The algorithm for the same-day surgery protocol. by Magnus Tveit (11442200)

The control algorithm of MPC for SSI. by Ahmed Abdelaleem (19066115)

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.…”

Comparison the results with and without Fourier Loss Function for Rain100H. by Chaobing Zheng (20896579)

<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.…”

Comparison between the proposed framework with and without Fourier Loss Function for Rain100H. by Chaobing Zheng (20896579)

Average quality scores of the models of the analysis set as a function of the <i>w</i>_<i>SP</i> with which the DOPE potential has been included in the objective function... by Giacomo Janson (8138517)

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

Cost functions implemented in Neuroptimus. by Máté Mohácsi (20469514)

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). …”

Genetic algorithms can be applied to higher-dimensional design problems. by Lu Hong (399882)

Comparison between the manual segmentation made by an expert (without the definition of the MVO region), and the segmentation made by each of the methods within CMRSegTools. by William A. Romero R. (13783210)

Posterior for parameter <i>θ</i> of the uniform toy model for different weights in the ABC distance function. by Jonathan U. Harrison (5447603)

“…In (b), is shown the Hellinger distance from the prior to approximate posterior distribution, which is maximized directly within Algorithm 2. In (c), the Hellinger distance from the approximate posterior to the exact posterior distribution (as sampled via MCMC) is displayed. …”

Flowchart of proposed fitness function algorithm. by Ferhat Yuna (21519851)

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…”

Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, th... by Ziyan Jin (21021976)

EELD load dispatch results for different algorithms without considering VPE. by Wenjing Wang (181404)

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

Subjects: