Multimodal Engagement Assessment in Children During Invented Story Paradigm With a Social Robot
A multimodal framework is proposed to assess children's engagement during storytelling interactions with a social robot. Gaze, physiological, and behavioral data are combined and validated against observer ratings. An automated gaze‐labeling strategy is introduced, and supervised classifiers achieve high accuracy. The study supports scalable engagement
Laura Fiorini +7 more
wiley +1 more source
Prediction of coal and gas outbursts based on physics informed neural networks and traditional machine learning models. [PDF]
Wang L, Jia B, Su G.
europepmc +1 more source
Liquid Crystalline Elastomers in Soft Robotics: Assessing Promise and Limitations
Liquid crystalline elastomers (LCEs) are programmable soft materials that undergo large, anisotropic deformation in response to external stimuli. Their molecular alignment encodes directional actuation in a monolithic structure, making them long‐standing candidates for soft robotic systems.
Justin M. Speregen, Timothy J. White
wiley +1 more source
Solving the Richards infiltration equation by coupling physics-informed neural networks with Hydrus-1D. [PDF]
Li Y, Sun Q, Fu Y, Wei J.
europepmc +1 more source
Compliant Pneumatic Feet with Real‐Time Stiffness Adaptation for Humanoid Locomotion
A compliant pneumatic foot with real‐time variable stiffness enables humanoid robots to adapt to changing terrains. Using onboard vision and pressure control, the foot modulates stiffness within each gait cycle, reducing impact forces and improving balance. The design, cast in soft silicone with embedded air chambers and Kevlar wrapping, offers durable,
Irene Frizza +3 more
wiley +1 more source
Physics-Informed Neural Networks with Unknown Partial Differential Equations: An Application in Multivariate Time Series. [PDF]
Mortezanejad SAF +2 more
europepmc +1 more source
[Prediction of hemodynamic parameters in pathological arterial vessels based on physics-informed neural networks]. [PDF]
Ji M +5 more
europepmc +1 more source
Physics-Informed Neural Networks for Depth-Dependent Constitutive Relationships of Gradient Nanostructured 316L Stainless Steel. [PDF]
Li H, Cheng Y, Wang Z, Wang X.
europepmc +1 more source
Response estimation and system identification of dynamical systems via physics-informed neural networks. [PDF]
Haywood-Alexander M +3 more
europepmc +1 more source
RVINN: a flexible modeling for inferring dynamic transcriptional and post-transcriptional regulation using physics-informed neural networks. [PDF]
Muto O, Guo Z, Yamaguchi R.
europepmc +1 more source

