Bayesian Joint Modeling for Longitudinal Magnitude Data With Informative Dropout: An Application to Critical Care Data. [PDF]
Teng W +3 more
europepmc +1 more source
Collaborative Visual Localization for Modular Self‐Reconfigurable Robots
Relative localization in modular self‐reconfigurable robots is challenged by hardware limitations, constrained fields of view, and sensor faults. This paper, based on the SnailBot platform, presents a vision‐based collaborative localization method that combines ArUco markers with learning‐based algorithms to enable robust pose estimation from ...
Guanqi Liang +4 more
wiley +1 more source
Effects of wearable technology on mental engagement and physical activity participation: self-regulatory motivation, attitude, flow, and dropout in new sport participation. [PDF]
Park SJ, Yang HJ, Choi C, Bum CH.
europepmc +1 more source
A digital twin framework is presented for modular soft continuum arms built from fiber‐reinforced pneumatic actuators. Cosserat rod models describe individual actuators, while virtual assemblies are formed through serial and parallel networks, preserving actuator‐level architecture and arm deformation.
Seung Hyun Kim +11 more
wiley +1 more source
Psychosocial Factors Explaining Academic Dropout among Scholarship Students at a Public University in Peru. [PDF]
Llonto-Caicedo Y +9 more
europepmc +1 more source
Interpretable Short‐Term Electric Load Forecasting
A temporal fusion transformer is implemented to generate day‐ahead forecasts of the hourly electrical load of a departmentbuilding at an Italian university. A forecasting performance improvement of more than 25% compared with established benchmark models and a provision of inherent robust interpretability insights reveal the potential of this model for
Alessandro Nicola +6 more
wiley +1 more source
Dropout from the maternity continuum of care and its determinants among women in Ethiopia: A systematic review and meta-analysis. [PDF]
Ferede YA +4 more
europepmc +1 more source
A hybrid Reinforcement Learning–Explainable AI framework integrates SHAP and LIME explanations directly into a Deep Q‐Network inference loop for real‐time ICU decision support. Trained on 18 142 mechanically ventilated stays from the eICU database, the system attains 93.0% decision accuracy, 20% fewer errors than RL alone, and a 91% clinician trust ...
Jannatul Ferdaus Disha +2 more
wiley +1 more source
Common errors in sample size calculation in clinical research: A focused critique. [PDF]
Madadizadeh F, Mahdavian A, Afzal G.
europepmc +1 more source

