Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
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Seeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\\n\\n
Over these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\\n\\n
We are excited about the present, and we look forward to sharing many more successes in the future.
\\n\\n
Thank you all for being part of the journey. 5,000 times thank you!
\\n\\n
Now with 5,000 titles available Open Access, which one will you read next?
Preparation of Space Experiments edited by international leading expert Dr. Vladimir Pletser, Director of Space Training Operations at Blue Abyss is the 5,000th Open Access book published by IntechOpen and our milestone publication!
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"This book presents some of the current trends in space microgravity research. The eleven chapters introduce various facets of space research in physical sciences, human physiology and technology developed using the microgravity environment not only to improve our fundamental understanding in these domains but also to adapt this new knowledge for application on earth." says the editor. Listen what else Dr. Pletser has to say...
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Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\n\n
Seeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\n\n
Over these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\n\n
We are excited about the present, and we look forward to sharing many more successes in the future.
\n\n
Thank you all for being part of the journey. 5,000 times thank you!
\n\n
Now with 5,000 titles available Open Access, which one will you read next?
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"7494",leadTitle:null,fullTitle:"Menstrual Cycle",title:"Menstrual Cycle",subtitle:null,reviewType:"peer-reviewed",abstract:"In this book, Menstrual Cycle, we cover certain interesting aspects of the physiology and endocrinology of the female body, as well as clinical diagnosis and treatment of various gynecological diseases and disorders in women of reproductive age. In this way, scholars, postgraduates, students, and others will be able to become acquainted with modern scientific research into menstrual cycle. For others, this book will be the impetus for further research in this area.",isbn:"978-1-78985-673-6",printIsbn:"978-1-78923-934-8",pdfIsbn:"978-1-78985-674-3",doi:"10.5772/intechopen.76123",price:119,priceEur:129,priceUsd:155,slug:"menstrual-cycle",numberOfPages:146,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"f3e1135f2d45bbaafe6e61b252108d05",bookSignature:"Olena Ivanivna Lutsenko",publishedDate:"June 12th 2019",coverURL:"https://cdn.intechopen.com/books/images_new/7494.jpg",numberOfDownloads:9932,numberOfWosCitations:1,numberOfCrossrefCitations:2,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:10,numberOfDimensionsCitationsByBook:0,hasAltmetrics:0,numberOfTotalCitations:13,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 13th 2018",dateEndSecondStepPublish:"May 3rd 2018",dateEndThirdStepPublish:"July 2nd 2018",dateEndFourthStepPublish:"September 20th 2018",dateEndFifthStepPublish:"November 19th 2018",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"225667",title:"Mrs.",name:"Olena",middleName:null,surname:"Lutsenko",slug:"olena-lutsenko",fullName:"Olena Lutsenko",profilePictureURL:"https://mts.intechopen.com/storage/users/225667/images/system/225667.jpg",biography:"Olena Lutsenko obtained her Ph.D. in 2021. Since 2015, she has had the opportunity to work on a departmental research topic on the fundamentalization of natural education for sustainable development. She is currently working on scientific topics at Oleksandr Dovzhenko Hlukhiv National Pedagogical University, Ukraine, and teaches medical and biological disciplines and features of teaching methods of these disciplines. She has published more than seventy-nine scientific papers, including five textbooks and five monographs. Her recent research focuses on the peculiarities of preparing future teachers for their professional activities, as well as on the introduction of modern educational technologies in educational activities.",institutionString:"Oleksandr Dovzhenko Hlukhiv National Pedagogical University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Oleksandr Dovzhenko Hlukhiv National Pedagogical University",institutionURL:null,country:{name:"Ukraine"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1069",title:"Reproductive Endocrinology and Infertility",slug:"obstetrics-and-gynecology-reproductive-endocrinology-and-infertility"}],chapters:[{id:"66096",title:"Introductory Chapter: Regulation of Ovarian-Menstrual Cycle as a Systemic Problem of Physiology of Humans",doi:"10.5772/intechopen.85065",slug:"introductory-chapter-regulation-of-ovarian-menstrual-cycle-as-a-systemic-problem-of-physiology-of-hu",totalDownloads:926,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:null,signatures:"Olena Lutsenko",downloadPdfUrl:"/chapter/pdf-download/66096",previewPdfUrl:"/chapter/pdf-preview/66096",authors:[{id:"225667",title:"Mrs.",name:"Olena",surname:"Lutsenko",slug:"olena-lutsenko",fullName:"Olena Lutsenko"}],corrections:null},{id:"64704",title:"Normal Menstrual Cycle",doi:"10.5772/intechopen.79876",slug:"normal-menstrual-cycle",totalDownloads:3130,totalCrossrefCites:2,totalDimensionsCites:5,hasAltmetrics:0,abstract:"Normal menstrual cycle represents a coordinated serial event, repeated month by month, at regular intervals, in which the hypothalamus participates along with the secretion of GnRH, the pituitary gland secreting follicle stimulating hormone and luteinizing hormone (LH), and the ovary which responds to those hormones, recruiting a dominant follicle and secreting estradiol and inhibin A. Estradiol stimulates endometrial proliferation and production of cervix mucus. A peak of estradiol triggers discharge of LH, responsible for ovulation and posterior secretion of progesterone by the corpus luteum, which in turn, involutionates 14 days later if it does not receive the stimulation of hCG (pregnancy). Normal menstrual cycles last 28 ± 7 days, being accepted a fluctuation of ±2 days in the same woman, as a normal pattern, what is described as a regular cycle. Normality of these events would allow to achieve a successful embrionary implantation in the case of looking for pregnancy. For this it is required that an adequate ovule to be fertilized is reached by a capacitated spermatozoon, during the ovulatory stage. Spermatozoon can survive as long as 5 days at feminine genital tractum, but the ovum is possible to be fecundated only during 12–24 hours. Fecundation occurs at the distal third of the fallopian tube and the fecundated zygote arrives in the state of a morula, to be implanted at the endometrium 4 days later. Once the state of blastocyst is reached, it is detached from its shaggy area (hatching) and it is implanted in a receptive endometrium when the window of implantation is open (days 7–9) postovulation. The first marker of pregnancy is the detection in maternal blood of β-hCG. No more than the 25% of fertile couples exposed to pregnancy can achieve gestation at the month of exposure.",signatures:"Barriga-Pooley Patricio and Brantes-Glavic Sergio",downloadPdfUrl:"/chapter/pdf-download/64704",previewPdfUrl:"/chapter/pdf-preview/64704",authors:[{id:"250273",title:"Associate Prof.",name:"Patricio",surname:"Barriga",slug:"patricio-barriga",fullName:"Patricio Barriga"}],corrections:null},{id:"63668",title:"Pre Menstrual Syndrome",doi:"10.5772/intechopen.80492",slug:"pre-menstrual-syndrome",totalDownloads:1161,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Approximately 80–90% of women experience some symptoms in the premenstrual period at some point in their reproductive years. Teenagers often present with moderate to severe symptoms, while women in the fourth decade of life appear to have worse symptoms with the severity of the disease worsening with increasing age up until menopause. Obesity and smoking have also been identified as risk factors. Symptoms could be physical, psychological, emotional, environmental and/or behavioral and affect the ability to perform normal daily activities as well as adversely affect interpersonal relationships. Though several theories have been propounded, the exact cause of premenstrual syndrome is unknown. Management of this disorder requires a multi-disciplinary approach involving the general practitioner, the general gynecologist or a gynecologist with a special interest in PMS, a mental health professional (psychiatrist, clinical psychologist or counselor), physiotherapist and dietician.",signatures:"Preye Fiebai, Avwebo Ochuko Ukueku and Rosemary Ogu",downloadPdfUrl:"/chapter/pdf-download/63668",previewPdfUrl:"/chapter/pdf-preview/63668",authors:[{id:"213063",title:"Prof.",name:"Rosemary",surname:"Ogu",slug:"rosemary-ogu",fullName:"Rosemary Ogu"},{id:"256597",title:"Dr.",name:"Avwebo",surname:"Otoide",slug:"avwebo-otoide",fullName:"Avwebo Otoide"},{id:"256598",title:"Dr.",name:"Preye",surname:"Fiebai",slug:"preye-fiebai",fullName:"Preye Fiebai"}],corrections:null},{id:"62617",title:"Menstrual Cycle and Physical Effort",doi:"10.5772/intechopen.79675",slug:"menstrual-cycle-and-physical-effort",totalDownloads:1323,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In addition to affecting the sexual organs in women, ovarian hormones have a wide impact on processes related to metabolism, water and electrolyte balance, thermoregulation, and redox balance. Differences in the estradiol and progesterone concentrations during the follicular and luteal phases, as well as the increase in the concentration of these hormones under the influence of physical exercise, may cause a different course of exercise response in women depending on the phase of menstrual cycle. Estrogens affect the metabolism of women by reducing the rate of gluconeogenesis and glycogenolysis and, at the same time, by increasing the share of lipids in covering energy requirements. Progesterone affects respiratory system parameters causing, among others, an increase in pulmonary ventilation. The resultant antagonistic action of progesterone and estradiol is the effect on thermoregulatory mechanisms. Increased estradiol concentration at the low progesterone concentration level causes water and electrolyte retention. In turn, an increased level of progesterone leads to loss of water and sodium, causing a decrease in the volume of plasma during the postovulatory phase of the menstrual cycle. The processes described above are related to metabolic changes affecting the ability to perform physical efforts.",signatures:"Magdalena Wiecek",downloadPdfUrl:"/chapter/pdf-download/62617",previewPdfUrl:"/chapter/pdf-preview/62617",authors:[{id:"253369",title:"Dr.",name:"Magdalena",surname:"Wiecek",slug:"magdalena-wiecek",fullName:"Magdalena Wiecek"}],corrections:null},{id:"63848",title:"Premature Ovarian Insufficiency",doi:"10.5772/intechopen.80090",slug:"premature-ovarian-insufficiency",totalDownloads:1103,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Premature ovarian insufficiency (POI) is a heterogeneous disorder, affecting approximately 1% of women before the age of 40. Heterogeneity of POI is reflected by various causes. The known causes are genetic defects, autoimmune ovarian damage, metabolic, iatrogenic following surgery, cancer therapy, and environmental factors. However, in most cases, the cause remains unknown (idiopathic POI). The main symptom is the absence of regular menstrual cycles, and the diagnosis is confirmed by the raised gonadotropins and low estradiol. The disorder usually leads to infertility and has long-term comorbidities such as cardiovascular diseases, osteoporosis, and cognitive impairments. Management includes the use of hormone replacement therapy till the age of natural menopause. In women having fertility issues, the spontaneous conception varies between 5 and 10%, and in vitro fertilization with donor oocytes remains the treatment of choice. Moreover, fertility preservation options can be offered to some patients with cancer and those at risk of early menopause, such as those with familial cases of POI. Further research is clearly needed, to identify new mechanisms which may improve the prediction of the early onset of the disease.",signatures:"Abdelhamid Benmachiche and Amel Dammene Debbih",downloadPdfUrl:"/chapter/pdf-download/63848",previewPdfUrl:"/chapter/pdf-preview/63848",authors:[{id:"254629",title:"M.D.",name:"Abdelhamid",surname:"Benmachiche",slug:"abdelhamid-benmachiche",fullName:"Abdelhamid Benmachiche"},{id:"265542",title:"Prof.",name:"Amel",surname:"Dammene Debbih",slug:"amel-dammene-debbih",fullName:"Amel Dammene Debbih"}],corrections:null},{id:"64960",title:"Secretory Phase and Implantation",doi:"10.5772/intechopen.81304",slug:"secretory-phase-and-implantation",totalDownloads:1285,totalCrossrefCites:0,totalDimensionsCites:2,hasAltmetrics:0,abstract:"This chapter will explore the latter phase of the menstrual cycle focusing on the secretory phase of the endometrium. In particular, focus will be on the mid-secretory endometrium and appropriate markers and hormonal environment for successful implantation. This will be put in the context of the luteal phase of ovulation and the hormonal support that progesterone provides. We will also review pathologic states, such as endometriosis and related progesterone resistance, which affect mid-secretory phase and implantation. Finally, we will provide a detailed review of the literature on what the current state of knowledge is regarding receptivity and the microenvironment of the mid-secretory endometrium which is essential to implantation.",signatures:"Courtney Marsh, Katelyn Schumacher, Warren B. Nothnick, Robert N. Taylor and Monique Monard",downloadPdfUrl:"/chapter/pdf-download/64960",previewPdfUrl:"/chapter/pdf-preview/64960",authors:[{id:"255491",title:"Dr.",name:"Courtney",surname:"Marsh",slug:"courtney-marsh",fullName:"Courtney Marsh"}],corrections:null},{id:"63858",title:"Standardization of Menstrual Cycle Data for the Analysis of Intensive Longitudinal Data",doi:"10.5772/intechopen.81504",slug:"standardization-of-menstrual-cycle-data-for-the-analysis-of-intensive-longitudinal-data",totalDownloads:1005,totalCrossrefCites:0,totalDimensionsCites:3,hasAltmetrics:0,abstract:"Daily diary methodology is becoming popular in human menstrual cycle (MC) research. However, variations in MC length makes it difficult to examine fluctuations in dependent variables (e.g., substance use levels), across the MC. Existing analytic approaches collapse data across MC phases, examining phase-related changes; however, a loss of potentially vital information can result when data is collapsed across phase. Additionally, current phase designation methods (phase designation and days within each phase) vary substantially across studies, making it difficult to interpret/compare results across studies. To address these problems, two methods were developed to standardize intensive longitudinal data collected via daily diary methodologies—phasic and continuous standardization. Phasic standardization accounts for individual variability in MC length by allowing luteal phase length differences while remaining phases are fixed, enabling the analysis of phasic variations. Alternatively, continuous standardization accounts for individual variability in MC length by standardizing the luteal phase to a seven-day phase, while remaining phases are fixed, allowing for the exploration of continuously reported variables across MC day. This chapter will discuss how to standardize daily diary data collected across the MC using phasic and continuous standardization methods and demonstrate the two standardization methods using two clinically-relevant hypothetical examples.",signatures:"Kayla M. Joyce and Sherry H. Stewart",downloadPdfUrl:"/chapter/pdf-download/63858",previewPdfUrl:"/chapter/pdf-preview/63858",authors:[{id:"251727",title:"Dr.",name:"Sherry",surname:"Stewart",slug:"sherry-stewart",fullName:"Sherry Stewart"},{id:"251728",title:"BSc.",name:"Kayla",surname:"Joyce",slug:"kayla-joyce",fullName:"Kayla Joyce"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"9558",title:"Active Learning",subtitle:"Theory and Practice",isOpenForSubmission:!1,hash:"c55b272766d51c3d563abc25c026b939",slug:"active-learning-theory-and-practice",bookSignature:"Olena Lutsenko and Gregory Lutsenko",coverURL:"https://cdn.intechopen.com/books/images_new/9558.jpg",editedByType:"Edited by",editors:[{id:"225667",title:"Mrs.",name:"Olena",surname:"Lutsenko",slug:"olena-lutsenko",fullName:"Olena Lutsenko"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"2575",title:"Prolactin",subtitle:null,isOpenForSubmission:!1,hash:"338ea99a4e29b28d7463a976a301711b",slug:"prolactin",bookSignature:"György M. 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1. Introduction
Trade is an integral part of economic activity and development. In global economic system, countries exchange various products as well as intermediate goods/inputs. International trade creates network of economic activity which are generally called trade networks or supply chains. Supply chains are the major enablers of world trade and the world is connected through supply chains. The world economy is strongly connected and influenced by supply chains and its developments. According various industry reports/studies, the best companies are the ones which are having best Supply Chains [1]. Global competition is forcing organizations to build suitable competencies to reduce cost and improve customer service on continuous basis. Corporations have realized that substantial cost savings and market benefits can be achieved by more effective management of their supply chains globally. Advances in information/communication systems and quantitative modeling are also being widely implemented, which provide the potential for access and analysis of comprehensive data/information from each element of the supply chain. One of the main objectives of supply chain is to link the markets, distribution system, manufacturing and allied processes and the procurement to serve its customers across different parts of the world at lower cost and higher service levels. Various developments in technologies, trade policies lead to strong economic growth of the world. Supply chains facilitated globalization of trade for a long time. Various disruptions challenged the globalization. COVID-19 crisis has led to the greatest disruption of supply chains worldwide. COVID-19 crisis not only disrupted the global supply chains and it has thrown lot of challenges to politicians, business and overall economy. COVID-19 pandemic has precipitated economic crisis due to disruption of supply chain, manufacturing activities, and suppressed demand. International Monetary Fund (IMF) has projected global economic growth to be negative 4.9%. This economic crisis has resulted in substantial erosion of market capitalization across the globe.
