Fire accidents are one of the serious security threats facing the metro, and the accurate determination of the index system and weights for fire assessment in underground stations is the key to conducting fire hazard assessment. Among them, the type and quantity of baggage, which varies with the number of passengers, is an important factor affecting the fire hazard assessment. This study is based on the combination of subjective and objective AHP (Analytic Hierarchy Process) with the available Particle Swarm Optimisation algorithm PSO (Particle Swarm Optimization) and the perfect CRITIC (Criteria Importance Through Intercriteria Correlation) empowered fuzzy evaluation method on the metro station fire hazard toughness indicator system and its weights were determined, and a fuzzy comprehensive evaluation model of metro station safety toughness under the influence of baggage was constructed. The practical application proves that the method provides a new perspective for the fire risk assessment of underground stations, and also provides a theoretical basis for the prevention and control of mobile fire load hazards in underground stations.

China’s annual government work report (GWR) contains terms with Chinese characteristics (TCC), reflecting unique policy frameworks. Translating these terms into English poses significant challenges due to cultural disparities between China and the West. This paper examines the English translation methods used for such terms, using the 2020 GWR as a case study, aiming to provide valuable insights for future translation practices.

In this study, the authors propose a method that combines CNN and LSTM networks to recognize facial expressions. To handle illumination changes and preserve edge information in the image, the method uses two different preprocessing techniques. The preprocessed image is then fed into two independent CNN layers for feature extraction. The extracted features are then fused with an LSTM layer to capture the temporal dynamics of facial expressions. To evaluate the method's performance, the authors use the FER2013 dataset, which contains over 35,000 facial images with seven different expressions. To ensure a balanced distribution of the expressions in the training and testing sets, a mixing matrix is generated. The models in FER on the FER2013 dataset with an accuracy of 73.72%. The use of Focal loss, a variant of cross-entropy loss, improves the model's performance, especially in handling class imbalance. Overall, the proposed method demonstrates strong generalization ability and robustness to variations in illumination and facial expressions. It has the potential to be applied in various real-world applications such as emotion recognition in virtual assistants, driver monitoring systems, and mental health diagnosis.

This article explores a method for evaluating the achievement of learning effectiveness based on virtual reality technology. The research analyzed the design and construction of a virtual learning environment, data collection of learner behavior, data analysis and evaluation methods, evaluation indicators and personalized feedback, as well as a case study of a virtual learning evaluation system. By using virtual reality technology to create an immersive learning environment, learners can gain an immersive learning experience, and evaluators can accurately record learners' behavior and performance. The learning effectiveness evaluation method based on virtual reality technology can improve learning effectiveness and teaching quality, promote educational innovation and development. These research results are of great significance for the evaluation of virtual learning effectiveness and personalized teaching in the field of education.