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)

Subjects:

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.…”

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)

Functional annotation and PPI analysis of turquoise module genes. by Minhui Xi (20114243)

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

The PF of ten comparative algorithms on the MMF1 test function. by Wenxing Wu (16853002)

“…<p>The PF of ten comparative algorithms on the MMF1 test function.</p>…”

The PS of ten comparative algorithms on the MMF1 test function. by Wenxing Wu (16853002)

“…<p>The PS of ten comparative algorithms on the MMF1 test function.</p>…”

Contrast enhancement of digital images using dragonfly algorithm by Soumyajit Saha (19726163)

“…Comparisons with state-of-art methods ensure the superiority of the proposed algorithm. The Python implementation of the proposed approach is available in this <a href="https://github.com/somnath796/DA_contrast_enhancement" target="_blank">Github repository</a>.…”

Pseudocode of the BBAC_I Algorithm. by Zezhi Lin (22521663)

Comparison of algorithm performance in ZDT1 and ZDT2 function tests. by Tao Dong (15551)

“…<p>Comparison of algorithm performance in ZDT1 and ZDT2 function tests.…”

Reward function related parameters. by Honglei Pang (22693724)

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)

Performance of algorithms compared to a smaller <i>RNN.</i> by Swathi Anil (17382903)

The parameters of algorithms. by Le Huu Binh (18340233)

Subjects:

Tunable parameters in stimuli generation module. ^<i>a</i>The fixation cross is displayed once before an experiment starts to help subjects concentrate. <sup><i>b</i></su... by Gursimran Singh (575288)

Subjects:

WST results for LLSKSO and other algorithms on benchmark test functions. (<i>α</i> = 0.05). by Ruyi Dong (9038174)

“…<p>WST results for LLSKSO and other algorithms on benchmark test functions. (<i>α</i> = 0.05).…”

WST results for LLSKSO and other algorithms on benchmark test functions. (<i>α</i> = 0.05). by Ruyi Dong (9038174)

“…<p>WST results for LLSKSO and other algorithms on benchmark test functions. (<i>α</i> = 0.05).…”