This chapter focus on how supply chains helped in building the economic development before COVID-19 crisis and how these will help to restart the economic development post COVID-19. We present a brief overview of supply chain management with some examples, impact of COVID-19 on supply chains and economy followed by economy recover framework, sectors and strategies.
1.1 Definition of supply chain and supply chain management
A supply chain is a system consists of people, material, transformation activities, organizations, information and resources used in fulfilling the demand for a product or service by a customer. Supply chain management deals with transformation of raw materials and components into a finished product/service that is delivered to the end customer [2]. Figure 1 shows a typical supply chain which consists of suppliers, manufacturers, distributors and final customer. These entities are connected through suitable transportation, warehousing and information sharing across the supply network.
Figure 1.
A typical supply chain network [2].
A supply chain is a network between a company and its suppliers to produce and distribute a specific product to the final buyer. Generally, supply chains deal with different activities, people, material, information, financial resource, knowledge & skills. Figure 2 shows various flows that need to be managed in a supply chain. Three important aspects of supply chains include adaptability, alignment and agility. The best supply chains identify structural shifts, sometimes before they occur, by capturing the latest data, filtering out noise, and tracking key patterns [3]. The supply chain also represents the steps it takes to get the product or service from its original state to the customer [2, 3]. Supply chain management is a crucial process because an optimized supply chain results in lower costs and a faster production cycle which helps companies to remain competitive in the business landscape. Some of the important areas that requires attention in supply chains include flow optimization, coordination, risk management, sustainability, safety and security. The best supply chains aren’t just fast and cost effective. They are also agile and adaptable, and they ensure that all their companies’ interests are aligned [3, 4].
Figure 2.
Various flows in supply chain [3].
Supply chain management uses different approaches for efficient integration of its stakeholders like suppliers, manufacturers, warehouses, retailers such that the merchandise is manufactured and supplied to the right customer on time at correct location at correct time at minimal cost and optimal service level [2, 3]. According to Institute for supply management, the supply chain management is defined as “the design and management of seamless, value-added process across organizational boundaries to meet the real needs of the end customer. The development and integration of people and technological resources are critical to successful supply chain integration”. Similarly, Supply Chain Council defines it as “Managing supply and demand, sourcing raw materials and parts, manufacturing and assembly, warehousing and inventory tracking, order entry and order management, distribution across all channels, and delivery to the customer”. Council of Supply Chain Management Professionals defined it as “the planning and management of sourcing and procurement, conversion, and all logistics management activities”. Also includes coordination with channel partners, which can be suppliers, intermediaries, third party service providers, and customers.
1.2 Supply chain building blocks
SCM is the management of a network of interconnected businesses involved in the provision of product and service packages required by the end customers. The network of interconnected businesses and comprises of three major building blocks Viz., structural, logical and informational [2, 3, 5, 6].
Structural Building Blocks include Suppliers, Manufacturing/Assembly Plants, Warehouses, Distribution Centers, Retailers/Customers, Logistics Network (inbound and outbound), Customers Orders etc. This can be visualized clearly from, Figure 3, a computer manufacturing company like IBM, HP, Dell etc. The relationship and their strength and number of entities drive the supply chain performance.
Figure 3.
Structural building blocks of a supply chain [2, 5].
Logical building blocks of a supply chain include both horizontal and vertical functions of a supply chain and the same is shown in Figure 4. Logical building blocks include strategic, tactical and operational decisions cutting across various processes like procurement, production, sales and services. Integration of these building blocks across the supply chain network requires suitable organization structure and accountability. Both structural and logical building blocks are connected/integrated through informational building blocks.
Figure 4.
Logical building blocks of a supply chain [5].
Information building blocks focus on material requirement planning (MRP), enterprise resource planning (ERP), electronic data interchange (EDI), internet technologies, sensor networks, E-Commerce, E-Markets, E-CRM, Decision Support Software, standards etc.
1.3 Supply chain decision spectrum
Organizations involved in supply chains need to take several decisions are different levels and different processes. A typical decision spectrum is shown in Figure 5 along with various decisions at different levels. Forecasting is one of the critical inputs that affects the efficiency and responsiveness of the entire supply chain both in short term and long term.
Figure 5.
Supply chain decision spectrum [5, 6].
Procurement & Inbound Logistics include two major components via., (i) managing procurements and (ii) manufacturing procurements. Managing procurement tasks comprise of control of inventory, development of quality standards, price negotiation, purchase of goods and services, financial purchases, aligning purchase to the company ethics and policies and disposal of waste. It also assists the organization to formulate strategies regarding criteria to choose suppliers for the company. The company can benefit from waste reduction and preservation of environmental assets, biodiversity and other finite assets. Manufacturing procurement efficiently manages the suppliers for the raw material with focus to obtain cheaper and quality raw materials.
Production Plan include safety stock and replenishment decisions and based on trends and market dynamics. If the production activities aren’t planned properly there is high chance to face the issues such as shortage of stock and heavy inventory. During COVID-19 sudden lockdown was imposed across the globe which has hit the production lines badly because of unexpected gap between demand and supply for essential products.
Outbound Logistics include demand management and order fulfillment. It takes care of fulfillment of demand of goods and services of customers at right time and quantity with better responsiveness. Demand management helps in understanding important aspects of customer demand by better forecasting of requirements and enhanced visibility across the supply chain. It also assists in enhance customers’ service, managing and improving inventory levels, enhance inventory planning and optimize promotion and trade planning, develop sales or demand forecasts. The main objectives of order fulfillment are to effectively help in faster order processing, faster order delivery to the customers’ and frequent fulfillment of products. Network designing and process designing are two components of order fulfillment which assists an organization in meeting its order fulfillment goals while minimizing the delivery costs.
Order fulfillment also takes into consideration of networking strategies such as allocating inventories in the physical buildings, transportation strategies such as trying new processes and carriers and making changes in the distribution centers. Therefore, the order fulfillment considers cross-functionality of the organization which builds coordination among main suppliers and customers.
Sales & Post Sales service takes care of needs of the supply chain stakeholders like dealers wholesalers and retail outlets. Decent long-term relationships with the retailer and wholesalers, frequent inventory inspection/management and capturing customer feedback are key components to understanding the demand of the product in the market.
There are two major aspects to be considered in post-sales. They are Customer relationship management (CRM), customer service management (CSM). CRM helps in understanding how to provide tailor-made products and services to meet the needs and demands of a customer. CRM through supply chain network and through the delivery of products builds competitive edge of a company against their competitors. The main responsibility of a customer service management lies in refining the relationship with the customers. Customer service management in the SCM building blocks effectively focus on managing customer service on the basis of customer’s preferences, tastes, and perceptions to deliver best of products and services.
1.4 Principles of supply chain management
Making rational decisions in the context of supply chain management is a complex one. To help managers decide how to proceed, [7] have suggested the following seven principles which were based on the stories of successful organizations in business. These principles include the following [7]:
Customer segmentation based on their needs
Logistics network customization
Observe market demand and plan accordingly
Postponement or delayed differentiation of product closer to the customer demand
Strategic sourcing
Use of SC wide technology strategy
Use of channel wide performance measures
Principle 1: Adapt supply chain serve customers based on their needs.
Principle 2: Use customized logistics network to serve requirements at lower cost based on segment.
Principle 3: Observe the market trends and plan to meet the demand across the supply chain consistently with suitable forecasts and optimal allocation of resources.
Principle 4: Use postponement or delayed differentiation in meeting the uncertain demand of the customer with better speed/response.
Principle 5: Use strategic alliances for sourcing of various raw materials/inputs at lower cost and better quality.
Principle 6: Use supply chain-wide technology strategy that supports multiple levels of decision making and gives a clear view of the flow of products, services, and information.
Principle 7: Use channel-spanning performance measures to measure success in fulfilling the end-user demand efficiently and effectively.
Many organizations around the work have benefited by using these principles and able to meet the customer demand effectively and profitably by strengthening their supply chains. Further, organizations have pursued various initiatives like integration of supply chain activities and these have resulted in improved asset utilization, reduced cost, and created competitive advantage on one hand and improved revenues on the other hand.
1.5 Performance measures of supply chains
Measuring supply chain performance is one of the most difficult tasks in business due to involvement of many stakeholders and different types of activities that they perform in different geographic locations and contexts. However, both industry practioners and researchers have evolved different performance measures which are based on cost, quality, delivery, efficiency, sustainability etc. Some of the mainly used performance measures include delivery performance to request, upside production flexibility/material availability, total supply chain costs, Cash-to-Cash Cycle Time, return on investment, inventory turns, fill rate, customer service level, revenue growth etc.
2. Methodology
In this study, we have adopted a generic method for literature search and industry practices in the area of supply chain management in different sectors, reports on economic development, global value chains, world trade and reports on COVID-19. Literature search has been carried out using key words like COVID-19, Supply Chain Management, economy recovery, world trade, global value chain, world GDP, healthcare from the databases like PubMed, Google Scholar, EBSCO, ENKI, ABI Info, etc. The search does not include other databases. Apart from these we have also used information regarding various stimulus packages and sectoral specific schemes for economic recovery in different countries. We have used the information from firms like McKinsey, The Economist, Gartner Inc., World economic forum, World trade organization etc. The author’s own research and consulting experience in the area of supply chain management and healthcare management. Apart from these we have also gathered the information from experts from different fields like policy makers, industry practitioners, consultants, medical physicians from healthcare sector at global level. We have excluded country specific details in the study.
3. Supply chain ecosystem and frameworks
Supply chains are in practice for a very long time in different forms facilitating the trade [5]. After world war II (WW-II), the importance of supply chains has increased significantly and from 1960s its influence has increased duo to developments in computers and information technology. Further, post 1990s due to advent of internet technologies and globalization of trade, the importance of supply chains has increased many fold and the GDP of the world has increased significantly [5, 6, 8] and the same is given in Section 3. Supply chain ecosystem and framework is brief described in the following sub sections.
3.1 Supply chain ecosystem
Figure 6 shows various stakeholders and their relationship in the context of supply chain ecosystem [9]. Supply chain ecosystem is a complex network connecting various stakeholders through suitable technology platforms and incentive systems. This include logistics service providers like third party, forth party logistics providers, reverse and returns, inbound and outbound. Transportation modes would include roadways, railways, airways and seaways. Other services like warehousing, courier and freight services and material handling. SC ecosystem would include platforms, software across different industry verticals like automobile, fast consumer goods, industrial goods, energy, health and other services.
Figure 6.
Supply chain ecosystem [9].
SC platforms are very critical and include connectivity management, application management and data management. Connectivity management include WAN, Wi-Fi, Hotspot, Bluetooth, RFID etc. Due to increase in complexity of SC network, the importance of software has increased significantly and most of the activities are managed through software tools. Software management in the context of SC include network management, streaming, data management, security, internet of things, asset management, asset maintenance, warehouse management, building management, fleet management etc. Advances in information and communication technologies like GPS has further improved the performance and control of supply chain activities. Due to rich data and advances like Artificial Intelligence (AI), Machine learning (ML) are facilitating in more rational and timely decisions across the supply chain.
3.2 SCOR model
Supply Chain Operations Reference (SCOR) model is very widely used industry standard model developed by supply chain council in 1996 [10]. SCOR model follows a hierarchical structure and has three levels. Level 1 include five process types viz., plan, source, make, delivery and return. The elements of SCOR model is shown in Figure 7 along with major functions and role of digitization at each stage of the supply chain. Level II include process categories which defines the configuration and level III include process activities in terms of inputs and outputs and performance measures. SCOR model employs five performance measures: reliability, responsiveness, flexibility, costs and asset management. In summary, the traditional SCOR Model has five processes, five performance measures and three levels describing SC comprehensively. Due to technological advancements in data capturing and analysis tools, the digitization across SC has attracted many stakeholders in improving the supply chain performance significantly. Digitization of supply chain functions at each stage is also shown in Figure 7. The most interesting and important element is handling of returns across various industry verticals. Returns in the supply chain have assumed significance due to electronic commerce industry. Of late, AI and Blockchain technologies are playing a critical role in managing the returns across various industry verticals by tracking the product usage history and other relevant information.
Figure 7.
SCOR model with traditional and digital focus [10].
SCOR model covers the following activities:
Covers all interactions of customers from order entry to final invoice/payment.
All transactions related to product (good or service) from supplier end to final customer, including product, spares, other equipment, software etc.
All interactions of the market from demand aggregation to demand fulfillment for each customer/order.
SCOR model allows environmental measures like carbon emissions, air pollution, liquid and solid waste, percent recycled waste etc. SCOR model connects emissions to the processes at source and provide a structure for measuring environmental performance and improvement areas. The hierarchical nature SCOR model allows strategic environmental footprints to be translated to specific activities and targets.
3.3 Healthcare value chain capabilities model
Though the supply chains are widely used in manufacturing industry to start with, subsequently the supply chain concepts and practices have been applied in other sectors including healthcare industry, pharmaceutical, food and agriculture, ecommerce, humanitarian, disaster relief etc. Healthcare is changing at an unprecedented pace, due to the impacts of technology, cost pressures from both payers and patients who are seeking quality care. Healthcare organizations including providers, retailers, distributors and wholesalers, manufacturers. Some of the organization who have adopted supply chain practices include Johnson and Johnson, Cleveland Clinic, Mercy, CVS Health, McKesson, Novo Nordisk, Medtronic, Stryker, Roche, Pfizer, Owens and Minor etc. For example, Mayo Clinic has improved the care delivery and reduction in cost by collaborating with its suppliers and adopting digital technologies in its operations. Figure 8 shows the framework suggested by Gartner in respect of healthcare organizations with a main objective of improving the human life at sustainable costs as well as quality. This model has five major processes viz., patient focus, collaboration, network visibility, cost to serve and change management supported by fundamental capabilities covering all the processes [11].
Figure 8.
Healthcare value chain capabilities model [11].
3.4 Sectoral specific supply chains
Due to inherent advantages and capabilities of supply chains, many organizations across different industry/business verticals have adopted supply chains and benefited immensely. Some of the major sectors include automobile, food and agriculture, e-commerce, healthcare including hospitals, pharmaceuticals, diagnostic services, medical devices etc., defense and government, energy and power, oil and gas etc. Supply chains of e-commerce, food and healthcare is briefly explained in this section.
3.5 Electronic commerce supply chains
Electronic commerce (e-commerce) supply chains have gained significant importance due to ability of supply chains in meeting the customer service and low cost. Many companies like Amazon, flipkart, Alibaba etc. have demonstrated the power of supply chain in their business operations. Figure 9 shows typical supply chain network of e-commerce supply chains. Figure 10 shows a macro view of supply chain management at Amazon [Amazon.com]. e-commerce supply chains are more robust compared to others mainly due to its ability to manage the disruptions during crisis like COVID-19. Particularly during COVID-19 crisis, Amazon, flipkart and other e-commerce companies were able to maintain the continuity of supply in spite restrictions like lockdowns, social/physical distance and limited time operations, shortage of manpower etc. These companies are able to recover fast due to its resilience and responsiveness capability. This can be seen from the latest trend in the share price of e-commerce and food supply chain companies.
