With the development of image processing technology and video analysis technology, forest fire monitoring technology based on video recognition is more and more important in the field of forest fire prevention and control. The objects currently applied to forest fire video image monitoring system monitoring are mainly flames and smoke. This paper proposes a forest fire risk monitoring and early warning algorithm, which integrates a deep learning model, infrared monitoring and early warning, and forest fire weather index. The algorithm first obtains the current visible image and infrared image of the same forest area, utilizing a smoke detection model based on deep learning to detect smoke in the visible image, and obtains the confidence level of the occurrence of fire in said visible image. Then, it determines whether the local temperature value of said infrared image exceeds a preset warning value, and obtains a judgment result based on the infrared image. It calculates again a current FWI based on environmental data, and determines a current fire danger level based on the current FWI. Finally, it determines whether or not to carry out a fire warning based on said fire danger level, said confidence level of the occurrence of fire in said visible image, and said judgment result based on the infrared image. The experimental results show that the accuracy of the algorithm in this paper reaches 94.12%, precision is 96.1%, recall is 93.67, and F1-score is 94.87. The algorithm in this paper can improve the accuracy of smoke identification at the early stage of forest fire danger occurrence, especially by excluding the interference caused by clouds, fog, dust, and so on, thus improving the fire danger warning accuracy.

Crowd gathering events deeply affect public safety. To enhance city management and avoid potential risks, many algorithms are designed for crowd analysis and deployed on video surveillance. Widely applied deep learning models also can be trained for crowd analysis. However, there are still few works focusing on crowd gathering behavior. Furthermore, as a result of the lack of interpretability of deep learning models, which also brings potential risk of being rejected by the users. In this paper, we categorize crowd behaviors into wandering, merging, walking gathering, standing gathering, and dispersing. Also, we propose an interpretable framework for crowd gathering understanding based on crowd density estimation model and proposed crowd descriptors, named Irregularity, Sparsity, Randomness, and Volatility. The experiments on the PETS2009 dataset demonstrate our method has outperformed the previous works on the crowd gathering understanding task. Moreover, we further analyze the framework performance with different crowd feature extraction models and the relations between our descriptors and crowd behavior. Besides, an ablation study is conducted to investigate the effectiveness of the descriptors and differences between density estimation models. The results demonstrate the effectiveness and the much better interpretability of our framework. Our descriptors also show significant contributions to the quantification of crowd gathering behaviors.

Over recent decades, the number of high-rise building fires has increased rapidly with urbanization. However, few studies have been conducted from the perspective of fire safety awareness among residents in high-rise buildings. This study investigated the fire safety literacy of people in high-rise buildings. High-rise buildings are an important part of the urban environment, and fires can cause environmental pollution. In this work, online and on-site questionnaire surveys were combined to investigate residents’ evacuation preparation, firefighting skills, and knowledge about firefighting equipment. A total of 3000 questionnaires were collected, of which 2026 were validated. The survey results showed that 27.79% of residents were unaware of the location of evacuation staircases, 55.43% were not aware of the location of refuge floors in super high-rise buildings, and 41.20% were not aware that fire doors should be in a normally closed state. The main findings of this investigation indicate that the fire safety knowledge of residents in high-rise buildings is gravely deficient. Therefore, it is recommended that fire safety awareness campaigns be enhanced in both school education and community activities, and residents in high-rise buildings should be organized to participate in regular fire drills.

Power-system safety is fundamental to protecting lives and ensuring reliable grid operation. Yet, hierarchical text classification (HTC) methods struggle with domain-dense accident narratives that require cross-sentence reasoning, often yielding limited fine-grained recognition, inconsistent label paths, and weak evidence traceability. We propose EG-HPT (Evidence-Grounded Hierarchy-Aware Prompt Tuning), which augments hierarchical prompt tuning with Global Pointer-based nested-entity recognition and a sentence–entity heterogeneous graph to aggregate cross-sentence cues; label-aware attention selects Top-k evidence nodes and a weighted InfoNCE objective aligns label and evidence representations, while a hierarchical separation loss and an ancestor-completeness constraint regularize the taxonomy. On a HFACS-based power-accident corpus, EG-HPT consistently outperforms strong baselines in Micro-F1, Macro-F1, and path-constrained Micro-F1 (C-Micro-F1), with ablations confirming the contributions of entity evidence and graph aggregation. These results indicate a deployable, interpretable solution for automated risk factor analysis, enabling auditable evidence chains and supporting multi-granularity accident intelligence in safety-critical operations.

Extremely high safety standards are required during nuclear power plants (NPPs) working. So it is critically important for process monitoring and fault detection and diagnosis (FDD). Through research on online FDD in NPPs, it helps operator timely to know the state of system to take the proper operation to prevent the accident further deterioration. Due to the complex system of