Medium-depth coaxial geothermal wells, as an efficient way to utilize geothermal energy, are now being vigorously promoted by many regions in China. In order to study its heat extraction performance and its influencing factors, COMSOL Multiphysics was used for modeling and orthogonal experimental analysis. The results show that the effects of all influencing factor on the heat extraction from geothermal wells are listed in the following order: ground temperature gradient, inlet flow rate, thermal conductivity of the backfill material, inlet temperature, and thermal conductivity of the inner tube. When designing geothermal wells, it is necessary to consider multiple factors to determine the optimal operating conditions. The radial temperature influence range of geothermal wells is about 10 m, which is a reference for the arrangement of multiple wells in the same area.

In order to explore the medication characteristics of expectorants in the Huatan prescriptions commonly used in treating ischemic stroke recently. “Huatan/Qutan/Ditan/Huotan/Daotan/Guntan/Banxia Baizhu Tianma Decoction/Xinglou Chengqi Decoction/Wendan Decoction/Erchen Decoction/Xiao xianxiong Decoction/Lingjiao Gouteng Decoction/Jieyu Dan” + “ischemic stroke/cerebral ischemia/cerebral infarction/cerebral embolism/cerebral thrombus” + “clinical” as the main topic on the China National Knowledge Network (from May 1, 1986 to April 1, 2024) was used to investigate the medication characteristics of expectorants, and selected literature meeting the criteria. Statistical analysis of the data was performed using Excel and IBM SPSS Modeler 18.0 software. The results show that Banxia and Tian nan-xing are the most frequently used expectorants in different stages of ischemic stroke, followed by Fuling, Shi chang-pu, Chenpi, and Gancao. The dosages of most expectorants are 10~15 g. In the analysis of Siqi and five flavors, Wen, Ping, Xin, Ku, and Gan have the highest frequency of occurrence in different stages of ischemic stroke. In the analysis of meridian tropism, the lung and spleen meridian have the highest frequency of occurrence. Most Huatan prescriptions are composed of 8~14 herbs in both the acute and rehabilitation stages, accounting for 82.51% and 78.05% of the total frequency, respectively. In the analysis of association rules, 25 traditional Chinese medicine combinations with strong correlation strength are obtained, and the most combinations of herbs are Huatan-Xifeng-Tongluo and Huatan-Huoxue-Qufeng. Most Huatan prescriptions are administered through the traditional decoction method and generally used in combination with Western medicine. It is concluded that the commonly used expectorants in Huatan prescriptions for treating ischemic stroke are mainly Banxia and Tian nan-xing, and the dosage ranges of most expectorants are 10~15 g. The number of herbs in Huatan prescriptions is mainly 8~14. The research results provide references and clinical data support for the application of expectorant Chinese medicine and Huatan prescriptions in the treatment of ischemic stroke and the development of research on treating ischemic stroke from the perspective of phlegm.

To address the issue of inaccuracies in groundwater level predictions due to the insufficient consideration of groundwater-related factors, clustering methods for observation wells based on spatial distance, hydrogeological attributes, and a hybrid of distance and attributes were proposed. The significance of inter-well connectivity in groundwater level prediction was validated. Four models were designed, which were applied to simulate and predict groundwater levels in the karst water region of Jinan and compared with actual observations. The prediction results indicate that the combined model incorporating the connectivity characteristics of karst aquifers, known as convolution-long short-term memory(ConvLSTM), outperforms the traditional long short-term memory(LSTM) model. Among the models, the mix-multivariate-convolution-long short-term memory(M-MV-ConvLSTM) model, which accounts for wells of the same category based on the hybrid distance-attribute clustering results (characterized by strong connectivity), achieves the highest prediction accuracy and the smallest error. The average root mean square error is approximately 0.457, and the Nash-Sutcliffe efficiency is approximately 0.216, demonstrating a higher prediction accuracy than the traditional LSTM model. The research results is positioned to serve as a reference for real-time groundwater level prediction in karst regions.

Addressing the lack of clarity regarding spontaneous imbibition displacement mechanisms and production management strategies for shale oil in the Cangdong Sag, a model was established that considered the “synergy of imbibition, flowback, and productivity”. The model was designed to dynamically reflect the mutual constraints and synergies between spontaneous imbibition displacement and engineering parameters such as flowback rates and soaking durations. Through comprehensive numerical simulations integrating geology and engineering across the entire lifecycle, the spontaneous imbibition displacement patterns and optimal flowback regimes for the C1 and C3 sweet spots in the Kong-2 Member, which served as the main development interval in the Cangdong Sag, were elucidated. The results indicate that spontaneous imbibition displacement continues to occur during the flowback process. The optimal soaking durations for the C1 and C3 sweet spots are determined to be between 37 and 42 days, with a reasonable flowback rate ranging from 20 to 40 tons per day. The research findings provide a theoretical foundation for studies on spontaneous imbibition mechanisms and reasonable production management strategies for deep shale oil and gas reservoirs.

Due to factors such as temperature, pressure, and production rate, integrity issues like annular leakage in gas well tubing frequently occur frequently. In order to study the influence of working conditions on the process of downhole oil pipe leakage, a high temperature and high pressure oil pipe leakage simulation model was established based on field parameters and compared with the existing mathematical model of small hole leakage. Based on the simulation model, the influence of flow field change, leakage aperture, casing pressure difference and leakage environment on the leakage process was analyzed. The results show that the changes of flow field mainly focus on the inside of the leak hole and the inlet and outlet of the leak hole. With the increase of the leak aperture, the leakage quantity, leakage velocity, pressure and density inside the leak hole increase. The greater the pressure difference between tubing and oil jacket annulus, the greater the leakage amount and leakage velocity, and the more drastic the change of pressure and density. The gas velocity and pressure in the upper part of the annular protective fluid are greater than that in the annular protective fluid section.

