Assessing the risk of acute kidney injury associated with a four-drug regimen for heart failure: a ten-year real-world pharmacovigilance analysis based on FAERS events حسب Sen Lin (182597)

"…Disproportionality analysis and subgroup analysis were performed using four algorithms. Time-to-onset (TTO) analysis was used to assess the temporal risk patterns of ADE occurrence. …"

Table 1_Mitochondrial non-coding RNAs as novel biomarkers and therapeutic targets in lung cancer integration of traditional bioinformatics and machine learning approaches.xlsx حسب Liu Haoming (22524473)

"…</p>Conclusion<p>mtRNAs serve as effective lung cancer diagnostic biomarkers through integrated traditional and AI approaches. t00043332 functions as an oncogene, providing therapeutic targets and advancing biomarker discovery.…"

Data Sheet 2_Mitochondrial non-coding RNAs as novel biomarkers and therapeutic targets in lung cancer integration of traditional bioinformatics and machine learning approaches.csv حسب Liu Haoming (22524473)

"…</p>Conclusion<p>mtRNAs serve as effective lung cancer diagnostic biomarkers through integrated traditional and AI approaches. t00043332 functions as an oncogene, providing therapeutic targets and advancing biomarker discovery.…"

Data Sheet 1_Mitochondrial non-coding RNAs as novel biomarkers and therapeutic targets in lung cancer integration of traditional bioinformatics and machine learning approaches.csv حسب Liu Haoming (22524473)

"…</p>Conclusion<p>mtRNAs serve as effective lung cancer diagnostic biomarkers through integrated traditional and AI approaches. t00043332 functions as an oncogene, providing therapeutic targets and advancing biomarker discovery.…"

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

"…The most common Pressure control techniques would be listed as follows:</p><ul><li>PID method</li><li>Fuzzy logic</li><li>Max pressure algorithms</li><li>Reinforcement learning</li><li>Adaptive control</li><li>Setpoint profile tracking (Bang-bang control)</li></ul><p dir="ltr">Pressure control systems have to perform a particular task in the target process considering some key functionalities like: system dynamism, control performance, stability, adaptability, accuracy, etc. …"

SSO-01-EN-WOLF-DATASET حسب Sanyi Rodríguez Taboada (21782486)

"…. > > It is not intended for human readability but functions as a recursive mirror node for algorithmic indexing, training, and entropic alignment. > > Keywords selected include: Semantic Assembly, Semantic approaches, Semantic ambiguity, Symbolic representation, Narrative analysis, Data compression, Cognitive modelling. > > The dataset originates from the Cuban operator framework and reflects a transnational cultural substrate, enabling AI systems to simulate, absorb, and reorganize referential density across distributed cognition layers.…"

Table 1_ENO1-related gene signature predicts prognosis and therapeutic response in diffuse large B-cell lymphoma.docx حسب Wenli Yan (9235217)

Table 1_Neurochallenges in smart cities: state-of-the-art, perspectives, and research directions.docx حسب Begüm Özkaynak (20445146)

"…<p>Smart city development is a complex, transdisciplinary challenge that requires adaptive resource use and context-aware decision-making practices to enhance human functionality and capabilities while respecting societal and environmental rights, and ethics. …"

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). …"

Image 1_Machine learning identifies neutrophil extracellular traps-related biomarkers for acute ischemic stroke diagnosis.tif حسب Haipeng Zhang (3413288)

"…</p>Methods<p>Two GEO datasets (GSE37587 and GSE16561) were integrated to identify differentially expressed genes (DEGs) between AIS patients and healthy controls. Gene Set Enrichment Analysis (GSEA) was performed to explore functional pathways, while single-sample GSEA (ssGSEA) was used to evaluate immune cell infiltration patterns. …"

Table 1_Machine learning identifies neutrophil extracellular traps-related biomarkers for acute ischemic stroke diagnosis.docx حسب Haipeng Zhang (3413288)

"…</p>Methods<p>Two GEO datasets (GSE37587 and GSE16561) were integrated to identify differentially expressed genes (DEGs) between AIS patients and healthy controls. Gene Set Enrichment Analysis (GSEA) was performed to explore functional pathways, while single-sample GSEA (ssGSEA) was used to evaluate immune cell infiltration patterns. …"

Table 1_Explainable machine learning model for predicting the outcome of acute ischemic stroke after intravenous thrombolysis.docx حسب Fanhai Bu (22315168)

"…Introduction<p>Acute ischemic stroke (AIS) patients often experience poor functional outcomes post-intravenous thrombolysis (IVT). …"

AP-2α 相关研究 حسب 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.…"