Wilcoxon signed-rank test results comparing IFOX with other optimization algorithms across CL, C17, C19, C21, C22, and RWPs. by Mahmood A. Jumaah (22272371)

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Convergence curves on the functions CL4, CL11, CL13, CL14, CL18, CL19, C17F1, C17F2, and C17F4. by Mahmood A. Jumaah (22272371)

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Application of the smooth-index to bandwidth-limited matrices (A,B) and to block-diagonal matrices (C,D). by Alexander Borst (32619)

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List of recurrent connections from the single column connectome of Drosophila. by Alexander Borst (32619)

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Steps for obtaining a complete Pareto frontier. by Tao Dong (15551)

“…Based on this algorithm, the dynamic economic emission dispatch model of combined heat and power system is constructed to optimize the economic and environmental benefits of the system. …”

Performance profiles of these algorithms on the basis of the number of function evaluations. by Xianfeng Ding (535904)

Image_1_Spectral density-based clustering algorithms for complex networks.pdf by Taiane Coelho Ramos (6207410)

Box plot of the algorithms based on 12 benchmark functions. by Chuang Zhang (2199421)

“…<p>Box plot of the algorithms based on 12 benchmark functions.</p>…”

Convergence behavior of the algorithms based on 12 benchmark functions. by Chuang Zhang (2199421)

“…<p>Convergence behavior of the algorithms based on 12 benchmark functions.</p>…”

Algorithm results based on FE simulated likelihood functions. by Nicolas Herzig (9230425)

A comparison between the static Python-based visualizations of the p65 activity in activated fibroblasts and the dynamic, HTML-based visualizations that use these same reduction me... by Hector Torres (11708207)

Steps of data preprocessing. by Bernhard Vennemann (7434353)

Representative neurons and their corresponding step functions. by Reem Khalil (10898163)

Overall Structure of FuzzyMath-based financial efficiency prediction. by Yanyan Dong (1304145)

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Table 12_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

“…Subsequently, the SNP solutions were estimated by back-solving the Genomic Estimated Breeding Values (GEBVs) predicted by ABCZ using the single-step GBLUP method. Genomic regions were identified using sliding windows of 175 consecutive SNPs, and the top 1% genomic windows, ranked based on their proportion of the additive genetic variance, were used to annotate positional candidate genes and genomic regions associated with each of the 16 traits.…”

Table 10_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

“…Subsequently, the SNP solutions were estimated by back-solving the Genomic Estimated Breeding Values (GEBVs) predicted by ABCZ using the single-step GBLUP method. Genomic regions were identified using sliding windows of 175 consecutive SNPs, and the top 1% genomic windows, ranked based on their proportion of the additive genetic variance, were used to annotate positional candidate genes and genomic regions associated with each of the 16 traits.…”

Table 15_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

“…Subsequently, the SNP solutions were estimated by back-solving the Genomic Estimated Breeding Values (GEBVs) predicted by ABCZ using the single-step GBLUP method. Genomic regions were identified using sliding windows of 175 consecutive SNPs, and the top 1% genomic windows, ranked based on their proportion of the additive genetic variance, were used to annotate positional candidate genes and genomic regions associated with each of the 16 traits.…”

Table 8_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

“…Subsequently, the SNP solutions were estimated by back-solving the Genomic Estimated Breeding Values (GEBVs) predicted by ABCZ using the single-step GBLUP method. Genomic regions were identified using sliding windows of 175 consecutive SNPs, and the top 1% genomic windows, ranked based on their proportion of the additive genetic variance, were used to annotate positional candidate genes and genomic regions associated with each of the 16 traits.…”

Table 6_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

“…Subsequently, the SNP solutions were estimated by back-solving the Genomic Estimated Breeding Values (GEBVs) predicted by ABCZ using the single-step GBLUP method. Genomic regions were identified using sliding windows of 175 consecutive SNPs, and the top 1% genomic windows, ranked based on their proportion of the additive genetic variance, were used to annotate positional candidate genes and genomic regions associated with each of the 16 traits.…”

Table 13_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

“…Subsequently, the SNP solutions were estimated by back-solving the Genomic Estimated Breeding Values (GEBVs) predicted by ABCZ using the single-step GBLUP method. Genomic regions were identified using sliding windows of 175 consecutive SNPs, and the top 1% genomic windows, ranked based on their proportion of the additive genetic variance, were used to annotate positional candidate genes and genomic regions associated with each of the 16 traits.…”