In order to elucidate the impact of varying sleep patterns on cognitive performance，this study leveraged the principle of complementarity among different chronotypes. This approach guided the strategic pairing of personnel for morning，evening，and night shifts，with the goal of enhancing operational safety and efficiency. The research involved a regimen of fixed sleep schedules，subjecting individuals with distinct sleep preferences to 30 hours of sleep deprivation. During this period，participants' HRV and levels of sustained attention were closely monitored. Moreover，the study utilized several established tools to evaluate fatigue in sleep-deprived individuals: the Karolinska Sleepiness Scale (KSS)，the Morningness-Eveningness Questionnaire (MEQ)，and the Pittsburgh Sleep Quality Index (PSQI). Findings revealed that，throughout the sleep deprivation period，individuals with a preference for evening activities exhibited significantly more pronounced variations in HRV time-domain indicators (RMSSD=38.301±17.056，P<0.001). These variations were characterized by greater fluctuation intensity and amplitude，as well as more evident periodicity. KSS scores across all chronotypes show a general upward trend，with those of intermediate chronotypes displaying the highest correlation with HRV frequency-domain indicators (LF/HF=0.769，P<0.05). Morning-oriented individuals demonstrated higher levels of sustained attention between 11:00 AM and 5:00 PM，with accuracy rate linear regression coefficients ranging from 1.5 to 1.7 (×10^-4). In contrast，individuals with intermediate sleep patterns peaked in attention from 7:00 AM to 12:00 PM，while evening-oriented participants exhibited significantly different patterns compared to the other two groups.

To have a deep understanding of the causal relationship between crane drivers' situation awareness information and behavior，the situation awareness and behavior response model of crane drivers was proposed by combining the ENDSLEY situation awareness model with the DEMATEL-AISM method. Firstly，the situation awareness theory was used to analyze the driver's behavioral response process and obtain information factors during the crane operation task. Secondly，the DEMATEL method was used to quantitatively analyze the association between the factors and determine a comprehensive influence matrix. Moreover，the attributes and characteristic values of the factors were analyzed to identify the key factors. Finally，a stable hierarchical structure of cause-effect attributes obtained by the AISM was used to propose the situation awareness and behavior response model of crane drivers. The results revealed that a five-layer information model consisted of 22 elements and influence relationships such as key elements of trajectory prediction and planning，and collision avoidance. Furthermore，the proposed model clarified the attribute characteristics，influence relationships，and influence degree among the information elements，offering a deep understanding of the crane driver's situation awareness and behavior response.

To reveal the characteristics of coal mine accidents in China in recent years，and put forward targeted countermeasures and suggestions for accident prevention，firstly，the major and catastrophic coal mine accidents in China from 2013 to 2023 were collected and analyzed from the aspects of the year，type，month，province and cause of the accident. Secondly，taking "2·22" particularly serious collapse accident in Inner Mongolia Xinjing coal mine as an example，the accident was analyzed based on 24Model. Finally，combined with the above analysis and research results，the accident prevention countermeasures and suggestions in line with the current situation of coal mine safety production in China were put forward. The analysis and research results show that the number of major accidents and deaths in coal mines has shown an overall downward trend，and the level of coal mine safety production in China has significantly improved since the 12^th Five Year Plan. Gas accidents are still the main accidents in coal mines in China，accounting for 51%. The fourth quarter of each year is a period of high incidence of coal mine accidents，accounting for 30.43% of the total number of accidents. Affected by geological conditions and occurrence，accidents often occur in the main coal producing areas. The proportion of accidents caused by unsafe human behavior is as high as 74%. When there are hidden dangers at both the individual and organizational levels and the dual prevention measures are not in place，accidents are likely to occur. Countermeasures and suggestions for reducing coal mine safety accidents are put forward from four dimensions，including safety supervision，strengthening safety through science and technology，team development and safety culture.

To accurately identify the entities of hydropower engineering construction safety specification，the named entity recognition model of hydropower engineering construction safety specification was constructed. The rich semantic information in the text was mined by the BERT. The semantic features of the specification were extracted by using BILSTM. The dependency relationship between entities was analyzed by relying on CRFs. The Technical Specification for Safety Protection in Construction of Water Conservancy and Hydropower Projects (SL714-2015) was taken as an example to calculate the named entity recognition model accuracy rate. The results show that the accuracy rate of the BERT-BILSTM-CRF model is 94.21%. Compared with the three traditional methods，the accuracy is significantly improved. The research will effectively assist in the intelligent management of safety regulations knowledge for hydropower engineering construction，and provide important support for the intelligent identification of construction safety hazards.

In order to identify the different dimensions of social justice in the risk communication of public crisis events for the promotion of sustainable social development，the theoretical mechanism between the four dimensions of social justice: procedural，interactive，distributive，and corrective justice and sustainable social，ecological，spatial，and temporal characteristics were explained by constructing a theoretical framework of risk justice. Then，we defined risk justice as the fair and reasonable quality of stakeholders' right to participate in the governance and management of public crisis events. To validate the constructed risk justice frameworks，we conducted a detailed content analysis of the risk management guideline，the Sendai Framework for Disaster Risk Reduction 2015-2030，which was used to elucidate the prospective potential of the risk justice framework. The results suggest that this risk management guideline emphasizes the social and spatial issues of distributive and procedural justice，and less consideration in interactivity，corrective justice，and temporal and ecological issues，which may lead to the negative impacts of public crisis events risk communication on the sustainable development of society.

