EFGs: A Complete and Accurate Implementation of Ertl’s Functional Group Detection Algorithm in RDKit حسب Gonzalo Colmenarejo (650249)

"…In this paper, a new RDKit/Python implementation of the algorithm is described, that is both accurate and complete. …"

Cleavage time as a function of scaled fiber diameter. حسب Roukayatou R. Ouedraogo (20460980)

Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results حسب 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 حسب 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.…"

PyNoetic’s stimuli generation and recording module, which supports both ERP and SSVEP. حسب Gursimran Singh (575288)

الموضوعات:

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

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

الموضوعات:

Median fiber cleavage time (black) and the fraction of successful runs (red) for different parameter values. حسب Roukayatou R. Ouedraogo (20460980)

Reward function related parameters. حسب Honglei Pang (22693724)

الموضوعات:

Summary of the contrasts obtained from the normalized images for both inclusions as a function of the host type and the wavelength. حسب María Victoria Waks-Serra (21578611)

The image of the Aekley function. حسب Zhengyu Xu (8550660)

Evolution of the rescaled hydraulic diameter () and complexity () in both simulations and seal tomograms as a function of simulation timestep and relative age (). حسب Jonathan E Kings (20818367)

The ALO algorithm optimization flowchart. حسب Wenjing Wang (181404)

"…The average running time of the proposed algorithm in Sphere function and Griebank function was 2.67s and 1.64s, respectively. …"

The IALO algorithm solution flowchart. حسب Wenjing Wang (181404)

"…The average running time of the proposed algorithm in Sphere function and Griebank function was 2.67s and 1.64s, respectively. …"

The impact of extrinsic noise sources on the measured SNR is plotted for both CW and TG against varying optical filter magnitudes. حسب Nicholas H. Vitale (20469289)

Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional حسب Ryan Stocks (16867476)

"…Kohn–Sham density functional theory (KS-DFT) has become a cornerstone for studying the electronic structure of molecules and materials. …"

A detailed process of iterative simulation coupled with bone density algorithm; (a) a function of stimulus and related bone density changes, and (b) iterative calculations of finit... حسب Hassan Mehboob (8960273)

The pseudocode for the NAFPSO algorithm. حسب Huichao Guo (14515171)

"…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …"

PSO algorithm flowchart. حسب Huichao Guo (14515171)

"…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …"

The traffic flow distribution diagram when traffic lights in both entrance and exit of the short-distance road section are red. حسب Yulin Tian (1457986)