Differential diagnosis of NCTs and WCTs.
\\n\\n
IntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\\n\\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\\n\\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\\n\\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\\n\\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\\n\\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\\n\\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\\n\\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\\n\\nFeel free to share this news on social media and help us mark this memorable moment!
\\n\\n\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/237"}},components:[{type:"htmlEditorComponent",content:'
After years of being acknowledged as the world's leading publisher of Open Access books, today, we are proud to announce we’ve successfully launched a portfolio of Open Science journals covering rapidly expanding areas of interdisciplinary research.
\n\n\n\nIntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\n\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\n\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\n\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\n\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\n\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\n\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\n\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\n\nFeel free to share this news on social media and help us mark this memorable moment!
\n\n\n'}],latestNews:[{slug:"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:"10397",leadTitle:null,fullTitle:"LabVIEW - A Flexible Environment for Modeling and Daily Laboratory Use",title:"LabVIEW",subtitle:"A Flexible Environment for Modeling and Daily Laboratory Use",reviewType:"peer-reviewed",abstract:"The LabVIEW software environment from National Instruments is used by engineers and scientists worldwide for a variety of applications. This book examines many of these applications, including modeling, data acquisition, monitoring electrical networks, studying the structural response of buildings to earthquakes, and more.",isbn:"978-1-83968-841-6",printIsbn:"978-1-83968-840-9",pdfIsbn:"978-1-83968-842-3",doi:"10.5772/intechopen.91583",price:119,priceEur:129,priceUsd:155,slug:"labview-a-flexible-environment-for-modeling-and-daily-laboratory-use",numberOfPages:184,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"44082cb927f5a6fd83b9b071b84d4619",bookSignature:"Riccardo de Asmundis",publishedDate:"July 28th 2021",coverURL:"https://cdn.intechopen.com/books/images_new/10397.jpg",numberOfDownloads:2999,numberOfWosCitations:0,numberOfCrossrefCitations:3,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:4,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:7,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 21st 2020",dateEndSecondStepPublish:"October 19th 2020",dateEndThirdStepPublish:"December 18th 2020",dateEndFourthStepPublish:"March 8th 2021",dateEndFifthStepPublish:"May 7th 2021",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"43508",title:"Prof.",name:"Riccardo",middleName:null,surname:"de Asmundis",slug:"riccardo-de-asmundis",fullName:"Riccardo de Asmundis",profilePictureURL:"https://mts.intechopen.com/storage/users/43508/images/system/43508.png",biography:"Riccardo de Asmundis is a physicist who graduated cum laude from the University of Naples “Federico II” in 1987, where he is also a contract professor teaching the Electronics Fundamentals course. He is a senior researcher at Istituto Nazionale di Fisica Nucleare (INFN) in Naples and a Certified LabVIEW Developer (CLD) and Professional Instructor (CPI) for National Instruments.\nHe is a member of international collaborations in high-energy physics, currently for the ATLAS and SND Experiments at CERN; he spent several decades abroad, designing particle detectors and data acquisition systems.\nDr. de Asmundis has authored more than 1100 publications in international reviews on topics such as high-energy physics, particle detectors, and photon detectors.\nHe owns his private electronic lab where he renovates electronic equipment for music, such as keyboards, synthesizers, organs, radios, and HiFi systems. He also plays classical piano.",institutionString:"INFN Sezione di Napoli",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"INFN Sezione di Napoli",institutionURL:null,country:{name:"Italy"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"611",title:"Signal Processing",slug:"numerical-analysis-and-scientific-computing-signal-processing"}],chapters:[{id:"75407",title:"Analyzing and Presenting Data with LabVIEW",doi:"10.5772/intechopen.96130",slug:"analyzing-and-presenting-data-with-labview",totalDownloads:586,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"LabVIEW is an abbreviation for Laboratory Virtual Instrument Engineering Workbench and allows scientists and engineers to develop and implement an interactive program. LabVIEW has been specially developed to take measurements, analyze data, and present the results to the user. You determine what the device looks like, rather than the manufacturer of the device. LabVIEW has a very large library of functions and subprograms (subVIs) that can help you during your programming and use without occupying memory. Hidden programming problems that you may encounter in traditional programming languages are less common in LabVIEW. LabVIEW also includes different applications such as serial device control, data analysis, data presentation, data storage and communication over the internet. Analysis library; It includes versatile and useful functions such as signal generation, signal processing filters, Windows statistics and regressions, linear algebra and array arithmetic. Due to the graphical nature of LabVIEW, it is an innate data presentation package. You can view the data in any form you want. Chart, graph and user-defined graph are among the output options that can be used. As a scientist or an engineer, you frequently measure physical changes such as temperature, pressure, time, mass, electric current, light intensity, radioactivity etc. You generally need to analyze and present the data. When you have large amounts of data, you need to use software to analyze and present the data. LabVIEW makes these actions easy for you. Because LabVIEW includes hundreds of built-in and add-on functions you need that make it easy to create a user-friendly interface. In this chapter, we focus on data analysis and presentation.",signatures:"Ahmet Mavi, Ahmet Özmen and Mehmet Ertuğrul",downloadPdfUrl:"/chapter/pdf-download/75407",previewPdfUrl:"/chapter/pdf-preview/75407",authors:[{id:"335908",title:"Prof.",name:"Mehmet",surname:"Ertugrul",slug:"mehmet-ertugrul",fullName:"Mehmet Ertugrul"},{id:"336013",title:"Prof.",name:"Ahmet",surname:"Mavi",slug:"ahmet-mavi",fullName:"Ahmet Mavi"},{id:"336014",title:"MSc.",name:"Ahmet",surname:"Özmen",slug:"ahmet-ozmen",fullName:"Ahmet Özmen"}],corrections:null},{id:"75654",title:"TeraVision: A LabVIEW Software for THz Hyper-Raman Spectroscopy",doi:"10.5772/intechopen.96663",slug:"teravision-a-labview-software-for-thz-hyper-raman-spectroscopy",totalDownloads:284,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Terahertz Time-Domain Spectroscopy (TDS) has emerged during the last two decades as a very popular technique for characterizing the low-energy excitations of several materials, gaseous, liquids and solids, as well as artificial materials as for instance epitaxial heterostructures and more. In recent years, the advances in THz technology allowed obtaining nonlinear optical effects with THz photons, showing remarkable results. In particular, THz Hyper-Raman Spectroscopy greatly expands the spectroscopic capability of the standard THz-TDS by combining intense and broadband THz pulses with a detailed analysis of the spectral content of the generated signal. It is evident that this improvement needs an adequate software support. The main parameter for coding the software which differs with respect to a standard THz-TDS software is the control of a motorized grating (monochromator), but several routines employed in the setup optimization stage rather than the actual measurement are needed as well. In this paper we present the TeraVision software, based on LabVIEW code, in order to highlight the solutions we adopted to tackle the main experimental challenges as well as to give a pleasant and user-friendly experience to expert users.",signatures:"Rohit Kumar, Qiucheng Yu, Domenico Paparo and Andrea Rubano",downloadPdfUrl:"/chapter/pdf-download/75654",previewPdfUrl:"/chapter/pdf-preview/75654",authors:[{id:"334702",title:"Dr.",name:"Andrea",surname:"Rubano",slug:"andrea-rubano",fullName:"Andrea Rubano"},{id:"347235",title:"Mr.",name:"Rohit",surname:"Kumar",slug:"rohit-kumar",fullName:"Rohit Kumar"},{id:"347236",title:"Mr.",name:"Qiucheng",surname:"Yu",slug:"qiucheng-yu",fullName:"Qiucheng Yu"},{id:"347237",title:"Dr.",name:"Domenico",surname:"Paparo",slug:"domenico-paparo",fullName:"Domenico Paparo"}],corrections:null},{id:"76638",title:"Cost-Effective Interfaces with Arduino-LabVIEW for an IOT-Based Remote Monitoring Application",doi:"10.5772/intechopen.97784",slug:"cost-effective-interfaces-with-arduino-labview-for-an-iot-based-remote-monitoring-application",totalDownloads:280,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"To date, research efforts have