HIL test results of split-μ road: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate. by Honglei Pang (22693724)

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Overview of our study for sampling candidate aptamer sequences using the API classifiers and MCTS. by Gwangho Lee (11027565)

“…<p>(A) shows the process of choosing the best model from the random forest classifier trained by the API classification benchmark dataset. (B) illustrates our iterative forward sampling algorithm to obtain the candidate aptamer sequences that bind to the given target protein. …”

PyNoetic’s classification module supports a range of popular and widely used ML classification models with just a single line of function call. by Gursimran Singh (575288)

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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. …”

Python code for a rule-based NLP model for mapping circular economy indicators to SDGs by Zahir Barahmand (18008947)

“…The package includes:</p><ul><li>The complete Python codebase implementing the classification algorithm</li><li>A detailed manual outlining model features, requirements, and usage instructions</li><li>Sample input CSV files and corresponding processed output files to demonstrate functionality</li><li>Keyword dictionaries for all 17 SDGs, distinguishing strong and weak matches</li></ul><p dir="ltr">These materials enable full reproducibility of the study, facilitate adaptation for related research, and offer transparency in the methodological framework.…”

Discovery of Protein Modifications Using Differential Tandem Mass Spectrometry Proteomics by Paolo Cifani (1575613)

“…Termed SAMPEI for spectral alignment-based modified peptide identification, this open-source algorithm is designed for the discovery of functional protein and peptide signaling modifications, without prior knowledge of their identities. …”

Discovery of Protein Modifications Using Differential Tandem Mass Spectrometry Proteomics by Paolo Cifani (1575613)

“…Termed SAMPEI for spectral alignment-based modified peptide identification, this open-source algorithm is designed for the discovery of functional protein and peptide signaling modifications, without prior knowledge of their identities. …”

Comparison of computational cost between SBOA and MESBOA. by Yuqi Xiong (12343771)

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-value on CEC2022 (dim = 20). by Yuqi Xiong (12343771)

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Flowchart of low-light image enhancement based on MESBOA. by Yuqi Xiong (12343771)

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Flowchart of low-light image enhancement based on MESBOA. by Yuqi Xiong (12343771)

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Precision elimination strategy. by Yuqi Xiong (12343771)

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Results of low-light image enhancement test. by Yuqi Xiong (12343771)

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Evaluation metrics obtained by SBOA and MESBOA. by Yuqi Xiong (12343771)

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Lens imaging opposition-based learning. by Yuqi Xiong (12343771)

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Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, th... by Ziyan Jin (21021976)

Modeling with uniform <i>σ</i> values. by Giacomo Janson (8138517)

Effect of <i>|Δd</i>_<i>n</i><i>|</i> perturbation on 3D modeling. by Giacomo Janson (8138517)

Revisiting the “satisfaction of spatial restraints” approach of MODELLER for protein homology modeling by Giacomo Janson (8138517)

“…This program implements the “modeling by satisfaction of spatial restraints” strategy and its core algorithm has not been altered significantly since the early 1990s. …”

Example of use of the slightly off-axis strategies. by Raul Castañeda (13932971)