Based on the detailed process mineralogy of gold ore from Jierwushake gold deposit in west Junggar, the selectability test was carried out. The results show that the valuable element in the ore is Au with a grade of 3.61 g/t. The types of gold minerals are natural gold, silver-gold, gold-tellurium and gold-selenium-silver ore, and the embedded states are encapsulated gold, fracture gold and intergranular gold. The particle size is mainly microparticle gold (0.2~10.0 μm), accounting for 96.96%, and the number of fine and above gold particles (>10 μm) account for 3.04%.The flotation-leaching combined process is recommended for mineral processing test: the Au grade of flotation-flotation concentrate is 53.14 g/t, and the recovery rate is 31.69%. After leaching the whole sludge of flotation tailings for 24 hours, the gold content in tailings is 0.20 g/t, and the leaching rate of gold operation is 89.56%, the Au total recovery rate of combined process is 93.31%, and the instructions are ideal.

In order to identify the characteristics of the geothermal field in Longmuwan, including its temperature field, hydrochemistry, and recharge sources, and to analyse its genetic model. The hydrogeochemistry, isotopes, and geothermal temperature measurement methods were employed, integrated with regional geological characteristics, the hydrochemical characteristics, heat reservoir temperature and recharge sources of the geothermal field were systematically analysed, and a conceptual genetic model was preliminarily constructed. The results indicate that the geothermal gradient of porous stratified reservoir is 5.23~8.25 ℃/100 m, while the fracture zoned reservoir has a gradient of 1.47~4.50 ℃/100 m, the deep heat reservoir temperature range is 87~115 ℃, with geothermal fluid circulation depths reaching 960~2 298 m in Longmuwan geothermal field. The hydrochemical types of geothermal fluid are HCO₃-Na·Ca type and Cl·HCO₃-Na type, primarily recharged by atmospheric precipitation. The calculated recharge elevation is 421~597 m, suggesting the recharge area is the hinterland of Jianfeng Ridge. Isotopic age results show that the formation age of geothermal water exceeds 6 000 a, and it has the characteristics of a long recharge pathway and slow groundwater flow.

Serving as a clean and renewable energy source, wind energy plays a significant role in mitigating the increasingly severe energy crisis. However, the fluctuation and randomness of wind speed pose severe challenges to the stable operation of power systems. To address this issue, a combined short-term wind speed forecasting model named CEEMDAN-RIME-CNN-BiLSTM-AM was proposed, which was based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), rime optimization algorithm (RIME), convolutional neural network (CNN), bidirectional long short-term memory network (BiLSTM), and attention mechanism (AM). Initially, the CEEMDAN algorithm was applied to the original wind speed series to obtain a series of relatively stable sub-modes, thereby reducing the volatility of the wind speed series. Subsequently, the CNN hyperparameters were optimized using the RIME algorithm to establish the CNN-RIME model for adaptive extraction and mining of wind speed data. Then, the BiLSTM-AM model was employed to forecast the processed data. Finally, the forecasting results of each sub-series were superimposed to obtain the final forecasting result. A comparative experiment was conducted using an actual wind speed dataset from a certain location. The proposed model demonstrates good forecasting performance in both single-step and multi-step forecasting, providing a reference for scheduling plans to maximize energy utilization and power supply.

Due to the limitations of traditional ice force measurement methods in terms of stability and reliability, and the high sensitivity and anti-interference capabilities of fiber optic sensing technology was given, a fiber optic ice force sensor was developed. The effectiveness of this sensor was evaluated in the context of its application in marine structures. Based on the fundamental principles of fiber optic sensing technology and the design requirements of the ice force sensor, the research, design, and installation processes of the sensor were described in detail, including the design calculations of the elastic element, the selection and arrangement of the fiber optic sensors, and the construction of the data acquisition system to ensure that the precision requirements for ice force measurement were met. A winter field measurement of ice force was conducted at an observation station in the northern Bohai Sea. Field ice force data were successfully collected and analyzed, and the actual monitoring performance of the sensor was evaluated. The experimental results indicate that the system exhibits good stability and reliability in practical applications. The developed fiber optic ice force sensor provides a new reliable technical means for ice force measurement in marine engineering and lays a foundation for further research in structural health monitoring.

Modern air traffic management systems necessitate efficient and accurate identification and classification of hazard-related text data to ensure flight safety. Air traffic control hazard data encompasses information on potential factors, conditions, or events that may adversely impact aviation safety. Existing text classification methods face challenges due to the diversity of data categories and imbalances within classes. An enhanced ensemble model based on the Stacking framework, incorporating a dual-weighting mechanism was proposed for improved performance. A dual-protection strategy was implemented to categorize hazards and safety risks systematically. The methodology employed the term frequency-inverse document frequency(TF-IDF)algorithm to extract and vectorize features from preprocessed hazard texts. To address class imbalance, the synthetic minority over-sampling technique(SMOTE) and adaptive synthetic sampling approach(ADASYN)algorithms were utilized to generate synthetic samples for minority classes. The Stacking ensemble model was refined by dynamically weighting the F₁ scores derived from cross-validation of base learners and integrating a sensitivity assessment mechanism across the ensemble. Experimental results on the constructed dataset demonstrate that the ADASYN-enhanced ensemble model achieves notable improvements in precision, recall, and F₁ scores by 0.9%, 1.1%, and 1.0%, respectively, effectively mitigating overfitting in majority classes. The proposed algorithm significantly enhances the classification performance of imbalanced hazard text categories, contributing to the advancement of safety risk management in air traffic control.