BOFdat Step 3: Identifying species-specific metabolic end goals. by Jean-Christophe Lachance (6619307)

“…<p>(A) After Step 3, the entire weight of the cell is accounted for by BOFdat. …”

User options and main steps of the generative algorithm. by Diana C. de Oliveira (9220729)

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). …”

Particle swarm optimization (PSO) algorithm to identify parameter values of model C-fiber axons and meet performance criteria. by Brandon J. Thio (5351147)

<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.…”

A schematic view of the step-by-step computational approach in the current study. by Hamed Rezaei (11085366)

“…<p>(<b>A</b>) modeling and predicting callus formation rate and callus fresh weight (outputs) based on Benzylaminopurine (BAP), Indole-3-butyric acid (IBA), Kinetin (KIN), and α-Naphthaleneacetic acid (NAA) as inputs through three machine learning algorithms including generalized regression neural network (GRNN), multilayer perception (MLP) and radial basis function (RBF); (<b>B</b>) optimization process through a genetic algorithm (GA); and (<b>C-D</b>) confirming the results of the optimization process in the validation experiment for callus formation rate and callus fresh weight, respectively.…”

Curve of step response signal of 6 algorithms. by Guangwei Liu (181992)

Working steps and operating algorithm of the developed GSMCS. by Abdallah Elshawadfy Elwakeel (19852263)

The proposed feature extraction algorithm. by Ali Ahmed (2567584)

Subjects:

Mean and covariance estimation of functional data streams by Mingxue Quan (13282368)

Subjects:

Steps of data preprocessing. by Bernhard Vennemann (7434353)

Representative neurons and their corresponding step functions. by Reem Khalil (10898163)

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

Objective function trend with static and dynamic step size. by Ludovica Adacher (11238189)

Swarm intelligence algorithms for multi-objective IMP: Step-by-step improvement by Francisco Muñoz (9455441)

“…Algorithms implemented on article "<b>A step-by-step solution for the multi-objective influence spread problem</b>". …”

Comparison of edge detector and CHT in detecting droplets. by Manibarathi Vaithiyanathan (6655274)

Faster Exact Exchange for Solids via occ-RI-K: Application to Combinatorially Optimized Range-Separated Hybrid Functionals for Simple Solids with Pseudopotentials Near the Basis Se... by Joonho Lee (1351974)

Subjects:

Comparison of algorithms in two cases. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Flow of the NSGA-II algorithm. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

A Python Package for the Localization of Protein Modifications in Mass Spectrometry Data by Anthony S. Barente (14035175)

Subjects: