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The reuse of industrial heritage sites has become increasingly prevalent in the context of hosting mega-events. This paper aims to investigate this trend, specifically in the context of Olympic Games or World Expos, and its impact on urban regeneration strategies. It introduces a methodological tool, namely, the relationship matrix, to identify key factors from 'best practices' criteria for managing industrial sites and planning mega-event legacies. We use this matrix to address two research objectives, namely, to determine the role of industrial heritage reuse in mega-event urban projects and to evaluate whether this creates a favourable environment for place-making. This matrix enables us to compare and analyse the Shanghai 2010 World Expo and the London 2012 Summer Olympic Games as illustrative case studies. While guidelines and strategies for mega-events and industrial heritage have evolved towards a proactive people-centred approach, our findings still reveal a bias towards material renewal in dominant discourses during the planning phase. This bias often overlooks or even excludes the memories and experiences of local communities. [ABSTRACT FROM AUTHOR]

Abstract The reuse of industrial heritage sites has become increasingly prevalent in the context of hosting mega-events. This paper aims to investigate this trend, specifically in the context of Olympic Games or World Expos, and its impact on urban regeneration strategies. It introduces a methodological tool, namely, the relationship matrix, to identify key factors from ‘best practices’ criteria for managing industrial sites and planning mega-event legacies. We use this matrix to address two research objectives, namely, to determine the role of industrial heritage reuse in mega-event urban projects and to evaluate whether this creates a favourable environment for place-making. This matrix enables us to compare and analyse the Shanghai 2010 World Expo and the London 2012 Summer Olympic Games as illustrative case studies. While guidelines and strategies for mega-events and industrial heritage have evolved towards a proactive people-centred approach, our findings still reveal a bias towards material renewal in dominant discourses during the planning phase. This bias often overlooks or even excludes the memories and experiences of local communities.

This paper analyzes the integration process of the Kano model and the QFD model, combines the advantages of the two, and proposes a method of obtaining the importance of user requirements based on the integration method of the QFD and Kano model. Organize older people’s smart tourism needs and use the Kano model analysis method to categorize each demand attribute. Obtain the self-scoring importance degree of the elderly demand, calculate the satisfaction index of the elderly demand, and get the comprehensive ranking of the importance degree centered on the elderly demand according to the SII, DDI, Ti, and K values of the demand elements. Clarify the elements of smart tourism APP interface design services, establish the relationship matrix between the needs of older people and the service index, and build the service quality house of smart tourism APP interface design for the elderly. Use the smart tourism APP functional interface planning strategy to present the functional design of the APP interface. It can be found that those with higher importance ratings are mainly basic functional requirements, attraction guides, and personalization center, of which the four importance indexes of U6 personalization center are U61=4.72, U62=4.75, U63=4.16, and U64=4.23. The calculation results of the quality house show that the importance of the service elements of the interface design of the APP for senior citizens’ smart tourism is ranked as APP content professionalism, service attitude, recommendation rationality, and service quality. Degree > service attitude > recommendation rationality > after-sale reliability.

This paper studies the use of artificial intelligence technology in the field of education and the way of empowering vocational education and constructs a wisdom teaching model of vocational education based on artificial intelligence. It also applies entropy weight and a fuzzy comprehensive evaluation model to determine evaluation indexes and weights, constructs a fuzzy relationship matrix, and synthesizes a fuzzy comprehensive evaluation model. Based on the model, the teaching effect of vocational education with artificial intelligence is studied, and the advantages of wisdom teaching in the creation of a learning environment and the triggering of students’ interest in learning, creative thinking and problem-solving ability are analyzed by comparing with traditional teaching methods. The results show that there is a significant difference between the effect of AI teaching and traditional teaching, p

Firstly, according to the actual situation of some new undergraduate colleges and universities upgraded from teacher training colleges, whose science and technology disciplines are relatively weak but humanities disciplines have an excellent history of nearly one hundred years, the humanities disciplines with strong creativity, such as education, art and media, are closely related through the construction of cultural and creative industry professional clusters. Secondly, based on the clustering algorithm, we classify and quantify the professional relationships of undergraduate institutions, which need to satisfy the assignment rules and perform algebraic operations on the relationship matrix to realize the classification of professional relationships. Then we searched the Chinese literature with “professional cluster” as the keyword to obtain the initial data, and used the clustering algorithm to visualize and analyze the research on professional cluster construction in undergraduate colleges and universities. The results show that when the default values of α1/α0 = 3 and γ[0,1] = 0.6 are used, the seven keywords with the highest emergence intensity are: architecture cluster, transformation development, undergraduate, professional faculty, school, undergraduate school, and big data, and the keyword clustering reflects the research hotspots of professional clusters, which can promote the construction of professional clusters.

Genes with important roles in reproduction traits have been described in pig X-chromosome (SSCX), which may contribute considerably to genetic variance and could impact the accuracy of genomic prediction. The phenotypic data set includes information on 82,580 litter size records from 34,346 sows. Additionally, there were 108,006 in the pedigree, and genotypes for 41,492 SNPs, of which 1,435 in SSCX, were available for 30,000 animals. Three different approaches were used to estimate variance components for the total number born and number of stillborn. In the first approach, only SNPs in the autosomes were used and the model included the additive genetic (u), permanent environment (p), and residual (e) random effects. In the second approach, the same model was adopted; however, the data included SNPs in the autosomes and in the X chromosome. For both models, it was assumed that u~N(0,H⊗Vu ), e~N(0,I⊗Ve ) and p~N(0,I⊗Vp ), wherein H is a relationship matrix that combines pedigree and genomic information; and, Vu, Vp, and Ve are the (co)variance matrices for the random additive genetic, permanent environment, and residual effects, respectively. In the third approach, the random additive genetic effects associated with the X-chromosome (s) were included in the model described before. Then, it was assumed that s~N(0,Hx ⊗Vs ), where Hx is the genomic relationship matrix for the X-chromosome and Vs is the additive genetic variance associated with the SSCX. All analyses were carried out using GIBBSF90+. Table 1 shows variance components for the three approaches. For both traits, additive genetic variance and heritabilities were similar across the approaches. The additive genetic variance for the SSCX was small, accounting for approximately two percent of the total additive genetic variance. Including the X chromosome as a separate effect and using Hx as a covariance matrix captured only a small part of the additive genetic variance. The additive variance across approaches did not differ. For total number born and number of stillborn, using the SSCX SNPs to build Hx did not provide extra benefit compared with fitting those SNP in the regular H matrix. [ABSTRACT FROM AUTHOR]

