In order to study the structural strength of platform fire fighting vehicles, and to address the impact of the structural design of its sub-frame, stabilizers, and booms on the stability and safety of the entire vehicle, ANSYS software simulation was used to study its structural strength, and experimental verification was conducted. A simplified 3D model of the sub-frame with stabilizers, and the booms were established separately, the stress distribution under various working conditions was simulated. Then, an experimental environment was set up for stress testing. The results show that when the entire boom is horizontally extended to the left or right of the fire fighting vehicle, it is more likely to experience the phenomenon of virtual legs, with the values of 22.5 mm and 17.5 mm, respectively. The maximum stress of the boom during the retraction and extension process occurs in the area of the folding arm's variable amplitude hinge point and the overlapping area of the telescopic arm. And the difference between the stress data obtained from experiments and simulations is about 4%. This not only provides good guidance for the security testing of platform fire fighting vehicles, but also verifies the credibility of the simulation method, which is helpful for the structural design and optimization of platform fire fighting vehicles.

Cultural relics protection units are important carriers of Chinese outstanding traditional culture. It is of great significance to study the spatial differentiation of cultural relics protection units and the differences in the results of multi-scale driving factors, in order to continuously promote the inheritance of history and culture, and to prosper the development of regional cultural undertakings and cultural industries. Huizhou cultural relics protection units were taken as the research object, and standard deviation ellipse, kernel density estimation and geographic detector methods were used in the analysis of spatial and temporal differentiation and driving factors of cultural relics protection units. The results show that the cultural heritage units in Huizhou region present the time series characteristics of “dispersed-concentrated-dispersed”, and the center of distribution gradually shifts from the middle to the south. The spatial aggregation of cultural heritage units is remarkable, the overall performance of the “V” distribution pattern, specifically concentrated in Shexian County and Jixi County. Natural geographic factors and human geographic factors have an impact on the spatial differentiation of cultural heritage units.

The pore structure is a pivotal determinant of the physical properties of tight sandstone reservoirs, and elucidating its characteristics holds great significance for oil and gas exploration and development. Taking the 4th member of the Xujiahe Formation tight sandstone reservoirs in Tianfu Gas Area as an example, the pore structure characteristics, fractal features, and fluid mobility of the Xu-4 sandstone in the study area were systematically analyzed through thin section identification, scanning electron microscopy observation, nuclear magnetic resonance testing, high-pressure mercury intrusion testing, and X-ray diffraction experiments, combined with fractal theory. The results indicate that the sandstones of the 4th member of the Xujiahe Formation in the study area are predominantly composed of feldspar lithic sandstone, belonging to an ultra-low pore-ultra-low permeability pore type reservoir. The pore type is primarily feldspar-dissolved pores, and the throat type is predominantly sheet throats. According to the morphology of the high-pressure mercury injection curves and nuclear magnetic resonance outcomes, the pore structure of the 4th member of the Xujiahe tight sandstone reservoir is categorized into three distinct types. Among them, the material properties of the I-type samples are the best, with larger pore-throat radii, good connectivity and sorting of pore-throat, strong fluid mobility, and the best reservoir quality. The pore structure and fluid mobility of the fourth member of the Xujiahe tight sandstone reservoir are affected by sedimentary structures and mineral content. Specifically, reservoirs with coarser grain sizes and better sorting demonstrate superior pore structure and fluid mobility. Furthermore, quartz, the primary rigid mineral in sandstones, exhibits resistance to compaction, thereby safeguarding the reservoir pores to a certain extent. However, calcite and clay minerals will occupy pore space, resulting in deterioration of the pore structure and fluid mobility of the reservoir.

In response to the challenges of low efficiency, high cost, and difficulty in deployment on mobile devices in current road damage detection technology, a novel road crack detection method based on the improved YOLOv8 algorithm, named YOLOv8 road crack (YOLOv8-RC), was proposed. The C2f module, based on the YOLOv8n architecture, was enhanced through the introduction of dynamic snake convolution technology, which more accurately identified tubular structural features and adaptively focuses on fine and curved local structures. Furthermore, a highly efficient multi-scale attention(EMA) mechanism was incorporated into the algorithm, effectively enhancing recognition accuracy. In the neck structure of the model, a weighted bidirectional pyramid network(BiFPN) was added to achieve multi-scale fusion of features, thereby optimizing both the accuracy and efficiency of the algorithm. Experimental results on the RDD2022-China-MotorBike and RDD2022-Japan datasets demonstrate that the improved YOLOv8n-RC model achieves mAP50 scores of 78.8% and 43.8%, respectively, representing improvements of 3.9% and 3% over the original YOLOv8n model. The total number of model parameters for the proposed algorithm is only 2.84 M, and the computational complexity is 7.8 G, underscoring the practicality and superiority of this method.

In order to resolve the problem that the volume of APP-based car-hailing industry increases and the trip counting results are susceptible to errors due to environmental influences, a novel method of APP-based car-hailing trip counting detection was proposed. The conversion mechanism between global navigation satellite system(GNSS) coordinate system and other coordinate systems was analyzed, and a network car counting device was designed by using loose coupling model and Kalman filter processing for combined navigation. The test results show that the distance counting device has a distance counting error of 0.42% in the straight road section and a distance counting error of 0.56% in the circular road section, and the distance counting accuracy meets the maximum error range required by the Verification Regulation of Taximeters(JJG 517—2016), which provides a new method for solving the distance counting error problem of APP-based car-hailing.

