In order to study the effect of nitrogen content on pyrolysis process of NC，Fourier Transform Infrared spectrometer (FTIR)，thermogravimetric analysis(TG)-FTIR and pyrolysis(Py) gas chromatography(GC)/mass spectrometry (MS) are used to reveal the structural characteristics，pyrolysis characteristics and process products of NCs with different nitrogen content. The results show that with the increase of nitrogen content，the amount of substituted nitro of NC increases，the pyrolysis reaction rate and reaction degree increase，the proportion of light gas increases and the product types increase，and a variety of chemical recombination forms appear at high temperature. In the pyrolysis process of NC，de-nitration reaction takes place first，and then large molecules are decomposed into small molecules，and then carbon skeleton and ring oxygen bridge fracture occurs. By identifying the common products of NC with different nitrogen content and the main nitrogen oxides in each stage，a mechanism of NC pyrolysis process based on the principle of temperature division is established.

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 prevent tunnel workers' unsafe behaviors，the effects of workers' cognitive biases on their unsafe behaviors and the role of risk perception in this process were investigated. Based on the literature，a conceptual model describing the relationship between workers' cognitive bias，risk perception，and unsafe behaviors was proposed. Moreover，measurement scales in tunnel construction scenarios were designed and a questionnaire survey was conducted. Then，the proposed conceptual model using regression analysis was used to investigate the effects of workers' cognitive bias on their unsafe behaviors. The results showed tunnel workers' cognitive bias positively affected their unsafe behaviors (effect value=0.713) and negatively impacted their risk cognition (effect value=-0.607). Workers' risk cognition negatively affected their unsafe behaviors (effect value=-0.617) and partially mediated the relationship between workers' cognitive bias and their unsafe behaviors (effect value=0.334).

To improve the emergency preparedness and response capabilities of spent fuel reprocessing nuclear accidents，a spent fuel reprocessing nuclear accident emergency scenario based on knowledge meta was proposed to address the uncertainty of the nuclear accident emergency evolution process，the importance of scenario analysis in emergency response decision-making，the complexity of the evolution process，and the difficulty of organization and implementation. The disaster event，causative agent，causal agent，and emergency response were determined，and then a dynamic scenario model for spent fuel reprocessing nuclear emergencies based on a DBN was developed to calculate the occurrence probability of key scenarios，evaluate the development trend of the scenarios，and analyze the evolution laws and paths. Taking the explosion of the high-release liquid storage tank of the Mayak spent fuel reprocessing plant as an example，the process of deduction of the scenario analysis method of the spent fuel reprocessing nuclear accident based on the knowledge meta theory and DBN was performed，and the results were further analyzed. The results showed that: the loss probability of emergency cooling water supply was 73%，the probability of explosion of the high-release waste liquid storage tank was 86%，the probability of radioactive nuclides transferred to animal and plant products and drinking water through multiple pathways was 87%，the probability of long-lived radioactive nuclides deposition in part of the area was 89%，and the probability of the event calming down and dying out was 72%. The probability of the accident contaminating the air，soil，and river was 89%，85%，and 81%，respectively. The probability of affecting public health and safety was 86%. The scenario evolution process is consistent with the emergency response development of the reprocessing storage tank explosion accident and its impact on the public and the environment，validating the model performance.

To standardize the content and quality of urban lifeline operational monitoring services，a standard system for urban lifeline operational monitoring services was proposed. Based on the mature experience of operational monitoring services and "Guidelines for standardization of organizations in service sector—Part 2: Standard system construction"，the proposed standard was divided into a general service basic standard system，service provision standard system，service guarantee standard system，and position standard system. A standardized and systematic operational monitoring service process was developed from the aspects of standard implementation foundation，operation service content，service quality assurance，and job responsibilities，to comprehensively guarantee the quality of urban lifeline operational monitoring services. The results indicated that the urban lifeline operational monitoring services standard system effectively addressed issues，such as untimely early warning responses and overlapping job functions. However，it should be continuously updated and improved in conjunction with industry development trends to fully promote the standard system implementation.

To deeply analyze the information transmission characteristics of large-scale sports events organizations，the effects of various organizations during the information transmission process were analyzed taking "5·22" cross-country race accident in Gansu Baiyin as an example. Firstly，an event organization information transmission model was proposed based on STAMP model. Moreover，the transmission process was divided into four stages: preparation，incident occurrence，emergency response，and post-incident handling，and analyzed from three levels: individual，enterprise，and government. Then，CN theory was used to develop an organizational information transmission network structure and identify key information nodes and paths. Finally，the entropy weight method was used to propose the information edge weight calculation model. The results indicated that the key information nodes were mainly concentrated at the individual and government levels，especially in the preparation stage when the information load of transmission paths was relatively high. However，enterprises showed insufficient responsibility at this stage，particularly in the acquisition and transmission of weather information，leading to impaired decision-making and actions at critical moments.