in the network technology increasingly innovation, weibo social media such as WeChat gradually become people thought together and important platform of communication, both theoretical knowledge and practical case studies, have proved that social media can be intuitive show people the focus of the current problems, to provide effective basis for market development and investment decisions. Especially in the comprehensive promotion of text mining technology, topic extraction related to corporate social media has been reasonably applied in the financial market. However, due to the problems of sparse and unstructured data, topic extraction in practical application has become the main subject explored by researchers and scholars. This paper studies enterprise microblogging social media, for example, using both the theme of the text content and platform interactive method, according to the relations between the two kinds of typical weibo relation and the matrix, and by using two consecutive nonnegative matrix decomposition process is analyzed, finally the corresponding distance as a result, and extracting keywords to express the relationship. The actual verification results show that the clustering accuracy and theme similarity of this method are more effective than traditional theme extraction methods in corporate social media.

In this present paper, the wicking effect of cotton woven fabrics in different directions and wicking effect of its constituent yarns has been studied. Two different types of weaves have been examined to study the wicking performance. Increased fineness in yarns of ply and single structure shows lower wickability individually. However, when used in plain and twill weave samples, improvement of wicking occurs with an initial increase in yarn fineness, but further increase in fineness deteriorates the wickability of fabrics except the diagonal direction of twill design. The regression analysis has been done to show the relationship between fabric wicking and its constituent yarn wicking. Highly positive correlations between fabric and constituent yarns are observed in relation to wicking irrespective of fineness of yarns and direction of test. However, twill weave shows stronger relationship compared to plain weave.

Nowadays, in some populations, the number of genotyped animals is too large to obtain the inverse of the genomic relationship matrix. The algorithm for proven and young animals (APY) can be used to overcome this problem. In the present work, different strategies for defining core animals in APY were tested using either simulated or real data. In particular, core definitions based on random choice or on the contribution to the genomic relationship matrix (GCONTR) calculated using Principal Component Analysis were tested. Core sizes able to explain 90, 95, 98, and 99% of the total variance of the genomic relationship matrix (G) were used. Analyzed phenotypes were three simulated traits for 3 000 individuals, and milkability records for 136 406 Italian Simmental cows. The number of genotypes was 4 100 for the simulated dataset, and 11 636 for the Simmental data, respectively. The GCONTR values in Simmental dataset were moderately correlated with the analyzed phenotype, and they showed a decreasing trend according to the year of birth of genotyped animals. The accuracy increased as the size of the core increased in both datasets. The inclusion in the core of animals with largest GCONTR values led to the lowest accuracies (0.50 and 0.71 for the simulated and Simmental datasets, respectively; average across traits and core sizes). On the contrary, the selection of animals with the lowest rank according to their contribution to the G provided slightly higher accuracies, especially in the simulated dataset (0.68 for the simulated dataset, and 0.76 for the Simmental data; average across traits and core sizes). In real data, particularly for larger sizes of core animals, the criteria of choice appear less important, confirming the results of earlier studies. Anyway, the inclusion in the core of animals with the lowest values of GCONTR led to increases in accuracy. These are preliminary results based on a small sample size that need to be confirmed on a larger number of genotypes.

In this present paper, the wicking effect of cotton woven fabrics in different directions and wicking effect of its constituent yarns has been studied. Two different types of weaves have been examined to study the wicking performance. Increased fineness in yarns of ply and single structure shows lower wickability individually. However, when used in plain and twill weave samples, improvement of wicking occurs with an initial increase in yarn fineness, but further increase in fineness deteriorates the wickability of fabrics except the diagonal direction of twill design. The regression analysis has been done to show the relationship between fabric wicking and its constituent yarn wicking. Highly positive correlations between fabric and constituent yarns are observed in relation to wicking irrespective of fineness of yarns and direction of test. However, twill weave shows stronger relationship compared to plain weave. [ABSTRACT FROM AUTHOR]

ABSTRACT: Single-step genomic BLUP (ssGBLUP) is a method for genomic prediction that integrates matrices of pedigree (A) and genomic (G) relationships into a single unified additive relationship matrix whose inverse is incorporated into a set of mixed model equations (MME) to compute genomic predictions. Pedigree information in dairy cattle is often incomplete. Missing pedigree potentially causes biases and inflation in genomic estimated breeding values (GEBV) obtained with ssGBLUP. Three major issues are associated with missing pedigree in ssGBLUP, namely biased predictions by selection, missing inbreeding in pedigree relationships, and incompatibility between G and A in level and scale. These issues can be solved using a proper model for unknown-parent groups (UPG). The theory behind the use of UPG is well established for pedigree BLUP, but not for ssGBLUP. This study reviews the development of the UPG model in pedigree BLUP, the properties of UPG models in ssGBLUP, and the effect of UPG on genetic trends and genomic predictions. Similarities and differences between UPG and metafounder (MF) models, a generalized UPG model, are also reviewed. A UPG model (QP) derived using a transformation of the MME has a good convergence behavior. However, with insufficient data, the QP model may yield biased genetic trends and may underestimate UPG. The QP model can be altered by removing the genomic relationships linking GEBV and UPG effects from MME. This altered QP model exhibits less bias in genetic trends and less inflation in genomic predictions than the QP model, especially with large data sets. Recently, a new model, which encapsulates the UPG equations into the pedigree relationships for genotyped animals, was proposed in simulated purebred populations. The MF model is a comprehensive solution to the missing pedigree issue. This model can be a choice for multibreed or crossbred evaluations if the data set allows the estimation of a reasonable relationship matrix for MF. Missing pedigree influences genetic trends, but its effect on the predictability of genetic merit for genotyped animals should be negligible when many proven bulls are genotyped. The SNP effects can be back-solved using GEBV from older genotyped animals, and these predicted SNP effects can be used to calculate GEBV for young-genotyped animals with missing parents.

