Helium is recognized as an extremely important yet highly scarce resource. In China, helium is primarily extracted from natural gas, where its low concentration presents significant challenges for extraction. Membrane separation technology for helium extraction from natural gas has been increasingly studied in recent years. However, the technology is still considered immature, and substantial experimental difficulties are encountered. Molecular dynamics (MD) simulations were employed as an effective approach to address these challenges. Recent advancements in membrane materials for MD simulations in helium extraction from natural gas were reviewed. Emphasis was placed on the methods used for constructing membrane models, the selection of simulation force fields, and the techniques applied to evaluate the separation performance of membrane materials. Two dimensional graphene like thin films and hybrid membrane materials were currently popular membrane materials. COMPASS and UFF force fields have a wide range of applications. The energy barrier for helium to pass through most membrane materials is low, and most membrane materials have high selectivity and permeability for helium and methane. The research results have good guiding significance for the practical production of membrane separation and helium extraction from natural gas.

Hydrothermal management technology is conducive to solving the problems of proton exchange membrane fuel cell(PEMFC), such as large heat dissipation demand, slow cold start, and short life. The dynamic response test of a high-power fuel cell was carried out, and the correlation between temperature and humidity and effective output voltage was verified by Pearson correlation coefficient. The influence of water management and thermal management on fuel cell was analyzed by literature review, and the current hydrothermal management and modeling methods were summarized. Water management methods mainly include reaction gas humidification, internal structure design, and drainage control, but it isn’t easy to achieve accurate online water content detection and closed-loop control. On the other hand, thermal management technology is relatively mature. The water cooling method of the traditional heat engine and the temperature control strategy is used to control the water pump and fan in the thermal management subsystem so that the temperature of the fuel cell and the temperature difference of inlet and outlet cooling water are kept in a reasonable range. However, there is a strong coupling between temperature and water distribution in the stack, so the single temperature variable and water variable study can not truly reflect the influence of temperature and water content on the performance of fuel cell. In the future, it is the key to improve the performance of fuel cell, effectively improve the parasitic power of appendage, and prolong its service life by using efficient hydrothermal coupling technology and considering the influence of temperature and water content comprehensively.

All the information regarding the distribution of karst caves sometimes cannot be fully reflected by means such as drilling, electrical prospecting and seismic exploration. Even with optimized design, it remains difficult to avoid the situation where pile foundations are constructed above karst caves and have to penetrate through them. The current research status of the bearing characteristics of pile foundations penetrating karst caves was analyzed from aspects like theoretical calculation, numerical simulation, indoor experiment, on-site experiment and karst cave filling materials. The following conclusions are obtained. When calculating the bearing capacity of pile foundations penetrating karst caves, the influence of the three-dimensional geometric dimensions of karst caves on the bearing characteristics of pile foundations must be comprehensively taken into account. The accuracy of parameter selection needs to be enhanced, the load transfer mechanism should be thoroughly understood, and theoretical models considering the influence of multiple factors comprehensively should be developed. In on-site experiments, emphasis should be placed on the accuracy and comprehensiveness of data collection. Unified standards and specifications ought to be established to enhance the representativeness, universality and applicability of the experimental results, thereby providing references for similar issues. Indoor models have investigated the influence law of the shape of karst caves on the bearing characteristics of pile foundations, analyzed the interaction mechanism between pile foundations and the roof of karst caves, and disclosed the stress characteristics and failure modes of pile foundations when penetrating karst caves. However, the shapes of simulated karst caves are relatively regular and single, and the influence of groundwater, temperature and the filling of karst caves on the bearing mechanism of pile foundations is rarely considered. The numerical simulation method is capable of simulating the bearing characteristics of pile foundations under complex stress states with different karst cave shapes, different pile types and different construction conditions. Nevertheless, the accuracy of parameter selection and simulation results requires improvement. The establishment of real geometric models of karst caves is a crucial development direction in the future. The research and optimization of karst cave filling materials are rather idealized, and their application under complex geological conditions is rather difficult. They usually exhibit performances that do not meet expectations. The research and development of karst cave filling materials should fully consider the adverse effects of special geological conditions, temperature changes and the chemical corrosion effect of the water environment in karst caves on the properties of filling materials.

Hyperspectral remote sensing widely uses unmanned aerial vehicles (UAV) as flight platforms for data collection, which has the advantages of flexibility and efficiency. However, due to UAV performance and environmental conditions, it is difficult for sensors to maintain a fixed shooting posture during the collection process, resulting in data misalignment, distortion, and deformation. While UAV positioning systems and inertial measurement devices provide real-time position and posture for hyperspectral cameras, achieving high accuracy often necessitates numerous ground control points for auxiliary geometric correction, which is time-consuming and labor-intensive. Therefore, it is necessary to study an efficient and time-saving data processing method to correct distortions in hyperspectral data acquisition. In order to efficiently and time-saving eliminate distortions in hyperspectral data during the acquisition process, an unmanned aerial vehicle (UAV) push scan hyperspectral camera data acquisition system was designed based on the principle of collinearity equations. The system integrates a high-precision inertial measurement system and synchronously collects LiDAR point cloud data in the measurement area. The high-precision terrain information contained in the LiDAR point cloud was used for geometric correction of hyperspectral data, and the influence of different density point cloud data on the geometric correction results was studied. Experiments have shown that using LiDAR point clouds improves accuracy by 67% compared to using average elevation geometric correction results. The use of LiDAR and hyperspectral cameras for synchronous acquisition has a significant effect on improving the accuracy of hyperspectral data.

In order to better study the spatial distribution of co-seismic landslides of the Luding earthquake, satellite images of Sentinel-2 on July 8 before and October 1 after the 2022 Luding earthquake were acquired, and a study area of 145.6 km² was obtained after cropping. Supervised classification was performed on the two images using the minimum distance method, maximum likelihood method, and neural network method, respectively, and the classification results were verified by calculating the overall accuracy and Kappa coefficient. Finally, the supervised classification results of the neural network method of the two images were selected for comparison to obtain the change detection results, and a total of 2 247 co-seismic landslides were identified in the study area, covering an area of 22.61 km², which accounted for 15.53% of the total study area. In the statistics of the slope direction, it was found that the slope direction of the co-seismic landslides radiated in the vertical direction of the originating fault to both sides, indicating that the co-seismic landslides were influenced by the originating fault. The analysis of elevation distribution pointed out that the landslides were mainly concentrated in the elevation range of 1 000 m to 2 000 m, accounting for more than 90% of the total. The analysis with other factors indicated that the landslide events were mainly concentrated near rivers, roads and on mountain slopes. The results of the principal component analysis indicates that the most important factor influencing the incidence of landslides in the same earthquake is the distribution of topographic deposits.

