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.

It is critical for urban and regional planners to examine spatial relationships and interactions between a port and its surrounding urban areas within a region’s spatial structure. This paper seeks to develop a targeted framework of causal relationships influencing the spatial structure changes in the Bushehr port-city. Hence, the study utilizes Fuzzy Cognitive Maps (FCMs), a computational technique adept at analyzing complex decision-making processes. FCMs are employed to identify concepts that act as drivers or barriers in the spatial structure changes of Bushehr port-city, thereby elucidating the causal relationships within this context. Additionally, the study evaluates these concepts’ relative significance and interrelationships. Data was collected through interviews with ten experts from diverse backgrounds, including specialists, academics, policymakers, and urban managers. The insights from these experts were analyzed using FCMapper and Pajek software to construct a collective FCM, which depicts the influential and affected concepts within the system. The resulting collective FCM consists of 16 concepts, representing the varied perspectives and expertise of the participants. Among these, the concepts of management and planning reform, economic growth of the city-port, and port development emerged as the three most central concepts. Moreover, the effects of all influential concepts on the spatial structure change in Bushehr port-city were evaluated through simulations conducted across four different scenarios. The analysis demonstrated that the system experiences the most significant impact under the fourth scenario, where the most substantial changes are observed in commercial and industrial growth and the planning of port-city separation policies.

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.