Convergence curves on the functions C22F1, C22F4, C22F5, C22F9, C22F10, C22F12, CBP, CSP, and TCP. by Mahmood A. Jumaah (22272371)

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Curve of step response signal of 6 algorithms. by Guangwei Liu (181992)

Attack and defense behaviors with the sensory–motor algorithm between a single predator and a single prey. by Hiroyuki Ichijo (13891153)

Subjects:

Convergence curves for multimodal function test. by Qinghe Guo (21519322)

Convergence curves for unimodal function test. by Qinghe Guo (21519322)

Implementation of the EMC² algorithm in Icy platform. by Thibault Lagache (492571)

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

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. …”

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

Exemplary application of DDQR in the storage and retrieval of DNA barcode sequence for plant material. by Yujun Wang (1947196)

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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)

Benchmark functions T-test for the proposed and compared algorithms. by El-Sayed M. El-kenawy (14581088)

Attack and defense behaviors between a single predator and a single prey using the sensory–motor algorithm. by Hiroyuki Ichijo (13891153)

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Steps of data preprocessing. by Bernhard Vennemann (7434353)

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

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.…”