demonstrated the stimulated need for the Internet of Things (IoT) based monitoring device in their laboratory. The benefits of remote laboratories in overcoming time constraints and the disadvantages of usability of conventional laboratories are well known. In addition to the current control engineering laboratories, a remote lab that incorporates an industry-relevant method has been established to assist in the understanding of data acquisition with cost-effective platform integration. However, one of the greatest challenges is the creation of a low-cost and user-friendly remote laboratory experiment that is ideal for interacting with the actual laboratory via a mobile device. The main objective of this work is therefore to build a remote laboratory system based on the IoT using the LabVIEW-Arduino interface with the example of proportional-integral-derivative (PID) tuning scheme for the LD-Didactic temperature plant. The practical work would include the implementation of the low-cost Arduino module connecting the actual plant to mobile devices. In addition, interfaces have been built using the Blynk application to allow communication between the end user and the laboratory equipment. In line with the Industrial Revolution 4.0 (IR 4.0), the proposed study structure called for the digitization of the current laboratory experiment method.",signatures:"Muhammad Asraf Hairuddin, Nur Dalila Khirul Ashar, Amar Faiz Zainal Abidin and Nooritawati Md Tahir",downloadPdfUrl:"/chapter/pdf-download/76638",previewPdfUrl:"/chapter/pdf-preview/76638",authors:[{id:"320847",title:"Mr.",name:"Amar Faiz Zainal",surname:"Abidin",slug:"amar-faiz-zainal-abidin",fullName:"Amar Faiz Zainal Abidin"},{id:"331209",title:"Dr.",name:"Muhammad Asraf",surname:"Hairuddin",slug:"muhammad-asraf-hairuddin",fullName:"Muhammad Asraf Hairuddin"},{id:"338481",title:"Mrs.",name:"Nur Dalila",surname:"Khirul Ashar",slug:"nur-dalila-khirul-ashar",fullName:"Nur Dalila Khirul Ashar"},{id:"414821",title:"Prof.",name:"Nooritawati",surname:"Md Tahir",slug:"nooritawati-md-tahir",fullName:"Nooritawati Md Tahir"}],corrections:null},{id:"75205",title:"LabView and Connections with Third-Party Hardware",doi:"10.5772/intechopen.96056",slug:"labview-and-connections-with-third-party-hardware",totalDownloads:310,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Data acquisition is a function that plays a fundamental role in the automatic supervision and system control, it combine the system (software and hardware) to the process to be controlled (real world). The field of application starts from research to automation, from industry to home automation, in practice everything that in some way must be performed without human supervision. Data acquisition systems are mainly used to measure physical phenomena such as: temperature, voltage, current, distance and pressure, shock and vibration, and displacement, RPM, angle and discrete events, weight. In order to measure it we need a DAQ , Data AcQuisition System, in this chapter we propose to use a cheap open source hardware: Arduino.",signatures:"Giuseppe Porzio",downloadPdfUrl:"/chapter/pdf-download/75205",previewPdfUrl:"/chapter/pdf-preview/75205",authors:[{id:"337183",title:"Dr.",name:"Giuseppe",surname:"Porzio",slug:"giuseppe-porzio",fullName:"Giuseppe Porzio"}],corrections:null},{id:"77102",title:"LabVIEW and Open Embedded System",doi:"10.5772/intechopen.98271",slug:"labview-and-open-embedded-system",totalDownloads:355,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In this chapter, discussions about the applications based on LabVIEW with a typical open embedded system such as Arduino. Instead of the applications with use of National Instrument hardware, an user can build an low-cost microcontroller-based system which user interface and procedure designed in LabVIEW. Recently, Arduino-based applications are one of interested developments, the embedded system is designed with the low-cost microcontroller of ATMEGA2560 and easy to program with IDE (Interface Development Environment). However, the difficulties of Arduino-based applications are user interface design while LabVIEW is an excellent utilities for the panel designs. The limitation of some LabVIEW applications is the requirements from combination with high performance NI hardwares. Under interactions between LabVIEW and IDE library designed by Arduino, the low-cost system can be easily built for experiments or prototype designs.",signatures:"Trinh Quang Duc",downloadPdfUrl:"/chapter/pdf-download/77102",previewPdfUrl:"/chapter/pdf-preview/77102",authors:[{id:"336496",title:"Ph.D.",name:"Trinh Quang",surname:"Duc",slug:"trinh-quang-duc",fullName:"Trinh Quang Duc"}],corrections:null},{id:"75665",title:"Certain Applications of LabVIEW in the Field of Electronics and Communication",doi:"10.5772/intechopen.96301",slug:"certain-applications-of-labview-in-the-field-of-electronics-and-communication",totalDownloads:288,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"The LabVIEW platform with graphical programming environment, will help to integrate the human machine interface controller with the software like MATLAB, Python etc. This platform plays the vital role in many pioneering areas like speech signal processing, bio medical signals like Electrocardiogram (ECG) and Electroencephalogram (EEG) processing, fault analysis in analog electronic circuits, Cognitive Radio(CR), Software Defined Radio (SDR), flexible and wearable electronics. Nowadays most engineering colleges redesign their laboratory curricula for the students to enhance the potential inclusion of remote based laboratory to facilitate and encourage the students to access the laboratory anywhere and anytime. This would help every young learner to bolster their innovation, if the laboratory environment is within the reach of their hand. LabVIEW is widely recognized for its flexibility and adaptability. Due to the versatile nature of LabVIEW in the Input- Output systems, it has find its broad applications in integrated systems. It can provide a smart assistance to deaf and dumb people for interpreting the sign language by gesture recognition using flex sensors, monitor the health condition of elderly people by predicting the abnormalities in the heart beat through remote access, and identify the stage of breast cancer from the Computed tomography (CT) and Magnetic resonance imaging (MRI) scans using image processing techniques. In this chapter, the previous work of authors who have extensively incorporated LabVIEW in the field of electronics and communication are discussed in detail.",signatures:"Prema Ramasamy, Shri Tharanyaa Jothimani Palanivelu and Abin Sathesan",downloadPdfUrl:"/chapter/pdf-download/75665",previewPdfUrl:"/chapter/pdf-preview/75665",authors:[{id:"336346",title:"Assistant Prof.",name:"Prema",surname:"Ramasamy",slug:"prema-ramasamy",fullName:"Prema Ramasamy"},{id:"345058",title:"Mrs.",name:"Shri",surname:"Tharanyaa",slug:"shri-tharanyaa",fullName:"Shri Tharanyaa"},{id:"345059",title:"Mr.",name:"Abin",surname:"Sathesan",slug:"abin-sathesan",fullName:"Abin Sathesan"}],corrections:null},{id:"75847",title:"Advanced Modeling of Single Degree of Freedom System for Earthquake Ground Motion Using LabVIEW Software",doi:"10.5772/intechopen.96341",slug:"advanced-modeling-of-single-degree-of-freedom-system-for-earthquake-ground-motion-using-labview-soft",totalDownloads:231,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,abstract:"In this paper, the structural responses at discrete time steps are evaluated to understand the linear dynamics characteristics of a structural system using LabVIEW (Laboratory Virtual Instrument Engineering Workbench) tool. Time History Analysis (THA) which is an essential procedure to design a reliable structure when the structure is subjected to dynamic loading is taken into consideration for the study. Direct integration method was used to find out the dynamic response of the structure as it is applicable for both linear as well as nonlinear range. Block diagram that perform step-by-step integration to analyze the linear single degree of freedom (SDOF) system has been prepared in LabVIEW. The processing of data is carried out till the equilibrium is satisfied at all discrete time points within the interval of solution instead of any time t. Different ground motion time histories were considered for THA and responses of the SDOF system are evaluated. The results from LabVIEW were validated and the accuracy of the algorithms generated are discussed. It is observed that the accuracy and stability of the final solution depends on the variation of displacement, velocity and acceleration that is assumed in each step. Thus, LabVIEW workbench can therefore be recognized as an effective instrument in structural engineering owing to its fast sampling features.",signatures:"R.B. Malathy, Govardhan Bhat and U.K. Dewangan",downloadPdfUrl:"/chapter/pdf-download/75847",previewPdfUrl:"/chapter/pdf-preview/75847",authors:[{id:"334233",title:"Ph.D. Student",name:"R.B.",surname:"Malathy",slug:"r.b.-malathy",fullName:"R.B. Malathy"},{id:"351748",title:"Dr.",name:"Govardhan",surname:"Bhat",slug:"govardhan-bhat",fullName:"Govardhan Bhat"},{id:"351750",title:"Dr",name:"U.K.",surname:"Dewangan",slug:"u.k.