Strengthening green and low-carbon environmental control technologies is critical because the industrial, agricultural, and construction sectors face three major challenges: high energy consumption, high emissions, and low energy efficiency in controlling temperature and humidity in specific areas. Heat pump-driven desiccant wheel air conditioning provides high evaporative temperature cooling, effective humidity management, and easy integration with renewable energy equipment. The typical heat pump-driven desiccant wheel air conditioning system was analyzed, the research progress of heat pump independently driven desiccant wheel air conditioning system under various coupling methods was reviewed, and the performance, regeneration temperature, and application occasions of heat pump independently driven desiccant wheel air conditioning system under various working conditions were summarized. Furthermore, the solar-assisted heat pump-driven desiccant wheel air conditioning system was introduced. Finally, existing challenges and future developments of heat pump-driven desiccant wheel air conditioning system were analyzed and projected.

Caragana is the main wind-breaking and sand-fixing vegetation in Inner Mongolia, with a wide planting area and a large area. There is a great demand for harvesting machine in the stubble and harvesting of caragana. Aiming at the problems of high fuel consumption and heavy pollution of traditional caragana harvester, the maximum speed, maximum gradient and pure electric driving range of the extended range hybrid caragana harvester designed by our research group were used as the objective functions to design the dynamic parameters. Firstly, according to the design parameters and performance requirements of the whole machine, the parameters of the range extender, drive motor and battery of the harvester were designed. Combined with the actual working parameters, AVL/Cruise software was used to simulate and find the optimal solution of the overall performance index. Combined with the actual working parameters, AVL/Cruise software was used to simulate and find the optimal solution of the overall performance index. The reasonableness of the simulation results is verified by the field test of the whole machine. Comparing the two results, it is found that the error of the maximum speed is 9.012% and the climbing error is 6.404%. The error of the two results is within the allowable range. The results show that the simulation model is reasonable and can provide reference for the optimization of dynamic parameters of the extended range hybrid Caragana harvester.

CO₂ flooding technology, recognized as a mature tertiary recovery method, is widely applied in complex small fault-block oilfields with strong heterogeneity. However, severe gas channeling is commonly observed during CO₂ flooding. As a result, the improvement in oil displacement efficiency remains low, typically below 10%. To address this, effective methods were explored to enhance oil displacement efficiency. Foam profile control and plugging were utilized as key techniques to achieve this enhancement.In the experiment, the JS oilfield was used as an example. The foam performance of the gas-soluble foaming plugging agent G-CF4 and the water-soluble foaming plugging agent W-CF1 was compared. The plugging agent with better foam performance was selected. Its plugging ability and oil displacement efficiency were tested.The results show that under target reservoir conditions, the optimal foaming plugging agent is 0.25% G-CF4.Moreover, the greater the permeability difference within the core combination, the stronger the plugging effect of G-CF4 in high-permeability cores.For a core combination with a permeability difference of 88 mD, the resistance coefficient of high-permeability cores is 2.5 times higher than that of a core combination with a permeability difference of 50 mD. In the core combination with 88 mD permeability difference, G-CF4 can maintain the resistance coefficient of high permeability cores above 9.2.The injection of 0.25% G-CF4 solution for 0.3 PV, followed by CO₂ flooding, improves oil displacement efficiency by 15% compared to CO₂ flooding alone.This study provides laboratory evidence supporting the optimization of foaming plugging technology in the JS oilfield.

The study focused on wrap-around reinforced soil retaining walls and proposed a calculation method for panel displacement. The horizontal displacement was divided into two components for calculation: the horizontal displacement caused by the strain of reinforcement and the overall horizontal displacement generated by the horizontal earth pressure acting on the back of the reinforced zone. When calculating the horizontal displacement caused by the reinforcement strain, the reinforced zone was divided into subzones through the potential failure surface of reinforced soil retaining wall and the natural repose angle of soil. The horizontal distribution of the reinforcement load was assumed, yielding a simplified calculation model for the horizontal displacement caused by reinforcement strain. For the calculation of the overall horizontal displacement of the reinforced zone, the zone was treated as a ‘cantilever beam’, taking into account the variation in elastic modulus of the reinforced zone with height. The theoretical results obtained through the proposed method were compared with experimental and numerical simulation results. The distribution trend of the displacements was basically consistent, indicating that the proposed method can effectively calculate the panel displacement of wrap-faced reinforced soil retaining walls.

Clouds play a crucial role in the atmospheric dynamics of the Earth, and precise segmentation of ground-based cloud images is essential for improving the accuracy of weather forecasting. In response to issues such as varying data quality, low data volume, and different capture angles in existing open-source cloud image datasets, a labeled standard ground-based cloud image dataset (Cloud-GT) was constructed using manual annotation and transfer learning methods. The color channel component threshold segmentation method was employed to eliminate sunlight interference. Furthermore, an improved U2Net-based ground-based cloud image segmentation technique was proposed. The model introduced channel attention modules and depth-wise separable convolution modules in the feature extraction unit, which greatly reduces the network model parameters while improving the effective feature extraction of ground-based cloud maps within the network. Finally, comparing and analyzing the method with classical segmentation networks, experimental results indicated that the method achieved classification pixel accuracy, mean class pixel accuracy, average intersection over union, intersection over union, and F₁ score of 84.03%, 90.88%, 84.13%, 74.12%, and 89.59%, respectively. In comparison with U2Net, UNet, and FCN, the method demonstrated a significant improvement in performance. In conclusion, the method not only substantially reduced the model parameters but also effectively enhanced segmentation accuracy, which provides the possibility of practical application.

