In order to solve the problem that the traditional spray technology in coal mine dust pollution control is not ideal and the spray dust reduction performance is low，Sodium dodecyl sulfate (SDS)，Coconut Diethanol Amide (CDEA) and Cocoamidopropyl Betaine(CAB-35) were selected in this paper. The wettability of the three surfactants was first analyzed macroscopically by contact angle test. Then，combined with molecular dynamics simulation and quantum chemical analysis，the action mechanism of single/compound surfactants on bituminous coal was studied from a microscopic perspective. The results show that among the single surfactants，SDS has the smallest contact angle and the largest molecular orbital energy difference，and is more likely to form high-strength hydrogen bonds with water molecules； the mixed surfactants all show better wettability of bituminous coal than single surfactants，and the contact angle is smaller when non-ionic surfactants are mixed with anionic or zwitterionic surfactants，and the contact angle reduction rate is also much greater than that of single solutions，and the synergistic effect is more prominent; In the CDEA+CAB-35 (4∶2) system，water and surfactants form more and higher strength hydrogen bonds，and the surfactant molecules pull each other to form a tight adsorption layer，which attracts water molecules to infiltrate the surface of coal dust and improves the wettability of coal dust to the best extent.

To improve the application of small-angle inclined piles in collapsible loess areas，finite element models of 2×2 pile groups with three different inclination angles were established. The modulus reduction method was employed to simulate the collapsibility effect of loess，and the influence of loading and soaking sequences on the bearing characteristics of pile groups with different inclination angles was analyzed. The results indicate that，based on the conducted tests of 0，10 and 15° pile groups，under both loading-before-soaking and soaking-before-loading conditions，the displacement of the pile cap and the settlement of the surrounding foundation soil are smaller for inclined pile groups compared to vertical pile groups. Moreover，the inclined pile group is less affected by the water-induced collapsibility of loess. However，the bending moment and shear force of the inclined piles are higher than those of the vertical piles. Compared to the loading-before-soaking condition，the soaking-before-loading condition results in smaller pile cap displacements and soil settlements but larger internal forces in the inclined pile shafts. Additionally，the shaft friction of inclined piles is smaller under the soaking-before-loading condition. Inclined pile groups with larger inclination angles demonstrate superior load-bearing capacity and resistance to loess collapsibility induced by soaking. Pre-soaking treatment of loess foundations effectively enhances the ultimate bearing capacity of pile group foundations.

In order to enhance emergency management in the field of gas pipeline networks，Gas-kBERT model was proposed. The model incorporated data from the gas pipeline network field expanded by Chat Generative Pre-Trained Transformer，(ChatGPT)and Chinese Gas Language Understanding Subject-Predicate-Object(CGLU-Spo) and related corpora were constructed in this field. By altering the model's masking (MASK) mechanism，domain knowledge was successfully injected into the model. Considering the professionalism and specificity of the gas pipeline network field，Gas-kBERT was pre-trained on various scales and contents of corpora and fine-tuned on named entity recognition and classification tasks within this field. Experimental results demonstrated that，compared to the general BERT model，Gas-kBERT exhibited significant performance improvements in F₁-score in text mining tasks in the gas pipeline network field. Specifically，in the named entity recognition task，the F₁-score was increased by 29.55%，and in the text classification task，the F₁-score improvement reached up to 83.33%. This study proves that the Gas-kBERT model performs exceptionally well in text mining tasks in the gas pipeline network field.

To enhance the evacuation efficiency of deeply buried subway stations，a standard subway station was selected to establish an elevator-assisted evacuation model for deeply buried subway stations. The average evacuation time of passengers was selected as the primary evaluation metric. Variation characteristics in evacuation efficiency were calculated and analyzed under the combined influence of various factors，including buried depth of the subway，passenger flow intensity，the proportion of passengers opting for elevator evacuation，elevator operating parameters，the number of elevators，and acceptable queue size through simulation. The results indicate that the advantages of elevator-assisted evacuation are more pronounced when the subway depth exceeds 30 m. When passengers maintain their original evacuation paths，the evacuation time is inversely related to the proportion of passengers choosing to use the elevator during off-peak periods，but positively related during peak hours. Furthermore，when passengers alter their evacuation paths due to queue size，evacuation efficiency improves under different buried depth scenarios. In a subway with a burial depth of 90 m and an acceptable queue size of 30，the overall evacuation efficiency reaches its peak. When planning subway exits，reasonably increasing the number of elevators and their rated load，as well as operating speed，can effectively balance evacuation efficiency with cost control.

To investigate the research dynamics，hotspots，and frontier trends in the field of emergency supplies scheduling，the data sources of China National Knowledge Infrastructure (CNKI) and Web of Science(WoS) were used to search and filter 321 Chinese and 497 English articles. The bibliometric and knowledge mapping software were used to conduct basic feature analysis and development trend analysis. The results show that the number of articles in the field of emergency supplies scheduling both domestically and internationally has a wavy growth. The overall research is in the rapid development stage. In domestic core author teams，there is relatively low collaboration density. While in international contexts，cross-national and cross-regional academic exchanges are frequent，with China，Canada，and Singapore serving as the core. The research focus of both domestic and international studies is basically the same，mainly revolving around model design，optimization algorithms，path location issues，etc. However，international research has been earlier and more in-depth in studying the psychological perception of disaster victims.

