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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\nThis achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
\n\nWe are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
\n\n\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:"4503",leadTitle:null,fullTitle:"Selected Topics in Applications of Quantum Mechanics",title:"Selected Topics in Applications of Quantum Mechanics",subtitle:null,reviewType:"peer-reviewed",abstract:"This book has two sections. 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2012",dateReviewed:"July 21st 2012",datePrePublished:null,datePublished:"December 19th 2012",book:{id:"3196",title:"New Generation of Electric Vehicles",subtitle:null,fullTitle:"New Generation of Electric Vehicles",slug:"new-generation-of-electric-vehicles",publishedDate:"December 19th 2012",bookSignature:"Zoran Stevic",coverURL:"https://cdn.intechopen.com/books/images_new/3196.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"30692",title:"Dr.",name:"Zoran",middleName:"M.",surname:"Stevic",slug:"zoran-stevic",fullName:"Zoran Stevic"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"154524",title:"Dr.",name:"Zlatomir",middleName:null,surname:"Zivanovic",fullName:"Zlatomir Zivanovic",slug:"zlatomir-zivanovic",email:"zzivanovic@vinca.rs",position:null,institution:{name:"University of 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Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"154524",title:"Dr.",name:"Zlatomir",middleName:null,surname:"Zivanovic",fullName:"Zlatomir Zivanovic",slug:"zlatomir-zivanovic",email:"zzivanovic@vinca.rs",position:null,institution:{name:"University of Belgrade",institutionURL:null,country:{name:"Serbia"}}},{id:"164696",title:"Dr.",name:"Zoran",middleName:null,surname:"Nikolic",fullName:"Zoran Nikolic",slug:"zoran-nikolic",email:"zor.nikolic@yahoo.com",position:null,institution:null}]},book:{id:"3196",title:"New Generation of Electric Vehicles",subtitle:null,fullTitle:"New Generation of Electric Vehicles",slug:"new-generation-of-electric-vehicles",publishedDate:"December 19th 2012",bookSignature:"Zoran Stevic",coverURL:"https://cdn.intechopen.com/books/images_new/3196.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"30692",title:"Dr.",name:"Zoran",middleName:"M.",surname:"Stevic",slug:"zoran-stevic",fullName:"Zoran Stevic"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},ofsBook:{item:{type:"book",id:"11433",leadTitle:null,title:"Human Migration in the Last Three Centuries",subtitle:null,reviewType:"peer-reviewed",abstract:"
\r\n\tIn March 2022, another book on human migration seems important when the events or tragedies unfolding in Eastern Europe are considered. People have always migrated and have moved, but, specifically looking at the last three hundred years, involuntary migration is on the rise. Involuntary migration does not only affect Europe; Asia, Africa, and North as well as South America, have had their fair share of natural catastrophes, invasions, and wars.
\r\n\tThis book will intend to look at different migrant patterns, voluntary and involuntary migration, over the last three centuries. What influenced people to leave their home countries, family, and friends and settle somewhere else? The book may include histories of the 19th century, consider tragedies and movements activated by political events in the 20th century, and/or look at recent events of the 21st century. Push and pull factors are important points. While most of us may be influenced in a negative way by the current happenings in Eastern Europe, the Russian invasion and resulting tragedies also demonstrate some very positive human traits – the preparedness of Ukraine’s surrounding countries to help those in need and to provide a safe place for the present.
\r\n\tWhether one looks at voluntary or involuntary migration into any country, after a period of adjustment, migrants do play a positive role. The research found that migrants contribute to the economy (food, shelter, employment, tax) and enrich a country’s cultural norms. Prerequisites for successful settlements are that the host society adopts a tolerant approach and that the migrants recognize the law and the language of the host country. Nothing is ever easy or without controversy, but I am a migrant (German Australian), and life in Australia has been relatively harmonious. Issues that could be considered in the book are multicultural societies (do monocultural societies still exist?) and theories of acculturation versus integration (settlement processes).
\r\n\tTwo further issues are very important in relation to human migration. There is climate change, global warming, and the environment, which clearly affect people’s movement. Small island populations are very concerned about rising sea levels. 2021 has also seen floods costing human lives: Turkey (August 2021), Brazil (December 2021), Chile (January 2021), and South India (November 2021), to name but a few. In Australia (March 2022), farms and whole townships in New South Wales and Queensland have been flooded for the second time in five years, and plans to resettle these towns are considered. Official and social media provide ample coverage of the events, which leads me to the next issue. There is today’s very important role of the media, of the official and social media. We are constantly bombarded with images of human war tragedies and flood victims. People in industrialized, western countries must be the best-informed populace. How far do the images and up-to-date TV news influence us, make us change our behavior, and perhaps even consider us more generous than we have been?
\r\n\tClimate change and the media are relatively new to the human migration debate, but both issues play important parts, and some interesting discussions are appreciated.
\r\n\t
Marker-assisted selection (MAS) is an important scheme in plant breeding since the 1990s, after promising analysis results for tagging genes or mapping QTL [1]. Marker assisted selection and molecular breeding have been used in the identification of underlying major genes in gene pools and their transfer to desirables traits of major plant breeding programs. Using of MAS have shown some shortcomings due to long selection schemes and also the look for vital marker-QTL associations being unable to capture “minor” gene effects. Thus marker-assisted selection (MAS) is difficult to improve traits having complex inheritance such as grain yield and abiotic stresses.
\nUsing whole-genome prediction models, the genomic selection (GS) strategy has paved the way to over-come these limitations. High-density molecular markers using is one of the main features of genomic selection. Therefore, each of the trait loci has the likelihood of being in linkage disequilibrium (LD) with a minimum of one marker locus within the entire breeding population. Genome selection strategy removes the need to mapping of genes and search for linked QTL–marker loci associated individually. Rather, Genomic selection accounts for bunches of predictors simultaneously and is characterized by constraining random estimates towards zero. Moreover, Genomic selection helpful for accelerate breeding cycles in such a way that the rate of annual genetic gain per unit of time and cost can be decreased. Genomic selection has been well established in the field of animal breeding, but is in its beginning in crops plants and forest tree breeding.
\nGenome-wide selection or genomic selection estimates marker effects across the full ordering of the breeding population (BP) supported the prediction model developed within the training population (TP). Training population could be a group of related individuals (such as half-sibs or lines) that are each phenotypes and genotypes. Breeding population typically is just genotyped not phenotypes. Hence, Genomic selection depends on the degree of genetic similarity between training population and breeding population within the Linkage disequilibrium between marker and trait loci. Breeding values have not been a preferred index in plant breeding, however it is in animal breeding. Once plan of genomic estimated breeding value (GEBV) was planned, it had been considered an unrealistic approach due to lack of enormous scale genotyping technologies. However, currently, it has been a possible approach with recent advances in high throughput genotyping platforms (3rd generation platforms). Generally processes of genomic selection and marker assisted selection used for Quantitative Traits are shown in Figure 1.
\nSelf-pollinated crop genomic selection vs. phenotypic/MAS selection timeline.
The main schemes of the two approaches are similar, wherever each marker assisted selection and genomic selection consist of breeding and training phases. In the training phase, phenotypes and genome-wide (GW) genotypes are investigated in an exceedingly set of a population, i.e., the mapping population in marker assisted selection and also the training population in genomic selection. Among populations, important relationships between phenotypes and genotypes are expected utilizing statistical models. Within the breeding phase, genotype data are obtained in an exceedingly breeding population, on the basis of genotypic information favorable individuals are selected. There are three prominent variations between the two approaches: (1) within the training section, quantitative trait loci (QTLs) are known in marker assisted selection whereas formulae for genetic estimation of breeding value prediction are generated in genomic selection, called genomic selection models; (2) within the breeding section, genotype data are solely needed for targeted regions in marker assisted selection, whereas genomic selection genotype data are considered to be mandatory in genomic selection (3) within the breeding phase, favorable individuals are selected on the bases of the linked markers in marker assisted selection, whereas GEBVs are used for selection in GS. Thus, GS collectively analyses all the genetic variance of every individual by summing the marker impacts of GEBV and it is expected to deal with little effect genes that cannot be captured by traditional MAS.
\nThe statistical ways employed by GS are comparatively new the plant-breeding community. The ways of marker-assisted selection (MAS) or marker-assisted recurrent selection (MARS) assume that the user is aware of that alleles are favorable, and what their average effects on the phenotype are. This assumption is viable for major-gene traits however not for quantitative traits that are influenced by several loci of little impact and the environment. To deal with quantitative traits, new statistical approaches that might account for this uncertainty were required to get the most effective predictions potential. Finding problem with locus identification, entailed that the consequences for all marker loci be at the same time estimated. Once a prediction based on allele effects, the allele becomes the unit of analysis. Alleles are so the units that need to be replicated inside and across environments. However that replication will occur in spite of the particular lines carrying the alleles such lines themselves no longer need to be replicated. Within the breeding context, removing the requirement for line replication opens the likelihood of dramatically increasing the amount of lines pushed through the pipeline of a breeding program, and successively of accelerating selection intensity.
\nGenomic selection is to assemble a training population for individuals for which both genotypes and phenotypes are available and use those data to create a statistical model that relates variation in observed genotypes marker loci to variation in the observed phenotypes of the individuals. Multiple generations of parents and progenies provided powerful training population than a single generation individuals and larger number of individual’s generations and markers provide more powerful training population (TP). The statistical model obtained from genotype and phenotype is then applied to a prediction population comprised of individuals for which genotypes are available, but phenotypes are not. GS is based on similarity between the training population (TP) and breeding population (BP) in the LD between marker loci and trait loci. This similarity may exist because breeding population is selected from training population or descended from training population or because density of markers is so high that every trait locus is in disequilibrium with at least one marker locus across the entire population of the target species. The training population is genotyped and phenotyped to train the genomic selection (GS) prediction model. In Genomic selection main role of phenotyping is to calculate effect of markers & cross validation. Genotypic information from the breeding material is then fed into the model to calculate genome estimated breeding values (GEBV) for these lines (Figure 2).