Traditional cloud classification methods exhibit limitations such as subjectivity and low efficiency and accuracy. To address these issues, a cloud classification prediction model based on the light gradient boosting machine (LightGBM) was proposed. Firstly, feature variables, including cloud top height, cloud bottom height, cloud layer thickness, average reflectivity factor, liquid water content, and duration obtained through millimeter-wave radar were utilized. A dataset was then constructed by combining these features with classification labels to meet the requirements of the model. This dataset was subsequently used to build a classification model that categorizes clouds into seven types: St, Sc, Cu, As, Ac, Cs, and Cc. The experimental results demonstrate that the model achieves an accuracy of 94.70%, precision of 94.68%, recall of 94.97%, and F₁ of 94.65%. These results indicate superior classification performance compared to other models. Therefore, the constructed LightGBM model shows significant effectiveness in cloud classification and recognition, exhibits strong applicability, and holds promising prospects for the automation of cloud recognition services.

The study of diagenesis of shale is an important part of the analysis of diagenesis system, pore structure and formation overpressure. Based on the data of X-ray diffraction, logging curve and measured porosity, the transformation amount of clay minerals in the hinterland of Junggar Basin was quantitatively analyzed. Combined with temperature, logging curve and porosity, the diagenetic evolution stage of shale was determined, and the mechanism of overpressure development was discussed. The study shows that the diagenetic evolution of shale in the hinterland of Junggar Basin can be divided into three stages. At the dominant stage of mechanical compaction from 0 m to 2 400 m, the clay mineral content in this stage is mainly affected by sediment sources, and the content and conversion amount have little change. The porosity calculated by acoustic logging is slightly larger than that calculated by density logging. The sonic time difference (AC), density (DEN) and compensated neutron (CNL) curves all decrease, while the resistivity (RT) curves increase. From 2 400 m to 4 200 m, during the transition stage between chemical compaction and mechanical compaction, illite-montmorillonite mixed layer began to transform into illite and chlorite, and the porosity calculated by density logging is equivalent to that calculated by acoustic logging. The curve of AC, DEN and CNL decreases slowly, and the RT curve slightly reverses. Below 4 200 m, in the dominant stage of chemical compaction, clay minerals begin to transform in large quantities, and feldspar begins to dissolve in large quantities. With the increase of depth, temperature and pressure gradually increase. When the temperature reaches about 120 ℃, kaolinite transforms into illite, and the content of potassium feldspar drops to almost 0%. The porosity calculated by density logging slightly deviates from the normal evolution trend, while the porosity calculated by sonic logging significantly deviates from the normal evolution trend, and the AC, DEN, CNL and RT curves all invert. Chemical compaction is one of the main causes of overpressure in the study area.

The operation of the Three Gorges Reservoir(TGR) generated a high amplitude of hydro-fluctuation belt(HFB). The preservation and restoration of the HFB had become a major scientific issue after water storage. The classification of bank slopes is the basis for carrying out the protection and restoration of HFB. Taking four typical drinking water sources of the TGR as the research objects, firstly, based on GF-2 remote sensing images covering the study area, and on the basis of radiometric calibration, orthoscopic correction, atmospheric correction, etc., combined with the samples of different bank slope types in the HFB obtained by UAV shooting and visual interpretation, and an object-oriented method for identifying bank slope types in the HFBa was constructed. Secondly, combined with random forest, support vector machine and neural network methods, the classification of bank slope types of typical water sources was carried out, and the classification effect of different machine learning methods was compared to realize the accurate identification of bank slope types in the HFB of typical water sources. Finally, the influence of pixel oriented and object oriented strategies on the classification accuracy of the bank slope in the fall zone was analyzed. The results show that the classification of bank slopes based on multiresolution segmentation-object-oriented classification is a convenient, cost-effective method, and has high accuracy. It can be used for classification of bank slope types in the large-scale HFB of the TGR. This method can solve the problems of internal spectral heterogeneity and increased homogeneity between objects in high-resolution remote sensing images, effectively improving the accuracy of slope classification.The study was of great significance in promoting ecological protection, restoration, and management of the HFB in the TGR, and maintaining important ecological security barriers in the Yangtze River Basin.

To promote occupational health and prevent musculoskeletal disorders among occupational drivers, the current status of functional movement ability of professional drivers was evaluated through functional movement screen (FMS), and the effect of exercise intervention was further explored. A stratified convenience sampling method was utilized to select 145 occupational drivers as participants, with their functional movement abilities rigorously evaluated using the FMS. Subsequently, 20 female drivers underwent an exercise intervention, with their exercise load monitored throughout, followed by a post-intervention FMS assessment. SPSS 29.0 software was used to analyze the FMS results, gender differences, and the effects of the exercise intervention. The results show that the FMS total scores of occupational drivers are generally below 14 points. Among the individual tests, the active straight leg raise scores highest, while the in-line lunge scores lowest. Gender differences are found in the scores for deep squat, hurdle step, active straight leg raise, and trunk stability push-up movements (P<0.05). Post-intervention, the basic functional movements, lower limb flexibility, and trunk stability patterns of female drivers show significant improvement, with all differences being statistically significant (P<0.05). The exercise intervention load is generally classified as moderate-intensity aerobic exercise. It is concluded that the functional movement capacity of occupational drivers is generally inadequate, and moderate-intensity exercise intervention is an effective means to improve the functional movement capacity of female drivers and prevent musculoskeletal disorders.

In order to study the deformation problem of the top coal area during the mining process of the fully mechanized top coal caving face, taking the fully mechanized top coal caving face of the Dongxia coal mine project as the engineering background, based on the specific coal seam characteristics of the fully mechanized top coal caving face, the support type and the parameters of support strength of the fully mechanized top coal caving face were analyzed. At the same time, combined with on-site monitoring data, the rated working resistance of the hydraulic support meets the on-site requirements of the working face. The discrete element software simulation analysis method was used to numerically simulate the deformation and support of the top coal during the mining process of the fully mechanized caving face in Dongxia coal mine project, and its application effect was verified. As the working face continues to move forward, the overlying basic roof undergoes periodic collapse, with an average cycle of about 20 m in step distance. In addition, the support effect of hydraulic support on the fully mechanized top coal caving working face did not change significantly with the increase of the advancing length. According to on-site monitoring data and calculation results, it is known that the selected hydraulic support can meet the support requirements of the fully mechanized mining face in the project.

