<b>Opti2Phase</b>: Python scripts for two-stage focal reducer by Morgan Najera (21540776)

“…</li></ul><p dir="ltr">The scripts rely on the following Python packages. Where available, repository links are provided:</p><ol><li><b>NumPy</b>, version 1.22.1</li><li><b>SciPy</b>, version 1.7.3</li><li><b>PyGAD</b>, version 3.0.1 — https://pygad.readthedocs.io/en/latest/#</li><li><b>bees-algorithm</b>, version 1.0.2 — https://pypi.org/project/bees-algorithm</li><li><b>KrakenOS</b>, version 1.0.0.19 — https://github.com/Garchupiter/Kraken-Optical-Simulator</li><li><b>matplotlib</b>, version 3.5.2</li></ol><p dir="ltr">All scripts are modular and organized to reflect the design stages described in the manuscript.…”

Convergence curves of different algorithms on the benchmark functions. by Aihua Guo (13267712)

Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information. by 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. …”

Power flow and cost optimization with Improved PSO algorithm (b). by Muhammad Ahmad Iqbal (11851338)

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Convergence graphs of the ten algorithms for the five functions from the CEC 2014 suite. by Juan Yao (293170)

Power flow and cost optimization with APSO variant-16 of APSO algorithm (b). by Muhammad Ahmad Iqbal (11851338)

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Linear relationship between ln(methadone concentration) and ln(methadone dose) by enantiomer and <i>CYP2B6</i> genotype function. by Andrew H. Talal (8727396)

Linear relationship between ln(methadone metabolism) and ln(methadone dose) by enantiomer and <i>CYP2B6</i> genotype function. by Andrew H. Talal (8727396)

Schematic diagram of the TEAPS algorithm. by Yoshiaki Kariya (13767985)

If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions. by Yuanchen Zhao (12905580)

“…(B) Nevertheless, even when the linear algorithm loses in <i>R</i>², the grouping it identifies can be a better representation of the underlying ground truth. …”

FAR-1: A Fast Integer Reduction Algorithm Compared to Collatz and Half-Collatz by Faizan Ali (21689861)

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

An expectation-maximization algorithm for finding noninvadable stationary states. by Robert Marsland (8616483)

“…<i>(b)</i> Metabolic byproducts move the relevant unperturbed state from <b>R</b>⁰ (gray ‘x’) to (black ‘x’), which is itself a function of the current environmental conditions. …”

<b>Algorithm Pseudocode</b> by Yibin Zhao (22425801)

Verification of the in-line PS function. by Raul Castañeda (13932971)

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

Output groupings of the 3 algorithms for a linear degradation chain of <i>N</i> = 3 metabolites. by Yuanchen Zhao (12905580)

The SCOPer algorithm can be run efficiently on multiple cores. by Nima Nouri (2183760)

The accuracy of different algorithms for TMD on SHL and HTC datasets. by Rui Li (4631)

Performance of PCA and Joint Autoencoder (JAE) denoising algorithms. by Ege Altan (11768156)

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