As a power drilling tool that can meet the needs of deep ground and high temperature drilling, the short life of thrust bearing is a key problem affecting drilling efficiency. Based on the CCD design method and finite element numerical simulation method, three sets of variables were selected as the design parameters, the orthogonal test between different structural parameters and abrasive wear life was carried out, and the optimal structural parameters of the bearing were determined by the range analysis method. The temperature-displacement coupling finite element model of bearing wear was established, and the influence of axial load, temperature and wear times on the wear depth of the bearing was studied considering the frictional heat generation of the bearing. The results show that the overall performance of the bearing with optimal structural parameters is improved, the stress concentration of the bearing is alleviated, the wear of the bearing is reduced, and the optimized structural life is increased by 4.6% compared with the initial life. This research method provides a reference value for the optimal design of this type of bearing. At the same time, the influence of different working conditions on bearing wear was analyzed. With the increase of axial load, the wear depth of the bearing increases. With the increase of the number of wear, the wear depth also shows an increasing trend. With the increase of bottom hole temperature, the wear depth decreases first and then increases gradually.

In response to problems of rapid excavation of deep shafts, such as lining cracking and high support costs, based on the engineering background of -906~-1 158 m section of an overseas copper and gold mine, the support parameters of shaft were studied by theoretical calculations, numerical simulations and field tests. In order to restrict deformation of the shaft and reduce the cost of support reasonably and effectively, a shaft model was established based on the engineering practice, the stability of surrounding rock with the different parameters was analyzed by FLAC^3D numerical software combined with fluid-structure interaction. The results demonstrate that for the class Ⅲ surrounding rock, “anchor net spraying+steel fiber concrete” support is adopted, and its parameters are as follows: bolt diameter 22 mm, length 2.3 m, shotcrete thickness 50 mm, row spacing 1 m×1 m, steel fiber concrete thickness 550 mm. For the locally existing class Ⅳ~Ⅴ surrounding rock, “anchor net spray+foam board+steel fiber concrete” support was proposed, and the thickness of buffer layer foam board is 100 mm, and the thickness of steel fiber concrete is 600 mm. Field test results show that the average convergence rate of surrounding rock is 0.18~0.31 mm/d after 412 h excavation, which meets the requirements of air inlet shaft construction, and the construction efficiency is improved by about 23.5% compared with the domestic deep shaft. This work can provide a guidance for the support design of shaft in soft-fractured strata with water-rich.

Aspect-level sentiment analysis detects the sentiment polarity of given aspect terms from a fine-grained perspective, providing decision support for e-commerce, consumers, and other groups by mining textual aspect sentiment. Different syntactic dependencies were treated equally in existing methods resulting in the influence of relation types in convolutional networks and the global information from semantic perspective being overlooked. To address these issues, considering the flexibility and complexity of graph structures, the excellent performance of auxiliary tasks in capturing aspect sentiment based on global semantic information and completing fine-grained aspect information, the model WRCN-CL (weight relational convolutional networks and complementary task) which incorporates two tasks: WRCN(weighted relational convolutional networks) and CL (complementary learning) was proposed. Specifically, Bi-LSTM (bidirectional long short-term memory network) was used to extract textual features, which were entered into WRCN and CL tasks separately. Aspect-related semantic information was collected from a global perspective to enhance knowledge, while the aspect representations from CL combined with GCN (graph convolutional networks) to deeply explore syntactic information based on positional and type-aware relational information in WRCN. The fused global and local features were then input into a pooling layer to obtain comprehensive information representation for improved classification performance. Experimental results demonstrate significant improvements with the accuracy of 83.49%、78.19%、75.89% on three public datasets compared to baseline models, proving the effectiveness of the proposed model in aspect-level sentiment analysis classification task.

Small sensors powered by wind-solar hybrid power supply do not require regular replacement of the power supply, and can be deployed in various remote areas. However, in practice, the photovoltaic panels are prone to accumulation of dirt, resulting in reduced power generation, and the rotating parts of small wind turbines are also very prone to failure. A wind-solar hybrid power device with a simple structure and self-cleaning capability was designed, and its solar and wind energy collection performance was experimentally evaluated based on meteorological statistical data. Moreover, the vibration cleaning efficacy of the photovoltaic panels under various dust coverage levels was compared. The research results show that the device can generate a maximum output power of 77.28 mW under simulated clear weather conditions (light intensity of 948.1 W/m²). The starting wind speed of wind energy collection based on piezoelectric and vortex-induced vibration effects is around 1.5 m/s, and a wind speed of 4.3 m/s in the experiments can generate an output power of 4.63 mW. After vortex-induced vibration cleaning of the photovoltaic module with different dust coverage densities, the output power of solar energy collection can be restored to over 84% of the clean state. This study presents a new technical solution for maintenance-free small-scale micro power generation equipment in remote areas.

A novel clustering approach combining Kmeans++ and PAM was introduced to segment the daily load curve chronologically for the dynamic reconfiguration of distribution networks incorporating time-varying wind solar power and loads. Multi-objective dynamic reconfiguration model of distribution network based on the optimal objectives of comprehensive cost, voltage offset and load balance. To enhance the computational efficiency of the model, an INOA(improved Nutcracker optimization algorithm) was proposed, which used Tent mapping+quasi-reflection learning to provide high-quality initial population. Dynamic fitness-distance balance selection method and tangential flight strategy were introduced to enhance the global search capability. The Cauchy-Gaussian variation perturbation was incorporated to augment the algorithm’s capability to escape from local optima. Using the IEEE 33-node system as a basis, the outcomes indicate that the suggested approach effectively achieves optimal load distribution and efficiently addresses the restructured model.