Machine vision system for quantification of aortic and pulmonic valvuloplasty catheter compliance حسب Jiazhe Tang (17596080)

"…Upon ballon inflation, the defocused image is then refocused though passive focusing algorithms used to identify the best focal position. …"

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). …"

Table 1_Image-assisted textural analysis of plagioclase crystals in volcanic rocks: an application to lavas erupted on 2021 at Pacaya volcano, Guatemala.xlsx حسب Roberto Visalli (8152251)

"…<p>The adoption of semi-automated image processing methods to investigate geo-petrological processes has grown quickly in recent years. …"

Data Sheet 1_Image-assisted textural analysis of plagioclase crystals in volcanic rocks: an application to lavas erupted on 2021 at Pacaya volcano, Guatemala.docx حسب Roberto Visalli (8152251)

"…<p>The adoption of semi-automated image processing methods to investigate geo-petrological processes has grown quickly in recent years. …"

Supplementary file 2_Educators’ reflections on AI-automated feedback in higher education: a structured integrative review of potentials, pitfalls, and ethical dimensions.docx حسب Latifah Hamdan Alghamdi (22611518)

"…Nonetheless, these advantages are persistently compromised by concerns regarding algorithmic bias, data privacy, the deterioration of teacher-student relationships, and inadequate professional growth. …"

Supplementary file 1_Educators’ reflections on AI-automated feedback in higher education: a structured integrative review of potentials, pitfalls, and ethical dimensions.docx حسب Latifah Hamdan Alghamdi (22611518)

"…Nonetheless, these advantages are persistently compromised by concerns regarding algorithmic bias, data privacy, the deterioration of teacher-student relationships, and inadequate professional growth. …"

Table 1_Reducing misdiagnosis in AI-driven medical diagnostics: a multidimensional framework for technical, ethical, and policy solutions.docx حسب Yue Li (102191)

"…The main research question is how technical limitations, ethical concerns, and unclear accountability hinder safe and equitable use of AI in real-world clinical practice, and what integrated solutions can minimize errors and promote trust.</p>Methods<p>We conducted a literature review and case analysis across major medical fields, evaluating failure modes such as data pathology, algorithmic bias, and human-AI interaction. …"

Image 6_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.tif حسب Liuqing Yang (128705)

"…Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. …"

Data Sheet 1_Beyond the current state of just-in-time adaptive interventions in mental health: a qualitative systematic review.pdf حسب Claire R. van Genugten (20626733)

"…To accomplish this, JITAIs often apply complex analytic techniques, such as machine learning or Bayesian algorithms to real- or near-time data acquired from smartphones and other sensors. …"

Presentation 1_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.pptx حسب Liuqing Yang (128705)

"…Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. …"

Image 4_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.tif حسب Liuqing Yang (128705)

"…Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. …"

Image 3_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.tif حسب Liuqing Yang (128705)

"…Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. …"

Image 7_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.tif حسب Liuqing Yang (128705)

"…Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. …"

Image 1_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.tif حسب Liuqing Yang (128705)

"…Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. …"

Image 2_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.tif حسب Liuqing Yang (128705)

"…Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. …"

Data Sheet 1_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.zip حسب Liuqing Yang (128705)

"…Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. …"

Table 1_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.docx حسب Liuqing Yang (128705)

"…Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. …"

Image 5_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.tif حسب Liuqing Yang (128705)

"…Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. …"

Annotated dataset of simulated voiding sound for urine flow estimation حسب Marcos Lazaro Alvarez (20107426)

"…Filename format: [device]_f_0_30s.wav ### Example: - `um_f_0s.wav` - `phone_f_0s.wav` - `oppo_f_0s.wav` ## Purpose The goal of this dataset is to provide a standardized audio repository for the development, training and validation of machine learning algorithms for voiding flow prediction. This enables researchers to: - Benchmark different approaches on a common dataset - Develop flow estimation models using synthetic audio before transferring them to real-world applications - Explore the spectral and temporal structure of urination-related audio signals ## Flow Generation - Pump Used: L600-1F precision peristaltic pump - Flow Range: 1–50 ml/s (based on ICS-reported ranges for male uroflowmetry) - Calibration: Pump flows were validated using a graduated cylinder - Noise Isolation: The pump was placed in a separate room (via 15m silicone tubing) to eliminate pump noise from recordings ## Recording Devices | Device | Sampling Rate | Frequency Range | Description | |---------|----------------|------------------|--------------------------------------| | UM | 192 kHz | 0–96 kHz | High-quality ultrasonic microphone | | Phone | 48 kHz | 0–24 kHz | Android smartphone (Mi A1) | | Watch | 44.1 kHz | 0–22.05 kHz | Oppo Smartwatch with built-in mic | Each recording was carried out using a custom mobile or desktop app with preset parameters. ## Recording Environment - Recordings were made in a bathroom with a standard ceramic toilet containing water at the bottom. - The nozzle height varied between 73–86 cm depending on flow rate to ensure consistent water impact. - Microphone heights: - UM: 84 cm - Phone: 95 cm - Watch: 86 cm (simulating wrist height) ## Data Collection Protocol 1. …"