Figure 9.
E-commerce supply chain [12].
Figure 10.
Amazon supply chain [13].
3.5.1 Food supply chains
A typical food supply chain with food safety information system and quality assurance system is shown in Figure 11 [14]. The food supply chain includes farm/farmer, distributor, factory, distributor/retailer and final customer. In case of food supply chains the most critical issue is food safety across the supply chain. These supply chains focus very heavily into food safety and traceability at each stage of the supply chain mainly at interfaces. Food supply chains uses information and communication technology widely. Quality and Compliance of standards across food supply chain is very critical and makes use of advanced technologies like RFID, temperature controls, GPS enabled systems for tracking and traceability. It follows standards like good manufacturing practices, ISO standards and TQM etc. [14].
Figure 11.
Food supply chain [14].
3.5.2 Healthcare supply chains
Healthcare supply chains are more complex than other supply chains due to presence of many stakeholders and responsiveness requirements. A typical healthcare supply chain is shown in Figure 12. Healthcare supply chains poses several challenges due to stringent regulatory requirements, safety and security and quality requirements. In spite all these challenges, many healthcare organizations benefited by adopting supply chain management approaches. According to Gartner study [11] on top 25 healthcare supply chains, top 5 organizations include Johnson and Johnson, Cleveland Clinic, Mercy, CVS health and Duke University Health system. Many of these organizations are using best practices like collaboration, digitalization, robust processes that are aligned with the overall objective as well as the elements listed in healthcare value chain capabilities model. The major strengths of healthcare supply chains include agility, alignment, adaptability, resilience and responsiveness. These organizations follow people, process and technology solutions in the care delivery. Of late, many healthcare organizations particularly using AI and Blockchain technologies for personalized care in large scale. Telemedicine and home care is growing very fast due to COVID-19 crisis where supply chain is very critical. Some of the organizations are using drones for delivery of medicines, pathology samples, food and equipment. Many start-ups also emerging in healthcare by bridging the gap predominantly through digital technologies and supply chains.
Figure 12.
Elements of healthcare supply chain [15].
3.6 Supply chain configurations (hub and spoke model, centralized vs decentralized SC)
Supply chain network structures and its configuration is very important in achieving the performance of supply chains. Predominantly used supply chain structures in the practice include centralized and decentralized [2] or Hub and spoke model. Figure 13 shows typical structure of centralized and decentralized supply chains. Centralized systems are more efficient and are generally used for high volume low value commodities like steel, cement, automobiles, computers etc. where cost is important. Whereas decentralized systems are predominantly used in e-commerce, healthcare and other services where responsiveness is most important. Hub and spoke models are predominantly used is both product and service organizations. A typical hub and spoke model of a Start-up delivering health services in Rural areas in India is shown in Figure 14. Generally, the Hubs are equipped with more resources that can help serving the customers located spoke level. Typically, these share many resources and innovate through collaborative mechanism and employ multi skilled people and technology to improve the efficiency and reach.
Figure 13.
Centralized and decentralized supply chain system [2].
Figure 14.
Hub and spoke model of a healthcare start-up [16].
4. Relation between economic development and global value chains
Supply chains have contributed significantly for the world trade for many years. Further, supply chains have facilitated the globalization of trade. Figure 15, shows the world trade over last four decades [8]. There is a sensitive relationship between economic development and supply chain because efficient management of the supply chain can reduce costs, maximize customer value, and maximize competitive advantage. It entails effective coordination and control of linked sectors, departments, systems, and organizations. According to a study by world economic forum, reducing supply chain barriers could increase world GDP better over import tariffs. Further, the study shows that 50% reduction in supply chain barriers can increase world trade by 14.5% and world GDP by 4.7% and these gains are more evenly distributed across various countries and also it can generate more employment [17].
Figure 15.
World trade-exports [17].
Global trade has increased owing to adoption of liberalization and globalization as national economic policies by several countries in post-Soviet era (post 1992). Global GDP has also expanded in tandem with global trade. An analysis of global merchandise trade and global GDP at market exchange rates with 2008 as base years shows coupled growth in global GDP and global trade as depicted in Figure 16 [18]. Due to emergence of global value chains, the global trade is shifting fast. This can be seen from Figure 16. Expansion of global GDP and its correlation with global trade increases the importance of worldwide network of production of goods. A study done by [8] suggests that contribution of intermediary goods in global trade of manufactured goods was more than that of finished goods during 2001–2008 and 2009–2014. This conclusion supports the existence of complex Global Value Chains (GVC) [19]. Furthermore, as per the available data, 57% of the global trade in 2015 was constituted by trade of intermediate goods [18]. OECD TiVA database shows that the Asian economies have the highest growth rates of contributions in GVCs [20].
Figure 16.
Relation between global trade and GDP [8, 18].
Due to increasing network of global supplies, patterns in global trade have shifted from ‘trade in goods’ to ‘trade in value added’ and ‘trade in tasks’ [19]. The Global Value Chain (GVC) Framework has emerged due to shifting pattern of global trade. It focusses on expanding and strengthening supply chain and value generation therein. Both developing and developed economies get benefited by participation into GVC [8]. Participation into GVC provides an exposure to the global best practices, technological know-how, and competence development. These result in higher economic growth and development [21].
4.1 Supply chain and global value chain (GVC)
GVC framework provides a strategic overview of global supply chain and integration of different characteristics of complex supply networks into GVC would provide a holistic perspective of various methodologies- operational and strategic [17]. Development of robust supply chain management practices have strengthened GVCs thereby easing cross-border movement of goods. It promotes domestic manufacturing and consumption. These in-turn result in growth of national GDPs. Therefore, to boost global economic growth promotion of global trade is essential. Sustenance of rapid growth in global trade is a function of participation of various countries into GVC which requires a robust supply chain management. From this analysis, it can be concluded that, development in supply-chain practices have contributed substantially to global economic growth.
World class organizations like Walmart, Apple, P & G, Amazon, IBM, Toyota, General Motors, Best Buy, Marks & Spencer’s, Zara, Sports goods companies, mobile companies, food chains have gained significantly by adopting supply chain practices in their business. Similarly, healthcare organizations like CVS Pharma, Cleveland clinic, Narayana Health of India, pharmaceutical companies, medical device and equipment companies and many others have gained significantly.
5. Impact of COVID-19 on supply chains
In 2019 the United States imported a staggering four $52 Billions of goods from China. COVID-19 crisis has created historical disruption to global supply chains. COVID-19 crisis has affected health of people, business and overall economy at global level. COVID-19 crisis is a wake-up call for supply chains and one way it created de-globalization of business and supply chains. Over dependence on one country like China had proved to be very disrupted at times of pandemic. Bloomberg reported in March 2020 that electronic makers are past the point of no return in the gradual migration from China. Further, the Chinese trade (both domestic and international) transactions dropped by around 56% in the mid- February 2020. Similarly, US, UK and Europe also gone through a drop of 26% in April 2020 and touched 17% in late April 2020. Sourcing from India it is recommended an incremental approach in bringing all Indian suppliers i.e., carefully select some low-risk and high reward programs to try out in India while maintaining Chinese base.
Due to COVID-19 crisis companies have fallen into one of two categories those that do not do anything hoping such a deception will not ever happen again those firms that heed the lessons of this crisis and make investments in mapping their supply networks so that they do not operate blind when the next crisis strikes, these are the ultimate winners. Some of the major challenges faced by supply chains due to COVID-19 include the following [22, 23]:
Lack of visibility due to higher levels of SC network complexity
High uncertainty on both supply and demand end
Limited production flexibility
Limited financial flexibility
In several countries consumer surveys show a likelihood of greater spending on groceries and less spending on discretionary categories. According to McKinsey study on marketing and sales survey conducted in Italy, Spain, UK and US during March, 2020 reveals that except groceries (grown around 18%) other sectors like quick service restaurants, restaurants, footwear, apparel, jewelry, accessories, furnishing and appliances have recorded negative growth ranging from 50–85%. Among all sectors, restaurants are the worst affected.
Another important incident of 2011 Tsunami in Japan can also help the business organizations around the world to learn important lessons. After the 2011 earthquake and tsunami had devastated north-east in Japan it led to the application of a new methodology that was developed that enables a sophisticated way to understand the exposure to risk associated with unlikely events such as COVID-19 pandemic. The ability of the supply chain to recover from the disaster should be considered by the supplier as a performance factor of the supply chains.
6. Economy recovery sectors and strategies
6.1 Recovery framework
McKinsey [22] has suggested the following three step framework for rapid recovery:
Identify and prioritize actions based on clear understanding and demand patterns of activities that can capture revenues quickly (B2B and B2C- multiple SCs) and opportunities need to be continuously updated
Act with urgency judicious allocation of resources to the activities prioritized to capture the growth. Engage new partners and new channels (online and offline), reallocation of resources to safe places quickly and reconfiguring the SC, shifting the promotional sales etc.
Develop a rapid-fire agile operating model - speed of decision making and agile teams (market insights, cash liberation, growth, digital market and post Covid-19 growth)
6.2 Recovery sectors
According to some of the major studies by McKinsey, Economist and others [22, 23, 24], the early starters of economy include health care including pharmaceuticals, medtech, diagnostics, hospital and home care services, telehealth/telemedicine, essential items, food and agriculture, FMCG, 3D printing, internet of things (IoT), AI/ML, robotics, smart systems, e-commerce industry. COVID-19 has put the medtech industry at center stage with unparalleled demand for diagnostic test PPE, ventilators, and critical medical supplies. McKinsey [22, 23] has built a detailed model of COVID-19 impact on medical procedures mainly for the United States on the Europe which is used to create a model for predicting the potential impact on medical device sales in consumables and implants. The models consider two broad scenarios for COVID-19 case growth V shape recovery and W shape recovery. In V shape recovery, it is estimated that the material procedures decline by around 70% in the second quarter and up to 45% in third quarter when compared to 2019. It is expected to see a rapid ramp up for the next three forth to catch up on delayed elective procedures. Whereas in W shaped recovery, procedures would decline by 69% and 45% in the second and third quarters respectively.
Telehealth potential has been realized after the COVID-19 crisis and telehealth consumer adoption has increased from 11% in 2019 to 76% in 2020 in US along. Approximately $250 billion or about 20% of all Medicare Medicaid and commercial OP office and home health spend could potentially be virtualized with the help of telehealth. Similar trend has been observed in many countries worldwide.
6.3 Recovery strategies and actions
It will not be that easy for many organizations to have a detailed analytical understanding of demand variability at local and national level. However, stress testing of supply chains with different scenarios with viable product demand and procedures will be very critical for managing the current scenario. Some of the methods suggested to overcome the impact on supply chains [22, 23, 25] includes the following:
Develop a high-risk supply chain disruption-monitoring and response program for countries impacted by the virus and the potential supply chain exposure from Tier 1 and below.
Assign high-risk weighting to suppliers and sub-tiers from emerging and developing countries with less developed healthcare systems that are less prepared.
Conduct a contract review to understand any financial implications of not being able to deliver supplies to manufacturing locations and customers.
Special attention for balancing of supply and demand, building suitable safety stocks are essential for business continuity, particularly with the unpredictable volatility of supply chain functions.
Identify various opportunities at supply side and diversify suppliers to ensure manufacturing capacity and raw material availability.
Establish a robust risk management models to monitor and prepare for shortages in material, manufacturing capacity and work closely with supply chain stakeholders particularly with critical suppliers.
Implement and utilize enhanced risk management, including scenario planning to create preemptive action plans.
Review the New Product Introduction process and utilize design measures to discover or develop alternative sources and routes in order to diversify your value chains. At the same time, analyze cascading implications of changes in volumes, quality and markets.
The most common approach is to use the bill of materials and focus on key components.
Some of the major actions suggested by McKinsey for economy recovery [23] is given below.
Strategy: Reposition the bran, Mergers and Acquisitions, Networked Ecosystems, create a new demand planning system.
Productivity: Take initiatives such as Marketing, improving sales productivity, Inside sales, data analytics for performance management.
Digital channels: Use scaled digital sales & services, E-commerce, Digital marketing etc.
Customer experience & insights: This can include, customer experience, Personalization, improved CRM, creating value proposition through suitable products, Analytics-driven sales and distribution, revenue pricing and promotion optimization.
Some of the actions suggested for recovery of medtech industry [22] include the following.
Accelerate capacity of equipment like PPE, Ventilators, other surgical items etc. that is essential to save the lives of critical COVID-19 patients.
Maintain capacity of equipment used in COVID-19 treatment or that requires replacement such as CT machine dialysis equipment or ECMO machines etc.
Deprioritisation of equipment not useful in COVID-19 treatment such as MRI surgical and mammography equipment and etc.
Supply chain management and reliability: More than 90% of global annual medical device exports come from countries that are now in some form of quarantine. There for Medtech company should consider activating supply contingency plans repositioning inventory to areas of greatest need and making all reasonable attempts to protect the health and safety of workers on the manufacturing floor.
Preserving cash: according to the McKinsey benchmarking analysis the potential for a rapid 3–5% increase in cash flow could be shown additional changes to production like just-in-time inventory pooling and rationalization, and standardization can free up more cash. These initiatives should be rigorously tracked in conjunction with appropriate cash controls and customer considerations.
Resource relocation and portfolio strategy: Now is the time to establish processes that can anticipate market demand and shift staffing as needed to quickly accommodate changes this would require increased investment in cross training the employees as well as new processes to rapidly scale up support services.
The company is required to fundamentally rethink the supply chain network and key suppliers as they were already facing pressure to localize in certain markets, after the crisis it will continue to be important for the companies to consider how to balance these pressures that can impact local supplies with potential desires for greater flexibility in capacity. These adaptations could include building more agile organizations, speeding time to market and aspiring to “absolute benchmarks” for product design and development and manufacturing efficiency.
Some of the lessons learned from fast food service organizations like KFC, McDonald etc. include the following which are based on digitalization:
Co-creation: use of user generated content with apps like radio KFC, RJ hunt and design your own bucket challenge.
Unique Experience: the simple equation is (cutting edge technology + ingenious ideas = unique experiences) Example, interactive campaigns.
Feedback fanatics- the secret to a great relationship is listening KFC ensure that every feedback was listen to and addressed.
Strengthening healthcare’s supply Chain.
These are the five specific capabilities that can have a dramatic impact on performance of the healthcare supply chain [26]:
Better segmentation of products, markets and customers.
Greater agility to reduce cost and increase flexibility.
Measurement and benchmarking.
Alignment with global standards.
Collaboration across the healthcare value chain.
How to address the shortage and improve the safety?
Supply chain issues create opportunities for counterfeiters and gray market vendors threatening patient safety and cutting into revenues of legitimate companies. Supply chain security breaches are increasing by an average of 33% every year not only in the emerging markets such as China, India & Brazil but also in the developed world. Better supply chain processes are central to increasing patient safety. Therefore, it is recommended that adopting a common global data standard and upgraded grading supply chain processes could/counterfeiting in half returning up to $15 Billion to $30 Billion in revenue to legitimate companies to reinvest in further improvements to patient care.
Building a new healthcare supply Chain
Organizations can learn from the experience of laptop manufacturer in Indian who can accept an order and deliver a customized computers to a European customer in almost a week. Other organizations like pharmaceutical and medical equipment companies can adopt two broad approaches based on internal factors and external factors. Internal factors include – segmentation, agility, measurement while the external factors include- alignment & collaboration [27].
Segmentation: Companies such as pharmaceutical and medical device generally follow one size fits all type of supply chains which are proved to be disastrous particularly during pandemic times. Best companies address these problems by segmenting the supply chains according to the nature of product and customers demand and preferences by developing suitable forecasting, production and distribution strategies for each category.
