016 and as expected, the monetary authority introduced a tight monetary policy by raising the cost of borrowing, the interest rate was steady at 14% from July 2017 to the first quarter of 2018 against 2016 which was 200 points higher. This is against the backdrop of relative improvement in the global economy.
Saban et al. [37] Investigated the responses of monetary policy variables of select emerging markets to oil market shocks. Using conventional and Fourier Toda Yamamoto methods. In their findings, the oil prices are sensitive to structural shifts and, the causality approach with gradual/smooth shifts indicates oil price shocks influencing the currencies of Indonesia and South Africa, interest rates in Brazil and India, and inflation in South Africa and Turkey.
Also in the summaries of Santos and Chris [38], used Johansen (1992) co-integration approach and the Toda and Yamamoto [39] causality testing procedure. Applying Wald coefficient test, the nominal interest rates, and expected inflation co-move together, in the long run, there is a uni-directional causality from expected inflation to nominal interest rates as suggested by the Fisher hypothesis in the closed economy context. While in the open economy context, the result showed that the expected inflation and international variables do not contain information that predicts the nominal interest rate.
In the empirical findings of Mohammed and Jauhari [40], they employed asymmetric causality test based on Toda and Yamamoto [39] causality approach to further the causal relationship between exchange rate and inflation differentials in Brunei, Malaysia, and Singapore. The results show the existence of Granger causality running from positive cumulative exchange rate shocks to shocks in inflation differentials for Brunei and Malaysia. Also, the asymmetric causality for Singapore runs from both positive and negative cumulative domestic inflation shocks to positive and negative exchange rate shocks respectively.
Chibvalo et al. [41] in their submissions, they employed the Toda-Yamamoto approach to Granger causality to test for a causal relationship between inflation and trade openness in Zambia. They established a bi-directional causality between inflation and trade openness. Further, there exists a positive relationship between inflation and trade openness in Zambia.
This analysis aims at investigating the effect and the interrelations existing between the impact of oil price fluctuation on the monetary instrument (Exchange rate, Inflation, Interest rate). The data were sourced from the Central Bank of Nigeria (CBN), National Bureau of Statistics (NBS) and Nigeria National Petroleum Corporation (NNPC). The data cover a period of 1995–2018 and the data is monthly. All our variables are in local currency. Therefore we used oil price, the interbank exchange rate as a proxy for exchange rate data, while the prime lending rate was used as a proxy for data on the interest rate and we used consumer price index for all commodity as a proxy for inflation.
A Toda and Yamamoto model (1995) (TY-VAR) was adopted in estimating the Modified WALD Granger Non-causality test (MWALD), Forecast Error Variance Decomposition (FEVD) and Impulse Response Function (IRF).
According to Salisu [42], Sims [43] and Toda and Yamamoto (TY-VAR) [39], Vector auto-regressions (VARs) are one of the widely used classes of models in applied econometrics, used as tools both for prediction and for model building and evaluation. It success lied on its flexibility and ease of application when dealing with the analysis of multivariate time series.
Practitioners have recently shown that the conventional asymptotic theory does not apply to hypothesis testing in levels VAR’s if the variables are integrated or co-integrated [39, 43]. And one of the deficiencies of the VAR application is the inability to ascertain the a priori expectation of the variables whether the variables are integrated, co-integrated, or (trend) stationary. This necessitates pretesting(s) for a unit root(s) and co-integration in the economic time series, asarequisite for estimating the VAR model, and also when the intentions are prioritized towards the estimation of cointegration and vector error correction model [44].
Conversely, the powers of the unit and also simulation experiments of Johansen tests for co-integrating are very sensitive to the values of the nuisance parameters in finite samples and hence not very reliable for sample sizes that are typical for economic time series [39, 45, 46].
To alleviate these problems, Toda and Yamamoto [39] as quoted by Shakya [47], Giles [48] proposes the augmented VAR modeling, that is the modified Wald test statistic (MWALD), which is more superiority to the ordinary Granger - causality tests, the method is flexible and easy to apply, since one can test linear or nonlinear restrictions on the coefficients by estimating a levels VAR and applying the Wald criterion, paying little attention or circumventing the integration and cointegration properties of the time series data [42, 44]. However, the model is not a substitute for the conventional pre-testing in time series analysis, but as a complementary to the conventional VAR [49].
In estimating the MWALD test for Granger causality, it is prerequisite to determine the maximum possible order of the integration of the basic variables (dmax). Although, the variables could be a mixture of I (0), I (1), and I (2), in such condition, dmax = 2. The determination of the optimal lag length (k) is very important, to avoid overstating or understating the true value of lag, to evade biased estimates of accepting the null hypothesis when it should be rejected, vice versa. By identifying dmax and k, a level VAR model of order (k + dmax) is estimated and zero restrictions test is conducted on lagged coefficients of the regressors up to lag k. This process certifies that the Wald test statistics have an asymptotical chi-square (χ2) distribution whose critical values can be used to draw a valid inference and conclusion [39, 44].
The model used in this research work borrowed a leave from the Toda and Yamamoto model (1995) as iterated in the work of Saban et al. [37], their model was adopted in this paper, to finding the inter-relationship between oil price and monetary variables. While they consider Granger Non-causality and structural shift, in our model we considered Granger Non-causality test, and substitute structural shift with Impulse Response Function (IRFs) and Forecast Error Variance Decomposition (FEVD). The TY-VAR is given by:
Where
The analysis aims at establishing the interrelationship that exist among the variables; i.e. oil price (lnoilpr), and monetary policy variable i.e. exchange rate (lnexchr), interest rates (lnintr), and inflation (lncpi). The specification considers each variable expressed as independent in the model as a function of its lag and the lag of other variables in the model. Here the exogenous error terms
Where
Although, the Todo-Yamamoto model, the MWALD test was introduced for ease of estimation by circumventing the presence of unit roots pre-testing problem, nevertheless, there is the need to determine the maximum order of integration of the variables, which is necessary for estimation of The MWALD test for Granger causality by Toda and Yamamoto [39]. Therefore, we ran the test for the Augmented Dickey-Fuller (ADF) test, Phillips – Perron (PP) test and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) unit root test, to ascertain the stationarity of the variables [45, 50, 51, 52, 53, 54].
From Tables 1 and 2, the unit-roots tests confirmed all our process to be considered integrated at the first difference and 1% level of significance using Augmented Dickey-Fuller (ADF) test and Phillips – Perron (PP).
Variable | ADF | |||||||
---|---|---|---|---|---|---|---|---|
Level | First Difference | |||||||
Constant | Prob. | Constant & Trend | Prob. | Constant | Prob. | Constant & Trend | Prob. | |
lnoilpr | −1.2206 | 0.6663 | −2.3779 | 0.3904 | −14.3220*** | 0.0000 | −14.3037*** | 0.0000 |
lnexchr | 0.3070 | 0.9784 | −1.5899 | 0.7949 | −11.6443*** | 0.0000 | −11.6786*** | 0.0000 |
lncpi | −1.4401 | 0.5626 | −5.3282*** | 0.0000 | −13.3181*** | 0.0000 | −13.3666*** | 0.0000 |
lnintr | −1.8216 | 0.3696 | −2.3214 | 0.4250 | −16.2688*** | 0.0000 | −16.2400*** | 0.0000 |
ADF stationarity tests.
Note: ***, ** and * denote significance at 1%, 5% and 10% respectively. ADF test the null hypothesis of ‘not stationary’ against the alternative of ‘stationary’. Source: E-views Version 9 software was used in the estimation.
Variable | PP | |||||||
---|---|---|---|---|---|---|---|---|
Level | First Difference | |||||||
Constant | Prob. | Constant & Trend | Prob. | Constant | Prob. | Constant & Trend | Prob. | |
lnoilpr | −1.2921 | −2.3897 | −14.3491*** | −14.3312*** | ||||
lnexchr | 1.0660 | −1.5040 | −9.8974*** | 0.0000 | −9.8872*** | 0.0000 | ||
lncpi | −1.7664 | 0.3968 | −5.5627*** | −13.2950*** | 0.0000 | −13.3455*** | 0.0000 | |
lnintr | −1.9316 | −2.4972 | −16.2641*** | 0.0000 | −16.2351*** | 0.0000 |
PP stationarity tests.
Note: Just like the ADF, the PP unit root test has the null hypothesis of ‘not stationary’ against the alternative, which is ‘stationary’. *, ** and *** indicate the level of significance at 10%, 5% and 1% respectively. Source: E-views Version 9 software was used in the estimation.
While Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) in Table 3 is in contrast to ADF and PP which indicated that the variables are at levels. This corroborates with the work of Yakubu and Abdul Jalil in their test of stationarity. A quick check on the line graphs in Figure 1 indicated that all the variables are at first difference I(1). Therefore, we stick to ADF and PP, and agree that dmax = 1.
Variable | KPSS | |||||||
---|---|---|---|---|---|---|---|---|
Level | First Difference | |||||||
Constant | Prob. | Constant & Trend | Prob. | Constant | Prob. | Constant & Trend | Prob. | |
lnoilpr | 1.8432*** | 0.2905*** | 0.0615 | 0.0359 | ||||
lnexchr | 1.7493*** | 0.2035** | 0.1959 | 0.0771 | ||||
lncpi | 0.2299*** | 0.1406* | 0.2440 | 0.1035 | ||||
Intr | 0.9826*** | 0.1353* | 0.0457 | 0.0454 |
KPSS stationarity tests.
Note: In contrast to ADF and PP, KPSS unit root test has the null hypothesis of ‘stationarity’ against the alternative, ‘not stationary’. ***, ** and * represent 1%, 5% and 10% level of significance respectively. Source: E-views Version 9 software was used in the estimation.
Graphical representation of original series at I(1) for oil price (doilpr), exchange rate (dexcri), CPI (dcpi) and interest rate (dintr).
The Modified Wald (MWALD) Test for Granger Causality requires the determination of optimal lag which is presented in Table 4. By default, we use LR: sequentially modified LR test statistic, FPE: Final prediction error, AIC; Akaike information criterion, SBC: Schwarz information criterion and Hannan-Quinn information criterion to determine the optimal lag for the estimation of VAR system. The SC and HQ minimize its value at lag 2 while LR and FPE minimizes at lag 3. According to Liew [55], Asghar and Abid [56] Estimating the lag length of the autoregressive process for a time series is imperative in econometrics. The selection is done to minimize the chance of underestimation while at the same time maximizing the chance of recovering the true lag length. Another important aspect of the lag selection criteria is to overcome the structural break. Though, studies indicated that HQC is found to surpass the rest by correctly identifying the true lag length. In contrast, AIC and FPE are better choices for a smaller sample. In Table 4 out of the two criteria, we propose three lags (lag 3) as the optimal lag.
