<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.…”

Reward function related parameters. by Honglei Pang (22693724)

“…Hardware-in-the-loop (HIL) validation confirms the algorithm’s robustness under extreme conditions, with lateral stability metrics maintained within safety thresholds.…”

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

“…Hardware-in-the-loop (HIL) validation confirms the algorithm’s robustness under extreme conditions, with lateral stability metrics maintained within safety thresholds.…”

EMB and SBW system structure. by Honglei Pang (22693724)

“…Hardware-in-the-loop (HIL) validation confirms the algorithm’s robustness under extreme conditions, with lateral stability metrics maintained within safety thresholds.…”

Raw data. by Honglei Pang (22693724)

“…Hardware-in-the-loop (HIL) validation confirms the algorithm’s robustness under extreme conditions, with lateral stability metrics maintained within safety thresholds.…”

Code program. by Honglei Pang (22693724)

“…Hardware-in-the-loop (HIL) validation confirms the algorithm’s robustness under extreme conditions, with lateral stability metrics maintained within safety thresholds.…”

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

“…Hardware-in-the-loop (HIL) validation confirms the algorithm’s robustness under extreme conditions, with lateral stability metrics maintained within safety thresholds.…”

Steering system model. by Honglei Pang (22693724)

“…Hardware-in-the-loop (HIL) validation confirms the algorithm’s robustness under extreme conditions, with lateral stability metrics maintained within safety thresholds.…”

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

“…Hardware-in-the-loop (HIL) validation confirms the algorithm’s robustness under extreme conditions, with lateral stability metrics maintained within safety thresholds.…”

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

“…Hardware-in-the-loop (HIL) validation confirms the algorithm’s robustness under extreme conditions, with lateral stability metrics maintained within safety thresholds.…”

Co-simulation architecture. by Honglei Pang (22693724)

“…Hardware-in-the-loop (HIL) validation confirms the algorithm’s robustness under extreme conditions, with lateral stability metrics maintained within safety thresholds.…”

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

“…Hardware-in-the-loop (HIL) validation confirms the algorithm’s robustness under extreme conditions, with lateral stability metrics maintained within safety thresholds.…”

Braking system model. by Honglei Pang (22693724)

“…Hardware-in-the-loop (HIL) validation confirms the algorithm’s robustness under extreme conditions, with lateral stability metrics maintained within safety thresholds.…”

Vehicle parameters. by Honglei Pang (22693724)

“…Hardware-in-the-loop (HIL) validation confirms the algorithm’s robustness under extreme conditions, with lateral stability metrics maintained within safety thresholds.…”

A comparison between the static Python-based visualizations of the p65 activity in activated fibroblasts and the dynamic, HTML-based visualizations that use these same reduction me... by Hector Torres (11708207)

Global Aridity Index and Potential Evapotranspiration (ET0) Database: Version 3.1 by Robert Zomer (12796235)

GridScopeRodents: High-Resolution Global Typical Rodents Distribution Projections from 2021 to 2100 under Diverse SSP-RCP Scenarios by Yang Lan (20927512)

“…Using occurrence data and environmental variable, we employ the Maximum Entropy (MaxEnt) algorithm within the species distribution modeling (SDM) framework to estimate occurrence probability at a spatial resolution of 1/12° (~10 km). …”

<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) by Erola Fenollosa (20977421)

“…<p dir="ltr">This dataset contains the data used in the article <a href="https://academic.oup.com/aob/advance-article/doi/10.1093/aob/mcaf043/8074229" rel="noreferrer" target="_blank">"Machine Learning and digital Imaging for Spatiotemporal Monitoring of Stress Dynamics in the clonal plant Carpobrotus edulis: Uncovering a Functional Mosaic</a>", which includes the complete set of collected leaf images, image features (predictors) and response variables used to train machine learning regression algorithms.…”

Brain-in-the-Loop Learning for Intelligent Vehicle Decision-Making by Xiaofei Zhang (16483224)

“…In this paper, we utilize functional near-infrared spectroscopy (fNIRS) signals as real-time human risk-perception feedback to establish a brain-in-the-loop (BiTL) trained artificial intelligence algorithm for decision-making. …”

Landscape17 by Vlad Carare (22092515)

“…</p><h3>Density functional theory calculations</h3><p dir="ltr">The reference potential energy landscapes were computed using density functional theory with the ωB97x hybrid-energy exchange correlation functional and a 6-31G(d) basis set within Psi4. …”