Based on the actual welding process of rectifier sample structure, the numerical simulation of high temperature vacuum nickel-based alloy brazing process was carried out, and the flow model of melting-wetting filling weld of nickel-based alloy filler metal in high temperature brazing vacuum furnace was established. The numerical calculation was based on the volume of fluid(VOF)method. Considering the factors such as surface tension, gravity, and latent heat of phase change, the laminar flow model was used to solve the flow behavior of the solder with time. The distribution of solid and liquid solder and the distribution of temperature field were calculated. The phenomenon of braze material loss and weld bead formation on the surface of the base material, along with a good filling effect of the braze material inside the weld seam, verifies the rationality of the arrangement of solder and temperature control in this brazing process. An empirical formula for the volume ratio of braze material filling the weld seam with respect to time and temperature has been provided, offering guidance for actual brazing processes.

The reservoir of Yanchang Formation in the southwest Ordos Basin is one of the typical tight sandstone reservoirs in China. The classification and evaluation of tight sandstone reservoir of Yanchang Formation in central Ordos Basin has attracted the attention of petroleum enterprises. In order to rationally and efficiently develop the Chang 8₁ tight sandstone reservoir in Ordos Basin, it is urgent to carry out reservoir classification and accurately evaluate the reservoir. Taking the Chang 8₁ tight sandstone reservoir in Wuqi area as an example, the fractal dimension of the target layer was calculated by NMR experimental data, and the micro-pore structure of the Chang 8₁ tight sandstone reservoir was quantitatively studied by combining the experimental data such as cast thin slice and scanning electron microscope. At the same time, the relationship between reservoir fractal dimension and reservoir physical properties was analyzed, and the fractal theory based on nuclear magnetic resonance experiment was used to classify and evaluate tight sandstone reservoirs. The results show that the rock type of Chang 8₁ tight sandstone reservoir in Wuqi area is mainly lithic feldspar sandstone, and the pore structure is mainly intergranular dissolved pores and feldspar dissolved pores. According to the T₂ spectral curve and fractal dimension method, reservoirs are divided into three categories. Each type of reservoir corresponds to the relevant fractal dimension, and the fractal dimension has a good correlation with the physical property parameters, which characterize the complexity of the reservoir pore structure. Combining nuclear magnetic resonance experiment with fractal dimension method to quantitatively characterize the microscopic characteristics of tight sandstone reservoir can improve the accuracy of reservoir classification.

Logging data constitutes the basis for oil and gas field development and evaluation. However, in actual mining, factors like poor wellbore stability and equipment failure give rise to the distortion or loss of logging data. A prediction model based on variational mode decomposition (VMD) was proposed to address the issues of unstable and inaccurate results in existing prediction models. The model combines convolutional neural networks (CNN), bidirectional long short term memory (Bi-LSTM), and attention mechanism to predict missing sections in well logging curves. With logging sequence data as input, the VMD algorithm was employed to decompose the sequence into a series of amplitude-modulated and frequency-modulated signal subsequences. The features were extracted by the CNN network and trained by the Bi-LSTM network. During training, the Attention mechanism was utilized to learn the importance weight of each time step dynamically. Finally, the predicted value of the logging curve was outputted. The method was applied to predict logging curves in the Biyang Block of Henan Province and compared with other common machine learning prediction models. The results show that the application effect of the CNN-BiLSTM-Att model improved based on VMD is remarkable, with an error of only the order of 10^-3 and a prediction accuracy of 92.02%. The research results provide new ideas for accurate prediction of logging curves.

The in-stage multi-cluster fracturing technology in horizontal wells is a key method for the efficient development of unconventional oil and gas. Due to the influence of reservoir heterogeneity, non-uniform perforation erosion, and fluid competition, the in-stage multiple perforation clusters cannot initiate simultaneously and propagate evenly in Mahu conglomerate Jinlong 2 well block reservoirs. The perforations with the excessive fluid intake can be plugged by injecting the ball-shaped or knot-shaped diverters. The sand carrier fluids can be dynamically distributed. In this way, the fracturing stage can be evenly stimulated and the reservoir productivity can be fully excavated. It is of great value to judge the temporary plugging effect accurately and timely. In this way, the operation scheme can be adjusted and the stimulation effect can be improved. The perforation plugging, the fracture initiation, and the fracture propagation were completely considered by integrating the three methods of diverter in-place pressurization, same displacement pressure boost, and curve superposition. The comprehensive discrimination method of the in-stage multi-cluster temporary plugging effect was generated and the supporting software was compiled to realize online monitoring and discrimination. The method demonstrates more than 85% consistent with the monitoring results of optical fiber and hawk-eye, which provides method and theoretical support for the upgrade and popularization of the temporary plugging technology.

As the oil and gas industry continues to expand into deeper layers and the development of deep geothermal resources progresses, the hardness of reservoir rocks increases, making rock breaking more difficult and raising development costs. To address the problem of difficult rock breaking in deep granite, the mechanism of electromagnetic radiation-assisted rock breaking was investigated. This method utilizes the interaction between electromagnetic waves and reservoir rocks to induce thermal stress damage or fracturing of the rock, thereby reducing rock strength and improving breaking efficiency. Firstly, based on the mineral composition of deep granite, a heterogeneous core model with random distribution of minerals was established. Secondly, a numerical model of electrothermal-solid-damage coupling of granite damaged by electromagnetic radiation was established. Finally, the electromagnetic field, temperature field, stress and damage distribution of granite under electromagnetic radiation were calculated by sequential coupling method. Due to the selective heating property of electromagnetic waves, the electromagnetic power loss density of biotite is 2~3 orders of magnitude higher than that of quartz and feldspar, resulting in different electromagnetic heating degrees and forming local hot spots near biotite. Based on the difference of temperature and thermal expansion coefficient of different minerals, a non-uniform stress distribution is formed. Quartz and biotite are strained, while feldspar is pressured. After 3 kW electromagnetic radiation for 5 min, the damage volume of granite is 42%, and the tensile damage of quartz is the main damage. The damage of granite under electromagnetic radiation is significant, the strength of rock is reduced, and the rock breaking of deep granite is promoted.

As the service life of China's oil and gas pipelines increase, the pipelines are inevitably subjected to defects such as corrosion and denting due to external factors. It becomes imperative that the impact of these defects, including denting and corrosion, on the ultimate bearing capacity of the pipelines be analyzed. A simulation was conducted on the safety of an X80 pipeline with dents and corrosion defects. By changing factors such as dent length, width, depth, and material type, the influence of a single dent area under the action of spherical and ellipsoidal pressure heads on the ultimate bearing capacity of the pipeline was analyzed. At the same time, the influence of various factors such as the size and spacing of dents and dents on pipeline safety was studied when composite defects such as dents and corrosion coexist. The results show that the length of dents, corrosion length, and corrosion depth are important factors affecting the ultimate bearing capacity of composite defects, and the damage to pipelines is greater when dents act in the vicinity of the corrosion center point.

