Numerical Weather Data-Driven Sensor Data Generation for PV Digital Twins: A Hybrid Model Approach by Jooseung Lee, Jimyung Kang, Sangwoo Son, Hui-Myoung Oh

“…To this end, a novel sensor data generation model based on numerical weather prediction (NWP) data is proposed to forecast the future operations of PV systems using DT systems. …”
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A Reproducible Method for Donor Site Computed Tomography Measurements in Abdominally Based Autologous Breast Reconstruction by Damini Tandon, MD, Arthur Sletten, MD, PhD, Austin Ha, MD, Gary B. Skolnick, BA, MBA, Paul Commean, BEE, Terence Myckatyn, MD

“…Larger patient cohorts must be leveraged to determine correlations between abdominal CT scan findings and donor site outcomes using machine learning algorithms that generate models for predicting abdominal donor site complications.…”
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FedDrip: Federated Learning With Diffusion-Generated Synthetic Image by Karin Huangsuwan, Timothy Liu, Simon See, Aik Beng Ng, Peerapon Vateekul

“…We also performed empirical experiments investigating the effects of prompting style, prompt correctness, and data availability on the inference of diffusion models, using the Fréchet Inception Distance (FID) metric for generative models.…”
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Automated skin lesion detection and prevalence estimation in Tamanend's bottlenose dolphins by Colin J. Murphy, Melissa A. Collier, Ann-Marie Jacoby, Eric M. Patterson, Megan M. Wallen, Janet Mann, Shweta Bansal

“…We also demonstrate the model's ability to address ecological questions across scales by generating model-based estimates of lesion prevalence and testing the effect of gregariousness on health status. …”
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Mt or not Mt: Temporal variation in detection probability in spatial capture-recapture and occupancy models by Sollmann, Rahel

“…I simulated data under different scenarios of temporal and spatio-temporal variation in detection, analyzed data with the data-generating model and an alternative model ignoring temporal variation in detection, and compared estimates between these two models with respect to relative bias, coefficient of variation (CV) and relative root mean squared error (RMSE). …”
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Prediction and Impact of Supersaturated Total Dissolved Gas in Flood Discharge of Flood Control Reservoirs in Southwest Mountain Areas: A Case Study in Jiangjiakou Reservoir by ZHANG Zhihao, ZHANG Peng, LIANG Ruifeng, WANG Qingfeng, WANG Yuanming, LI Kefeng

“…This paper develops an impact assessment framework of supersaturated TDG during high dam flood discharge by coupling the longitudinal one-dimensional hydrodynamic model, the supersaturated TDG generation model, the hybrid model, and the longitudinal one-dimensional supersaturated TDG transportation and dissipation model. …”
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Affective valence predictors from real-world based short sprint interval training by Stefano Benítez-Flores, Flávio A. de S. Castro, Eduardo Caldas Costa, Daniel Boullosa, Todd A. Astorino

“…The regression model was significant (F3,61 ​= ​5.57; p ​= ​0.002) and only three variables significantly entered the generated model: ΔHRRend-120s end (p ​= ​0.002; VIF ​= ​2.58; 40.8%), time ≥ 90% HRpeak (p ​= ​0.001; VIF ​= ​1.26; 31.6%), and RMSSDend (p ​= ​0.025; VIF ​= ​2.23; 27.6%). …”
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