Simple Summary: Compared to BLUP, in single-step genomic BLUP, G − 1 − A 22 − 1 is added to the inverse of the pedigree relationship matrix ( A − 1 ), forming H − 1 , where G is the genomic relationship matrix, and A 22 is the block of A for genotyped animals. Incompatibility between G and A may cause inflated genetic variance. Blending and tuning G with A 22 partially solves the problem. However, conditioning H − 1 might still be needed, which is usually performed via τ G − 1 − ω A 22 − 1 . This may violate the properties upon which H is built. Alternative ways of weighting the H − 1 components are presented to prevent/minimise violations of the properties of H. The single-step genomic BLUP (ssGBLUP) is used worldwide for the simultaneous genetic evaluation of genotyped and non-genotyped animals. It is easily extendible to all BLUP models by replacing the pedigree-based additive genetic relationship matrix (A) with an augmented pedigree–genomic relationship matrix (H). Theoretically, H does not introduce any artificially inflated variance. However, inflated genetic variances have been observed due to the incomparability between the genomic relationship matrix (G) and A used in H. Usually, G is blended and tuned with A 22 (the block of A for genotyped animals) to improve its numerical condition and compatibility. If deflation/inflation is still needed, a common approach is weighting G − 1 − A 22 − 1 in the form of τ G − 1 − ω A 22 − 1 , added to A − 1 to form H − 1 . In some situations, this can violate the conditional properties upon which H is built. Different ways of weighting the H − 1 components ( A − 1 , G − 1 , A 22 − 1 , and H − 1 itself) were studied to avoid/minimise the violations of the conditional properties of H. Data were simulated on ten populations and twenty generations. Responses to weighting different components of H − 1 were measured in terms of the regression of phenotypes on the estimated breeding values (the lower the slope, the higher the inflation) and the correlation between phenotypes and the estimated breeding values (predictive ability). Increasing the weight on H − 1 increased the inflation. The responses to weighting G − 1 were similar to those for H − 1 . Increasing the weight on A − 1 (together with A 22 − 1 ) was not influential and slightly increased the inflation. Predictive ability is a direct function of the slope of the regression line and followed similar trends. Responses to weighting G − 1 − A 22 − 1 depend on the inflation/deflation of evaluations from A − 1 to H − 1 and the compatibility of the two matrices with the heritability used in the model. One possibility is a combination of weighting G − 1 − A 22 − 1 and weighting H − 1 . Given recent advances in ssGBLUP, conditioning H − 1 might become an interim solution from the past and then not be needed in the future. [ABSTRACT FROM AUTHOR]

This study investigated the main factors influencing the genetic variance and the variance of breeding values (EBV). The first is the variance of genetic values in the base population, and the latter is the variance of genetic values in the population under evaluation. These variances are important as improper variances can lead to systematic bias. The inverse of the genetic relationship matrix (K-1) and the phenotypic variance are the main factors influencing the genetic variance and heritability (h2). These factors and h2 are also the main factors influencing the variance of EBVs. Pedigree- and genomic-based relationship matrices (A and G as K) and phenotypes on 599 wheat lines were used. Also, data were simulated, and a hybrid (genomic-pedigree) relationship matrix (H as K) and phenotypes were used. First, matrix K underwent a transformation (K* = wK + a11' + ßI), and the responses in the mean and variation of diag(K-1) and offdiag(K-1) elements, and genetic variance in the form of h2 were recorded. Then, the original K was inverted, and matrix K-1 underwent the same transformations as K, and the responses in the h2 estimate and the variance of EBVs in the forms of correlation and regression coefficients with the EBVs estimated based on the original K-1 were recorded. In response to weighting K by w, the estimated genetic variance changed by 1/w. We found that µ(diag(K)) - µ(offdiag(K)) influences the genetic variance. As such, a did not change the genetic variance, and increasing ß increased the estimated genetic variance. Weighting K-1 by w was equivalent to weighting K by 1/w. Using the weighted K-1 together with its corresponding h2, EBVs remained unchanged, which shows the importance of using variance components that are compatible with the K-1. Increasing ßI added to K-1 increased the estimated genetic variance, and the effect of a11' was minor. We found that larger variation of diag(K-1) and higher concentration of offdiag(K-1) around the mean (0) are responsible for lower h2 estimate and variance of EBVs. [ABSTRACT FROM AUTHOR]

This study investigated the main factors influencing the genetic variance and the variance of breeding values (EBV). The first is the variance of genetic values in the base population, and the latter is the variance of genetic values in the population under evaluation. These variances are important as improper variances can lead to systematic bias. The inverse of the genetic relationship matrix (K−1) and the phenotypic variance are the main factors influencing the genetic variance and heritability (h2). These factors and h2 are also the main factors influencing the variance of EBVs. Pedigree- and genomic-based relationship matrices (A and G as K) and phenotypes on 599 wheat lines were used. Also, data were simulated, and a hybrid (genomic-pedigree) relationship matrix (H as K) and phenotypes were used. First, matrix K underwent a transformation (K* = wK + α11′ + βI), and the responses in the mean and variation of diag(K−1) and offdiag(K−1) elements, and genetic variance in the form of h2 were recorded. Then, the original K was inverted, and matrix K−1 underwent the same transformations as K, and the responses in the h2 estimate and the variance of EBVs in the forms of correlation and regression coefficients with the EBVs estimated based on the original K−1 were recorded. In response to weighting K by w, the estimated genetic variance changed by 1/w. We found that μ(diag(K)) − μ(offdiag(K)) influences the genetic variance. As such, α did not change the genetic variance, and increasing β increased the estimated genetic variance. Weighting K−1 by w was equivalent to weighting K by 1/w. Using the weighted K−1 together with its corresponding h2, EBVs remained unchanged, which shows the importance of using variance components that are compatible with the K−1. Increasing βI added to K−1 increased the estimated genetic variance, and the effect of α11′ was minor. We found that larger variation of diag(K−1) and higher concentration of offdiag(K−1) around the mean (0) are responsible for lower h2 estimate and variance of EBVs.

