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.

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.

The efficient utilization of electromagnetic spectrum resources has become a significant concern in the domain of wireless communications, with EMSM(electromagnetic spectrum map) playing a crucial role in visually representing spectrum usage within a specific task area and providing valuable support for the optimization of wireless networks. To address the challenges associated with generating fine-grained EMSMs under conditions of complex scenes and limited spatial point monitoring data, an improved DRN(deep residual network) model, ES-AFB(enhanced with a spatial attention feature block), was proposed. This model drew inspiration from image super-resolution techniques and leveraged the strong spatial characteristics of EMSMs to design a deep residual network capable of extracting the correlation and spectral features of EMSMs. The enhanced spatial attention feature block was utilized to mine the intrinsic implicit spatial features of coarse-grained EMSMs. Subsequently, the data size was reconfigured through the network’s multilayer up-sampling module, enabling the achievement of a more effective fine-grained image restoration. This approach allows for the generation of high-quality fine-grained EMSMs using limited coarse-grained monitoring data. The effectiveness of the algorithm is validated through simulation experiments, with the root-mean-square error of the EMSMs generated from actual data being found to be no more than 3%.

In order to study the effect of different scanning strategies on laser deposition of nickel-based alloy matrix, the process of deposition of IN718 alloy powder on IN718 alloy matrix under four different scanning strategies was numerically simulated by ABAQUS software, and tested under the same conditions. The heat source verification results show that the heat source model is accurate and effective, and the numerical simulation process accords with the actual deposition process. The analysis results of temperature field, stress field and deformation field show that the thermal influence of different side scanning is less than that of same side scanning and reciprocating scanning is lower than that of unidirectional scanning, and the peak temperature is lower, thus the residual stress is lower, and the deformation degree of the matrix can be effectively reduced. The experimental results show that the numerical simulation process is accurate and effective. It is concluded that the residual stress value and deformation can be reduced effectively by using the reciprocating scanning method on different sides.

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.

The development of a high-grade waterway network in the Upper Yangtze River will play a crucial role in promoting the economic and social development in the Chengdu-Chongqing region. Waterway improvement through underwater blasting to removal the obstructing rocks is still one of the most effective method in mountainous river management, but its impact on aquatic organisms, particularly on passively drifting fish eggs and larvae, has raised significant ecological concerns. Long-term observational data of drifting larvae in the Upper Yangtze River were analyzed, focusing on their temporal distribution patterns and spatial variations in species composition and abundance. The propagation mechanisms of underwater blasting shock waves were examined, and the primary factors affecting shock wave intensity were identified. Current research on blast-induced fish damage was reviewed, with special attention paid to the effects of shock waves on eggs and larvae. Based on the documented drift patterns of eggs and larvae and the characteristics of shock waves, comprehensive ecological protection measures were proposed. These measures included temporal and spatial avoidance strategies, optimized blasting techniques, and the use of bubble curtain technology. These findings provide valuable insights for achieving sustainable development that balances waterway construction with ecological conservation in the Upper Yangtze River.

China's deep coalbed methane resources have enormous potential. As a clean and high-quality energy, it is of great significance to ensure China's energy security and green and low-carbon development. The development of middle-shallow coalbed methane in China has basically been industrialized, but the development scale is far from the national planning goal. With the new progress made in the exploration and development of deep coalbed methane in recent years, it has provided new impetus for the rapid development of the coalbed methane industry. But when it comes to deep exploration and development, it faces a series of new geological, engineering, and other challenges that need to be tackled. In view of this, based on the latest national oil and gas resource evaluation data, the current situation of coalbed methane exploration and development in China was summarized, and the latest progress in deep coalbed methane exploration and development in typical blocks in China was evaluated. The new breakthroughs in geological evaluation, drilling, fracturing, and extraction of deep coalbed methane in recent years were summarized, and it is pointed out that there are problems in the current exploration and development of deep coalbed methane in China, such as low resource evaluation level, low adaptability of key technologies, and few mature demonstration projects that can be promoted and referenced.On this basis, four countermeasures and suggestions for the development of deep coalbed methane industry were proposed: strengthening resource exploration, strengthening key technical research, speeding up the construction of high-efficiency development demonstration zones, strengthening the co-exploration and co-exploitation of deep coal measures gas, so as to provide reference for promoting the exploration and development of deep coalbed methane in China.

