ADF stationarity tests.
\\n\\n
IntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\\n\\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\\n\\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\\n\\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\\n\\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\\n\\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\\n\\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\\n\\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\\n\\nFeel free to share this news on social media and help us mark this memorable moment!
\\n\\n\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/237"}},components:[{type:"htmlEditorComponent",content:'
After years of being acknowledged as the world's leading publisher of Open Access books, today, we are proud to announce we’ve successfully launched a portfolio of Open Science journals covering rapidly expanding areas of interdisciplinary research.
\n\n\n\nIntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\n\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\n\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\n\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\n\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\n\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\n\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\n\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\n\nFeel free to share this news on social media and help us mark this memorable moment!
\n\n\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"5966",leadTitle:null,fullTitle:"Heuristics and Hyper-Heuristics - Principles and Applications",title:"Heuristics and Hyper-Heuristics",subtitle:"Principles and Applications",reviewType:"peer-reviewed",abstract:"In the last few years, the society is witnessing ever-growing levels of complexity in the optimization paradigms lying at the core of different applications and processes. This augmented complexity has motivated the adoption of heuristic methods as a means to balance the Pareto trade-off between computational efficiency and the quality of the produced solutions to the problem at hand. The momentum gained by heuristics in practical applications spans further towards hyper-heuristics, which allow constructing ensembles of simple heuristics to handle efficiently several problems of a single class. In this context, this short book compiles selected applications of heuristics and hyper-heuristics for combinatorial optimization problems, including scheduling and other assorted application scenarios.",isbn:"978-953-51-3384-1",printIsbn:"978-953-51-3383-4",pdfIsbn:"978-953-51-4677-3",doi:"10.5772/66267",price:119,priceEur:129,priceUsd:155,slug:"heuristics-and-hyper-heuristics-principles-and-applications",numberOfPages:136,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"da699185a8b84a430d96d54bc35acdb2",bookSignature:"Javier Del Ser Lorente",publishedDate:"August 30th 2017",coverURL:"https://cdn.intechopen.com/books/images_new/5966.jpg",numberOfDownloads:8949,numberOfWosCitations:12,numberOfCrossrefCitations:3,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:14,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:29,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 10th 2016",dateEndSecondStepPublish:"December 1st 2016",dateEndThirdStepPublish:"February 27th 2017",dateEndFourthStepPublish:"May 28th 2017",dateEndFifthStepPublish:"July 27th 2017",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"5",institution:{name:"Tecnalia",institutionURL:null,country:{name:"Spain"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"549",title:"Algorithm Analysis",slug:"computer-science-and-engineering-algorithm-analysis"}],chapters:[{id:"55554",title:"Hyper‐Heuristics and Metaheuristics for Selected Bio‐Inspired Combinatorial Optimization Problems",doi:"10.5772/intechopen.69225",slug:"hyper-heuristics-and-metaheuristics-for-selected-bio-inspired-combinatorial-optimization-problems",totalDownloads:1592,totalCrossrefCites:0,totalDimensionsCites:7,hasAltmetrics:0,abstract:"Many decision and optimization problems arising in bioinformatics field are time demanding, and several algorithms are designed to solve these problems or to improve their current best solution approach. Modeling and implementing a new heuristic algorithm may be time‐consuming but has strong motivations: on the one hand, even a small improvement of the new solution may be worth the long time spent on the construction of a new method; on the other hand, there are problems for which good‐enough solutions are acceptable which could be achieved at a much lower computational cost. In the first case, specially designed heuristics or