CPU usage of benchmark function: (a) CPU Usage during the Execution of Benchmark Functions F21-F30, (b) CPU Usage during the Execution of Benchmark Functions F31-F40 and (c) CPU Us... حسب Ruyi Dong (9038174)

Iteration curve of benchmark function: (a) Convergence curves of the average best fitness for functions F21-F30, (b) Convergence curves of the average best fitness for functions F3... حسب Ruyi Dong (9038174)

DataSheet1_The evolution of flexibility and function in the Fc domains of IgM, IgY, and IgE.pdf حسب 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. …"

Significantly dysregulated proteins (|log2FC | ≤  0.5, adj. p-value ≤  0.05) in SCA1 (805 proteins). حسب Aimilia-Christina Vagiona (17584758)

AODV algorithm for route selection at node destination. حسب Tibebu Legesse (21030014)

Flowchart of the general algorithm for one trial. حسب Junying Wang (509009)

Computational and resource efficiency of different algorithms. حسب Rui Li (4631)

Pipeline of the interactor algorithm. حسب Jose Cleydson F. Silva (8685594)

Flowchart of MOSRS algorithm. حسب Vahid Goodarzimehr (21343124)

Objective function values of the algorithms across individual problem instances grouped by scale: (a) Small, (b) Medium, (c) Large, (d) Super-Large. حسب Panfei Li (22379086)

"…<p>Objective function values of the algorithms across individual problem instances grouped by scale: (a) Small, (b) Medium, (c) Large, (d) Super-Large.…"

Reliability of the optimized frequency parameters and sFC matrix edges in the high-dimensional cases as well as of the GoF. حسب Kevin J. Wischnewski (21354521)

EASP-AODV algorithm for route discovery at the intermediate node. حسب Tibebu Legesse (21030014)

Functional enrichment and correlation analysis of hub DE-SRGs. حسب Xihao Shen (20347942)

الموضوعات:

Comparison of different algorithms. حسب Jiandong Qiu (20389944)

Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information. حسب Yuanchen Zhao (12905580)

"…<p>(A), (B) Algorithm performance, evaluated over 50 simulated datasets generated as described in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012590#pcbi.1012590.g001" target="_blank">Fig 1</a> with <i>N</i> = 3 true groups, 900 samples and 10% simulated measurement noise. …"

Code snippets to add user-specific custom functionality in PyNoetic. حسب Gursimran Singh (575288)

الموضوعات:

The training process for PV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. حسب Hiam Alquran (17636906)

The training process for AV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. حسب Hiam Alquran (17636906)

The training process for TV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. حسب Hiam Alquran (17636906)

The training process for MV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. حسب Hiam Alquran (17636906)