The silica with the highest content in sandstone was taken as the main research object. The SiO₂ surfaces underwent hydroxyl (—OH) and methyl (—CH₃) treatments to represent the hydrophilic and hydrophobic walls, respectively. LAMMPS software was utilized to implement molecular dynamics simulations and replicate the process of supercritical CO₂ extraction of crude oil components. The results indicate that the temperature is 333.15 K and the pressure is 20 MPa. The hydroxylated silica surface extracted 5.07% more saturated hydrocarbon molecules using supercritical CO₂ compared to the methylated silica surface. The interaction energy between the oil components and the two wall surfaces is mutually attractive. The interaction energy of saturated hydrocarbon molecules decreases by 77.52% for hydroxylated silica surface and 46.04% for methylated silica surface, respectively. Additionally, the interaction energy of methylated silica on saturated hydrocarbon is greater than that of the hydroxylated silica surface.It is important to note that carbon dioxide easily extracts saturated hydrocarbons with short molecular chains. The diffusion coefficients of crude oil components under two surface conditions are saturated hydrocarbons > resins > aromatic hydrocarbons > asphaltenes.

In order to enrich and improve the geohazard-pregnant mechanism of structural mélange, taking the North Lancang River suture zone as an example, the structural petrology characteristics of the structural mélange were dissected, then the physical and mechanical characteristics were identified through field geological investigation and laboratory test analysis, and finally its geohazard effect were revealed. The results show that the tectonic mélange in the northern Lancang River suture zone is sandwiched between granite and granite gneiss on the southwest and volcano-sedimentary rocks on the northeast as a non-abutment layer. The main rock types are phyllite and slate with a small amount of basalt, limestone and siliceous rock blocks, showing obvious “hybrid accumulation” characteristics. Under the coupling process of tectonic uplift, differential weathering and river erosion, it gradually evolved into a mountain deep canyon landform, which is the topographic basis for the development of geohazard. In terms of structural petrology, the structural mélange in the North Lancang River suture zone shows the characteristics of “one weak and three strong”, that is, weak metamorphism, strong deformation, strong alteration and strong orientation, which leads to serious deterioration of rocks and is the source basis of geohazard. In terms of physical and mechanical properties, the tectonic mélange appears as a set of easy to slip soft rock “formation”, rich in clay minerals, with high porosity and water absorption, low compressive and shear strength, which provides structural conditions for the development of geohazards. Tectonic activity and tectonic stress are strong in the North Lancang River suture zone, which provides dynamic conditions for the development of geohazards. The research results are conducive to further enriching and improving the geohazard breeding mechanism of tectonic mélange.

A mathematical model was established to address the multi-constraint, large-scale three-dimensional bin packing problem. A hybrid metaheuristic algorithm combining an improved whale algorithm with simulated annealing was proposed. The algorithm discretized the whale algorithm, including individual encoding and updating mechanisms, and utilized simulated annealing to overcome local optima traps. Moreover, a heuristic loading rule was designed for decoding and optimizing the packing solution. The algorithm was evaluated using standard packing instances from Bischoff and Ratcliff's OR-Library, as well as real-world cargo order data, covering a range of cargo types from weakly heterogeneous to strongly heterogeneous. The proposed algorithm achieved a balance between global and local search capabilities, resulting in high packing efficiency for various types of containers. Specifically, the average container filling rate were 92.24% for weakly heterogeneous cargo, 88.78% for strongly heterogeneous cargo, and an overall average of 91.29%. This result provides valuable insights and references for the study of three-dimensional bin packing problems.