\nGenomic selection scheme. Information on phenotype and genotype for a training population allows estimating parameters for the model. (Modified: Castro et al. 2012) [
Traditional marker assisted selection, whereas helpful for merely transmitted traits controlled by few loci, loses effectiveness because the number of loci will increase. This is often true for individual quantitative traits or once multiple traits are below selection. Quantitative traits like grain yield, abiotic stress have verified hard to enhance with marker-assisted selection. The main limitations are (i) tiny population sizes and traditional statistical strategies that have inadequate power to find and accurately estimate effects of small-effect quantitative trait loci (QTL) and (ii) gene x gene interactions (epistasis) and (iii) genotype x environment interactions (G.E) that have restricted the exchangeability of quantitative trait loci result estimates across populations and environments. The Beavis effect is a statistical phenomenon in biology that refers to the overestimation of the effect size of quantitative trait loci (QTL) as a result of small sample sizes in QTL studies.
\nThe availability of low cost and extensive molecular markers in plants has allowed breeders to raise however molecular markers might best be used to win breeding progress. Additionally advances in high-throughput genotyping have markedly reduced the value per data point of molecular markers and increasing genome coverage. This reduction was in the main the results of three parallel developments [2] (i) the invention of huge numbers of single nucleotide polymorphism (SNP) markers in several species; (ii) development of high-throughput technologies, like multiplexing and gel-free deoxyribonucleic acid arrays, for screening SNP polymorphisms; and (iii) automation of the marker-genotyping method, together with efficient procedures for deoxyribonucleic acid extraction [2]. Phenotyping prices are increased Genotyping prices are being reduced and marker densities are being increased speedily.
\nStatistical strategies are inadequate for improving polygenic traits controlled by several loci of small impact. There will be more markers (explanatory variables) than lines (observations) that introduce statistical issues. Drawback of small p (number of traits) and enormous m (number of markers) ends up in a lack of degrees of freedom. The foremost acceptable statistical model is required to at the same time estimate several marker effects from a limited range of phenotypes. In so-called “large p, small m” problems, standard multiple linear regression cannot be used without variable selection, that conflicts with the initial goal of avoiding marker selection. To overcome these issues, a range of ways, e.g., best linear unbiased prediction, ridge regression, Bayesian regression, kernel regression and machine learning methods are projected to develop prediction models for genomic selection.
\nThe most economical use of GS is to exchange expensive and long phenotyping by a prediction of the genetic worth of the character below selection (or any multi trait index). Thus, the foremost expected advantage is to shorten selection cycles. However, to learn from shorter cycles, the genetic gain per selection cycle ought to be near that predicted from phenotypic or combined MAS + phenotypic selection. Progeny testing schemes have a high accuracy of selection, however the time interval is also additional, takes long term to perform a cycle of selection that decreases the genetic gain. The univariate breeder’s equation was used for the GS-BPs as a result of they include just one stage of selection [3]. Selection accuracy is adequate to the correlation between selection criteria and breeding value (i.e., correlation between phenotypes or GEBVs and true breeding values [TBVs]). In oxen, Schaeffer [4] determined that the time and value savings exploitation GS with GEBV accuracy of 0.75 would increase genetic gain twofold and supply a price savings of ninety two in comparison to the present ways. The power to calculate extremely correct GEBVs and also the potential to drastically cut back makeup analysis frequency and selection cycle time expedited a speedy adoption of genomic selection and is revolutionizing the oxen breeding trade (Figure 3).
\nGenomic selection scheme.
The basic model may be denoted as
\nwhere \n
Evaluating GEBV accuracy through cross validation (CV). CV entails splitting the data into training and validation set. The ratio of observations in each set varies, but often a fivefold CV is used, that is, the data set is randomly divided into five sets, with four sets being combined to form the training set and the remaining set designated as the validation set. Each subset of the data is used as the validation set once, before applying of the prediction model to the breeding population, the accuracy of the model should be tested. For this, most of the training population is used to create a prediction model, which is then used to estimate the genomic estimation breeding values of the remaining individuals in the training population, using genotypic data only. This permits researchers to “test” and refine the prediction model to make sure the prediction accuracy is high enough that future predictions are often relied upon. Once valid, the model is often applied to a breeding population to calculate GEBVs of lines that genotypical, however no phenotypical, information is available.
\nThe prediction accuracy of the GEBVs is evaluated by the correlation between the GEBVs and empirically estimated breeding values, r(GEBV: EBV), where the EBV can be obtained in a number of ways, most simply, as a phenotypic mean. This correlation provides an estimate of selection accuracy and thus directly relates GEBV prediction accuracy to selection response [2]. Other statistics such as mean-square error (MSE) are used occasionally [3]. Genomic selection accuracy is defined as the correlation between GEBV and the true breeding value (TBV), that is, r(GEBV:TBV). Since we can only measure r(GEBV:EBV), this measure needs to be converted to an estimate of r(GEBV:TBV). To do so, it is assumed that r(GEBV:EBV) = r(GEBV:TBV) X r(EBV:TBV). This assumption is correct if the only component common between the GEBV and the EBV is the TBV itself. In other words, the assumption holds if GEBV = TBV + e1 and EBV = TBV + e2, where e1 and e2 are uncorrelated residuals. The assumption could be violated if the training and validation data were collected in the same environment. In that case, genotype by environment (G.E) interaction would generate a common component of error in both GEBV and EBV, biasing their correlation upward. Thus, training and validation data should be collected in different environments to ensure sound estimates of GEBV prediction accuracy. The correction, r(EBV:TBV), accounts for the fact that the EBV in the validation set is not free of error. When the EBVs are phenotypes, r(EBV:TBV) is equal to the square root of heritability (h) within the validation set [2].
\nAccurate GEBV predictions offer the possibility that future elite and parental lines will be selected on GEBV rather than phenoypic data from extensive field testing. Immediate impact would be a great increase in speed of breeding cycle increasing selection gains per unit time. Thus, GS could radically change the practice of field evaluation for breeders. Of course, regardless of the breeding method used, final field evaluations of varieties across the target environments will be needed before they are distributed to farmers.
\nBreeding cycle time is shortened by removing phenotypic evaluation of lines before selection as parents for the next cycle. Model training and line development cycle length will be crop and breeding program specific. In a GS breeding schema, genome-wide DNA markers are used to predict which individuals in a breeding population are most valuable as parents of the next generation of offspring. The prediction model is additionally continuously rejuvenated as genotypical and phenotypic data from elite lines derived from the collaborating breeding programs is incorporated into the prediction models. In this manner, new germplasm may be infused into the system at any point. As lines derived from the recently infused germplasm advance within the breeding process, their genotypical and phenotypic data may be incorporated into the prediction models.
\nThe purpose of phenotyping now is to pick the best lines from a segregating population and to judge fewer lines with larger replication in every cycle of selection. However during a GS driven breeding cycle, the aim of phenotyping is to estimate or re-estimate marker effects. It is far from clear at this point whether or not it will be advantageous to evaluate solely the best lines or to evaluate few lines with high replication. So separates the germplasm improvement cycle from the prediction model improvement cycle. Indeed, if we tend to use the rules for optimum QTL linkage mapping, analysis should include not just the best however the best and the the} worst lines Figure 3 also emphasizes the requirement for model updating and re-evaluation. Marker effects might amendment as a results of allomorph frequency changes [6] or of epistatic gene action. Model updating with every breeding cycle should mitigate reduced gains from GS caused by these mechanisms. Thus, GS may radically change the practice of field evaluation for breeders. Of course, despite the breeding technique used, final field evaluations of varieties across the target environments are going to be needed before they are distributed to farmers.
\nAccuracy declines as generation number between the last model update and selection candidates increases [4, 5, 6], because selection causes changes in variances, allele frequencies, and LD relationships between markers and QTL [4]. Under random mating, simulations have shown model accuracy to decrease by about 5% per generation [5, 6], but accuracy decrease was much more rapid under selection.
\nThe response of genomic selection is that the output of varied factors responsible for estimation accuracy of GEBVs. These factors are interconnected in an exceedingly advanced and comprehensive manner. They include model performances, sample size and relatedness, marker density, gene effects, heritability and genetic design, and therefore the extent and distribution of linkage disequilibrium between markers and QTL.
\nThe most important characteristic of the population is its effective size. An obvious measure of population size is its census: how many individuals it contains. But populations with the same census size can behave quite differently. For a population of a given rate of inbreeding, the effective size is equal to the census size of a randomly mating (“ideal”) population that would have that same rate.
\nAccuracy due to genetic relationships can represent from a small minority to a large majority proportion of the overall accuracy. The combination of long-distance LD due to pedigree relatedness (e.g., full sibs and half sibs) and short-distance ancestral LD due to small effective population size are among the key features of our training population. With improved marker technology, large TPs that use a representative sample of germplasm in a given breeding program may be a good strategy for long-term accuracy over a broad range of families. It has been observed to be monotonic increase in the prediction accuracy for grain yield with increasing population size without any substantial decrease in the slope (Figure 4). Studies in this the size of the training population is of crucial importance in genomic selection. The impact of the population size on the accuracy of genomic selection is less pronounced for fewer characters like grain moisture, which might be due to presence of larger variance among populations that can be efficiently utilized by few individuals per population. Parameters such as effective population size and QTL number strongly influence marker densities and TP sizes required for acceptable accuracy. Indeed, simulations similar to those of Meuwissen et al. [6] have shown that marker density needs to scale with effective population size [7]. Until very low marker densities were reached, marker number had very little, if any, effect on prediction accuracies within families from various plant species [8]. Likewise, GEBV accuracy of several traits in cattle, including net merit, was hardly affected when as many as 75% of the original markers were masked. Adequate marker density and TP size depend on QTL number and trait heritability. Calus and Veerkamp [9] used the average r2 between adjacent markers as a measure of marker density relative to decay of linkage disequilibrium. They found that for a highly heritability trait, average adjacent marker r2 of 0.15 was sufficient, but for a low heritability trait, increasing the r2 to 0.20 improved prediction accuracy. Heritability dramatically affects TP sizes required for successful GS, especially at h2 less than 0.40 [3].
\nRelation between number of plants in the training population and accuracy of genomic selection for traits with different heritabilities.
Genetic drift is an important cause in generating LD, the non-independence of alleles at different loci. This non-independence allows marker alleles to predict the allelic state of nearby QTL, enabling marker genotypes to predict the phenotype. The LD intensity decays with greater distance between two markers. Decay rates which vary widely across species, populations, and genomes due to forces of mutation, recombination, population size, population mating marker density must increase with increases in Ne*c, where Ne is the effective population size and c is the recombination rate between loci. LD patterns. Marker density can be inferred by the rate of LD decay across the genome as inferred by the relationship b/w inter marker coefficient of determination r2 and genetic distance. LD estimates can be used to determine target marker densities for GS at equilibrium, drift generating LD is balanced by recombination, causing it to decay, such that nearby loci are expected to be in higher LD than faraway loci. LD has a major effect on the operability of GS, so it has to be well understood before performing GS. LD is defined as the non-random association of alleles at different loci. It has been found that for high heritability trait average adjacent marker r2 of 0.15 is sufficient but for low heritability trait increasing r2 value to 0.2 improve accuracy of GEBV predictions.