With the transformation of the modern power system, the application of grid-connected inverters is increasing, and the transient synchronization stability of inverters after disturbance is becoming prominent. Previous research on transient synchronization stability mainly focuses on the dynamic of the phase-locked loop, with less consideration of the impact of outer loop control, resulting in a cognitive bottleneck in understanding transient synchronization mechanism. To address these issues, the grid-following voltage source inverter system was modelled, taking into account the inverter control strategy and limiting elements in detail. Subsequently, the impact of different voltage drops on the stability region of the equilibrium point was analyzed, and the effects of outer loop control proportional integral (PI) and limiting elements on transient synchronization were discussed. The influence mechanism of outer loop control on the transient synchronization stability of inverters was revealed systematically. Finally, the effectiveness of the proposed theory was verified in PSCAD/EMTDC.

Fracturing and packing is a key technology for maintaining and enhancing production in medium-to high-permeability unconsolidated sandstone reservoirs. However, after production begins, the loose cementation of the reservoir, combined with proppant embedment and formation sand invasion, significantly reduces fracture conductivity. Currently, there is a lack of methods to predict fracture conductivity under the combined effects of proppant embedment and formation sand blockage in such reservoirs. A fracturing and packing simulation device was used to conduct composite experiments on proppant embedment and formation sand blockage under closure stresses ranging from 5 MPa to 20 MPa, unconsolidated rock plate samples were used to simulate fracture surfaces. Based on the experimental results, the controlling factors and developed models were analyzed to predict permeability loss due to proppant compaction, fracture width loss caused by embedment, and dynamic permeability changes due to formation sand blockage. The results show that proppant embedment and compaction after fracture closure significantly reduce fracture conductivity, with the main factors being closure stress, reservoir strength, and particle sizes of the proppant and formation sand. Formation sand blockage also exhibits a time-dependent effect, contributing to dynamic conductivity decline. In a typical unconsolidated sandstone reservoir in the Bohai Oilfield, the calculated fracture width loss due to embedment is approximately 19.34%, permeability loss from closure and compaction is about 34.15%, and dynamic permeability loss from formation sand invasion is around 22.89%. The combined effect of these factors results in a total fracture conductivity loss of approximately 59.06%. To prevent excessive blockage, it is recommended that the initial fracture width be maintained at no less than 12.5 mm, large-particle proppants be used, and production rates be controlled during the early production phase. The research results provide important guidance for optimizing fracturing and packing parameters and improving production in unconsolidated sandstone reservoirs.

Aiming at the limitations of traditional synthetic aperture radar interferometry (InSAR) technology in monitoring karst collapse, a small baseline subset (SBAS)-InSAR surface deformation monitoring method integrating permanent scatterer (PS) technology was proposed to monitor the deformation characteristics of shallowly buried karst collapse groups. The study area was deliberately selected as the Dongdiu District in Libo County, Qiannan Prefecture, Guizhou Province. A dataset composed of 83 Sentinel-1A imagery acquisitions from January 30, 2020, to December 21, 2022, was thoroughly compiled and subsequently analyzed by time-series InSAR in a rigorous manner. The results show that during the period from 2020 to 2022, the collapse-prone areas undergo a phase of accelerated development in deformation rate. The monitoring results closely mirror the actual boundaries of the delineated collapse zones. The maximum deformation rate recorded within the collapse zones is -167.5 mm/a, predominantly occurring in regions with the most concentrated collapses. Moreover, a novel set of criteria for identifying karst collapse clusters was introduced, which was based on time-series InSAR technology. These criteria were founded on the analysis of the uniformity in the trends of deformation accumulation curves and the detection of local abrupt changes among any three interconnected points within the monitoring area. Such features were proposed as early indicators of the development of clustered karst collapses. The research findings are anticipated to provide valuable perspectives for the identification and characterization of the developmental processes associated with clustered, shallowly buried karst collapses.

The systematic construction of a biomechanical analysis of the full swing technique is considered essential to addressing the core issues and resolving technical problems from their root causes. Targeted special physical training is a powerful guarantee for the full utilization of technical skills. The research findings on the golf full swing technique were reviewed, its biomechanical characteristics were discussed and summarized. By further analyzing the strengths and weaknesses of full swing techniques in players of different genders and skill levels, rational recommendations for physical training were proposed, providing valuable insights for optimizing athletes' full swing techniques and enhancing the level of scientific training. During the full swing, the limbs follow the principle of proximal-to-distal motion, and muscle contractions adhere to the stretch-shortening cycle principle. Weight transfer is rationally adjusted based on specific swing patterns, and the terminal joint release effect is strengthened, contributing to the maximization of clubhead speed. In the downswing, the peak angular velocities of turnk and hip axial rotations, along with the timing and the peak speed of wrist release, are identified as the primary factors influencing clubhead speed. These factors also represent the key technical differences between male and female players. Strength and conditioning is regarded as a crucial pathway for improving full swing performance. Golfers are advised to focus on developing specific physical qualities, including upper limb muscle strength and explosiveness, lower limb muscle strength and explosiveness, as well as core stability and rotational power.

In recent years, artificial intelligence has demonstrated strong pattern recognition and classification capabilities across various fields, providing new insights for lithology identification. Starting from three methods: support vector machines, neural networks, and ensemble learning, the basic principles, advantages and disadvantages of these machine learning algorithms were reviewed, as well as their research progress and application in the field of uranium ore bed lithology identification. The results show that machine learning can effectively identify the correlation between logging data and different lithologies through model training, transforming the process of lithology identification into a machine learning process. This can greatly improve the automation level and accuracy of lithology identification, holding significant practical importance and a broad development prospect.