Image 3_Development of machine learning models with explainable AI for frailty risk prediction and their web-based application in community public health.jpeg حسب Seungmi Kim (11071440)

"…However, external validation, recalibration, and subgroup fairness assessments are needed to ensure generalizability and clinical adoption.</p>…"

Table 1_Development of machine learning models with explainable AI for frailty risk prediction and their web-based application in community public health.xlsx حسب Seungmi Kim (11071440)

"…However, external validation, recalibration, and subgroup fairness assessments are needed to ensure generalizability and clinical adoption.</p>…"

Data Sheet 1_Development of machine learning models with explainable AI for frailty risk prediction and their web-based application in community public health.pdf حسب Seungmi Kim (11071440)

"…However, external validation, recalibration, and subgroup fairness assessments are needed to ensure generalizability and clinical adoption.</p>…"

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. …"

Collaborative research: CyberTraining: Implementation: Medium: Training users, developers, and instructors at the chemistry/physics/materials science interface حسب Francesco Paesani (5128004)

"…Using computational tools as functional components of discipline-specific curricula and adopting informal learning events allow us to overcome common barriers given by feelings of non-belonging and low self-confidence, which are typical of learning programming for non-computer-science students.…"

Robust Matrix Completion with Heavy-Tailed Noise حسب Bingyan Wang (275853)

"…Prior theory falls short of explaining the empirical results and is unable to capture the optimal dependence of the estimation error on the noise level. In this article, we adopt an adaptive Huber loss to accommodate heavy-tailed noise, which is robust against large and possibly asymmetric errors when the parameter in the Huber loss function is carefully designed to balance the Huberization biases and robustness to outliers. …"

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_Viroscope™: a universal solution for plant virus and viroid diagnostics using HTS and cloud-based analysis.docx حسب Verónica Morgante (13991745)

"…Universally applicable across plant tissue, Viroscope™ detects all known viruses and viroids in public databases while employing innovative metrics for functional assessment. The cloud-based platform facilitates global adoption of HTS technology by phytosanitary agencies through user-friendly reports that enable rapid and informed decision-making.…"

Table 3_Viroscope™: a universal solution for plant virus and viroid diagnostics using HTS and cloud-based analysis.docx حسب Verónica Morgante (13991745)

"…Universally applicable across plant tissue, Viroscope™ detects all known viruses and viroids in public databases while employing innovative metrics for functional assessment. The cloud-based platform facilitates global adoption of HTS technology by phytosanitary agencies through user-friendly reports that enable rapid and informed decision-making.…"

Table 2_Viroscope™: a universal solution for plant virus and viroid diagnostics using HTS and cloud-based analysis.xlsx حسب Verónica Morgante (13991745)

"…Universally applicable across plant tissue, Viroscope™ detects all known viruses and viroids in public databases while employing innovative metrics for functional assessment. The cloud-based platform facilitates global adoption of HTS technology by phytosanitary agencies through user-friendly reports that enable rapid and informed decision-making.…"