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    China Safety Science Journal    2025, 35 (8): 272-272.  
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    Research review and progress of coal mine gas explosion risk assessment
    LI Min, WANG Dan, HE Shan, SHI Shiliang, WANG Deming, LU Yi
    China Safety Science Journal    2025, 35 (2): 127-136.   DOI: 10.16265/j.cnki.issn1003-3033.2025.02.0963
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    Gas explosion disaster is the most serious coal mine accidents. In order to summarize the research progress of gas explosion risk assessment, firstly, the risk factors of gas explosion were identified. Then the shortcomings of existing risk assessment methods were analyzed, and the following conclusions were drawn by sorting out relevant literature. The analysis shows that there are subjective problems in identification method and evaluation method of coal mine gas explosion risk sources. There are also some problems with risk factors, such as the uncertainty of gas source and change, the unknown ignition source, the uncertainty of ventilation and air control. The application of objective weighting method and evaluation method based on mathematical theory can improve the accuracy of weighting and evaluation results, but the computational complexity limits its wide application. Although the application of computer models has made the assessment of coal mine gas explosion risk more accurate, it is necessary to solve the problem of expanding the integration of data collection and deep learning. Based on the current research status and existing problems, the future risk assessment of coal mine gas explosion can develop in the direction of multi-source data fusion technology, deeply mining precursory warning information, establishing intelligent models of disaster information based on information depth perception and data mining, and realizing dynamic risk assessment of coal mine gas explosion.

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    A review of research for civil aviation safety resilience
    YUAN Leping, ZHENG Ying, GU Zekun, GAO Yuyu
    China Safety Science Journal    2025, 35 (2): 1-9.   DOI: 10.16265/j.cnki.issn1003-3033.2025.02.0661
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    In order to systematically review the research and development status of safety resilience in the field of civil aviation at home and abroad, and deepen the research on safety resilience in the field of civil aviation, firstly, the concept of aviation safety resilience was explored by reviewing policy documents, standards and research literature related to air transport systems in recent years. Then the research and application of safety resilience in civil aviation airport, air traffic control, flight operation and other fields were discussed. Finally, the existing problems were analyzed and corresponding suggestions and prospects are put forward. The results show that the safety management of civil aviation has formed a relatively perfect system, and the safety resilience covers all stages of safety management before, during and after, but it still cannot meet the needs of perfect connection and integration with the existing safety management system. Current studies focus more on the resilience of airports and route networks. In terms flight operation, more measures are taken to improve safety resilience based on the actual flight operation. The basic research on aviation resilience assessment is relatively lacking, and the research on aviation personnel resilience at operational level is far from enough. In the future, relevant research should be carried out around the individual operation resilience, enrich the basic research of resilience assessment, further deepen the research and form a relatively stable discipline system, pay attention to the connection between safety resilience and safety management system, and assess individual operation resilience from the perspective of safety.

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    Failure causes of emergency response in oil and gas pipeline accidents and social network analysis
    GONG Yunhua, ZHANG Zhe
    China Safety Science Journal    2025, 35 (1): 209-215.   DOI: 10.16265/j.cnki.issn1003-3033.2025.01.1536
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    To avoid worsening the consequences of oil and gas pipeline accidents due to emergency failures, the causes of emergency failure in 27 accidents at home and abroad were analyzed using the HFACS model. Based on the results of grounded theory (GT) statistical coding analysis, a classification model of failure causes of emergency response in oil and gas pipeline accidents was proposed. SNA was used to develop the relationship network of the causes of emergency failures in oil and gas pipeline accidents. The core-periphery, centrality, and association direction index analyses were used to identify core factors and factors with high association and strong mediating roles in the classification model of the causes of emergency failures in oil and gas pipeline accidents. The results indicated that the classification model of emergency failure causes in oil and gas pipeline accidents was divided into five levels: government and emergency department factors, operator organizational factors, operator unsafe supervision, preconditions for unsafe behavior of on-site personnel, and unsafe behavior of on-site personnel. The emergency failure causes were further divided into 16 bottom-level factors, among which there were 9 core factors: inadequate safety supervision by government and emergency departments, ineffective emergency rescue, regulations defects, insufficient supervision by pipeline operators, technical environment, and skill errors. Skill errors, regulations or procedure defects, technical environment, and insufficient supervision by operators were highly associated factors. Moreover, pipeline operators' regulation defects, procedure defects, technical environment, insufficient supervision, improper resource management, and decision-making errors were strong mediating factors.

