In order to study the influence characteristics of different driving conditions on driving behavior of small-clearance interchanges, eye tracker was used to collect the drivers' eye movement data in the real vehicle experiment. k-medoids dynamic clustering method was used to divide the drivers' visual interest area. A comparative analysis of gaze behavior and saccade characteristics of drivers was conducted of under mainline entry and exit conditions at conventional clear-distance and small-clearance interchanges and across different driving stages. The driving behavior characteristics of drivers under high-density interchange of the expressway were revealed. The results show that drivers mainly focus on the area of road ahead and instrument panel, and they also pay special attention to the area of the left mirror when driving into mainline conditions. The 500 ms gaze time is a common time window for drivers to capture key information. The mean value of the gaze time on small-clearance interchange is higher than that on regular-clearance interchanges. There is a clear bias in observing the horizontal and vertical information in different driving phases. The mean gaze duration was higher in the small-clearance interchanges than in the conventional interchanges. The reduction of clear distance of the interchanges increases drivers' scanning amplitude and changed the scanning direction, which is most obvious in the interweaving section. The scanning amplitude is uniformly distributed in the range of 200 px. There is no significant difference in the mean distribution of fixation time between drivers of different driving ages. There is no significant monotonic relationship between the drivers' gaze duration and scanning speed and amplitude, while there is a significant positive correlation between scanning speed and amplitude of scanning.

In order to explore the role of intelligent and digital empowerment in the modernization of emergency management systems and capacities, this study identified the key influencing factors and clarifies their interrelationships. CiteSpace was applied to cluster and analyze relevant domestic and international literature, while NVivo12plus, combined with expert consultations, was used to identify 16 influencing factors from four dimensions: environment, mechanism, technology, and data. DEMATEL method was employed to calculate the influence, dependence, centrality, and causality of each factor, and ISM was used to construct a hierarchical structure and reveal their internal logical relationships. By integrating the DEMATEL and ISM models, five key factors were identified, namely political and legal frameworks, cultural awareness, demand and supply, management strategies and planning, and managerial attention and support, which rank highest in multiple indicators and play a significant role in promoting modernization.

CBR can effectively address the issue of unclear elements in safety risk management. However, existing research lacks systematic reviews of its application status and development trends. To systematically summarize the current state of research on the application of CBR in construction safety risk management and to identify future research directions, this paper comprehensively reviewed 45 Chinese and 50 English papers, employing bibliometric analysis and content analysis. The results show that the number of publications shows a fluctuating upward trend and tends to stabilize, spanning multiple disciplines such as environmental science and computer science. Key scholars demonstrate both commonalities and differences in collaboration themes, with China ranking first in publication volume. The research themes have evolved from knowledge construction and methodological exploration toward integration and intelligent convergence. CBR has been widely applied in safety risk identification, analysis and assessment, response, and knowledge management, yet limitations remain regarding control targets, application scenarios, knowledge management, data integration, and workflow optimization. Future research should focus on expanding control targets, developing whole-process management scenarios, establishing collaborative knowledge management mechanisms, integrating multi-source heterogeneous data, and improving workflows to advance the development of intelligent safety risk management in construction projects.

To study the impact of cognitive bias on energy safety engineering decision-making, the Fukushima nuclear power plant accident was taken as the research object. A full-cycle decision-making analysis chain, covering risk identification (earthquake and tsunami assessment), crisis disposal (cooling system failure treatment), and aftermath management (information disclosure decision-making), was built through retrospective analysis of the accident timeline and key decision points. Cognitive psychology theory was used to analyze decision-making bias phenomena in energy engineering safety management, and the specific mechanisms of these biases in emergency response and risk assessment were revealed. Results show that six typical cognitive biases are present in the emergency decision-making during this accident, including confirmation bias, anchoring effect, representative heuristics, framing effect, loss aversion, and overconfidence. This analysis demonstrates that cognitive bias identification can enhance energy engineering safety management, and improve the effectiveness of safety emergency responses.

To decouple the structural complexity of industrial and supply chains, address systemic risks, and enhance supply chain resilience, bidding transaction big data was employed. Taking the vaccine sector as an example, a framework for constructing a supply chain knowledge graph was designed, and a systematic supply chain knowledge graph was established. On this basis, complex network techniques were applied to examine the vulnerability and potential security risks of China's vaccine industry supply chain network from 2011 to 2023. The research encompassed complex knowledge queries of the industrial chain, an analysis of city degree distribution patterns, and simulations and analyses of supply chain risks. The study shows that the vaccine industry chain exhibits spatial imbalance, particularly between eastern and western regions. The production structure is highly dependent, with approximately 61.3% of vaccine varieties relying on a single manufacturer or overseas agent. Manufacturers with high centrality constitute potential risk points within the vaccine supply chain network, where disruptions to about 33 enterprises significantly hinder vaccine supply. Compared with core cities, the cumulative effects of cities with lower network status, such as Chongqing, Dalian, Shenzhen, and Shenyang, have a more pronounced impact on the efficiency of vaccine circulation.

