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

EFGs: A Complete and Accurate Implementation of Ertl’s Functional Group Detection Algorithm in RDKit حسب 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. …"

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.…"

Python-based Monte Carlo Simulation Tool حسب Yangfan Jiang (9372782)

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

الموضوعات:

Genetic algorithm process of this paper. حسب Jiajie Liu (4560088)

One iteration of the NSGA-II algorithm. حسب Erick Axel Padilla-García (16839233)

Standard benchmark 28 test functions. حسب Qianhang Du (13410314)

Efficient algorithms to discover alterations with complementary functional association in cancer حسب Rebecca Sarto Basso (6728921)

"…We provide analytic evidence of the effectiveness of UNCOVER in finding high-quality solutions and show experimentally that UNCOVER finds sets of alterations significantly associated with functional targets in a variety of scenarios. In particular, we show that our algorithms find sets which are better than the ones obtained by the state-of-the-art method, even when sets are evaluated using the statistical score employed by the latter. …"

Average quality scores of the models of the analysis set as a function of the <i>w</i>_<i>SP</i> with which the DOPE potential has been included in the objective function... حسب Giacomo Janson (8138517)

An expectation-maximization algorithm for finding noninvadable stationary states. حسب Robert Marsland (8616483)

"…<i>(b)</i> Metabolic byproducts move the relevant unperturbed state from <b>R</b>⁰ (gray ‘x’) to (black ‘x’), which is itself a function of the current environmental conditions. …"

Comparison of details of different algorithms. حسب Hao Wu (65943)

الموضوعات:

Structure of the LMPEC algorithm model. حسب Hao Wu (65943)

الموضوعات:

Python scripts used to model CPU errors by Poisson distribution density mass function and to draw graph of Poisson events (needs Matplot, SciPy, PysimpleGUI and NumPy) حسب Santhosh Kumar Rajamani (13023678)

Benchmark functions. حسب Yun Qi (560401)

Experimental results of basic unimodal benchmark functions (<i>f</i>1−<i>f</i>7). حسب Jiahao Fan (11236401)

الموضوعات:

Experimental results of basic multi-modal benchmark functions (<i>f</i>8−<i>f</i>13). حسب Jiahao Fan (11236401)

الموضوعات:

Experimental results of CEC 2013 composition benchmark functions (<i>f</i>44−<i>f</i>51). حسب Jiahao Fan (11236401)

الموضوعات:

Convergence process on CEC 2013 multi-modal benchmark functions (<i>f</i>29−<i>f</i>36). حسب Jiahao Fan (11236401)

الموضوعات: