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FAR-1: A Fast Integer Reduction Algorithm Compared to Collatz and Half-Collatz
Published 2025Subjects: -
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EFGs: A Complete and Accurate Implementation of Ertl’s Functional Group Detection Algorithm in RDKit
Published 2025“…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. …”
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Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results
Published 2025“…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). …”
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<b>Opti2Phase</b>: Python scripts for two-stage focal reducer
Published 2025“…</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.…”
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Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional
Published 2025“…Two approaches, both leveraging batched dense linear algebra, are found to outperform the others across a suite of molecular benchmarks. We show that batched formation of the XC matrix from the density matrix yields the best performance for large (>O(103) basis functions), sparse systems such as glycine chains and water clusters. …”
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Algorithm results based on FE simulated likelihood functions.
Published 2020“…(B) Bayesian iterations for nodule depth estimation with a constant stiffness <i>K</i><sub><i>θ</i></sub> = 0.68Nm. (C) KL divergence and stiffness changes across sweeps with the likelihood functions obtained by FEM simulation.…”
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An expectation-maximization algorithm for finding noninvadable stationary states.
Published 2020“…<i>(b)</i> Metabolic byproducts move the relevant unperturbed state from <b>R</b><sup>0</sup> (gray ‘x’) to (black ‘x’), which is itself a function of the current environmental conditions. …”
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Reconstruction performance across algorithms, dynamics, cell types, and recording length.
Published 2024“…<b>D</b> The mean average precision score (APS, upper panel) and Matthews correlation coefficient (MCC, lower panel), estimated across all connections, obtained from all inference algorithms and the eANN across three different dynamical regimes. …”
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Improved Ant Colony Algorithm
Published 2024“…To achieve this, we integrate the hyperbolic tangent function, fine-tuning the ACO algorithm's behavior to adaptively adjust its search strategy across iterations.(2) Recognizing the tendency of heuristic algorithms to converge prematurely into local optima, we devise a max-min ant colony strategy. …”
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Prediction performance of different optimization algorithms.
Published 2021“…<p>(A) 3 algorithms were compared in terms of the residuals of the cost function of the optimized TF on 7 mice datasets (Derivative free algorithm failed in optimizing a TF in a mouse). …”