-dewangan",fullName:"U.K. Dewangan"}],corrections:null},{id:"75701",title:"LabVIEW as Power Disturbances Classification Tools",doi:"10.5772/intechopen.96079",slug:"labview-as-power-disturbances-classification-tools",totalDownloads:292,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Power disturbances monitoring is one of the important aspects on dealing power quality issue in electrical system. The aims of conducting monitoring process are to identify the real culprit which contribute to the Power Quality (PQ) problem. One of the vital steps during monitoring process is the classifying various type of power disturbance. This classification process is very important to give a right direction towards proposing the correct mitigation technique. In order to produce reliable classification technique, the devices which has a flexibility on accommodating the software and hardware part need to be deployed. The software is need for algorithm development such as signal processing, Artificial Intelligent (AI) as well as statistical analysis. On the hardware part, the device’s ability to acquire the electrical parameter within the electrical system operation is very important. The data acquisition based on the voltage and current is essential to be feed in the classification algorithm in software side. On the other hand, the interfacing devices and data acquisition module need to be developed at the hardware side, LabVIEW manage to accommodate both software and hardware need and further development of the LabVIEW for this purpose will be elaborated in this chapter.",signatures:"Ahmad Farid Abidin and Mohd Abdul Talib Mat Yusoh",downloadPdfUrl:"/chapter/pdf-download/75701",previewPdfUrl:"/chapter/pdf-preview/75701",authors:[{id:"333066",title:"Associate Prof.",name:"ahmad farid",surname:"abidin",slug:"ahmad-farid-abidin",fullName:"ahmad farid abidin"},{id:"335323",title:"Dr.",name:"Mohd Abdul Talib",surname:"Mat Yusoh",slug:"mohd-abdul-talib-mat-yusoh",fullName:"Mohd Abdul Talib Mat Yusoh"}],corrections:null},{id:"76484",title:"Digital System Design",doi:"10.5772/intechopen.97611",slug:"digital-system-design",totalDownloads:374,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The main objective of this chapter is to study and design various combinational circuits like Verification of Boolean Expression, Multiplexer, Demultiplexer Circuits, Code Converters circuits using LabVIEW tools. This chapter will make the user more comfortable towards learning of Design of Digital Systems. The various types of Boolean Expressions like SOP and POS, Combinational circuits like Adder circuit (Half adder and full adder), Subtractor circuit (Half Subtractor, Full Subtractor), some code converters like Binary to Gray and Gray to Binary, BCD to Gray and Gray to BCD and also Sequential circuits with D flip flop is also being carried out using this LabVIEW.",signatures:"Janani Rajaraman",downloadPdfUrl:"/chapter/pdf-download/76484",previewPdfUrl:"/chapter/pdf-preview/76484",authors:[{id:"334243",title:"Assistant Prof.",name:"Janani",surname:"Rajaraman",slug:"janani-rajaraman",fullName:"Janani Rajaraman"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"31",title:"Modeling, Programming and Simulations Using LabVIEW™ 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Scheduling is essentially the short-term execution plan of a production planning model. Production scheduling consists of the activities performed in a manufacturing company in order to manage and control the execution of a production process. A schedule is an assignment problem that describes into details (in terms of minutes or seconds) which activities must be performed and how the factory’s resources should be utilized to satisfy the plan. Detailed scheduling is essentially the problem of allocating machines to competing jobs over time, subject to the constraints. Each work center can process one job at a time and each machine can handle at most one task at a time. A scheduling problem, typically, assumes a fixed number of jobs and each job has its own parameters (i.e., tasks, the necessary sequential constraints, the time estimates for each operation and the required resources, no cancellations). All scheduling approaches require some estimate of how long it takes to perform the work. Scheduling affects, and is affected by, the shop floor organization. All scheduling changes can be projected over time enabling the identification and analysis of starting time, completion times, idle time of resources, lateness, etc….
A right scheduling plan can drive the forecast to anticipate completion date for each released part and to provide data for deciding what to work on next. Questions about “Can we do it?” and/or “How are we doing?” presume the existence of approaches for optimisation. The aim of a scheduling study is, in general, to perform the tasks in order to comply with priority rules and to respond to strategy. An optimal short-term production planning model aims at gaining time and saving opportunities. It starts from the execution orders and it tries to allocate, in the best possible way, the production of the different items to the facilities. A good schedule starts from planning and springs from respecting resource conflicts, managing the release of jobs to a shop and optimizing completion time of all jobs. It defines the starting time of each task and determines whatever and how delivery promises can be met. The minimization of one or more objectives has to be accomplished (e.g., the number of jobs that are shipped late, the minimization set up costs, the maximum completion time of jobs, maximization of throughput, etc.). Criteria could be ranked from applying simple rules to determine which job has to be processed next at which work-centre (i.e., dispatching) or to the use of advanced optimizing methods that try to maximize the performance of the given environment. Fortunately many of these objectives are mutually supportive (e.g., reducing manufacturing lead time reduces work in process and increases probability to meeting due dates). To identify the exact sequence among a plethora of possible combinations, the final schedule needs to apply rules in order to quantify urgency of each order (e.g., assigned order’s due date - defined as global exploited strategy; amount of processing that each order requires - generally the basis of a local visibility strategy). It’s up to operations management to optimize the use of limited resources. Rules combined into The etymology of the word heuristic derives from a Greek word The term metaheuristc originates from union of prefix
This research examines the main characteristics of the most promising meta-heuristic approaches for the general process of a Job Shop Scheduling Problems (i.e., JSSP). Being a NP complete and highly constrained problem, the resolution of the JSSP is recognized as a key point for the factory optimization process [4]. The chapter examines the soundness and key contributions of the 7 meta-heuristics (i.e., Genetics Approaches, Ants Colony Optimization, Bees Algorithm, Electromagnetic Like Algorithm, Simulating Annealing, Tabu Search and Neural Networks), those that improved the production scheduling vision. It reviews their accomplishments and it discusses the perspectives of each meta approach. The work represents a practitioner guide to the implementation of these meta-heuristics in scheduling job shop processes. It focuses on the logic, the parameters, representation schemata and operators they need.
The two key problems in production scheduling are „priorities“ and „capacity“. Wight (1974) described s
Practical scheduling problems, although more highly constrained, are high difficult to solve due to the number and variety of jobs, tasks and potentially conflicting goals. Recently, a lot of Advanced Production Scheduling tools arose into the market (e.g., Aspen PlantTM Scheduler family, Asprova, R2T – Resourse To Time, DS APS – DemandSolutions APS, DMS – Dynafact Manufacturing System, i68Group, ICRON-APS, JobPack, iFRP, Infor SCM, SchedulePro, Optiflow-Le, Production One APS, MQM – Machine Queue Management, MOM4, JDA software, Rob-ex, Schedlyzer, OMP Plus, MLS and MLP, Oracle Advanced Scheduling, Ortec Schedule, ORTEMS Productionscheduler, Outperform, AIMMS, Planet Together, Preactor, Quintiq, FactoryTalk Scheduler, SAP APO-PP/DS, and others). Each of these automatically reports graphs. Their goal is to drive the scheduling for assigned manufacturing processes. They implement rules and optimise an isolated sub-problem but none of the them will optimise a multi stage resource assignment and sequencing problem.
In a Job Shop (i.e., JS) problem a classic and most general factory environment, different tasks or operations must be performed to complete a job [10]; moreover, priorities and capacity problems are faced for different jobs, multiple tasks and different routes. In this contest, each job has its own individual flow pattern through assigned machines, each machine can process only one operation at a time and each operation can be processed by only one machine at a time. The purpose of the procedure is to obtain a schedule which aims to complete all jobs and, at the same time, to minimize (or maximize) the objective function. Mathematically, the JS Scheduling Problem (i.e., JSSP) can be characterized as a combinatorial optimization problem. It has been generally shown to be NP-hard A problem is NP-complete if exists no algorithm that solves the problem in a polynomial time. A problem is NP-hard if it is possible to show that it can solve a NP-complete problem.