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    Human reliability analysis of emergency behaviors for hazardous chemical accidents in chemical industry parks
    WANG Dongdong, YANG Angbin, WANG Zhihao, ZHAO Jingrong, DONG Guoyu, TONG Ruipeng
    China Safety Science Journal    2025, 35 (2): 21-27.   DOI: 10.16265/j.cnki.issn1003-3033.2025.02.1078
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    To explore human error in complex emergency rescue scenarios for hazardous chemical accidents in chemical industry parks and improve human reliability in emergency rescue actions, a comprehensive analysis method was established to quantitatively evaluate the human reliability of emergency rescue for hazardous chemical accidents. Firstly, based on the laws, regulations and standards related to the emergency rescue of hazardous chemical accidents, 20 emergency behaviors in chemical industry parks were summarized and extracted. Secondly, cognitive reliability and error analysis method(CREAM) was introduced to determine the probability of human error. Analytic hierarchy process(AHP) and entropy weight method were combined to quantify the severity of errors in emergency behaviors. Finally, from the perspectives of possibility and severity, the weak aspects of emergency behaviors in chemical industry parks were explored, and the strategies for enhancing emergency rescue capabilities in chemical industry parks were discussed. The practical application of the method was verified with the example of N chemical industry park. The results show that 20 emergency behaviors were divided into 3 clusters. There are 4 emergency behaviors identified that needed to be prioritized for improvement in the petrochemical zone: risk assessment, fire-fighting, initial disposal of enterprises and rescue of people in distress. For these emergency behaviors, it was proposed that N chemical industry park should focus on optimizing the accident information transmission mechanism, improving decision-making and command effectiveness, and strengthening rapid response and disaster identification capabilities, so as to provide countermeasures and suggestions for improving its emergency rescue capability.

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    Research on aircraft control system fault risk assessment based on hybrid probability models
    SHI Tongyu, GAO Yi, WANG Yantao
    China Safety Science Journal    2025, 35 (2): 10-20.   DOI: 10.16265/j.cnki.issn1003-3033.2025.02.0601
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    To address the flight safety risks posed by faults in aircraft control systems, a composite framework for fault risk assessment based on IRPN was proposed. This framework comprehensively considered four key risk factors: fault probability, severity, detectability, and risk damping. First, system fault modes were deduced bidirectionally using FMECA-FTA method. Second, human and environmental factors were incorporated, and a Bayesian network approach was employed to construct a hybrid probability model for calculating fault probabilities. Third, fault severity was categorized into three evaluation parameters, which were comprehensively assessed using the Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation methods. Next, utilizing resources such as pilot quick reference manuals and aircraft type design manuals, a criterion-based reasoning method was applied to establish detectability scoring criteria, allowing for a more scientific evaluation of fault mode detectability levels. Finally, the FRAM was introduced to define risk damping coefficients, characterizing the propagation of risk during the evolution of fault risks. The computational validation was carried out with the case of jamming failure mode of aircraft flap seam wing actuation system. The research results show that its IRPN assessment result is 158, which is in perfect agreement with the actual operation. The validity and accuracy of the failure composite risk index calculated by the IRPN composite risk assessment framework are confirmed by the failure mode example simulation and the real verification of unsafe events.

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    Empirical analysis of construction engineering safety impact network influenced by five-party responsible entities
    CHEN Dawei, YANG Zhe, YU Rui, CAO Weiqiang
    China Safety Science Journal    2025, 35 (4): 1-8.   DOI: 10.16265/j.cnki.issn1003-3033.2025.04.1638
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    In order to effectively prevent construction safety accidents, a safety responsibility network model of the five responsible parties was developed based on complex network topology characteristics to reveal the nonlinear coupling mechanism of their safety responsibilities, supported by accident case studies. First, relevant regulations, standards, and literature were analyzed to extract safety responsibility checklists for each party, and a responsibility matrix was constructed. Subsequently, Neo4j software was utilized to visualize the network diagram illustrating the safety influence relationships among the five parties in construction projects. Finally, topological metrics were applied to explore the interdependencies between the parties across multiple dimensions. The results indicate that the construction client plays a central and dominant role in project safety, with particularly significant influence on the contractor. The contractor's impact is primarily concentrated within its organizational boundaries, and its responsibility implementation is susceptible to external influences from other parties. The survey and design units indirectly affect other parties through the construction client. The supervision unit maintains systemic balance and regulation through monitoring and feedback mechanisms. The identification of critical decision-making nodes and passive response factors provides network topology-based evidence for differentiated management of responsible parties.