To address the issues of insufficient resolution in the high-density electrical resistivity method and limited accuracy in anomaly indentification for road collapse hazard detection, resolution tests for road collapse hazard detection based on high-density electrical resistivity method and investigation of an anomaly identification method using GMM clustering were conducted. Forward modeling was performed using the finite difference method, while inversion process was carried out using the Gauss-Newton method. Numerical simulations were conducted to assess the effect of different electrode spacing configurations on detection resolution. In the context of pipeline leakage-induced road collapse, geoelectric models for underground anomalies at various stages of development were designed, and GMM clustering analysis was applied to optimize the inversion results of the high-density electrical resistivity method. The results demonstrate that adjusting the electrode spacing and measurement parameters can significantly improve detection resolution. At a depth of 4.5 meters, the location and shape of underground anomalies at a scale of 1 meter can be effectively characterized by reducing the electrode spacing. An electrode spacing of 0.5 meters can balance detection accuracy and computational efficiency, corresponding to half the scale of the target anomaly. For anomalies buried at the same depth, the resistivity recovery of low-resistance anomalies is superior to that of high-resistance anomalies, providing the basis for parameter optimization for detecting various anomaly types. The feasibility of high-density electrical resistivity method to detect leakage-induced detects at different stages is demonstrated through tests on underground cavity models induced by pipeline leakage, while the identification accuracy of anomaly regions is further enhanced by the GMM-based clustering analysis.

In order to prevent structural safety accidents in commercial buildings during the operation and maintenance phase and enhance the objectivity and accuracy of structural safety assessments for commercial buildings, the following steps were taken: First, a structural safety evaluation system for the operation and maintenance phase of commercial buildings was established. The weights of each evaluation indicator within the system were determined using a combined objective-subjective weighting method based on the Analytic Hierarchy Process (AHP) and the Criteria Importance Through Intercriteria Correlation (CRITIC) method. Second, by incorporating the improved TOPSIS method with interval numbers, the interval values of safety evaluation indicators corresponding to different structural safety levels of commercial buildings were determined, yielding relative proximity values under different safety condition levels. Finally, taking a commercial complex building in Changsha City as an example, the safety condition was evaluated based on on-site inspection data, and the results were compared with those from the inspection report issued by a professional inspection institution after appraisal to verify the accuracy and applicability of the established indicator system and calculation results. The study indicates that by combining industry standards and expert experience to establish the evaluation indicator system, and using the combined weighting method and the interval-based TOPSIS evaluation method improved with interval numbers on the basis of existing research, the objectivity and accuracy of structural safety assessment for commercial buildings can be enhanced.

In order to enhance the flight training capabilities of trainees, identify and evaluate flight trainees' situational awareness, an index system for evaluating flight trainees' situational awareness was established based on grounded theory. The combined weights were calculated using game theory to integrate subjective weights, which were determined by the G1 method, and objective weights, which were derived from the criteria importance through intercriteria correlation(CRITIC) method improved by the coefficient of variation method. Subsequently, a combined weighting cloud model was proposed: the digital characteristic values of the evaluation indices were input into a cloud generator to obtain situational awareness ratings, and the model was verified for the use of an example to evaluate the situation awareness of a flight trainee. The results indicate that the cloud model can comprehensively evaluate the situational awareness of flight trainees. The overall situational awareness of Trainee A was rated as excellent. Among the indicators, the flight discipline metric yielded the best evaluation result, contributing significantly to the maintenance of situational awareness. The flight operation skills metric was rated between the best and the poorest, while the professional knowledge indicator received the lowest score. Major deficiencies were identified in two primary evaluation indicators, emergency handling capability and adaptability to operational environments, which were found to adversely affect the maintenance of situational awareness.

Safety management of long-distance oil and gas pipeline enterprises has been regarded as essential for ensuring energy security and maintaining social stability. As a key approach to enhancing safety management efficiency, audits of QHSE management systems are increasingly emphasized. In this study, traditional audit modes—including international safety rating audits, full-factor quantitative audits, and system certification audits—were systematically compared and analyzed. Additionally, the features and applicable contexts of emerging audit modes, such as data-driven audits and AI-based audits, were further explored. It was found that each audit mode presented distinct advantages and limitations, depending on the enterprise's stage of development, risk management demands, and resource capabilities. Accordingly, a comprehensive audit framework integrating conventional methods and intelligent technologies was constructed. Optimization strategies were proposed, including phased audit mode selection, risk-oriented focus, capacity enhancement, digital and intelligent transformation, and the establishment of a closed-loop rectification mechanism. The findings show that a multi-mode integrated audit system significantly improves the safety performance of long-distance oil and gas pipeline enterprises. The conclusions provide theoretical guidance and practical reference for the selection and optimization of QHSE audit modes in the oil and gas industry.

In order to effectively prevent the occurrence of civil aviation maintenance related events, a sample of 2 687 voluntary reports from the US Aviation Safety Reporting System (ASRS) database (2001-2023) was examined. Fourteen risk factors, including work environment and human factors, were analyzed using CiteSpace. Strong couplings between these factors were identified through UCINET and Gephi. The results show the following findings: Among the nine single risk factors, key risks are identified as landing gear system malfunctions, poor lighting, and rain (with frequencies of 103, 64, and 45, respectively). Among the five coupled risk factors, key risks are found to be airframe maintenance, communication breakdown, and human factors (with co-occurrence frequencies of 1 646, 1 448, and 1 206, respectively). The analysis of all risk factor couplings reveals that the strongest coupling exists between airframe maintenance and powerplant maintenance (with a weight value of 3 568). Additionally, a significant coupling relationship is observed between procedures and improper landing gear system operations (with a weight value of 118).