Agility: This refers to building and operating supply chains that can better respond to demand shifts due to occurrence of unexpected disruptions in the business. The approaches include cross functional process, understanding of demand and supply scenarios and circumstances, effective communication and transparency across the supply chain.
Measurement: Generally, FMCG companies use data driven metrics such as manufacturing index for producing appropriate quantity of stock keeping units (SKUs) across countries and plants. Organizations also uses commercially available benchmarking tools and approaches for guidance and identification of opportunities along with supply chains.
Alignment: Refers to building of a cost effective supply chain that could align around global standards that support data interchange processes and capabilities to reduce the errors etc.
Collaboration: Many successful organizations collaborate in areas where they have a strength and share the benefits. Collaboration focus on (i) Selection of partners for the potential value of the collaboration; (ii) capabilities and willingness to act as a team; (iii) dedicate resources to collaboration and involve senior leadership in it; (iv) jointly manage performance and measure impact; (v) start out with a long-term perspective.
According to [25], the supply chains needs a stress test in terms of time to recover (TTR) and time to survive (TTS). TTR is the time it takes for a particular player/stakeholder (supplier, manufacturer, distributor, retailer etc.) in the supply chain to restore full functionality after disruption. TTS is the maximum duration that supply chain can match supply with demand after facility disruption. TTS also estimate each measure under different scenarios of business and Identify its ability to recover from the disaster. Organizations need to have a backup plan when TTR of a stakeholder or facility is greater than its TTS. This helps the organization in quantifying the cost of disruptions and prepare mitigation plans for the most critical parts of the supply chain [25].
At macro level, the economic recovery requires actions such as government stimulus, digitalization, advance technologies like 3D printing, up-skilling/multiskilling of workforce, restructuring of supply chains with better resilience and response, collaboration/alliances, facilitating innovation by start-ups, strengthening research collaboration between government, industry and academia is also very critical for faster recovery.
7. Conclusions
There is a strong relationship between world trade, GDP and supply chain investments around the world during last more than two centuries starting from 1800. As globalization has increased, the world’s supply chains have become substantially more interconnected. Moreover, as emerging market economies have steadily come to account for a greater proportion of global GDP, goods often have more stages to pass through before reaching the end consumer.
During last seven decades (starting from 1960s) the economic growth of the world is very significant and also seen many disruptions like Tsunamis, 911 Terrorist attacks, pandemics like COVID-19. Among all the COVID-19 crisis is more significant in terms of health and economy. COVID-19 has led to nearly 5% negative growth of world economy. From national lockdowns to closed airspace and borders, Covid-19 has resulted in unprecedented disruption to the mechanics of most economies, regardless of their size or stage of development. In particular, the erection of these barriers has placed a major strain on the world’s supply chains, including essential linkages relating to food and medicines. COVID-19 also created tension between major economies of the world and disrupted global supply chains significantly. Supply chain leaders face pressure to rethink traditional distribution and supplier models. For example, Amazon looks to strengthen its healthcare influence through the expansion of services in the medical supply chain, industry stakeholders are reconsidering traditional hospital-supplier relationships. Data, analytics and technology are playing an increasingly important role in supply chain strategy. A 2018 Global Healthcare Exchange survey [28] showed roughly 60 percent of respondents indicated data and analytics were the highest priority areas for improvement. These changes and trends have pushed the role of supply chain management into new territory. Now, supply chain leaders are positioned to help lead their organizations to higher levels of customer service with more efficient models [29, 30]. To ensure success amid this changing environment, business leaders including healthcare organizations should place an emphasis on technology, business practices and customer service.
\n',keywords:"COVID-19, E-commerce, economic development, economic recovery, GDP, healthcare, pandemic, supply chain management, world trade",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/73634.pdf",chapterXML:"https://mts.intechopen.com/source/xml/73634.xml",downloadPdfUrl:"/chapter/pdf-download/73634",previewPdfUrl:"/chapter/pdf-preview/73634",totalDownloads:747,totalViews:0,totalCrossrefCites:0,totalDimensionsCites:2,totalAltmetricsMentions:0,impactScore:2,impactScorePercentile:76,impactScoreQuartile:4,hasAltmetrics:0,dateSubmitted:"August 19th 2020",dateReviewed:"September 25th 2020",datePrePublished:"October 16th 2020",datePublished:"November 17th 2021",dateFinished:"October 16th 2020",readingETA:"0",abstract:"The increase in World Trade has led to significant growth in world GDP over last 100 years particularly. Supply chains have become the major enablers of world trade and the world is connected through supply chains. Any disruptions in any part of the world has led to disruptions in supply chains and economic recessions. Crisis like Tsunamis, earthquakes, 911 terror attacks, epidemics/pandemics like COVID-19 etc. have affected the businesses worldwide. COVID-19 pandemic has precipitated economic crisis due to disruption of supply chains and suppressed demand for many products and services worldwide. International Monetary Fund (IMF) has projected global economic growth to be negative 4.9%. This economic crisis has resulted in substantial erosion of market capitalization across the globe. The impact of COVID-19 is very significant on both health of the people and economy worldwide. Almost all businesses and governments are trying its best to save people from health and economic crisis. This requires rebuilding of supply chains through appropriate configuration with reliable sources of supply, collaboration, manufacturing and distribution of goods and services. Sectors like essential items, pharmaceutical, e-commerce have started early recovery of economy. However, other sectors require suitable interventions from government, business organizations in their policies and practices and use of digital technologies for economic recovery.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/73634",risUrl:"/chapter/ris/73634",book:{id:"10624",slug:"contemporary-developments-and-perspectives-in-international-health-security-volume-2"},signatures:"Venkataramanaiah Saddikuti, Sagar Galwankar and Akilesh Sai Saddikuti Venkat",authors:[{id:"292211",title:"Associate Prof.",name:"Venkataramanaiah",middleName:null,surname:"Saddikuti",fullName:"Venkataramanaiah Saddikuti",slug:"venkataramanaiah-saddikuti",email:"svenkat@iiml.ac.in",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"293168",title:"Dr.",name:"Sagar C.",middleName:null,surname:"Galwankar",fullName:"Sagar C. Galwankar",slug:"sagar-c.-galwankar",email:"gcsagar@yahoo.com",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/293168/images/system/293168.jpeg",institution:{name:"University of South Florida",institutionURL:null,country:{name:"United States of America"}}},{id:"321650",title:"Mr.",name:"Akilesh Sai",middleName:null,surname:"S V",fullName:"Akilesh Sai S V",slug:"akilesh-sai-s-v",email:"akileshsai2509@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Sapienza University of Rome",institutionURL:null,country:{name:"Italy"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_1_2",title:"1.1 Definition of supply chain and supply chain management",level:"2"},{id:"sec_2_2",title:"1.2 Supply chain building blocks",level:"2"},{id:"sec_3_2",title:"1.3 Supply chain decision spectrum",level:"2"},{id:"sec_4_2",title:"1.4 Principles of supply chain management",level:"2"},{id:"sec_5_2",title:"1.5 Performance measures of supply chains",level:"2"},{id:"sec_7",title:"2. Methodology",level:"1"},{id:"sec_8",title:"3. Supply chain ecosystem and frameworks",level:"1"},{id:"sec_8_2",title:"3.1 Supply chain ecosystem",level:"2"},{id:"sec_9_2",title:"3.2 SCOR model",level:"2"},{id:"sec_10_2",title:"3.3 Healthcare value chain capabilities model",level:"2"},{id:"sec_11_2",title:"3.4 Sectoral specific supply chains",level:"2"},{id:"sec_12_2",title:"3.5 Electronic commerce supply chains",level:"2"},{id:"sec_12_3",title:"3.5.1 Food supply chains",level:"3"},{id:"sec_13_3",title:"3.5.2 Healthcare supply chains",level:"3"},{id:"sec_15_2",title:"3.6 Supply chain configurations (hub and spoke model, centralized vs decentralized SC)",level:"2"},{id:"sec_17",title:"4. Relation between economic development and global value chains",level:"1"},{id:"sec_17_2",title:"4.1 Supply chain and global value chain (GVC)",level:"2"},{id:"sec_19",title:"5. Impact of COVID-19 on supply chains",level:"1"},{id:"sec_20",title:"6. Economy recovery sectors and strategies",level:"1"},{id:"sec_20_2",title:"6.1 Recovery framework",level:"2"},{id:"sec_21_2",title:"6.2 Recovery sectors",level:"2"},{id:"sec_22_2",title:"6.3 Recovery strategies and actions",level:"2"},{id:"sec_24",title:"7. Conclusions",level:"1"}],chapterReferences:[{id:"B1",body:'Conn S. Supply Chain Top 25. 2019. Available from: https://www.gartner.com/en/newsroom/press-releases/2020-05-20-gartner-announces-rankings-of-the-2020-supply-chain-top-25 [Accessed on 2020-08-10].'},{id:"B2",body:'Simchi-Levi D, Kaminsky P, Simchi-Levi E, Shankar R. Designing and Managing the Supply Chain: Concepts, Strategies and Case Studies, McGraw Hill Higher Education; 2017.'},{id:"B3",body:'Lee H L. The Triple-A Supply Chain, Harvard Business Review. 2004; 102-112.'},{id:"B4",body:'Council of Supply Chain Management Professionals, https://cscmp.org/ [Accessed on 2020-09-20]'},{id:"B5",body:'Sunil Chopra and Kalra (2019), Supply Chain Management: Strategy, Planning & Operation, 7th Edition, Pearson Education India.'},{id:"B6",body:'Russel R S and Taylor B W (2019), Operations and Supply Chain Management, 8th Edition, Wiley.'},{id:"B7",body:'Anderson D L, Britt F F, Favre D J. The 7 Principles of Supply Chain Management, Supply Chain Management Review. 1997; 3-8.'},{id:"B8",body:'Degain C, Meng B, Wang Z. Recent trends in global trade and global value chains. 2017. Available from: https://www.researchgate.net/publication/320043874_Recent_trends_in_global_trade_and_global_value_chains [Accessed on 2020-08-10].'},{id:"B9",body:'Ganesh K. Supply Chain Ecosystem, McKinsey (email communication received on 2020-07-20].'},{id:"B10",body:'SCC. 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Innovations in Healthcare Management: Cost effective and sustainable solutions, CRC Press, New York. 2015.'},{id:"B16",body:'Chaudhuri A, Prætorius T. and Venkataramanaiah S. iKure Techsoft: Providing technology enabled affordable health care in rural India, Asian Case Research Journal, World Scientific. 2018. 385-411.'},{id:"B17",body:'Golini R, Caniato F, Kalchschmidt M. Linking global value chains and supply chain management: evidence from the electric motors industry. Production Planning and Control. 2016; 27: 934-951.'},{id:"B18",body:'World Trade Statistical Review. Available from: https://www.wto.org/english/res_e/statis_e/wts2019_e/wts2019_e.pdf [Accessed on 2020-08-10].'},{id:"B19",body:'Word Trade Organisation. Trade Patterns and Global Value Chains. Available from: https://www.wto.org/english/res_e/booksp_e/stat_tradepat_globvalchains_e.pdf [Accessed on 2020-11-10].'},{id:"B20",body:'Trade in Value Added (TiVA). Principal Indicators. 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Florida State University, Sarasota Memorial Hospital, Sarasota, USA
'},{corresp:null,contributorFullName:"Akilesh Sai Saddikuti Venkat",address:null,affiliation:'
Sapienza University, Italy
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1. Introduction
This chapter describes the application of Evolutionary Computation to the task of digital circuit design. Although many Electronic Design Automation (EDA) tools exist to aid designers, digital circuit design remains a time consuming and expensive task that requires skilled engineers.
The cost of errors in silicon is enormous and this has led to extremely powerful and accurate simulators that designers use to verify their designs before committing them to silicon. These simulators provide a huge opportunity for Evolutionary Computation as they can be used to test individuals.
This chapter gives an overview about how digital integrated circuits are designed and how the tools used to develop them have evolved over the past few decades. These tools, when linked together with GE produce a system we call the Automatic Design of Digital Circuits (ADDC), which can evolve circuits using massive levels of abstraction rather than simple logic gates.
We demonstrate the system on three real-world problems, including one with 220 test cases, showing that ADDC successfully evolves solutions in all cases.
2. Background
Digital circuit design began in the 1960’s with the arrival of semiconductor transistor based circuits and the Integrated Circuit (IC). Up until the 2010’s, Moore’s Law has successfully predicted the shrinking in size of manufacturing technologies allowing for lower cost, faster speeds and lower power consumption. However in the last decade, this shrinking has slowed due to the difficulties involved in the fabrication process. Integrated circuits are extremely common in many products today and their use is not always obvious.
Integrated circuits come in three different varieties; Digital, Analogue or Mixed-Signal. Digital integrated circuits process digital information, often represented using bits, bytes or words. Many of these circuits employ the use of one or more processors (often referred to as a core) with support logic, memories and I/O interfaces. The microprocessor is a famous example of a digital circuit. Analogue integrated circuits are used for handling continuous-time signals and to perform operations such as amplification, analogue filtering and power management. Mixed-Signal integrated circuits contain both analogue and digital circuitry in the same package and use ADC (Analogue to Digital Converters) and DACs (Digital to Analogue Converters) to share information between both domains.
In modern circuit design, signal processing tends to be performed in the digital domain instead of the analogue domain. This is due to the reliability of digital circuitry and the existence of advanced digital algorithms with performance that cannot be obtained with analog circuitry alone [1]. This move towards using digital designs for signal processing has required the use of circuit representations like Hardware Description Languages (HDLs) to be used to describe these extremely complex circuits. New devices such as Complex Programmable Logic Devices (CPLDs) and Field Programmable Gate Arrays (FPGAs) are increasingly being used due to their ability to replicate the behaviour of these circuits without requiring the fabrication of new chips. The following sections will go more in-depth into HDLs, the differences between CPLD and FPGA devices and an overview of the Digital Design Flow.
2.1 Hardware description languages
The first modern HDL, Very High Speed Integrated Circuit Hardware Description Language (VHSIC-HDL), more commonly known as VHDL, was created in 1983. VHDL was developed for the US Department of Defense as part of the VHSIC project. The project was launched in 1980 [2], while the first version of VHDL was launched in 1983 by Intermetrics Inc., Texas Instruments and IBM [3, 4]. VHDL is a verbose and strongly-typed language. It grew steadily in popularity, resulting in both logic simulators and logic synthesis tools being developed for it. IEEE Standard VHDL was standardised in 1986 [5] with the adoption of VHDL version 7.2 and was finalised in 1987 in the IEEE Standard 1076-1987 [5]. VHDL would become the first HDL language that would gain widespread adoption, and is still in use today.
Another modern HDL developed around this time was Verilog, created by Phil Moorby in 1983 [6] while working for Gateway Design Automation, who were acquired by Cadence Design Systems in 1989 [7]. In comparison to VHDL, Verilog is less verbose and is a weakly-typed language. Originally it was designed only for logic simulation, but later had logic synthesis features added after the language gained widespread popularity. Verilog-XL, a Verilog simulator owned by Cadence, became the de facto Verilog simulator throughout the 1990’s. Due to the increasing popularity of VHDL, Cadence created the the Open Verilog International (OVI, now known as Accellera) organisation and transferred the rights of Verilog into the public domain [8]. Verilog was eventually standardised in IEEE Standard 1364-1995 [6]. Verilog would be superseded by SystemVerilog in IEEE Standard 1800-2005 [9], adding features for design verification. SystemVerilog is more popular than VHDL today due to the language being less verbose and having a similar structure to the C programming language. Table 1 provides a comparision between VHDL and SystemVerilog hardware description languages.