In order to study the influence mechanism of energetic particles on the combustion and explosion reaction of solid-liquid mixed fuel, a 20 L spherical cloud combustion and explosion characteristics test system was used to study the combustion and explosion characteristics of solid-liquid fuel-air dispersion systems with different mass fractions of energetic substances. Under low concentration conditions, the explosion pressure, maximum pressure rise rate, reaction time and explosion lower limit of different fuels were measured. Based on the combination of solid-liquid dispersed particles, the impact of energetic particles on the combustion and explosion characteristics of solid-liquid mixed fuel was analyzed. The results show that in the 1,3,5-trinitroperhy-dro-1,3,5-triazine(RDX)/aluminum powder mixed fuel system, the explosion pressure and maximum pressure rise rate first increase and then decrease with the increase of RDX mass fraction, with the maximum values being 1 516.17 kPa and 116.17 kPa/ms respectively. For RDX/ aluminum powder/nitromethane mixed fuel system, the addition of RDX causes the explosion pressure to continuously decrease, reaching 427.99 kPa. When the RDX mass fraction is low, RDX inhibits the combustion explosion of the mixed fuel. At the same time, it was found that the change pattern of mixed fuel reaction time is completely opposite to the change pattern of maximum pressure rise rate.

In order to explore the dust deposition characteristics on the surface of photovoltaic(PV) modules. For the dust accumulation problem of ground-mounted photovoltaic, the deposition characteristics of dust particles with different particle sizes were investigated from two factors, namely, tilt angle and wind speed, by using CFD numerical simulation method. The results show that as the tilt angle of the PV module increases, the deposition rate of dust on the surface of the PV module gradually decreases, in addition, it is found that when the PV module is facing the wind, the rate of dust deposition increases with the increase of wind speed, and the maximum particle size of dust deposition will become larger with the increase of wind speed. At the same time, with the increase of particle size, the deposition of dust on the surface of the PV module is the first to increase and then decrease. The study is of great significance in solving the problem of dust accumulation in photovoltaic power plants.

The offshore environment is complex and volatile, and the combined wind and wave loads can generate large vibrations on the floating wind turbine platform and tower top, posing a serious threat to the structural safety of the wind turbine system. To cope with this challenge, a tuned mass damper (TMD) was installed in the nacelle of the barge floating wind turbine to form a hybrid mass damper (HMD) using active driving force. The H∞ algorithm was used for the drive force control. The effects of no control, passive control, and H∞ control were compared through simulation. The results show that the H∞ control can effectively reduce the longitudinal angle of the platform and the longitudinal displacement of the top of the tower, with obvious vibration suppression effects.

In order to smooth out the output fluctuation of wind power generation system, a hybrid energy storage dual-layer fuzzy control strategy based on wind power prediction was constructed by adjusting the control strategy of hybrid energy storage system (HESS) to meet the fluctuation limit of grid connection. Firstly, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) was used to decompose the original wind power data. Secondly, the improved Adam algorithm and Transformer model were combined to predict each component, and the prediction results are superimposed as the final prediction result. Finally, based on the predicted wind power fluctuation state and state of charge (SOC) of the hybrid energy storage system, the dual-layer fuzzy control strategy was adopted to adjust the hybrid energy storage system to ensure that the overcharge and overdischarge of the hybrid energy storage system are reduced under the premise of smooth grid connection of wind power. The results demonstrate that the proposed control strategy achieves lower fluctuation indices in wind power output, ensuring reliable grid connection. Moreover, it maintains the SOC of the HESS within an optimal range, leading to an overall enhancement in the system's comprehensive performance.

With the development of DC (direct current ) distribution networks and the large-scale integration of distributed energy storage and photovoltaics into the distribution network, the structure of the distribution network has undergone revolutionary changes. After a short circuit fault occurs in the DC distribution network, the short circuit voltage drops sharply, the short circuit current rises rapidly, and the stability of the power system operation is disrupted. To address this issue, a model for inter pole and single pole short circuit faults in DC systems was proposed. Firstly, by sampling voltage data at both ends of the DC line, the voltage equation was written, and the transition resistance was eliminated. Then, a fitness function was constructed, and the adaptive optimization red fox algorithm with faster convergence speed and higher positioning accuracy was used to calculate the distance from the fault point to the protection installation site for fault location in the DC distribution network. Based on the red fox algorithm, combined with the isolation forest algorithm to remove abnormal data, the algorithm performance and accuracy were improved by improving adjustable feedback factors and introducing genetic crossover operators. When the sampling frequency is low, the accuracy of fault localization is improved through adaptive interpolation. Simulation verification was conducted in Simulink, and the results show that the method has strong resistance to transition resistance, small positioning error, is not affected by system parameters, and can effectively reduce the impact of low sampling frequency on fault localization, and has good robustness.

To comprehensively and deeply analyze the overall benefits of provincial-level construction of new type of power load management system, a study on the comprehensive evaluation system for such systems was conducted. Firstly, an evaluation index system that considers technical, social, environmental, and economic benefits was established. Secondly, the subjective weights of the comprehensive benefits were determined through an improved analytic hierarchy process. Given that the index system encompasses both qualitative and quantitative indicators, the objective weights were determined based on the concept of intuitionistic fuzzy numbers, and the comprehensive weights were determined through cooperative game theory. Finally, the concept of perpendicular distance was introduced to enhance the traditional technique for order preference by similarity to ideal solution (TOPSIS) method, thereby improving the accuracy of the evaluation results. By applying the proposed evaluation system to evaluate the construction of new power load management systems in different provinces, the results show that the studied system exhibits strong systematicity, scientificity, accuracy, and feasibility in the comprehensive and multidimensional benefit evaluation of the construction of new power load management systems.

Lightning is the main cause of active distribution line fault. It is of great significance to study the lightning risk assessment of active distribution network. The distribution line with distributed photovoltaic system in Nanjing area was taken as the research object. The calculation model of lightning overvoltage on distribution lines with distributed photovoltaic system was established, and the interaction between photovoltaic side and distribution line side during lightning strike was analyzed. The electrical geometric model on both sides was constructed. The trip rates of the photovoltaic side and the line side were calculated, and the risk was evaluated according to the calculation results. The results show that when lightning strikes the nearest tower on the photovoltaic side, the lightning trip-out rate on the photovoltaic side increases from 27.52 times/(100 km·a) to 29.63 times/(100 km·a), and the lightning risk is higher. When the photovoltaic side is struck by lightning, the tripping rate of the adjacent three towers affected by the lightning intrusion wave is doubled, and the risk level is also higher.

