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

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

<b>Opti2Phase</b>: Python scripts for two-stage focal reducer by Morgan Najera (21540776)

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Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

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

The ALO algorithm optimization flowchart. by Wenjing Wang (181404)

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The IALO algorithm solution flowchart. by Wenjing Wang (181404)

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The image of the Aekley function. by Zhengyu Xu (8550660)

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

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Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional by Ryan Stocks (16867476)

“…Kohn–Sham density functional theory (KS-DFT) has become a cornerstone for studying the electronic structure of molecules and materials. …”

The pseudocode for the NAFPSO algorithm. by Huichao Guo (14515171)

“…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. …”

PSO algorithm flowchart. by Huichao Guo (14515171)

“…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. …”

Reward function related parameters. by Honglei Pang (22693724)

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Comparison of different algorithms. by Dawei Wang (471687)

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Study proposed algorithm. by Ainhoa Pérez-Guerrero (21377457)

Subjects: “…assess microvascular function…”

The benchmark functions. by Shuhui Yu (1535500)

Gillespie algorithm simulation parameters. by Nicholas H. Vitale (20469289)

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Scheduling time of five algorithms. by Huichao Guo (14515171)

“…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. …”

Convergence speed of five algorithms. by Huichao Guo (14515171)

“…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. …”

A Genetic Algorithm Approach for Compact Wave Function Representations in Spin-Adapted Bases by Maru Song (22593561)

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PyNoetic’s stimuli generation and recording module, which supports both ERP and SSVEP. by Gursimran Singh (575288)

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