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.

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.

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.

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.

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.

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.

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.