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    Risk assessment of biological sample transport by UAVs based on Bayesian networks
    LIU Qing, SHEN Tian
    China Safety Science Journal    2025, 35 (1): 16-24.   DOI: 10.16265/j.cnki.issn1003-3033.2025.01.0441
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    To quantify the transportation risks associated with biological samples using UAVs, this study first identified 32 risk factors across five dimensions-human, machine, environment, management, and hazard-based on national standards and relevant literature. A BN for risk assessment was constructed using Netica software, with prior probabilities determined through expert knowledge and fuzzy set quantitative analysis. The proposed risk assessment model was then used for bidirectional reasoning and scenario analysis. A case study of a UAV company in Shenzhen was presented to evaluate the transportation risks of biological samples and identify key influencing factors. The results indicate that the risk probability of biological sample transportation, as calculated through forward reasoning, is approximately 2.203×10-5. The primary risk factors are related to hazardous materials, followed by equipment and facility-related issues. The core risk factors influencing biological sample transportation include the size, quantity and weight of hazardous material packages, the temperature control effectiveness of specialized cold chain logistics boxes, the integrity of emergency response plans, emergency handling capabilities, safety management and education, and the presence of obstacles.

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    Mechanism of risk perception on safety cognition of construction workers in underground caverns
    JIANG Xin, LI Fengbiao, PENG Jiayu, JIAN Li, JIN Lianghai
    China Safety Science Journal    2025, 35 (3): 1-9.   DOI: 10.16265/j.cnki.issn1003-3033.2025.03.1121
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    In order to improve the safety cognition level of underground caverns construction workers, safety requirement and safety capability were introduced as mediating variables, and occupational burnout was used as a moderating variable to construct a moderated chain mediation model. A structured questionnaire was designed using 5 scales: risk perception, safety requirement, safety capability, occupational burnout and safety cognition. A questionnaire survey and data analysis were conducted on 312 underground caverns construction workers, and SPSS 26.0 and AMOS 26.0 software were used to test the mediating effect and moderating effect.The results show that risk perception directly and positively affects the safety cognition of underground cavern construction workers, while occupational burnout plays a negative moderating role between the two. Risk perception also affects the safety cognition of construction workers through the independent mediating effect of safety requirement and safety capability, as well as through the chain mediating effect of safety requirement and safety capability. Therefore, motivating and improving the risk perception, safety requirement and safety capability of construction workers, while reducing their occupational burnout, can effectively improve their safety cognition level.

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    Exploration and practice of collaborative training for safety discipline talents between universities and institutions: from integration of science and education to integration of industry and education
    TONG Ruipeng, WANG Leyao, HAN Jixiang, ZHANG-JIANG Bonan, KANG Rongxue, AN Yu
    China Safety Science Journal    2025, 35 (7): 1-7.   DOI: 10.16265/j.cnki.issn1003-3033.2025.07.1357
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    To cultivate talents with both theoretical innovation and practical abilities in safety science and engineering discipline of universities, this paper explored the educational practice of safety discipline from the integration of science and education to the integration of production and education. First, the integration concepts of science and education and integration of production and education were analyzed under the new Degree Law, establishing the connotation requirements and an educational framework for science-industry-education synergy. Utilizing macro, meso, and micro perspectives, this study constructed robust educational systems, management mechanisms and training programs. Examples from School of Emergency Management and Safety Engineering of China University of Mining and Technology-Beijing and National Academy of Safety Science and Engineering illustrated the effective cultivation of talent seeds, the creation of nurturing environments, and the achievement of substantial educational outcomes. Results indicate that this collaborative model significantly enhances the comprehensive development of safety professionals and improves the overall quality of education in the safety discipline.

