Data Sheet 1_Real defect partial discharge identification method for power cables joints based on integrated PJS-M and GA-SVM algorithm with multi-source fusion.zip by Ling-Xuan Zhang (22116859)

“…These results demonstrate that the proposed method significantly improves the accuracy of identifying complex real-type defects in 10 kV cable intermediate joints under multi-source feature conditions, providing a reliable diagnostic basis and technical reference for partial discharge detection in industrial applications.…”

Early Parkinson’s disease identification via hybrid feature selection from multi-feature subsets and optimized CatBoost with SMOTE by Subhashree Mohapatra (17387852)

Data Sheet 1_Antiviral therapy can effectively suppress irAEs in HBV positive hepatocellular carcinoma treated with ICIs: validation based on multi machine learning.zip by Shuxian Pan (8868356)

“…</p>Methods<p>Data from 274 hepatitis B virus positive tumor patients who received PD-1 or/and CTLA4 inhibitor treatment and had immune cell detection results were collected from Henan Cancer Hospital for retrospective analysis. …”

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). …”