Endogenous variables: LNOILPR LNEXCHR LNCPI LNINTR | ||||||
---|---|---|---|---|---|---|
Lag | LogL | LR | FPE | AIC | SC | HQ |
0 | 1024.270 | NA | 8.68e-09 | −7.210389 | −7.158863 | −7.189729 |
1 | 3293.435 | 4458.148 | 1.05e-15 | −23.13382 | −22.87619 | −23.03052 |
2 | 3342.568 | 95.13951 | 8.35e-16 | −23.36797 | −22.90424* | −23.18203* |
3 | 3364.257 | 41.38540* | 8.02e-16* | −23.40817* | −22.73834 | −23.13959 |
4 | 3375.620 | 21.36093 | 8.29e-16 | −23.37540 | −22.49947 | −23.02418 |
5 | 3381.763 | 11.37514 | 8.89e-16 | −23.30575 | −22.22371 | −22.87189 |
VAR lag order selection criteria.
indicates lag order selected by the criterion. LR: sequential modified LR test statistic (each test at 5% level), FPE: Final prediction error, AIC: Akaike information criterion, SC: Schwarz information criterion and HQ: Hannan-Quinn information criterion. Source: E-views Version 9 software was used in the estimation.
The orthogonal impulse response are based on recursive causal ordering, if the ordering is reversed different sets of structural shocks will be identified, and this gives a different impulse response function (IRF) and forecast error variance decomposition (FEVD), except if the error terms contemporaneous correlations are low [57]. According to Lutkepohl [58] given a sample size of T, the determinant of the reordering of the variables is given by
The ordering of variables suggested by Sims (1981, 1980) as iterated in the work of Yakubu and Abdul Jalil [44], Duasa [46], is to start with the most exogenous variables in the system and ended by the most endogenous variable. Table 5 shows the residual correlation matrix result, the result shows that there is no instantaneous correlation between the variables because the variables are not significantly different from zero (at a 5% level of significance) [59]. This is based on the sample size in this analysis, we need at least a correlation of 31% that is above 5% level of significance to satisfy the call for reordering of the variables. Since there is no strong correlation among the variable we assumed the arrangement of our variables are in order.
LNOILPR | LNEXCHR | LNCPI | LNINTR | |
---|---|---|---|---|
LNOILPR | 1.000000 | |||
LNEXCHR | 0.156275 | 1.000000 | ||
LNCPI | 0.025236 | 0.038583 | 1.000000 | |
LNINTR | 0.052056 | 0.144681 | −0.057944 | 1.000000 |
Correlation matrix for TY-VAR.
Before the estimation of the Causality Test, Forecast Error Variance Decomposition (FEVD) and Impulse Response Functions (IRFs). The VAR residual serial correlation test is needed to verify the adequacy of the lag selection criterion used in the estimation of a chosen multivariate model, it is applied to test a set of restrictions on a model that is unrestricted, and it is based on the restricted maximum likelihood test (ML) [42, 60, 61]. From the TY-VAR estimated output for the residual serial correlation test in Table 6, the null hypothesis for the test is that there is no serial correlation. The result submits that there is no evidence of serial correlation. Which indicate the acceptance of the null hypothesis that the restriction (lags) place on the model is adequate.
Lags | LM-Stat | Prob |
---|---|---|
1 | 13.85744 | 0.6093 |
2 | 8.875657 | 0.9184 |
3 | 15.67327 | 0.4760 |
4 | 12.71378 | 0.6936 |
TY-VAR residual serial correlation LM tests.
Probs from chi-square with 16 df.
In the test for normality, to examine whether the residuals are normally distributed. We employed the null hypothesis H0: residuals are normally distributed. From Table 7 we rejected the null hypothesis of normality of residuals of each equation as well as all the equations combined at 5% level of significance since p-value of all the variables are zero. Hence, we concluded that residuals are not normally distributed [62].
Component | Jarque-Bera | df | Prob. |
---|---|---|---|
1 | 15.36714 | 2 | 0.0005 |
2 | 4572.449 | 2 | 0.0000 |
3 | 389.0131 | 2 | 0.0000 |
4 | 382.0722 | 2 | 0.0000 |
Joint | 5358.902 | 8 | 0.0000 |
Jarque-Bera normality test result.
df and Prob stands for the degree of freedom and probability
Although, the credibility of Iarque-Bera test of normality with application to VAR has been questioned specifically for an I(1). Jarque-Bera normality of the series does not guarantee normality of distributions, it only signifies normality of the first four moments of a distributions [58]. According to Lutz and Ufuk [63] in their remarks, they posited that Jarque-Bera test based on asymptotic critical values can be very unreliable. In their submissions, they gave the asymptotic critical values of 1–100% in their Monte Carlo analysis of VAR. They presented that the size distortions of the asymptotic test persevere even for sample sizes as large as 5000 observations.
From Table 8 we have the lnoilpr as the dependent variable, at 5% level of significance, we accept the null hypothesis that there is no causality between, the lnexchr, lncpi and lnintr on the dependent variable. Also, the combination of all the independent variables do not granger caused changes in the dependent variable. This indicates the exogeneity of oil price which is been determined by many factors that are exogenous to both net importers and exporters of oil, Nigerian inclusive. According to Humbatova and Hajiyev [24] posited that the determinants of oil price range from financial factors, lack of production capacities in oil production, the decline in the world oil reserves, natural disasters, political events and processes, and no one country has the monopoly of determining oil price.
Excluded | Chi-sq | df | Prob. |
---|---|---|---|
LNEXCHR | 0.297326 | 3 | 0.9605 |
LNCPI | 2.517571 | 3 | 0.4721 |
LNINTR | 2.072927 | 3 | 0.5574 |
All | 5.503884 | 9 | 0.7884 |
Granger causality test WALD test for Eq. (2) for the dependent variable: LNOILPR.
From Table 9 we have the lnexchr as the dependent variable, at 10% level of significance, we reject the null hypothesis that there is no causality between loilpr and lnexchr. The exchange rate plays a significant role in determining the oil price both to net exporters and net importers. Specifically, oil is priced in U.S. dollars. According to Farley [64] submissions, each decrease and increase in the dollar or the price of the commodity (oil) generates an instantaneous realignment between the US dollar and other currencies. These correlated is more significant in countries with significant oil reserves that depend largely on crude exports and they experience more economic damage than those with more diverse resources. In the presentations of Bützer [65], he established that oil Net exporters tend to respond against depreciation pressures by running down foreign exchange reserves, particularly after oil demand shocks, but also global demand shocks (which also decrease oil prices). This is sometimes supplemented by a nominal depreciation of exchange rates. These invariably indicate that oil demand shocks are a relevant factor for their exchange rates. While we accept the null hypothesis that there is no causality between, the lncpi and lnintr on the dependent variable. Also, the combination of all the independent variables do not Granger cause changes in the dependent variable.
Excluded | Chi-sq | df | Prob. |
---|---|---|---|
LNOILPR | 6.426225* | 3 | 0.0926 |
LNCPI | 2.889761 | 3 | 0.4089 |
LNINTR | 1.567570 | 3 | 0.6668 |
All | 11.29767 | 9 | 0.2559 |
Granger causality test WALD test for Eq. (3) for the dependent variable: LNEXCHR.
Also from Table 10 we have the lncpi as the dependent variable, at 10% level of significance, we reject the null hypothesis and accept the alternative hypothesis that there is causality from lnexchr and linintr to lncpi. Exchange rate plays a vital role in determining prices in Nigeria, as an economy that has some element of a Dutch disease syndrome, and relied heavily on importation of basic necessity, when we factor out oil exportation from the total export, the non-oil balance of trade approximately stood at negative 7114 billion for 2017 as stated in our introduction. Therefore, appreciation in the exchange rate can cause inflation (lncpi) (Katz, 1973). The interest rate is one of the instruments used by the monetary authority to regulate the economy either during inflation or deflationary periods, the interest rate affects the demand and allocation of the available loanable funds the level, and pattern of consumption and investment ([66] p. 15). Before 2016 recession in Nigeria, the inflation rate was at a single digit of 9.55% in 2015, during the recession, the inflation rate was at double-digit 18.55% in 2016 and the central bank introduced a tight monetary policy, by raising the interest rate steady at 14 per cent from July 2017 to the first quarter of 2018 against 2016 which is 200 points higher [36].
Excluded | Chi-sq | df | Prob. |
---|---|---|---|
LNOILPR | 1.151935 | 3 | 0.7646 |
LNEXCR | 6.824049* | 3 | 0.0777 |
LNINTR | 7.771454* | 3 | 0.0510 |
All | 14.75625** | 9 | 0.0979 |
Granger causality test WALD test for Eq. (4) for the dependent variable: LNCPI.
Also, the combination of all the independent variables (lnoilpr, lnexchr and lnintr) does Granger cause changes in the dependent variable lncpi at 5%, but lnexchr and lnintr are more pronounced in the causality. While we accept the null hypothesis that lnoilpr do not granger cause lncpi.
In Table 11 we have lnintr as the dependent variable, we reject the null hypothesis and accept the alternative hypothesis that at 5% levels of significance that there is a causality which is from lnoilpr and lnexchr to the endogenous variable lnintr, while there is no any causality with the log of lncpi on the dependent variable. Also, the combination of all the independent variables Granger cause changes in the dependent variable at a 5% level of significance. The relationship of lnoilpr and lnintr may not be exclusive but via the exchange rate, in the boom period the net exporter of oil has more dollars to expend, vice versa during deflationary periods, both periods has a direct link to economic growth. To avoid these inflationary or deflationary tendencies, the central bank may engage in the sterilization process through open market operation, by manipulating the short-term interest rate, that is by increasing interest rates to discourage borrowing during inflationary periods or decrease the interest rate to encourage borrowing during deflationary periods. The relation is said to be inverse and this shows how oil price and exchange rate influences the monetary policy of net oil exporters.
Excluded | Chi-sq | df | Prob. |
---|---|---|---|
LNOILPR | 14.66233** | 3 | 0.0021 |
LNEXCR | 10.44319** | 3 | 0.0152 |
LNCPI | 3.488718 | 3 | 0.3222 |
All | 31.49615** | 9 | 0.0002 |
Granger causality test WALD test for Eq. (5) for dependent variable: LNINTR.
From the estimated TY-VAR, we compute forecast error variance decompositions (FEVD and impulse response functions (IRF), which serve as means for evaluating the dynamics of the interrelationship, interactions, and strength of causal relations among the variables in the system. The impulse response functions trace the effects of a shock to one endogenous variable on to the other variables in the VAR, variance decomposition separates the variation in an endogenous variable into the component shocks to the VAR [10, 46].