\nSince, then marker of choice is very important to accurate estimate GEBVBs, different platforms are available
\nSingle nucleotide polymorphisms (SNPs) differentiate individuals based on variations detected at the level of a single nucleotide base in the genome. SNPs have become the marker of choice for crop genetics and breeding applications because of their high abundance in genomes, and the availability of a wide array of genotyping platforms with various multiplex capabilities for SNP analysis [10]. Recent breakthroughs in next generation sequencing (NGS) technologies enabled millions of sequences reads to be generated from a single run at a more affordable cost. The ability to perform GS requires routine genotyping at a high number of loci. Single nucleotide polymorphisms (SNPs) differentiate individuals based on variations detected at the level of a single nucleotide base in the genome. SNPs have become the marker of choice for crop genetics and breeding applications because of their high abundance in genomes, and the availability of a wide array of genotyping platforms with various multiplex capabilities for SNP analysis [10]. Recent breakthroughs in next generation sequencing (NGS) technologies enabled millions of sequences reads to be generated from a single run at a more affordable cost. The resulting large amount of data provided sequence depth adequate for de novo sequence assembly, which has made the SNP discovery on a large scale a feasible task, particularly for species without completed genome sequences. Successful results on large-scale discovery of SNPs based on NGS methods have been reported in several plant species, including both and polyploid species, and more are on the way. The development of highly parallel SNP assays, such as Illumina’s Golden Gate assay with 1536-plex platform enabled the genome-wide studies previously not feasible for economically important crops. Using these techniques, SNP-based high-density genetic maps are now available in several crop plants such as soybean, maize, barley and wheat. Thus, genotyping lines for use in GS using SNP and direct resequencing with next-generation.
\nAdvances in next generation technologies have driven the costs of DNA sequencing down to the point that genotyping-by sequencing (GBS) is now feasible for high diversity, large genome species. GBS is a highly multiplexed approach is based on high-throughput, next-generation sequencing of genomic subsets targeted by restriction enzymes (REs). Genotyping by sequencing (GBS) in any large genome species requires reduction of genome complexity. Genotyping-by-sequencing can be applied to different populations or even different species without any prior genomic knowledge as marker discovery is simultaneous with the genotyping of the population. GBS sequence allows access to any sequence within low copy genomic regions or regulatory regions controlling the expression of plant genes responsible for agronomically important phenotypes are often located in non-coding DNA. The use of GBS for GS, therefore, should be applicable to a range of model and non-model crop species to implement genomics-assisted breeding. Genotyping-by sequencing combines marker discovery and genotyping of large populations, making it a superb marker platform for breeding applications even within the absence of a reference genome sequence or previous polymorphism discovery. Additionally, the pliability and low price of GBS create this an ideal approach for genomics-assisted breeding.
\n\n
The marker effects are calculable from the training population and used directly for GS within the involved breeding population, and QTL discovery, mapping, etc., are not needed.
Each simulation and empirical studies reveal that GS produces larger gains per unit time than constitution selection. For instance, a simulation study in maize showed GS to be superior to MARS, notably for traits having low heritability. Further, GS will predict the performance of breeding lines additional accurately than that supported pedigree data, and GS appears to be an efficient tool for rising the potency of rice breeding.
The selection index approach integrates appropriately weighted data from multiple traits into an index that is the premise for concurrent selection for the concerned traits. The genome-wide marker data is integrated into a range index either alone or in conjunction with phenotype data on one or additional traits. Simulation studies show that the on top of combined selection index approach of GS can increase the effectiveness of selection, considerably for low heritability traits.
GS would tend to cut back the speed of inbreeding and also the loss of genetic variability as compared to selection based on breeding values calculable from phenotype data; this may be achieved while not sacrificing selection gains. This might be notably vital in species that show severe inbreeding depression.
Genomic selection scheme consist of phenotyping for each selection cycle within the breeding population is not needed. This greatly reduces the length of breeding cycle, notably in perennial species. For instance, GS was calculable to reduce the selection cycle time from 19 years to simply 6 years just in case of oil palm (
Genomic selection would possibly enable breeders to pick out parents for crossbreeding programs from among those lines that have not been evaluated within the target environment. This selection would be supported GEBVs of these lines estimated for their adaptation to the target environment. This could facilitate germplasm exchange and their utilization in breeding programs.
Genotype X environment interaction could be a vital a part of phenotype and its estimation is sort of demanding. GS can utilize information on marker genotype and trait phenotype accumulated over time in varied analysis programs covering a variety of environments and integrate an identical in GEBV estimates of the various individuals/lines. This could enable GEBV estimation even for traits that they have never been tested.
Theoretically, GEBV estimates is employed for the selection of parents for crossing programs and, possibly, for the development of hybrid varieties. These applications, however, ought to expect validation of the concept in apply.
\n
GS has still not become popular plant breeding community primarily due to low evidence for its sensible utility. In fact, most discussions on its utility are for the most part statistical treatments and simulations that are not simply appreciated by plant breeders.
The potential value of GS should be assessed with caution because GS has been mostly evaluated in simulation studies. There is an imperative have to be compelled to judge genomic selection in crop breeding situations to demonstrate its practical utility.
The marker effects and, as a result, GEBV estimates would possibly modification attributable to changes in gene frequencies and epistatic interactions. This is often ready to necessitate amendment of the GS model with every breeding cycle therefore the gains from GS are not reduced.
The accuracy of GEBV estimates has been evaluated exploitation simulation models based on additive genetic variance. These models ignore epistatic effects that does not seem to be realistic. It has been argued that since biological process makes alone a small contribution to the breeding value, the employment of solely additive genetic models for GS is additionally expected to maximize selection gains. However, this argument are planning to be entirely valid only for self-fertilizing species, where homozygous lines are used as parents as well as varieties.
Our information concerning the genetic design of quantitative traits is severely restricted, that limits our ability to develop applicable models of GS to realize the most prediction accuracy.
The selection response declines at a faster rate under GS than with pedigree selection. This may be reduced by continually together with new markers for the prediction of GEBVs. The long response under GS can also be raised by putting higher weights on the low-frequency favorable alleles, considerably within the start of GS program.GS is simpler than phenotypic selection on per unit time basis only if off-season/greenhouse facilities are accustomed grow up to three generations per annum. The utility and also the cost-effectiveness of GS would be uncertain wherever such facilities are not offered.
The necessity for genotyping of an oversized variety of marker loci in every generation of selection adds considerably to the price of breeding programs. It has been projected that, inside the future, a bigger stress are going to be placed on the use of marker information than on composition information. Such a shift, however, would need the value of one marker information to be merely 1/5000 the price of phenotyping one entry.
Implementation of GS would need intensive infrastructure and completely different resources, which might get on the so much aspect the reach of most moderate size public sector breeding programs, considerably within the developing countries. To boot, planning and execution of GS is kind of exigent, and additionally the breeders would be required to reorient their approach to the breeding programs.
Currently, the lion’s share of research on GS has been performed in livestock breeding, where effective population size, extent of LD, breeding objectives, experimental design, and other characteristics of populations and breeding programs are quite different from those of crop species. Nevertheless, a great number of findings within this literature are very illuminating for GS in crops and should be studied and built upon by crop geneticists and breeders. The application of powerful, relatively new statistical methods to the problem of high dimensional marker data for GS has been nearly as important to the development of GS as the creation of high-density marker platforms and greater computing power. The methods can be classified by what type of genetic architecture they try to capture. Somewhat surprisingly, RR-BLUP, which makes the ostensibly unrealistic assumption that genetic effects are uniformly spread across the genome, often performs as well as more sophisticated models. Exceptions do exist, though, and there is abundant evidence that BayesB is superior for traits with strong QTL effects. Additionally, since BayesB better identifies markers in strong LD with QTL than RR-BLUP, it maintains accuracy for more generations. Finally, the question of whether or not to model epistasis remains open. If epistasis is important for a particular trait in a particular population, the kernel methods and machine-learning techniques such as SVM may be preferable. It is important for the practitioner to consider such issues or test methods on a relevant data set before a method for GEBV calculation is chosen. Although the increasing marker density, training population size, and trait heritability are obvious ways to improve GEBV accuracy; these options add cost to the program. Implementing algorithms for markers imputation and training population design holds the potential for essentially free additional accuracy, leading to greater overall GS efficiency.
\nThe current drops in genotyping costs, while phenotyping costs remain constant or increase, suggest that efforts to understand how to choose which lines to phenotype on the basis of their genotype, that is, how to design training populations, will be rewarding. Combining training populations from different populations is another way to boost accuracy when individual populations lack sufficient size and assuming that the marker densities required are available. With respect to maximizing long-term selection, we discussed several promising approaches that strive to retain favorable, low-frequency alleles while minimizing loss of short-term gain. Both simulation and empirical results for GS have been quite impressive. Empirical results of GS accuracy in crops, however, are not yet available for the public sector, except in the form of CV within families. Further empirical studies of the effects of statistical models, marker density, TP size and composition, and different selection criteria for the effectiveness of GS in breeding populations are urgently needed. In addition, while the CV approach can be instructive, an important caveat should be mentioned. In CV, the training and validation sets belong to the same population. But in GS, the selected candidates will rarely belong to the same population as the training set and may well be several generations removed from it. Recombination during meiosis between generations erodes the association between marker and QTL, systematically reducing accuracy. The effect of selection on allele frequencies and the Bulmer effect can also have detrimental effects on accuracy. In order to realistically evaluate GS for crops, studies designed for this purpose should be performed.
