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A recent study conducted at Xi'an University of Science and Technology in Xi'an, People's Republic of China, focused on the accurate recognition of micro-expressions in demanding scenarios such as clinical psychotherapy and criminal interrogation. The study proposed a novel architecture based on a multi-scale 3D residual convolutional neural network, which effectively captured weak and fleeting facial features and improved recognition performance. The algorithm's performance was evaluated using public datasets, and it achieved recognition accuracies of 74.6%, 84.77%, and 91.35% on these datasets. The study concluded that the proposed algorithm could serve as an important reference for researchers working on high-precision micro-expression recognition. [Extracted from the article]

A study conducted by researchers at Guangzhou University in China explores the principles and factors that influence the selection of emergency medical facilities for public health emergencies in megacities. The study proposes a logistic regression model and a site selection model for these facilities, using genetic algorithms (GAs) for simulation and analysis. The results show that the improved GA model outperforms traditional models, providing decision-makers with stable technical support and better action plans. The research findings offer a theoretical and decision-making basis for the location of government emergency medical facilities and guidance for enterprises constructing such facilities. [Extracted from the article]