Historical data from plant breeding programs provide valuable resources to study the response of genotypes to the changing environment (i.e. genotype‐by‐environment interaction). Such data have been used to evaluate the pattern of genotype performance across regions or locations, but its use to evaluate the long‐term pattern of genotype performance across environments (i.e. locations‐by‐years) has been hampered by the lack of common genotypes across years. This lack of common genotypes is due to the structure of the breeding program, especially for annual crops, where only a proportion of selected genotypes are tested in subsequent years. This has resulted in a sparse prediction of the performance of genotypes across years (i.e. a genotype‐by‐year table). A genomic prediction method that fitted both a relationship matrix among genotypes and a relationship matrix among environments (i.e. years) could overcome this limitation and produce a dense genotype‐by‐year table, thereby enabling some evaluation of long‐term genotype performance. In this paper, we applied the genomic prediction model to the yield data from CIMMYT's Elite Spring Wheat Yield Trials (ESWYT) to visualise the pattern of genotype performance over 25 years. [ABSTRACT FROM AUTHOR]

Combinatorial testing is a prevailing approach to finding out faults in the extremely complex systems of train operation for railway. While combinatorial testing is applied to Chinese Train Control Systems (CTCS) for revealing faults, some potential faults tend to be arduous to trigger due to the interaction of multiple test cases. For the sake of accurate and efficient fault localization of CTCS under the circumstances of masking effects, a novel method of locating the faults of CTCS using the relationship matrix with frequent terms has been proposed in the paper. To begin with, failed test cases from CTCS were made full use of to calculate the frequentness of failure‐inducing elements for ordered tables. Then, the relationship matrix with frequent terms for CTCS was established to work out the suspiciousness of failure‐inducing combinations by the following relation of failure‐inducing elements in the above tables. Furthermore, an iterative approach to locating failure‐inducing combinations was adopted to obtain the full list of rankings of failure‐inducing combinations, among which top combinations, especially MFS (Minimal Failure‐causing Schema), were prone to discover the faults of CTCS. Finally, the effectiveness and accuracy of the proposed method for CTCS was confirmed on the simulation platform of CTCS for Beijing‐Shenyang High‐speed Rail. Empirical studies suggest that compared to the method of fault forest, the average suspiciousness in the proposed method is 12.17% higher than that and the number of additional test cases are reduced by 31.7% on average, which can produce more precise and efficient results of identifying the faults of CTCS in case of masking effects. The research achievements can provide some references for the safety evaluation and system optimization of CTCS for high‐speed rail. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Single-step genomic BLUP (ssGBLUP) is a method for genomic prediction that integrates matrices of pedigree (A) and genomic (G) relationships into a single unified additive relationship matrix whose inverse is incorporated into a set of mixed model equations (MME) to compute genomic predictions. Pedigree information in dairy cattle is often incomplete. Missing pedigree potentially causes biases and inflation in genomic estimated breeding values (GEBV) obtained with ssGBLUP. Three major issues are associated with missing pedigree in ssGBLUP, namely biased predictions by selection, missing inbreeding in pedigree relationships, and incompatibility between G and A in level and scale. These issues can be solved using a proper model for unknown-parent groups (UPG). The theory behind the use of UPG is well established for pedigree BLUP, but not for ssGBLUP. This study reviews the development of the UPG model in pedigree BLUP, the properties of UPG models in ssGBLUP, and the effect of UPG on genetic trends and genomic predictions. Similarities and differences between UPG and metafounder (MF) models, a generalized UPG model, are also reviewed. A UPG model (QP) derived using a transformation of the MME has a good convergence behavior. However, with insufficient data, the QP model may yield biased genetic trends and may underestimate UPG. The QP model can be altered by removing the genomic relationships linking GEBV and UPG effects from MME. This altered QP model exhibits less bias in genetic trends and less inflation in genomic predictions than the QP model, especially with large data sets. Recently, a new model, which encapsulates the UPG equations into the pedigree relationships for genotyped animals, was proposed in simulated purebred populations. The MF model is a comprehensive solution to the missing pedigree issue. This model can be a choice for multibreed or crossbred evaluations if the data set allows the estimation of a reasonable relationship matrix for MF. Missing pedigree influences genetic trends, but its effect on the predictability of genetic merit for genotyped animals should be negligible when many proven bulls are genotyped. The SNP effects can be back-solved using GEBV from older genotyped animals, and these predicted SNP effects can be used to calculate GEBV for young-genotyped animals with missing parents. [ABSTRACT FROM AUTHOR]

City development strategies (CDS) are of great importance in reducing the pressure created by urban development processes in the city, providing services equally to each individual and ensuring the sustainability of natural resources. Within the scope of this research, firstly, Bartın Province Strategic Plan (2020-2024) was analyzed by comparing themes that should be addressed in urban development. In another analysis phase, relationship matrices were created to determine the harmony of the criteria and strategic goals of urban planning and management with the "Integrated Urban Development Strategy and Action Plan - KENTGES, 2010-2023". Data in matrices; after classifying them as compatible, indirect compatible and full compatible, the items associated with indirect compatible and their reasons are briefly summarized. As a result of the comparisons obtained, the deficiencies of Bartın's urban development strategies were determined and suggestions for ensuring sustainable urban development were given. [ABSTRACT FROM AUTHOR]

Abstract Background R is a multi-platform statistical software and an object oriented programming language. The package archive network for R provides CRAN repository that features over 15,000 free open source packages, at the time of writing this article ( https://cran.r-project.org/web/packages , accessed in October 2019). The package ggroups is introduced in this article. The purpose of this package is providing functions for checking and processing the pedigree, calculation of the additive genetic relationship matrix and its inverse, which are used to study the population structure and predicting the genetic merit of animals. Calculation of the dominance relationship matrix and its inverse are also covered. A concept in animal breeding is genetic groups, which is about the inequality of the average genetic merits for groups of unknown parents. The package provides functions for the calculation of the matrix of genetic group contributions (Q). Calculating Q is computationally demanding, and depending on the size of the pedigree and the number of genetic groups, it might not be feasible using personal computers. Therefore, a computationally optimised function and its parallel processing alternative are provided in the package. Results Using sample data, outputs from different functions of the package were presented to illustrate a real experience of working with the package. Conclusions The presented R package is a free and open source tool mainly for quantitative geneticists and ecologists, who deal with pedigree data. It provides numerous functions for handling pedigree data, and calculating various pedigree-based matrices. Some of the functions are computationally optimised for large-scale data.