In order to enhance the reliability and safety of gas network operations and improve the fault diagnosis capabilities for gas network leaks，while addressing issues such as the scarcity of real gas network leak data samples and variations in operating conditions，a gas network leak localization method based on transfer learning was proposed. Firstly，the Random Forest feature importance ranking method was used to select five pressure monitoring points in the TGNET simulation network. Subsequently，pressure monitoring point data under three different pressure conditions were respectively used as the source domain and target domain input features. The traditional JDA method of transfer learning was improved to reduce the feature distance between the source domain and the target domain. Furthermore，the CS algorithm was employed to optimize the dimensionality after mapping d' and the learning rate λ parameters of the improved transfer learning algorithm，ultimately achieving the diagnosis of unlabeled target domain leak segments. The results indicated that the proposed leak localization method for complex gas networks can effectively improve the localization accuracy of unlabeled gas network leaks，achieving higher accuracy compared to traditional.

In order to improve the efficiency and accuracy of safety risk management in machinery manufacturing enterprises，the Bayesian network and machine vision technology were combined. Based on improved YOLOv5，Intersection over Union(IoU) values of safety hazard events occurring at the operation site were calculated. By leveraging the audit risk assessment in conjunction with AHP to derive the danger weights，the prior probabilities of the root nodes of Bayesian network were determined. Bayesian network model and design management system were established to realize closed-loop control. A safety risk management model of machinery manufacturing enterprises was constructed and verified by examples. The results show that the model has a more accurate identification and evaluation ability，and can find some potential safety hazards，so as to optimize the current management process. At the same time，the model also successfully realizes the effective combination of qualitative and quantitative analysis，integrates the expert experience and data quantification results，and confirms each other，so that the risk assessment results have a certain improvement in scientificity and reliability，which can provide a practical new idea for safety risk management.

In order to improve urban governance and promote sustainable development，the resilience measurement of core cities in Chinese mainland was analyzed based on panel data from 25 core cities (municipalities directly under the central government，provincial capitals，and regional capitals) between 2011 and 2020. Technique for order preference by similarity to an ideal solution(TOPSIS)-Entropy Weight Method was applied. The resilience situation for 2026 and 2029 was predicted using a BP neural network model. This research aimed to explore the dynamic spatial differentiation of regional resilience. The results show that the standard deviation of the resilience index across cities fluctuates around 0.180，with the resilience disparity between cities remaining relatively stable. However，some cities show a downward trend in their resilience index year by year. The standard deviation of the predicted resilience index for 2026 decreases to 0.173，indicating a reduction in the resilience disparity between cities and a narrowing of the resilience gap. In the four time points of 2014，2020，2026，and 2029，the spatial heterogeneity of urban resilience evolves relatively stably. The urban resilience rankings are as follows: Eastern region > Central region > Western region > Northeastern region. Among them，the economic and infrastructure resilience in the Eastern region is the highest，while the social and ecological resilience in the Central region is the highest.

To optimize the mixed inert gas fire suppression technique for goafs，the effects of CO₂/N₂ ratio and O₂ volume fraction on the low-temperature oxidation characteristics of coal were investigated. Taking the long flame coal from Dongxia mine in Gansu as the research subject，a temperature-programmed experimental system combined with gas chromatography analysis was employed to carry out low-temperature oxidation experiments under various CO₂/N₂ ratios (ranging from 0∶10 to 10∶0) and O₂ volume fractions (6%，10%，14%，and 18%). The results indicate that at the same O₂ volume fraction，as the CO₂/N₂ ratio increases，the rates of oxygen consumption and CO production of the long-flame coal gradually slow down，with the decline rates increasing as the temperature rises. The apparent activation energy of the long-flame coal increases gradually with the increase in the CO₂/N₂ ratio during the slow oxidation stage，while it initially increases and then decreases during the accelerated and rapid oxidation stages，reaching the maximum value at a CO₂/N₂ ratio of 4∶6.

In order to improve and advance the nuclear radiation detection and monitoring integrated technology based on pixel sensors，a radiation noise suppression and nuclear detection method leveraging the parallel advantages of FPGA was proposed，with corresponding programs developed. By analyzing the characteristics of radiation noise signals，radiation noise suppression and two-dimensional wavelet transform programs based on FPGA were developed to output clear radiation field images. The images were decomposed into horizontal，vertical，and diagonal components，and the results of linear fitting statistics for each component were investigated to identify the component with the best linear fit. The research results demonstrate that the FPGA program modules effectively execute radiation noise suppression and nuclear detection functions in images. After noise reduction，the peak signal-to-noise ratio(PSNR) of the images is improved by approximately 11 dB. The diagonal component is shown to best characterize the radiation response information of the images，achieving a linearity of 0.99624 in linear fitting for different dose rates.