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

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Steering system model. by 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... by 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... by Honglei Pang (22693724)

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

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

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Co-simulation architecture. by Honglei Pang (22693724)

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

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Braking system model. by Honglei Pang (22693724)

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Vehicle parameters. by 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. by Honglei Pang (22693724)

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

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Hybrid feature selection algorithm of CSCO-ROA. by 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. 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. …”

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

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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.…”

Table_3_In silico identification and verification of ferroptosis-related genes in type 2 diabetic islets.xls by Meiqi Yin (694849)

“…Ferroptosis-related DE-mRNA functional and pathway enrichment analysis in T2D islet were performed. …”