Enhancing emergency competencies in healthcare professionals via murder mystery games: An innovative gamification learning-based approach.

Background: Enhancing the emergency competencies of healthcare professionals is essential for ensuring patient safety, optimizing emergency response efficiency, and fostering effective team collaboration. However, traditional simulation-based methods often struggle to accurately replicate real-life emergencies, resulting in outcomes that may not fully reflect actual performance, thereby undermining their effectiveness in training and developing the critical skills needed for emergency situations.

Objective: This study evaluated the effectiveness of using murder mystery games (MMGs) as a gamified learning method to enhance the emergency competencies of healthcare professionals.

Photoactuators Based on Plastically Flexible α-Cyanostilbene Molecular Crystals Driven by the Solid-State [2+2] Cycloaddition Reaction.

Organic photomechanical molecular crystals are promising candidates for photoactuators, which have potential applications as smart materials in various fields. However, it is still challenging to fabricate photomechanical molecular crystals with flexibility because most of the molecular crystals are brittle and the mechanism of flexible crystals remains controversial. Here, a plastically flexible α-cyanostilbene crystal has been synthesized that can undergo solid-state [2+2] cycloaddition reaction under violet or UV irradiation and exhibits excellent photomechanical bending properties.

Minimalistic fully convolution networks (MFCN): pixel-level classification for hyperspectral image with few labeled samples.

Most of the existing deep learning methods for hyperspectral image (HSI) classification use pixel-wise or patch-wise classification. In this paper, we propose an image-wise classification method, where the network input is the original hyperspectral cube rather than the spectral curve of each pixel (i.e.