In order to explore the effect of dry-wet cycles in acidic environment on the physical and mechanical properties of limestone, and to evaluate the long-term stability of limestone rock mass in this environment, the limestone of the Jinfo Mountain of the Nanchuan District in Chongqing was selected as research subject. The limestone specimens were exposed to dry-wet cycles under neutral and acidic environments. The specimens were treated through mass loss test, hygroscopic property test, uniaxial compression test and tensile test. The results show that under the condition of the same pH of the soaking solution, with the increase of the times of dry-wet cycles, the mass loss rate and saturation water absorption rate of specimens increase; the tensile strength, uniaxial compressive strength and elastic modulus gradually decrease; with the same times of dry-wet cycles, the lower the pH of the soaking solution leads to the more serious the loss of physical and mechanical properties. Based on the experimental results, the damage theory, Weibull distribution, Lemaitre strain equivalence hypothesis and Mohr-Coulomb (M-C) strength criterion, the damage constitutive model of limestone by using a quadratic function to characterize the nonlinear features of the compaction stage of stress-strain curve was established and validated.

The occurrence of natural gas leaks in buried gas pipelines is a serious safety event that can have significant economic and environmental impacts. For large-diameter high-pressure gas transmission pipelines, the computational fluid dynamics (CFD) method was used to establish a three-dimensional numerical model that included a${1.4}\mathrm{\;m}$diameter pipeline and the surrounding soil, to study the leakage characteristics of high-pressure gas through a pre-set leak hole in the soil. The CFD model considered the soil as a porous medium material, used the Redlich-Kwong equation of state to describe the temperature-pressure effects of high-pressure gas, and combined species transport and turbulence models to study the impact of leak hole diameter and internal pipeline pressure on leakage rate and temperature distribution. The results show that the leakage rate increases with the increase of hole diameter and pressure. When the leak hole diameter varies from 10 to${50}\mathrm{\;{mm}}$, the leakage rate increases by${77.78}\%$. Ambient temperature can cause the soil temperature field distribution to take different forms. When the ambient temperature is low, the temperature-pressure effect produced by the leakage of high-temperature gas inside the pipeline will be weakened. When the ambient temperature is close to the temperature of the gas inside the pipeline, a detectable temperature change area is produced in the buried range of 0.7 to 1.2 m above the leak hole. The research results help to understand the leakage characteristics and temperature change patterns of buried large-diameter high-pressure gas transmission pipelines, providing a theoretical basis for the layout of pipeline leak monitoring optical cables.

Semantic segmentation of remote sensing images plays a crucial role in agriculture production, urban planning, and other fields. However, due to factors like imaging distance, lighting conditions, objects, and environment, there is a problem of semantic ambiguity in remote sensing images, which leads to uncertainty in segmentation. A multi-scale context attention (MSCA) method that combined pyramid pooling with attention mechanisms to better utilize contextual information was proposed for this problem. Additionally, this method significantly reduced the computational complexity and memory usage of attention methods. Experimental results on the ISPRS Potsdam dataset demonstrate that the MSCA method achieves superior segmentation performance for target classification with ambiguous semantic information in remote sensing images while almost not increasing memory consumption and maintaining consistent inference speed.

In order to select advanced technologies applicable to civil aircraft, technical characteristics from various fields were integrated to develop an evaluation framework. Five key evaluation dimensions were identified: technology competitiveness, technology readiness assessment, economic impact, engineering methods, and technology standards. From practical case studies, these dimensions were derived and used as the basis for an evaluation index system. A technology application perspective was adopted, utilizing a cloud model and a reverse cloud generator to determine indicator weights. This approach incorporated technical standards from different industries, airworthiness standards, and the entire life cycle of civil aircraft to create comprehensive evaluation guidelines. The results show that this approach effectively compares advanced technologies across different industries, differentiates similar technologies at various levels, and eliminates those that offer no benefit or are unsuitable for civil aircraft. This evaluation approach successfully selects advanced technologies with a high degree of compatibility with civil aircraft.

As a critical unconventional oil and gas resource within the global energy framework, heavy oil has garnered significant attention for its development efficiency. Although steam flooding technology has improved the efficiency of heavy oil production, the phenomenon of steam breakthrough negatively impacts thermal efficiency and reservoir development. Traditional prediction methods have shown inadequate precision and delayed response when dealing with long-term oilfield time series data. Data from 13 steam flooding well groups in the Shengli oilfield heavy oil block were utilized. An innovative approach was adopted, using the instantaneous temperature ratio between production and injection wells as an indicator of steam breakthrough time. Pearson correlation coefficient analysis was employed to select key factors related to steam breakthrough time. Based on these factors, a deep learning model built on the Transformer architecture was developed, achieving accurate predictions of the instantaneous temperature ratio. The predictions closely aligned with oilfield observation data, demonstrating higher prediction accuracy and stability compared to traditional long short-term memory (LSTM) models. The research results not only provide a new perspective for the precise prediction of steam breakthrough time in heavy oil reservoirs but also further validate the extensive potential of deep learning technology in oilfield development applications, supporting the construction of intelligent oilfield management and decision support systems.