The single-step genomic BLUP (ssGBLUP) is used worldwide for the simultaneous genetic evaluation of genotyped and non-genotyped animals. It is easily extendible to all BLUP models by replacing the pedigree-based additive genetic relationship matrix (A) with an augmented pedigree–genomic relationship matrix (H). Theoretically, H does not introduce any artificially inflated variance. However, inflated genetic variances have been observed due to the incomparability between the genomic relationship matrix (G) and A used in H. Usually, G is blended and tuned with A22 (the block of A for genotyped animals) to improve its numerical condition and compatibility. If deflation/inflation is still needed, a common approach is weighting G−1−A22−1 in the form of τG−1−ωA22−1, added to A−1 to form H−1. In some situations, this can violate the conditional properties upon which H is built. Different ways of weighting the H−1 components (A−1, G−1, A22−1, and H−1 itself) were studied to avoid/minimise the violations of the conditional properties of H. Data were simulated on ten populations and twenty generations. Responses to weighting different components of H−1 were measured in terms of the regression of phenotypes on the estimated breeding values (the lower the slope, the higher the inflation) and the correlation between phenotypes and the estimated breeding values (predictive ability). Increasing the weight on H−1 increased the inflation. The responses to weighting G−1 were similar to those for H−1. Increasing the weight on A−1 (together with A22−1) was not influential and slightly increased the inflation. Predictive ability is a direct function of the slope of the regression line and followed similar trends. Responses to weighting G−1−A22−1 depend on the inflation/deflation of evaluations from A−1 to H−1 and the compatibility of the two matrices with the heritability used in the model. One possibility is a combination of weighting G−1−A22−1 and weighting H−1. Given recent advances in ssGBLUP, conditioning H−1 might become an interim solution from the past and then not be needed in the future.

Purpose: Quality Function Deployment, (QFD) is a methodology which helps to satisfy customer requirements through the selection of appropriate Technical Attributes (TAs). The rationale of this article is to provide a method lending statistical support to the selection of TAs. The purpose is to determine the statistical significance of TAs through the derivation of associated significance (P) values. Design/Methodology/Approach: We demonstrate our methodology with reference to an original QFD case study aimed at improving the educational system in high schools in Pakistan; and then with five further published case studies obtained from literature. Mean weights of TAs are determined. Considering each TA mean weight to be a Test Statistic, a weighted matrix is generated from the VOCs’ importance ratings, and ratings in the relationship matrix. Finally using R, P-values for the means of original TAs are determined from the hypothetical population of means of TAs. Findings: Each TA’s P-value evaluates its significance/insignificance in terms of distance from the grand mean. P-values indirectly set the prioritization of TAs. Implications/Originality/Value: The novel aspect of this study is extension of mean weights of TAs, to also provide P-values for TAs. TAs with significant importance can be resolved on priority basis, while other can be fixed with appropriateness.

In this article, we view business models as complex deal-making activity systems organized to create, deliver, and capture value. Unlike some other viewpoints, we emphasize both system components and their interconnection. Business activities are carried out by a network of actors drawing on a network of resources, and individual firms seek to configure these intersecting networks to enhance their competitive positioning. The business model literature refers to the significance of antecedent activities in providing context – opportunities the firm decides to pursue, the strategy adopted, and requisite capabilities. Drawing on this literature, we propose an approach to framing business model context. Drawing on the information systems literature, we identify a toolkit facilitating activity system architecture design. We suggest how this both draws out the underlying complexity of a business model and shows how a multiplicity of views makes sense.

Reduced models are equivalent models to the full model that enable reduction in the computational demand for solving the problem, here, mixed model equations for estimating breeding values of selection candidates. Since phenotyped animals provide data to the model, the aim of this study was to reduce animal models to those equations corresponding to phenotyped animals. Non-phenotyped ancestral animals have normally been included in analyses as they facilitate formation of the inverse numerator relationship matrix. However, a reduced model can exclude those animals and obtain identical solutions for the breeding values of the animals of interest. Solutions corresponding to non-phenotyped animals can be back-solved from the solutions of phenotyped animals and specific blocks of the inverted relationship matrix. This idea was extended to other forms of animal model and the results from each reduced model (and back-solving) were identical to the results from the corresponding full model. Previous studies have been mainly focused on reduced animal models that absorb equations corresponding to non-parents and solve equations only for parents of phenotyped animals. These two types of reduced animal model can be combined to formulate only equations corresponding to phenotyped parents of phenotyped progeny.

