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

“…A major challenge in bioprocess simulation is the lack of physical and chemical property databases for biochemicals. A Python-based algorithm was developed for estimating the nonrandom two-liquid (NRTL) model parameters of aqueous binary systems in a straightforward manner from simplified molecular-input line-entry specification (SMILES) strings of substances in a system. …”

<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.…”

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

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Reward function related parameters. by Honglei Pang (22693724)

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Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information. by Yuanchen Zhao (12905580)

“…Whiskers show the lowest and highest values within 1.5 × <i>IQR</i> from the first and third quartiles, respectively. …”

Heatmap results from the global sensitivity analysis for the generation of villous vessels within a placentone. by Diana C. de Oliveira (9220729)

Geometric interpretation and data visualisation equivalent describing the DI algorithmic pipeline. by Borjan Milinkovic (21349908)

Cost functions implemented in Neuroptimus. by Máté Mohácsi (20469514)

Examples of between- and within-community Beta distributions. by Alexandria M. Jensen (6494957)

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Elementary effects (EE) results for the generation of villous vessels within a placentone employing the input parameter ranges from Table 1. by Diana C. de Oliveira (9220729)

Biological Function Assignment across Taxonomic Levels in Mass-Spectrometry-Based Metaproteomics via a Modified Expectation Maximization Algorithm by Gelio Alves (51850)

“…To overcome this limitation, we implemented an expectation-maximization (EM) algorithm, along with a biological function database, within the MiCId workflow. …”

P-values from analysis of ABIDE dataset for continuous outcome of VIQ. by Alexandria M. Jensen (6494957)

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Test functions. by Kejia Liu (5699651)

“…The escape rate from local optima within DGEP reached 35% higher than what standard GEP could achieve. …”

P-values from analysis of ABIDE dataset for binary outcome of diagnostic group. by Alexandria M. Jensen (6494957)

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Multimodal reference functions. by Ruiyu Zhan (21602031)

“…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”

NRPStransformer, an Accurate Adenylation Domain Specificity Prediction Algorithm for Genome Mining of Nonribosomal Peptides by Zhihan Zhang (1403308)

“…Leveraging the sequences within the flavodoxin-like subdomain, we developed a substrate specificity prediction algorithm using a protein language model, achieving 92% overall prediction accuracy for 43 frequently observed amino acids, significantly improving the prediction reliability. …”

The convergence curves of the test functions. by Ruiyu Zhan (21602031)

“…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”

Single-peaked reference functions. by Ruiyu Zhan (21602031)

“…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”

Fitness comparison on test function. by Kejia Liu (5699651)

“…The escape rate from local optima within DGEP reached 35% higher than what standard GEP could achieve. …”

Image 4_Construction of a right ventricular function assessment model in patients undergoing invasive mechanical ventilation based on VExUS grading and the classification and regre... by Jun Gao (203098)

“…Objective<p>Investigate the correlation between right ventricular function ultrasound indicators and the Venous Excess Ultrasound (VExUS) grading system in patients undergoing invasive mechanical ventilation (IMV), and develop a right ventricular function assessment model using VExUS grading and the Classification and Regression Tree (CART) algorithm.…”