VHDL
SystemVerilog
Standardised in 1987
Standardised in 1995 (Verilog) and 2005 (SystemVerilog)
More Verbose
Less Verbose
ADA-like
C-like
Case Insensitive
Case Sensitive
Support for Digital, Analog and Mixed-Signal Designs
Support for Digital Designs only
Table 1.
A comparison between VHDL and verilog hardware description languages.
With the introduction of Hardware Description Languages for digital circuit design, two discrete time based simulation methods came into prominence. Both cycle-driven and event-driven simulation methods were orders of magnitude faster than the traditional continuous time based simulation method “SPICE”. One limitation of the cycle-driven simulation method is that the output is only updated on each clock edge This means it can only be used for synchronous digital designs, but is much faster than event-driven simulation. It also cannot detect glitches and doesn’t take the timing of the design into consideration.
Event-driven simulation updates the output on any input event meaning it can be used for both synchronous and asynchronous designs. Although still quicker than SPICE methods, it is much slower than cycle-driven simulation. Modern circuit designs utilise techniques such as clock and power gating, allowing parts of a design to be “turned off”. This can help reduce the simulation time of an event-driven simulation, bringing it closer to cycle-driven simulation while providing a more accurate simulation. Table 2 provides a comparison between cycle-driven and event-driven simulation methods. Practically all commercial and open-source simulation tools today utilise one of these methods.
Cycle-driven simulation
Event-driven simulation
Evaluation every clock cycle
Evaluation at minimum time-step or greater
Synchronous Designs only
Synchronous and Asynchronous Designs
Behavioural Simulation only
Behavioural, Functional and Timing Simulations
Faster Simulation Speed
Slower Simulation Speed
Table 2.
A comparison between cycle-driven simulation and event-driven simulation.
2.2 CPLD vs FPGA devices
As digital designs grew in complexity, early Programmable Logic Devices (PLD) such as Programmable Array Logic (PAL) became obsolete as they could only replicate the behaviour of a few hundred logic gates. To address this shortcoming, PALs were soon replaced by Complex Programmable Logic Devices (CPLD). Modern CPLDs are able to replicate the behaviour of hundreds of thousands of logic gates. One advantage of CPLD devices is that they use non-volatile memory to store their configuration. As a result, their logic is already configured at power-up. This makes them ideal devices for systems where the logic is required to be ready for initialisation, such as glue logic for circuits.
Figure 1 shows the internal structure of a CPLD. These logic blocks consist of programmable PAL blocks. The inputs can be connected together to different AND gates using programmable fuses. The OR gate connections are fixed and cannot be reconfigured. Although less configurable than a PLA (which contains both programmable AND and OR planes), this reduction in complexity makes PAL blocks cheaper to manufacture. In order for PAL blocks to be able to implement sequential designs, a D flip-flop can be used to store the state of the output. CPLDs can connect multiple logic blocks together using the programmable interconnection matrix in order to implement more complex designs.
Figure 1.
Structure of a Complex Programmable Logic Device (CPLD) and Programmable Array Logic (PAL) block. The programmable AND plane and the fixed OR plane are shown on the right.
While CPLD devices are still used for specific tasks, the most common PLD in use today is the Field Programmable Gate Array (FPGA). These devices are quite similar in structure to the mask-programmed gate array (MPGA) [11] which was one of the first commercial programmable PLDs available. One benefit of using FPGAs is that they can be electronically reconfigured, whereas the previous MPGAs configuration was specified at the time of manufacture. The first FPGA, the Xilinx XC2064 was invented by Ross Freeman and Bernard Vonderschmitt in 1985 [12]. Early FPGAs were mainly used in the telecommunications and networking sectors as they were often cheaper than manufacturing custom silicon for these tasks.
Figure 2 shows the internal structure of the FPGA. Similarities can be seen between FPGA and CPLD devices where a programmable interconnect is used to connect programmable logic blocks. In an FPGA, the Configurable Logic Blocks (CLB) consist of Look Up Tables (LUTs). The output of these programmable memories are defined by their input signals. The multiplexer then selects either the output of the LUT or the D flip flop to allow for combinational or sequential logic, similar to PAL blocks in CPLDs. These blocks can then be connected together using the Switching Blocks (SB). Modern FPGAs are able to replicate the behaviour of tens of millions of logic gates and contain logic like RAM and multipliers. Today, they are often used in high-performance computing applications due to their performance and efficiency over processor-based algorithms.
Figure 2.
Structure of a Field Programmable Gate Array (FPGA). The Complex Logic Block (CLB) consists of a programmable memory called a Look Up Table (LUT), a D flip flop to store state and a multiplexer to select the output signal. The programmable Switching Block (SB) is used to connect the CLBs together.
2.3 Digital design flow
In digital design, it is often not practical to use gate-level descriptions. Instead, a representation called Register Transfer Level (RTL) is used. RTL allows for a high-level model of the design to be represented without having to think about the low-level logic structures required to implement the functionality [13]. This abstraction uses constructs like logic statements, arithmetic operations and control flow. Similarities can be drawn between programming using mnemonics in Assembly Language compared to functional programming in C. Using RTL allows the designer to focus on the functionality of the design rather than on the implementation. Figure 3 presents the different stages a digital design must goes through in order to convert a RTL representation into an implementable design.
Figure 3.
A flowchart showing the different stages of the logic synthesis/digital design process.
When a high level language is used for programming, the code written by the programmer must first go through a process called compilation before the code is executed. Similarly with digital hardware, a design specified using RTL (often using a HDL like VHDL or SystemVerilog) must go through a process called logic synthesis. This process analyses the given RTL and converts it into a set of primitives that is functionally equivalent. Primitives are the basic building blocks of any logic design and consists of both combinational and sequential blocks. Examples include boolean logic (NOT/AND/OR/XOR etc.), multiplexers and flip-flops. This output is stored in a file called a net-list, which contains a list of all the primitive blocks and the nets that connect them together.
The net-list generated from the logic synthesis is not optimized and must go through a process known as logic minimization. There can be many parameters to optimize for, such as area usage, power consumption and timing delay. There are many different methods that can be used to perform this optimization. Some early algorithmic methods include Karnaugh maps [13] and the Quine–McCluskey algorithm [14]. However as designs have become increasing complex, these algorithmic methods are not computationally feasible. This has lead to the use of heuristic optimizers such as ESPRESSO [15] and BOOM [16]. When using heuristic optimizers, it cannot be guaranteed that the minimized design is the global minimum. However in practice, these methods are sufficient and are widely used in logic synthesis tools today.
Following the logic minimization process, the optimized net-list is in an intermediate representation. A process called Technology Mapping must be performed before the design can be implemented in silicon or on a PLD. For silicon, this intermediate representation is compared against a library of available “building blocks” called leaf-level cells. The mapper then selects and connects these leaf-level cells, rebuilding the circuit. Further optimization may be performed here as the available leaf cells may be able to replace multiple blocks in the intermediate representation. For PLDs, the process is similar. The PAL blocks in CPLDs and the LUTs in FPGAs can be configured to replace one or multiple blocks. These are then connected together using the programmable interconnects. In comparison to logic minimization, the optimizations performed here are much simpler. After the technology mapping process is complete, the designer now has an implementable design. This is often in the GDSII/OASIS format for silicon manufacturing and in a bitstream format for PLDs. The top EDA companies for ASIC digital design tools include Cadence Design Systems, Siemens and Synopsys, with Xilinx and Intel providing FPGA tools.
2.4 Grammatical evolution and circuit design
Grammatical Evolution (GE), the tool used in this chapter and described in detail in the next section, has been used to evolve Verilog circuits, such as the one-bit adder and D-type latch at the gate level [17]. Notably, the one-bit adder is frequently used as a case study to evolve combinational circuits at the gate-level through GE [18, 19, 20]. However, gate-level evolution is less likely to scale to highly complex circuits from scratch [21]. In response to scalability issues inherent in gate level evolution, [22] proposed functional level evolution through Genetic Algorithms, which uses higher-level functions such as multiplexers, adders, subtractors instead of primitive gates to help reduce the search space. Similarly, [23] evolved a 3-bit multiplier using only binary multiplexers. 9- and 25-Median approximate circuits have also been designed at the functional level through Cartesian GP [24]. We address the scalability concern by performing circuit evolution through GE at a more abstract level – RTL modeling, where the focus is on describing the circuit’s behavior [25, 26].
3. Grammatical evolution
Biological evolution has been a source of inspiration for many techniques that formed the field of evolutionary computation (EC), and has been used to address a wide range of problem domains ranging from the small to the huge, solving molecular to astronomical related problems. One of the most successful evolutionary techniques is GP, introduced by John R. Koza in his book “Genetic Programming—On the programming of Computers by Means of Natural Selection” [27], which mimics natural selection in an iterative way to find an optimal (best) solution. Algorithm 1 details the steps required to implement a standard GP. A survey of the different GP techniques current available in the literature is out of the scope of this work, the interested reader can find in [28] a comprehensive review of various aspects and techniques of GP and their categorization.
Grammatical evolution (GE) is an evolutionary computation and, more specifically, a genetic programming (GP) technique [29] that addresses the closure issue of Koza-style GP, which effectively confines GP to single-type problems. This is achieved through the use of a grammar, generally in Backus-Naur Form (BNF) [29, 30], or Attribute Grammar (AG) [31, 32, 33, 34].
The GE system shown in Figure 4 automatically generates programs using three main components: (i) grammar; (ii) cost function; and (iii) search engine. The grammar describes the program’s syntax, the cost function evaluates the quality of each program, and the search engine, typically a GA, searches within the program space defined by the grammar.
Figure 4.
The GE system uses a search engine (typically a GA) to generate solutions for a given problem, by recombining the genetic material (genotype) and mapped onto programs (phenotype) according to a language specification (interpreter/compiler).
In GE, a typical representation for an individual is a binary string grouped into codons (e.g. 8 bits). The linear representation of the genome allows the application of genetic operators such as crossover and mutation in the manner of a typical GA, unlike tree-based GP.
3.1 Initialisation
In GP, the standard initialisation is the ramped-half-and-half (RHH) technique, introduced in [27]. In order to ensure diversity in the population, GP individuals typically represented as trees are created with different depths. The RHH technique uses two methods to create a tree: full and grow. Typically there is a probability of 0.5 to select either method for a particular individual. The full method creates trees with full branches at the maximum specified depth, whereas the grow method creates trees with different length of branches and different depth size up to the maximum allowed.
Sensible Initialisation is an adaptation of ramped-half-and-half initialisation routine in GP [35]. SI requires the grammar to labelled— whether a production rule is recursive or non-recursive and the minimum derivation tree depth to fully expand a production rule. SI requires a maximum tree depth to be specified prior. Similar to ramped-half-and-half in GP, SI applies both the grow and full method. When applying the grow method any production can be selected while the full method chooses only recursive productions. Both grow and full methods are subject to the constraints of not exceeding the maximum specified tree depth and the availability of enough tree depth budget to fully expand all non-terminals to terminals.
3.2 GE operators
Generally, GE uses a one-point crossover as it has been shown to be effective [36]. In crossover, two individuals are selected as parents and a single crossover point within each parent’s genome is randomly chosen, dividing the genome into two halves: left and right sub-genomes. The right sub-genomes of both parents are swapped to create two offspring. However, crossover points that lie within non-coding regions (unused codon(s) from the mapping step) may not be so useful. As a result, a variant of one-point crossover known as effective crossover, which constrains the selection of the crossover point to be within the effective length of an individual’s genome is preferred. Point mutation is a commonly used mutation operator in GE. Each bit within the binary string genome is mutated or flipped using the specified mutation probability. However, neutral mutations can occur whereby mutated codons select the same productions as the original codon; as a result, the corresponding phenotype remains the same.
3.3 GE example
To illustrate the application of GE, we first explain the evolutionary process using a mathematical optimisation problem as study case.
GE begins with the start symbol of the grammar, then the codons are used to select and apply the grammar production rules to finally build a program. This mapping process is illustrated in Figure 5 with a simple example, where the production rules in the grammar contains a set of user-defined functions: maxab, minab, additionab, subtractionab, multiplicationab, divisionab, const, and X, which is a value (or a vector) sampled from the independent variable.
Figure 5.
Example of a GE genotype-phenotype mapping process for the Iris dataset, where the binary genotype is grouped into codons (e.g. 8 bits; red & blue), transcribed into an integer string, then used to select production rules from a predefined grammar (BNF-Grammar), and finally translated into a sequence of rules to build a solution (phenotype).
The production rules for each non-terminal are indexed starting from 0 and, when selecting a production rule (starting with the left-most non-terminal of the developing program) the next codon value in the genome is read and interpreted using the formula: p=c%r, where c represents the current codon value, % represents the modulus operator, and r is the number of production rules for the left-most non-terminal.
To prevent reaching the end of the genome without consuming all the available codons, then a wrapping process is used to continuing reading from the beginning of the genome. This mapping process stops when all of the non-terminal symbols have been replaced, in order to get a valid program. An exemption to this process is in the case when it fails to replace all of the non-terminal symbols after a maximum number of iterations, then it is considered an invalid individual and it is penalized with the lowest possible fitness.
4. ADDC
ADDC is an evolutionary HDL circuit design framework mainly driven by GE. ADDC requires a grammar and a testbench as inputs for circuit evolution and verification respectively. The designed grammar must be BNF compliant and must satisfy the grammar sufficiency property. Thus, the grammar must contain all the necessary building blocks required to potentially evolve an optimal circuit. ADDC is technology agnostic and easily configurable as the choice of HDL and simulator are left to the user to choose. Illustrated in Figure 6 is ADDC’s design flow for functional evolution of circuits.
Figure 6.
ADDC Functional Circuit Evolution Overview.
During the initial phase of the circuit design process, ADDC creates an initial population of circuit designs using a suitable GE initialisation routine such as sensible initialisation. These individuals then undergo fitness evaluation. The fitness evaluation phase entails a number of steps. First, the genotype (genome) of each individual is translated to a HDL (SystemVerilog in this work) circuit design (phenotype) by the GE mapper, using the grammar designed for the circuit. Functional simulation of each circuit takes place, assuming all circuit designs are valid. For these experiments, Icarus Verilog, a lightweight and open-source Verilog simulator is used. The simulator uses a testbench which must be provided by the user for circuit verification. A testbench is a verification description written in the same HDL as the circuit that ensures the device under test (DUT) or evolved circuit meets functional and timing requirements. A typical testbench uses a test vector(s) which contains circuit inputs and their respective expected outputs. For example, test vector(s) for combinational circuits are usually created from their truth table. The simulator drives these input(s) through the DUT and verifies if the circuit output is the desired output. The sum of the number of passed cases out of the total number of cases represents the fitness value of the circuit. After all circuits have been evaluated, ADDC makes available the best circuit design if the specified termination criterion is satisfied, otherwise the circuit design evolution continues.
The next phase is reproduction, where usually individuals with either good overall fitness score or individuals that perform best on certain cases are selected for crossover and mutation to create a new population of circuit designs. Lexicase selection performs well on circuit design benchmarks [25, 26], hence selected as the choice of selection routine. Also, depending on the genetic algorithm (GA) of choice, for example steady state, generational GAs etc., events like replacement or elitism may take place in creating the new population. The new population undergoes fitness evaluation in similar manner as described in the previous section. The process continues until the termination criterion is satisfied and the best circuit design returned as solution.
5. Experiments
Three circuit benchmark problems are considered, namely: Hamming Code (7,4) Encoder (corresponding decoder evolved in [25]), Seven Segment Display [25, 37] and 16-to-4 Multiplexer [25, 38, 39]. These problems are not only standard benchmarks in circuit design literature, but are used in real world applications. For example, hamming codes, seven segment displays and multiplexers are used in satellite communication, digital calculators and telephone networks respectively.