In order to improve the accuracy of network traffic classification, a traffic classification method combining an attention mechanism and a convolutional neural network was proposed. An attention mechanism layer was designed and implemented on the basis of the convolutional neural network model, which received the output of the fully connected layer as input, calculated the weight of the input features, and multiplied it by the original features to strengthen the key features. This, in turn, helped to improve the model's ability to capture key information. Secondly, in order to solve the problem that the model was overfitting to the high-proportion category due to the unbalanced sample number of network traffic categories, and it was difficult to identify the small-proportion categories, a method to augment the dataset was proposed. Considering the perspective of hyperparameter combination optimization, a hyperparameter search strategy based on Bayesian optimization and five-fold cross-validation was proposed to optimize the hyperparameter combination of the model. The combination of hyperparameters of the model was determined by the above methods. The public dataset was used for the above experiments and model tests. The results show that compared with other methods, the overall accuracy, precision, and F₁ score are significantly improved, which verifies that the proposed method has better classification performance.

Exploring the spatiotemporal evolution of FVC(fractional vegetation cover) in the East Pamirs Plateau and the driving mechanisms of FVC by the driving forces, providing scientific data support and reference for vegetation protection in the study area. Based on Landsat remote sensing image data, meteorological data, DEM (digital elevation model)data and other data in 1993, 2000,2007,2014 and 2021, pixel dichotomous model, Markov transition model, spatial auto-correlation analysis and geodetector were used to research on the spatiotemporal evolution and detection driving forces of vegetation coverage in the East Pamirs Plateau. Results show as follows. The FVC in the East Pamirs Plateau is exhibiting a fluctuating upward trend, in terms of spatial distribution, is overall characterized by “higher in the east, lower in the west and extremely low in the medium”. The spatial autocorrelation analysis of vegetation coverage in the East Pamirs Plateau from 1993 to 2021 showed a significant positive spatial autocorrelation, with the global Moran's index of FVC 0.27~0.40. Local spatial autocorrelation shows that the vegetation coverage in local areas is dominated by low-low aggregation and high-high aggregation. Factor detection results showed that the explanatory power (q) of various driving factors on the spatial heterogeneity of vegetation coverage in the study area fluctuated to varying degrees from 1993 to 2021, with land use and DEM were the main driving factors of FVC in the East Pamirs Plateau. Results of the interaction detection showed that the influence of interaction between driving factors is greater than that of a single driving factor, and the interaction results are all enhanced.

In order to strengthen the heat-mass transfer performance of humid air-spray water outside staggered tube bundles (STB), an analytical model was constructed for heat-mass transfer performance of humid air-spray evaporative cooling in staggered tube bundles based on the coupled method of DPM(discrete phase model) and Wall film model. The verification results show that the error was less than 1.1% for simulation and parameter results. Meanwhile, the influences were studied for three key structural parameters on heat-mass transfer performance. The results show that the heat transfer performance is improved between tube wall and spray water with the increase of longitudinal and transverse spacing of tube bundles. However, the mass transfer performance decreases of humid air-spray water with the increase of transverse spacing. Meanwhile, Nusselt number increases by 33.3% with the increase of longitudinal spacing from 30 to 70 mm, increases by 73.5% with the increase of transverse spacing from 10 to 50 mm. Besides, the heat transfer performance proves to be better when contact area increases between tube bundle and spray water with larger pipe diameter. At a certain transverse and longitudinal spacing, the lowest humid air temperature and highest enthalpy are located on the maximum pipe diameter (24 mm), and its decreasing and increasing degrees are 11.2% and 35.6%, respectively. The above results can provide a theoretical basis for optimizing the structure of staggered tube bundles and improving the heat-mass transfer efficiency.

Rock mass classification is a fundamental component in tunnel engineering construction. With the rapid advancement of mechanized and intelligent construction technologies in China, drilling-parameter-based intelligent rock mass classification methods have become crucial in facilitating smart mechanized tunneling. This need is especially pronounced in the mountainous regions of Western China, where complex terrain and challenging construction, combined with limited experience in mechanized tunneling and the restricted applicability of current intelligent rock mass classification methods, make mechanized construction crucial for improving project quality and effectively controlling construction risks. A predictive method was proposed for intelligent rock mass classification using drilling measurement parameters. Focusing on multiple long tunnels as research subjects, on-site drilling parameters were collected and rock mass mechanical tests was conducted to construct a drilling parameter database, then intelligent algorithms was applied, such as support vector regression (SVR) and particle swarm optimization-back propagation (PSO-BP), to develop a predictive model for rock mass classification. The result indicates that the absolute value of correlation coefficient |r_s| between drilling parameters and rock mass classification indices is greater than 0.6, demonstrating a significant correlation, where torque and rotational speed show the strongest correlation with rock mass classification indices. A standardized parameter index database with 574 ideal samples was established through data-cleaning tools. Comparative analysis of predictive accuracy across intelligent algorithms indicated that the PSO-BP model demonstrated the best performance. The PSO-BP neural network-based prediction model was validated by transient electromagnetic (TEM) and tunnel seismic prediction (TSP) advanced geological forecasting, confirming its accuracy in predicting rock mass classification and providing reliable support for mechanized tunnel excavation.

