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

Subjects:

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)

Subjects:

MOMA compared to MFA-derived estimates, carbon yield efficiencies and CBA co-factor profile comparison across unconstrained, manually curated and experimentally constrained solutio... by Laura de Arroyo Garcia (9226096)

“…MOMA ranges were estimated using the wild type solution as a reference and sequentially implementing the single-gene knockouts studied by Long et al. (2019) [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1008125#pcbi.1008125.ref046" target="_blank">46</a>], with biomass formation as the objective function. MFA ranges were extracted from a pre-existing dataset (Long et al., 2019), using a Python algorithm to select the minimal and maximal flux ranges.…”

PREDICTION OF DEM PARAMETERS OF COATED FERTILIZER PARTICLES BASED ON GA-BP NEURAL NETWORK by Xin Du (208780)

“…The predicted values matched the expected output values, indicating that the GA-BP neural network can accurately predict the nonlinear function output, and the network predicted output can be approximated as the actual output of the function. …”

Spatiotemporal Soil Erosion Dataset for the Yarlung Tsangpo River Basin (1990–2100) by peng xin (21382394)

“…Bias correction was conducted using a 25-year baseline (1990–2014), with adjustments made monthly to correct for seasonal biases. The corrected bias functions were then applied to adjust the years (2020–2100) of daily rainfall data using the "ibicus" package, an open-source Python tool for bias adjustment and climate model evaluation. …”

Barro Colorado Island 50-ha plot aerial photogrammetry orthomosaics and digital surface models for 2018-2023: Globally and locally aligned time series. by Vicente Vasquez (13550731)

“…We first computed a grid of co-registration point using arosics.COREG_LOCAL function and used the highest reliability point position to globally align to the closest date orthomosaics. …”

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). …”