It has to undergo a discrete number of operations (i.e.,
Let:
JSSP, marked as
To accommodate extreme variability in different parts of a job shop, schedulers separate workloads in each work-centres rather than aggregating them [14]. Of more than 100 different rules proposed by researchers and applied by practitioners exist, some have become common in Operations Management systems: First come- First served, Shortest Processing Time, Earliest Due Date, Slack Time Remaining, Slack Time Remaining For each Operation, Critical Ratio, Operation Due Date, etc. [15]. Besides these, Makespan is often the performance feature in the study of resource allocation [16]. Makespan represents the time elapsed from the start of the first task to the end of the last task in schedule. The minimisation of makespan arranges tasks in order to level the differences between the completion time of each work phase. It tries to smooth picks in work-centre occupancy to obtain batching in load assignment per time. Although direct time constraints, such as minimization of processing time or earliest due date, are sufficient to optimize industrial scheduling problems, for the reasons as above the minimization of the makespan is preferable for general/global optimization performances because it enhances the overall efficiency in shop floor and reduces manufacturing lead time variability [17].
Thus, in JSSP optimization variant of
where
and
The possible representation of a JS problem could be done through a Gantt chart or through a Network representation.
Gantt (1916) created innovative charts for visualizing planned and actual production [18]. According to Cox
A Network representation was first introduced by Roy and Sussman [20]. The representation is based on “
JS processes are mathematically described as disjunctive graph
Each job-tasks pair Hard constraints are physical ones, while soft constraints are generally those related to human factor e.g., relaxation, fatigue etc…
Let sv be the starting time of an operation to a node
Minimize s* subject to:
Disjunctive graph representation. There are disjunctive arcs between every pair of tasks that has to be processed on the same machine (dashed lines) and conjunctive arcs between every pair of tasks that are in the same job (dotted lines). Omitting processing time, the problem specification is
s* is equal to the completion time of the last operation of the schedule, which is therefore equal to
In order to obtain a scheduling solution and to evaluate makespan, we have to collect all feasible permutations of tasks to transform the undirected arcs in directed ones in such a way that there are no cycles.
The total number of nodes,
The number of arcs defines the possible combination paths. Each path from source to sink is a candidate solution for JSSP. The routing graph is reported in figure 2:
Problem routing representation.
A logic has to be implemented in order to translate the scheduling problem into an algorithm structure. Academic researches on scheduling problems have produced countless papers [23]. Scheduling has been faced from many perspectives, using formulations and tools of various disciplines such as control theory, physical science and artificial intelligence systems [24]. Criteria for optimization could be ranked from applying simple priority rules to determine which job has to be processed next at the work-centres (i.e., dispatching) to the use of advanced optimizing methods that try to maximize the performance of the given environment [25]. Their way to solution is generally approximate – heuristics – but it constitutes promising alternatives to the exact methods and becomes the only one possible when dimension and/or complexity of the problem is outstanding [26].
Guidelines in using heuristics in combinatorial optimization can be found in Hertz (2003) [27]. A classification of heuristic methods was proposed by Zanakis et al. (1989) [28]. Heuristics are generally classified into
Relatively simple rules in guiding heuristic, with exploitation and exploration, are capable to produce better quality solutions than other algorithms from the literature for some classes of instances. These variants originate the class of meta-heuristic approaches [31]. The meta-heuristics The term metaheuristics was introduced by F. Glover in the paper about Tabu search.
As optimization techniques, metaheuristics are stochastic algorithms aiming to solve a broad range of hard optimization problems, for which one does not know more effective traditional methods. Often inspired by analogies with reality, such as physics science, Simulated Annealing [33] and Electromagnetic like Methods [34], biology (Genetic Algorithms [35], Tabu Search [36]) and ethnology (Ant Colony [37,], Bees Algorithm [38]), human science (Neural Networks [39]), they are generally of discrete origin but can be adapted to the other types of problems.
The methodology of a GAs - based on the evolutionary strategy- trasforms a population (set) of individual objects, each with an associated
The theory of evolutionary computing was formalized by Holland in 1975 [40]. GAs are stochastic search procedures for combinatorial optimization problems based on Darwinian principle of natural reproduction, survival and environment’s adaptability [41]. The theory of evolution is biologically explained, the individuals with a stronger fitness are considered better able to survive.. Cells, with one or more strings of DNA (i.e., a chromosome), make up an individual. The gene (i.e., a bit of chromosome located into its particular locus) is, responsible for encoding traits (i.e., alleles). Physical manifestations are raised into genotype (i.e., disposition of genes). Each genotype has is physical manifestation into phenotype. According to these parameters is possible to define a fitness value. Combining individuals through a crossover (i.e., recombination of genetic characteristics of parents) across the sexual reproduction, the chromosomal inheritance process performs to offspring. In each epoch a stochastic mutation procedure occurs. The implemented algorithm is able to simulate the natural process of evolution, coupling solution of scheduling route in order to determinate an optimal tasks assignment. Generally, GA has different basic component: representation, initial population, evaluation function, the reproduction selection scheme, genetic operators (mutation and crossover) and stopping criteria. Central to success of any GA is the suitability of its representation to the problem at hand [42]. This is the encoding from the solution of the problem domain to the genetic representation.
During the last decades, different representation’s schemata for JS have been proposed, such as
The Genetic Algorithms (GAs) model; 3a. the pseudo-code of a GA; 3b. the flow chart of a general GA.
A mutation operator is applied changing the genes into the same genotype (in order to generate only feasible solutions, i.e., without the rejection procedure). Mutation allows to diversify the search over a broader solution domain and it is needed when there is low level of crossover. Among solutions, the allocation with favourable fitness will have higher probability to be selected through the selection mechanisms.
Another important issue for the GA is the selection mechanism (e.g., Tournament Selection procedure and Roulette Wheel as commonly used [44] - their performances are quite similar attending in the convergence time). The
It is very important, for the GAs success, to select the correct ratio between crossover and mutation, because the first one allows to allows to diversify a search field, while a mutation to modify a solution.
If we are on a pic-nic and peer into our cake bitten by a colony of ants, moving in a tidy way and caring on a lay-out that is the optimal one in view of stumbling-blocks and length, we discover how remarkable is nature and we find its evolution as the inspiring source for investigations on intelligence operation scheduling techniques [45]. Natural ants are capable to establish the shortest route path from their colony to feeding sources, relying on the phenomena of It is an organic compound highly volatile that shares on central neural system as an actions’ releaser.
The same behaviour of natural ants can be overcome in an artificial system with an artificial communication strategy regard as a direct metaphoric representation of natural evolution. The essential idea of an ACO model is that „good solutions are not the result of a sporadic good approach to the problem but the incremental output of good partial solutions item. Artificial ants are quite different by their natural progenitors, maintaining a memory of the step before the last one [37]. Computationally, ACO [46] are population based approach built on stochastic solution construction procedures with a retroactive control improvement, that build solution route with a probabilistic approach and through a suitable selection procedure by taking into account:
The initial schedule is constructed by taking into account heuristic information, initial pheromone setting and, if several routes are applicable, a self-created selection procedure chooses the task to process. The same process is followed during the whole run time. The probabilistic approach focused on pheromone. Path’s attractive raises with path choice and probability increases with the number of times that the same path was chosen before [47]. At the same time, the employment of heuristic information can guide the ants towards the most promising solutions and additionally, the use of an agent’s colony can give the algorithm:
The approach focuses on co-operative ant colony food retrieval applied to scheduling routing problems. Colorni et al, basing on studies of Dorigo
The Ant Colony Optimization (ACO) model; 4a. the pseudo-code of an ACO algorithm; 4b. the flow chart of a general ACO procedure.
