Table 11_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

“…These findings contribute to gene prioritization efforts, facilitating the identification of functional candidate genes that can enhance genomic selection strategies for economically important traits in Nellore cattle.…”

Table 1_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

“…These findings contribute to gene prioritization efforts, facilitating the identification of functional candidate genes that can enhance genomic selection strategies for economically important traits in Nellore cattle.…”

Table 4_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

“…These findings contribute to gene prioritization efforts, facilitating the identification of functional candidate genes that can enhance genomic selection strategies for economically important traits in Nellore cattle.…”

Table 7_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

“…These findings contribute to gene prioritization efforts, facilitating the identification of functional candidate genes that can enhance genomic selection strategies for economically important traits in Nellore cattle.…”

Table 3_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

“…These findings contribute to gene prioritization efforts, facilitating the identification of functional candidate genes that can enhance genomic selection strategies for economically important traits in Nellore cattle.…”

Diversity of the population of en-CSA with respect to benchmark functions. by Sarah A. Alzakari (19704611)

Subjects:

CEC2022 and CEC2017 benchmark functions. by Chengcheng Ding (9013862)

Greedy optimization algorithm. by Alexis Bénichou (19832361)

Cost function calculated by QA with different hyperparameters. by Takuro Matsuta (21608505)

Subjects: “…currently available algorithms…”

Cost function calculated by SA with different hyperparameters. by Takuro Matsuta (21608505)

Subjects: “…currently available algorithms…”

Pt Cluster Catalysts and Alkane to Aromatic Conversion Evaluated Using a Combination of Swarm Intelligence and Density Functional Theory by Michael T. Davenport (7826936)

“…Here, we report density functional theory (DFT) calculations combined with a swarm intelligence algorithm to determine Pt positioning and interaction with the zeolite framework and detailed comparison of reaction pathways. …”

Time response of the en-CSA based condenser with respect to different algorithms of CSA, RUN, PDO and RIME. by Sarah A. Alzakari (19704611)

Subjects:

Confusion matrix values for different size of test samples and different choice of random states. by Abeer Aljohani (18497914)

Flowchart of simple ant colony algorithm. by Yang Yu (4292)

“…The improvements to the ant colony algorithm are made in four key areas: the definition of heuristic information, the weighting of parameters, the state selection strategy, and the pheromone update strategy. …”

High-dimensional benchmark test functions. by Ruyi Dong (9038174)

“…In addition, to verify the performance and robustness of LLSKSO, comparison experiments between LLSKSO and 10 well-known algorithms are conducted on 50 benchmark test functions. …”

Description of test functions. by Lijie Sun (224007)

Q-Table values for each state-action pair at selected generations (mean over 20 runs). by Zhongfeng Li (3965060)

Force variations under different contact states. by Guohua Cao (697580)

Cleavage time as a function of scaled fiber diameter. by Roukayatou R. Ouedraogo (20460980)

MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis by Metodi V. Metodiev (6089009)

Subjects: “…probabilistic scoring function…”