To address the challenges of high-dimensional features, large computational demand, and difficulty in improving the accuracy of secondary return water temperature prediction models for heat stations, a secondary return water temperature prediction model based on the xtreme gradient boosting-artifical neural network(XGBoost-ANN) was proposed. The feature screening layer uses XGBoost algorithm to calculate the importance scores of the original data features and determine the main features that affect the secondary backwater temperature, thus reducing the complexity of the model and improving the computational efficiency. Three layers of feedforward ANN were trained by Bayesian regularization algorithm as the secondary backwater temperature prediction layer, and the initial weights and thresholds of the ANN model were optimized by grey wolf optimizer (GWO) algorithm. The weights and thresholds of the ANN model were represented by grey wolf position vector. The fitness function was introduced to evaluate the performance of each set of weights and thresholds to help the model avoid falling into local optimality at the initial stage of training, so as to improve the performance and generalization ability of the model. Experimental results demonstrate that the constructed XGBoost-GWO-ANN secondary return water temperature prediction model achieved significant improvements. Compared to the model before feature filtering, the root mean squared error(RMSE) is reduced by 26.8%, the R² is increased by 11.3%, and the model inference time is shortened by 46.1%. Furthermore, the optimization of the initial ANN weights and thresholds using the GWO algorithm improve the RMSE by 20.0% and the R² by 3.4% compared to the unoptimized ANN model. These results indicate that the accuracy and generalization ability of the proposed prediction model are effectively enhanced.

Aiming at the unmanned autonomous positioning task of mobile robot during the maintenance of nuclear reactor internals, an underwater geometric positioning method combining inertial measurement unit(IMU) and monocular visual distance measurement was proposed. Combining with the actual environment demand, the principle of distance measurement with monocular camera was described briefly. Aiming at the limitation of the underwater nuclear radiation environment on the use of sensors, a method based on monocular vision correction of inertial measurement unit position estimation integral error and geometric positioning was proposed. Simulation experiments show that the positioning method can effectively correct the accumulated error generated in the positioning process of the inertial measurement unit, and improve the positioning accuracy of the robot in underwater motion.

The natural orifice intervention using continuum robots faces challenges such as tortuous and narrow intervention paths, as well as compressive forces exerted by soft tissues in the orifice. To address the issue in the delivery process where existing planning methods struggle to balance multiple control objectives, resulting in difficulty in reaching deeper positions, an autonomous planning scheme based on residual reinforcement learning was proposed. The method enables the autonomous delivery of continuum robots through natural orifices. A feedback deviation model between the delivery posture of the continuum robot and the spatial state of the natural orifice was established to control the posture target during the delivery process. Simultaneously, a Markov model of the overall motion process of the continuum robot was constructed to train the reinforcement learning algorithm. A residual strategy, generated by combining posture feedback control with reinforcement learning control, was used to output the optimal actions for the continuum robot's delivery process. Experiments conducted in a simulated bronchial orifice show that the proposed method converges over 60% faster than existing methods and can plan smooth, collision-free trajectories for the continuum robot's intervention through the orifice, outperforming existing methods in several key metrics.

In the field of unmanned systems, heterogeneous cross-domain collaboration is recognized as an effective solution to current bottlenecks, integrating functional redundancy and complementary capabilities across different platforms. However, traditional rope-hook recovery systems, due to their high stiffness, result in significant instantaneous impact forces during aircraft hooking, which leads to undesired landing attitudes and an inability to land precisely. Furthermore, these systems are less flexible and have slower response times compared to robotic arms in achieving altitude tracking. To address these challenges, admittance control for robotic arm compliance was introduced, with the goal of enhancing landing stability, preventing structural damage caused by excessive lateral forces, and ensuring stability. In addition, the roll compensation process was optimized through a combination of short-time Fourier transform-fast Fourier transform(STFT-FFT) prediction algorithms and active disturbance rejection control, leading to smoother and more stable compensation responses. Simulation tests confirmed the effectiveness of the admittance control strategy and the optimization of the compensation response algorithm, resulting in improvements in both landing stability and system flexibility. This research presents novel methods for unmanned aircraft recovery and offers valuable insights for the development of future unmanned systems.

To address the cumbersome calibration process of fisheye cameras and its inapplicability to everyday scene images, a novel convolutional neural network(CNN)-based method was proposed that simultaneously calibrates the intrinsic parameters of fisheye lenses and corrects image distortion. The accuracy of fisheye camera calibration and image distortion correction was improved by predicting the displacement of pixel points under different distortion parameters. A coordinate attention module was introduced in the encoding part to enhance the model's accuracy and generalization ability to increase attention to image position information. Additionally, a cross-scale fusion module was designed in the skip connections to enhance image detail features. To address the issues of dataset scarcity and incomplete distortion parameter distribution, a new large-scale dataset labeled with corresponding distortion parameters and images after distortion correction was created. Experimental results show that compared to other fisheye camera calibration methods, this method achieves a reprojection error of 0.312 pixel, indicating the highest calibration accuracy. Additionally, compared to other image distortion correction methods, a peak signal to noise ratio(PSNR) of 38.055 dB and an structural similarity(SSIM) of 0.874 are achieved, indicating the best quality of image distortion correction.

As a large transportation hub, airport terminals have complex structures, and the evacuation efficiency becomes extremely important when an emergency occurs. To improve the evacuation efficiency of the terminal building, an improved A* algorithm has been proposed for selecting the optimal evacuation path based on the actual distribution of personnel. Firstly, the flow of personnel in the terminal building was simulated, and data on the distribution of personnel was obtained. Then the time-varying distribution of personnel in each area was considered in the cost calculation of path selection. Finally, the A* algorithm was improved in terms of traversal methods, network weights, other factors, and congested paths were replanned to avoid congestion. The results indicate that considering the distribution of passengers in the terminal improves the evacuation paths at each node, which leads to shorter evacuation time compared to traditional A* algorithm. It also allows for the avoidance of congested paths. This study can provide theoretical and methodological support for the rapid evacuation of passengers in the terminal under emergencies.

Currently, mainstream gait recognition methods often rely on stacked convolutional layers to gradually expand the receptive field and integrate local features. These methods mostly use shallow networks, which have limitations in extracting global features from gait images and lack attention to temporal cycle feature information. Therefore, a deep neural network algorithm combining Transformer and 3D convolution, named 3D convolutional gait recognition network based on AdaptFormer and Spect-Conv (3D-ASgaitNet)was proposed. Firstly, the initial residual convolution layer converts the binary contour data into a floating-point encoded feature map to provide dense low-level structural features. On this basis, the spectral layer enhances the feature extraction ability through the joint processing of frequency domain and time domain, and uses the pseudo-3D residual convolution module to further extract advanced spatio-temporal features. Finally, AdaptFormer module was integrated to provide flexible feature transformation capability through lightweight down-sampling and up-sampling network structure to adapt to different data distribution and task requirements. 3D-ASgaitNet was carried out on four publicly available indoor datasets (CASIA-B, OU-MVLP) and outdoor datasets (GREW, Gait3D), and achieved recognition accuracy rates of 99.84%, 87.83%, 45.32% and 72.12%, respectively. Experimental results show that the recognition accuracy of the proposed method in CASIA-B and Gait3D data sets is close to the performance of SOTA.