In simulating FEVD and IFRs, the VAR innovations can be contemporaneously correlated. That is a shock in one variable can work through the contemporaneous correlation with innovations in other variables. The responses of a variable to innovations in another variable of interest cannot be adequately represented in isolation, due to the facts that shock to individual variables cannot be separately identified due to contemporaneous correlation [46].
In our analyses, we applied Cholesky approach which uses the inverse of the Cholesky factor of the residual covariance matrix to orthogonalise impulses (innovations) as recommended by Sims (1980) as quoted by Duasa [46] and (Breitung, Bruggemann, and [58]) to solve this identification problem. The strategy requires a pre-specified causal ordering of the variables, which we estimated in Table 5 for the correlation matrix. The results of FEVD are displayed in Tables 12–15, while the IRFs represented in Figures 2–17 in appendix 1, respectively.
Period | S.E. | LNOILPR | LNEXCHR | LNCPI | LNINTR |
---|---|---|---|---|---|
1 | 0.039283 | 100.0000 | 0.000000 | 0.000000 | 0.000000 |
2 | 0.059667 | 99.56602 | 0.007555 | 0.357862 | 0.068566 |
3 | 0.074387 | 99.31622 | 0.077847 | 0.518729 | 0.087200 |
4 | 0.087239 | 99.17720 | 0.135794 | 0.615055 | 0.071949 |
5 | 0.099720 | 99.16728 | 0.123200 | 0.650960 | 0.058563 |
6 | 0.112282 | 99.16645 | 0.102858 | 0.650544 | 0.080151 |
12 | 0.191020 | 98.36406 | 0.104402 | 0.630791 | 0.900743 |
18 | 0.276129 | 96.71609 | 0.060657 | 0.908562 | 2.314688 |
24 | 0.366613 | 94.33976 | 0.064427 | 1.477383 | 4.118426 |
30 | 0.457642 | 91.03687 | 0.223173 | 2.331971 | 6.407984 |
36 | 0.541764 | 86.40256 | 0.693518 | 3.520289 | 9.383636 |
42 | 0.611323 | 79.78047 | 1.802594 | 5.120937 | 13.29600 |
43 | 0.621214 | 78.43050 | 2.090483 | 5.429983 | 14.04904 |
44 | 0.630655 | 77.00398 | 2.418306 | 5.749531 | 14.82819 |
45 | 0.639696 | 75.50135 | 2.790808 | 6.077919 | 15.62992 |
46 | 0.648412 | 73.92544 | 3.212962 | 6.412711 | 16.44889 |
47 | 0.656906 | 72.28230 | 3.689787 | 6.750497 | 17.27741 |
48 | 0.665310 | 70.58226 | 4.226078 | 7.086683 | 18.10498 |
Variance decomposition of LNOILPR.
Note: SE refers to the total variance error in forecasting LNOILPR. Other columns represent the percentage of the variance attributable to shocks in the residual of the respective variables.
Variance Decomposition of LNEXCHR: | |||||
---|---|---|---|---|---|
Period | S.E. | LNOILPR | LNEXCHR | LNCPI | LNINTR |
1 | 0.008667 | 2.442191 | 97.55781 | 0.000000 | 0.000000 |
2 | 0.016018 | 1.303029 | 98.47056 | 0.226099 | 0.000307 |
3 | 0.020768 | 0.793908 | 98.43015 | 0.646775 | 0.129165 |
4 | 0.024011 | 0.693289 | 97.87271 | 1.034284 | 0.399717 |
5 | 0.026961 | 0.553215 | 97.54321 | 1.309243 | 0.594331 |
6 | 0.030343 | 0.647208 | 97.17916 | 1.485892 | 0.687736 |
12 | 0.059365 | 4.366737 | 92.68622 | 2.025025 | 0.922015 |
18 | 0.109801 | 12.31598 | 84.79549 | 2.160683 | 0.727839 |
24 | 0.199812 | 21.01359 | 76.31208 | 2.242682 | 0.431654 |
30 | 0.358345 | 28.27847 | 69.15410 | 2.361025 | 0.206413 |
36 | 0.633138 | 33.57260 | 63.83346 | 2.514318 | 0.079625 |
42 | 1.103690 | 37.11351 | 60.17595 | 2.683471 | 0.027067 |
43 | 1.209424 | 37.56150 | 59.70328 | 2.711909 | 0.023305 |
44 | 1.324903 | 37.97407 | 59.26509 | 2.740223 | 0.020615 |
45 | 1.451006 | 38.35307 | 58.85970 | 2.768351 | 0.018878 |
46 | 1.588692 | 38.70034 | 58.48544 | 2.796232 | 0.017984 |
47 | 1.739007 | 39.01764 | 58.14072 | 2.823810 | 0.017829 |
48 | 1.903092 | 39.30669 | 57.82396 | 2.851035 | 0.018316 |
Variance decomposition of LNEXCHR.
Note: SE refers to the total variance error in forecasting LNEXCHR. Other columns represent the percentage of the variance attributable to shocks in the residual of the respective variables.
Variance Decomposition of LNCPI: | |||||
---|---|---|---|---|---|
Period | S.E. | LNOILPR | LNEXCHR | LNCPI | LNINTR |
1 | 0.006843 | 0.063687 | 0.122994 | 99.81332 | 0.000000 |
2 | 0.010614 | 0.111687 | 1.169617 | 97.78015 | 0.938541 |
3 | 0.013902 | 0.104867 | 1.709240 | 96.72400 | 1.461890 |
4 | 0.016436 | 0.118843 | 2.348794 | 96.05369 | 1.478675 |
5 | 0.018494 | 0.094052 | 3.766938 | 94.89832 | 1.240691 |
6 | 0.020348 | 0.110542 | 6.716716 | 92.14406 | 1.028684 |
12 | 0.034150 | 0.390382 | 43.39555 | 54.73213 | 1.481945 |
18 | 0.058790 | 0.800621 | 71.63000 | 26.51978 | 1.049596 |
24 | 0.102887 | 2.477121 | 83.86813 | 13.27275 | 0.382003 |
30 | 0.182422 | 6.425699 | 86.02039 | 7.403793 | 0.150115 |
36 | 0.326127 | 12.33701 | 82.73589 | 4.811637 | 0.115460 |
42 | 0.583692 | 18.92647 | 77.30909 | 3.668364 | 0.096074 |
43 | 0.642926 | 19.99991 | 76.35470 | 3.554354 | 0.091028 |
44 | 0.708053 | 21.05415 | 75.40480 | 3.455500 | 0.085552 |
45 | 0.779631 | 22.08580 | 74.46447 | 3.369987 | 0.079739 |
46 | 0.858270 | 23.09198 | 73.53809 | 3.296234 | 0.073694 |
47 | 0.944635 | 24.07026 | 72.62936 | 3.232858 | 0.067521 |
48 | 1.039451 | 25.01867 | 71.74135 | 3.178651 | 0.061323 |
Variance decomposition of LNCPI.
Note: SE refers to the total variance error in forecasting LNCPI. Other columns represent the percentage of the variance attributable to shocks in the residual of the respective variables.
Variance Decomposition of LNINTR: | |||||
---|---|---|---|---|---|
Period | S.E. | LNOILPR | LNEXCHR | LNCPI | LNINTR |
1 | 0.011298 | 0.270981 | 1.911153 | 0.411721 | 97.40614 |
2 | 0.015682 | 1.162856 | 3.384292 | 0.236829 | 95.21602 |
3 | 0.019164 | 0.778732 | 7.551086 | 0.251113 | 91.41907 |
4 | 0.021868 | 1.545252 | 10.35243 | 0.690563 | 87.41175 |
5 | 0.024147 | 4.317860 | 10.80310 | 1.548061 | 83.33098 |
6 | 0.026278 | 8.517769 | 10.19189 | 2.639437 | 78.65090 |
12 | 0.042469 | 43.36535 | 7.083532 | 7.537991 | 42.01312 |
18 | 0.068739 | 68.22092 | 7.817425 | 6.432329 | 17.52932 |
24 | 0.105922 | 75.25005 | 13.09986 | 4.196117 | 7.453977 |
30 | 0.154692 | 69.22610 | 23.97069 | 2.633864 | 4.169344 |
36 | 0.219876 | 52.16768 | 42.34294 | 1.710601 | 3.778773 |
42 | 0.320347 | 28.71560 | 65.93762 | 1.220443 | 4.126342 |
43 | 0.343392 | 25.06914 | 69.62387 | 1.173901 | 4.133092 |
44 | 0.369026 | 21.71282 | 73.04019 | 1.136972 | 4.110024 |
45 | 0.397622 | 18.71892 | 76.11686 | 1.109481 | 4.054735 |
46 | 0.429590 | 16.14604 | 78.79614 | 1.091170 | 3.966652 |
47 | 0.465375 | 14.03524 | 81.03610 | 1.081687 | 3.846977 |
48 | 0.505456 | 12.40799 | 82.81296 | 1.080569 | 3.698485 |
Variance decomposition of LNINTR.
Cholesky Ordering: LNOILPR LNEXCHR LNCPI LNINTR. Note: SE refers to the total variance error in forecasting LNINTR. Other columns represent the percentage of the variance attributable to shocks in the residual of the respective variables.
Impulse response function of lnoilpr to lnoilpr.
Impulse response function of lnoilpr to lnexchr.
Impulse response function of lnoilpr to lncpi.
Impulse response function of lnoilpr to lnintr.
Impulse response function of lnexchr to lnoilpr.
Impulse response function of lnexchr to lnexchr.
Impulse response function of lnexchr to lncpi.
Impulse response function of lnexchr to lnintr.
Impulse response function of lncpi to lnoilpr.
Impulse response function of lncpi to lnexchr.
Impulse response function of lncpi to lncpi.
Impulse response function of lncpi to lnintr.
Impulse response function of lnintr to lnoilpr.
Impulse response function of lnintr to lnexchr.
Impulse response function of lnintr to lncpi.
Impulse response function of lnintr to lnintr.
We explored the Cholesky factorization in the E-Views software and forecast the interrelationship of the variables up 48 months equal to 4 years. Table 10 is the Table for FEVD for lnoilpr as a dependent variable for 48 periods (4 years) forecast. In forecasting a variable, shocks in the residual of the forecasted variable contribute more to its variance than the shocks in other variables in the first period. The shocks in oil price-output contributed more to its variance, from 100% in the first period down to 70.58% in the 48 period (4th year) of the forecast period. This is followed by lnintr that contributed 4.11% in the 24th period to about 18.11% in the 48 period (4th year). This followed by lncpi that contributed 1.48% at the 24th period to 7.09 at the 48 periods and last is the lnexchr contributions from 0.06% in the 24th period to 4.22% in the 48 periods. This shows monetary policy influences the fluctuation inherent with the oil price and in the future, it shows that lnintr will respond highly to oil price shocks. While the contemporaneous relationship between the oil prices as the endogenous variables (lncpi and lnexchr) in our model are very insignificant. This is an indication that it will take a longer time into the future, for variables other than lnintr to influence the impact of oil prices.