The selection of technological bases in the machining of prismatic components may be formalized by means of geometric information in IGS format. The use of IGS files for formalized determination of surface properties is analyzed for purposes of basing in the presence of constraints on the surface extent in the prismatic component. [ABSTRACT FROM AUTHOR]

Reduced models are equivalent models to the full model that enable reduction in the computational demand for solving the problem, here, mixed model equations for estimating breeding values of selection candidates. Since phenotyped animals provide data to the model, the aim of this study was to reduce animal models to those equations corresponding to phenotyped animals. Non-phenotyped ancestral animals have normally been included in analyses as they facilitate formation of the inverse numerator relationship matrix. However, a reduced model can exclude those animals and obtain identical solutions for the breeding values of the animals of interest. Solutions corresponding to non-phenotyped animals can be back-solved from the solutions of phenotyped animals and specific blocks of the inverted relationship matrix. This idea was extended to other forms of animal model and the results from each reduced model (and back-solving) were identical to the results from the corresponding full model. Previous studies have been mainly focused on reduced animal models that absorb equations corresponding to non-parents and solve equations only for parents of phenotyped animals. These two types of reduced animal model can be combined to formulate only equations corresponding to phenotyped parents of phenotyped progeny. [ABSTRACT FROM AUTHOR]

Estimated breeding values using best linear unbiased prediction (BLUP) through pedigree relationship can enhance selection efficiency and save time as well as resources in autotetraploid potato breeding program. Here, we used historical preliminary yield evaluation trials data of 469–619 breeding lines for tuber yield and late blight resistance to estimate heritability and BLUP based breeding values modelling auto-tetraploid inheritance in mixed model analysis. The pedigree file had a depth of 3–4 generations with total 370 individuals including 111 founders. Heritability estimates varied from 0.15 for marketable tuber yield to 0.47 for late blight resistance computed using A matrix. The prediction accuracy for total tuber yield, marketable tuber yield and late blight resistance (AUDPC) was 0.53 ± 0.02, 0.44 ± 0.02 and 0.81 ± 0.01, respectively. The prediction accuracy was highest for late blight resistance and moderate for total and marketable tuber yield. The prediction bias measured as regression of observed phenotype values on predicted values for late blight resistance was almost nil in comparison to total and marketable tuber yield. Moderate to high prediction accuracies for tuber yields and late blight resistance suggest the selection of genotypes based on EBVs in Indian potato breeding programme for higher genetic gain.

We considered Bayesian estimation of polygenic effects, in particular heritability in relation to a class of linear mixed models implemented in R (R Core Team 2018). Our approach is applicable to both family-based and population-based studies in human genetics with which a genetic relationship matrix can be derived either from family structure or genome-wide data. Using a simulated and a real data, we demonstrate our implementation of the models in the generic statistical software systems JAGS (Plummer 2017) and Stan (Carpenter et al. 2017) as well as several R packages. In doing so, we have not only provided facilities in R linking standalone programs such as GCTA (Yang, Lee, Goddard, and Visscher 2011) and other packages in R but also addressed some technical issues in the analysis. Our experience with a host of general and special software systems will facilitate investigation into more complex models for both human and nonhuman genetics.

Copyright of Canadian Journal of Animal Science is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: R is a multi-platform statistical software and an object oriented programming language. The package archive network for R provides CRAN repository that features over 15,000 free open source packages, at the time of writing this article (https://cran.r-project.org/web/packages, accessed in October 2019). The package ggroups is introduced in this article. The purpose of this package is providing functions for checking and processing the pedigree, calculation of the additive genetic relationship matrix and its inverse, which are used to study the population structure and predicting the genetic merit of animals. Calculation of the dominance relationship matrix and its inverse are also covered. A concept in animal breeding is genetic groups, which is about the inequality of the average genetic merits for groups of unknown parents. The package provides functions for the calculation of the matrix of genetic group contributions (Q). Calculating Q is computationally demanding, and depending on the size of the pedigree and the number of genetic groups, it might not be feasible using personal computers. Therefore, a computationally optimised function and its parallel processing alternative are provided in the package. Results: Using sample data, outputs from different functions of the package were presented to illustrate a real experience of working with the package. Conclusions: The presented R package is a free and open source tool mainly for quantitative geneticists and ecologists, who deal with pedigree data. It provides numerous functions for handling pedigree data, and calculating various pedigree-based matrices. Some of the functions are computationally optimised for large-scale data. [ABSTRACT FROM AUTHOR]

In order to solve the problem of inaccurate description of news content features and user interest features in mobile cloud computing, proposed a multimedia text classification algorithm that utilizes multi-tag potential Dirichlet distribution. The algorithm is based on the traditional latent Dirichlet allocation (LDA) model and assumes a linear relationship between user tags and potential topics. Therefore, a relational matrix is introduced in the LDA model to describe the corresponding relationship between the tag and the topic, so that the probability distribution of the tag on the word can be inferred from the probability distribution of the topic on the word. The algorithm first learns the probability distribution table of label words by Gibbs sampling method, then infers the probability distribution of new documents on labels according to the model parameters, so as to realize the purpose of predicting the corresponding multiple labels of documents. In order to improve the ability of the algorithm to deal with massive data, the parallel algorithm has been improved. Since the bottleneck of the algorithm lies mainly in the serial nature of global variable updating and communication, the core idea of our parallelization is that in massive text training, global delay updating and asynchronous communication will not affect the final training results. Experiments show that the proposed algorithm has greatly improved the training efficiency. The classification accuracy is higher than that of Naive Bayesian algorithm and Support Vector Machine (SVM) algorithm proposed in other literatures. The average classification accuracy can achieve at about 95%, and it can be used as a general parallel framework of supervised LDA algorithm. [ABSTRACT FROM AUTHOR]

The performance of contaminated site management in terms of its sustainability can be inherently uncertain, considering that this performance depends not only on individual site characteristics and variability, but also on integrated (and potentially long-term) environmental, social, economic and technical impacts. The present study proposes a combination weights-based TOPSIS model (Technique for Order Preference by Similarity to Ideal Solution), following a multi-criteria decision analysis (MCDA) approach, to quantitatively analyze the most influential measures and indices over the entire life cycle of contaminated site management (from site investigation to land reuse) in China. Results indicate that the sustainability performance of different sites varies widely due to differences in the best management practices (BMPs) implemented. Environmental dimensions in strategy design and remediation implementation processes contribute most to the overall sustainability performance when whole life-cycle management is considered, with social dimensions relatively under-emphasised. Based on a sensitivity analysis of the TOPSIS method, random variations of indices' weights did not result in significant alteration of the modelled sustainability performance of the evaluated sites, which indicates that the method provides a relatively robust approach. The MCDA approach developed enables stakeholders to adopt optimal BMPs for risk control and to enhance sustainability performance in contaminated site management, covering the whole life cycle of site remediation and redevelopment, although further work is required to determine its international applicability. • A novel (lifecycle-based) MCDA framework for sustainability evaluation is presented. • Sensitivity analysis is conducted to show accuracy of the improved TOPSIS approach. • Factors in multi-dimensions jointly influence sustainability of site management. • Recommendations to enable more effective decision making are proposed. • Challenges remain in extending application of the proposed method beyond China. [ABSTRACT FROM AUTHOR]