A skeleton-based architectural point cloud fusion method was explored to address the challenges of model incompleteness in real-world 3D architectural models. The process begins with reverse modeling of the architectural scene using 3D point clouds. The acquired reverse point cloud data was combined with the original architectural design data to generate a forward point cloud model. A method based on the rotational symmetry axis (ROSA) was then applied to extract skeleton lines from both the reverse and forward point cloud models. The fusion of the forward and reverse point cloud models was achieved by skeleton matching, resulting in a reconstructed 3D model with improved completeness. Experimental validation shows that this method significantly reduces areas of model incompleteness, providing new insights and methods for 3D modeling and reverse engineering in architectural reality capture.

In order to improve the unidirectional linear resource metabolism pattern within communities and optimize the urban resource circulation system, a green productive regeneration method for urban communities based on image 3D reconstruction and multi-criteria decision making was proposed with the help of drone low-altitude photogrammetry technology and using Agisoft PhotoScan, Cloud Compare, and Yaahp. By constructing a 3D point cloud model of the community's built environment and a spatial production potential evaluation model, the suitability of different spaces for production within the community was analyzed, and regeneration design was carried out. The results show that the regenerated community using this method can meet 43.8% of its vegetable supply demand, process 87.28% of its organic waste, supplement 23.46% of its irrigation water needs, and effectively reduce food mileage carbon emissions and building energy consumption. It is concluded that the community's green productive regeneration can significantly reduce its external resource dependency, thus transforming its resource metabolism pattern.

To address the issue of bridge collisions induced by earthquakes, seismic vulnerability curves for the overall system and individual components of conventional and irregular box girder highway bridges, with and without collisions, were developed and compared based on nonlinear time history analysis using the OpenSees finite element software. Four damage states, ranging from minor to severe, were established. The fragility function was employed to elucidate the interaction between structural irregularities and collisions between the bridge deck and the abutment on the seismic vulnerability of multi-span box girder highway bridges. A conversion coefficient, r_p, was introduced to quantify the impact of collisions on the vulnerabilities of bridge components and the overall system. Traditional analysis methods, including static and simplified analyses, alongside technical seismic models, were applied to adjust the vulnerability values associated with collisions and irregularities. The findings reveal that collisions exert adverse effects on all structural components. As the damage level increase, the variability in RP values for the ductility of bridge columns and anti-falling beams also increase. Specifically, for earthquake number 2, the r_p values under damage conditions are 0.95 for mild(DC₁) and 1.02 for moderate(DC₂). Under pulse type seismic motion, collisions significantly increase the degree of damage to engineering demand parameters (EDP). For earthquake number 3, the average r_p value for foundation translation was 0.71 with collisions and 0.57 without, highlighting the significant destructive influence of collisions on foundation translation. By comparing the median values across all categories, it was observed that the median values for earthquake numbers 3 and 1A were lower than those for earthquake numbers 2 and 1B, indicating that collisions caused more severe damage under lower ground motion intensities. This study provides a valuable reference for seismic bridge design, offering insights to improve design specifications, enhance the seismic performance of bridge structures, and deliver significant engineering and practical applications.

Vertical joint steel anchor ring grouting connection (referred to as vertical seam tight splicing connection) shear wall system uses steel anchor ring connection for vertical joints, followed by secondary grouting connection. Based on actual projects of the enterprise, the feasibility of vertical joint tight fitting connection has been verified through experimental research, seismic performance analysis, and design and construction practice.

Uplift piles, in accordance with their structural properties, effectively sustain the structural uplift loads and have emerged as an efficacious solution to address the anti-floating issue. The precise determination of the internal forces within uplift piles is crucial for comprehending their load-bearing characteristics. Nevertheless, the tensile capacity of concrete is relatively feeble. Once the load attains a specific magnitude, its elastic modulus will decline, rendering the traditional axial force calculation methods inapplicable. By leveraging the optical frequency domain reflectometry(OFDR) strain measurement technology and conducting indoor model tests of uplift piles, the strain distribution and evolution patterns of both steel bars and concrete during the pulling process were analyzed. The alterations in the elastic modulus of concrete throughout the tension-failure process were thereby obtained. A method for optimizing the axial force calculation, which exploits the relationship curve between the concrete strain and elastic modulus, was put forward. This enables the accurate acquisition of the axial force of the pile body and its subsequent application in practical engineering projects. The test results indicate that under the condition of small loads, the OFDR technology can identify the locations where concrete cracks emerge based on the strain curve of the pile body. In the event of pile body failure under large loads, the elastic modulus of concrete can be rectified using the relationship curve between strain and elastic modulus. Compared with traditional calculation methods, the relative error of the axial force throughout the entire process can be confined within 5%. The viability of this approach has been corroborated in actual engineering endeavors, and the optimized axial force calculation exhibits enhanced precision.

The prediction of dynamic stress-strain relationship of composite geomembrane is one of the key problems involved in the long-term operation of seepage prevention projects of earth-rock dams. In order to study the effect of cyclic loading on the stress and strain of the composite geomembrane, cyclic loading and unloading tests were carried out under three different stress levels of 10%, 20% and 30%, and the creep tests under 10% to 80% stress levels were carried out to compare and analyze the creep and creep recovery characteristics of the composite geomembrane. The expression of strain under cyclic loading and accumulation condition was presented, and the constitutive model of composite geomembrane boundary surface under cyclic loading condition was constructed. The results show that the deformation of composite geomembrane increases instantaneously at the moment of loading and unloading, and then gradually slows down with the increase of time. After complete unloading, when the stress level is less than 30%, the deformation of the composite geomembrane can almost recover. When the stress level is greater than or equal to 40%, the residual deformation increases gradually with the increase of the stress level. The effect of cyclic loading times on the creep deformation of geomembrane is not a simple linear relationship. In addition, an improved three-parameter viscoelastic model was established, and it is verified that the model can reproduce the creep recovery process of composite geomembrane under different load levels. The research results can provide a reference for the long-term deformation analysis of composite geomembrane in earth-rock dams.