metaheuristics are needed, while the latter hyper‐heuristics can be proposed. The paper will describe both approaches in different domain problems.",signatures:"Aleksandra Swiercz",downloadPdfUrl:"/chapter/pdf-download/55554",previewPdfUrl:"/chapter/pdf-preview/55554",authors:[{id:"203032",title:"Ph.D.",name:"Aleksandra",surname:"Swiercz",slug:"aleksandra-swiercz",fullName:"Aleksandra Swiercz"}],corrections:null},{id:"55594",title:"Multi‐Objective Hyper‐Heuristics",doi:"10.5772/intechopen.69222",slug:"multi-objective-hyper-heuristics",totalDownloads:1484,totalCrossrefCites:1,totalDimensionsCites:0,hasAltmetrics:1,abstract:"Multi‐objective hyper‐heuristics is a search method or learning mechanism that operates over a fixed set of low‐level heuristics to solve multi‐objective optimization problems by controlling and combining the strengths of those heuristics. Although numerous papers on hyper‐heuristics have been published and several studies are still underway, most research has focused on single‐objective optimization. Work on hyper‐heuristics for multi‐objective optimization remains limited. This chapter draws attention to this area of research to help researchers and PhD students understand and reuse these methods. It also provides the basic concepts of multi‐objective optimization and hyper‐heuristics to facilitate a better understanding of the related research areas, in addition to exploring hyper‐heuristic methodologies that address multi‐objective optimization. Some design issues related to the development of hyper‐heuristic framework for multi‐objective optimization are discussed. The chapter concludes with a case study of multi‐objective selection hyper‐heuristics and its application on a real‐world problem.",signatures:"Mashael Suliaman Maashi",downloadPdfUrl:"/chapter/pdf-download/55594",previewPdfUrl:"/chapter/pdf-preview/55594",authors:[{id:"201702",title:"Dr.",name:"Mashael",surname:"Maashi",slug:"mashael-maashi",fullName:"Mashael Maashi"}],corrections:null},{id:"56264",title:"Heuristics Techniques for Scheduling Problems with Reducing Waiting Time Variance",doi:"10.5772/intechopen.69224",slug:"heuristics-techniques-for-scheduling-problems-with-reducing-waiting-time-variance",totalDownloads:1751,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"In real computational world, scheduling is a decision making process. This is nothing but a systematic schedule through which a large numbers of tasks are assigned to the processors. Due to the resource limitation, creation of such schedule is a real challenge. This creates the interest of developing a qualitative scheduler for the processors. These processors are either single or parallel. One of the criteria for improving the efficiency of scheduler is waiting time variance (WTV). Minimizing the WTV of a task is a NP-hard problem. Achieving the quality of service (QoS) in a single or parallel processor by minimizing the WTV is a problem of task scheduling. To enhance the performance of a single or parallel processor, it is required to develop a stable and none overlap scheduler by minimizing WTV. An automated scheduler's performance is always measured by the attributes of QoS. One of the attributes of QoS is ‘Timeliness’. First, this chapter presents the importance of heuristics with five heuristic-based solutions. Then applies these heuristics on 1‖WTV minimization problem and three heuristics with a unique task distribution mechanism on Qm|prec|WTV minimization problem. The experimental result shows the performance of heuristic in the form of graph for consonant problems.",signatures:"Satyasundara Mahapatra, Rati Ranjan Dash and Sateesh K. Pradhan",downloadPdfUrl:"/chapter/pdf-download/56264",previewPdfUrl:"/chapter/pdf-preview/56264",authors:[{id:"201253",title:"Dr.",name:"Satyasundara",surname:"Mahapatra",slug:"satyasundara-mahapatra",fullName:"Satyasundara Mahapatra"},{id:"203077",title:"Dr.",name:"Rati Ranjan",surname:"Dash",slug:"rati-ranjan-dash",fullName:"Rati Ranjan Dash"},{id:"203078",title:"Dr.",name:"Sateesh Kumar",surname:"Pradhan",slug:"sateesh-kumar-pradhan",fullName:"Sateesh Kumar Pradhan"}],corrections:null},{id:"55810",title:"Efficient Heuristics for Scheduling with Release and Delivery Times",doi:"10.5772/intechopen.69223",slug:"efficient-heuristics-for-scheduling-with-release-and-delivery-times",totalDownloads:1200,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In this chapter, we describe efficient heuristics for scheduling jobs with release and delivery times with the objective to minimize the maximum job completion time. These heuristics are essentially based on a commonly used scheduling theory in Jackson’s extended heuristic. We