In order to improve the effectiveness of oil and gas pipeline accident prevention strategies，a classification model for the causes of oil and gas pipeline accidents was developed，and social network analysis was applied to the classification model. Firstly，the STAMP model and HFACS model were combined to get the control structure of oil and gas pipeline accident prevention，and then the causes of 35 oil and gas pipeline accidents at home and abroad were analyzed according to the control structure. The analysis results were coded using grounded theory to get the classification model for the causes of oil and gas pipeline accidents. Social network analysis methods were applied to construct a relationship network of factors related to oil and gas pipeline accidents，and core edge analysis，centrality analysis，and correlation direction index analysis were used to identify the core factors and factors with high correlation and strong influence in the oil and gas pipeline accident classification model. The research results show that the classification model for the causes of oil and gas pipeline accidents included 6 levels and 22 bottom cause factors，which are government and regulatory factors，third-party factors，operator organizational factors，operator unsafe supervision and the prerequisites for unsafe behavior of on-site personnel. Among the causal factors，the internal factors of the government and regulatory authorities，organizational factors of operators，unsafe supervision of operators，and third-party factors are core factors. System flaws，insufficient supervision，improper operation plans，third-party sabotage behavior，pipeline and weld defects，construction/repair/accessory issues，and skill errors are factors with high correlation and strong influence.

To effectively implement safety production investment in bridge construction projects，GRA was used to analyze the correlation between the influencing factors of construction safety behavior from both the project supervision organization and construction units，and then the core causal factors were determined. Subsequently，a SD model was proposed to investigate the interaction mechanisms among the core causal factors. Finally，the vulnerability and resilience of the core causal factors affecting construction safety behavior were analyzed. The results indicated that extreme fines imposed by construction units，high supervision costs for project monitoring organizations，and low initial proportions between parties were the key vulnerability factors of the safety behavior in bridge construction. Reasonable economic fines，appropriate supervision costs，and enhanced training to improve frontline personnel's safety awareness significantly improved the construction safety behavior resilience.

To optimize operator fatigue risk management and reduce the impact of fatigue on operator performance，the monitoring tasks before and after fatigue induction were conducted. Firstly，the fatigue-inducing task of the 2-back paradigm and the monitoring task of the oddball paradigm were designed with the digital main control room of a pressurized-water reactor nuclear power plant，and the control interfaces of low，medium，and high complexity were used. Then，human trials with 23 participants were performed to obtain subjective fatigue ratings，behavioral data，and EEG signals. Moreover，the effectiveness of fatigue induction was verified using relevant fatigue indicators. Finally，the participator's behavioral data and event-related potential P3 components under three different complexity interfaces before and after fatigue induction were analyzed. The results showed that the shortest 2-back task lasting 30 to 60 min induced fatigue. Fatigue or interface complexity increase resulted in a decrease in the monitoring behavior performance，and the maximum performance difference was observed between high-complexity and medium-complexity interfaces under fatigue conditions. Moreover，a 4.9% decrease in accuracy and a 10.4% increase in reaction time were observed. The trend of P3 latency was consistent with that of reaction time，and P3 amplitude increased significantly only under high-complexity interfaces. Based on the performance analysis and event-related potential data，it was concluded that the interface complexity increased the negative impact of fatigue on operators' mental workload.

In the new security pattern，the power grid safety management was promoted to transform from the traditional mode to the digital，information，and intelligent mode during the life-cycle of safety production. Based on patent data of China's power grid intelligent security from 2008 to 2023，the patent map was performed using network centrality analysis and main path analysis. Then，key patent technology subgroups were obtained to systematically analyze technological development and key points of power grid intelligent safety. Based on patent maps and expert opinions，the entropy weight-TOPSIS method was used to evaluate the support of China's existing intelligent safety technology community for power grid security and intelligence. The results showed there were 11 key technology communities for the power grid intelligent safety technology，and power grid intelligent safety process technology was the most excellent knowledge group. Among the 11 types of power grid intelligent safety technology communities，technologies including intelligent control，intelligent dispatching，and network communication safety were with higher support for power grid intelligent safety. Intelligent security technology can well support the safety control and stability capabilities in the traditional safety goals of the power grid. Furthermore，it supports the safety governance and active security capabilities in the modern safety goal of the power grid. However，the support for security resilience capabilities was insufficient.

In order to improve the safety analysis of old buildings，a method was introduced for quantitatively predicting their structural safety based on digital twins. Identifying risk factors in the buildings' structures，setting up an intelligent monitoring system，and using monitoring data in numerical simulations to assess the safety of critical structural elements were included. The method provided quantitative predictions for damaged areas and the extent of destruction in the buildings. This study validates the proposed method using an old building in Xi'an with a history of over 70 years. A two-year structural safety monitoring was conducted following an actual engineering design monitoring plan. The numerical model of the building was created using Revit software and imported into ABAQUS. Monitoring data was used as external parameters in numerical simulations，with the assumption that monitored values exceeding settlement thresholds indicate a quantitative prediction of building damage. The outcome demonstrates that the old building is currently in a safe condition. However，if the settlement of the building exceeds the safety threshold，the likelihood of structural damage occurring at the front side of the stairs will be increased. The study verifies the high feasibility of a quantitative prediction method that integrates real-time monitoring data with numerical simulations based on digital twin technology for analyzing and assessing the overall and local structural safety of aging buildings.