Engineered cementitious composites (ECC) have good crack width control ability and self-healing potential, which can effectively improve the performance and durability of concrete structures and has been used in various environmental conditions. The healing ability of ECC varies under different environmental conditions, and currently there is relatively little research on the healing behavior of ECC in different environments. In order to study the effects of different environmental conditions on the self-healing behavior of ECC, three different environments including 0.5 mol/L NaCl solution, saturated Ca(OH)₂ solution, and natural environment were selected. Pre-cracked specimens were placed in each of the three environments, and the healing behavior and mechanism in different environments were explored through crack closure tests, strength recovery tests, electron microscopy scanning, and energy spectrum analysis. The experimental results show that the cracks in ECC under different healing environments have varying degrees of closure. As the healing cycle gradually increases, the degree of crack healing also gradually improves. NaCl solution and saturated Ca(OH)₂ solution have a positive effect on the crack healing. The ranking of the healing ability of ECC under different environments is obtained as follows: saturated Ca(OH)₂ solution > NaCl solution > natural environment. Healing materials in natural environment mainly include calcium-silicate-hydrate (C-S-H) gel and Ca(OH)₂. The healing material at the crack site in NaCl solution is mainly filled in the form of ettringite and Ca(OH)₂. In a saturated Ca(OH)₂ solution environment, a large amount of healing material is generated at the cracks, mainly in the form of CaCO₃ filling the crack.

To address the challenges of complex geology, dense urban spaces, high component production requirements, and difficult excavation parameter control in the intelligent construction of ultra-large diameter shield tunnels, a systematic study of key links in the shield construction industry chain was conducted based on the experience and digitalization needs of completed and ongoing projects. building information modeling(BIM), big data, IoT, and artificial intelligence technologies were applied to build a digital architecture and establish a full lifecycle coding system. A data platform was developed to promote the digitalization of design data, intelligent segment production, tunnel intelligent excavation, and lifecycle management. The research shows that the parametric drive greatly improves the efficiency of digital design, optimizes the precision of segment production and drilling, and enhances the ability to control the whole life cycle. It can be seen that digital means effectively solve the technical problems in construction, improve the ability of construction control, and provide a feasible digital solution for the construction of large-diameter shield tunnel.

The double semi-trailer truck train has strong transportation capacity and relatively low transportation costs. However, compared to semi-trailers, double semi-trailer truck train has more vehicle units, resulting in higher driving difficulty and lower lateral stability at high speeds. To address this issue, a control strategy combining model predictive control (MPC) with differential braking was proposed. Based on the principles of MPC, an MPC lateral stability controller for double semi-trailer truck trains has been designed. MPC regulates the yaw moments of three vehicle units, with differential braking technology dynamically allocating braking forces to individual wheels. The double semi-trailer truck train model has been built in Trucksim, and a simplified vehicle model has been established in MATLAB/Simulink. Through joint simulation of Trucksim and MATLAB/Simulink, the effectiveness of the designed system was verified under different vehicle speeds, loads, and friction coefficients. The research results indicate that the designed controller effectively reduces the centroid yaw angle, lateral acceleration, and yaw rate of each vehicle unit, thereby enhancing the stability of double semi-trailer truck train during high-speed lane changes.

High-precision vehicle trajectory data is crucial for the realization of intelligent transportation systems. However, existing vehicle trajectory sensing technologies are limited by the range of data collection, making it challenging to obtain full-period and full-area vehicle trajectory data, which cannot meet the demands for trajectory tracking accuracy and real-time performance in practical applications. Considering the characteristics of vehicle trajectory data across radar scenarios, a cross-device vehicle trajectory tracking method was proposed based on radar data. Firstly, trajectory data was filtered based on the lower bound of the confidence interval, and the position and velocity of vehicle trajectories were smoothed and denoised using Kalman filtering. Next, the trajectory timestamp, position coordinates, speed, direction, and lane number from the radar detection area were used as model inputs, while the position information of non-overlapping areas was used as output to construct a non-overlapping area trajectory prediction model based on long short-term memory (LSTM). Subsequently, a fast dynamic time warping (DTW) algorithm based on trajectory condition constraints and search area limitations was proposed to compute trajectory similarity, enabling cross-radar device vehicle trajectory tracking. Finally, verification was conducted using overlapping and non-overlapping scenario data from radar detection on highways. Experimental results show that the trajectory tracking accuracy in overlapping scenarios reaches 92.2%, and the trajectory matching accuracy in non-overlapping areas reaches 90.3%.

Container terminal yard as the hub of the operation area on both land and sea sides, the space allocation will directly affect the overall operation efficiency of the terminal. In order to reasonably allocate the space of U-shaped yard to improve the efficiency of container loading, two blocks sharing one intelligent guided vehicle(IGV) lane were formed into a group. From the perspective of reducing lane congestion, considering the constraints of high and low workloads of adjacent operation areas within the block groups, and with the goal of minimizing the total loading operation time and balancing the number of containers in the block groups, the first stage of the export container allocation model was constructed, and an immunity compensation mechanism and Metropolis criterion were introduced. And an improved adaptive genetic algorithm was designed by introducing immune compensation mechanism and Metropolis criterion. Based on the allocation results of the first stage, a priority descending balanced stockpiling strategy was established to ensure the continuous operation of the yard crane, and the second stage of the container allocation model was constructed, and a heuristic algorithm was designed to solve the problem. The design of large-scale case experiments shows that the algorithm is able to give effective export container stockpiling area delineation and container space allocation scheme, and the performance of the algorithm is verified by different scale comparison experiments, which shows that the algorithm has a faster convergence speed and is able to optimize 10% of the target value, which improves the overall operational efficiency of the U-shaped automated terminal.

Reliability analysis and allocation were conducted on the propulsion system of multi rotor electric vertical takeoff and landing (eVTOL) aircraft. Firstly, to solve the problem of insufficient accumulation of reliability historical data of multi-rotor eVTOL aircraft, a reliability analysis model was established by using fuzzy Bayesian network (FBN) to supplement the reliability prior data, and reliability posterior inference was carried out to assist the key link of the positioning system. Secondly, based on the FBN reliability analysis model of the system, an improved advisory group on reliability of electronic equipment(AGREE) reliability allocation method was proposed. Reliability distribution of eVTOL propulsion systems with different configurations was carried out. The results show that the FBN reliability analysis model supplements the propulsion system reliability data and can effectively identify the system weak links. The reliability allocation results of the improved AGREE allocation method meet the reliability requirements for eVTOL aircraft in SC-VTOL-01, while the reliability allocation results obtained by this method are more reasonable, reflecting the differences between different configurations, subsystems, and components.