Table 13, is the Variance Decomposition for dependent variable lnexchr, the contributions to itself were 97.56% in the 1st period, to about 57.82% in the 48 period (4th year) into the future. This followed by the contributions of lnoilpr with 28.28% at the 24th period and 39.31% at the 48th period. While lncpi and lnintr contributed 2.58% and 0.02% all at the 48th period. The error variance in forecasting lnexchr from lnoilpr is high, which indicates that shocks in the residuals of lnoilpr will have much effect in determining the lnexchr in the future.
Table 14 is forecast error variance decomposition of LNCPI as the predictant, the predictant contributes 99.81%, 54.73%, 3.18% in the 1st, 12th and 48th periods to itself, which indicates that the contributions of lncpi to itself declined in 4 years. While lnexchr contributes more to the error variance in forecasting lncpi, contributing about 43.40% up to 82.74% for the periods 12th and 36th then declined to 71.74%in the 48th period (4th year). While lnoilpr contributions started from 24th period with 2.47% and keep increasing up to 25.02% in the 48th period. Whereas lnintr contributions are insignificant. This has brought a clearer picture that lnexchr and lnoilpr are the major determinant of inflation in the economy.
Table 15 illustrated the forecast error variance decomposition of lnintr, contributing to its future error variation of 97.41%, 42.01% and 54.34% for the 1st, 12th and declined to 3.70% at the 48th period (4th year), this is followed by lnexchr which contributes 1.91%, 10.19% for the 1st and 6th periods, it declined for some periods and pick up again and continue rising to 82.81% in the 48th period (4th year).
This is trailed behind by lnoilpr, contributing 4.32% and 43.37% in the 6th and 12th, 75.25% at 24th period and started declining up to 12.41% at the 48th period (4th year). This indicates also a strong relationship into the future. The forecast error variance decomposition of the variables estimates also coincides with the result we obtained in the estimates we derived in Table 11, which also indicates that our estimates are good to go with for future implementation of policies.
In Figure 2, from appendix 1, the Oil price (lnoilp) responded contemporaneously by the change in its own shocks, which is positive and not dissipating. The implication is that hick in the price of oil may mean high revenue, but the consequences is, as an import based economic of non-oil goods and refined petroleum product, with domestic regulation of prices (subsidies), the policy will confine government’s ability to finance the import bills as well as meet other international obligations [8]. While the response of oil price (lnoilpr) to change in Exchange rate (lnexchr) is insignificant in Figure 3. Inflation (lncpi), and Interest rate (lnintr) in Figures 4, and 5 showed some level of positive response.
In Figure 6, there is a slightly positive response of Exchange (lnexchr) to change Oil price (lnoilpr) in the sixth lag period. This show how influential oil is in determining exchange rate, since high price of oil means more revenue (foreign income), also Exchange (lnexchr) responded instantaneously, a positive response, to change in its self (Figure 7.). In Figure 8, there is slight positive response of lnexchr to change in lncpi and Figure 9 showed a small inverse response of lnexchr to change in lnintr.
In Figures 10 and 13, Inflation (lncpi) did not show a meaningful response to orthogonal change in the price of oil (lnoilpr) and Interest rate (lnintr). While Figure 11, showed a positive response in Inflation (lncpi) to change in the Exchange rate (lnexchr), that is from the second lag period up to the tenth lag period in increasing order, this indicate that inflation will continue since the response is not dissipating unless there is a policy to induce deflation. Whereas in Figure 12 there is an instantaneous response of Inflation (lncpi) to change in Inflation (lncpi) in a high positive level, with a slight drop towards the tenth period which indicates tendencies of achieving normality in the future.
Figure 14, showed that there is an inverse response of Interest rate (lnintr) to one standard deviation change in the price of oil (lnoilpr) from the second lag period in an increasing order up to the tenth period, this is expected because the assumption is that interest rate has an inverse relationship with the oil price. Also Figure 15 indicated an instantaneous positive response of interest rate (lnintr) to change in the Exchange rate (lnexchr), in the third and fourth period, before it dying off which indicates that there is propensities of achieving normality in the long run. In Figure 16 Interest rate (lnintr) responds contemporaneously to change in Inflation (lncpi), with a positive increase from the fourth period and finally, in Figure 17 Inflation (lncpi) responded significantly to change Inflation (lncpi). The impulse response functions further complement the Forecast Error Variance Decomposition by given a portrait of the direction of the inter-relationships of variables.
In this research work, we explored the Toda-Yamamoto Modified Wald Test (MWALD) to examine the impact of oil price fluctuation on the monetary instrument in Nigeria, by looking at their causal relationships. The study covered the period 1995 to 2018 and the data are monthly data, to establish the contemporaneous relationships between these macroeconomic indicators. Among other analyses are the Granger Causality, FEVD and IRFs.
The review showed the direction of causality and FEVD into the future for 48 months equivalent to four years (short-run), between oil price, Exchange rate, Inflation, and Interest rate.
From the analyses of Toda-Yamamoto Granger Causality WALD Test, the review presented that there is unidirectional causality from lnoilpr to lnexchr in Table 9. This is consistence with the result we obtained in the estimation forecast error variance decomposition of lnexchr (Table 13) as the predictant, where the predictant contributes 97.56% in the 1st period, to about 57.82% in the 48 period (4th year) into the future. This was followed by the contributions of lnoilpr with 28.28% at the 24th period and 39.31% at the 48th period. While lncpi and lnintr contributed 2.58% and 0.02% all at the 48th period. This was also complemented by for IRFs in Figure 7 in the appendix.
Also from granger causality of lncpi as a dependent variable in Table 10 there is unidirectional causality from lnexchr and lnintr to lncpi, also the combination of all the three independent variables (lnoilpr, lnexchr and lnintr) granger cause lncpi but lnexchr and lnintr have more contributions. This is also in tandem with the result of FEVD for dependent variable lncpi in Table 14 where the dependent variable contributions to itself were 99.81%, 54.73%, 3.18% in the 1st, 12th and 48th periods, which indicates that the contributions of lncpi to itself declined in 4 years. While lnexchr contributes more to the error variance in forecasting lncpi, contributing about 43.40% up to 82.74% for the periods 12th and 36th periods (3rd years) then declined to 71.74%in the 48th period (4th year). While lnoilpr contributions started from 24th period with 2.47% and keep increasing up to 25.02% in the 48th period (4th year). This is also affirmed in Figure 11 in the appendix.
Similarly in the estimation of Granger Causality WALD Test for lnintr, it responded positively to change in lnoilpr and lnexchr. This is also in agreement with the estimation of forecast error variance decomposition of lnintr as an endogenous variable, contributing to its future error variation of 97.41%, 42.01% and 54.34% for the 1st, 12th periods and declined to 3.70% at the 48th period (4th year), this is followed by lnexchr which contributes 1.91%, 10.19% for the 1st and 6th perods, it declined for some periods and pick up again and continue rising to 82.81% in the 48th period (4th year). This is trailed behind by lnoilpr, contributing 4.32% and 43.37% in the 6th and 12th, 75.25% at 24th period and started declining up to 12.41% at the 48th period (4th year). This indicated that the major determinant factors of interest rate policy in Nigeria are change in price of oil and exchange rate in the long run. This also conforms to the outcome of the IRF in Figure 14, which specified further that the relation between lnintr and lnoilpr is an inverse relationship, while lnexchr, lncpi and lnintr in Figures 15–17 are positive.
The object of this is work is to establish a direct link between oil price and some selected monetary instruments in Nigeria, and our a priori expectations were achieved, we were able to established that oil price has a direct influence on the exchange rate, interest rate and inflation rate. It is known facts that Nigeria is an oil-producing economy and at the same time also an import-based economy of non-oil products. The major sources of financing the import come from oil revenue. As an oil-producing economy, there are tendencies of having Dutch disease syndrome and economic pass-through [9]. Both in theory and empirical analyses one can conclude that oil price is a strong determining factor of the rate of exchange, it has a direct link to inflationary or deflationary tendencies and also influences the monetary policies in Nigeria in terms of cost of borrowing.
Therefore, in implementation of monetary policy by the policymakers, attention should be drawn to price level of import from the external market, that is by concurrently monitoring the domestic market and the economy of the country’s trading partners. On a general note, there should be diversification of the economy from oil to the non-oil economy to avoid the Dutch disease syndrome.