\nClearly, exciting times are ahead of us as public breeding programs launch GS efforts. This review compiles several immediately useful results for breeders wanting to maximize gains through GS. Knowledge of breeding program parameters (effective population size, extent of LD, and trait heritability) allows marker density and training population size to be determined using analytical formulae. The greatest impact of GS on gain per unit time will come from shortening the breeding cycle [11]. Therefore, redesigning crossing and population development schemes incorporate GS as early as possible will likely be the most effective. Consequently, phenotyping resources will need to be shifted from early generation, evaluation for selection to evaluation for model training. The importance of epistasis will need to be assessed for each trait. A major paradigm in plant breeding since the availability of molecular marker technology is that mapping and characterizing the genetic loci that control a trait will lead to improved breeding. Often, one of the rationales for cloning of QTL is to develop the “perfect market” for MAS, perhaps based on a functional polymorphism. In contrast, an advantage of the GS is precisely its black box approach to exploiting genotyping technology to expedite genetic progress. This is an advantage in our view because it does not rely on a “breeding by design” engineering approach to cultivar development requiring knowledge of biological function before the creation of phenotypes. Breeders can therefore use GS without the large upfront cost of obtaining that knowledge. In addition, GS can maintain the creative nature of phenotypic selection which couples random mutation and recombination to sometimes arrive at solutions outside the engineer’s scope.
\nSupport from the teaching section Department of plant breeding and Genetics, Punjab Agricultural University, Ludhiana India.
\nMAS | marker-assisted selection |
GS | genomic selection |
BV | breeding value |
GEBV | genomic estimated breeding value |
CV | cross-validation |
TP | training population |
BP | Breeding population |
LD | linkage disequilibrium |
In society, people have different opinions and are influenced by the opinions of others. It is opinion dynamics that simulate what kind of opinion distribution it will form. Ideally, people in a society should be bound together by trust. However, in reality, people often distrust each other and rebel against each other. In this chapter, we will apply opinion dynamics to take into account the distrust between such people and describe how trust and distrust affect the composition of society.
Opinion dynamics is a field that has been studied for a long time with applications to consensus building and elections in society [1, 2]. The transition of social discussions leading to consensus building is an old problem, but it is also an important theme in the analysis of various communications on the Internet in modern society. The opinion dynamics of binary opinions (agree and disagree or agree and ignore) have long been studied in analogy with magnetic physics [3, 4, 5, 6, 7, 8, 9]. In addition, since 2000, the Bounded Confidence Model, which analyzes opinions not as binary values but as continuously varying quantities, has been presented, and more precise studies have been conducted [10, 11, 12, 13, 14].
However, the conventional Bounded Confidence Model implicitly assumes social consensus. In Gérard Weisbuch et al. [10] and Hegselmann-Krause [11], which are representative theories of the Bounded Confidence Model, the opinions of individual people are expressed as Ii(t) in the following equation. Here, the coefficient Dij, which indicates the degree of influence by other people’s opinions, is limited to positive values.
In the Bounded Confidence Model, the coefficient is considered to be a factor that represents the speed of convergence of opinions. If the coefficient is limited to a positive value, the opinions of everyone converge without fail, and the larger the positive value, the faster the convergence. In other words, it is not the results of individual simulations that cause the convergence of social opinions, but rather the Bounded Confidence Model [10, 11, 12, 13, 14] itself, in which the convergence of social opinions is inherent from the beginning.
The reality of opinions in society is that not all opinions can be agreed upon. In social issues, it is rather rare to reach a consensus. In reality, we all experience cases where we feel opposition to someone’s opinion. Therefore, Ishii and Kawahata extended the Bounded Confidence Model by introducing repulsion and distrust of opinions [15, 16, 17, 18, 19, 20]. Simply put, the extension is that the coefficients are not limited to positive values, but negative values are introduced, and positive values indicate a trust relationship, while negative values indicate a distrust relationship. If the coefficient is negative, the opinions will be separated from each other every moment. In other words, they will never reach a consensus. This new theory of opinion dynamics is called the Trust-Distrust Model.
Using this theory of opinion dynamics, calculations have been made for the case of a person who is charismatically popular in society [20] and for the case of a person who is disliked by society as a whole [18], and calculations can also be made for the case of a society splitting, so this theory of opinion dynamics has the potential to enable social simulation calculations for many social movements.
In addition, the theory of opinion dynamics with multiple axes of opinion has been proposed by Ishii and Okano, and analysis has been conducted with two axes of opinion, so-called “official stance” and “real opinion” [21].
Even between individuals with limited time and space, active exchange of opinions has become possible [22]. In recent years, there are more and more cases in which the prerequisite information for conventional communication (e.g., the other person’s gender, appearance, tone of voice) cannot be established without exchanging personal information. In recent years, however, immediate two-way communication with excerpts of personal information such as letters and pictograms has become the norm. However, there are advantages and disadvantages to using information assets in the form of personalized data, which are excerpts of personal information as described above. The above discussion has already started in the 1950s when the use of the Internet was limited in the U.S. and the former Soviet Union; in the early 1990s, the Internet became available to the general public and the discussion was accelerated based on the concept of the information highway. Today, the status of information asset management and personalized data management differs from country to country. This has led to various problems in terms of economic loss and education related to the development of human resources involved in the proper management of information assets using data (e.g., data scientist training, legal development, moral and ethical education in handling data). In Japan, on the other hand, with the spread of mobile communications, the flat-rate system for telecommunications was applied early and actively operated at a rapid pace from the late 1990s to the early 2000s. In particular, the flat-rate system was introduced at a lower cost than in neighboring Asian countries, and advanced efforts were made in terms of information transmission. However, against the backdrop of this rapid progress, it is difficult to say that awareness-raising and legislation regarding the use of the Internet among the compulsory education generation and the generation that is not familiar with Internet literacy and cyber security (assumed to be socially vulnerable groups such as children and the elderly) has progressed. It is possible that communication is repeatedly evolving. In recent years, there have been cases of fake news being disseminated on a large scale. As a result, there have been cases where misconceptions about personal information have spread. In some cases, this may even occur in the community, resulting in a “big wave of information” on an individual basis. While we cannot be certain that there are adequate warnings and laws regarding how to use the Internet, communication may continue to evolve. Therefore, social networking services are always at risk of becoming hotbeds of conflicts and criminal activities that sometimes spill over into society as a whole, and risk management for them has been actively discussed in recent years. In particular, the COVID-19 disaster has increased the need for risk management due to the increased use of online communication. This issue raises concerns not only about the parties involved, but also about the responsibility of those who accidentally spread fake news that pose a great risk to the lives of both parties. How to deal with such cases will need to be discussed in the future. On the other hand, there are concerns about the emergence of a new “digital divide”. In the past, the divide over the superiority of handling computer technology itself was a hot topic in Japan from 2004 to 2005. However, the new “digital divide” assumes that computer technology is available to some extent regardless of gender or age. The differences are differences in literacy due to differences in the ability to transmit information (such as loudness of voice) and extract information. It can be assumed that there will be cases of false understanding, such as being evaluated by the number of people on the web. In this regard, since the beginning of this year, social networking sites have taken measures such as speech control and account restrictions to ensure fairness in elections (e.g. in the US and English-speaking countries). However, in order to ensure fairness, there is a limit to large-scale policing through mechanical processes in the Japanese sphere, which has a complex linguistic context including English, katakana, hiragana, and kanji. Therefore, it can be said that education also requires reading comprehension in all kinds of texts and a perspective on preserving the information resources of individuals. In this regard, those who are vulnerable in the information environment, such as the generation that has not been adequately educated on cyber security, may be at risk of various fragmentation. As a result of this information gap, a threshold of distrust and trust in communication occurs, and sometimes there are scattered cases of major mistakes such as major social fragmentation, deadly attacks, and slander against completely disinterested entities. In the case of socially vulnerable people, there is a limit to the legal measures that can be taken without financial benefits such as hiring a lawyer, and there is a risk that socially vulnerable people who should be protected will be left defenseless or denounced. To remedy them, social protection and remedy mechanisms in online communities, such as digital citizenship, are also urgently needed, and even within those communities, consensus building, trust building, and to some extent, thresholds occur. In addition, slander and defamation may be committed without the person being aware of it and he or she may be held responsible for it. Only those who are in a superior position to apply the law are protected and enjoy many benefits, while those who are not in a position to denounce based on legal grounds may cry themselves to sleep or suffer losses without any social guarantee. In such cases, although there are problems such as surveillance society, digital citizenship, and other network communication in neighborly relations, the formation of communities that protect each other regardless of social class is more important. And there are expected to work as part of care work in online communities. In these elements, it can be said that mutual care communication based on mutual “trust“ and very close relationships, neighborly relationships, is promoted. It can be hypothesized that these online pseudo-societies, which promote the building of invisible trust relationships formed between distant and nearby communities, have something in common with the wider society. Since the rapid spread of public networks, there have been growing expectations for elucidating the mechanisms of social phenomena that have become difficult to visualize and quantify [23]. However, in order to analyze the exchange of opinions left in the vast amount of log data in modern society, it goes without saying that a theory that corresponds to quantitative analysis, focusing on integration with analysis to large-scale data, is necessary. In addition, slander and defamation may be committed without the person being aware of it and he or she may be held responsible for it. Only those who are in a superior position to apply the law are protected and enjoy many benefits, while those who are in a position not to be denounced on legal grounds may cry themselves to sleep or suffer losses, without any social guarantee. Similar functions are ensured in functions such as suggestions in online search behavior and product recommendations in e-commerce, etc. In addition, opinions that infer our trust or distrust, which constitute the recommendation function, become “opinion aggregates” or “generalization models” that are automatically returned to us through public networks. These are the results of online consensus building; in COVID-19, generalized models and recommendations for various social crisis situations will be developed and analyzed based on large-scale data such as our behavior logs and opinions. However, the global spread of public networks has not been positive in all aspects, and while COVID-19 has increased excessively, problems such as online slander have also been highlighted. This chapter touches on those issues as well. In particular, a case can be envisioned where public opinion is formed from the aftermath of unconscious consensus building. This is the case today, when populism and propaganda are rampant. However, the use of online media was pioneered in the 2020 U.S. presidential election, and typical social networking sites such as Facebook and Twitter have been suppressed, and regulations and laws are being revised at a rapid pace. From this point of view, it can be inferred that the nature of online communication is entering a transitional period after COVID-19 and the 2020 U.S. presidential election. It is now possible to pseudo-analyze various opinions in society through online logs. Theories for analyzing the process of consensus building in society (or small groups) have long been proposed and studied from various perspectives [10, 11, 12, 13, 14]. However, in order to analyze the exchange of opinions left in the vast amount of log data of modern society, it goes without saying that a theory that corresponds to quantitative analysis, focusing on integration with analysis to large-scale data, is necessary. There are two main types of theories of opinion dynamics. One is the theory that treats contradictory conditions and discrete opinions as 1 (trust) and 0 (distrust), or 1 (trust) and -1 (distrust). In presidential elections in the U.S. and France, and in referendums such as those seen in Brexit, this dichotomous theory is more likely to be applied because voting takes place when there is one clear winner. The other method is the theory that regards opinions as a continuous value with one (or many) dimensions. For example, consensus building is often considered in this way [15, 16, 17, 18, 19, 20]. As for the discussion of public health risk management in the COVID-19 disaster, which is imminent every day as described above, the number of articles being updated and recorrected is increasing every day. Changes in information on the web provide a bird’s eye view of the situation, which is often different from the expected case. In addition, there is an urgent need to “democratize security” in order to appeal to, resolve, and protect vulnerable members of society who do not fully understand cyber security. Depending on future legal decisions, significant changes may occur. In addition, there is a need to share security awareness in cyberspace as well as offline crime arrest rates in society. In addition, in various online communities, organizations may be formed to protect each other’s security in the form of blockchain, just like the “Ren” (ex. creation critics’ community) formed in the Edo period in Japan. In the aforementioned communities, there is a communication and consensus that can only be established if there is a clear relationship of trust and distrust. In recent years, while consensus-based communication has increased, disparities and security issues have also been detected, and more and more fatal flaws and security errors in online communities have been uncovered that were not previously apparent. The mechanism by which these problems are discovered can occur when there is a sense of distrust among a certain number of people in a community. In the context of information and communication known as “technological warfare” or “quiet information warfare,” the threshold values of parameters related to the sense of trust and distrust among communities are important information for communication to take place, but they are difficult to determine, quantify, and visualize clearly. Therefore, it is necessary to reason based on mathematical models, develop arguments and predictions, and confront possible risks and potential social problems. These issues, as well as election prediction, are themes that involve implicit understandings, such as floating and fixed votes, and consensus among regions, so we try to consider them together with social discussions in consensus building [15, 16, 17, 18, 19, 20, 21].