present basic structural properties of the solutions delivered by Jackson’s heuristic and then illustrate how one can exploit them to build efficient heuristics.",signatures:"Nodari Vakhania",downloadPdfUrl:"/chapter/pdf-download/55810",previewPdfUrl:"/chapter/pdf-preview/55810",authors:[{id:"202585",title:"Prof.",name:"Nodari",surname:"Vakhania",slug:"nodari-vakhania",fullName:"Nodari Vakhania"}],corrections:null},{id:"55704",title:"Advanced Particle Filter Methods",doi:"10.5772/intechopen.69236",slug:"advanced-particle-filter-methods",totalDownloads:1580,totalCrossrefCites:2,totalDimensionsCites:6,hasAltmetrics:1,abstract:"This chapter presents a set of algorithmic methods based on particle filter heuristics. We start with an introduction to particle filters, which covers the main motivation and related works. Then, the generic framework for particle filter algorithm is presented, followed by two important use cases regarding indoor positioning and multitarget tracking; for both problems, modified particle filter algorithms are presented followed by experimental results, implementation remarks, and a discussion. Finally, a short list of conclusion and future work are presented.",signatures:"Roi Yozevitch and Boaz Ben-Moshe",downloadPdfUrl:"/chapter/pdf-download/55704",previewPdfUrl:"/chapter/pdf-preview/55704",authors:[{id:"203049",title:"Prof.",name:"Boaz",surname:"Benmoshe",slug:"boaz-benmoshe",fullName:"Boaz Benmoshe"},{id:"203051",title:"Mr.",name:"Roi",surname:"Yozevitch",slug:"roi-yozevitch",fullName:"Roi Yozevitch"}],corrections:null},{id:"55968",title:"On the Use of Hybrid Heuristics for Providing Service to Select the Return Channel in an Interactive Digital TV Environment",doi:"10.5772/intechopen.69615",slug:"on-the-use-of-hybrid-heuristics-for-providing-service-to-select-the-return-channel-in-an-interactive",totalDownloads:1343,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The technologies used to link the end-user to a telecommunication infrastructure, has been changing over time due to the consolidation of new access technologies. Moreover, the emergence of new tools for information dissemination, such as interactive digital TV, makes the selection of access technology, factor of fundamental importance. One of the greatest advantages of using digital TV as means to disseminate information is the installation of applications. In this chapter, a load characterization of a typical application embedded in a digital TV is performed to determine its behavior. However, it is important to note that applications send information through an access technology. Therefore, this chapter, based on the study on load characterization, developed a methodology combining Bayesian networks and technique for order preference by similarity to ideal solution (TOPSIS) analytical approach to provide support to service providers to opt for a technology (power line communication, PLC, wireless, wired, etc.) for the return channel.",signatures:"Marcos César da Rocha Seruffo, Ádamo Lima de Santana, Carlos\nRenato Lisboa Francês and Nandamudi Lankalapalli Vijaykumar",downloadPdfUrl:"/chapter/pdf-download/55968",previewPdfUrl:"/chapter/pdf-preview/55968",authors:[{id:"10493",title:"Dr.",name:"Adamo",surname:"Lima De Santana",slug:"adamo-lima-de-santana",fullName:"Adamo Lima De Santana"},{id:"202549",title:"Dr.",name:"Marcos",surname:"Seruffo",slug:"marcos-seruffo",fullName:"Marcos Seruffo"},{id:"202551",title:"Dr.",name:"Nadamundi",surname:"Vijaykumar",slug:"nadamundi-vijaykumar",fullName:"Nadamundi Vijaykumar"},{id:"202552",title:"Dr.",name:"Carlos Renato",surname:"Francês",slug:"carlos-renato-frances",fullName:"Carlos Renato Francês"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"149",title:"Recent Advances on Video Coding",subtitle:null,isOpenForSubmission:!1,hash:"3e7cf16c546740b7b07ff0b182637f23",slug:"recent-advances-on-video-coding",bookSignature:"Javier Del Ser Lorente",coverURL:"https://cdn.intechopen.com/books/images_new/149.jpg",editedByType:"Edited by",editors:[{id:"49813",title:"Dr.",name:"Javier",surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7726",title:"Swarm Intelligence",subtitle:"Recent Advances, New Perspectives and 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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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\\n"}]'},components:[{type:"htmlEditorComponent",content:'Copyright is the term used to describe the rights related to the publication and distribution of original Works. Most importantly from a publisher's perspective, copyright governs how Authors, publishers and the general public can use, publish, and distribute publications.
