gmxapi usage examples. حسب M. Eric Irrgang (4487278)

University of Arizona authors' scholarly works published and cited works year 2022 from OpenAlex حسب Yan Han (11106597)

"…It is also possible to filter the results to include only <b>"journal" type publications</b> using the primary_location.source.type = "journal" parameter in the oa_fetch function.…"

University of Arizona authors' scholarly works published and cited works year 2023 from OpenAlex حسب Yan Han (11106597)

"…It is also possible to filter the results to include only <b>"journal" type publications</b> using the primary_location.source.type = "journal" parameter in the oa_fetch function.…"

University of Arizona authors' scholarly works published and cited works year 2021 from OpenAlex حسب Yan Han (11106597)

"…It is also possible to filter the results to include only <b>"journal" type publications</b> using the primary_location.source.type = "journal" parameter in the oa_fetch function.…"

University of Arizona authors' scholarly works published and cited works year 2024 from OpenAlex حسب Yan Han (11106597)

"…It is also possible to filter the results to include only <b>"journal" type publications</b> using the primary_location.source.type = "journal" parameter in the oa_fetch function.…"

University of Arizona authors' scholarly works published and cited works year 2020 from OpenAlex حسب Yan Han (11106597)

"…It is also possible to filter the results to include only <b>"journal" type publications</b> using the primary_location.source.type = "journal" parameter in the oa_fetch function.…"

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows حسب 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). …"