The strength reduction method is an essential approach for calculating the slope safety factor, with its computation reliant on the criterion for slope instability during the process of strength reduction. Among the commonly employed criteria for slope instability, displacement mutation stands out; however, its determination remains relatively subjective at present. The maximum slope displacement was considered as a function of the strength reduction factor based on the strength reduction method. The standard deviations of the maximum slope displacements for different strength reduction factors were calculated. Consequently, a method for determining the slope safety factor within the strength reduction method, based on the standard deviations of the maximum slope displacements, was proposed. The method was validated through a typical case study and subsequently applied to a practical engineering project. The results demonstrate that the proposed method can objectively identify the occurrence of slope displacement mutation during the strength reduction process and effectively ascertain the slope safety factor. The proposed method is especially applicable for practical slope engineering projects requiring quantitative comparison of safety factors for different cases.

Natural cooling is one of the more energy-efficient and widely used cooling methods in data center air conditioning systems. Based on the climatic characteristics of Suzhou, Beijing, Guiyang, Guangzhou and Urumqi, the natural cooling system model of data centers was established by TRNSYS, and then the energy saving effect of data centers under the same chilled water supply and return temperatures in different regions was studied. The results show that when the chilled water supply/return temperatures are 15/22 ℃, the longest time for complete natural cooling is in Urumqi, accounting for 67.4% of the year, and the shortest time is in Guangzhou, accounting for 10.2% of the year; the longest time for complete mechanical cooling is in Guangzhou, accounting for 63.0% of the year, and the shortest time is in Urumqi, accounting for 4.3% of the year; the longest time for part of natural cooling is in Guiyang, accounting for 31.8% of the year, and the shortest time is in Beijing, accounting for 4.3% of the year. The longest part of natural cooling time is in Guiyang, accounting for 31.8% of the year, and the shortest is in Beijing, accounting for 18.6% of the year. At full load, the lowest annual average system power usage effectiveness (PUE) was in Urumqi at 1.227, and the highest PUE was in Guangzhou at 1.299. The average annual PUE of the five cities decreases as the load factor increases, and the effect of air conditioning load on the average annual PUE becomes smaller as the load factor increases. The findings provide theoretical support for guiding the application of natural cooling technology in data centers in different regions.

As essential components in power conversion modules, rectifiers are extensively utilized in power supply systems such as inverters, where their operational reliability directly influences the overall system performance. In order to enhance the reliability of rectifiers, it is critical to conduct lifespan predictions for sensitive components, particularly rectifier diodes. A predictive model was proposed that employs an improved grey wolf optimization (GWO) algorithm to optimize the hyperparameters of a simple recurrent unit (SRU) network. Initially, a power cycling accelerated aging test was performed on the diode, followed by an analysis of its characteristic parameters, with forward voltage drop identified as the primary aging indicator. Subsequently, the improved GWO algorithm was applied to optimize SRU hyperparameters—such as learning rate, number of hidden layers, and iteration count—thereby establishing a hybrid predictive model. Finally, the model was trained and validated using aging test data, with predictive accuracy compared against alternative models. The results show that the proposed model achieves superior predictive accuracy, and the data-driven predictive approach enhances the precision of diode lifespan estimation compared to conventional analytical modeling methods, thereby contributing to enhanced operational reliability of rectifiers.