In the opinion dynamics proposed by Ishii named Trust-Distrust Model, the time evolution of people’s opinions in the society is expressed by the following Equation [16].
The first term on the right-hand side is the influence of external media such as advertising, mass media reports, and government publicity, where A(t) is the influence from mass media from time to time, and the coefficient ci is the coefficient of how much influence each person receives from that mass media. The coefficient Dij can be negative [15, 16]. Here, the function f(Ii,Ij) is a cutoff function that is ignored when the opinions are farther apart than a certain degree. Hegselmann-Krause [11] uses a simple step function, but here we use the Sigmoid function in the sense of a smooth cutoff.
Here, the coefficients of trust and distrust, Dij and Dji, are considered to be independent. Usually, Dij is an asymmetric matrix with Dij ̸= Dji. Moreover, Dijand Dji can take positive and negative values with different signs. A positive value means that i trusts j, while a negative value means that i does not trust j. Also, m is the strength of will of agent “i”. For large values of m, the agent “i” is not so much influenced by mass media or other people’s opinions.
The Trust-Distrust Model can be used to calculate the case of a person who is charismatically popular in society [22] and the case of a person who is disliked by society as a whole [18], and it can also be used to calculate the case of a society splitting up [23, 24, 25], so the Trust-Distrust Model has the potential to provide social simulation calculations for many social movements.
Here is a simple calculation using Trust-Distrust Model. Figure 1 shows the opinion dynamics for the case of two people, where the left side of Figure 1 shows the case where the two people trust each other (DAB > 0, DBA > 0). The right panel of Figure 1 shows the case where two people in the calculation are shown as “A” and “B”. distrust each other (DAB < 0, DBA < 0). The case of mutual trust can be found in Hegselmann-Krause [11], but the case of distrust cannot be calculated without this theory.
Example of trust-distrust model calculation using
In this Trust-Distrust Model, the influence of the mass media is expressed by the first term on the right side of Eq. (2) called ciA(t). Here, A(t) is the amount of mass media coverage of the focal topic. The quantity is simply the product of the number of seconds and the number of channels that handle the topic, and the coefficient ci on this means that we can handle the fact that each person is affected differently by this mass media.
Based on Eq. (2), the individual opinions of the people, Ii(t), are calculated over time. We assume that opinions can take values from -∞ to +∞; Hegselmann-Krause [11] has 0 to 1, but Trust-Distrust Model has no upper bound on extreme opinions (and no lower bound if negative). In this case, the initial opinions of people are distributed as uniform random numbers in the range of −20 to +20.
What is important in Trust-Distrust Model is the coefficient Dij represented in Eq. (2). In a complete network where all people are connected to all people, there are N2 coefficients Dij that express trust or distrust between individual people. Ishii and Kawahata have shown that if more than 55% of the N2 Dij are positive, that is, trustworthy, the system will form a consensus [17]. This result is also true for random networks [26].
When people in a society are bound together by trust, they reach a consensus. This is the implicit assumption and conclusion of the bounded confidence model. The time required to reach consensus and whether one or more opinions are reached can be analyzed from the calculations of the bounded confidence model.
However, if people in the society as a whole are not necessarily bound by trust, it becomes uncertain whether they will reach a consensus or not. If all the people in a society distrust each other, it is obvious that they will not reach a consensus. Then, there is an interesting question that can be confirmed by a mathematical model: what is the ratio of trust and distrust that will lead to consensus formation?
First, we use the Trust-Distrust Model to calculate whether the entire society, assuming 300 people, will form a consensus in a situation where people’s connections are mixed with trust and mistrust. Assume that these 300 people are connected by a complete network. Suppose that the coefficient of trust Dij connecting people occurs in a specified proportion of cases where the coefficient is a positive value determined by a random number between 0 and 1 and a negative value determined by a random number between −1 and 0. Let T be the proportion of positive or negative values of the trust coefficient Dij. If T = 1, the every trust coefficient Dij is positive. For example, if T = 0.5, then the positive and negative values are 50–50.
The results of the calculations are shown in Figure 2 and Figure 3 [19]. Figure 2 plots the highest value of the opinion distribution for calculations from T = 0.45 to T = 1. Since the calculations are for 300 people, the vertical axis of Figure 2 is 300 if consensus is achieved. The highest value of the distribution is over 200, indicating that the situation is close to consensus formation. On the other hand, at T = 0.45, the highest value of the opinion distribution is less than 20, suggesting that the opinion distribution does not have a sharp peak. Therefore, at T = 0.45, the situation is far from consensus building.
Variation of the highest value of the opinion distribution with the proportion T of positive and negative values of the coefficient of confidence Dij.
The changes in the opinion distribution due to the ratio of positive and negative values of the coefficient of confidence Dij, T, are calculated for T = 0.5, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, and 0.60. N = 1000 in this calculation. The probability of people connecting in a random network is set to 30%.
The above results were calculated for a complete network of 300 people. Since a complete network cannot be realized in society, calculations for the case where people are connected in a different network structure are also presented. The calculations were done for random networks and scale-free networks.
This can be seen in Figure 3, which shows the computation of the opinion distributions for T = 0.5, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, and 0.60. Let us assume that the entire society has 1000 people and is connected by a random network. The probability of people being connected is set to be 30%. As can be seen here, when T = 0.55 or higher, the opinion distribution has a sharp peak, indicating that a consensus has been formed. However, at T = 0.54, there is a peak, but it is not sharp, and at T = 0.53 or lower, the distribution of opinions is flattening out, clearly indicating that consensus has not been formed. The calculation for 300 people in the complete network is very similar to this calculation.
It is noteworthy that the highest value of the opinion distribution in Figure 2 changes rapidly with the change of T. The peak of the opinion distribution appears after T = 0.5, and the height of the peak becomes higher after T = 0.55. In other words, the value of T determines whether a society is consensus-building or not. We can see that the borderline between the two is approximately T = 0.55.
The abrupt change in the highest value of the opinion distribution seen in Figure 2 suggests that there is a borderline at around T = 0.55 where society may or may not reach a consensus. In other words, if more than 55% of the relationships in the entire social network are trust relationships, consensus building is achieved in the entire society. This means that it is not necessary for all relationships to be trusting in order for the entire society to reach consensus, but if more than 55% of the relationships are trusting, the society will reach consensus.
This conclusion suggests that in a democracy, for example, if more than 55% of the people support a certain policy in an election, it is possible for society to reach a consensus. It also suggests that it is difficult to reach a consensus when there is a strong opposition between those in favor and those against, such as when the number of those in favor is less than 55%. Thus, this conclusion is interesting as an application to political science.
The conclusion that 55% is the borderline of social consensus is very striking. However, I wonder if this conclusion is the same no matter what network structure people are connected to. Figure 4 below shows a calculation for a random network of 1000 people, where the probability of joining the random network is assumed to be 1%.
The changes in the opinion distribution due to the ratio of positive and negative values of the coefficient of confidence Dij, T, are calculated for T = 0.8, 0.75, 0.72, 0.70, 0.65, and 0.60. N = 1000 in this calculation. The probability of people connecting in a random network is set to 1%.
Figure 4 shows that the sharp peak of the opinion distribution disappears completely at T = 0.6, and the sharp peak representing consensus emerges at about T = 0.75. In other words, if people’s connections are sparse, such as the probability of joining in a random network is 1%, 55% is not the boundary of consensus formation.
For this quantitative check, we calculate the following quantity. This is the sum of the differences in the opinions of N people.
This W is W = 1 if the width of the opinion distribution remains the same over time, W < 1 if consensus is reached, and W > 1 if the opinion distribution is divergent without consensus.
Let us examine quantitatively the finding from previous researches [26, 27] that consensus is formed when positive trust between people in a society is at least 55% of all relationships. In Figure 5, we show the T dependence of W for various values of trustΔ. Dij is between -Δ to Δ. The calculation of Figure 5 is N = 1600, the connection rate of the random network is 30%. Since there are fluctuations due to random numbers, the calculated values are averaged over five times. The green horizontal line represents W = 1. In other words, if the calculation is below this green line, the society forms a consensus.
The calculated W as a function of T, the proportion of positive values of the trust coefficient Dij. N = 1600. Δ = 1.0. The average value of 10 calculations is used. The proportion 0.01, 0.05, 0.1, 0.5 and 1 is shown.