Constraints are introduced hanging from jobs and resources. Fitness is introduced to translate how good the explored route was. Artificial ants live in a computer realized world. They have an overview of the problem instance they are going to solve across a visibility factor. In the Job Shop side of ACO implementation the visibility has chosen tied with the run time of the task (Eq. 7). The information was about the inquired task’s (i.e.,
The colony is composed of a fixed number of agents
Where:
For each cycle the agents of the colony are going out of source in search of food. When all colony agents have constructed a complete path, i.e. the sequence of feasible order of visited nodes, a pheromone update rule is applied (Eq. 9):
Besides ants’ activity,
The laid pheromone on the inquired path is evaluated taking into consideration how many agents chose that path and how was the objective value of that path (Eq. 10). The weight of the solution goodness is the makespan (i.e.,
Visibility and updated pheromone trail fixes the probability (i.e., the fitness values) of each node (i.e., task) at each iteration; for each cycle, it is evaluated the output of the objective function (
A colony of bees exploits, in multiple directions simultaneously, food sources in the form of antera with plentiful amounts of nectar or pollen. They are able to cover kilometric distances for good foraging fields [50]. Flower paths are covered based on a stigmergic approach – more nectar places should be visited by more bees [51].
The foraging strategies in colonies of bees starts by scout bees – a percentage of beehive population. They wave randomly from one patch to another. Returning at the hive, those scout bees deposit their nectar or polled and start a recruiting mechanism rated above a certain quality threshold on nectar stored [52]. The recruiting mechanism is properly a launching into a wild dance over the honeycomb. This natural process is known as waggle dance” [53]. Bees, stirring up for discovery, flutter in a number from one to one hundred circuits with a waving and returning phase. The waving phase contains information about direction and distance of flower patches. Waving phases in ascending order on vertical honeycomb suggest flower patches on straightforward line with sunbeams. This information is passed using a kind of dance, that is possible to be developed on right or on left. So through this dance, it is possible to understand the distance from the flower, the presence of nectar and the sunbeam side to choose [54].
The waggle dance is used as a guide or a map to evaluate merits of explored different patches and to exploit better solutions. After waggle dancing on the dance floor, the dancer (i.e. the scout bee) goes back to the flower patch with follower bees that were waiting inside the hive. A squadron moves forward into the patches. More follower bees are sent to more promising patches, while harvest paths are explored but they are not carried out in the long term. A swarm intelligent approach is constituted [55]. This allows the colony to gather food quickly and efficiently with a recursive recruiting mechanism [56].
The Bees Algorithm (i.e., BA) is a population-based search; it is inspired to this natural process [38]. In its basic version, the algorithm performs a kind of neighbourhood search combined with random search. Advanced mechanisms could be guided by genetics [57] or taboo operators [58]. The standard Bees Algorithm first developed in Pham and Karaboga in 2006 [59, 60] requires a set of parameters: no. of scout bees (
The honey bees‘ effective foraging strategy can be applied in operation management problems such as JSSP. For each solution, a complete schedule of operations in JSP is produced. The makespan of the solution is analogous to the profitability of the food source in terms of distance and sweetness of the nectar. Bees,
The Bees Algorithm model; 6a. the BA pseudo code; 6b. the flow chart of a general BA procedure.
Together with scouting, this differential recruitment is the key operation of the BA. Once a feasible solution is found, each bee will return to the hive to perform a waggle dance. The output of the waggle dance will be represented by a list of “elite solutions”,
The Electromagnetic Like Algorithm is a population based meta-heuristics proposed by Birbil and Fang [61] to tackle with combinatorial optimisation problems. Algorithm is based on the natural law of attraction and repulsion between charges (Coulomb’s law) [62]. EM simulates electromagnetic interaction [63]. The algorithm evaluates fitness of solutions considering charge of particles. Each particle represents a solution. Two points into the space had different charges in relation to what electromagnetic field acts on them [64]. An electrostatic force, in repulsion or attraction, manifests between two points charges. The electrostatic force is directly proportional to the magnitudes of each charge and inversely proportional to the square of the distance between the charges. The fixed charge at time iteration (
Where
The particles move along with total force and so diversified solutions are generated. The following formulation is the resultant force of particle
The following notes described an adapted version of EM for JSSP. According to this application, the initial population is obtained by choosing randomly from the list or pending tasks, as for the feasibility of solution, particles’ path. The generic pseudo-code for the EM is reported in figure 6. Each particle is initially located into a source node (see disjunctive graph of figure 2). Particle is uniquely defined by a charge and a location into the node’s space. Particle’s position in each node is defined in a multigrid discrete set. While moving, particle jumps in a node based on its attraction force, defined in module and direction and way. If the force from starting line to arrival is in relation of positive inequality, the particles will be located in a plane position in linear dependence with force intensity. A selection mechanism could be set in order to decide where particle is directed, based on node force intensity. Force is therefore the resultant of particles acting in node. A solution for the JS is obtained only after a complete path from the source to the sink and the resulting force is updated according to the normalized makespan of different solutions.
The Electromagnetic like Method;
The simulated annealing was presented by Scott Kirkpatrick
Name and inspiration come from annealing in metallurgy, a technique that, through the heating and a controlled process of cooling, can increase the dimensions of the crystals inside the fuse piece and can reduce the defects inside the crystals structure. The technique deals with the minimization of the global energy
As follows there is the pseudo-code of a general simulated annealing procedure:
The Simulated Annealing model; 7a. the SA pseudo code; 7b. the flow chart of a general SA procedure
For the scheduling issues, the application of the SA techniques requires the solutions fitness generated by each iteration, that is generally associated to the cost of a specific scheduling solution; the cost is represented by the temperature that is reduced for each iteration [68]. The acceptance probability can be measured as following:
Another facet to be analysed is the stopping criteria, which can be fixed as the total number of iterations of the procedure to be computed.
Tabu search (Glover, 1986) is an iterative search approach characterised by the use of a flexible memory [69]. The process with which tabu search overcomes local optimality is based on the evaluation function that chooses the highest evaluation solution at each iteration. The evaluation function selects the move, in the neighbourhood of the current solution, that produces the most improvement or the least deterioration in the objective function. Since, movement are accepted based on a probability function, a tabu list is employed to store characteristics of accepted moves so to classify them as taboo (i.e., to be avoided) in the later iteration. This is used to dodge cycling movements. A strategy called forbidding is employed to control and update the tabu list. This method was formalized by Glover [69]. An algorithm based on tabu search requires some elements: (i) the move, (ii) the neighbourhood, (iii) an initial solution, (iv) a search strategy, (v) a memory, (vi) an objective function and (vii) a stop criterion. The of TS is based on the definition of a first feasible solution S, which is stored as the current seed and the best solution, at each iteration, after the set of the neighbours is selected between the possible solutions deriving from the application of a movement. The value of the objective function is evaluated for all the possible movements, and the best one is chosen. The new solution is accepted even if its value is worse than the previous one, and the movement is recorded in a list, named taboo list.
For the problem of the scheduling in the job shops, generally a row of assignments of
In figure 8 are reported the pseudo-code and the flowchart for the application of TS to JSSP.
The Tabu Search approach; 8a. the TS pseudo code; 8b. the flow chart of a general TS procedure.
Neural networks are a technique based on models of biological brain structure. Artificial Neural Networks (NN), firstly developed by McCulloch and Pitts in 1943, are a mathematical model which wants to reproduce the learning process of human brain [72]. They are used to simulate and analyse complex systems starting from known input/output examples. An algorithm processes data through its interconnected network of processing units compared to neurons. Consider the Neural Network procedure to be a “black box”. For any particular set of inputs (particular scheduling instance), the black box will give a set of outputs that are suggested actions to solve the problem, even though output cannot be generated by a known mathematical function. NNs are an adaptive system, constituted by several artificial neurons interconnected to form a complex network, those change their structure depending on internal or external information. In other words, this model is not programmed to solve a problem but it learns how to do that, by performing a
The basic architecture of a neural network, starting from the taxonomy of the problems faceable with NNs, consists of three layers of neurons: the
The error of NNs is set according to a testing phase (to confirm the actual predictive power of the network while adjusting the weights of links). After having built a training set of examples coming from historical data and having chosen the kind of architecture to use (among feed-forward networks, recurrent networks), the most important step of the implementation of NNs is the learning process. Through the training, the network can infer the relation between input and output defining the “strength” (weight) of connections between single neurons. This means that, from a very large number of extremely simple processing units (neurons), each of them performing a weighted sum of its inputs and then firing a binary signal if the total input exceeds a certain level (activation threshold), the network manages to perform extremely complex tasks. It is important to note that different categories of learning algorithms exists: (i) supervised learning, with which the network learns the connection between input and output thank to known examples coming from historical data; (ii) unsupervised learning, in which only input values are known and similar stimulations activate close neurons otherwise different stimulations activate distant neurons; and (iii) reinforcement learning, which is a retro-activated algorithm capable to define new values of the connection weights starting from the observation of the changes in the environment. Supervised learning by back error propagation (BEP) algorithm has become the most popular method of training NNs. Application of BEP in Neural Network for production scheduling is in: Dagli et al. (1991) [73], Cedimoglu (1993) [74], Sim et al. (1994) [75], Kim et al. (1995) [76].