Due to the negative impact of abandoned powdered clay on land waste and pollution, it is beneficial to improve the powdered clay and use it for backfill in engineering construction.The effects of lignin fiber content and cement content on the unconfined compressive strength of silty clay excavated from a tunnel along the Yangtze River in Hangzhou,Zhejiang Province were studied. The formation mechanism of the compressive strength was analyzed. Finally,the pore microstructure of the sample was quantitatively analyzed by SEM experiment. The results show that the compressive strength reached the maximum value when the lignin fiber content is 4%,and the unconfined compressive strength was greater than that when the lignin fiber content is 2%, 6% and 8%. No matter how much lignin fiber content is,the unconfined compressive strength q'_u increases gradually with the increase of cement content. With the addition of lignin fiber,the average diameter of pores gradually concentrated in the range of particle size less than 1 μm,and the proportion of pores 1~2 μm and 2~4 μm increased with the increase of cement content. With the increase of lignin fiber and cement content,pore abundance mainly concentrated in the range of 0.2~0.5.With the addition of lignin fiber,the particle abundance mainly concentrated in the range of 0.1~0.6,and the particle distribution showed a “mountain” pattern with the addition of cement.

Soil seed banks serve as the foundation for plant restoration and succession, playing a crucial role in sustaining biodiversity and ecological balance. The effect of short-term enclosure and grazing ban on soil seed bank was studied in the marsh meadow of Napahai Lake in northwest Yunnan Plateau. Through field sampling and seed germination identification, the effects of grazing and short-term confinement (3 years) on species composition, diversity and functional group structure of topsoil seed bank were compared. The findings show that short-term grazing exclusion significantly alters the diversity of the soil seed bank, reducing the Shannon-Wiener and Pielou indices (P<0.05) and increasing the density of sedge seeds (P<0.05). Weeds dominate in both enclosed and grazed conditions, with grasses and sedges following, and legumes being minimal. The percentage of weed species decreases, while that of sedge species increases under grazing exclusion, with no significant change observed in grasses and legumes. Weed importance values are significantly negatively correlated with grass and sedge importance values (P<0.05), and grass density is significantly positively correlated with sedge density (P<0.05). These results indicate that while short-term grazing exclusion reduces the diversity of the soil seed bank in swamp meadows, it also enhances the replenishment and recovery of sedge seeds, offering new insights for the restoration management of wetland ecosystems.

On-line and efficient monitoring of leakage faults in district heating network can effectively increase the quality of heat transmission and reduce energy consumption. However, the data feature extraction ability of conventional leakage fault diagnosis method is limited, and it is difficult to deal with the high dimensional nonlinear pressure flow monitoring data for complex heating network, which makes its diagnostic performance weak. Therefore, a fault diagnosis model of heating network leakage based on convolutional neural network (CNN) and Transformer was proposed. The proposed CNN-Transformer diagnostic model combines CNN and Transformer network to realize joint learning of different time scales and spatial features. The CNN network was used to extract local features, and the Transformer network was used to capture global features. The validity of the model was verified by simulating the fault data set of the annular heating pipe network system. The results show that the proposed CNN-Transformer diagnosis model based on multi-stage feature extraction and fusion mechanism of fault features significantly improves the accuracy of leak diagnosis. The CNN-Transformer method has the highest accuracy on the test set, with an accuracy increase of 13.21%, 7.49%, 6.1% and 4.62%, respectively, compared to other fault diagnosis methods including long short-term memory network, gate recurrent network, CNN and Transformer.