Abstract Background Single-step genomic best linear unbiased prediction (BLUP) evaluation combines relationship information from pedigree and genomic marker data. The inclusion of the genomic information into mixed model equations requires the inverse of the combined relationship matrix $${\mathbf{H}}$$ H , which has a dense matrix block for genotyped animals. Methods To avoid inversion of dense matrices, single-step genomic BLUP can be transformed to single-step single nucleotide polymorphism BLUP (SNP-BLUP) which have observed and imputed marker coefficients. Simple block LDL type decompositions of the single-step relationship matrix $${\mathbf{H}}$$ H were derived to obtain different types of linearly equivalent single-step genomic mixed model equations with different sets of reparametrized random effects. For non-genotyped animals, the imputed marker coefficient terms in the single-step SNP-BLUP were calculated on-the-fly during the iterative solution using sparse matrix decompositions without storing the imputed genotypes. Residual polygenic effects were added to genotyped animals and transmitted to non-genotyped animals using relationship coefficients that are similar to imputed genotypes. The relationships were further orthogonalized to improve convergence of iterative methods. Results All presented single-step SNP-BLUP models can be solved efficiently using iterative methods that rely on iteration on data and sparse matrix approaches. The efficiency, accuracy and iteration convergence of the derived mixed model equations were tested with a small dataset that included 73,579 animals of which 2885 were genotyped with 37,526 SNPs. Conclusions Inversion of the large and dense genomic relationship matrix was avoided in single-step evaluation by using fully orthogonalized single-step SNP-BLUP formulations. The number of iterations until convergence was smaller in single-step SNP-BLUP formulations than in the original single-step GBLUP when heritability was low, but increased above that of the original single-step when heritability was high.

One of the factors most important to achieving global competitiveness is good-quality security management. Two significant concerns in undertaking security management are spiralling preventive costs and the occurrence of security incident. Although there have been many studies on technical security management, few have been investigated from an organisational security management perspective. Furthermore, quality management and security management have a similar ultimate goal – namely, to prevent defects. Mistake-proofing is a preventive action taken to identify and eliminate the root causes of defects. The ultimate goal of the current study is the application of the mistake-proofing tools in security management. A taxonomy of security management approaches is defined from the organisational security management perspective, and by applying the Delphi method, a relationship matrix between the taxonomy of security management and the 10 types of human error is investigated. The relationship matrix can be used as a mistake-proofing tool in creating preventive strategies that relate to organisational security management. The feasibility and usability of the relationship matrix are demonstrated by taking up three case studies of security failure. [ABSTRACT FROM AUTHOR]

Genetic evaluations are computed to assess the genetic merit of animals based on mixed model equations. An important assumption for setting up these equations is that all genetic relationships among animals are available and correct. The objectives of the present study were to estimate the effects of incomplete pedigree and paternity errors on genetic evaluation. Data and pedigree of 100 dairy herds were obtained from Animal Breeding Center of Iran. Final data edited included milk yield records from 302860 first lactation Holstein cows. DMU Trace program was used for tracing ancestors and creating the full pedigree of animals. To simulate incomplete and wrong pedigrees, different scenarios including 8, 12, 16, 20, 24 percent of paternal identification numbers were removed or replaced using R program. Breeding values for milk yield was predicted by animal model using DMU program. Spearman's rank correlation was estimated for superior animals in different scenarios using SAS software. Estimates of heritability for full, incomplete and wrong pedigrees were 0.29, 0.26 and 0.27, respectively. The results showed a high variation in ranking of animals and determination of superior animals (P<0.01). As an example, at 12% level scenario, Spearman's rank correlation of BVs predicted from full pedigree with incomplete and wrong pedigrees were 0.65 and 0.60, respectively. Selection effectiveness, defined as the ratio of common superior animals in alternative scenarios, was decreased by increasing the rate of misidentification and errors (P<0.01). Incorrect pedigree and misidentification of animals could reduce accuracy of breeding values and consequently bias in animals ranking. [ABSTRACT FROM AUTHOR]

In recent years, in the aviation industry, the weak engineering controls and lapses associated with safety management systems (SMSs) are responsible for the seemingly unprecedented disasters. A previous study has confirmed the difficulties experienced by safety managers with SMSs and the need to direct research to this area of investigation for more insights and progress in the evaluation and maintenance of SMSs in the aviation industry. The purpose of this work is to examine the application of Decision Trial and Evaluation Laboratory (DEMATEL) to the aviation industry in developing countries with illustration using the Nigerian aviation survey data for the validation of the method. The advantage of the procedure over other decision making methods is in its ability to apply feedback in its decision making. It also affords us the opportunity of breaking down the complex aviation SMS components and elements which are multi-variate in nature through the analysis of the contributions of the diverse system criteria from the perspective of cause and effects, which in turn yields easier and yet more effective aviation transportation accident pre-corrective actions. In this work, six revised components of an SMS were identified and DEMATEL was applied to obtain their direct and indirect impacts and influences on the overall SMS performance. Data collection was by the survey questionnaire, which served as the initial direct-relation matrix, coded in Matlab software for establishing the impact relation map (IRM). The IRM was then plotted in MS Excel spread-sheet software. From our results, safety structure and regulation has the highest impact level on an SMS with a corresponding positive relation level value. In conclusion, the results agree with those of previous researchers that used grey relational analysis. Thus, DEMATEL serves as a great tool and resource for the safety manager.