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    China Safety Science Journal    2025, 35 (10): 247-247.  
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    System fault evolution process based on quantum thought description
    CUI Tiejun, LI Shasha, DENG Wenhao
    China Safety Science Journal    2025, 35 (5): 1-7.   DOI: 10.16265/j.cnki.issn1003-3033.2024.05.0671
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    In order to study the SFEP and its characteristics, a method for describing SFEP using quantum concepts is proposed. The relationship between SFEP and event function states was discussed. The principle of describing SFEP by quantum thought was studied. The mathematical model of quantum description of SFEP was constructed. The measurement collapse of SFEP quantum states was realized, and the failure mode and its probability were finally determined. The results indicate that SFEP has diversity and uncertainty, event quantum state superposition, measurement collapse, bipolar state, which is the basis of studying SFEP through quantum thought. SFEP can be decomposed into multiple layers, with distinct objects and quantum state superposition modes in each layer. The SFEP quantum states are derived from the superposition of the quantum states of objects at each layer, which is composed of the polynomial product of the probability amplitude of the objects in each layer. The restriction conditions for the formation of the probability amplitude of quantum states of each layer are given. Each dimension of the SFEP quantum state vector is a fault state. The measurement of the factor phase value makes the SFEP quantum state collapse, and the possible failure mode and occurrence probability are obtained.

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    Visual behavior for construction site hazard identification under different cognitive loads
    DENG Hongze, KONG Yuanyuan, XU Sheng
    China Safety Science Journal    2025, 35 (1): 40-49.   DOI: 10.16265/j.cnki.issn1003-3033.2025.01.0685
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    In order to explore the effect of cognitive load on workers' hazard identification behavior, a cognitive experiment based on eye-tracking technology for construction site hazard identification was designed firstly. In this experiment, a N-digit task was introduced to increase the cognitive load. Secondly, the gaze and glance data were collected to analyze the static attention allocation, and the scanning path was processed to extract the dynamic transfer characteristics of attention. Finally, three parameters of variance review probability (RP), transition probability (TP) and switching probability (SP) were selected as the quantitative parameter values to classify the scanning patterns of hazard identification, which explored the influence of cognitive load on hidden hazard identification from the perspective of visual behavior performance. The results show that the level of cognitive load negatively affects hazard identification performance. The subjects with high cognitive load show longer first fixation time, fewer fixation counts and saccade counts, and there is no significant difference in fixation percentage and mean fixation duration. Additionally, based on the attention characteristics, three scanning patterns are identified: sequential inspection, repeated comparison and random discovery. With the improvement of cognitive load level, subjects will pay more attention to identifying single hazard but neglect others during sequential inspection, and reduce the attention in the hazard area but still keep the fixation point quickly and frequently switching during repeated comparison, while the number and time of inspection of hazard areas are reducing simultaneously during random discovery.

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    Pattern identification and risk assessment model of civil aircraft abnormal flight based on QAR
    WANG Feiyin, YUAN Jintong, LIU Xiaochen, TAN Wei, WANG Lei
    China Safety Science Journal    2025, 35 (2): 160-167.   DOI: 10.16265/j.cnki.issn1003-3033.2025.02.0779
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    In order to assess and monitor flight risks in real-time, clustering analysis was utilized to explore the abnormal patterns embedded in QAR data, and the influencing factors of abnormal flight patterns of civil aircraft were analyzed. The Euclidean distance was employed to characterize the similarity between samples of QAR parameters, establishing an abnormal flight pattern recognition model based on K-means to define the deviation degree of abnormal patterns. By considering the number of fatal accidents and the proportion of deaths in global commercial jet accidents, in conjunction with the deviation degree of abnormal patterns, the duration of abnormal patterns, flight phases, the likelihood of unexpected safety events, and the severity of consequences following unexpected safety events, a quantified assessment method for civil aviation flight risks based on QAR data was proposed. The feasibility of abnormal flight pattern recognition and risk quantification models for civil aircraft was validated through the practical QAR data of a certain airline. The results indicate that abnormal patterns are more prevalent during the cruising phase and critical moments at the transitions between flight phases. Significant differences are observed in the distribution of abnormal flight patterns and risks across different flights and flight phases. The average total risk value for flights is 166.94, with outliers exceeding 386.97. The abnormal flight risk during the takeoff roll phase is relatively low, with an average of 5.95, while the risk during the cruising phase is relatively high, with an average of 93.46.