Figure 5 shows that the condition for consensus is satisfied at about T = 0.53–0.55, regardless of the size of Dij. In particular, when Δ = 1.0, we can see that when T is close to 0.55, there is a sharp inclination toward consensus. Therefore, the 55% consensus threshold from previous studies is supported. However, the threshold for consensus depends very much on the connection rate of the network: in the calculation for N = 1600, if Δ is 1.0, then W = 1 is T = 0.545 when the connection probability of the random network is 30%, but T = 0.69 when the connection probability is 1%. This means that if the network is sparsely connected, the threshold value of T will rapidly increase. In other words, if the network is sparsely connected, it will be difficult for society to reach a consensus.
In our previous work [27], we have performed the same type of calculations on scale-free networks, which are said to be closer to real human connections in society than random networks. However, in the case of scale-free networks, a clear consensus threshold such as 55% does not emerge.
People in society are not uniform, but each individual is unique. A person who is especially popular among many people is called a charismatic person. In this section, we will use the Trust-Distrust Model to simulate the case of a charismatic person who is trusted by many people.
Here, a charismatic person is one who is popular with many people in society. Although being popular among others is not synonymous with being trusted by others, in this Trust-Distrust Model, a charismatic person is considered to be a positive value with a high coefficient of trust Dij from others to the charismatic person. Thus, a charismatic person is defined as follows. The coefficient of trust, Dij, is the strength with which person “i” is influenced by a person “j”. Therefore, if the charismatic person is represented by “c” and Dic is the trust from person “i” to the charismatic person. Dic is larger than the influence from other people, then the charismatic person will have more influence.
Figure 6 shows the case where there is one charismatic person in a society of 300 people. It can be seen that many people have their opinions close to those of charismatic person. Thus, a charismatic person will be able to attract people with similar opinions. The more positive and larger the value of Dic, the stronger the effect. This is called being popular in society.
Simulation of a single charismatic person. The charismatic person is trusted by the people in the society with a trust coefficient Dic = 10, and the trust coefficients between other people in the society are determined by random numbers in the range of +1 to −1. The arrows show the opinion distribution of the charismatic person. The blue line in the opinion trajectory represents the opinion of a charismatic person, while the green line is a sample of the opinion trajectory of an ordinary person.
Figure 7 shows the case where there are two charismatic people in the society. These two people are popular and have many people who agree with their opinions. If the two charismatic people are far apart in their opinions, a middle opinion group will be formed between their opinions, but if their opinions are close, there will be no middle ground and the society will be divided between them.
Simulation of two charismatic persons. The charismatic persons are trusted by the people in the society with a trust coefficient Dic = 10. The blue line and red line in the opinion trajectory represent the opinions of a charismatic person, while the green line is a sample of the opinion trajectory of an ordinary person.
Another feature that distinguishes the Trust-Distrust Model from the traditional bounded confidence model is that it can calculate the effect of advertising on the formation of social opinion. In this section, we will consider the impact of advertising on people’s opinions of society. In general, advertising is the use of mass media to convey people’s messages [28]. Here, we do not touch on the specific method of advertising or the content of advertising but set the impact of advertising per unit time on people as A(t). A(t) can be thought of as the amount of advertising per day, e.g., the amount of money spent on advertising.
The first term on the right-hand side of Eq. (2) is A(t), where A(t) represents the strength of advertising added to society from time to time. This term of the impact of advertising is adopted with reference to the term introduced in the mathematical model of hit phenomena [29, 30], which analyzes the impact of advertising on society.
In this section, the opinions people have are expressed as one-dimensional numerical values. Therefore, an opinion with a positive value simply means that it is expressed as a positive numerical value, not that it is an affirmative opinion. The situation is the same for opinions with a negative value. Therefore, whether an opinion is positive or negative only implies the direction of the opinion on a particular topic. Whether an opinion is positive or negative does not mean that it supports or does not support a particular topic. For example, on the topic of cola, it is possible to assign a positive value to an opinion that likes Coca-Cola and a negative value to an opinion that likes Pepsi-Cola. Conversely, it is also possible to make the opinion that you like Pepsi-Cola a positive opinion and the opinion that you like Coca-Cola a negative opinion.
Figure 8 shows the effect of advertising on the distribution of opinions. From left to right, the strength of advertising is A(t) = 0, 0.5, and 5.0. When A(t) = 5.0 on the right, social opinion distribution moves significantly in the positive direction. In other words, using Eq. (2), we can include the influence of advertising in our calculations.
It shows the effect of advertising on the distribution of opinions. From left to right, a(t) = 0, 0.5, 5.0. When a(t) = 5.0 on the right, social opinion moves significantly in the positive direction.
If we define the advertising term A(t) as follows, we can concentrate the opinions of the people in the society into an arbitrary opinion.
Here, a represents how narrowly the opinion distribution should be concentrated, and b specifies where the opinion distribution should be concentrated. By setting these a and b, we can decide which and how much of society’s opinions should be concentrated. An example of this is shown in Figure 9. However, what kind of advertising can have this kind of effect is still another question.
Calculation of the concentration of the distribution of opinions in society under the influence of advertising, using
An example of this extreme simulation is shown in Figure 10. Here, the opinion of the whole society is negative at first, but due to the influence of strong advertising, the opinion of all people in the society changes to a positive value. We do not know what kind of advertising can actually have this kind of effect on society, but we have shown that it is possible in principle as a mathematical model.
Calculation of the concentration of the distribution of opinions in society under the influence of advertising, using
In the first term on the right-hand side of Eq. (2) of the Trust-Distrust Model, which represents the influence of advertising, the influence of advertising can be added separately to each person in society by setting the coefficient ci. This shows that it is possible to calculate micro-targeting, which is known in the field of marketing.
Eq. (2) also shows that people are influenced both by advertising from the mass media and by the people they are connected to in society. Today, with the development of social media, some people are not exposed to information from mass media such as television. Therefore, we will use the Trust-Distrust Model to investigate whether people who are not exposed to information from the mass media are indirectly influenced by the mass media through the influence of people who are connected to them in society [31].
In Figure 11, we set the number of people in society as a whole at 1000, of which 100 people, or 10%, are not affected by mass media. The connections between people are random networks, and the calculations for the percentage of connections are shown as 30%, 10%, 5%, and 0.5%. In Figure 12, the trajectory of the opinions of those who are influenced by the mass media is depicted in pink, and the trajectory of the opinions of those who are not influenced by the mass media is depicted in blue.
Simulation of the movement of people who are not reached by the influence of mass media. Suppose the number of people in the society is 1000, and 100 people are not reached by the influence of mass media. Calculations are shown for random networks with connection probabilities of 30%, 10%, 5%, and 0.5%. The trajectory of the opinions of those who are influenced by the mass media is pink, and the trajectory of the opinions of those who are not reached by the mass media is blue. The coefficient of people’s trust is set at a uniform random number in the range of 1 to −1, and the proportion of positive values is T = 0.6. The proportion of positive values is T = 0.6. The strength of advertising is a = 5.
Polarization of the distribution of opinions in society. (a) Polarization of opinions obtained by the bounded confidence model. The coefficient of trust Dij > 0 for everyone in the pink locus of opinion. (b) Polarization of opinion obtained with the trust-distrust model. The red and blue groups in the locus of opinion are consensus with Dij > 0 within the group and distrust with Dij < 0 between the groups.
The calculation results show that when people’s connections are sparse, some of the people who have not received the influence of mass media do not receive the influence of mass media even though they are connected to people in the society, and their opinions are about −40 and the trajectory of their opinions is horizontal. Even in that case, many people’s opinions are moving in the direction influenced by the mass media, that is, in the positive direction, because of the connections between people in society, even if the influence of the mass media does not reach them.
On the other hand, when people are closely connected in random networks, as seen in the case of 30%, even those who are not reached by mass media influence reach consensus with those who are, indicating that opinions are moving in a positive direction influenced by mass media.
The Trust-Distrust Model takes into account not only trust and consensus among people in a society but also distrust and opposition among people. Thus, phenomena such as social division can be reproduced in the simulation. Social divisions are often caused by serious conflicts in society, which is different from the phenomenon calculated by the Bounded Confidence Model, in which there are multiple consensus opinions because the opinions are far apart. In this sense, the Trust-Distrust Model seems to be a more suitable opinion dynamics theory for dealing with social fragmentation and division.
The most typical example of social division would be the American Civil War. The American society at that time was divided into two positions, and the war took the form of a war between two uncompromising and polarized groups. Another example would be the Reformation in Europe in the 16th century. Modern American society also seems to be divided into conservative and liberal, as seen in the 2020 presidential election. In Japan, during the Meiji Restoration in the mid-19th century, Japanese society was divided into conservative and reformist factions, and there was a civil war that lasted over a year. In addition to the past examples of wars, many countries are divided over whether to prioritize medical countermeasures or minimize economic damage in response to the spread of COVID-19 today, for example. Such divisions of opinion in society cannot be handled by the Bounded Confidence Model, since they clearly disagree with each other and with the opinions of others.
In the bounded confidence model, people in the society are basically in a trust relationship. In the bounded confidence model, people in the society are basically in a trusting relationship, and the cause of the polarization of opinions is therefore not affected by distant opinions. In the bounded confidence model, people are not influenced by opinions that are too far apart from their own, so the distribution of opinions in society becomes multipolar and coalesces into multiple opinions [10, 11].
However, in the case of the Trust-Distrust Model, it can be assumed that people in a society are divided into, say, two groups, and the groups are in conflict with each other and distrust each other. Figure 12 shows the polarization of opinions in the bounded confidence model and in the trust-distrust model. Figure 13 shows the polarization of opinions in the bounded confidence model and the trust-distrust model. Although they look the same, in the bounded confidence model, all people in society are bound by trust, while in the trust-distrust model, people in society are divided by distrust.
In-group and out-group based on Tajfel’s proposal. TA and TB are the proportions of positive values of the coefficient of trust Dij within groups a and B, and TAB is the proportion of positive values of the coefficient of trust Dij between groups.
More generally, we think of a society as being divided into multiple endogroups. A distinction is made between the relations between people within an endogroup and the relations between an endogroup and people in another endogroup. Tajfel’s idea [32] is to describe the relationship between an in-group and another in-group as an out-group.
This polarization of social opinion based on the Trust-Distrust Model is expressed in the concept of In-group and Out-group proposed by Tajfel [32], and Figure 13 shows a schematic diagram of the opinions of people in society according to Tajfel’s concept. In Figure 14, TA and TB are the proportions of positive values of the coefficient of trust Dij within groups A and B, and TAB is the proportion of positive values of the coefficient of trust Dij between groups. If TAB = 0, then the two groups are completely split as in Figure 13 (b).