Fog computing plays an important role in industrial and information process. The programs in fog computing, such as iFogSim toolkit, usually contain some infeasible paths (paths that cannot be covered), which makes it impossible to compare algorithm in models that require covering all paths. In this paper, we proposed a mathematical model to build automated test case generation based on path coverage (ATCG-PC) in fog computing programs as a single-objective problem. Single objective helps to reduce the cost of evaluation functions, which is proportional to the number of test cases. When infeasible paths are contained in tested programs, algorithms can also be compared in this model. In this paper, classical differential evolution (DE) is used to solve the ATCG-PC. However, it is difficult for DE to use generated test cases covering remaining paths in the ATCG-PC of fog computing. Therefore, we proposed a test-case-path relationship matrix to empower DE (RP-DE). Experiment results show that RP-DE uses significantly less test cases and achieves higher path coverage rate than compared state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In quality function deployment (QFD), establishing the relationship matrix is quite an important step to transform ambiguous and qualitative customer requirements into concrete and quantitative technical characteristics. Owing to the inherent imprecision and fuzziness of the matrix, the fuzzy linear regression (FLR) is gradually applied into QFD to establish it. However, with regard to an FLR model, the h value is a critical parameter whose setting is always an aporia and it is commonly determined by decision makers. To a certain extent, this subjective assignment fades the effectiveness of FLR in the application of QFD. Aiming to this problem, FLR models with optimized parameters h obtained by maximizing system credibility are introduced into QFD in this paper, in which relationship coefficients are assumed as asymmetric triangular fuzzy numbers. Moreover, a systematic approach is developed to identify the relationship matrix in QFD, whose application is demonstrated through a packing machine example. The final results show that FLR models with optimized h values can always achieve a more reliable relationship matrix. Besides, a comparative study on symmetric and asymmetric cases is elaborated detailedly. [ABSTRACT FROM AUTHOR]

Abstract: The inbreeding coefficients are considered in breeding decisions, and the inverse numerator relationship matrix A−1 is a prerequisite for breeding value estimation. Polyandry and haploid males are among the specifics of relationships between honey bees. Brascamp and Bijma (2014) averaged out the manifold possible relationships among honey bees that appear to have the same parents in a pedigree and assigned a single entry in A to animals that behave as a unit, for example, the workers of a hive. Their methods of calculation connected full‐sibs in the variance matrix of the Mendelian sampling terms D, via nonzero off‐diagonal elements. This impedes the inversion of A and the closely connected calculation of inbreeding coefficients, because efficient algorithms for this task take D to be a diagonal matrix. Memory limitations necessitate their use for large data sets. We adapted the quickest of them to the block diagonal matrix D, that is postulated for the honey bee. To our knowledge, the presented algorithm is the first one that facilitates the method of Brascamp and Bijma (2014) on large data sets. [ABSTRACT FROM AUTHOR]

Summary: Single‐step models including dominance can be an enormous computational task and can even be prohibitive for practical application. In this study, we try to answer the question whether a reduced single‐step model is able to estimate breeding values of bulls and breeding values, dominance deviations and total genetic values of cows with acceptable quality. Genetic values and phenotypes were simulated (500 repetitions) for a small Fleckvieh pedigree consisting of 371 bulls (180 thereof genotyped) and 553 cows (40 thereof genotyped). This pedigree was virtually extended for 2,407 non‐genotyped daughters. Genetic values were estimated with the single‐step model and with different reduced single‐step models. Including more relatives of genotyped cows in the reduced single‐step model resulted in a better agreement of results with the single‐step model. Accuracies of genetic values were largest with single‐step and smallest with reduced single‐step when only the cows genotyped were modelled. The results indicate that a reduced single‐step model is suitable to estimate breeding values of bulls and breeding values, dominance deviations and total genetic values of cows with acceptable quality. [ABSTRACT FROM AUTHOR]

A combined matrix that exploits genealogy together with marker-based information could improve the selection of elite individuals in breeding programs. We present genetic parameters for adaptive and growth traits in beef cattle by exploring linear combinations of pedigree-based (A) and marker-based (G) relationship matrices. We use a data set with 2,111 Brahman (BB) and 2,550 Tropical Composite (TC) cattle with genotypes for 729,068 SNP, and phenotypes for five traits. A weighted relationship matrix (WRM) combining G and A was constructed as WRM = λG + (1 - λ)A. The weight (λ) was explored at values from 0.0 to 1.0, at 0.1 intervals. Additionally, four alternative G matrices, in the WRM, were evaluated according to the selection of SNP used to generate them: 1) Gw: all autosomal SNP with minor allele frequency (MAF) > 1%; 2) Gg: autosomal SNP with MAF > 1% and mapped inside to gene coding regions; 3) Gp: autosomal SNP with MAF > 1% and previously reported to have significant pleiotropic effect in these two populations; and 4) Gc: autosomal SNP with MAF > 1% and with significant correlated effects previously reported in both BB and TC populations. In addition, two A matrices were evaluated: 1) A: all relationships between animals were considered after tracing back known ancestors; and 2) Ad: a distorted A matrix where a random 1% of the off-diagonal nonzero values were set to zero to simulate relationship errors. Five independent Ad matrices were explored each with a different random 1% of relationships masked. Criteria for comparing the resulting WRM included estimates of heritability (h2) and cross-validation accuracy (ACC) of genomic estimated breeding values. The choice of WRM had a greater impact on h2 than on ACC estimates. The 1% errors introduced in pedigree relationships generated large distortion in genetic parameters and ACC estimates. However, employing a λ > 0.7 was an efficient mechanism to compensate for the errors in A. Additionally, although significant (P-value < 0.0001), we found no consistent relationship between the type of SNP used to compute G and h2 or ACC estimates. We devised the optimal value of λ for maximum h2 and ACC at λ = 0.7 suggesting a 70% and 30% weighting to genomic and genealogical information, respectively, as an optimal strategy to compensate for pedigree errors, to improve genetic parameters estimates and lead to more accurate selection decisions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Univesity of Thi-Qar is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)