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As a response, technological, economic, policy, or legislation measures are adopted.",book:{id:"5289",slug:"landscape-ecology-the-influences-of-land-use-and-anthropogenic-impacts-of-landscape-creation",title:"Landscape Ecology",fullTitle:"Landscape Ecology - The Influences of Land Use and Anthropogenic Impacts of Landscape Creation"},signatures:"Radoslava Kanianska",authors:[{id:"184781",title:"Ph.D.",name:"Radoslava",middleName:null,surname:"Kanianska",slug:"radoslava-kanianska",fullName:"Radoslava Kanianska"}]},{id:"45414",doi:"10.5772/55749",title:"Role of Ecotourism in Sustainable Development",slug:"role-of-ecotourism-in-sustainable-development",totalDownloads:30268,totalCrossrefCites:52,totalDimensionsCites:69,abstract:null,book:{id:"3560",slug:"advances-in-landscape-architecture",title:"Advances in Landscape Architecture",fullTitle:"Advances in Landscape Architecture"},signatures:"Tuğba Kiper",authors:[{id:"135574",title:"Dr.",name:"Tuğba",middleName:null,surname:"Kiper",slug:"tugba-kiper",fullName:"Tuğba Kiper"}]},{id:"12592",doi:"10.5772/12877",title:"Effects of Herbicide Glyphosate and Glyphosate-Based Formulations on Aquatic Ecosystems",slug:"effects-of-herbicide-glyphosate-and-glyphosate-based-formulations-on-aquatic-ecosystems",totalDownloads:5699,totalCrossrefCites:9,totalDimensionsCites:64,abstract:null,book:{id:"17",slug:"herbicides-and-environment",title:"Herbicides and Environment",fullTitle:"Herbicides and Environment"},signatures:"Gonzalo Luis Pérez, María Solange Vera and Leandro Miranda",authors:[{id:"13369",title:"Dr.",name:"Gonzalo Luis",middleName:null,surname:"Pérez",slug:"gonzalo-luis-perez",fullName:"Gonzalo Luis Pérez"},{id:"14902",title:"Dr.",name:"María Solange",middleName:null,surname:"Vera",slug:"maria-solange-vera",fullName:"María Solange Vera"},{id:"14903",title:"Dr.",name:"Leandro",middleName:null,surname:"Miranda",slug:"leandro-miranda",fullName:"Leandro Miranda"}]},{id:"38612",doi:"10.5772/51091",title:"Soil Fauna Diversity - Function, Soil Degradation, Biological Indices, Soil Restoration",slug:"soil-fauna-diversity-function-soil-degradation-biological-indices-soil-restoration",totalDownloads:7713,totalCrossrefCites:24,totalDimensionsCites:54,abstract:null,book:{id:"3007",slug:"biodiversity-conservation-and-utilization-in-a-diverse-world",title:"Biodiversity Conservation and Utilization in a Diverse World",fullTitle:"Biodiversity Conservation and Utilization in a Diverse World"},signatures:"Cristina Menta",authors:[{id:"145410",title:"PhD.",name:"Cristina",middleName:null,surname:"Menta",slug:"cristina-menta",fullName:"Cristina Menta"}]},{id:"12591",doi:"10.5772/13620",title:"Impacts of Biochar (Black Carbon) Additions on the Sorption and Efficacy of Herbicides",slug:"impacts-of-biochar-black-carbon-additions-on-the-sorption-and-efficacy-of-herbicides",totalDownloads:3971,totalCrossrefCites:3,totalDimensionsCites:44,abstract:null,book:{id:"17",slug:"herbicides-and-environment",title:"Herbicides and Environment",fullTitle:"Herbicides and Environment"},signatures:"Alegría Cabrera Mesa and Kurt Spokas",authors:[{id:"13745",title:"Dr.",name:"Kurt",middleName:null,surname:"Spokas",slug:"kurt-spokas",fullName:"Kurt Spokas"},{id:"23738",title:"Dr.",name:"Alegría",middleName:null,surname:"Cabrera Mesa",slug:"alegria-cabrera-mesa",fullName:"Alegría Cabrera Mesa"}]}],mostDownloadedChaptersLast30Days:[{id:"45422",title:"Urban Waterfront Regenerations",slug:"urban-waterfront-regenerations",totalDownloads:13880,totalCrossrefCites:4,totalDimensionsCites:11,abstract:null,book:{id:"3560",slug:"advances-in-landscape-architecture",title:"Advances in Landscape Architecture",fullTitle:"Advances in Landscape Architecture"},signatures:"Umut Pekin Timur",authors:[{id:"165480",title:"Dr.",name:"Umut",middleName:null,surname:"Pekin Timur",slug:"umut-pekin-timur",fullName:"Umut Pekin Timur"}]},{id:"51201",title:"Agriculture and Its Impact on Land‐Use, Environment, and Ecosystem Services",slug:"agriculture-and-its-impact-on-land-use-environment-and-ecosystem-services",totalDownloads:8420,totalCrossrefCites:34,totalDimensionsCites:70,abstract:"Human expansion throughout the world caused that agriculture is a dominant form of land management globally. Human influence on the land is accelerating because of rapid population growth and increasing food requirements. To stress the interactions between society and the environment, the driving forces (D), pressures (P), states (S), impacts (I), and response (R) (DPSIR) framework approach was used for analyzing and assessing the influence of agriculture on land use, environment, and ecosystem services. The DPSIR model was used to identify a series of core indicators and to establish the nature of interactions between different driving forces, pressures, states, impacts, and responses. We assessed selected indicators at global, national, and local levels. Driving force indicators describe growing population trend and linking land‐use patterns. The driving forces exert pressure on the environment assessed by indicators describing development in fertilizer and pesticides consumption, by number of livestock, and by intensification joined growing release of ammonia and greenhouse gas (GHG) emissions from agriculture, and water abstraction. The pressure reflects in the state of environment, mainly expressed by soil and water quality indicators. Negative changes in the state then have negative impacts on landscape, e.g., traditional landscape disappearance, biodiversity, climate, and ecosystem services. As a response, technological, economic, policy, or legislation measures are adopted.",book:{id:"5289",slug:"landscape-ecology-the-influences-of-land-use-and-anthropogenic-impacts-of-landscape-creation",title:"Landscape Ecology",fullTitle:"Landscape Ecology - The Influences of Land Use and Anthropogenic Impacts of Landscape Creation"},signatures:"Radoslava Kanianska",authors:[{id:"184781",title:"Ph.D.",name:"Radoslava",middleName:null,surname:"Kanianska",slug:"radoslava-kanianska",fullName:"Radoslava Kanianska"}]},{id:"45403",title:"Urban Landscapes and Identity",slug:"urban-landscapes-and-identity",totalDownloads:9064,totalCrossrefCites:19,totalDimensionsCites:25,abstract:null,book:{id:"3560",slug:"advances-in-landscape-architecture",title:"Advances in Landscape Architecture",fullTitle:"Advances in Landscape Architecture"},signatures:"Isil Kaymaz",authors:[{id:"127507",title:"Dr.",name:"Isil",middleName:null,surname:"Kaymaz",slug:"isil-kaymaz",fullName:"Isil Kaymaz"}]},{id:"45414",title:"Role of Ecotourism in Sustainable Development",slug:"role-of-ecotourism-in-sustainable-development",totalDownloads:30268,totalCrossrefCites:52,totalDimensionsCites:69,abstract:null,book:{id:"3560",slug:"advances-in-landscape-architecture",title:"Advances in Landscape Architecture",fullTitle:"Advances in Landscape Architecture"},signatures:"Tuğba Kiper",authors:[{id:"135574",title:"Dr.",name:"Tuğba",middleName:null,surname:"Kiper",slug:"tugba-kiper",fullName:"Tuğba Kiper"}]},{id:"63289",title:"Land Acquisition and Use in Nigeria: Implications for Sustainable Food and Livelihood Security",slug:"land-acquisition-and-use-in-nigeria-implications-for-sustainable-food-and-livelihood-security",totalDownloads:3524,totalCrossrefCites:5,totalDimensionsCites:9,abstract:"Land acquisition and use remain a critical issue of great policy relevance in developing countries such as Nigeria. This study therefore examined land acquisition and use in Nigeria within the context of food and livelihood security. The chapter used secondary data obtained from the World Bank website, National Bureau of Statistics (NBS) and other sources. It was found that there are gender, location and income-group considerations in the allocation of land in Nigeria. While the urban land market is relatively more formal, the rural land market is informal and the transactions were not documented in most cases. The study found that bureaucratic bottlenecks, high cost of registering land and long registration procedures, and inconsistent policy regimes impede the development of land market in Nigeria. Based on the findings of the study, it was recommended that the government should reduce and make the processes to be completed in registering lands in Nigeria easier. The Land Use Act 1978 should be amended to capture the prevailing realities around customary laws and informal markets. The government should reduce the cost of land registration in Nigeria. Multilateral organisations and government should co-create and co-finance innovative interventions to improve activities in the land market.",book:{id:"7476",slug:"land-use-assessing-the-past-envisioning-the-future",title:"Land Use",fullTitle:"Land Use - Assessing the Past, Envisioning the Future"},signatures:"Isaac B. Oluwatayo, Omowunmi Timothy and Ayodeji O. Ojo",authors:[{id:"250347",title:"Associate Prof.",name:"Isaac",middleName:null,surname:"Oluwatayo",slug:"isaac-oluwatayo",fullName:"Isaac Oluwatayo"}]}],onlineFirstChaptersFilter:{topicId:"126",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:99,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:288,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. 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Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). 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He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. 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He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. 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Dr. Aydin is currently a Fellow of Higher Education Academy, UK, a member of EPSRC College, a senior member of IEEE and a senior member of ACM. In addition to being a member of advisory committees of many international conferences, he is an Editorial Board Member of various peer-reviewed international journals. He has served as guest editor for a number of special issues of peer-reviewed international journals.",institutionString:null,institution:{name:"University of the West of England",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:17,paginationItems:[{id:"81791",title:"Self-Supervised Contrastive Representation Learning in Computer Vision",doi:"10.5772/intechopen.104785",signatures:"Yalin Bastanlar and Semih Orhan",slug:"self-supervised-contrastive-representation-learning-in-computer-vision",totalDownloads:12,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Pattern Recognition - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11442.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"79345",title:"Application of Jump Diffusion Models in Insurance Claim Estimation",doi:"10.5772/intechopen.99853",signatures:"Leonard Mushunje, Chiedza Elvina Mashiri, Edina Chandiwana and Maxwell Mashasha",slug:"application-of-jump-diffusion-models-in-insurance-claim-estimation-1",totalDownloads:2,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Data Clustering",coverURL:"https://cdn.intechopen.com/books/images_new/10820.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"81557",title:"Object Tracking Using Adapted Optical Flow",doi:"10.5772/intechopen.102863",signatures:"Ronaldo Ferreira, Joaquim José de Castro Ferreira and António José Ribeiro Neves",slug:"object-tracking-using-adapted-optical-flow",totalDownloads:10,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Information Extraction and Object Tracking in Digital Video",coverURL:"https://cdn.intechopen.com/books/images_new/10652.jpg",subseries:{id:"24",title:"Computer Vision"}}},{id:"81558",title:"Thresholding Image Techniques for Plant Segmentation",doi:"10.5772/intechopen.104587",signatures:"Miguel Ángel Castillo-Martínez, Francisco Javier Gallegos-Funes, Blanca E. Carvajal-Gámez, Guillermo Urriolagoitia-Sosa and Alberto J. 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(Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. 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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. 