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

“…Ertl’s algorithm is an approach to extract functional groups in arbitrary organic molecules that does not depend on predefined libraries of functional groups. …”

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.…”

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

Subjects:

Convergence curves of various algorithms under test functions. by Jiayuan Wang (3765376)

Convergence curves of various algorithms under test functions. by Jiayuan Wang (3765376)

The convergence curve of each optimization algorithm under the test function. by Hao Yang (328526)

Types of training algorithms. by Caixia Yan (6647072)

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

“…In this work, we present a comparative study of four GPU-accelerated algorithms for evaluating the KS-DFT exchange–correlation (XC) potential with an atom-centered Gaussian basis. …”

Reward function related parameters. by Honglei Pang (22693724)

Subjects:

Parameters of the genetic algorithm. by Jian Ma (170138)

A Genetic Algorithm Approach for Compact Wave Function Representations in Spin-Adapted Bases by Maru Song (22593561)

Subjects:

The structure of genetic algorithm (GA). by Ali Akbar Moosavi (17769033)

“…First, physico-chemical inputs as bulk density (BD), initial water content (W_i), saturated water content (W_s), mean weight diameter (MWD), and geometric mean diameter (GMD) of aggregates, pH, electrical conductivity (EC), and calcium carbonate equivalent (CCE) were measured. Then, radial basis functions (RBFNNs), multilayer perceptron (MLPNNs), hybrid genetic algorithm (GA-NNs), and particle swarm optimization (PSO-NNs) neural networks were utilized to develop PTFs and compared their accuracy with the traditional regression model (MLR) using statistical indices. …”

Flowchart of the particle swarm optimization algorithm. by Jian Ma (170138)

Flowchart of the particle swarm optimization algorithm. by Jian Ma (170138)

Immune infiltration analysis by ssGSEA algorithm. by Yubin Xue (1537042)

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

Algorithm pseudocode. by Cheng-jie Chen (22272090)

“…On the basis of the Retinex model, multi-level decomposition of brightness is carried out, and different filtering parameters are set to obtain multiple illumination and reflection images with different scale information; Using exponential function and Sigmoid function to process reflection images and illumination images separately, reducing external interference on images of different scales, and solving the difficulty of enhancing images with uneven illumination, high noise, low illumination, and loss of details. …”

Mean and standard deviation of RBF classifier performance across different training algorithms with cross-validation. by Mohammad Hossein Hadizadeh Isfahani (22634016)

Mean and standard deviation of MLP classifier performance across different training algorithms with cross-validation. by Mohammad Hossein Hadizadeh Isfahani (22634016)