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.

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.

After fracturing, the solid particles carried by the high speed liquid will cause serious erosion to the oil nozzle, and it is difficult to ensure the stable operation of the oil nozzle. To address the serious erosion problem of the nozzle, numerical simulation was employed to study the erosion wear of the nozzle, and the influence patterns of sand content, sand grain diameter, sand grain density, pump displacement, and liquid viscosity on the erosion wear of the nozzle were analyzed. The research indicates that: when the sand content and liquid viscosity increase, the maximum erosion rate exhibits linear growth; when the sand grain density and pump displacement increase, the maximum erosion rate exhibits exponential growth; and when the sand grain diameter increases, the maximum erosion rate shows exponential decrease. The orthogonal test method is used to judge the significance of each factor. The factors affecting the erosion wear of the nozzle are as follows: sand content ratio > pump displacement > sand density > sand diameter > liquid viscosity.Based on the results of numerical simulation, the machine learning method is used to compare and analyze SVR(support vector regression), CNN(convolutional neural network), BP(back propagation) neural network and RFR(random forest regression) algorithm to predict the erosion wear results of oil nozzle respectively. By preferring the SVR algorithm and adopting the particle swarm optimization algorithm to optimize the prediction model, a better nozzle erosion prediction model is obtained.

The high-precision fault diagnosis of cross modal high-dimensional fault data under unsupervised conditions is a challenging problem. To address this issue, a rotating machinery fault diagnosis method based on unsupervised cross-modal Euler discriminant space (UCEDS) was proposed. In this method, cross-modal fault data samples were mapped to Euler representations through cosine metrics to enhance the differences and separability between different types of fault samples. Then, an unsupervised cross modal Euler discriminant space learning model was constructed in this space, and the analytical solution of the model was theoretically derived. This model not only considered the local neighborhood structure of fault samples, but also effectively discovered the local structural information of complex and nonlinear fault feature samples. At the same time, on the basis of cross modal consistent discriminative fusion, it further improved the complementarity between low dimensional discriminative feature subsets. Targeted experiments on the Paderborn fault bearing dataseht showed that the proposed UCEDS method had superior fault diagnosis and classification performance.

The estimation of the state of health (SOH) for lithium-ion batteries is considered crucial for ensuring the safe and stable operation of battery management system. However, the accurate estimation of SOH has been a challenge due to the capacity regeneration phenomenon during the discharge process of lithium-ion batteries. To improve estimation accuracy, a hybrid model based on variational mode decomposition (VMD) and bidirectional long short-term memory network with attention mechanism (BiLSTM-ATT) was proposed. First, the battery capacity was decomposed using the VMD algorithm, producing a set of stable sub-sequences. Then, permutation entropy was introduced to reconstruct the sub-sequences to reduce computational complexity. The reconstructed sequences were input into the BiLSTM-ATT model, and feature weights were assigned by the attention mechanism. The SOH values were trained and estimated by the BiLSTM model. Finally, the complete SOH estimation result was obtained by summing all estimated values. Validation was performed using the CS2_36, CS2_38, and CX2_35 datasets from the CALCE lithium battery dataset. The results show that the proposed algorithm maintains a root mean square error within 0.6% and a mean absolute error within 0.4%, which demonstrates higher accuracy and performance compared to other estimation models.

At present, complex interstitial lung diseases have the problems of low classification accuracy and lack of auxiliary diagnostic information. To address these problems, an image retrieval framework based on multi-feature fusion and supervised contrastive learning methods was proposed. Interstitial lung disease features were extracted using Res-Net50 and radiomics feature extraction modules. In order to fuse two features of different modalities and scales, a feature fusion module was designed that can jointly represent the spatial calculation feature correlation of two features. The feature discrimination between interstitial lung disease categories was improved through supervised contrastive learning methods, and a typical interstitial lung disease database was retrieved. The highest precision, recall rate and F₁ score were obtained in the retrieval task of interstitial lung disease data, and a silhouette coefficient of 0.482 was obtained in the feature vector discrimination index for image retrieval. The experimental results show that compared with the traditional deep learning single feature modality method, the proposed method can effectively improve the classification retrieval accuracy of interstitial lung disease images and improve the interpretability of interstitial lung disease diagnosis.

