Activation functions: Sigmoid function and ReLU function. by Wan-Hua Zhang (21601139)

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)

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Geometric interpretation and data visualisation equivalent describing the DI algorithmic pipeline. by Borjan Milinkovic (21349908)

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

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Adaptive H-REMD simulation algorithm. by Danial Pourjafar-Dehkordi (7165523)

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

“…<i>(c)</i> Pseudocode for self-consistently computing <b>R</b>* and , which is identical to standard expectation-maximization algorithms employed for problems with latent variables in machine learning.…”

Function <i>χ</i>_<i>opt</i> superimposed with the phase-field function indicating the channel geometry. by Martina Bukač (20161460)

The phase-field function for three different network configurations considered in this work. by Martina Bukač (20161460)

<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.…”

Listed python codes of our algorithm with explanations (Python 3.9 recommended). by Takayuki Niizato (162226)

Python cv coding screenshots of all developed fingerprints on distinct surfaces. by Nisha Yadav (366131)

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Idea of the search for a domain/motif within the range from 1 to W positions (in the window) instead of an only predetermined position. by Alexander V. Spirov (11302085)

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Identification and functional analysis of hub genes. by Wei Ya Lan (22403712)

“…(C, D) Top 10 hub genes identified using the Maximal Clique Centrality (MCC) algorithm; darker colors indicate higher centrality within the PPI network. …”

Pictorial representation of isomorphically scaling down spots within a fixed field of view and corresponding imaging configuration. by Nicholas H. Vitale (20469289)

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MSVM-RFE algorithm for screening key genes. by Ya xi Zhu (11624730)

Heatmap results from the global sensitivity analysis for the generation of villous vessels within a placentone. by Diana C. de Oliveira (9220729)

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Python-based Monte Carlo Simulation Tool by Yangfan Jiang (9372782)

Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

“…This algorithm conducts a series of procedures: (1) fragmentation of the molecules into functional groups from SMILES, (2) calculation of activity coefficients under predetermined temperature and mole fraction conditions by employing universal quasi-chemical functional group activity coefficient (UNIFAC) model, and (3) regression of NRTL model parameters by employing UNIFAC model simulation results in the differential evolution algorithm (DEA) and Nelder–Mead method (NMM). …”

Elementary effects (EE) results for the generation of villous vessels within a placentone employing the input parameter ranges from Table 1. by Diana C. de Oliveira (9220729)

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Test functions. by Kejia Liu (5699651)

“…<div><p>The fast developments in artificial intelligence together with evolutionary algorithms have not solved all the difficulties that Gene Expression Programming (GEP) encounters when maintaining population diversity and preventing premature convergence. …”