Exploration of a deep learning-based mechanism for predicting the work competence of community caregivers

To predict the workability of community nursing staff and provide corresponding training strategies based on the results. In this study, a nursing staff workability prediction model based on R-GCN-GRU was constructed. In the process of community nursing staff workability feature extraction, the attention mechanism is introduced, combined with the degree of association between the captured nodes of the R-GCN network and the long-term memory capacity of the GRU network, and the model optimization is carried out using the cross-entropy loss function. Finally, the workability of community caregivers in a city in Guangdong Province was predicted to verify the accuracy of the model from multiple perspectives. The results showed that clinical handling ability, keen observation ability, and communication ability were more valued by most caregivers, and their selection rates all reached 98.4%. On the other hand, clinical research, organizational management, and innovation abilities were relatively low. In the ability prediction of individual characteristics, the highest income personnel’s working ability was second only to the lowest salary personnel reaching 44.61±6.03. The working ability of older age and higher-position nursing staff, and nursing staff with more than 25 years of service reached 45.62±6.14, 48.30±5.22, and 45.86±5.52, respectively

Unraveling the Internal Structure of 3D Printed Stimuli-Responsive Materials Using a Molecular Probe

Stimuli-responsive 3D printing, or 4D printing, offers unparalleled potential in various research fields, enabling the combination of complicated mechanical design with programmable functionalities. The switchable polymer network structures, e.g., crystalline domains, free volume, and phase separation, are the key to achieving macroscopic responsiveness. However, despite a growing repertoire of new materials, most studies rely on rudimentary imaging techniques to visualize the materials’ shape change under external stimuli. Seldomly could such macroscopic behavior be correlated with the nanoscopic structures and dynamics of polymers. Here, leveraging the AIE phenomena, we introduce a novel method that can offer direct insights into the network structures and the chain mobility of the printed polymers. We developed a new photo-polymerizable polyurethane with multiple responsive characteristics, including temperature, mechanical strain, and pH, as an example of 4D printing materials. By embedding AIEgen in the polymer matrix, we demonstrated that the emission intensity and wavelength can serve as reporters and correlate the intramolecular motions of the AIEgen with the stimuli-responsive properties of the polymers. These observations were confirmed by small-angle X-ray scattering revealing the underpinning structural evolution. With potential applications for real-time structural monitoring, this study provides a new tool for the characterization of 4D printed materials