Similarity between the allegiance matrices of the multiplex SC-FC graph, the FC graph alone, and the SC graph alone, as a function of the topological scale for the group. by Arian Ashourvan (6685232)

Local topological scales of hierarchical community organization in structural and functional brain graphs. by Arian Ashourvan (6685232)

Comparison between different PSO algorithms on function 4–6. by Lai Xu (1828831)

Comparison between different PSO algorithms on function 1–3. by Lai Xu (1828831)

Comparison between different PSO algorithms on function 7–8. by Lai Xu (1828831)

Results obtained by different algorithms on 23 benchmark functions. by Yuqi Xiong (12343771)

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RMSE values of different algorithms for different bands in the "Kherson" region. by YueSheng Jiang (19267984)

RMSE values of different algorithms for different bands in the "PY" region. by YueSheng Jiang (19267984)

Convergence curves obtained by different algorithms on 23 benchmark functions. by Yuqi Xiong (12343771)

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Different algorithms in 23 benchmark functions to obtain partial boxplot. by Yuqi Xiong (12343771)

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Results obtained by different algorithms on CEC2022 benchmark functions. by Yuqi Xiong (12343771)

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Convergence curves obtained by different algorithms on CEC2022 benchmark functions. by Yuqi Xiong (12343771)

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Loss function value changes in CNN training changes with epochs for both training dataset and testing dataset. by Jiaxi Wang (545353)

DataSheet1_The evolution of flexibility and function in the Fc domains of IgM, IgY, and IgE.pdf by Rosaleen A. Calvert (10039787)

“…Introduction<p>Antibody Fc regions harbour the binding sites for receptors that mediate effector functions following antigen engagement by the Fab regions. …”

Mutations of the gene expression fold change (FC) over wildtype. by Fangzhou Shen (5140994)

Compare algorithm parameter settings. by Yuqi Xiong (12343771)

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The Precision-Recall curve of both the models. by Aastha Vatsyayan (9981916)

“…</p><p>Results</p><p>Using an independent validation dataset of ACMG & AMP classified variants, as well as a patient set of functionally validated variants, we showed how both algorithms perform and can be used to classify large numbers of variants in clinical as well as research settings.…”

A parabolic relationship between prediction errors and learning rates obtained by a cubic learning algorithm. by Boluwatife Ikwunne (22238697)

Raw task performance on both experiments, with respect to conditions. by Anis Najar (9745561)

Principal component analysis applied to both datasets. by Anny K. G. Rodrigues (11689849)