Python implementation of the Trajectory Adaptive Multilevel Sampling algorithm for rare events and improvements by Pascal Wang (10130612)

“…In `main.py`, the parameters for the TAMS algorithm are specified (trajectory time, time step, score function, number of particles, type of score threshold, maximum number of iterations, noise level etc.). …”

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

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

Adaptive H-REMD simulation algorithm. by Danial Pourjafar-Dehkordi (7165523)

Comparison of UACI among different algorithms. by Feixian Liu (22477943)

“…Based on the 2D-SQSM, this paper further designs a highly robust color image encryption algorithm. First, by introducing different hash functions multiple times, the correlation between the key and plaintext is enhanced, significantly improving resistance against brute-force attacks; second, cyclic shifting and segmentation-recombination operations are applied separately to the three RGB channels to effectively disrupt pixel distribution and significantly reduce spatial correlation between pixels; finally, the chaotic sequence generated by the 2D-SQSM is utilized for XOR diffusion, further enhancing the randomness and diffusion capability of the ciphertext. …”

Comparison of NPCR among different algorithms. by Feixian Liu (22477943)

“…Based on the 2D-SQSM, this paper further designs a highly robust color image encryption algorithm. First, by introducing different hash functions multiple times, the correlation between the key and plaintext is enhanced, significantly improving resistance against brute-force attacks; second, cyclic shifting and segmentation-recombination operations are applied separately to the three RGB channels to effectively disrupt pixel distribution and significantly reduce spatial correlation between pixels; finally, the chaotic sequence generated by the 2D-SQSM is utilized for XOR diffusion, further enhancing the randomness and diffusion capability of the ciphertext. …”

Elapsed time of algorithms on benchmark functions. by Muhammad Zubair Rehman (11243478)

“…<p>Elapsed time of algorithms on benchmark functions.</p>…”

Python cv coding screenshots of all developed fingerprints on distinct surfaces. by Nisha Yadav (366131)

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Code snippets to add user-specific custom functionality in PyNoetic. by Gursimran Singh (575288)

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

Modular architecture design of PyNoetic showing all its constituent functions. by Gursimran Singh (575288)

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Comparison of PyNoetic’s functionality for BCI design with other existing state-of-the-art frameworks. by Gursimran Singh (575288)

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Average function evaluation times of the three optimization algorithms. by Shuhui Yu (1535500)

“…<p>Average function evaluation times of the three optimization algorithms.…”

<b>Opti2Phase</b>: Python scripts for two-stage focal reducer by 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.…”

Overview of functionality supported by PyNoetic and its various modules, including the live analysis and programmable flowchart. by Gursimran Singh (575288)

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Average CPU time (s) of all the referenced algorithm on benchmark function. by Aihua Guo (13267712)

“…<p>Average CPU time (s) of all the referenced algorithm on benchmark function.…”

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

“…<div><p>This paper aims to solve the scheduling optimization problem in the emergency management of long-distance natural gas pipelines, with the goal of minimizing the total scheduling time. To this end, the objective function of the minimum total scheduling time is established, and the relevant constraints are set. …”

Completion times for different algorithms. by Jianbin Zheng (587000)

“…The algorithm employs recurrent neural networks to capture and process historical information. …”

Results obtained by different algorithms on 23 benchmark functions. by Yuqi Xiong (12343771)

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Comparison of the running time(in ms) of the three algorithms. by Yaling Zhang (1687540)