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

“…For a RDKit molecule, it provides (i) a PNG binary string with an image of the molecule with color-highlighted functional groups; (ii) a list of sets of atom indices (idx), each set corresponding to a functional group; (iii) a list of pseudo-SMILES canonicalized strings for the full functional groups; and (iv) a list of RDKit labeled mol objects, one for each full functional group. …”

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

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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.…”

Depiction of how the label of is assigned as a function of whether are identical (row =), nested (rows ), or partially overlapping (row “else”), and as a function of the labels of... by Jordan Moutet (21800145)

Matlab code of a function used in S2 File. by Hiroko Kato Solvang (5718250)

Immune infiltration analysis by CIBERSORT algorithm. by Lanying Shen (22755930)

Immune infiltration analysis by CIBERSORT algorithm. by Lanying Shen (22755930)

Membership functions. by Tuan Anh Nguyen (121944)

Line charts of the elbow method and the k-value selection algorithm: (A) elbow method; (B) k-value selection algorithm. by Jingnan Yan (20727331)

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Flowchart of improved FOX optimization algorithm. by Mahmood A. Jumaah (22272371)

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Fixing λ_<i>L</i> = 1 and adjusting the size of λ_<i>G</i>, with the gradual increase of λ_<i>G</i>, the time resolution of the high-frequency part of... by Li Jiashen (20552690)

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Spectrograms extracted from the generalized S-transform corresponding to the adjustment parameters λ_<i>G</i> = 0.05, λ_<i>L</i> = 0.01. by Li Jiashen (20552690)

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Generalized S-transform result plots of the signal h(t) corresponding to the fixed parameter λ_<i>G</i> = 2, adjusting the size of the parameter λ_<i>L</i>. by Li Jiashen (20552690)

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Generalized S-transform result plots of the signal h(t) corresponding to the fixed parameter λ_<i>L</i> = 10, adjusting the size of the parameter λ_<i>G</i>. by Li Jiashen (20552690)

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If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions. by Yuanchen Zhao (12905580)

“…Both versions are evaluated on the same synthetic datasets with a 3-group ground truth. Each algorithm return a set of coarsened <i>variables</i> (a grouping of species into three groups) and a <i>model</i> that uses these variables to predict the function. …”

Compare algorithm parameter settings. by Yuqi Xiong (12343771)

“…<div><p>Metaheuristic optimization algorithms often face challenges such as complex modeling, limited adaptability, and a tendency to get trapped in local optima when solving complex optimization problems. …”

AR(1) error term, <i>ρ</i> = 0.5, <i>σ</i>² = 0.1 versus i.i.d., <i>σ</i>² = 1 for all the methods; <i>K</i> = 3. by Susana Conde (5495675)

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Circulatory system-based optimization algorithm with dynamic penalty function for optimum design of large-scale water distribution networks by Veli Batmaz (20800175)

“…However, existing heuristic algorithms for WDN optimization often rely on specific parameters, limiting their performance and hindering their applicability to diverse network configurations, particularly in large-scale systems. …”

Exponentially attenuated sinusoidal function. by Hang Zhao (143592)

“…A multi-objective optimization method based on non-dominated sorting was integrated into the crayfish optimization algorithm (MOCOA). To optimize the key parameters <i>K</i> and in variational mode decomposition (VMD), a MOCOA-VMD technique specifically tailored for ECG signal processing was developed. …”