The mostly NNs architectures used for JSSP are: searching network (Hopfield net) and
In figure 9 it is possible to see the pseudo-code and the flow chart for the neural networks.
The NNs model; 9a. the implemented NNs pseudo code; 9b. the flow chart of generic NNs.
In this chapter, it was faced the most intricate problem (i.e., Job Shop) in order to explain approaches for scheduling in manufacturing. The JSP is one of the most formidable issues in the domain of optimization and operational research. Many methods were proposed, but only application of approximate methods (metaheuristics) allowed to efficiently solve large scheduling instances. Most of the best performing metaheuristics for JSSP were described and illustrated.
The likelihood of solving JSP can be greatly improved by finding an appropriate problem representation in computer domain. The acyclic graph representation is a quite good way to model alternatives in scheduling. How to fit approaches with problem domain (industrial manufacturing system) is generally a case in issue. Approaches are obviously affected by data and the results are subject to tuning of algorithm’s parameters. A common rule is: less parameters generate more stable performances but local optimum solutions. Moreover, the problem has to be concisely encoded such that the job sequence will respect zoning and sequence constraints. All the proposed approaches use probabilistic transition rules and fitness information function of payoff (i.e., the objective function).
ACO and BE manifest common performances in JSSP. They do not need a coding system. This factor makes the approaches more reactive to the particular problem instance in issue. Notwithstanding, too many parameters have to be controlled in order to assure diversification of search. GAs surpasses their cousins in the request for robustness. The matching between genotype and phenotype across the schemata must be investigated in GAs in order to obtain promising results. The difficult of GA is to translate a correct phenotype from a starting genotype. A right balancing between crossover and mutation effect can control the performance of this algorithm. The EM approach is generally affected by local stability that avoid global exploration and global performance. It is, moreover, subject to infeasibility in solutions because of its way to approach at the problem. SA and TS, as quite simpler approaches, dominate the panorama of metaheuristics proposal for JS scheduling. They manifest simplicity in implementation and reduction in computation effort but suffer in local optimum falls. These approaches are generally used to improve performances of previous methodologies and they enhance their initial score. The influence of initial solutions on the results, for overall approaches, is marked. Performances of NNs are generally affected by the learning process, over fitting. Too much data slow down the learning process without improving in optimal solution. Neural Network is, moreover, affected by difficulties in including job constraints with network representation. The activating signal needs to be subordinated to the constraints analysis.
Based on authors experience and reported paragraphs, it is difficult to definitively choose any of those techniques as outstanding in comparison with the others. Measurement of output and cost-justification (computational time and complexity) are vital to making good decision about which approach has to be implemented. They are vital for a good scheduling in operations management. In many cases there are not enough data to compare – benchmark instances, as from literature for scheduling could be useful - those methods thoroughly. In most cases it is evident that the efficiency of a given technique is problem dependent. It is possible that the parameters may be set in such way that the results of the algorithms are excellent for those benchmark problems but would be inferior for others. Thus, comparison of methods creates many problems and usually leads to the conclusion that there is no the only best technique. There is, however, a group of several methods that dominates, both in terms of quality of solutions and computational time. But this definition is case dependent.
What is important to notice here is: performance is usually not improved by algorithms for scheduling; it is improved by supporting the human scheduler and creating a direct (visual) link between scheduling actions and performances. It is reasonable to expect that humans will intervene in any schedule. Humans are smarter and more adaptable than computers. Even if users don’t intervene, other external changes will happen that impact the schedule. Contingent maintenance plan and product quality may affect performance of scheduling. An algorithmic approach could be obviously helpful but it has to be used as a computerised support to the scheduling decision - evaluation of large amount of paths - where computational tractability is high. So it makes sense to see what optimal configuration is before committing to the final answer.
Differential diagnosis and treatment of tachycardias is a common dilemma encountered by physicians or cardiologists. Although such tachycardias often occur in patients with a normal heart, they may cause bothersome symptoms and rarely represent life-threatening conditions. Among these tachycardias with a heart rate greater than 100 beats per minute (bpm), the narrow QRS complex tachycardias (NCTs) are defined by the presence in a 12-lead electrocardiogram (ECG) of a QRS complex duration less than 120 ms and the wide QRS complex tachycardias (WCTs) are defined by the presence in a 12-lead ECG of a QRS complex duration more than 120 ms (Figure 1) [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]. The NCTs are typically of supraventricular origin above or within the His bundle, although rarely narrow complex ventricular tachycardias (VT) have been reported in the literature in which early activation of the His bundle can also occur in high septal VT, resulting in relatively narrow QRS complexes of 110–140 ms (Table 1, [1, 2, 3, 4, 5]]. The WCTs can be VT or supraventricular tachycardia (SVT) with right or left bundle branch block (BBB) or right or left accessory pathway (Table 1, [6, 7, 8, 9, 10]]. Because administration of medications based on misdiagnosis of these tachycardias can be harmful and sometimes fatal, diagnosis of these tachycardias is critical [11, 12, 13]. The accurate, rapid diagnosis in patients with these tachycardias still remains a significant clinical dilemma, because the published numerous ECG algorithms and criteria are complicated and difficult to recall in urgent clinical situations [11, 12, 13]. We have reviewed ECG findings of the NCTs and WCTs in order to reduce the possible diagnostic errors on the ECGs.
Differential diagnostic algorithm of NCTs and WCTs.
The NCTs with QRS duration less than 120 ms | The WCTs with QRS duration more than 120 ms |
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Differential diagnosis of NCTs and WCTs.
SVT; supra-ventricular tachycardia, AVNRT; atrio-ventricular nodal re-entrant tachycardia, AVRT; atrio-ventricular reciprocating tachycardia, AV; atrio-ventricular, VT; ventricular tachycardia, BBB; bundle branch block, AF; atrial fibrillation, VA; ventriculo-atrial.
The NCTs are common problems encountered in clinical situations [1, 2, 3, 4, 5, 14, 15, 16, 17, 18, 19, 20, 21]. The key to approaching the diagnosis of these arrhythmias is identifying atrial activity (P waves) on the ECG and classifying these tachycardias according to the presence of AV dissociation (Figure 2) and then re-classifying according to long RP or short RP (Table 2) [1, 2, 3, 4, 5, 14, 15, 16, 17, 18, 19, 20, 21]. On the basis of these algorithm, a differential diagnosis can be generated, logical therapy can be delivered for termination of the tachycardia, and a plan can be developed to prevent recurrence.
Differential diagnostic algorithm of NCTs with regular rhythm.
Short RP (RP < PR) | Long RP (RP > PR) |
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Differential diagnosis of NCTs according to RP interval.
AVNRT; atrio-ventricular nodal re-entrant tachycardia, AVRT; atrio-ventricular reciprocating tachycardia, PJRT; Permanent junctional reciprocating tachycardia.
Short RP tachycardias are defined as regular tachycardias in which interval from QRS complex to P wave (upper arrows, Figure 3) much less than interval from P wave to subsequent QRS complex, whereas long RP tachycardias are defined as regular tachycardias in which interval from QRS complex to P wave much more than interval from P wave to subsequent QRS complex (lower arrows, Figure 3) [1, 2, 3, 4, 5, 14, 15, 16, 17, 18, 19, 20, 21].
Schematic demonstration of short RP and long RP.
Sinus tachycardia is defined as an increase in sinus rate to more than 100 bpm with regular rhythm. The rate increases gradually and may show beat to beat variation. Although generally identifiable by a P wave of normal morphologic features that precedes each QRS complex, sinus tachycardia can be difficult to recognize when the P wave begins to fuse with the T wave of the preceding QRS complex. Sinus tachycardia is usually a physiological response such as fever, anxiety, pain, hyperthyroidism but may be precipitated by sympathomimetic drugs or endocrine disturbances [5, 14, 15, 16, 17, 18, 19, 20, 21].
