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.

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 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 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 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 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.

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.

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.

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.

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.