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On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. 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From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. 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Hundreds of thousand people lost their lives and loss of billions of dollars’ properties occurred in these disasters. Occurred medium or high-intensity magnitude earthquakes in last twenty years showed that these loses continue. For reinforced concrete (R/C) buildings, inappropriate design such as soft and weak stories, strong beam–weak column, short column, hammering, unconfined gable wall and in-plane/out-of-plane movement of the walls causes damages. These are the main reasons. In addition to this, low quality of structural materials, poor workmanship, lack of engineering services, and construction with insufficient detailing of the structural elements are the another reasons of damages. Main reasons of masonry building damages in terms of design faults can be shown as heavy earthen roofs, inappropriate detailing of wall to wall connection and wall to roof connection, absence of bond beams, large openings. However, construction of buildings by using local materials with poor workmanship on the base of traditional rules is the other reason of failures for these buildings. In this book chapter, earthquakes and reasons of damages arose from earthquakes for reinforced concrete and masonry structures were presented. In addition to this, appropriate solutions are suggested.",book:{id:"5499",slug:"earthquakes-tectonics-hazard-and-risk-mitigation",title:"Earthquakes",fullTitle:"Earthquakes - Tectonics, Hazard and Risk Mitigation"},signatures:"Burak Yön, Erkut Sayın and Onur Onat",authors:[{id:"192483",title:"Dr.",name:"Burak",middleName:null,surname:"Yön",slug:"burak-yon",fullName:"Burak Yön"},{id:"192486",title:"Dr.",name:"Erkut",middleName:null,surname:"Sayın",slug:"erkut-sayin",fullName:"Erkut Sayın"},{id:"192487",title:"Dr.",name:"Onur",middleName:null,surname:"Onat",slug:"onur-onat",fullName:"Onur Onat"}]},{id:"52565",doi:"10.5772/65511",title:"Earthquake Prediction",slug:"earthquake-prediction",totalDownloads:2049,totalCrossrefCites:11,totalDimensionsCites:21,abstract:"Among the countless natural disasters, earthquakes are capable to inflict vast devastation to a large number of buildings and constructions at the blink of an eye. Lack of knowledge and awareness on earthquake as well as its comeback is conspicuous and results in disaster; leading to bitter memories. Therefore, earthquake forecast has been a polemical study theme that has defied even the most intelligent of minds. In this chapter, an attempt was made to do an extensive overview in the area of the earthquake prediction as well as classifying them into the main strategies comprising short‐, immediate‐, and long‐term prediction. An example of each strategy was carried out by mentioning their corresponding approaches/algorithms, such as ΔCFS, CN, MSc, M8, ANN, FFBPANN, KNN, GRNN, RBF, and LMBP; depending on the importance of each strategy. Based on these, it was concluded that, after the Tohoku‐Oki earthquake with M9.0, the current orientation of the Headquarters for earthquake Research Promotion of MEXT in Japan declare that, their mission would be long‐term statistical forecast of seismicity. Even, it is claimed that they do not emphasize on short‐term forecasting. Besides, intermediate‐term estimations are not capable to be used for prevention of all damages and protect all human life, but they may be utilized to undertake certain affordable activities to decrease damage, losses, and modify postdisaster relief. And, despite the long‐term prediction is more concerned by researchers, there is no certain satisfactory level to content them. De facto, the made covenant of 1970 that investigators will be capable to forecast/predict ground excitations within a decade, still remains unmet.",book:{id:"5499",slug:"earthquakes-tectonics-hazard-and-risk-mitigation",title:"Earthquakes",fullTitle:"Earthquakes - Tectonics, Hazard and Risk Mitigation"},signatures:"Khaled Ghaedi and Zainah Ibrahim",authors:[{id:"190572",title:"Dr.",name:"Khaled",middleName:null,surname:"Ghaedi",slug:"khaled-ghaedi",fullName:"Khaled Ghaedi"},{id:"196228",title:"Prof.",name:"Zainah",middleName:null,surname:"Ibrahim",slug:"zainah-ibrahim",fullName:"Zainah Ibrahim"}]}],mostDownloadedChaptersLast30Days:[{id:"41664",title:"Volcanic Natural Resources and Volcanic Landscape Protection: An Overview",slug:"volcanic-natural-resources-and-volcanic-landscape-protection-an-overview",totalDownloads:3782,totalCrossrefCites:2,totalDimensionsCites:4,abstract:null,book:{id:"3088",slug:"updates-in-volcanology-new-advances-in-understanding-volcanic-systems",title:"Updates