The morphologic appearance of sinus nodal reentrant tachycardia is identical to that of sinus tachycardia. In contrast to sinus tachycardia, the rate is very regular and initiation and termination are abrupt without an underlying physiological stimulus. Vagal maneuver may be successful in stopping the arrhythmia [5, 14, 15, 16, 17, 18, 19, 20, 21].
Atrial tachycardia (AT) is usually a NCTs accounting for 5–15% of SVT. Other than sinus tachycardia, AT is the most common long RP tachycardia. In AT, an atrial source outside the sinoatrial node due to focal automatic activity or re-entry circuit activates the atria. Accordingly, P-wave morphologic characteristics vary depending on the site of this source. Digitalis toxicity should be suspected in patients with paroxysmal AT with AV block [5, 14, 15, 16, 17, 18, 19, 20, 21, 22].
Atrial flutter is a reentrant rhythm of the right atrium typically with an atrial rate of 250 to 350 beats/min. The flutter may circulate in a counterclockwise direction around the tricuspid annulus in the frontal plane (typical, counterclockwise flutter) or in a clockwise direction (atypical, clockwise flutter). P waves have a characteristic “sawtooth” appearance, and 2:1 AV block is common. Because one flutter wave occurs in the ST-T segment and another flutter wave occurs before each QRS complex in atrial flutter with 2:1 AV conduction, atrial flutter is neither a short RP nor a long RP tachycardia [5, 14, 15, 16, 17, 18, 19, 20, 21, 22].
Non-paroxysmal junctional tachycardia (NPJT) is a tachycardia that arises in the AV junction. Although often described as a short RP tachycardia, because NPJT causes ventricular activation almost concurrently with atrial activation, a substantial portion (25%), which is described as a long RP tachycardia, actually show P waves that slightly precede the QRS complex. and in some cases, AV dissociation may be present. Unlike AVNRT and AVRT, initiation and termination are gradual. NPJT is often associated with digitalis intoxication, inferior myocardial infarction, myocarditis, and mitral valve surgical procedures [5, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23].
AV nodal reentrant tachycardia (AVNRT) is characterized by a tachycardia with supraventricular origin, with sudden onset and termination generally at rates between 150 and 250 beats/min and is the most common cause of SVT except atrial fibrillation, atrial flutter, and sinus tachycardia. In majority of patients (noted as the “typical” or “slow-fast” AVNRT), anterograde conduction to the ventricle occurs over the “slow” pathway and retrograde conduction to the atrium occurs over the “fast” pathway and the atria are activated either simultaneously with or just after activations of the ventricles and this common type is classified as a short RP tachycardia. Rarely, in “atypical” or “fast-slow” AVNRT, the reentry occurs in the opposite direction in which anterograde conduction occurs over the “fast” pathway, while retrograde conduction occurs over “slow” pathway, and this rare type is classified as a long RP tachycardia [24, 25, 26, 27, 28, 29, 30, 31].
AV reentrant tachycardia (AVRT) involves reentry between the atria and ventricles with use of the AV node-His bundle conduction as the anterograde and slow pathway and an accessory conduction as the retrograde and fast pathway. This pattern is also known as orthodromic reciprocating tachycardia (ORT). This type is not apparent by analysis of the ECG during sinus rhythm because the ventricle is not pre-excited and the accessory pathway is said to be “concealed”. In tachycardia, retrograde conduction over the accessory pathway is fast and yields a short RP tachycardia [24, 25, 26, 27, 28, 29, 30, 31].
In contradistinction to ORT resulting in NCTs, antidromic AVRT has anterograde conduction over the accessory pathway and retrograde conduction over the AV node-His bundle resulting in WCTs [24, 25, 26, 27, 28, 29, 30, 31].
The following factors are important differences between AVNRT and AVRT [24, 25, 26, 27, 28, 29, 30, 31]:
In contradistinction to AVNRT, an 1:1 relationship is necessary for AVRT because both the atria and the ventricles are part of the reentry circuit. Therefore, if AV block occurs during tachycardia, AVRT is excluded.
If bundle branch block occurs during ORT and the length of the tachycardia cycle increases, AVNRT is excluded because the His-Purkinje system is not part of the tachycardia reentry circuit in AVNRT. The converse is not necessarily true because the absence of cycle length change with the occurrence of bundle branch block does not exclude AVRT.
As discussed with AVRT, certain types of reentrant circuits exist in which the accessory AV connection has AV nodal properties such as slow conduction. In PJRT, excitation over the postero-septal accessory pathway conducts very slowly, because of a long and tortuous route of pathway. Tachycardia is maintained by anterograde AV nodal conduction and retrograde conduction over slow accessory pathway. Because of slow conduction property of accessory pathway, retrograde atrial activation is delayed, and a long RP tachycardia results. Patients with this type of accessory pathway almost never have preexcitation (a delta wave) on ECGs during sinus rhythm [5, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23].
Atrial tachycardia with atrioventricular block is typically seen with digoxin toxicity. The ventricular rhythm is usually regular but may be irregular if atrioventricular block is variable [5, 14, 15, 16, 17, 18, 19, 20, 21].
MAT is characterized by P waves with variable morphologies and variable PR intervals. Differential diagnosis between MAT and atrial fibrillation can be possible by the presence of isoelectric baselines between the P waves in MAT. MAT is seen typically in patients with chronic obstructive pulmonary disease or digoxin toxicity [5, 14, 15, 16, 17, 18, 19, 20, 21, 32].
Atrial flutter is due to a re-entry circuit in the right atrium with secondary activation of the left atrium. This produces atrial contractions at a rate of about 300 beats/min as flutter (F) waves. F waves show broad and saw-tooth appearances and are best seen in lead V1 and the inferior leads [5, 14, 15, 16, 17, 18, 19, 20, 21].
This is the most common sustained arrhythmia with overall prevalence is 1% to 1.5%. Atrial fibrillation is caused by multiple re-entrant circuits or “wavelets” of activation sweeping around the atrial myocardium without effective atrial contraction. Atrial fibrillation is seen on the ECG as irregular baseline undulations of variable amplitude and morphology (called f waves) discharging at a frequency of 350 to 600 beats/min.
With normal conduction, ventricular rate shows frequency between 100 and 150 beats/min. Atrial fibrillation with slow ventricular responses or AV block is seen typically in patients with digoxin toxicity [5, 14, 15, 16, 17, 18, 19, 20, 21, 33, 34, 35, 36].
Monomorphic ventricular tachycardia is common in patients with a history of previous myocardial infarction. Other rare causes of monomorphic VT include right or left ventricular outflow tract ventricular tachycardia and right ventricular dysplasia.
Ventricular flutter appears as a sine wave pattern with regular, large oscillations on the ECG and can progress to ventricular fibrillation [37, 38, 39, 40, 41, 42, 43, 44].
Antidromic AVRT includes a reentrant circuit with accessory pathway as the anterograde pathway, and AV node–His bundle as the retrograde pathway. Some patients (3 to 8%) with WPW syndrome show mechanisms of antidromic AVRT [24, 25, 26, 27, 28, 29, 30, 31].
SVT with aberrant conduction/BBB
Atrial tachycardia with accessory pathway
Junctional tachycardia with accessory pathway
Polymorphic VT is most commonly caused by abnormalities of ventricular muscle repolarization. The predisposition to this problem usually manifests on the ECG as a prolongation of the QT interval. Congenital problems include long QT syndrome and catecholaminergic polymorphic ventricular tachycardia. Acquired problems are usually related to drug toxicity or electrolyte abnormalities, myocardial ischemia. Class III anti-arrhythmic drugs such as sotalol and amiodarone prolong the QT interval and may in some circumstances be pro-arrhythmic. Other relatively common drugs include some antibiotics and antihistamines [37, 38, 39, 40, 41, 42, 43, 44].
Ventricular fibrillation is a terminal arrhythmia in which ventricular contractions are uncoordinated and too weak to eject blood. The ECG shows irregular, chaotic deflections of varying amplitude and shape [37, 38, 39, 40, 41, 42, 43, 44].