5.1 Benchmark problems
5.1.1 Hamming code (7,4) encoder
Hamming codes are a linear error-correcting codes capable of detecting a single error and at most two errors, but are only capable of correcting a single error. They belong to a category of codes referred to as Linear Block Codes. A Hamming Code (7,4) Encoder encodes a 4-bit data word into a 7-bit code word prior to data transmission by generating and adding three parity bits to the data word.
The structure of the code words generated by hamming codes can be classified into two categories: systematic and non-systematic encodings. The structure of systematic encoding separates the data word and code word while the data word and parity bits are interspersed in non-systematic encoding. We adopt systematic encoding as it is easier to separate the data word from the parity bits.
The grammar designed for evolving the Hamming Code (7,4) Encoder is shown in Figure 7. The circuit’s interface is defined using the begin‐module rule. The grammar uses four bitwise operators and a bitwise negation operator in Verilog defined using bitwise‐op and bitwise‐neg rules respectively. Hamming Code (7,4) Encoder generates three redundant bits stated earlier and these have been defined using parity‐bit‐1, parity‐bit‐2 and parity‐bit‐3 rules. The expr rule is use by the parity‐bit‐∗ rules to evolve variable length expressions that generate the correct parity bit. The grammar also features an important Verilog construct, the always procedural block, defined using always‐block which behaves similarly to an infinite loop and which is triggered by a change in any signal (indicated with ∗) to evaluate the statements within its scope.
Figure 7.
Hamming code (7,4) encoder grammar.
5.1.2 Seven segment display
A Seven Segment Display is an electronic device used for the display of decimal numerals. It is also capable of displaying letters, though some letters such as K, X, Z etc. are difficult to recognise on the device. The specification for Seven Segment Display considered in this work supports decimal numerals (0-9) and A-F letter representations. These numbers and characters are encoded as a 4-bit binary number (0000–1111) referred to as binary coded decimal (BCD) which are sent as inputs to the Seven Segment Display. The 4-bit binary numbers starting from 0000 to 1001 are used to encode decimal numbers 0 to 9 respectively; while 1010 to 1111 are used to encode letters A-F. Upon receipt, the Seven Segment generates a 7-bit binary number (each bit corresponding an ON/OFF state of a segment) to turn on the appropriate LEDs to display the digit/letter. Figure 8 shows the grammar designed to evolve the seven segment display. The BCD and the 7-bit binary numbers are defined by the bcd and seven‐segment rules respectively. The grammar uses switch-case construct defined using the switch‐case as well as the always procedural block (always). begin−module defines the circuit interface of the Seven Segment Display.
Figure 8.
Seven segment display grammar.
5.1.3 16-to-4 multiplexer
A multiplexer is a multiple-input single-output device that accepts data (data lines) and an address (select lines) as inputs and uses the address to select the corresponding data line to be transmitted. The 16-to-4 multiplexer has 16 data lines and 4 select lines.
Figure 9 shows the grammar designed to evolve the multiplexer. Similar to the Seven Segment Display Grammar, the 16-to-4 Multiplexer Grammar also uses the always procedural block. However, here an if-else (if‐else) construct is used, although the switch-case construct is also suitable in this context. The addresses used to select a data line as output are defined using the address rule. The address is used to generate a conditional statement (cond) by the if‐else rule to determine the data line to select. The data‐index defines the indexes of the 16-bit data which is used by the data‐bitrule to select data line. begin‐module defines the circuit interface of the 16-to-4 Multiplexer.
Figure 9.
16-to-4 multiplexer grammar.
5.2 Evolutionary parameters
Experimental parameters used for running the experiments are shown in Table 3. The generation number and population size were selected based on preliminary experiments. The generation sizes used for the preliminary experiments were 50, 100 and 200; the population sizes were 100, 200, 500, 1000 and 2000. For each problem, 5 independent runs were conducted. The choice of generation number and population size for the actual experiments were based on setups with majority of the runs with mean best fitness of the final generation within the fourth quartile of the maximum fitness. The other parameters used remain the same as used in [25, 26].
Parameter
Value
Initialization
Sensible Initialization
No of generations
50 for Hamming Code (7,4) Encoder for Seven Segment Display & 16-to-4 Multiplexer
Mutation rate
0.01
Crossover rate
0.8
Replacement rate
0.5
No of independent runs
50
Population
1,000
Selection
Lexicase Parent Selection
Table 3.
Experimental run parameters.
50 generations were used for evolving the Hamming Code (7,4) Encoder, while 100 generations was used for each of the Seven Segment Display and 16-to-4 Multiplexer designs as preliminary results revealed these problems were relatively challenging to evolve compared to the Hamming Code (7,4) Encoder. All other parameters remain the same for all benchmark problems.
5.3 Training and testing
The number of training and testing cases are tabulated in Table 4.
Benchmark problem
No of training cases
No of testing cases
Hamming Code (7,4) Encoder
112
—
Seven Segment Display (with A-F letter representations)
16
—
16-to-4 Multiplexer
4100
5000
Table 4.
Number of training and testing cases.
Each of the Hamming Code (7,4) Encoder and Seven Segment Display have only 16 cases. However, for the Hamming Code (7,4) Encoder every correct bit in each bit position in the codeword is counted as part of the total fitness score for a candidate circuit, giving a total of 112 (7×16) cases. Given that Hamming Code (7,4) and Seven Segment Display have so few cases, it is feasible to perform exhaustive testing during training, leaving no cases for testing as shown in Table 4.
On the other hand, the 16-to-4 Multiplexer has 220 cases making exhaustive testing infeasible. As a result we uniformly sample 4,100 and 5,000 cases for training and testing respectively as shown in Table 4.
5.4 Results and discussions
Results obtained from experiments conducted demonstrate ADDC is ideal for evolving digital circuit designs due to the use GE and a HDL which permits designs to be done at a more abstract level. The evolutionary performance for the experiments conducted for Hamming Code (7,4) Encoder, Seven Segment Display and 16-to-4 Multiplexer described in Section 5.1 are visualized in Figures 10–12 respectively. The success rate per benchmark problem is tabulated in Table 5. A representative solution per each circuit benchmark problem is shown in Figures 13–15 in the Appendix. The advantages and disadvantages of the proposed approach is discussed in Section 5.7.
Figure 10.
Mean best and mean average across runs for hamming code (7,4) encoder.
Figure 11.
Mean best and mean average across runs for seven segment display.
Figure 12.
Mean best and mean average across runs for 16-to-4 multiplexer.
Benchmark problem
Success rate
Hamming Code (7,4) Encoder
50/50
Seven Segment Display (with A-F letter representations)
30/50
16-to-4 Multiplexer
43/50
Table 5.
Success rate for benchmark problems.
5.5 Success rate
A successful run is a single independent evolutionary run that evolved an optimal circuit for the target problem. Fifty independent runs were conducted for all three benchmark problems. The success rate is the number of successful runs divided by the total number of evolutionary runs (i.e. 50) as tabulated in Table 5. A 100% success rate was attained for the Hamming Code (7,4) Encoder. The Seven Segment Display and 16-to-4 Multiplexer obtained 60 and 86% success rates, respectively.
5.6 Evolutionary performance
Visualization of the evolutionary performance as evolution progressed for Hamming Code (7,4) Encoder, Seven Segment Display and 16-to-4 Multiplexer are shown in Figures 10–12 respectively.
The red line represents the mean best fitness per generations across the 50 independent runs conducted, while the black line represents the mean average fitness. Also plotted are error bars representing the standard error. The error bars are short to non-existent indicating small variability between the fitnesses of individuals. Furthermore, all three plots reveal a steady and progressive increase in fitness as the evolution progressed indicating the evolutionary search is continuously searching regions of the solutions where fitter individuals are located. Hamming Code (7,4) Encoder and 16-to-4 Multiplexer problems discover individual(s) that solve more that 50% of the test cases from the initial generations while the Seven Segment Display evolves individual(s) that solve 25% of the test cases on average.
5.7 Advantages and disadvantages of proposed approach
First, evolved circuit designs are quite interpretable compared to gate-level designs. This is due to the high level of abstraction at which these designs are performed which focuses on evolving circuit behaviours as opposed to evolving gate-level designs. Gate-level design approaches are challenging to scale to complex circuits [40]. The use of constructs such as if-else, switch-case, always procedural blocks, bitwise operators etc., makes it easier for humans to understand the behaviour of a circuit and if required effect manual modifications. Second, verification of circuits is easier as the circuit behaviour are easier to interpret enabling robust testbenches to be written.
Third, like any other methodology, there exist few disadvantages. The use of HDL requires the user to have technical knowledge about the HDL of choice— how to design grammars free of syntax errors and modelling errors. Syntax errors are easier to find and fix as most simulators will report such errors at the functional simulation phase. Modelling errors are a bit more challenging to fix, as they are only noticeable during synthesis (conversion of RTL or high level designs to gate-level representation) phase of the circuit design when designed grammars do not adhere to the guidelines of the HDL of choice. For example, fully functional representative solutions for the 16-to-4 Multiplexer and Seven Segment Display shown in Figures 13 and 14 respectively may not be directly synthesizable (depending on the synthesis tool), as the case statements and if conditions are not mutually exclusive. Some of these errors can be fixed by defining new rules, modifying existing rules, the use of attribute grammars in order to impose the necessary constraints etc. The redundant logic can be gotten rid of by via a number of approaches: manually by hardware designer, design of SystemVerilog/Verilog redundant logic removal algorithm, use unique case statements (for switch case constructs) etc.
The choice of operators to use for evolving circuits is key as it has been shown to increase simulation time of circuits if inappropriate operators are chosen [26]. Furthermore, some circuit designs may contain redundant block of code which impede interpretability as observed in representative best solutions for 16-to-4 Multiplexer and Seven Segment Display shown in Figures 13 and 14 respectively in Appendix. Only 16 of the if conditions and case statements are valid for the 16-to-4 Multiplexer and Seven Segment Display representation circuit designs respectively.
6. Conclusions and future directions
We have presented a system for the automated design of digital circuits, ADDC. ADDC is the next logical step in the evolution of Electronic Design Automation and this chapter has described how the history of integrated circuits has led to the confluence of GE, circuit simulators and HDLs. ADDC has been demonstrated on three difficult, real-world problems and was successful on all three of them, including one with 220 possible inputs.
The HDLs employed here are hugely powerful and expressive. Digital designers often operate at very high levels of abstraction using IP blocks, which is somewhat similar to using libraries when programming software. IP blocks are typically very powerful and can have complexities equivalent to tens of thousands of gates. Making some of these blocks available to ADDC will dramatically increase its scalability and doing so will simply involve expanding the grammar.
As the problems scale up, the number of test cases can become astronomical, as was the case in this chapter. While in this case we randomly sampled the training and test cases, it is also possible to use a more intelligent approach. Recent work [41] has investigated using clustering to select a representative set of test cases. This will permit us to operate at greater scales with confidence.
A circuit that functions correctly on a simulator is not guaranteed to be fit for purpose when rendered in silicon. This is because there are often other considerations, such as silicon area, power dissipation and delay. Future work will use multi-objective optimisation to include pressure on individuals to adhere to these constraints too.
While some of our automatically generated circuits have successfully been implemented in silicon on a Xilinx Artix-7 FPGA, e.g., an 8-to-1 multiplexer [25], ADDC does not yet include that step in its toolchain; to be fully automated it will need to include this.
Acknowledgments
The authors are supported by Research Grants 13/RC/2094 and 16/IA/4605 from the Science Foundation Ireland and by Lero, the Irish Software Engineering Research Centre (www.lero.ie). The third is partially financed by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil (CAPES), Finance Code 001, and Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ).