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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",institutionURL:null,country:{name:"Romania"}}}]},{type:"book",id:"9963",title:"Advances and Applications in Deep Learning",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/9963.jpg",slug:"advances-and-applications-in-deep-learning",publishedDate:"December 9th 2020",editedByType:"Edited by",bookSignature:"Marco Antonio Aceves-Fernandez",hash:"0d51ba46f22e55cb89140f60d86a071e",volumeInSeries:4,fullTitle:"Advances and Applications in Deep Learning",editors:[{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. 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His research interest focuses on computational chemistry and molecular modeling of diverse systems of pharmacological, food, and alternative energy interests by resorting to DFT and Conceptual DFT. He has authored a coauthored more than 255 peer-reviewed papers, 32 book chapters, and 2 edited books. He has delivered speeches at many international and domestic conferences. He serves as a reviewer for more than eighty international journals, books, and research proposals as well as an editor for special issues of renowned scientific journals.",institutionString:"Centro de Investigación en Materiales Avanzados",institution:{name:"Centro de Investigación en Materiales Avanzados",country:{name:"Mexico"}}},{id:"76477",title:"Prof.",name:"Mirza",middleName:null,surname:"Hasanuzzaman",slug:"mirza-hasanuzzaman",fullName:"Mirza Hasanuzzaman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/76477/images/system/76477.png",biography:"Dr. Mirza Hasanuzzaman is a Professor of Agronomy at Sher-e-Bangla Agricultural University, Bangladesh. He received his Ph.D. in Plant Stress Physiology and Antioxidant Metabolism from Ehime University, Japan, with a scholarship from the Japanese Government (MEXT). Later, he completed his postdoctoral research at the Center of Molecular Biosciences, University of the Ryukyus, Japan, as a recipient of the Japan Society for the Promotion of Science (JSPS) postdoctoral fellowship. He was also the recipient of the Australian Government Endeavour Research Fellowship for postdoctoral research as an adjunct senior researcher at the University of Tasmania, Australia. Dr. Hasanuzzaman’s current work is focused on the physiological and molecular mechanisms of environmental stress tolerance. Dr. Hasanuzzaman has published more than 150 articles in peer-reviewed journals. He has edited ten books and written more than forty book chapters on important aspects of plant physiology, plant stress tolerance, and crop production. According to Scopus, Dr. Hasanuzzaman’s publications have received more than 10,500 citations with an h-index of 53. He has been named a Highly Cited Researcher by Clarivate. He is an editor and reviewer for more than fifty peer-reviewed international journals and was a recipient of the “Publons Peer Review Award” in 2017, 2018, and 2019. He has been honored by different authorities for his outstanding performance in various fields like research and education, and he has received the World Academy of Science Young Scientist Award (2014) and the University Grants Commission (UGC) Award 2018. He is a fellow of the Bangladesh Academy of Sciences (BAS) and the Royal Society of Biology.",institutionString:"Sher-e-Bangla Agricultural University",institution:{name:"Sher-e-Bangla Agricultural University",country:{name:"Bangladesh"}}},{id:"187859",title:"Prof.",name:"Kusal",middleName:"K.",surname:"Das",slug:"kusal-das",fullName:"Kusal Das",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSBDeQAO/Profile_Picture_1623411145568",biography:"Kusal K. Das is a Distinguished Chair Professor of Physiology, Shri B. M. Patil Medical College and Director, Centre for Advanced Medical Research (CAMR), BLDE (Deemed to be University), Vijayapur, Karnataka, India. Dr. Das did his M.S. and Ph.D. in Human Physiology from the University of Calcutta, Kolkata. His area of research is focused on understanding of molecular mechanisms of heavy metal activated low oxygen sensing pathways in vascular pathophysiology. He has invented a new method of estimation of serum vitamin E. His expertise in critical experimental protocols on vascular functions in experimental animals was well documented by his quality of publications. He was a Visiting Professor of Medicine at University of Leeds, United Kingdom (2014-2016) and Tulane University, New Orleans, USA (2017). For his immense contribution in medical research Ministry of Science and Technology, Government of India conferred him 'G.P. Chatterjee Memorial Research Prize-2019” and he is also the recipient of 'Dr.Raja Ramanna State Scientist Award 2015” by Government of Karnataka. He is a Fellow of the Royal Society of Biology (FRSB), London and Honorary Fellow of Karnataka Science and Technology Academy, Department of Science and Technology, Government of Karnataka.",institutionString:"BLDE (Deemed to be University), India",institution:null},{id:"243660",title:"Dr.",name:"Mallanagouda Shivanagouda",middleName:null,surname:"Biradar",slug:"mallanagouda-shivanagouda-biradar",fullName:"Mallanagouda Shivanagouda Biradar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/243660/images/system/243660.jpeg",biography:"M. S. Biradar is Vice Chancellor and Professor of Medicine of\nBLDE (Deemed to be University), Vijayapura, Karnataka, India.\nHe obtained his MD with a gold medal in General Medicine and\nhas devoted himself to medical teaching, research, and administrations. He has also immensely contributed to medical research\non vascular medicine, which is reflected by his numerous publications including books and book chapters. Professor Biradar was\nalso Visiting Professor at Tulane University School of Medicine, New Orleans, USA.",institutionString:"BLDE (Deemed to be University)",institution:{name:"BLDE University",country:{name:"India"}}},{id:"289796",title:"Dr.",name:"Swastika",middleName:null,surname:"Das",slug:"swastika-das",fullName:"Swastika Das",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/289796/images/system/289796.jpeg",biography:"Swastika N. Das is Professor of Chemistry at the V. P. Dr. P. G.\nHalakatti College of Engineering and Technology, BLDE (Deemed\nto be University), Vijayapura, Karnataka, India. She obtained an\nMSc, MPhil, and PhD in Chemistry from Sambalpur University,\nOdisha, India. Her areas of research interest are medicinal chemistry, chemical kinetics, and free radical chemistry. She is a member\nof the investigators who invented a new modified method of estimation of serum vitamin E. She has authored numerous publications including book\nchapters and is a mentor of doctoral curriculum at her university.",institutionString:"BLDEA’s V.P.Dr.P.G.Halakatti College of Engineering & Technology",institution:{name:"BLDE University",country:{name:"India"}}},{id:"248459",title:"Dr.",name:"Akikazu",middleName:null,surname:"Takada",slug:"akikazu-takada",fullName:"Akikazu Takada",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248459/images/system/248459.png",biography:"Akikazu Takada was born in Japan, 1935. After graduation from\nKeio University School of Medicine and finishing his post-graduate studies, he worked at Roswell Park Memorial Institute NY,\nUSA. He then took a professorship at Hamamatsu University\nSchool of Medicine. In thrombosis studies, he found the SK\npotentiator that enhances plasminogen activation by streptokinase. He is very much interested in simultaneous measurements\nof fatty acids, amino acids, and tryptophan degradation products. By using fatty\nacid analyses, he indicated that plasma levels of trans-fatty acids of old men were\nfar higher in the US than Japanese men. . He also showed that eicosapentaenoic acid\n(EPA) and docosahexaenoic acid (DHA) levels are higher, and arachidonic acid\nlevels are lower in Japanese than US people. By using simultaneous LC/MS analyses\nof plasma levels of tryptophan metabolites, he recently found that plasma levels of\nserotonin, kynurenine, or 5-HIAA were higher in patients of mono- and bipolar\ndepression, which are significantly different from observations reported before. In\nview of recent reports that plasma tryptophan metabolites are mainly produced by\nmicrobiota. He is now working on the relationships between microbiota and depression or autism.",institutionString:"Hamamatsu University School of Medicine",institution:{name:"Hamamatsu University School of Medicine",country:{name:"Japan"}}},{id:"137240",title:"Prof.",name:"Mohammed",middleName:null,surname:"Khalid",slug:"mohammed-khalid",fullName:"Mohammed Khalid",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/137240/images/system/137240.png",biography:"Mohammed Khalid received his B.S. degree in chemistry in 2000 and Ph.D. degree in physical chemistry in 2007 from the University of Khartoum, Sudan. He moved to School of Chemistry, Faculty of Science, University of Sydney, Australia in 2009 and joined Dr. Ron Clarke as a postdoctoral fellow where he worked on the interaction of ATP with the phosphoenzyme of the Na+/K+-ATPase and dual mechanisms of allosteric acceleration of the Na+/K+-ATPase by ATP; then he went back to Department of Chemistry, University of Khartoum as an assistant professor, and in 2014 he was promoted as an associate professor. In 2011, he joined the staff of Department of Chemistry at Taif University, Saudi Arabia, where he is currently an assistant professor. His research interests include the following: P-Type ATPase enzyme kinetics and mechanisms, kinetics and mechanisms of redox reactions, autocatalytic reactions, computational enzyme kinetics, allosteric acceleration of P-type ATPases by ATP, exploring of allosteric sites of ATPases, and interaction of ATP with ATPases located in cell membranes.",institutionString:"Taif University",institution:{name:"Taif University",country:{name:"Saudi Arabia"}}},{id:"63810",title:"Prof.",name:"Jorge",middleName:null,surname:"Morales-Montor",slug:"jorge-morales-montor",fullName:"Jorge Morales-Montor",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/63810/images/system/63810.png",biography:"Dr. Jorge Morales-Montor was recognized with the Lola and Igo Flisser PUIS Award for best graduate thesis at the national level in the field of parasitology. He received a fellowship from the Fogarty Foundation to perform postdoctoral research stay at the University of Georgia. He has 153 journal articles to his credit. He has also edited several books and published more than fifty-five book chapters. He is a member of the Mexican Academy of Sciences, Latin American Academy of Sciences, and the National Academy of Medicine. He has received more than thirty-five awards and has supervised numerous bachelor’s, master’s, and Ph.D. students. Dr. Morales-Montor is the past president of the Mexican Society of Parasitology.",institutionString:"National Autonomous University of Mexico",institution:{name:"National Autonomous University of Mexico",country:{name:"Mexico"}}},{id:"217215",title:"Dr.",name:"Palash",middleName:null,surname:"Mandal",slug:"palash-mandal",fullName:"Palash Mandal",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/217215/images/system/217215.jpeg",biography:null,institutionString:"Charusat University",institution:null},{id:"49739",title:"Dr.",name:"Leszek",middleName:null,surname:"Szablewski",slug:"leszek-szablewski",fullName:"Leszek Szablewski",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49739/images/system/49739.jpg",biography:"Leszek Szablewski is a professor of medical sciences. He received his M.S. in the Faculty of Biology from the University of Warsaw and his PhD degree from the Institute of Experimental Biology Polish Academy of Sciences. He habilitated in the Medical University of Warsaw, and he obtained his degree of Professor from the President of Poland. Professor Szablewski is the Head of Chair and Department of General Biology and Parasitology, Medical University of Warsaw. Professor Szablewski has published over 80 peer-reviewed papers in journals such as Journal of Alzheimer’s Disease, Biochim. Biophys. Acta Reviews of Cancer, Biol. Chem., J. Biomed. Sci., and Diabetes/Metabol. Res. Rev, Endocrine. He