Fracturing and packing is a key technology for maintaining and enhancing production in medium-to high-permeability unconsolidated sandstone reservoirs. However, after production begins, the loose cementation of the reservoir, combined with proppant embedment and formation sand invasion, significantly reduces fracture conductivity. Currently, there is a lack of methods to predict fracture conductivity under the combined effects of proppant embedment and formation sand blockage in such reservoirs. A fracturing and packing simulation device was used to conduct composite experiments on proppant embedment and formation sand blockage under closure stresses ranging from 5 MPa to 20 MPa, unconsolidated rock plate samples were used to simulate fracture surfaces. Based on the experimental results, the controlling factors and developed models were analyzed to predict permeability loss due to proppant compaction, fracture width loss caused by embedment, and dynamic permeability changes due to formation sand blockage. The results show that proppant embedment and compaction after fracture closure significantly reduce fracture conductivity, with the main factors being closure stress, reservoir strength, and particle sizes of the proppant and formation sand. Formation sand blockage also exhibits a time-dependent effect, contributing to dynamic conductivity decline. In a typical unconsolidated sandstone reservoir in the Bohai Oilfield, the calculated fracture width loss due to embedment is approximately 19.34%, permeability loss from closure and compaction is about 34.15%, and dynamic permeability loss from formation sand invasion is around 22.89%. The combined effect of these factors results in a total fracture conductivity loss of approximately 59.06%. To prevent excessive blockage, it is recommended that the initial fracture width be maintained at no less than 12.5 mm, large-particle proppants be used, and production rates be controlled during the early production phase. The research results provide important guidance for optimizing fracturing and packing parameters and improving production in unconsolidated sandstone reservoirs.

The traditional particle swarm optimization (PSO) algorithm still has shortcomings in terms of performance and efficiency of cloud computing task scheduling, such as low local search efficiency and limited search accuracy, which often makes it difficult to find the global optimal solution and easily falls into the local optimal solution. To solve this problem, an improved particle swarm optimization task scheduling algorithm(IPSO) was proposed. Firstly, a opposition-based learning strategy was used to create a more homogeneous initial population and the Rate of convergence of this algorithm was enhanced. Secondly, in the particle update process, the sine cosine algorithm(SCA) was introduced to enhance the optimization ability of the particles and balance the two processes of global search and local development. Finally, a search behavior based on average fitness was added to further expand the search solution space to find better optimal solutions and prevent falling into local optima. Experimental verification was conducted on the CloudSim simulation platform. The experimental results show that the improved particle swarm algorithm has significant advantages in reducing the cost and maximum completion time of system tasks. In particular, when the number of tasks reaches 500, IPSO improves the total cost by 10%, 4.6%, 8.6%, 9.2%, 8.2%, 10.4% and 11.3% respectively compared with adaptive particle swarm optimization (AdPSO), sine cosine algorithm-particle swarm optimization (SCA-PSO), simulated annealing particle swarm optimization (SAPSO), enhanced phagocytosis genetic algorithm (EPGA), competitive crossover mechanism genetic algorithm (C2PGA), opposition based learning-particle swarm optimization (OBL-PSO) and PSO, and improves the maximum completion time by 34.1%, 27%, 41.7%, 28.5%, 21.6%, 50.3% and 54.8% respectively, which verifies the feasibility and effectiveness of IPSO in solving cloud computing task scheduling problems under different task scales.

Aiming at the current problem of rail transit feeder buses being affected by competition from shared motorcycles, which has led to a significant loss of passenger flow, the service quality of feeder buses was studied and evaluated in order to enhance the competitiveness of feeder buses. Firstly, a questionnaire was designed to collect passenger satisfaction data, and the object importance of each service index of the feeder bus in the passenger perspective was obtained through the random forest algorithm. The subject importance degree of each service indicator under the experts' perspective was obtained through the analytic hierarchy process(AHP), and the competitive importance degree of the service indicators under the competition with shared motorcycles was obtained. Next, the evaluator weight determination method based on the stakeholder perspective was used to weight the combination of the three importance degrees to obtain the comprehensive importance degree of the feeder bus service indicators, and the importance-performance analysis(IPA) matrix was constructed to classify the indicator improvement priority. Finally, the technique for order preference by similarity to an ideal solution (TOPSIS) was used to confirm the specific priority of the service indicators to be improved by combining the comprehensive importance degree and satisfaction degree. The results show that waiting time, transfer fare and ride congestion are the three most effective indicators for improving the service quality of rail-connected buses, and the priority weights for improvement are 0.368, 0.235, and 0.164, respectively. Among them, the waiting time shows high importance under all three perspectives, and is the most prioritized key factor for improvement. Two service indicators, transfer fare and travel time, have high importance in the expert and competitive perspectives, respectively, suggesting that perspectives other than passenger perceptions can also reveal the key role of different indicators in improving service quality. A proposed comprehensive assessment method based on the importance of service indicators in multiple perspectives and the quantification of improvement priority, which can more accurately assess the service quality of feeder buses and provide the direction of improvement.