\n',keywords:"digital design, VLSI design, microelectronics design, evolvable hardware, HDL, verilog, grammatical evolution",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/82121.pdf",chapterXML:"https://mts.intechopen.com/source/xml/82121.xml",downloadPdfUrl:"/chapter/pdf-download/82121",previewPdfUrl:"/chapter/pdf-preview/82121",totalDownloads:14,totalViews:0,totalCrossrefCites:0,dateSubmitted:null,dateReviewed:"March 8th 2022",datePrePublished:"June 4th 2022",datePublished:null,dateFinished:"June 4th 2022",readingETA:"0",abstract:"Digital circuits are one of the most important enabling technologies in the world today. Powerful tools, such as Hardware Description Languages (HDLs) have evolved over the past number of decades to allow designers to operate at high levels of abstraction and expressiveness, rather than at the gate level, which circuits are actually constructed from. Similarly, highly accurate digital circuit simulators permit designers to test their circuits before committing them to silicon. This is still a highly complex and generally manual task, however, with complex circuits taking months or even years to go from planning to silicon. We show how Grammatical Evolution (GE) can harness the standard tools of silicon design and be used to create a fully automatic circuit design system. Specifically, we use a HDL known as SystemVerilog and Icarus, a free, but powerful simulator, to generate circuits from high level descriptions. 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Conclusions and future directions",level:"1"},{id:"sec_24",title:"Acknowledgments",level:"1"},{id:"sec_27",title:"Conflict of interest",level:"1"},{id:"sec_25",title:"Figure 13.",level:"1"}],chapterReferences:[{id:"B1",body:'Conway T, Conway R, Mulvaney K, Mahony SO, Billon C, Khan MK, et al. Low power application specific processor for ISM band transceiver. In: IET Irish Signals and Systems Conference (ISSC 2010). Cork, Ireland: IET; 2010. pp. 272-277. Available from: https://digital-library.theiet.org/content/conferences/10.1049/cp.2010.0525'},{id:"B2",body:'Barbe DF. VHSIC Systems and Technology. Computer. 1981;14(2):13-22'},{id:"B3",body:'Dewey A. VHSIC Hardware Description (VHDL) Development Program. In: 20th Design Automation Conference Proceedings. Miami Beach (FL): IEEE Press; 1983. pp. 625-628'},{id:"B4",body:'Dewey A, Gadient A. VHDL Motivation. IEEE Design Test of Computers. 1986;3(2):12-16'},{id:"B5",body:'IEEE Standard VHDL Language Reference Manual. IEEE Std 1076-1987; 1988'},{id:"B6",body:'IEEE Standard Hardware Description Language Based on the Verilog(R) Hardware Description Language. IEEE Std 1364-1995. 1996.'},{id:"B7",body:'Ap. COMPANY NEWS; Cadence to Buy Gateway Design. The New York Times. 1989. Available from: https://www.nytimes.com/1989/10/05/business/company-news-cadence-to-buy-gateway-design.html [Accessed: January 10, 2022]'},{id:"B8",body:'Verilog Hardware Description Language Reference Manual (LRM). Version 1.0. Open Verilog International (OVI); 1991'},{id:"B9",body:'IEEE Standard for SystemVerilog: Unified Hardware Design, Specification and Verification Language. IEEE Std 1800-2005. 2005'},{id:"B10",body:'Section 8—XC157. In: The Integrated Circuit Data Book. Motorola Semiconductor Products Inc.; 1968. p. 3'},{id:"B11",body:'Freeman R. Xilinx Inc. Configurable Electrical Circuit Having Configurable Logic Elements and Configurable Interconnects. US4870302; 1989'},{id:"B12",body:'De la Guia Solaz M, Conway R. Razor based programmable truncated multiply and accumulate, energy-reduction for efficient digital signal processing. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 2014;23(1):189-193'},{id:"B13",body:'Karnaugh M. The Map Method for Synthesis of Combinational Logic Circuits. Transactions of the American Institute of Electrical Engineers, Part I: Communication and Electronics. 1953;72(5):593-599'},{id:"B14",body:'McCluskey EJ. Minimization of Boolean Functions. The Bell System Technical Journal. 1956;35(6):1417-1444'},{id:"B15",body:'Brayton R, Hachtel G, Hemachandra L, Newton A, Sangiovanni-Vincentelli A. A Comparison of Logic Minimization Strategies Using ESPRESSO: An APL Program Package for Partitioned Logic Minimization. In: Proceedings of the International Symposium on Circuits and Systems. New York (NY): IEEE Press; 1982. pp. 42-48'},{id:"B16",body:'Hlavicka J, Fiser P. BOOMa—A heuristic boolean minimizer. In: IEEE/ACM International Conference on Computer Aided Design. ICCAD 2001. IEEE/ACM Digest of Technical Papers (Cat. No.01CH37281). San Jose, CA, USA: IEEE; 2001. pp. 439-442'},{id:"B17",body:'Cullen J. Evolving Digital Circuits in an Industry Standard Hardware Description Language. In: Li X, Kirley M, Zhang M, Green D, Ciesielski V, Abbass H, et al., editors. Simulated Evolution and Learning. Berlin, Heidelberg: Springer; 2008. pp. 514-523'},{id:"B18",body:'Youssef A, Majeed B, Ryan C. Optimizing combinational logic circuits using Grammatical Evolution. In: 2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES); 2021. pp. 87–92'},{id:"B19",body:'Karpuzcu UR. Automatic verilog code generation through grammatical evolution. In: Proceedings of the 7th Annual Workshop on Genetic and Evolutionary Computation. GECCO ’05. New York, NY, USA: Association for Computing Machinery; 2005. pp. 394–397. 10.1145/1102256.1102346.'},{id:"B20",body:'Kratochvil O, Osmera P, Popelka O. Parallel grammatical evolution for circuit optimization. In: Ao SI, Douglas C, Grundfest WS, Burgstone J, editors. Proceedings of the World Congress on Engineering and Computer Science, WCECS ’09. vol. II. International Association of Engineers. San Francisco, USA: Newswood Limited; 2009. pp. 1032–1037. Available from: http://www.iaeng.org/publication/WCECS2009/WCECS2009_pp1032-1037.pdf'},{id:"B21",body:'Vassilev VK, Miller JE. Scalability problems of digital circuit evolution evolvability and efficient designs. In: Proceedings. The Second NASA/DoD Workshop on Evolvable Hardware; 2000. pp. 55–64'},{id:"B22",body:'Murakawa M, Yoshizawa S, Kajitani I, Furuya T, Iwata M, Higuchi T. Hardware evolution at function level. In: Voigt HM, Ebeling W, Rechenberg I, Schwefel HP, editors. Parallel Problem Solving from Nature — PPSN IV. Springer: Berlin, Heidelberg; 1996. pp. 62-71'},{id:"B23",body:'Vassilev VK, Miller JF. Embedding landscape neutrality to build a bridge from the conventional to a more efficient three-bit multiplier circuit. In: Proceedings of Genetic and Evolutionary Computation Conference. Morgan Kaufmann; 2000'},{id:"B24",body:'Vasicek Z, Sekanina L. Evolutionary approach to approximate digital circuits design. IEEE Transactions on Evolutionary Computation. 2015;19(3):432-444'},{id:"B25",body:'Ryan C, Tetteh M, Dias D. Behavioural Modelling of Digital Circuits in System Verilog using Grammatical Evolution. In: Proceedings of the 12th International Joint Conference on Computational Intelligence—ECTA,. INSTICC. SciTePress; 2020. pp. 28–39'},{id:"B26",body:'Tetteh MK, Mota Dias D, Ryan C. Evolution of complex combinational logic circuits using grammatical evolution with systemverilog. In: Hu T, Lourenço N, Medvet E, editors. Genetic Programming. Cham: Springer International Publishing; 2021. pp. 146-161'},{id:"B27",body:'Koza JR. Genetic Programming—On the programming of Computers by Means of Natural Selection. Complex Adaptive Systems. Cambridge (MA): MIT Press; 1992'},{id:"B28",body:'Eiben SJ, Agoston E. From evolutionary computation to the evolution of things. Nature. 2015;521(7553):476-482. DOI: 10.1038/nature14544'},{id:"B29",body:'Ryan C, Collins JJ, O’Neill M. Grammatical evolution: Evolving programs for an arbitrary language. In: Banzhaf W, Poli R, Schoenauer M, Fogarty TC, editors. EuroGP. vol. 1391 of Lecture Notes in Computer Science. Berlin: Springer; 1998. pp. 83-96'},{id:"B30",body:'O’Neill M, Ryan C. Grammatical evolution. IEEE Trans Evolutionary Computation. 2001;5(4):349-358'},{id:"B31",body:'Patten JV, Ryan C. Attributed grammatical evolution using shared memory spaces and dynamically typed semantic function specification. In: Genetic Programming—18th European Conference, EuroGP 2015, Copenhagen, Denmark, April 8-10, 2015, Proceedings. 2015. pp. 105-112. DOI: 10.1007/978-3-319-16501-1_9'},{id:"B32",body:'Karim MR, Ryan C. On improving grammatical evolution performance in symbolic regression with attribute grammar. In: Genetic and Evolutionary Computation Conference, GECCO ’14, Vancouver, BC, Canada, July 12-16, 2014, Companion Material Proceedings. 2014. pp. 139-140. DOI: 10.1145/2598394.2598488'},{id:"B33",body:'Karim MR, Ryan C. A new approach to solving 0-1 multiconstraint knapsack problems using attribute grammar with lookahead. In: Genetic Programming—14th European Conference, EuroGP 2011, Torino, Italy, April 27-29, 2011. Proceedings. 2011. pp. 250-261'},{id:"B34",body:'Karim MR, Ryan C. Degeneracy reduction or duplicate elimination? an analysis on the performance of attributed grammatical evolution with lookahead to solve the multiple knapsack problem. In: Nature Inspired Cooperative Strategies for Optimization, NICSO 2011, Cluj-Napoca, Romania, 2011. Vol. 387. Berlin: Springer. Studies in computational intelligence; 2011. pp. 247-266. DOI: 10.1007/978-3-642-24094-2_18'},{id:"B35",body:'Ryan C, Azad R. Sensible initialisation in grammatical evolution. In: GECCO 2003: Proceedings Of The Bird Of A Feather Workshops, Genetic And Evolutionary Computation Conference. Chigaco (IL): AAAI; 2003. pp. 142-145'},{id:"B36",body:'O’Neill M, Ryan C, Keijzer M, Cattolico M. Crossover in Grammatical Evolution. The Search Continues. Genetic Programming. 2001:337-347'},{id:"B37",body:'Miller J. Cartesian Genetic Programming. Cartesian Genetic Programming2011. pp. 17-34. DOI: 10.1007/978-3-642-17310-3_2'},{id:"B38",body:'Kruse, R., Borgelt, C., Klawonn, F., Moewes, C., Steinbrecher, M. & Held, P. Fundamental evolutionary algorithms. Computational Intelligence: A Methodological Introduction. London (UK): Springer London; pp. 227-274 (2013), 10.1007/978-1-4471-5013-8_13'},{id:"B39",body:'Fredivianus, N., Prothmann, H. & Schmeck, H. XCS revisited: A novel discovery component for the extended classifier system. Simulated Evolution and Learning. Berlin, Heidelberg: Springer Berlin Heidelberg. Lecture Notes in Computer Science; vol. 6457. 10.1007/978-3-642-17298-4_30 pp. 289-298 (2010)'},{id:"B40",body:'Zdenek, V. Bridging the Gap Between Evolvable Hardware and Industry Using Cartesian Genetic Programming. 2018. 10.1007/978-3-319-67997-6_2'},{id:"B41",body:'Ryan C, Kshirsagar M, Gupt K, Rosenbauer L, Sullivan J. Hierarchical clustering driven test case selection in digital circuits. In: Proceedings of the 16th International Conference on Software Technologies—ICSOFT,. INSTICC. SciTePress; 2021. pp. 589–596.'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Conor Ryan",address:"conor.ryan@ul.ie",affiliation:'
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UK Research and Innovation (former Research Councils UK (RCUK) - including AHRC, BBSRC, ESRC, EPSRC, MRC, NERC, STFC.) Processing charges for books/book chapters can be covered through RCUK block grants which are allocated to most universities in the UK, which then handle the OA publication funding requests. It is at the discretion of the university whether it will approve the request.)
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Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. 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The performance of these filters will be compared exploitation the applied mathematics parameter Peak Signal-to-Noise Ratio (PSNR).",book:{id:"6144",slug:"high-resolution-neuroimaging-basic-physical-principles-and-clinical-applications",title:"High-Resolution Neuroimaging",fullTitle:"High-Resolution Neuroimaging - Basic Physical Principles and Clinical Applications"},signatures:"Hanafy M. Ali",authors:[{id:"213318",title:"Dr.",name:"Hanafy",middleName:"M.",surname:"Ali",slug:"hanafy-ali",fullName:"Hanafy Ali"}]},{id:"46296",doi:"10.5772/57398",title:"Physiological Role of Amyloid Beta in Neural Cells: The Cellular Trophic Activity",slug:"physiological-role-of-amyloid-beta-in-neural-cells-the-cellular-trophic-activity",totalDownloads:5907,totalCrossrefCites:19,totalDimensionsCites:32,abstract:null,book:{id:"3846",slug:"neurochemistry",title:"Neurochemistry",fullTitle:"Neurochemistry"},signatures:"M. del C. Cárdenas-Aguayo, M. del C. Silva-Lucero, M. Cortes-Ortiz,\nB. Jiménez-Ramos, L. 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Luna-Muñoz and M.A.\nMeraz-Ríos",authors:[{id:"42225",title:"Dr.",name:"Jose",middleName:null,surname:"Luna-Muñoz",slug:"jose-luna-munoz",fullName:"Jose Luna-Muñoz"},{id:"114746",title:"Dr.",name:"Marco",middleName:null,surname:"Meraz-Ríos",slug:"marco-meraz-rios",fullName:"Marco Meraz-Ríos"},{id:"169616",title:"Dr.",name:"Maria del Carmen",middleName:null,surname:"Cardenas-Aguayo",slug:"maria-del-carmen-cardenas-aguayo",fullName:"Maria del Carmen Cardenas-Aguayo"},{id:"169857",title:"Dr.",name:"Maria del Carmen",middleName:null,surname:"Silva-Lucero",slug:"maria-del-carmen-silva-lucero",fullName:"Maria del Carmen Silva-Lucero"},{id:"169858",title:"Dr.",name:"Maribel",middleName:null,surname:"Cortes-Ortiz",slug:"maribel-cortes-ortiz",fullName:"Maribel Cortes-Ortiz"},{id:"169859",title:"Dr.",name:"Berenice",middleName:null,surname:"Jimenez-Ramos",slug:"berenice-jimenez-ramos",fullName:"Berenice Jimenez-Ramos"},{id:"169860",title:"Dr.",name:"Laura",middleName:null,surname:"Gomez-Virgilio",slug:"laura-gomez-virgilio",fullName:"Laura Gomez-Virgilio"},{id:"169861",title:"Dr.",name:"Gerardo",middleName:null,surname:"Ramirez-Rodriguez",slug:"gerardo-ramirez-rodriguez",fullName:"Gerardo Ramirez-Rodriguez"},{id:"169862",title:"Dr.",name:"Eduardo",middleName:null,surname:"Vera-Arroyo",slug:"eduardo-vera-arroyo",fullName:"Eduardo Vera-Arroyo"},{id:"169863",title:"Dr.",name:"Rosana Sofia",middleName:null,surname:"Fiorentino-Perez",slug:"rosana-sofia-fiorentino-perez",fullName:"Rosana Sofia Fiorentino-Perez"},{id:"169864",title:"Dr.",name:"Ubaldo",middleName:null,surname:"Garcia",slug:"ubaldo-garcia",fullName:"Ubaldo Garcia"}]},{id:"41589",doi:"10.5772/50323",title:"The Role of the Amygdala in Anxiety Disorders",slug:"the-role-of-the-amygdala-in-anxiety-disorders",totalDownloads:9707,totalCrossrefCites:4,totalDimensionsCites:28,abstract:null,book:{id:"2599",slug:"the-amygdala-a-discrete-multitasking-manager",title:"The Amygdala",fullTitle:"The Amygdala - A Discrete Multitasking Manager"},signatures:"Gina L. Forster, Andrew M. Novick, Jamie L. Scholl and Michael J. Watt",authors:[{id:"145620",title:"Dr.",name:"Gina",middleName:null,surname:"Forster",slug:"gina-forster",fullName:"Gina Forster"},{id:"146553",title:"BSc.",name:"Andrew",middleName:null,surname:"Novick",slug:"andrew-novick",fullName:"Andrew Novick"},{id:"146554",title:"MSc.",name:"Jamie",middleName:null,surname:"Scholl",slug:"jamie-scholl",fullName:"Jamie Scholl"},{id:"146555",title:"Dr.",name:"Michael",middleName:null,surname:"Watt",slug:"michael-watt",fullName:"Michael Watt"}]},{id:"26258",doi:"10.5772/28300",title:"Excitotoxicity and Oxidative Stress in Acute Ischemic Stroke",slug:"excitotoxicity-and-oxidative-stress-in-acute-ischemic-stroke",totalDownloads:7183,totalCrossrefCites:6,totalDimensionsCites:25,abstract:null,book:{id:"931",slug:"acute-ischemic-stroke",title:"Acute Ischemic Stroke",fullTitle:"Acute Ischemic Stroke"},signatures:"Ramón Rama Bretón and Julio César García Rodríguez",authors:[{id:"73430",title:"Prof.",name:"Ramon",middleName:null,surname:"Rama",slug:"ramon-rama",fullName:"Ramon Rama"},{id:"124643",title:"Prof.",name:"Julio Cesar",middleName:null,surname:"García",slug:"julio-cesar-garcia",fullName:"Julio Cesar García"}]},{id:"62072",doi:"10.5772/intechopen.78695",title:"Brain-Computer Interface and Motor Imagery Training: The Role of Visual Feedback and Embodiment",slug:"brain-computer-interface-and-motor-imagery-training-the-role-of-visual-feedback-and-embodiment",totalDownloads:1455,totalCrossrefCites:13,totalDimensionsCites:24,abstract:"Controlling a brain-computer interface (BCI) is a difficult task that requires extensive training. Particularly in the case of motor imagery BCIs, users may need several training sessions before they learn how to generate desired brain activity and reach an acceptable performance. A typical training protocol for such BCIs includes execution of a motor imagery task by the user, followed by presentation of an extending bar or a moving object on a computer screen. In this chapter, we discuss the importance of a visual feedback that resembles human actions, the effect of human factors such as confidence and motivation, and the role of embodiment in the learning process of a motor imagery task. Our results from a series of experiments in which users BCI-operated a humanlike android robot confirm that realistic visual feedback can induce a sense of embodiment, which promotes a significant learning of the motor imagery task in a short amount of time. We review the impact of humanlike visual feedback in optimized modulation of brain activity by the BCI users.",book:{id:"6610",slug:"evolving-bci-therapy-engaging-brain-state-dynamics",title:"Evolving BCI Therapy",fullTitle:"Evolving BCI Therapy - Engaging Brain State Dynamics"},signatures:"Maryam Alimardani, Shuichi Nishio and Hiroshi Ishiguro",authors:[{id:"11981",title:"Prof.",name:"Hiroshi",middleName:null,surname:"Ishiguro",slug:"hiroshi-ishiguro",fullName:"Hiroshi Ishiguro"},{id:"231131",title:"Dr.",name:"Maryam",middleName:null,surname:"Alimardani",slug:"maryam-alimardani",fullName:"Maryam