is the author of two books and four book chapters. He has edited four books, written 15 scripts for students, is the ad hoc reviewer of over 30 peer-reviewed journals, and editorial member of peer-reviewed journals. Prof. Szablewski’s research focuses on cell physiology, genetics, and pathophysiology. He works on the damage caused by lack of glucose homeostasis and changes in the expression and/or function of glucose transporters due to various diseases. He has given lectures, seminars, and exercises for students at the Medical University.",institutionString:"Medical University of Warsaw",institution:{name:"Medical University of Warsaw",country:{name:"Poland"}}},{id:"173123",title:"Dr.",name:"Maitham",middleName:null,surname:"Khajah",slug:"maitham-khajah",fullName:"Maitham Khajah",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/173123/images/system/173123.jpeg",biography:"Dr. Maitham A. Khajah received his degree in Pharmacy from Faculty of Pharmacy, Kuwait University, in 2003 and obtained his PhD degree in December 2009 from the University of Calgary, Canada (Gastrointestinal Science and Immunology). Since January 2010 he has been assistant professor in Kuwait University, Faculty of Pharmacy, Department of Pharmacology and Therapeutics. His research interest are molecular targets for the treatment of inflammatory bowel disease (IBD) and the mechanisms responsible for immune cell chemotaxis. He cosupervised many students for the MSc Molecular Biology Program, College of Graduate Studies, Kuwait University. Ever since joining Kuwait University in 2010, he got various grants as PI and Co-I. He was awarded the Best Young Researcher Award by Kuwait University, Research Sector, for the Year 2013–2014. He was a member in the organizing committee for three conferences organized by Kuwait University, Faculty of Pharmacy, as cochair and a member in the scientific committee (the 3rd, 4th, and 5th Kuwait International Pharmacy Conference).",institutionString:"Kuwait University",institution:{name:"Kuwait University",country:{name:"Kuwait"}}},{id:"195136",title:"Dr.",name:"Aya",middleName:null,surname:"Adel",slug:"aya-adel",fullName:"Aya Adel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/195136/images/system/195136.jpg",biography:"Dr. Adel works as an Assistant Lecturer in the unit of Phoniatrics, Department of Otolaryngology, Ain Shams University in Cairo, Egypt. Dr. Adel is especially interested in joint attention and its impairment in autism spectrum disorder",institutionString:"Ain Shams University",institution:{name:"Ain Shams University",country:{name:"Egypt"}}},{id:"94911",title:"Dr.",name:"Boulenouar",middleName:null,surname:"Mesraoua",slug:"boulenouar-mesraoua",fullName:"Boulenouar Mesraoua",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94911/images/system/94911.png",biography:"Dr Boulenouar Mesraoua is the Associate Professor of Clinical Neurology at Weill Cornell Medical College-Qatar and a Consultant Neurologist at Hamad Medical Corporation at the Neuroscience Department; He graduated as a Medical Doctor from the University of Oran, Algeria; he then moved to Belgium, the City of Liege, for a Residency in Internal Medicine and Neurology at Liege University; after getting the Belgian Board of Neurology (with high marks), he went to the National Hospital for Nervous Diseases, Queen Square, London, United Kingdom for a fellowship in Clinical Neurophysiology, under Pr Willison ; Dr Mesraoua had also further training in Epilepsy and Continuous EEG Monitoring for two years (from 2001-2003) in the Neurophysiology department of Zurich University, Switzerland, under late Pr Hans Gregor Wieser ,an internationally known epileptologist expert. \n\nDr B. Mesraoua is the Director of the Neurology Fellowship Program at the Neurology Section and an active member of the newly created Comprehensive Epilepsy Program at Hamad General Hospital, Doha, Qatar; he is also Assistant Director of the Residency Program at the Qatar Medical School. \nDr B. Mesraoua's main interests are Epilepsy, Multiple Sclerosis, and Clinical Neurology; He is the Chairman and the Organizer of the well known Qatar Epilepsy Symposium, he is running yearly for the past 14 years and which is considered a landmark in the Gulf region; He has also started last year , together with other epileptologists from Qatar, the region and elsewhere, a yearly International Epilepsy School Course, which was attended by many neurologists from the Area.\n\nInternationally, Dr Mesraoua is an active and elected member of the Commission on Eastern Mediterranean Region (EMR ) , a regional branch of the International League Against Epilepsy (ILAE), where he represents the Middle East and North Africa(MENA ) and where he holds the position of chief of the Epilepsy Epidemiology Section; Dr Mesraoua is a member of the American Academy of Neurology, the Europeen Academy of Neurology and the American Epilepsy Society.\n\nDr Mesraoua's main objectives are to encourage frequent gathering of the epileptologists/neurologists from the MENA region and the rest of the world, promote Epilepsy Teaching in the MENA Region, and encourage multicenter studies involving neurologists and epileptologists in the MENA region, particularly epilepsy epidemiological studies. \n\nDr. Mesraoua is the recipient of two research Grants, as the Lead Principal Investigator (750.000 USD and 250.000 USD) from the Qatar National Research Fund (QNRF) and the Hamad Hospital Internal Research Grant (IRGC), on the following topics : “Continuous EEG Monitoring in the ICU “ and on “Alpha-lactoalbumin , proof of concept in the treatment of epilepsy” .Dr Mesraoua is a reviewer for the journal \"seizures\" (Europeen Epilepsy Journal ) as well as dove journals ; Dr Mesraoua is the author and co-author of many peer reviewed publications and four book chapters in the field of Epilepsy and Clinical Neurology",institutionString:"Weill Cornell Medical College in Qatar",institution:{name:"Weill Cornell Medical College in Qatar",country:{name:"Qatar"}}},{id:"282429",title:"Prof.",name:"Covanis",middleName:null,surname:"Athanasios",slug:"covanis-athanasios",fullName:"Covanis Athanasios",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/282429/images/system/282429.jpg",biography:null,institutionString:"Neurology-Neurophysiology Department of the Children Hospital Agia Sophia",institution:null},{id:"190980",title:"Prof.",name:"Marwa",middleName:null,surname:"Mahmoud Saleh",slug:"marwa-mahmoud-saleh",fullName:"Marwa Mahmoud Saleh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/190980/images/system/190980.jpg",biography:"Professor Marwa Mahmoud Saleh is a doctor of medicine and currently works in the unit of Phoniatrics, Department of Otolaryngology, Ain Shams University in Cairo, Egypt. She got her doctoral degree in 1991 and her doctoral thesis was accomplished in the University of Iowa, United States. Her publications covered a multitude of topics as videokymography, cochlear implants, stuttering, and dysphagia. She has lectured Egyptian phonology for many years. Her recent research interest is joint attention in autism.",institutionString:"Ain Shams University",institution:{name:"Ain Shams University",country:{name:"Egypt"}}},{id:"259190",title:"Dr.",name:"Syed Ali Raza",middleName:null,surname:"Naqvi",slug:"syed-ali-raza-naqvi",fullName:"Syed Ali Raza Naqvi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259190/images/system/259190.png",biography:"Dr. Naqvi is a radioanalytical chemist and is working as an associate professor of analytical chemistry in the Department of Chemistry, Government College University, Faisalabad, Pakistan. Advance separation techniques, nuclear analytical techniques and radiopharmaceutical analysis are the main courses that he is teaching to graduate and post-graduate students. In the research area, he is focusing on the development of organic- and biomolecule-based radiopharmaceuticals for diagnosis and therapy of infectious and cancerous diseases. Under the supervision of Dr. Naqvi, three students have completed their Ph.D. degrees and 41 students have completed their MS degrees. He has completed three research projects and is currently working on 2 projects entitled “Radiolabeling of fluoroquinolone derivatives for the diagnosis of deep-seated bacterial infections” and “Radiolabeled minigastrin peptides for diagnosis and therapy of NETs”. He has published about 100 research articles in international reputed journals and 7 book chapters. Pakistan Institute of Nuclear Science & Technology (PINSTECH) Islamabad, Punjab Institute of Nuclear Medicine (PINM), Faisalabad and Institute of Nuclear Medicine and Radiology (INOR) Abbottabad are the main collaborating institutes.",institutionString:"Government College University",institution:{name:"Government College University, Faisalabad",country:{name:"Pakistan"}}},{id:"58390",title:"Dr.",name:"Gyula",middleName:null,surname:"Mozsik",slug:"gyula-mozsik",fullName:"Gyula Mozsik",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/58390/images/system/58390.png",biography:"Gyula Mózsik MD, Ph.D., ScD (med), is an emeritus professor of Medicine at the First Department of Medicine, Univesity of Pécs, Hungary. He was head of this department from 1993 to 2003. His specializations are medicine, gastroenterology, clinical pharmacology, clinical nutrition, and dietetics. His research fields are biochemical pharmacological examinations in the human gastrointestinal (GI) mucosa, mechanisms of retinoids, drugs, capsaicin-sensitive afferent nerves, and innovative pharmacological, pharmaceutical, and nutritional (dietary) research in humans. He has published about 360 peer-reviewed papers, 197 book chapters, 692 abstracts, 19 monographs, and has edited 37 books. He has given about 1120 regular and review lectures. He has organized thirty-eight national and international congresses and symposia. He is the founder of the International Conference on Ulcer Research (ICUR); International Union of Pharmacology, Gastrointestinal Section (IUPHAR-GI); Brain-Gut Society symposiums, and gastrointestinal cytoprotective symposiums. He received the Andre Robert Award from IUPHAR-GI in 2014. Fifteen of his students have been appointed as full professors in Egypt, Cuba, and Hungary.",institutionString:"University of Pécs",institution:{name:"University of Pecs",country:{name:"Hungary"}}},{id:"277367",title:"M.Sc.",name:"Daniel",middleName:"Martin",surname:"Márquez López",slug:"daniel-marquez-lopez",fullName:"Daniel Márquez López",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/277367/images/7909_n.jpg",biography:"Msc Daniel Martin Márquez López has a bachelor degree in Industrial Chemical Engineering, a Master of science degree in the same área and he is a PhD candidate for the Instituto Politécnico Nacional. His Works are realted to the Green chemistry field, biolubricants, biodiesel, transesterification reactions for biodiesel production and the manipulation of oils for therapeutic purposes.",institutionString:null,institution:{name:"Instituto Politécnico Nacional",country:{name:"Mexico"}}},{id:"196544",title:"Prof.",name:"Angel",middleName:null,surname:"Catala",slug:"angel-catala",fullName:"Angel Catala",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/196544/images/system/196544.jpg",biography:"Angel Catalá studied chemistry at Universidad Nacional de La Plata, Argentina, where he received a Ph.D. in Chemistry (Biological Branch) in 1965. From 1964 to 1974, he worked as an Assistant in Biochemistry at the School of Medicine at the same university. From 1974 to 1976, he was a fellow of the National Institutes of Health (NIH) at the University of Connecticut, Health Center, USA. From 1985 to 2004, he served as a Full Professor of Biochemistry at the Universidad Nacional de La Plata. He is a member of the National Research Council (CONICET), Argentina, and the Argentine Society for Biochemistry and Molecular Biology (SAIB). His laboratory has been interested for many years in the lipid peroxidation of biological membranes from various tissues and different species. Dr. Catalá has directed twelve doctoral