Two typical examples of the distribution of opinions in a divided society. (a), TA = TB = 0.8. TAB = 0. Group A and Group B form a consensus as In-group. However, with TAB = 0. (b), TA = TB = 0.5. TAB = 0.
Figure 14 shows two typical examples of the distribution of opinions in a divided society. In (a), TA = TB = 0.8. TAB = 0. Group A and Group B form a consensus as In-group. However, with TAB = 0, the trust between the groups is zero. On the other hand, in (b), TA = TB = 0.5. TAB = 0, Group A and Group B do not form a consensus because of insufficient trust in the group, but the trajectories of the two groups are repulsive and do not mix because of distrust in the Out-group.
A typical example of (a) in Figure 14 would be the American Civil War, where society was completely divided, and war broke out. However, as far as the votes for the 2020 presidential election in the United States are concerned, the two candidates are competing in each state, and there is no regional division.
Figure 15 shows the results when TA and TB are fixed at 0.55 and TAB is varied. Here, TAB is not zero, so even with TAB = 0.3, Group A, and Group B mix a little. When TAB = 0.8, the two groups are in an out-group trust relationship, and they form a single consensus. For these detailed calculations, please refer to References [33, 34].
Calculations using the trust-distrust model when society is divided into Group A and Group B. TA = TB = 0.55. TAB = 0.3, 0.5, 0.6, 0.8. The opinion trajectories of people in Group A are in red and those of people in Group B are in blue.
In this paper, we introduced a new theory of opinion dynamics, the Trust-Distrust Model. Trust and mistrust play a very important role in this opinion dynamics theory. Trust brings people to a consensus, while distrust makes people repel. The Trust-Distrust Model is a theory that is suitable for simulating this situation.
The Bounded Confidence Model is a theory of opinion dynamics in which opinions take continuous values, and the Trust-Distrust Model is an extension of the Bounded Confidence Model. The Trust-Distrust Model is an extension of the Bounded Confidence Model in two respects: the coefficient Dij is seen as the coefficient of trust between people, and when this value is negative, the relationship is distrustful. Also, the influence of mass media was incorporated as an external field to the differential equation that determines opinion. The extension of distrust as negative trust facilitates the simulation of social phenomena such as social divisions. It is possible to simulate consensus building as an In-group for each group in the society, and trust and distrust as Out-group among groups in detail. In this sense, the Trust-Distrust Model is a theory that facilitates the simulation of a real, complex society. The main theme of this paper is the consensus of information: “trust-distrust”, the discussion of social impact through communication by various media formed by implicit understanding is represented by resistance to authority, populism, and risk. The focus tends to be on issues. Depending on the content and nature of the news, positive dissenting or agreeing opinions may have both similar and different tendencies depending on the source and content, and the ability of stakeholders to communicate in the discussion. The simulation results suggest that the network structure is significantly changed by the above. On SNS, we have already gradually introduced a mechanism to anticipate risks, such as (1) a mechanism to prohibit hackers from accessing the system with a system that is increasing in number mechanically, and (2) a mechanism to prohibit accounts due to posted content. Has been done. However, unpredictable behavior can occur. In addition, by accumulating information collectively, patterns for manipulating information will continue to grow. As mentioned above, in the 2020 US presidential election, strict regulations were imposed on large-scale web-based speech control and erroneous information transmission channels including bots. From this research, the network structure changes drastically due to the spread of erroneous information, the participation of untrustworthy information, the balance of the spread of reliable information, and the construction of the related party network, and the opinion is that phase transition occurs at a certain threshold. It was suggested. Significant changes may occur in the future due to future legislative decisions. Furthermore, we think that it is necessary to have a shared awareness not only of the crime clearance rate offline but also of security awareness in cyberspace as a social convention. In that respect as well, it is important to check facts in an online-offline environment and form a communication community in consideration of the reliability of information for a diverse risk society, or if it is distrustful for a risk society, it is wrong. It is necessary to consider various cases such as discussions when problems are overloaded, and it can be said that it is necessary to learn from past cases and prepare for them from hypothetical simulation results and case studies. In the future, there will be an increase in two-way communication across time and space by anonymizing personal information such as letters and pictograms, and extracting them “as cryptographic asset data” to represent social events. However, there are advantages and disadvantages to using information assets in the form of personalized data, which are excerpts of personal information as described above. In the future, the discussion of trust value in the above data will accelerate in indicators such as personal credit scoring. In this paper, the Trust-Distrust Model will be discussed with respect to theories that also address charismatic people, the effects of advertising, and social divisions. Furthermore, simulations of the Trust-Distrust Model show that 55% agreement is sufficient to build social consensus. By working on this theory, we hope to use it to discuss and predict social risk in future discussions in credit scoring.
This work is supported by JSPS KAKENHI Grant Number JP19K04881.
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There are several ways to apply robust optimization and the choice of form is typical of the problem that is being solved. In this paper, the basic concepts of robust optimization are developed, the different types of robustness are defined in detail, the main areas in which it has been applied are described and finally, the future lines of research that appear in this area are included.",book:{id:"6587",slug:"nature-inspired-methods-for-stochastic-robust-and-dynamic-optimization",title:"Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization",fullTitle:"Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization"},signatures:"José García and Alvaro Peña",authors:[{id:"227809",title:"Ph.D.",name:"Jose",middleName:null,surname:"Garcia",slug:"jose-garcia",fullName:"Jose Garcia"},{id:"240407",title:"Dr.",name:"Alvaro",middleName:null,surname:"Peña",slug:"alvaro-pena",fullName:"Alvaro Peña"}]},{id:"51131",doi:"10.5772/63785",title:"Survey of Meta-Heuristic Algorithms for Deep Learning Training",slug:"survey-of-meta-heuristic-algorithms-for-deep-learning-training",totalDownloads:3136,totalCrossrefCites:15,totalDimensionsCites:24,abstract:"Deep learning (DL) is a type of machine learning that mimics the thinking patterns of a human brain to learn the new abstract features automatically by deep and hierarchical layers. DL is implemented by deep neural network (DNN) which has multi-hidden layers. DNN is developed from traditional artificial neural network (ANN). However, in the training process of DL, it has certain inefficiency due to very long training time required. Meta-heuristic aims to find good or near-optimal solutions at a reasonable computational cost. In this article, meta-heuristic algorithms are reviewed, such as genetic algorithm (GA) and particle swarm optimization (PSO), for traditional neural network’s training and parameter optimization. Thereafter the possibilities of applying meta-heuristic algorithms on DL training and parameter optimization are discussed.",book:{id:"5165",slug:"optimization-algorithms-methods-and-applications",title:"Optimization Algorithms",fullTitle:"Optimization Algorithms - Methods and Applications"},signatures:"Zhonghuan Tian and Simon Fong",authors:[{id:"1952",title:"Dr.",name:"Simon",middleName:null,surname:"Fong",slug:"simon-fong",fullName:"Simon Fong"},{id:"186166",title:"MSc.",name:"Zhonghuan",middleName:null,surname:"Tien",slug:"zhonghuan-tien",fullName:"Zhonghuan Tien"}]},{id:"51209",doi:"10.5772/62472",title:"A Review and Comparative Study of Firefly Algorithm and its Modified Versions",slug:"a-review-and-comparative-study-of-firefly-algorithm-and-its-modified-versions",totalDownloads:2907,totalCrossrefCites:15,totalDimensionsCites:21,abstract:"Firefly algorithm is one of the well-known swarm-based algorithms which gained popularity within a short time and has different applications. It is easy to understand and implement. The existing studies show that it is prone to premature convergence and suggest the relaxation of having constant parameters. To boost the performance of the algorithm, different modifications are done by several researchers. In this chapter, we will review these modifications done on the standard firefly algorithm based on parameter modification, modified search strategy and change the solution space to make the search easy using different probability distributions. The modifications are done for continuous as well as non-continuous problems. Different studies including hybridization of firefly algorithm with other algorithms, extended firefly algorithm for multiobjective as well as multilevel optimization problems, for dynamic problems, constraint handling and convergence study will also be briefly reviewed. A simulation-based comparison will also be provided to analyse the performance of the standard as well as the modified versions of the algorithm.",book:{id:"5165",slug:"optimization-algorithms-methods-and-applications",title:"Optimization Algorithms",fullTitle:"Optimization Algorithms - Methods and Applications"},signatures:"Waqar A. Khan, Nawaf N. Hamadneh, Surafel L. Tilahun and Jean\nM. T. Ngnotchouye",authors:[{id:"180330",title:"Dr.",name:"Surafel",middleName:null,surname:"Tilahun",slug:"surafel-tilahun",fullName:"Surafel Tilahun"},{id:"180784",title:"Dr.",name:"Waqar Ahmed",middleName:null,surname:"Khan",slug:"waqar-ahmed-khan",fullName:"Waqar Ahmed Khan"},{id:"185148",title:"Dr.",name:"Nawaf",middleName:null,surname:"Hamadneh",slug:"nawaf-hamadneh",fullName:"Nawaf Hamadneh"},{id:"185149",title:"Dr.",name:"Jean M. T.",middleName:null,surname:"Ngnotchouye",slug:"jean-m.-t.