The role of web-based flipped learning in EFL learners' critical thinking and learner engagement. [PDF]
Pang Y.
europepmc +1 more source
AI‐based tools enable rapid characterization of bacterial ultrastructure in low‐dose cryogenic transmission electron microscopy. The envelope thickness tool quantifies membrane thickness and anisotropy. The flagella module analyzes filament morphology and detects cell‐flagella contacts.
Sita Sirisha Madugula +10 more
wiley +1 more source
Effects of Infection Control Education Program Using Flipped Learning Based on the ARCS Model for Nursing Students: A Mixed Method. [PDF]
Jeong D, Park C, Eun Y.
europepmc +1 more source
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia +2 more
wiley +1 more source
Comparing the Effectiveness of Two New CPR Training Methods in Korea: Medical Virtual Reality Simulation and Flipped Learning. [PDF]
Kim EA, Cho KJ.
europepmc +1 more source
Machine learning serves as a central engine for the intelligent characterization of two‐dimensional materials by integrating multimodal techniques, including optical microscopy, spectroscopy, electron microscopy, and scanning probe microscopy (SPM). This unified framework enables automated, high‐throughput, and quantitative extraction of structural ...
Zhi‐Long Cao, Jia‐Xu Yan
wiley +1 more source
Investigation of the effect of flipped learning on neuroanatomy education in dentistry faculty students. [PDF]
Akbaytürk N, Aygün T.
europepmc +1 more source
Measuring the Impact of Augmented Reality in Flipped Learning Mode on Critical Thinking, Learning Motivation, and Knowledge of Engineering Students. [PDF]
Dutta R, Mantri A, Singh G, Singh NP.
europepmc +1 more source
A Generative AI Framework to Predict Cardiomyocyte Contraction Function From Single Static Images
A single static hiPSC‐cardiomyocyte image is fed into a U‐Net‐GAN framework, which directly predicts a pixel‐resolved contraction heatmap without time‐lapse imaging. StyleGAN2‐generated synthetic cell–heatmap pairs augment training, improving prediction fidelity (SSIM = 0.84).
Andrew Kowalczewski +5 more
wiley +1 more source
Student engagement and self-regulated learning across standard and AI-enhanced flipped learning: a within-subjects longitudinal study. [PDF]
Myagmarkhorloo C +2 more
europepmc +1 more source