In order to accurately predict the material demand in the transitional resettlement stage of earthquakes and improve the efficiency and accuracy of emergency material mobilization，the factors that have a great impact on the number of resettled population were determined based on the historical seismic data in China. A prediction model of the resettled population based on GWO-BP was established，which combined with the quantitative relationship between the population and emergency supplies，to predict the material demand in the transitional resettlement stage after the earthquake. The experimental results show that the GWO-BP neural network model exhibits high accuracy and stability in predicting the number of relocated populations，and can effectively predict the number of relocated populations in disaster areas，thereby calculating the corresponding material demand. GWO-BP neural network model has a certain application value in predicting material demand in post-earthquake transitional resettlement stage，and can provide a reference for the decision-making of emergency material procurement after the earthquake.

In the application process of blockchain technology，insufficient attention has been paid to the research on safety barriers that are more suitable for preventing complex system safety problems. To solve this problem，firstly，the safety requirements of blockchain itself and the support of safety barrier theory were introduced，which was combined with the security application of blockchain technology in the industrial field. Then，the main safety barrier models within qualitative and quantitative perspectives were summarized，so were the progress of security analysis model of software system and of performance evaluation of safety barrier. Then，the research status of safety precautions related to security risks of blockchain technology was summarized. Finally，in accordance with the trend of coupling coordination in safety barrier models，a research framework of safety barrier models related to the application of blockchain technology was put forward，which was based on the research progress of quantitative methods studying complex system coupling coordination and complex causal mechanism. It was a framework system including safety analysis，situational construction，system modeling，mechanism analysis，effect assessment and implementation path. The results show that the research on the safety barrier models related to blockchain technology should cover static series diagram pattern with Bow-Tie model as core and ARAMIS(Accidental Risk Assessment Methodology for Industries System) and coupling perspective STAMP (Systems Theoretic Accident Model and Processes) models as integrators，dynamic evolution mechanism research that includes dynamic Bayesian network analysis by transforming the static models into BN，and the effect assessment of safety barrier system. The study on coupling coordination and nonlinear causal analysis focusing on entropy deepen this coupling integration research system.

In order to study the wall dynamic response and damage characteristics of deep roadway with high geostress in gas explosion accidents，a mathematical model and a physical analysis model of the roadway wall dynamic response damage were established by using LS-Dyna software，and the numerical model was verified. The displacement，stress and damage characteristics of the roadway wall under the dynamic and static loads of gas explosion impact and geostress were analyzed by numerical simulation. The response and damage change of the roadway wall under different geostress conditions (horizontal geostress and vertical geostress) and gas explosion impact loads were investigated. The results show that high geostress causes the initial damage deformation. The stress concentration and initial damage are greatest at the corner，but its dynamic response is smaller than that at the roof. Under the dynamic and static loads of gas explosion impact and geostress，the damage strain of each part of the roadway increases with the increase of geostress，among which the damage degree at the corners is most affected and most serious，followed by the roof position. The increase of geostress makes the initial damage deformation of the roadway wall more serious，but it obviously weakens the propagation of the shock wave in the surrounding rock.

Excessive damage to aircraft braking moving discs will directly threaten flight safety. In order to solve the problems of low detection efficiency and strong subjectivity in the evaluation of damage degree of braking moving disc based on manual experience，an intelligent evaluation algorithm for damage degree and replacement need of aircraft braking moving disc based on image segmentation was proposed. Firstly，the classification and feature information of braking moving disc damage were analyzed. Then，based on the U-Net model，semantic segmentation was performed on the braking moving disc damage image dataset. A quantitative analysis model for the proportion of braking moving disc damage area and a calculation model for the maximum radial width of the fall off area were constructed. Finally，the model was validated using image data of the braking moving disc of Cessna525. The results indicate that the U-Net model has a good image segmentation effect on the types of braking moving disc damage，with average accuracy，average recall，average pixel accuracy，and average intersection to union(IoU) of 90.75%，91.25%，90.25%，and 87.25%，respectively. The evaluation results of braking moving disc are basically consistent with the evaluation conclusions of experienced mechanical engineers，and the evaluation results are objective，accurate，and highly visualized，which proves the rationality and feasibility of the algorithm proposed in this paper.