in Volcanology",fullTitle:"Updates in Volcanology - New Advances in Understanding Volcanic Systems"},signatures:"Jiaqi Liu, Jiali Liu, Xiaoyu Chen and Wenfeng Guo",authors:[{id:"60000",title:"Prof.",name:"Jiaqi",middleName:null,surname:"Liu",slug:"jiaqi-liu",fullName:"Jiaqi Liu"}]},{id:"31815",title:"Disaster Management Based on Business Process Model Through the Plant Lifecycle",slug:"disaster-management-based-on-business-process-model-through-the-plant-lifecycle",totalDownloads:2727,totalCrossrefCites:6,totalDimensionsCites:10,abstract:null,book:{id:"600",slug:"approaches-to-managing-disaster-assessing-hazards-emergencies-and-disaster-impacts",title:"Approaches to Managing Disaster",fullTitle:"Approaches to Managing Disaster - Assessing Hazards, Emergencies and Disaster Impacts"},signatures:"Yukiyasu Shimada, Teiji Kitajima, Tetsuo Fuchino and Kazuhiro Takeda",authors:[{id:"70197",title:"Dr.",name:"Yukiyasu",middleName:null,surname:"Shimada",slug:"yukiyasu-shimada",fullName:"Yukiyasu Shimada"},{id:"82055",title:"Dr.",name:"Tetsuo",middleName:null,surname:"Fuchino",slug:"tetsuo-fuchino",fullName:"Tetsuo Fuchino"},{id:"82056",title:"Prof.",name:"Teiji",middleName:null,surname:"Kitajima",slug:"teiji-kitajima",fullName:"Teiji Kitajima"},{id:"121284",title:"Dr.",name:"Kazuhiro",middleName:null,surname:"Takeda",slug:"kazuhiro-takeda",fullName:"Kazuhiro Takeda"}]},{id:"52524",title:"Earthquakes and Structural Damages",slug:"earthquakes-and-structural-damages",totalDownloads:3388,totalCrossrefCites:14,totalDimensionsCites:21,abstract:"Earthquakes are the most destructive natural hazards throughout human history. Hundreds of thousand people lost their lives and loss of billions of dollars’ properties occurred in these disasters. Occurred medium or high-intensity magnitude earthquakes in last twenty years showed that these loses continue. For reinforced concrete (R/C) buildings, inappropriate design such as soft and weak stories, strong beam–weak column, short column, hammering, unconfined gable wall and in-plane/out-of-plane movement of the walls causes damages. These are the main reasons. In addition to this, low quality of structural materials, poor workmanship, lack of engineering services, and construction with insufficient detailing of the structural elements are the another reasons of damages. Main reasons of masonry building damages in terms of design faults can be shown as heavy earthen roofs, inappropriate detailing of wall to wall connection and wall to roof connection, absence of bond beams, large openings. However, construction of buildings by using local materials with poor workmanship on the base of traditional rules is the other reason of failures for these buildings. In this book chapter, earthquakes and reasons of damages arose from earthquakes for reinforced concrete and masonry structures were presented. In addition to this, appropriate solutions are suggested.",book:{id:"5499",slug:"earthquakes-tectonics-hazard-and-risk-mitigation",title:"Earthquakes",fullTitle:"Earthquakes - Tectonics, Hazard and Risk Mitigation"},signatures:"Burak Yön, Erkut Sayın and Onur Onat",authors:[{id:"192483",title:"Dr.",name:"Burak",middleName:null,surname:"Yön",slug:"burak-yon",fullName:"Burak Yön"},{id:"192486",title:"Dr.",name:"Erkut",middleName:null,surname:"Sayın",slug:"erkut-sayin",fullName:"Erkut Sayın"},{id:"192487",title:"Dr.",name:"Onur",middleName:null,surname:"Onat",slug:"onur-onat",fullName:"Onur Onat"}]},{id:"41478",title:"Monogenetic Basaltic Volcanoes: Genetic Classification, Growth, Geomorphology and Degradation",slug:"monogenetic-basaltic-volcanoes-genetic-classification-growth-geomorphology-and-degradation",totalDownloads:6195,totalCrossrefCites:71,totalDimensionsCites:142,abstract:null,book:{id:"3088",slug:"updates-in-volcanology-new-advances-in-understanding-volcanic-systems",title:"Updates in Volcanology",fullTitle:"Updates in Volcanology - New Advances in Understanding Volcanic Systems"},signatures:"Gábor Kereszturi and Károly Németh",authors:[{id:"51162",title:"Dr.",name:"Károly",middleName:null,surname:"Németh",slug:"karoly-nemeth",fullName:"Károly Németh"},{id:"62029",title:"Dr.",name:"Gabor",middleName:null,surname:"Kereszturi",slug:"gabor-kereszturi",fullName:"Gabor Kereszturi"}]},{id:"62769",title:"Disaster Mitigation Model of Eruption Based on Local Wisdom in Indonesia",slug:"disaster-mitigation-model-of-eruption-based-on-local-wisdom-in-indonesia",totalDownloads:1462,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"Kelud is one of the most active volcanoes in Indonesia and suffered a major eruption in 2014. Although they are not part of the super volcano, the impact of the eruption is extraordinary. However, the eruption is not too worrying for the surrounding community. The lack of disaster