With the increase of air cargo volume, cargo plans are frequently interrupted due to disruptions in cargo demand, so rescheduling flight schedules is the core issue for air cargo recovery. An air cargo recovery model based on spatio-temporal network method was proposed with the goal of maximizing the profits of airlines under the disturbance of temporary increase in demand. Aircraft routes, cargo routes and flights were reorganized in the model and the initial flight plan was preserved as much as possible by adding penalty factors. In order to verify the effectiveness of the model, the model was solved using CPLEX solver. The proposed spatio-temporal network-based air cargo recovery model was compared with the model in reference. The results show that the proposed model has significant advantages in computational efficiency and finding optimal values, and the advantages become more apparent with the increase of the case size. The sensitivity of the model's solution results to the time window width and aircraft carrying capacity was analyzed. The results show that the narrower the time window, the slower the solution speed, while as the time window width increases, the solution speed accelerates and tends to stabilize. As the carrying capacity of the aircraft gradually increases, the solving speed of the model becomes faster and tends to be stable.

Studying the characteristics of airport traffic flow fluctuation range is fundamental for efficient traffic management and control. Mastering these characteristics plays a crucial role in maintaining the stability and effectiveness of overall airport operations. Considering the irreversibility of time and the cumulative impact of traffic congestion, which occurs when airport traffic exceeds facility capacity within certain time intervals, a method for constructing an adaptive crossing network was proposed. From the perspective of complex network topology, both the overall characteristics of the network and the centrality of nodes were analyzed. The integrated centrality of nodes was calculated using the independent weighting coefficient method, enabling the identification of key time nodes that are core hubs of strong fluctuations within the network. The results show that the adaptive crossing network, mapped based on the traffic data from Beijing Daxing International Airport, exhibits characteristics of complexity and order, featuring scale-free properties, assortativity, and a distinct community structure. The time period from 21:20 to 22:25 (nodes 257~269) ranks highly across various centrality measures, indicating a significant fluctuation impact range, and thus, these nodes are identified as core hub nodes within the network. The integrated centrality synthesizes various topological centrality features of the network, and through quantitative analysis, effectively characterizes the strong fluctuation nodes within the network. This method provides a theoretical basis and practical reference for the optimization of airport traffic flow management and the study of abnormal fluctuations, offering a new perspective for enhancing airport operational efficiency and safety.

In order to address the challenges posed by multifaceted risk factors in air traffic control operations, a comprehensive analysis of unsafe operational incident reports was performed to extract risk-related information and identify underlying patterns. The latent Dirichlet allocation (LDA) model was utilized to uncover key risk topics and associated keywords, and the evolutionary relationships among different risk themes were systematically analyzed. A semantic network for the civil aviation air traffic control domain was constructed using the bidirectional encoder representation from Transformers(BERT) model to examine the interconnections and potential dependencies among risk topics. This network provides a theoretical foundation for quantifying the association between keywords. The findings indicate that the proposed approach enhances the digital representation of safety risks in air traffic control operations. It is concluded that the results offer valuable insights for advancing risk assessment and mitigation strategies in civil aviation air traffic control systems. The relevant research results can better mine air traffic control unsafe information and lay a foundation for accurately perceiving air traffic control operations risks.

The oxidation of Sb(III) occurred rapidly in aerobic and dark groundwater environments, with previous studies suggesting that co-oxidation of Fe(II) and Sb(III) may be the predominant driving mechanism. However, there is a lack of field evidence confirming environmental isotope fractionation. Therefore, 20 groups of Magunao aquifer (${\mathrm{Dx}}_{3}^{4}$ water) samples were collected from the North mine of Xikuangshan antimony mining area in Hunan Province to compare the differences in environmental isotopes (δ⁵⁶Fe, δ¹³C, and δ³⁴S) between high- and low-Sb groundwater and investigate the fractionation process of these isotopes. The results reveal that total Sb(^TSb) concentrations ranged from 5.30 μg/L to 20 700 μg/L, with a mean concentration of 3 660.61 μg/L. Additionally, Sb(V) is found to be the most dominant valence state for Sb in ${\mathrm{Dx}}_{3}^{4}$ water. The neutral-alkaline and oxygen-enriched conditions in ${\mathrm{Dx}}_{3}^{4}$ water facilitate the co-oxidation of FeS₂ and Sb₂S₃, as well as induce fractionation of δ¹⁸${\mathrm{O}}_{\mathrm{S}{\mathrm{O}}_{4}}$, δ³⁴${\mathrm{S}}_{\mathrm{S}{\mathrm{O}}_{4}}$and δ⁵⁶Fe between sediments and groundwater, resulting in the increase of $\mathrm{S}{\mathrm{O}}_{4}^{2-}$,total Fe (^TFe) and Sb(Ⅴ) contents in high Sb groundwater. Furthermore, microbial activities promote the oxidative decomposition of organic carbon, thereby enhancing the co-oxidation rate of Fe(Ⅱ) and Sb(Ⅲ). This conclusion unveils a novel mechanism for aerobic oxidation of Sb(III) in dark groundwater environments while providing a scientific foundation for advancing our understanding of the Sb geochemical cycle and preventing environmental pollution from high Sb groundwater.

In order to further explore the pipe-soil interaction and the failure mechanism of buried pipelines during the collapse process, the element birth and death technology and the displacement load technology were used to simulate the soil collapse process. The coupling models considering the pipe-soil nonlinear effect were constructed respectively to analyze the failure law of buried pipelines under collapse. The results show that the bottom of the pipe jacking is the control point, the axial stress is the control stress, and the junction of the non-subsidence area and the subsidence area and the center of the subsidence area are dangerous sections. Applying displacement load technology to element birth and death technology, the location of the most dangerous section transitions from the junction of non-subsidence area and subsidence area to the center of subsidence area, and the peak value of Von-Mises stress increases from 4.3 MPa to 6.09 MPa. The application of displacement load technology makes the lower part of the pipeline always in contact with the soil, and the pipeline is subjected to strong shear near the junction of the non-subsidence area and the subsidence area. The element birth and death technology is lost with the soil, and the pipe-soil state gradually changes from contact to separation, which is a weak shear effect. The Von-Mises stress peak is larger and the radial stress is smaller, which can better reflect the actual situation. According to the stress and settlement values, the maximum Von-Mises stress prediction formula is fitted, and the error is within 7%. The research results can provide reference for the safe operation of pipelines and the selection of collapse simulation methods.

According to the fault data of a certain type EMU traction system in China in 2022, the location and frequency distribution of key faulty equipment were analyzed, and the impact duration caused by various failure problems was counted. These two are combined as the spatio-temporal characteristics of EMU traction system faults. The distribution model was selected to compare the duration distribution characteristics of various faults, and the K-S test method was used to analyze the fitting effects. The results show that the fault influence time of pantograph, traction converter and roof high-voltage cable is the most suitable for the logistic distribution model fitting, while the lognormal distribution is most suitable for the traction transformer, main circuit breaker and traction motor. It is of great significance to predict the failure time of EMU traction system, point out the optimization direction of system equipment maintenance, and improve the efficiency of train operation and scheduling.