Pressure control techniques in freeze-drying by Geoff Smith (6064268)

“…PID (Proportional Integral Derivative) controllers provide most of those functionalities and they operate in a closed loop system (called PID loop) by which the system reads a sensor output, to provide a constant feedback, against which the desired actuator output may be calculated at a regular interval (e.g., time interval) known as a fixed loop rate. …”

Data Sheet 1_A comprehensive analysis of the prognostic value, expression characteristics and immune correlation of MKI67 in cancers.docx by Xiaolan Pan (136582)

“…However, the definite prognostic value of Ki67 against a specific cancer type has remained vague. …”

Table 2_Comprehensive analysis of immunogenic cell death-related genes in liver ischemia-reperfusion injury.xlsx by Kai Lu (166354)

“…The RF and SVM machine learning algorithms were finally chosen to construct the models. …”

Table 1_Comprehensive analysis of immunogenic cell death-related genes in liver ischemia-reperfusion injury.xlsx by Kai Lu (166354)

“…The RF and SVM machine learning algorithms were finally chosen to construct the models. …”

Image 1_Immune-molecular nexus in reproductive disorders: mechanisms linking POI and RSA.pdf by Chen Chen (6544)

“…The analysis involved machine learning algorithms, mcode and Cytoscape, revealing important hub genes. …”

Supplementary file 1_Comprehensive analysis of phagocytosis regulatory genes in bladder cancer: implications for prognosis and immunotherapy.xlsx by Xueming Ma (2119150)

“…</p>Methods<p>Multi-omics data from the TCGA and GEO databases were integrated, and strict data preprocessing was carried out. A variety of algorithms and analysis techniques, such as Kaplan-Meier analysis, Cox regression analysis, and ConsensusClusterPlus clustering analysis, were used to identify PRGs related to the prognosis of bladder cancer patients, and functional analysis and clustering analysis were conducted in depth. …”

Data Sheet 1_Inflammation-related biomarkers and berberine therapy in post-stroke depression: evidence from bioinformatics, machine learning, and experimental validation.docx by Wei Liu (20030)

“…Differentially expressed genes (DEGs) were identified using the limma package, followed by functional enrichment analysis with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). …”

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). …”

AP-2α 相关研究 by Ya-Hong Wang (21080642)

“…</p><p dir="ltr"><b>Figure 4 | Comparative transcriptomic analysis of the functional regulation of </b><b><i>VdAP-2α</i></b><b> in </b><b><i>Verticillium dahliae.…”