victims caused by the eruption in 2014 became a successful representation of disaster mitigation models owned by local communities in answering the eruption problem. The easy evacuation process and quickly post-eruption rehabilitation illustrate a pattern of environmental adaptation around the volcano. This discussion focuses on how the people behavior around the volcano in responding to the challenge of eruption? How the role of local government in preparing the community in the face of an eruption, and what actions are done so that the rehabilitation process can take place quickly? To answer all these questions, the researchers collected relevant data through observation, documentation, and interviews with the local communities and local government representatives directly involved in disaster mitigation measures. In addition, the researchers also revealed local traditions that are considered capable of supporting the process of preparing the community in answering the eruption challenges and becoming part of disaster mitigation in the volcanic region.",book:{id:"6821",slug:"natural-hazards-risk-assessment-and-vulnerability-reduction",title:"Natural Hazards",fullTitle:"Natural Hazards - Risk Assessment and Vulnerability Reduction"},signatures:"Eko Hariyono and Solaiman Liliasari",authors:[{id:"214360",title:"Dr.",name:"Eko",middleName:null,surname:"Hariyono",slug:"eko-hariyono",fullName:"Eko Hariyono"},{id:"219699",title:"Prof.",name:"Liliasari",middleName:null,surname:"S",slug:"liliasari-s",fullName:"Liliasari S"}]}],onlineFirstChaptersFilter:{topicId:"106",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"81241",title:"Physiological and Molecular Adaptation of Sugarcane under Drought vis-a-vis Root System Traits",slug:"physiological-and-molecular-adaptation-of-sugarcane-under-drought-vis-a-vis-root-system-traits",totalDownloads:21,totalDimensionsCites:0,doi:"10.5772/intechopen.103795",abstract:"Among various abiotic stresses, water is reported as a rare entity in many parts of the world. Decreased frequency of precipitation and global temperature rise will further aggravate the situation in future. Being C4 plant, sugarcane requires generous water for the proper growth. Plant root system primarily supports above-ground growth by anchoring in the soil and facilitates water and nutrients uptake from the soil. The plasticity and dynamic nature of roots endow plants for the uptake of vital nutrients from the soil even under soil moisture conditions. In sugarcane, the major part of root system are generally observed in the upper soil layers, while limited water availability shifts the root growth towards the lower soil layer to sustained water uptake. In addition, root traits are directly related to physiological traits of the shoot to cope up with water limited situations via reduction in stomatal conductance and an upsurge in density and deep root traits, adaptations at biochemical and molecular level which includes osmotic adjustment and ROS detoxification. Under stressed conditions, these complex interactive systems adjust homeo-statically to minimize the adverse impacts of stress and sustain balanced metabolism. Therefore, the present chapter deals with physiological and biochemical traits along with root traits that helps for better productivity of sugarcane under water-limited conditions.",book:{id:"11131",title:"Drought - Impacts and Management",coverURL:"https://cdn.intechopen.com/books/images_new/11131.jpg"},signatures:"Pooja Dhansu, Arun Kumar Raja, Krishnapriya Vengavasi, Ravinder Kumar, Adhini S. Pazhany, Ashwani Kumar, Naresh Kumar, Anita Mann and Shashi Kant Pandey"},{id:"79973",title:"Impacts of Drought on Homestead Plant Diversity in Barind Tract of Bangladesh",slug:"impacts-of-drought-on-homestead-plant-diversity-in-barind-tract-of-bangladesh",totalDownloads:6,totalDimensionsCites:0,doi:"10.5772/intechopen.101885",abstract:"Homestead is a great place for household food access, diet, and nutrition. Drought affects homestead plant diversity and reduces production, availability, and diversity that lead toward less supply and consumption. Drought detains moisture and degrades the soil that supports plant growth. Homestead provides regular bread and income in the rural areas with an effective means for both economic and environmental well-being. People are getting a good amount of subsidiary income without any extra care and effort. In managing homestead land and drought, the household needs necessary technical and managerial training. In reducing drought effects to the homestead, action research needs to be carried out on available knowledge, effective practices, water management, and the adoption of local varieties and knowledge to develop effective homestead integration. Government initiatives, community