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    Work safety solution for China's chemical (hazardous chemicals) enterprise
    WANG Haoshui
    China Safety Science Journal    2025, 35 (5): 16-22.   DOI: 10.16265/j.cnki.issn1003-3033.2025.05.0097
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    In order to improve the work safety management level of China's chemical (hazardous chemicals) industry, firstly, the formation of chemical process safety management was expounded, including the traditional management method and the proposal of process safety concept. Then, the characteristics and specific content of the elements of the chemical process management elements system in China were analyzed, including 8 modules and 20 elements. Finally, a systematic solution was proposed, that was, four-element solution with safety leadership, management barrier (responsibility, equipment, management and capability), and safety culture as the primary-level elements. The results show that core elements of four-element solution are the concept, safety leadership, management barrier (responsibility, equipment, management, capability) and safety culture. Among them, safety leadership is the most critical element, leading the concept into practice, responsibility division, equipment upgrading, management system construction, capability improvement and culture cultivation. Other elements together constitute the skeleton of work safety management, and realize systematization. Businesses can help prevent accidents and improve the overall safety level of the industry by taking the lead and promoting the implementation of various elements through participation of all staff.

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    Unsafe behavior recognition model of high climbing workers based on vision
    ZHANG Zehui, ZHANG Qianlong, XU Xiaobin, ZHAO Zuguo, WANG Haiquan, LI Hao
    China Safety Science Journal    2025, 35 (2): 144-151.   DOI: 10.16265/j.cnki.issn1003-3033.2025.02.0278
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    In order to accurately identify unsafe behaviors during the climbing process of high-altitude workers, this paper proposed an unsafe behavior recognizing method for high climbing workers based on vision, which included the human pose estimation and the one-dimensional convolutional unsafe behavior recognition models. Quantized autoencoder was used to structurally model human key points in human pose estimation, enabling the detection of human key point coordinates. Combining with safety behavior knowledge in high climbing operations, the unsafe behavior recognition model was constructed based on one-dimensional convolutional neural network model, and it was validated by industrial data experiments. Experimental results show that the accuracy of this method is 93.91% and 90.34% on unobstructed and partially obstructed datasets, respectively. Moreover, compared with support vector machines (SVM) and K-nearest neighbor (KNN), this method has stronger generalization capability.

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    Aircraft rivet and detachment anomaly detection algorithm based on improved YOLOv8n
    XIA Zhenghong, HE Hu, YANG Lei, WU Jianjun, LIU Lu
    China Safety Science Journal    2025, 35 (2): 66-72.   DOI: 10.16265/j.cnki.issn1003-3033.2025.02.0880
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    In order to address the issue of missed detections during small target detection of aircraft rivets, an improved YOLOv8n algorithm for the detection of aircraft rivets and their anomalies was proposed. First, by adding a small object detection head, the shallow detail information in the backbone network was better fused, enhancing the model's feature fusion capability and its ability to recognize and locate small rivet targets. Second, the first two convolutions in the backbone network were replaced with SPD-Conv, which reduces information loss during down sampling through the combination of feature map reorganization and non-stride convolutions. Finally, large separable kernel attention (LSKA) was integrated into the spatial pyramid pooling fast (SPPF) module, capturing the dependencies between spatial and channel dimensions by calculating spatial and channel weights on each feature map and adjusting the feature maps to enhance the algorithm's ability to extract and recognize rivet feature information. Ablation experiments and comparative experiments were conducted based on a self-built aircraft rivet dataset. The results show that the proposed algorithm can achieve real-time identification of aircraft rivets and their anomalies, with precision, recall, and mean average precision (mAP) values improved by 6.5%, 16%, and 15%, respectively, compared to the YOLOv8n algorithm. The detection performance is also significantly better than other mainstream algorithms.

