The HIL simulation data flowchart. حسب Honglei Pang (22693724)

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Braking performance and lateral stability on curved road surfaces: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sidesli... حسب Honglei Pang (22693724)

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Steering system model. حسب Honglei Pang (22693724)

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Braking performance and lateral stability on split-μ road surfaces: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sidesl... حسب Honglei Pang (22693724)

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Simulation Results Plot of the EMB System Response: (a) Step response curve of the EMB system; (b) Sine response curve of the EMB system; (c) Steady state error curve of the EMB sy... حسب Honglei Pang (22693724)

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Hyperparameter Configurations in PPO Training. حسب Honglei Pang (22693724)

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Main parameters of steering system. حسب Honglei Pang (22693724)

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

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Co-simulation architecture. حسب Honglei Pang (22693724)

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Overall framework diagram of the study. حسب Honglei Pang (22693724)

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Braking system model. حسب Honglei Pang (22693724)

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Vehicle parameters. حسب Honglei Pang (22693724)

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

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Comparison of average reward values in various simulation scenarios. حسب Honglei Pang (22693724)

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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. حسب Honglei Pang (22693724)

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Hybrid feature selection algorithm of CSCO-ROA. حسب Afnan M. Alhassan (18349378)

"…Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. The suggested strategy facilitates the acquisition of precise functionality attributes, hence simplifying the detection procedure. …"

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

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BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data حسب 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. …"

Comparison of the user-friendly graphical interfaces and functions of the single nucleotide polymorphism (SNP) analysis pipelines. حسب Dong-Jun Lee (14068673)

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Python code for a rule-based NLP model for mapping circular economy indicators to SDGs حسب 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.…"