theses, published more than 100 papers in peer-reviewed journals, several chapters in books, and edited twelve books. He received awards at the 40th International Conference Biochemistry of Lipids 1999 in Dijon, France. He is the winner of the Bimbo Pan-American Nutrition, Food Science and Technology Award 2006 and 2012, South America, Human Nutrition, Professional Category. In 2006, he won the Bernardo Houssay award in pharmacology, in recognition of his meritorious works of research. Dr. Catalá belongs to the editorial board of several journals including Journal of Lipids; International Review of Biophysical Chemistry; Frontiers in Membrane Physiology and Biophysics; World Journal of Experimental Medicine and Biochemistry Research International; World Journal of Biological Chemistry, Diabetes, and the Pancreas; International Journal of Chronic Diseases & Therapy; and International Journal of Nutrition. He is the co-editor of The Open Biology Journal and associate editor for Oxidative Medicine and Cellular Longevity.",institutionString:"Universidad Nacional de La Plata",institution:{name:"National University of La Plata",country:{name:"Argentina"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",slug:"francisco-javier-martin-romero",fullName:"Francisco Javier Martin-Romero",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",biography:"Francisco Javier Martín-Romero (Javier) is a Professor of Biochemistry and Molecular Biology at the University of Extremadura, Spain. He is also a group leader at the Biomarkers Institute of Molecular Pathology. Javier received his Ph.D. in 1998 in Biochemistry and Biophysics. At the National Cancer Institute (National Institute of Health, Bethesda, MD) he worked as a research associate on the molecular biology of selenium and its role in health and disease. After postdoctoral collaborations with Carlos Gutierrez-Merino (University of Extremadura, Spain) and Dario Alessi (University of Dundee, UK), he established his own laboratory in 2008. The interest of Javier's lab is the study of cell signaling with a special focus on Ca2+ signaling, and how Ca2+ transport modulates the cytoskeleton, migration, differentiation, cell death, etc. He is especially interested in the study of Ca2+ channels, and the role of STIM1 in the initiation of pathological events.",institutionString:null,institution:{name:"University of Extremadura",country:{name:"Spain"}}},{id:"217323",title:"Prof.",name:"Guang-Jer",middleName:null,surname:"Wu",slug:"guang-jer-wu",fullName:"Guang-Jer Wu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/217323/images/8027_n.jpg",biography:null,institutionString:null,institution:null},{id:"148546",title:"Dr.",name:"Norma Francenia",middleName:null,surname:"Santos-Sánchez",slug:"norma-francenia-santos-sanchez",fullName:"Norma Francenia Santos-Sánchez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/148546/images/4640_n.jpg",biography:null,institutionString:null,institution:null},{id:"272889",title:"Dr.",name:"Narendra",middleName:null,surname:"Maddu",slug:"narendra-maddu",fullName:"Narendra Maddu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/272889/images/10758_n.jpg",biography:null,institutionString:null,institution:null},{id:"242491",title:"Prof.",name:"Angelica",middleName:null,surname:"Rueda",slug:"angelica-rueda",fullName:"Angelica Rueda",position:"Investigador Cinvestav 3B",profilePictureURL:"https://mts.intechopen.com/storage/users/242491/images/6765_n.jpg",biography:null,institutionString:null,institution:null},{id:"88631",title:"Dr.",name:"Ivan",middleName:null,surname:"Petyaev",slug:"ivan-petyaev",fullName:"Ivan Petyaev",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Lycotec (United Kingdom)",country:{name:"United Kingdom"}}},{id:"423869",title:"Ms.",name:"Smita",middleName:null,surname:"Rai",slug:"smita-rai",fullName:"Smita Rai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Integral University",country:{name:"India"}}},{id:"424024",title:"Prof.",name:"Swati",middleName:null,surname:"Sharma",slug:"swati-sharma",fullName:"Swati Sharma",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Integral University",country:{name:"India"}}},{id:"439112",title:"MSc.",name:"Touseef",middleName:null,surname:"Fatima",slug:"touseef-fatima",fullName:"Touseef Fatima",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Integral University",country:{name:"India"}}},{id:"424836",title:"Dr.",name:"Orsolya",middleName:null,surname:"Borsai",slug:"orsolya-borsai",fullName:"Orsolya Borsai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca",country:{name:"Romania"}}},{id:"422262",title:"Ph.D.",name:"Paola Andrea",middleName:null,surname:"Palmeros-Suárez",slug:"paola-andrea-palmeros-suarez",fullName:"Paola Andrea Palmeros-Suárez",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Guadalajara",country:{name:"Mexico"}}}]}},subseries:{item:{id:"5",type:"subseries",title:"Parasitic Infectious Diseases",keywords:"Blood Borne Parasites, Intestinal Parasites, Protozoa, Helminths, Arthropods, Water Born Parasites, Epidemiology, Molecular Biology, Systematics, Genomics, Proteomics, Ecology",scope:"Parasitic diseases have evolved alongside their human hosts. In many cases, these diseases have adapted so well that they have developed efficient resilience methods in the human host and can live in the host for years. Others, particularly some blood parasites, can cause very acute diseases and are responsible for millions of deaths yearly. Many parasitic diseases are classified as neglected tropical diseases because they have received minimal funding over recent years and, in many cases, are under-reported despite the critical role they play in morbidity and mortality among human and animal hosts. The current topic, Parasitic Infectious Diseases, in the Infectious Diseases Series aims to publish studies on the systematics, epidemiology, molecular biology, genomics, pathogenesis, genetics, and clinical significance of parasitic diseases from blood borne to intestinal parasites as well as zoonotic parasites. We hope to cover all aspects of parasitic diseases to provide current and relevant research data on these very important diseases. In the current atmosphere of the Coronavirus pandemic, communities around the world, particularly those in different underdeveloped areas, are faced with the growing challenges of the high burden of parasitic diseases. At the same time, they are faced with the Covid-19 pandemic leading to what some authors have called potential syndemics that might worsen the outcome of such infections. Therefore, it is important to conduct studies that examine parasitic infections in the context of the coronavirus pandemic for the benefit of all communities to help foster more informed decisions for the betterment of human and animal health.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11401,editor:{id:"67907",title:"Dr.",name:"Amidou",middleName:null,surname:"Samie",slug:"amidou-samie",fullName:"Amidou Samie",profilePictureURL:"https://mts.intechopen.com/storage/users/67907/images/system/67907.jpg",biography:"Dr. Amidou Samie is an Associate Professor of Microbiology at the University of Venda, in South Africa, where he graduated for his PhD in May 2008. He joined the Department of Microbiology the same year and has been giving lectures on topics covering parasitology, immunology, molecular biology and industrial microbiology. He is currently a rated researcher by the National Research Foundation of South Africa at category C2. He has published widely in the field of infectious diseases and has overseen several MSc’s and PhDs. His research activities mostly cover topics on infectious diseases from epidemiology to control. His particular interest lies in the study of intestinal protozoan parasites and opportunistic infections among HIV patients as well as the potential impact of childhood diarrhoea on growth and child development. He also conducts research on water-borne diseases and water quality and is involved in the evaluation of point-of-use water treatment technologies using silver and copper nanoparticles in collaboration with the University of Virginia, USA. He also studies the use of medicinal plants for the control of infectious diseases as well as antimicrobial drug resistance.",institutionString:null,institution:{name:"University of Venda",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null,series:{id:"6",title:"Infectious Diseases",doi:"10.5772/intechopen.71852",issn:"2631-6188"},editorialBoard:[{id:"188881",title:"Dr.",name:"Fernando José",middleName:null,surname:"Andrade-Narváez",slug:"fernando-jose-andrade-narvaez",fullName:"Fernando José Andrade-Narváez",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRIV7QAO/Profile_Picture_1628834308121",institutionString:null,institution:{name:"Autonomous University of Yucatán",institutionURL:null,country:{name:"Mexico"}}},{id:"269120",title:"Dr.",name:"Rajeev",middleName:"K.",surname:"Tyagi",slug:"rajeev-tyagi",fullName:"Rajeev Tyagi",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRaBqQAK/Profile_Picture_1644331884726",institutionString:"CSIR - Institute of Microbial Technology, India",institution:null},{id:"336849",title:"Prof.",name:"Ricardo",middleName:null,surname:"Izurieta",slug:"ricardo-izurieta",fullName:"Ricardo Izurieta",profilePictureURL:"https://mts.intechopen.com/storage/users/293169/images/system/293169.png",institutionString:null,institution:{name:"University of South Florida",institutionURL:null,country:{name:"United States of America"}}}]},onlineFirstChapters:{paginationCount:1,paginationItems:[{id:"81644",title:"Perspective Chapter: Ethics of Using Placebo Controlled Trials for Covid-19 Vaccine Development in Vulnerable Populations",doi:"10.5772/intechopen.104776",signatures:"Lesley Burgess, Jurie Jordaan and Matthew Wilson",slug:"perspective-chapter-ethics-of-using-placebo-controlled-trials-for-covid-19-vaccine-development-in-vu",totalDownloads:9,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"SARS-CoV-2 Variants - Two Years After",coverURL:"https://cdn.intechopen.com/books/images_new/11573.jpg",subseries:{id:"6",title:"Viral Infectious Diseases"}}}]},publishedBooks:{},testimonialsList:[{id:"18",text:"It was great publishing with IntechOpen, the process was straightforward and I had support all along.",author:{id:"71579",name:"Berend",surname:"Olivier",institutionString:"Utrecht University",profilePictureURL:"https://mts.intechopen.com/storage/users/71579/images/system/71579.png",slug:"berend-olivier",institution:{id:"253",name:"Utrecht University",country:{id:null,name:"Netherlands"}}}},{id:"8",text:"I work with IntechOpen for a number of reasons: their professionalism, their mission in support of Open Access publishing, and the quality of their peer-reviewed publications, but also because they believe in equality.",author:{id:"202192",name:"Catrin",surname:"Rutland",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/202192/images/system/202192.png",slug:"catrin-rutland",institution:{id:"134",name:"University of Nottingham",country:{id:null,name:"United Kingdom"}}}},{id:"27",text:"The opportunity to work with a prestigious publisher allows for the possibility to collaborate with more research groups interested in animal nutrition, leading to the development of new feeding strategies and food valuation while being more sustainable with the environment, allowing more readers to learn about the subject.",author:{id:"175967",name:"Manuel",surname:"Gonzalez Ronquillo",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/175967/images/system/175967.png",slug:"manuel-gonzalez-ronquillo",institution:{id:"6221",name:"Universidad Autónoma del Estado de México",country:{id:null,name:"Mexico"}}}}]},submityourwork:{pteSeriesList:[],lsSeriesList:[],hsSeriesList:[],sshSeriesList:[],subseriesList:[],annualVolumeBook:{},thematicCollection:[],selectedSeries:null,selectedSubseries:null},seriesLanding:{item:null},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"chapter.detail",path:"/chapters/5786",hash:"",query:{},params:{id:"5786"},fullPath:"/chapters/5786",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()