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    Text mining of causes of hot working accidents based on 24Model
    NIU Maohui, LI Weijun, LIU Yin, WANG Lu
    China Safety Science Journal    2025, 35 (3): 151-158.   DOI: 10.16265/j.cnki.issn1003-3033.2025.03.0757
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    In order to systematically explore the root causes of industrial hot work accidents through a large amount of text data, a text mining method based on 24Model was proposed. Firstly, 220 hot work accident reports were collected and sorted as datasets, and a 24Model classifier based on Bidirectional Encoder Representations from Transformers (BERT) was constructed. The pre-trained model was used to train and evaluate the accident report dataset to construct a classification model. Then, through the combination weight of the Keyword extraction algorithm based on BERT (KeyBERT) and Term Frequency-Inverse Document Frequency (TF-IDF) algorithms, combined with the 24Model framework, a keyword index system for hot work accident text was established. Finally, the interrelationships between accident causes were obtained through the analysis of the network co-occurrence relationship between text-mining keywords. The results show that the BERT-based 24Model classifier model can systematically and accurately determine the causative categories of hot work accidents. The weight of the safety management system was the largest among the 4-level keyword index systems obtained through the combination of weights. Furthermore, 7 key causative factors of hot work accidents were obtained by combining them with the co-occurrence network analysis. This shows that 24Model can strengthen the interpretability of text mining results, which provides an important reference for the prevention and management of hot work accidents.

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    Influence mechanism of psychological fatigue on unsafe behaviors of construction workers
    YAN Haoran, LI Ming, XU Xia, JIANG Xiangting
    China Safety Science Journal    2025, 35 (7): 15-21.   DOI: 10.16265/j.cnki.issn1003-3033.2025.07.1461
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    To reduce the negative impact of psychological fatigue on construction workers' safety, the relationship between psychological fatigue and UB and their underlying mechanisms were investigated. First, based on the characteristics of construction workers and literature review, psychological fatigue was divided into three dimensions: reduced motivation (RM), cognitive weakness (CW), and emotional exhaustion (EE). Secondly, based on protection motivation theory (PMT) and Conservation of Resources Theory (COR), HP was introduced as a mediating variable and SS was introduced as a moderating variable. Finally, Structural Equation Modeling and hierarchical regression analysis were used to explore the mechanism through which psychological fatigue affects construction workers' UB. The results show that psychological fatigue is found to have a significantly positive impact on UB through the three dimensions of RM, CW, and EE; HP is identified to partially mediate the relationship between psychological fatigue and UB. Both the direct effect of psychological fatigue on UB and the mediating role of HP between them are moderated by SS.

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    Research on tunnel fire detection based on improved YOLOv8s model
    WANG Chunyuan, LIU Quanjie
    China Safety Science Journal    2025, 35 (3): 69-76.   DOI: 10.16265/j.cnki.issn1003-3033.2025.03.1181
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    To accurately and efficiently detect fires in complex tunnel environments, an enhanced YOLOv8s-based tunnel fire detection algorithm was proposed. Firstly, the Cross-Stage Partial Transformer Block (CSP-PTB) module was introduced to reconstruct the backbone network structure, thereby reducing computational complexity while preserving feature extraction capabilities. Secondly, CBAM was integrated to enhance the perception of the model of key areas and improve the discriminative power of feature representation. Finally, the Normalized Wasserstein Distance (NWD) loss function was employed to optimize the training process, effectively addressing the issue of insufficient detection accuracy for small targets. Experimental results demonstrate that the improved YOLOv8s model achieves a mean average precision (mAP) of 0.848, representing a 2% improvement over the original YOLOv8s model. The recall rate reachs 0.812, marking a significant increase of 9.3% compared to the original model. Additionally, the computational cost (GFLOPS) of the improved model is reduced by 6.7%, achieving dual objectives of performance enhancement and efficiency optimization. Compared with mainstream object detection models such as Faster R-CNN(Faster Region-based Convolutional Neural Network), SSD(Single Shot MultiBox Detector), and YOLOv5s, the improved model exhibits superior performance, with mAP improvements of 7.3%, 10.1%, and 4.2%, respectively, thus meeting the stringent requirements for tunnel fire detection.

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