Geothermal tail water reinjection is the main bottleneck that restricts the development and utilization of geothermal resources in Lanzhou Basin. In order to break through the technological gap of geothermal tail water reinjection in Lanzhou Basin sandstone-type thermal storage, relying on the geothermal heating demonstration project in Pengjiaping, Lanzhou City, for the first time, the natural reinjection experiment with graded flow rates of 15, 20, 25, 28 m³/h and graded pressure pressurised reinjection experiment of 0.1, 0.2, 0.3, 0.4, 0.5, 0.6 MPa were designed and carried out, the maximum stable natural reinjection volume of 27.96 m³/h and the stable reinjection volume of 51.68 m³/h for 0.667 MPa pressure were firstly obtained from the sandstone-type thermal storage of Pengjiaping, Lanzhou Basin, and the impact of geothermal tail water reinjection on the water level, water temperature, and temperature field of the extraction wells was also investigated. After a heating season of productive reinjection experiment verification, a set of suitable and feasible sandstone-type thermal storage geothermal tail water reinjection technology process has been successfully explored in Lanzhou Basin, which is of great reference and significance for the large-scale and high-quality development and utilization of geothermal resources in Lanzhou Basin.

In order to find out the susceptibility of debris flow after fire at different time points, the burned land where a serious fire occurred in Lushan Mountain, Changshou Township in March 2020 was selected as a demonstration research area. Based on the idea of “space for time”, the whole study was carried out. Through laboratory experiments, the root soil mechanical parameters of the study area at different time after fire were obtained. By using the experimental parameters obtained, the slope instability coefficient of different years after fire was obtained through the slope instability model. According to the slope stability division standard, the slope instability area of different years after fire was obtained. Finally, the source strength indexes of different years after fire were extracted. The dynamic evaluation index system of post-fire debris flow susceptibility at small watershed scale was established by taking source strength as static evaluation index and topographic and geomorphic index as static evaluation index. Using the entropy weight method to calculate the weight of index factors combined with the comprehensive index method, the dynamic susceptibility assessment of debris flow was carried out on the burned land of Lushan Mountain in Changshou Township. Based on the results, targeted remediation of watersheds that remain highly susceptible for many years after a fire and those that are highly susceptible within a short period of time can effectively prevent and reduce the probability of mud slides, while also saving economic costs and achieving truly effective disaster prevention and mitigation.

The Yingxiongling shale oil reservoirs in the Qaidam Basin are notably characterized by the development of the laminated shale and thin-layered shale. In order to identify the superior sweet spots, an experimental study on the physical and mechanical properties of laminated and thin-layered shales was conducted, and the sweet spots of the Yingxiongling shale oil reservoirs were systematically evaluated in conjunction with the analysis of oilfield production data. The results show that laminated shale exhibits higher total organic carbon (TOC) content, stronger hydrocarbon generation capacity, and higher initial oil saturation compared to thin-layered shale. Although laminated shale has lower porosity, it exhibits stronger anisotropy, higher stress sensitivity coefficient, and more developed initial natural microcracks. Additionally, the laminated shale demonstrates high horizontal permeability, strong fluid absorption capacity, and effective imbibition oil displacement ability. Its lower compressive strength facilitates the formation of complex fracture networks. The experimental research results are consistent with the in site fluid production analysis. The fracture morphology generated by the laminated shale is more complex with strong oil displacement ability and high oil displacement efficiency, indicating that the laminated shales produce fluids first; therefore, it is concluded that the laminated shales are the preferred sweet spots. The findings of this study have important theoretical significance for the exploration and development of shale oil in Yingxiongling.