Aiming at the prominent problems of ignoring the unnatural connection relationship and interaction relationship between human bodies in two-person interaction recognition algorithm, a two-person interaction recognition network based on improved spatial temporal graph convolutional model was proposed. Firstly, the edge features of joint point data were aggregated by edge convolution to capture the unnatural connectivity relations inherent in the human body. Secondly, the interaction relationship graph between two people was constructed by using the improved relationship network. Furthermore, the branch of edge convolution and the interaction relationship graph were embedded into the spatial temporal graph convolutional network block, which were constructed as an edge-graph convolutional block and interaction relation graph convolutional block. Finally, an improved spatial temporal graph convolution algorithm was proposed to capture both the unnatural connection relationship and the interaction relationship, so as to realized the recognition of two-person interaction behavior. To verify the effectiveness of the network, it was tested on the international public large-scale standard dataset NTU RGB + D. The experimental results show that the network obtain a recognition accuracy of 97.77%, which is an improvement of 4. 28 percentage points compared to the baseline spatial temporal graph convolutional network. It improves the expressiveness of two-person interaction behavioral features, and achieves a better recognition effect than the existing state-of-the-art network models.

In order to accurately calculate the hydrodynamic parameters of the slope rill at any point during the erosion process, and to avoid errors caused by using the average flow rate to calculate the hydrodynamic parameters in the traditional method. Based on the variability and complexity of the development process of slope rills, as well as the characteristics of water sand two-phase flow, the Euler-Euler two-phase flow model was used to calculate and analyze the morphological evolution characteristics and erosion mechanisms of slope rills at different stages of expansion erosion. The results show that the Euler-Euler two-phase flow model can accurately describe the morphology evolution process of slope rill in expanded erosion. Based on the morphology evolution characteristics of slope rill at different stages of expanded erosion, the expanded erosion of slope rill is divided into the period when the rill sidewall is slightly spreading and eroding (the early stage), the period when the expanded erosion become severe with a significant increase in the number and area of amalgamated arcs (the middle stage), and the period when the expanded erosion basically ceased and the rill morphology stabilized (the late stage). The influential factors of slope gradient, initial flow rate, and preset rill width on the Darcy-Weisbach resistance coefficient, Reynolds number, and real-time flow rate are significant. Optimal characterization parameters for different stages of slope rill development, such as erosion arc length and hydraulic radius, are proposed, aiding in determining the specific period of slope rill development and predicting the development trend of rill morphology through changes in these parameters. The research results provide a theoretical basis for soil erosion control measures and are of great significance for soil and water conservation.

As a widely distributed and abundant clean energy source, geothermal energy may lead to inefficient resource utilization and a series of ecological environmental issues when improperly developed. The typical geothermal distribution area in Linqing, Liaocheng City, Shandong Province was selects as the research object. Based on detailed geothermal geological survey data, the construction of a "multi-well coordination system" geothermal heating model was explored and the feasibility analysis with operational benefit was conducted. The results demonstrate that the proposed coordinated multi-well geothermal heating mode can reduce geothermal resource extraction by 41.46% while maintaining equivalent heating coverage and quality standards, significantly enhancing maximum utilization efficiency of geothermal resources. The system simultaneously achieves geothermal tailwater reinjection with favorable economic returns. The static investment payback period approximates 3 years, and the total revenue over 20 heating seasons reaches 25.742 million yuan. Through zoning division implementation in key operational areas, this model effectively addresses challenges including dense well distribution, uneven development patterns, and difficulties in reinjection well construction. The findings provide technical references and application demonstrations for geothermal heating development in other regions.

Reinforcement and water sealing effect difficult to achieve by conventional stratum grouting method in strong seepage sandy soil stratum. In order to study the mechanical characteristics of freezing-grouting combination in water-rich sand stratum, the stress-strain relationship and its influencing factors of artificially frozen cement sand were studied by triaxial tests, and the variation law and strength mechanism of stress-strain relationship of samples under different freezing temperature, curing age and confining pressure were discussed. The results show that the stress-strain curve of frozen cement-sand has a certain strain-hardened nonlinear ductility stage including compaction stage linear elasticity stage nonlinear ductility stage and strain-hardening stage. The non-linear increase of cohesion and internal angle of frozen cement-sand due to the curing age of freezing temperature increases the shear strength of frozen cement-sand, and the non-linear ductility phase strain ratio enhances the brittleness of frozen cement-sand. The increase of confining pressure increases the shear strength of frozen cement-sand, and the proportion of strain in nonlinear ductility stage improves the ductility of frozen cement-sand. Based on Mohr-Coulomb strength criterion, a non-linear strength prediction model of frozen cement sand was established, which considered the influence of freezing temperature and curing age. The error between the predicted results and the measured values is less than 5% The research results can provide parameter support for the fine design of freezing-grouting combined reinforcement scheme for water-rich sandy soil stratum.

Capsule networks can encode the properties and spatial relationships of skin cancer image features, thereby overcoming the disadvantage of information loss in the pooling process of convolutional neural networks. Aiming at the problem that only shallow features can be extracted and the convergence performance of the squash function in capsule networks, a ResNeXt cascaded with capsule networks was proposed for Rs-Capsnet networks. Firstly, the complex features of the image were learned using the ResNeXt network. The Inception module and the residual connection were used to extract the deep features, and the weights of the feature map were adjusted and delivered to the capsule module through the CBAM attention module. Then, an improved squash function capsule network was used to complete the classification. Finally, the improved network was compared with mainstream models. The results show that Rs-Capsnet exhibits better performance in skin cancer image classification.