Antidromic AVRT with variable VA conduction
Pre-excited AF (AF with ventricular pre-excitation)
Torsades de pointes
The ECG demonstrates a polymorphic VT characterized by the QRS complexes of changing amplitude that appear to twist around the isoelectric line and occur at the rates of 200 to 250 beats/min. Most data suggest that early afterdepolarizations are responsible for both the QT prolongation and the torsades de pointes. The most common causes are congenital severe bradycardia, potassium depletion and use of class IA and IC drugs. Clinical features depend on whether torsades de pointes is due to acquired or congenital long QT syndrome. Some episodes may persist and progress to ventricular fibrillation, leading to sudden death. In congenital long QT syndrome, long QT intervals predispose the patient to an R-on-T phenomenon, wherein the R-wave, representing ventricular depolarization, occurs during the relative refractory period at the end of repolarization [37, 38, 39, 40, 41, 42, 43, 44].
AF or atrial flutter or focal atrial tachcyardia with varying block conducted with aberration
There are several algorithms that are currently used to help distinguish Supraventricular Tachycardia (SVT) with aberrancy and Ventricular Tachycardia (VT) (Table 3). Many of these algorithms and criteria have limitations [44, 45, 46, 47, 48, 49, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71].
[RBBB morphology] Identical activation vector = SVT.
If the initial 20 ms of the QRS are the same in WCT as in sinus rhythm, SVT is favored with a positive predictive value (PPV) of 92%. The sinus rhythm ECG must be available for this analysis.
[RBBB morphology] An rSR’ where S crosses baseline = SVT with a PPV of 91%.
[RBBB morphology] Triphasic QRS in V1 = SVT with a PPV of 92%.
[RBBB morphology, LBBB morphology] Precordial concordance = VT. A QRS, which is predominantly positive or predominantly negative in every precordial lead, overwhelmingly favors VT with specificity of 95–100% and a PPV of 89–100% [44, 45, 46, 47, 48, 49, 50, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71].
AV dissociation = VT. Of all criteria, this is the most secure with specificity of 100% and PPV of 100%. It holds true regardless of bundle branch pattern or other morphology criteria.
[RBBB morphology] QRS duration >140 ms = VT with specificity of 57–75% and PPV of 89%.
[RBBB morphology] Left axis deviation = VT with PPVs of 88–94%. With extreme left axis (more negative than −90°), the PPV is 98%.
[RBBB morphology] Mono- or biphasic QRS morphologies in V1 favors VT with PPV of 82–83
If the V1 QRS is triphasic, an R:S ratio < 1 in V6 (that is, R wave smaller than S wave) favors VT with PPV of 90%.
[RBBB morphology] Rsr’ (‘Rabbit ears’) = VT. In an unusual triphasic V1, with the left R wave taller than the right, and the S wave not crossing the baseline, favors VT with PPV of 100% [44, 45, 46, 47, 48, 49, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71].
A history of myocardial infarction, QRS morphology in leads aVF and V1 ([1] predominant negative deflection in aVF in tachycardia with RBBB pattern and Q wave, [2] a monophasic or biphasic waveform in V1 in tachycardia with RBBB pattern, [3] QS or qR waveform in tachycardia with LBBB pattern favored a diagnosis of VT) and frontal plane axis > 40° when compared with baseline the ECG favored a diagnosis of VT. The presence of AV dissociation and/or the presence of premature ventricular beats during sinus rhythm that show morphologies same to that observed in tachycardia favored a diagnosis of VT [51, 52, 53, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71].
[LBBB morphology] V1 or V2 with initial R > 30 ms = VT.
[LBBB morphology] V1 or V2 QRS onset to nadir of S wave >60 ms = VT.
[LBBB morphology] V1 or V2 with notching on the S wave downstroke = VT.
[LBBB morphology] Any Q in V6 = VT [54, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71].
An R-wave peak time, with the interval from QRS onset to first change in polarity (R or S peak) in lead II ≥ 50 ms, independent of whether the complex is positive or negative, has been reported to have a sensitivity of 93% and specificity of 99% for identifying VT (Figure 4) [55, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71].
The R-wave peak time (RWPT) in lead II.
Vereckei et al. published four-step algorithms with the incorporation of new criteria of Vi/Vt (Figure 5) [56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71].
Vereckei aVR algorithm.
The four steps were used in the following sequence:
If an initial R wave was present in lead aVR, VT was diagnosed.
If an initial, non-dominant q or r in aVR > 40 ms, VT was diagnosed.
If the morphology of WCT did not correspond to BBB or fascicular block, VT was diagnosed.
In the last step when the Vi/Vt ratio, obtained by measuring the voltage of the initial 40 ms (Vi) and the terminal 40 ms of a QRS (Vt) in any ECG lead, was ≤1 the diagnosis of VT, if the Vi/Vt was >1 the diagnosis of SVT was made (Figure 5).
During WCT due to SVT, after the initial rapid septal activation over the normal His-Purkinje system, the slow intraventricular activation occurs in the mid to terminal portion of the QRS, thus the Vi/Vt > 1.
Brugada algorithm is the most widely known and commonly used algorithm (Figure 6) [57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71].
Brugada algorithm.
Brugada algorithm is as follows:
Is there concordance present in the precordial leads (leads V1-V6)?
“Are all of the QRS complexes completely upright, or downward in the precordial leads?”
If the answer is yes, then VT is the diagnosis
Is the R to S interval (between the onset of the R wave and the nadir of the S wave) > 100 ms in any one precordial lead?
If the answer is present, then VT is the diagnosis
Is AV dissociation present?
“AV dissociation occurs when P waves are seen at different rates than the QRS complexes.”
If the answer is present, then VT is the diagnosis
Examine the morphology of the QRS complex to see if it meets the specific criteria for VT, as Table 4.
QRS duration | |
---|---|
| |
| |
| |
| |
| |
| |
| |
Positive or negative concordance in all precordial leads | |
| |
| |
| |
| |
Lead V1 | Lead V6 |
|
|
| |
| |
Lead V1–2 | Lead V6 |
|
|
| (Absence of Q wave favors SVT) |
| |
ECG criteria favoring ventricular rather than supra-ventricular tachycardia in WCTs.
RBBB; right bundle branch block, LBBB; left bundle branch block, SVT; supra-ventricular tachycardia.
Right bundle branch block morphology | Lead V1: Monophasic R, biphasic qR, broad R (>40 ms), Rsr’ (the so-called ‘rabbit ears’ sign) Lead V6: R:S ratio < 1 |
---|---|
Left bundle branch block morphology | Lead V1–2: Broad R wave, slurred or notched downstroke of S wave, delayed nadir of S wave Lead V6: Q or QR or QS wave |
Morphology criteria for VT in leads V1, V2 and V6.
The ECG criteria or algorithms for the diagnosis of NCTs and WCTs has undergone evolution and development in concert with the field of cardiology itself, but the necessity of a correct diagnosis remains unchanged [57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71]. The world has not yet seen the ‘one criterion to end all criteria’ or ‘simplest criterion’ with high sensitivity and specificity, and it seems unlikely to appear in our near future. Therefore, physicians or cardiologists should be cautioned against overreliance in these ECG criteria or algorithms for the interpretation of the ECGs.
This is a brief overview of the main steps involved in publishing with IntechOpen Compacts, Monographs and Edited Books. Once you submit your proposal you will be appointed a Author Service Manager who will be your single point of contact and lead you through all the described steps below.
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In all cases, cyclical ups and downs depend not only on internal system cyclical processes and their factors in countries but also on the consequences of intercountry interaction. The ability to measure and predict business cycles, taking into account their mutual influence, is a prerequisite for the development of an adequate business policy of countries and their associations.",book:{id:"6703",slug:"statistics-growing-data-sets-and-growing-demand-for-statistics",title:"Statistics",fullTitle:"Statistics - Growing Data Sets and Growing Demand for Statistics"},signatures:"Elena Zarova",authors:null},{id:"60246",title:"Statistical Research of Investment Appeal of Russian Regions",slug:"statistical-research-of-investment-appeal-of-russian-regions",totalDownloads:986,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"In this chapter, the methodological results directed on realization statistical research of investment appeal of Russian regions are offered. 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He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. 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