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.