The optimization result of 10 benchmark functions for different algorithms (D = 10). by Yu Li (183836)

The optimization result of 10 benchmark functions for different algorithms (D = 30). by Yu Li (183836)

The result of CEC2010 standard functions for different algorithms (D = 30). by Yu Li (183836)

The optimization result of 10 benchmark functions for different algorithms (D = 100). by Yu Li (183836)

The convergence curves of the function in different dimensions. by Yu Li (183836)

Convergence curves of multimodal functions <i>f</i>₇. by Yu Li (183836)

Convergence curves of unimodal functions <i>f</i>₂. by Yu Li (183836)

Convergence curves of unimodal functions <i>f</i>₂. by Yu Li (183836)

Convergence curves of multimodal functions <i>f</i>₇. by Yu Li (183836)

Convergence curve of functions <i>f</i>_<i>1</i><i>-f</i>_<i>10</i> in D = 10. by Yu Li (183836)

Python implementation of the Trajectory Adaptive Multilevel Sampling algorithm for rare events and improvements by Pascal Wang (10130612)

“…<div>This directory contains Python 3 scripts implementing the Trajectory Adaptive Multilevel Sampling algorithm (TAMS), a variant of Adaptive Multilevel Splitting (AMS), for the study of rare events. …”

Reward function related parameters. by Honglei Pang (22693724)

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Differential analysis between the high- and low-risk score groups and functional analyses of DEGs. by Jian Wen (286688)

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Immune microenvironment (A) Comparison of immune cell infiltration between the high-risk and low-risk groups using the ssGSEA algorithm. by Chengting Wu (21485612)

“…<p>(B) Comparison of immune function between the high-risk and low-risk groups using the ssGSEA algorithm. …”

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

Summary of results of naïve Bayes algorithms. by Christopher E. Niemczak (8586861)

“…<div><p>Importance</p><p>Predicting neurocognitive deficits using complex auditory assessments could change how cognitive dysfunction is identified, and monitored over time. …”

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). …”

<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.…”

HIL test results of low-adhesion road: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate. by Honglei Pang (22693724)

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