engagement and not harming the environment, and efficient uses of water could be great solutions for the adverse effects of drought on the homestead plant diversity.",book:{id:"11131",title:"Drought - Impacts and Management",coverURL:"https://cdn.intechopen.com/books/images_new/11131.jpg"},signatures:"Md. Shafiqul Islam and Md. Nazrul Islam Mukul"},{id:"82110",title:"Hydrological Drought Index Based on Discharge",slug:"hydrological-drought-index-based-on-discharge",totalDownloads:28,totalDimensionsCites:0,doi:"10.5772/intechopen.104625",abstract:"Drought is a natural phenomenon causing disasters and its period of occurrence can be predicted in recent times based on several methods using the same or different variables. The prediction is usually associated with the climate interactions in the form of rainfall or discharge patterns which can be analyzed using the return period. Therefore, this research was conducted in four different stages of data acquisition and validation, drought analysis method based on the data, drought prediction method based on hydrology, and sample applications to determine the debit availability in other watersheds. Historical rainfall data converted to dependable rainfall at 80% probability were used as input for the rainfall-discharge analysis while the hydrological drought analysis was conducted using the drought threshold value. Moreover, the drought was predicted using an artificial neural network model while historical data were used to verify the hydrological character of the prediction model. The results of the analysis conducted were further used to predict the water balance in different river areas due to the fact that each area has a different hydrological character. Meanwhile, the watersheds used as case research showed that the model has reliability of up to 80%.",book:{id:"11131",title:"Drought - Impacts and Management",coverURL:"https://cdn.intechopen.com/books/images_new/11131.jpg"},signatures:"R. Rintis Hadiani, Bambang Suharto, Agus Suharyanto and Suhardjono"},{id:"81203",title:"Climate Change: A Real Danger to Human and Animal Survival",slug:"climate-change-a-real-danger-to-human-and-animal-survival",totalDownloads:42,totalDimensionsCites:0,doi:"10.5772/intechopen.103022",abstract:"Some countries in Southern Africa where hit by either a storm or cyclone or both in 2019 alone manifesting a changing climate. Infrastructure and cropping land was destroyed, both animal and human lives were lost due to the flooding events. Drought is a common phenomenon in this region, often occurring once in three years. This has affected food, feed and nutritional security of both humans and livestock. Saline soils unsuitable for agriculture, other animal and plant life are expanding fast due to insufficient precipitation. Soil degradation is on the rise, leaving soils with poor water holding capacity to support sustainable agriculture. Climate change is changing the environment and new pests and diseases for both crops and livestock are emerging. World governments, industries and general populace should find better ways of reducing air pollution by greenhouse gases which have a net effect of damaging the ozone layer and increasing atmospheric temperatures. At the same time, plant and animal breeding should aim at improving crop cultivars and animal breeds that resist to the constraints such as drought and heat stress brought by climate change. The human population is increasing at an alarming rate and need both food and nutritional security.",book:{id:"11131",title:"Drought - Impacts and Management",coverURL:"https://cdn.intechopen.com/books/images_new/11131.jpg"},signatures:"Godwill Makunde, Nation Chikumba, Walter Svinurai and Xavier Mhike"},{id:"81810",title:"Water Shortages: Cause of Water Safety in Sub-Saharan Africa",slug:"water-shortages-cause-of-water-safety-in-sub-saharan-africa",totalDownloads:27,totalDimensionsCites:0,doi:"10.5772/intechopen.103927",abstract:"This chapter highlights a high rate of water crisis across sub-Saharan Africa (SSA) despite its huge hydro-potential. Factors contributing to water stress include rainfall deficit and drought, increased water requirements, population growth, urbanization, and poverty. Coupled with the uneven distribution of water resources and mismanagement of water facilities, the gap between the demand for water and available supply has deepened. This has led almost half of the SSA population to drink water from unprotected sources. Moreover, many millions travel far distances and spend several hours daily to collect water. Children and women are mainly involved in water collection. The growing scarcity of water in Africa has a negative impact on economic growth. Besides, water shortages are at the heart of many social crises in SSA and have become directly or