Alimardani"},{id:"231134",title:"Dr.",name:"Shuichi",middleName:null,surname:"Nishio",slug:"shuichi-nishio",fullName:"Shuichi Nishio"}]}],mostDownloadedChaptersLast30Days:[{id:"29764",title:"Underlying Causes of Paresthesia",slug:"underlying-causes-of-paresthesia",totalDownloads:192987,totalCrossrefCites:3,totalDimensionsCites:7,abstract:null,book:{id:"1069",slug:"paresthesia",title:"Paresthesia",fullTitle:"Paresthesia"},signatures:"Mahdi Sharif-Alhoseini, Vafa Rahimi-Movaghar and Alexander R. Vaccaro",authors:[{id:"91165",title:"Prof.",name:"Vafa",middleName:null,surname:"Rahimi-Movaghar",slug:"vafa-rahimi-movaghar",fullName:"Vafa Rahimi-Movaghar"}]},{id:"63258",title:"Anatomy and Function of the Hypothalamus",slug:"anatomy-and-function-of-the-hypothalamus",totalDownloads:4596,totalCrossrefCites:6,totalDimensionsCites:12,abstract:"The hypothalamus is a small but important area of the brain formed by various nucleus and nervous fibers. Through its neuronal connections, it is involved in many complex functions of the organism such as vegetative system control, homeostasis of the organism, thermoregulation, and also in adjusting the emotional behavior. The hypothalamus is involved in different daily activities like eating or drinking, in the control of the body’s temperature and energy maintenance, and in the process of memorizing. It also modulates the endocrine system through its connections with the pituitary gland. Precise anatomical description along with a correct characterization of the component structures is essential for understanding its functions.",book:{id:"6331",slug:"hypothalamus-in-health-and-diseases",title:"Hypothalamus in Health and Diseases",fullTitle:"Hypothalamus in Health and Diseases"},signatures:"Miana Gabriela Pop, Carmen Crivii and Iulian Opincariu",authors:null},{id:"57103",title:"GABA and Glutamate: Their Transmitter Role in the CNS and Pancreatic Islets",slug:"gaba-and-glutamate-their-transmitter-role-in-the-cns-and-pancreatic-islets",totalDownloads:3523,totalCrossrefCites:3,totalDimensionsCites:9,abstract:"Glutamate and gamma-aminobutyric acid (GABA) are the major neurotransmitters in the mammalian brain. Inhibitory GABA and excitatory glutamate work together to control many processes, including the brain’s overall level of excitation. The contributions of GABA and glutamate in extra-neuronal signaling are by far less widely recognized. In this chapter, we first discuss the role of both neurotransmitters during development, emphasizing the importance of the shift from excitatory to inhibitory GABAergic neurotransmission. The second part summarizes the biosynthesis and role of GABA and glutamate in neurotransmission in the mature brain, and major neurological disorders associated with glutamate and GABA receptors and GABA release mechanisms. The final part focuses on extra-neuronal glutamatergic and GABAergic signaling in pancreatic islets of Langerhans, and possible associations with type 1 diabetes mellitus.",book:{id:"6237",slug:"gaba-and-glutamate-new-developments-in-neurotransmission-research",title:"GABA And Glutamate",fullTitle:"GABA And Glutamate - New Developments In Neurotransmission Research"},signatures:"Christiane S. Hampe, Hiroshi Mitoma and Mario Manto",authors:[{id:"210220",title:"Prof.",name:"Christiane",middleName:null,surname:"Hampe",slug:"christiane-hampe",fullName:"Christiane Hampe"},{id:"210485",title:"Prof.",name:"Mario",middleName:null,surname:"Manto",slug:"mario-manto",fullName:"Mario Manto"},{id:"210486",title:"Prof.",name:"Hiroshi",middleName:null,surname:"Mitoma",slug:"hiroshi-mitoma",fullName:"Hiroshi Mitoma"}]},{id:"35802",title:"Cross-Cultural/Linguistic Differences in the Prevalence of Developmental Dyslexia and the Hypothesis of Granularity and Transparency",slug:"cross-cultural-linguistic-differences-in-the-prevalence-of-developmental-dyslexia-and-the-hypothesis",totalDownloads:3609,totalCrossrefCites:2,totalDimensionsCites:7,abstract:null,book:{id:"673",slug:"dyslexia-a-comprehensive-and-international-approach",title:"Dyslexia",fullTitle:"Dyslexia - A Comprehensive and International Approach"},signatures:"Taeko N. Wydell",authors:[{id:"87489",title:"Prof.",name:"Taeko",middleName:"N.",surname:"Wydell",slug:"taeko-wydell",fullName:"Taeko Wydell"}]},{id:"58597",title:"Testosterone and Erectile Function: A Review of Evidence from Basic Research",slug:"testosterone-and-erectile-function-a-review-of-evidence-from-basic-research",totalDownloads:1349,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"Androgens are essential for male physical activity and normal erectile function. Hence, age-related testosterone deficiency, known as late-onset hypogonadism (LOH), is considered a risk factor for erectile dysfunction (ED). This chapter summarizes relevant basic research reports examining the effects of testosterone on erectile function. Testosterone affects several organs and is especially active on the erectile tissue. The mechanism of testosterone deficiency effects on erectile function and the results of testosterone replacement therapy (TRT) have been well studied. Testosterone affects nitric oxide (NO) production and phosphodiesterase type 5 (PDE-5) expression in the corpus cavernosum through molecular pathways, preserves smooth muscle contractility by regulating both contraction and relaxation, and maintains the structure of the corpus cavernosum. Interestingly, testosterone deficiency has relationship to neurological diseases, which leads to ED. Testosterone replacement therapy is widely used to treat patients with testosterone deficiency; however, this treatment might also induce some problems. Basic research suggests that PDE-5 inhibitors, L-citrulline, and/or resveratrol therapy might be effective therapeutic options for testosterone deficiency-induced ED. Future research should confirm these findings through more specific experiments using molecular tools and may shed more light on endocrine-related ED and its possible treatments.",book:{id:"5994",slug:"sex-hormones-in-neurodegenerative-processes-and-diseases",title:"Sex Hormones in Neurodegenerative Processes and Diseases",fullTitle:"Sex Hormones in Neurodegenerative Processes and Diseases"},signatures:"Tomoya Kataoka and Kazunori Kimura",authors:[{id:"219042",title:"Ph.D.",name:"Tomoya",middleName:null,surname:"Kataoka",slug:"tomoya-kataoka",fullName:"Tomoya Kataoka"},{id:"229066",title:"Prof.",name:"Kazunori",middleName:null,surname:"Kimura",slug:"kazunori-kimura",fullName:"Kazunori Kimura"}]}],onlineFirstChaptersFilter:{topicId:"18",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"82319",title:"Traumatic Optic Neuropathy",slug:"traumatic-optic-neuropathy",totalDownloads:2,totalDimensionsCites:0,doi:"10.5772/intechopen.104731",abstract:"Traumatic optic neuropathy (TON) is a specific neurological sequence of traumatic brain injury (TBI). It has a different mechanism than other most neurologic complications of head trauma and its consequences can be devastating. The damage can be from direct penetrating trauma or bone fracture injuring the optic nerve directly or secondary to indirect blunt trauma (usually causing traction). The diagnosis of TON is based on the clinical history and examination findings indicative of optic neuropathy, especially the presence of defective pupillary light response. TON can cause only mild vision loss but, in some cases, severe vision loss is present. Imaging findings can support the diagnosis, and provide information on the mechanism as well as treatment options. The treatment options include observation alone, systemic steroids, erythropoietin, surgical decompression of the optic canal, or combination. The evidence base for these various treatment options is controversial and each treatment has its side effects and risks. Poor prognostic factors include poor visual acuity at presentation, loss of consciousness, no improvement in vision in the first 48 hours, and evidence of optic canal fractures on neuroimaging.",book:{id:"11367",title:"Traumatic Brain Injury",coverURL:"https://cdn.intechopen.com/books/images_new/11367.jpg"},signatures:"Ainat Klein and Wahbi Wahbi"},{id:"82203",title:"Resting-State Brain Network Analysis Methods and Applications",slug:"resting-state-brain-network-analysis-methods-and-applications",totalDownloads:19,totalDimensionsCites:0,doi:"10.5772/intechopen.104827",abstract:"Resting-state fMRI has been widely applied in clinical research. Brain networks constructed by functional connectivity can reveal alterations related to disease and treatment. One of the major concerns of brain network application under clinical situations is how to analyze groups of data to find the potential biomarkers that can aid in diagnosis. In this paper, we briefly review common methods to construct brain networks from resting-state fMRI data, including different ways of the node definition and edge calculation. We focus on using a brain atlas to define nodes and estimate edges by static and dynamic functional connectivity. The directed connectivity method is also mentioned. We then discuss the challenges and pitfalls when analyzing groups of brain networks, including functional connectivity alterations, graph theory attributes analysis, and network-based statistics. Finally, we review the clinical application of resting-state fMRI in neurorehabilitation of spinal cord injury patients and stroke patients, the research on the mechanism and early diagnosis of neurodegenerative diseases, such as multiple system atrophy, as well as the research on brain functional network alteration of glioma patients.",book:{id:"11742",title:"Neurophysiology",coverURL:"https://cdn.intechopen.com/books/images_new/11742.jpg"},signatures:"Yunxiang Ge and Weibei Dou"},{id:"82099",title:"Recent Advances in the Development of Biofluid-Based Prognostic Biomarkers of Diffuse Axonal Injury",slug:"recent-advances-in-the-development-of-biofluid-based-prognostic-biomarkers-of-diffuse-axonal-injury",totalDownloads:13,totalDimensionsCites:0,doi:"10.5772/intechopen.104933",abstract:"Even though head injury is a silent pandemic of the century producing immense social and economic impact, predictive models have not been established to develop strategies promoting the development of reliable diagnostic tools and effective therapeutics capable of improving the prognosis. Diffuse axonal injury (DAI) is a type of traumatic brain injury (TBI) that results from a blunt injury to the brain. Discovering biomarkers for DAI have been a matter of debate and research. A number of studies have reported biomarkers that are correlated with severity of TBI but no conclusive and reproducible clinical evidence regarding the same has been put forward till now. Additionally, many DAI biomarkers have limitations so that they cannot be generalized for universal applications. The properties of these biomarkers should be extensively researched along with the development of novel biomarkers to aid important clinical decisions for the benefit of the society. This chapter summarizes the existing biofluid-based biomarkers, critically examines their limitations and highlights the possibilities of a few novel biomolecules as prognostic biomarkers of DAI.",book:{id:"11367",title:"Traumatic Brain Injury",coverURL:"https://cdn.intechopen.com/books/images_new/11367.jpg"},signatures:"Vinu V. Gopal, Rinku Raj Mullasseril and Goutam Chandra"},{id:"81998",title:"Understanding the Neuropathophysiology of Psychiatry Disorder Using Transcranial Magnetic Stimulation",slug:"understanding-the-neuropathophysiology-of-psychiatry-disorder-using-transcranial-magnetic-stimulatio",totalDownloads:5,totalDimensionsCites:0,doi:"10.5772/intechopen.103748",abstract:"Transcranial magnetic stimulation (TMS) is a safe and non-invasive tool that allows researchers to probe and modulate intracortical circuits. The most important aspect of TMS is its ability to directly stimulate the cortical neurons, generating action potentials, without much effect on intervening tissue. This property can be leveraged to provide insight into the pathophysiology of various neuropsychiatric disorders. Using multiple patterns of stimulations (single, paired, or repetitive), different neurophysiological parameters can be elicited. Various TMS protocol helps in understanding the neurobiological basis of disorder and specific behaviors by allowing direct probing of the cortical areas and their interconnected networks. While single-pulse TMS can provide insight into the excitability and integrity of the corticospinal tract, paired-pulse TMS (ppTMS) can provide further insight into cortico-cortical connections and repetitive TMS (rTMS) into cortical mapping and modulating plasticity.",book:{id:"11742",title:"Neurophysiology",coverURL:"https://cdn.intechopen.com/books/images_new/11742.jpg"},signatures:"Jitender Jakhar, Manish Sarkar and Nand Kumar"},{id:"81646",title:"Cortical Plasticity under Ketamine: From Synapse to Map",slug:"cortical-plasticity-under-ketamine-from-synapse-to-map",totalDownloads:17,totalDimensionsCites:0,doi:"10.5772/intechopen.104787",abstract:"Sensory systems need to process signals in a highly dynamic way to efficiently respond to variations in the animal’s environment. For instance, several studies showed that the visual system is subject to neuroplasticity since the neurons’ firing changes according to stimulus properties. This dynamic information processing might be supported by a network reorganization. Since antidepressants influence neurotransmission, they can be used to explore synaptic plasticity sustaining cortical map reorganization. To this goal, we investigated in the primary visual cortex (V1 of mouse and cat), the impact of ketamine on neuroplasticity through changes in neuronal orientation selectivity and the functional connectivity between V1 cells, using cross correlation analyses. We found that ketamine affects cortical orientation selectivity and alters the functional connectivity within an assembly. These data clearly highlight the role of the antidepressant drugs in inducing or modeling short-term plasticity in V1 which suggests that cortical processing is optimized and adapted to the properties of the stimulus.",book:{id:"11374",title:"Sensory Nervous System - Computational Neuroimaging Investigations of Topographical Organization in Human Sensory Cortex",coverURL:"https://cdn.intechopen.com/books/images_new/11374.jpg"},signatures:"Ouelhazi Afef, Rudy Lussiez and Molotchnikoff Stephane"},{id:"81582",title:"The Role of Cognitive Reserve in Executive Functioning and Its Relationship to Cognitive Decline and Dementia",slug:"the-role-of-cognitive-reserve-in-executive-functioning-and-its-relationship-to-cognitive-decline-and",totalDownloads:30,totalDimensionsCites:0,doi:"10.5772/intechopen.104646",abstract:"In this chapter, we explore how cognitive reserve is implicated in coping with the negative consequences of brain pathology and age-related cognitive decline. Individual differences in cognitive performance are based on different brain mechanisms (neural reserve and neural compensation), and reflect, among others, the effect of education, occupational attainment, leisure activities, and social involvement. These cognitive reserve proxies have been extensively associated with efficient executive functioning. We discuss and focus particularly on the compensation mechanisms related to the frontal lobe and its protective role, in maintaining cognitive performance in old age or even mitigating the clinical expression of dementia.",book:{id:"11742",title:"Neurophysiology",coverURL:"https://cdn.intechopen.com/books/images_new/11742.jpg"},signatures:"Gabriela Álvares-Pereira, Carolina Maruta and Maria Vânia Silva-Nunes"}],onlineFirstChaptersTotal:13},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:318,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:105,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:15,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. This Series is intended for researchers and students alike interested in this fascinating field and its many applications.",coverUrl:"https://cdn.intechopen.com/series/covers/14.jpg",latestPublicationDate:"June 11th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:9,editor:{id:"218714",title:"Prof.",name:"Andries",middleName:null,surname:"Engelbrecht",slug:"andries-engelbrecht",fullName:"Andries Engelbrecht",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRNR8QAO/Profile_Picture_1622640468300",biography:"Andries Engelbrecht received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999 respectively. He is currently appointed as the Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). In addition to a number of research articles, he has written two books, Computational Intelligence: An Introduction and Fundamentals of Computational Swarm Intelligence.",institutionString:null,institution:{name:"Stellenbosch University",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:6,paginationItems:[{id:"22",title:"Applied Intelligence",coverUrl:"https://cdn.intechopen.com/series_topics/covers/22.jpg",isOpenForSubmission:!0,editor:{id:"27170",title:"Prof.",name:"Carlos",middleName:"M.",surname:"Travieso-Gonzalez",slug:"carlos-travieso-gonzalez",fullName:"Carlos Travieso-Gonzalez",profilePictureURL:"https://mts.intechopen.com/storage/users/27170/images/system/27170.jpeg",biography:"Carlos M. Travieso-González received his MSc degree in Telecommunication Engineering at Polytechnic University of Catalonia (UPC), Spain in 1997, and his Ph.D. degree in 2002 at the University of Las Palmas de Gran Canaria (ULPGC-Spain). He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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