Search alternatives:
within function » fibrin function (Expand Search), python function (Expand Search), protein function (Expand Search)
algorithm both » algorithm blood (Expand Search), algorithm b (Expand Search), algorithm etc (Expand Search)
both function » body function (Expand Search), growth function (Expand Search), beach function (Expand Search)
within function » fibrin function (Expand Search), python function (Expand Search), protein function (Expand Search)
algorithm both » algorithm blood (Expand Search), algorithm b (Expand Search), algorithm etc (Expand Search)
both function » body function (Expand Search), growth function (Expand Search), beach function (Expand Search)
-
61
Functional Projection <i>K</i>-means
Published 2025“…In contrast to existing literature, which largely considers the smoothing as a pre-processing step, in our proposal regularization is integrated with the identification of both subspace and cluster partition. An alternating least squares algorithm is introduced to compute model parameter estimates. …”
-
62
Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional
Published 2025“…Kohn–Sham density functional theory (KS-DFT) has become a cornerstone for studying the electronic structure of molecules and materials. …”
-
63
-
64
-
65
-
66
The pseudocode for the NAFPSO algorithm.
Published 2025“…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”
-
67
PSO algorithm flowchart.
Published 2025“…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”
-
68
-
69
-
70
-
71
Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information.
Published 2024“…Whiskers show the lowest and highest values within 1.5 × <i>IQR</i> from the first and third quartiles, respectively. …”
-
72
-
73
-
74
-
75
Gillespie algorithm simulation parameters.
Published 2024“…Both the ensemble and stochastic models presented in this work have been verified using Monte Carlo molecular dynamic simulations that utilize the Gillespie algorithm. …”
-
76
Scheduling time of five algorithms.
Published 2025“…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”
-
77
Convergence speed of five algorithms.
Published 2025“…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”
-
78
-
79
EFGs: A Complete and Accurate Implementation of Ertl’s Functional Group Detection Algorithm in RDKit
Published 2025“…In this paper, a new RDKit/Python implementation of the algorithm is described, that is both accurate and complete. …”
-
80
A Genetic Algorithm Approach for Compact Wave Function Representations in Spin-Adapted Bases
Published 2025“…Crucially, we propose fitness functions based on approximate measures of the wave function compactness, which enable inexpensive genetic algorithm searches. …”