indirectly the first cause of death in Africa linked to waterborne diseases. The prevailing water-related diseases include malaria, typhoid fever, cholera, poliomyelitis, etc. To attain the African agenda 2063, national governments in SSA need a multidisciplinary approach integrating, supervising informal settlements of the population in urban and peri-urban areas; improving water storage capacity; increasing irrigation potential for agriculture; and having a good understanding of the epidemiology of waterborne diseases.",book:{id:"11131",title:"Drought - Impacts and Management",coverURL:"https://cdn.intechopen.com/books/images_new/11131.jpg"},signatures:"Chelea Matchawe, Patrice Bonny, Germaine Yandang, Huguette Cecile Yangoua Mafo and Bonglaisin J. Nsawir"},{id:"81584",title:"Reducing the Effects of Drought and Degradation of Agricultural Soils, in the Context of Climate Change, through the Application of Regenerative Ecological Technologies",slug:"reducing-the-effects-of-drought-and-degradation-of-agricultural-soils-in-the-context-of-climate-chan",totalDownloads:42,totalDimensionsCites:0,doi:"10.5772/intechopen.104446",abstract:"The agricultural sector has a limited capacity for expansion, consequently, deficient technologies based on the widespread use of synthetic chemicals have been implemented in the last decades, having a major negative impact on natural ecosystems, biodiversity, and environmental services. Desertification, land degradation, and drought, combined with human activity and environmental changes, cause important soil losses and a reduction in natural defenses against droughts and floods. The combined impact of climate change, land mismanagement and unsustainable freshwater use has long been affecting agricultural productivity, the most common cause being unsustainable land management practices. This chapter aims to briefly assess the most effective strategies for reducing the impact of climate change on agricultural crops, as well as to prevent or reverse the process of desertification and systematic loss in food quality and quantity. 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That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"426586",title:"Dr.",name:"Oladunni A.",middleName:null,surname:"Daramola",slug:"oladunni-a.-daramola",fullName:"Oladunni A. Daramola",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Federal University of Technology",country:{name:"Nigeria"}}},{id:"357014",title:"Prof.",name:"Leon",middleName:null,surname:"Bobrowski",slug:"leon-bobrowski",fullName:"Leon Bobrowski",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Bialystok University of Technology",country:{name:"Poland"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"354126",title:"Dr.",name:"Setiawan",middleName:null,surname:"Hadi",slug:"setiawan-hadi",fullName:"Setiawan Hadi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Padjadjaran University",country:{name:"Indonesia"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"332603",title:"Prof.",name:"Kumar S.",middleName:null,surname:"Ray",slug:"kumar-s.-ray",fullName:"Kumar S. Ray",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Statistical Institute",country:{name:"India"}}},{id:"415409",title:"Prof.",name:"Maghsoud",middleName:null,surname:"Amiri",slug:"maghsoud-amiri",fullName:"Maghsoud Amiri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Allameh Tabataba'i University",country:{name:"Iran"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. 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In recent years, emerging technologies such as multi-omics, high-throughput technologies, and genome editing tools could assist plant physiologists in unraveling molecular mechanisms in specific critical pathways. The global picture of physiological processes in plants needs to be investigated continually to increase our knowledge, and the resulting technologies will benefit sustainable agriculture.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/13.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11409,editor:{id:"332229",title:"Prof.",name:"Jen-Tsung",middleName:null,surname:"Chen",slug:"jen-tsung-chen",fullName:"Jen-Tsung Chen",profilePictureURL:"https://mts.intechopen.com/storage/users/332229/images/system/332229.png",biography:"Dr. Jen-Tsung Chen is currently a professor at the National University of Kaohsiung, Taiwan. He teaches cell biology, genomics, proteomics, medicinal plant biotechnology, and plant tissue culture. Dr. Chen\\'s research interests include bioactive compounds, chromatography techniques, in vitro culture, medicinal plants, phytochemicals, and plant biotechnology. 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Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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