Pseudocode of the BBAC_I Algorithm. by Zezhi Lin (22521663)

CageCavityCalc (<i>C</i>3): A Computational Tool for Calculating and Visualizing Cavities in Molecular Cages by Vicente Martí-Centelles (1422415)

“…Efficiently predicting such properties is critical for accelerating the discovery of novel functional cages. Herein, we introduce <i>CageCavityCalc</i> (<i>C</i>3), a Python-based tool for calculating the cavity size of molecular cages. …”

CageCavityCalc (<i>C</i>3): A Computational Tool for Calculating and Visualizing Cavities in Molecular Cages by Vicente Martí-Centelles (1422415)

“…Efficiently predicting such properties is critical for accelerating the discovery of novel functional cages. Herein, we introduce <i>CageCavityCalc</i> (<i>C</i>3), a Python-based tool for calculating the cavity size of molecular cages. …”

CageCavityCalc (<i>C</i>3): A Computational Tool for Calculating and Visualizing Cavities in Molecular Cages by Vicente Martí-Centelles (1422415)

“…Efficiently predicting such properties is critical for accelerating the discovery of novel functional cages. Herein, we introduce <i>CageCavityCalc</i> (<i>C</i>3), a Python-based tool for calculating the cavity size of molecular cages. …”

Algorithm 1. by Jeffrey O. Agushaka (11326679)

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

Approximate energy consumed per particles in <i>nano Joules</i> (<i>nJ</i>) for various computation platforms and algorithms (A: first algorithm, B: second algorithm, C: third algo... by Abedalmuhdi Almomany (18547669)

Comparison of algorithm performance in ZDT1 and ZDT2 function tests. by Tao Dong (15551)

“…<p>Comparison of algorithm performance in ZDT1 and ZDT2 function tests.…”

<i>K</i>-CDFs: A Nonparametric Clustering Algorithm via Cumulative Distribution Function by Jicai Liu (11419050)

“…<p>We propose a novel partitioning clustering procedure based on the cumulative distribution function (CDF), called <i>K</i>-CDFs. For univariate data, the <i>K</i>-CDFs represent the cluster centers by empirical CDFs and assign each observation to the closest center measured by the Cram<math><mrow><mi>e</mi><mo>´</mo></mrow></math>r-von Mises distance. …”

Flowchart for Algorithm 1. by Sagvan Y. Musa (17797611)

Convergence curves of the IMGO and comparison algorithms on functions <i>f</i>1−<i>f</i>7. by Ying Li (38224)

“…<p>Convergence curves of the IMGO and comparison algorithms on functions <i>f</i>1−<i>f</i>7.</p>…”

Parameters <i>p</i> identified by the APLM algorithm. by HaiYan Jiang (4455199)

Performance of algorithms compared to a smaller <i>RNN.</i> by Swathi Anil (17382903)

Convergence curves of various algorithms under test functions. by Jiayuan Wang (3765376)

Convergence curves of various algorithms under test functions. by Jiayuan Wang (3765376)

BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data by Jean-Christophe Lachance (6619307)

“…Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a <b>B</b>iomass <b>O</b>bjective <b>F</b>unction from experimental <b>dat</b>a. …”

Flowchart of the algorithm. by Xiaowen Zhang (83669)

Subjects:

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

Subjects:

Standard benchmark functions used for the experimentation of EOSA and other similar optimization algorithms. by Tehnan I. A. Mohamed (16846175)

Critical values of membership functions learned from the study cohort by the algorithm. by Yufeng Zhang (195768)