<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)

<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.…”

CSPP instance by peixiang wang (19499344)

“…<p dir="ltr">This Python script (<code>instance_generator.py</code>) is a tool designed to <b>programmatically generate synthetic instance data for container stowage and logistics problems.…”

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows by Pierre-Alexis DELAROCHE (22092572)

“…Performance Profiling Algorithms Energy Measurement Methodology # Pseudo-algorithmic representation of measurement protocol def capture_energy_metrics(workflow_type: WorkflowEnum, asset_vector: List[PhotoAsset]) -> EnergyProfile: baseline_power = sample_idle_power_draw(duration=30) with PowerMonitoringContext() as pmc: start_timestamp = rdtsc() # Read time-stamp counter if workflow_type == WorkflowEnum.LOCAL: result = execute_local_pipeline(asset_vector) elif workflow_type == WorkflowEnum.CLOUD: result = execute_cloud_pipeline(asset_vector) end_timestamp = rdtsc() energy_profile = EnergyProfile( duration=cycles_to_seconds(end_timestamp - start_timestamp), peak_power=pmc.get_peak_consumption(), average_power=pmc.get_mean_consumption(), total_energy=integrate_power_curve(pmc.get_power_trace()) ) return energy_profile Statistical Analysis Framework Our analytical pipeline employs advanced statistical methodologies including: Variance Decomposition: ANOVA with nested factors for hardware configuration effects Regression Analysis: Generalized Linear Models (GLM) with log-link functions for energy modeling Temporal Analysis: Fourier transform-based frequency domain analysis of power consumption patterns Cluster Analysis: K-means clustering with Euclidean distance metrics for workflow classification Data Validation and Quality Assurance Measurement Uncertainty Quantification All energy measurements incorporate systematic and random error propagation analysis: Instrument Precision: ±0.1W for CPU power, ±0.5W for GPU power Temporal Resolution: 1ms sampling with Nyquist frequency considerations Calibration Protocol: NIST-traceable power standards with periodic recalibration Environmental Controls: Temperature-compensated measurements in climate-controlled facility Outlier Detection Algorithms Statistical outliers are identified using the Interquartile Range (IQR) method with Tukey's fence criteria (Q₁ - 1.5×IQR, Q₃ + 1.5×IQR). …”