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.…"

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

"…In this paper, a new RDKit/Python implementation of the algorithm is described, that is both accurate and complete. …"

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

الموضوعات:

Reward function related parameters. حسب Honglei Pang (22693724)

الموضوعات:

Summary of greedy algorithms presented in this section. Details of approximation factors for FastGreedy and UnitGreedy may be found in [26] and [25], respectively. حسب Jeffrey Keithley (14626551)

الموضوعات:

Percentage totBurden and percentage maxBurden reduced by UnitGreedy, SingletonGreedy, FastGreedy and baselines for = 0.535 in Iowa for FRED (first column) and SafeGraph (second col... حسب Jeffrey Keithley (14626551)

الموضوعات:

Metapopulation model notation. حسب Jeffrey Keithley (14626551)

الموضوعات:

Estimates of for each problem instance. حسب Jeffrey Keithley (14626551)

الموضوعات:

The instance of MaxCasesAverted and MaxPeaksReduced is a graph <i>G</i> constructed from the given instance of Max k-SI. حسب Jeffrey Keithley (14626551)

الموضوعات:

Percentage totBurden and percentage maxBurden reduced by UnitGreedy, FastGreedy and baselines for = 0.525 in Texas for FRED (first column) and SafeGraph (second column). حسب Jeffrey Keithley (14626551)

الموضوعات:

Approximation factors for each problem instance. حسب Jeffrey Keithley (14626551)

الموضوعات:

Percentage totBurden and percentage maxBurden reduced by all approaches for = 0.5345 in New Hampshire for FRED (first column) and SafeGraph (second column). حسب Jeffrey Keithley (14626551)

الموضوعات:

FastGreedy, UnitGreedy, SingletonGreedy, 3-EnumGreedy, and POMS comparison with respect to practical running time (estimated for POMS) to achieve approximation guarantee for New Ha... حسب Jeffrey Keithley (14626551)

الموضوعات:

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

"…However, solving the RNP problem in WMN is difficult because it is NP-hard. As a result, this problem can only be solved using approximate optimization algorithms such as heuristics and meta-heuristics. …"

Optimization process of fitness function . حسب Yuehong Zhang (82779)

A detailed process of iterative simulation coupled with bone density algorithm; (a) a function of stimulus and related bone density changes, and (b) iterative calculations of finit... حسب Hassan Mehboob (8960273)

Cost function calculated by QA with different hyperparameters. حسب Takuro Matsuta (21608505)

الموضوعات: "…currently available algorithms…"

Cost function calculated by SA with different hyperparameters. حسب Takuro Matsuta (21608505)

الموضوعات: "…currently available algorithms…"

Objective functions and solving methods for PVRP problems with time windows, multiple depots, and a heterogeneous fleet. حسب Alejandro Arenas-Vasco (19797133)