-ngnotchouye",fullName:"Jean M. T. Ngnotchouye"}]},{id:"61251",doi:"10.5772/intechopen.76979",title:"A Brief Survey on Intelligent Swarm-Based Algorithms for Solving Optimization Problems",slug:"a-brief-survey-on-intelligent-swarm-based-algorithms-for-solving-optimization-problems",totalDownloads:1618,totalCrossrefCites:8,totalDimensionsCites:12,abstract:"This chapter presents an overview of optimization techniques followed by a brief survey on several swarm-based natural inspired algorithms which were introduced in the last decade. These techniques were inspired by the natural processes of plants, foraging behaviors of insects and social behaviors of animals. These swam intelligent methods have been tested on various standard benchmark problems and are capable in solving a wide range of optimization issues including stochastic, robust and dynamic problems.",book:{id:"6587",slug:"nature-inspired-methods-for-stochastic-robust-and-dynamic-optimization",title:"Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization",fullTitle:"Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization"},signatures:"Siew Mooi Lim and Kuan Yew Leong",authors:[{id:"229799",title:"Dr.",name:"Siew Mooi",middleName:null,surname:"Lim",slug:"siew-mooi-lim",fullName:"Siew Mooi Lim"},{id:"231023",title:"Dr.",name:"Kuan Yew",middleName:null,surname:"Leong",slug:"kuan-yew-leong",fullName:"Kuan Yew Leong"}]},{id:"68118",doi:"10.5772/intechopen.88185",title:"Overview of Multi-Objective Optimization Approaches in Construction Project Management",slug:"overview-of-multi-objective-optimization-approaches-in-construction-project-management",totalDownloads:1180,totalCrossrefCites:5,totalDimensionsCites:12,abstract:"The difficulties that are met in construction projects include budget issues, contractual time constraints, complying with sustainability rating systems, meeting local building codes, and achieving the desired quality level, to name but a few. Construction researchers have proposed and construction practitioners have used optimization strategies to meet various objectives over the years. They started out by optimizing one objective at a time (e.g., minimizing construction cost) while disregarding others. Because the objectives of construction projects often conflict with each other, single-objective optimization does not offer practical solutions as optimizing one objective would often adversely affect the other objectives that are not being optimized. They then experimented with multi-objective optimization. The many multi-objective optimization approaches that they used have their own advantages and drawbacks when used in some scenarios with different sets of objectives. In this chapter, a review is presented of 16 multi-objective optimization approaches used in 55 research studies performed in the construction industry and that were published in the period 2012–2016. The discussion highlights the strengths and weaknesses of these approaches when used in different scenarios.",book:{id:"8521",slug:"multicriteria-optimization-pareto-optimality-and-threshold-optimality",title:"Multicriteria Optimization",fullTitle:"Multicriteria Optimization - Pareto-Optimality and Threshold-Optimality"},signatures:"Ibraheem Alothaimeen and David Arditi",authors:[{id:"304595",title:"Dr.",name:"David",middleName:null,surname:"Arditi",slug:"david-arditi",fullName:"David Arditi"},{id:"304596",title:"Dr.",name:"Ibraheem",middleName:null,surname:"Alothaimeen",slug:"ibraheem-alothaimeen",fullName:"Ibraheem Alothaimeen"}]}],mostDownloadedChaptersLast30Days:[{id:"60097",title:"Robust Optimization: Concepts and Applications",slug:"robust-optimization-concepts-and-applications",totalDownloads:2533,totalCrossrefCites:21,totalDimensionsCites:29,abstract:"Robust optimization is an emerging area in research that allows addressing different optimization problems and specifically industrial optimization problems where there is a degree of uncertainty in some of the variables involved. There are several ways to apply robust optimization and the choice of form is typical of the problem that is being solved. In this paper, the basic concepts of robust optimization are developed, the different types of robustness are defined in detail, the main areas in which it has been applied are described and finally, the future lines of research that appear in this area are included.",book:{id:"6587",slug:"nature-inspired-methods-for-stochastic-robust-and-dynamic-optimization",title:"Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization",fullTitle:"Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization"},signatures:"José García and Alvaro Peña",authors:[{id:"227809",title:"Ph.D.",name:"Jose",middleName:null,surname:"Garcia",slug:"jose-garcia",fullName:"Jose Garcia"},{id:"240407",title:"Dr.",name:"Alvaro",middleName:null,surname:"Peña",slug:"alvaro-pena",fullName:"Alvaro Peña"}]},{id:"76058",title:"Ultrasonic Detection of Down Syndrome Using Multiscale Quantiser with Convolutional Neural Network",slug:"ultrasonic-detection-of-down-syndrome-using-multiscale-quantiser-with-convolutional-neural-network",totalDownloads:367,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Down Syndrome is a genetic condition that occurs when there is an extra copy of a chromosome 21 in the newly formed fetus. EIF is observed as one of the possible symptoms of DS. But in comparison to the other symptoms like nasal bone hypoplasia, increased thickness in the nuchal fold, EIF is very much less prone to DS. Hence, recommending the pregnant women with EIF to undergo the diagnostic process like amniocentesis, CVS and PUBS is not always a right choice as these diagnostic processes suffer serious drawbacks like miscarriage, uterine infections. This chapter “Ultrasonic Detection of Down Syndrome Using Multiscale Quantiser With Convolutional Neural Network” presents a new ultrasonic method to detect EIF that can cause DS. Ultrasonic Detection of Down Syndrome Using Multiscale Quantiser with Convolutional Neural Network entails two stages namely i) training phase and ii) testing phase. Training phase aims at learning the features of EIF that can cause DS whereas testing phase classifies the EIF into DS positive or DS negative based on the knowledge cluster formed during the training phase. A new algorithm Multiscale Quantiser with the convolutional neural network is used in the training phase. Enhanced Learning Vector Classifier is used in the testing phase to differentiate the normal EIF from EIF causing DS. The performance of the proposed system is analysed in terms of sensitivity, accuracy and specificity.",book:{id:"9965",slug:"computational-optimization-techniques-and-applications",title:"Computational Optimization Techniques and Applications",fullTitle:"Computational Optimization Techniques and Applications"},signatures:"Michael Dinesh Simon and A.R. Kavitha",authors:[{id:"213441",title:"Dr.",name:"A.R.Kavitha",middleName:null,surname:"Balaji",slug:"a.r.kavitha-balaji",fullName:"A.R.Kavitha Balaji"},{id:"335252",title:"Dr.",name:"Michael",middleName:null,surname:"Dinesh Simon",slug:"michael-dinesh-simon",fullName:"Michael Dinesh Simon"}]},{id:"51131",title:"Survey of Meta-Heuristic Algorithms for Deep Learning Training",slug:"survey-of-meta-heuristic-algorithms-for-deep-learning-training",totalDownloads:3136,totalCrossrefCites:15,totalDimensionsCites:24,abstract:"Deep learning (DL) is a type of machine learning that mimics the thinking patterns of a human brain to learn the new abstract features automatically by deep and hierarchical layers. DL is implemented by deep neural network (DNN) which has multi-hidden layers. DNN is developed from traditional artificial neural network (ANN). However, in the training process of DL, it has certain inefficiency due to very long training time required. Meta-heuristic aims to find good or near-optimal solutions at a reasonable computational cost. In this article, meta-heuristic algorithms are reviewed, such as genetic algorithm (GA) and particle swarm optimization (PSO), for traditional neural network’s training and parameter optimization. Thereafter the possibilities of applying meta-heuristic algorithms on DL training and parameter optimization are discussed.",book:{id:"5165",slug:"optimization-algorithms-methods-and-applications",title:"Optimization Algorithms",fullTitle:"Optimization Algorithms - Methods and Applications"},signatures:"Zhonghuan Tian and Simon Fong",authors:[{id:"1952",title:"Dr.",name:"Simon",middleName:null,surname:"Fong",slug:"simon-fong",fullName:"Simon Fong"},{id:"186166",title:"MSc.",name:"Zhonghuan",middleName:null,surname:"Tien",slug:"zhonghuan-tien",fullName:"Zhonghuan Tien"}]},{id:"58127",title:"Particle Swarm Optimization Solution for Power System Operation Problems",slug:"particle-swarm-optimization-solution-for-power-system-operation-problems",totalDownloads:1658,totalCrossrefCites:2,totalDimensionsCites:3,abstract:"Application of particle swarm optimization (PSO) algorithm on power system operation is studied in this chapter. Relay protection coordination in distribution networks and economic dispatch of generators in the grid are defined as two of power system-related optimization problems where they are solved using PSO. Two case study systems are conducted. The first case study system investigates applicability of PSO on providing proper overcurrent relay settings in the grid, while in the second case study system, the economic dispatch of a 15-unit system is solved where PSO successfully provides the optimum power output of generators with minimum fuel costs to satisfy the load demands and operation constraints. The simulation results in comparison with other methods show the effectiveness of PSO against other algorithms with higher quality of solution and less fuel costs on the same test system.",book:{id:"6363",slug:"particle-swarm-optimization-with-applications",title:"Particle Swarm Optimization with Applications",fullTitle:"Particle Swarm Optimization with Applications"},signatures:"Mostafa Kheshti and Lei Ding",authors:[{id:"120842",title:"Associate Prof.",name:"Mostafa",middleName:null,surname:"Kheshti",slug:"mostafa-kheshti",fullName:"Mostafa Kheshti"},{id:"213017",title:"Prof.",name:"Lei",middleName:null,surname:"Ding",slug:"lei-ding",fullName:"Lei Ding"}]},{id:"51209",title:"A Review and Comparative Study of Firefly Algorithm and its Modified Versions",slug:"a-review-and-comparative-study-of-firefly-algorithm-and-its-modified-versions",totalDownloads:2907,totalCrossrefCites:15,totalDimensionsCites:21,abstract:"Firefly algorithm is one of the well-known swarm-based algorithms which gained popularity within a short time and has different applications. It is easy to understand and implement. The existing studies show that it is prone to premature convergence and suggest the relaxation of having constant parameters. To boost the performance of the algorithm, different modifications are done by several researchers. In this chapter, we will review these modifications done on the standard firefly algorithm based on parameter modification, modified search strategy and change the solution space to make the search easy using different probability distributions. The modifications are done for continuous as well as non-continuous problems. Different studies including hybridization of firefly algorithm with other algorithms, extended firefly algorithm for multiobjective as well as multilevel optimization problems, for dynamic problems, constraint handling and convergence study will also be briefly reviewed. A simulation-based comparison will also be provided to analyse the performance of the standard as well as the modified versions of the algorithm.",book:{id:"5165",slug:"optimization-algorithms-methods-and-applications",title:"Optimization Algorithms",fullTitle:"Optimization Algorithms - Methods and Applications"},signatures:"Waqar A. Khan, Nawaf N. Hamadneh, Surafel L. Tilahun and Jean\nM. T. Ngnotchouye",authors:[{id:"180330",title:"Dr.",name:"Surafel",middleName:null,surname:"Tilahun",slug:"surafel-tilahun",fullName:"Surafel Tilahun"},{id:"180784",title:"Dr.",name:"Waqar Ahmed",middleName:null,surname:"Khan",slug:"waqar-ahmed-khan",fullName:"Waqar Ahmed Khan"},{id:"185148",title:"Dr.",name:"Nawaf",middleName:null,surname:"Hamadneh",slug:"nawaf-hamadneh",fullName:"Nawaf Hamadneh"},{id:"185149",title:"Dr.",name:"Jean M. T.",middleName:null,surname:"Ngnotchouye",slug:"jean-m.-t.-ngnotchouye",fullName:"Jean M. T. 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