The confined space and fluctuating brightness levels inside and outside highway tunnels result in notable disparities in driving behaviors across various sections. It's difficult to achieve differential management of various sections within tunnels due to the challenge of implementing uniform warning and control across the entire roadway. Based on the Tongji road trajectory sharing platform (TJRD TS), continuous microscopic parameters of vehicles were extracted to quantify driving characteristics using eight indicators. This approach was aimed at analyzing the differences in driving behavior and safety risks of vehicles at different tunnel locations. Based on unsupervised learning algorithms, a segmenting method was proposed for highway tunnel sections that considers driving characteristics. Firstly, principal components analysis (PCA) was employed to determine the main features representing driving behavior and traffic safety. Subsequently, the K-means clustering algorithm was utilized to divide the distribution of main features along the tunnel direction into segments. Finally, the rationality of tunnel section division was validated through significance analysis. The results show that the driving behavior and safety vary significantly at different positions within the tunnel. Based on driving characteristics, the tunnel sections are segmented into six parts using PCA-K-means clustering: approach section, entrance section, transition section, middle section, exit section, and departure section. The entrance and transition sections exhibit high variability in speed changes and unstable traffic flow, while conflict frequencies are high in the transition and exit sections, with vehicle deceleration and acceleration reaching peak values of 14.89% and 15.65%, respectively. The research results reveal the evolution pattern of vehicle driving characteristics within tunnels and facilitates effective segmentation of highway tunnels. The research results contribute to the formulation of proactive safety control strategies for tunnel vehicles and the realization of precise vehicle-road cooperative control.

Remote sensing images are characterized by diverse scales, dense arrangement and small target sizes, etc. Aiming at the problem that there is much background noise in remote sensing images and vehicle targets are small and difficult to be acquired. A vehicle target detection algorithm based on improved feature fusion method, Atiny-YOLO was proposed. Firstly, an additional detection layer for small targets was introduced into the Neck layer of YOLOv5 so as to generated a small target detection algorithm for drone remote sensing images. Neck layer to introduce an additional detection layer for small targets, so as to generated a larger-scale feature map and effectively identified the detailed features of small objects. Secondly, a split operation was added to the C3 module to reuse the image feature information, and the Swin Transformer module was further optimized to improve the usage rate of the effective information. Lastly, by improving the feature fusion channel, the detection accuracy was improved while the model parameters were reducing the model parameters. The Atiny-YOLO algorithm was tested on the AU-AIR(aerial universal autonomous inspection and recognition) dataset. The experimental results show that the average detection accuracy of the Atiny-YOLO algorithm compared to the baseline algorithm is improved by about 2.9%. It reaches 95.5% and the detection speed reaches 234 frames/s. These results verify that the Atiny-YOLO algorithm meets the real-time performance while the model detection accuracy is greatly improved.

The mechanical properties of carbon fiber reinforced polymer (CFRP) composites are significantly impacted by residual stresses, which can even induce material cracking. Consequently, the accurate measurement of interlayer non-uniform residual stresses in CFRP laminates is of paramount importance for improving their manufacturing processes. The incremental hole-drilling method was employed to measure the interlayer non-uniform residual stresses in CFRP laminates. Finite element simulation was used to calculate the standard coefficient matrix between the released residual stresses and strains released in each layer. Coefficient matrix in conjunction with the measured strains was utilized to compute the residual stresses within each layer of the CFRP. The results indicate that the CFRP laminates exhibit an overall stress distribution characterized by compressive stresses externally and tensile stresses internally along the thickness direction. Furthermore, the measurement variance of residual stresses increases with the increase in drilling depth, and the interlayer residual stress values and their non-uniformity are higher in the layers closer to the center of the plate.

To enhance the long-term displacement prediction accuracy of landslides, the GCformer model was applied to landslide displacement forecasting, and a novel landslide displacement prediction approach grounded in the GCformer model was proposed. This methodology leveraged rainfall and displacement as input variables, utilized the GConv_msk module to capture the global information of the sequence, and combined a linear scaling technique of sequence length to efficiently extract data features. Concurrently, the PatchTST model was employed to automatically extract short-term and long-term signals from the sequence data, in order to obtain more comprehensive historical information and bolster the model's robustness and modeling capability. Finally, the landslide displacement monitoring data from Jinliuping Village and Yuanshitan Village in Huichuan County, Dingxi City, Gansu Province, were utilized for case validation. The findings demonstrate that the proposed model exhibits superior prediction accuracy and reliability. In comparison to the Autoformer model and the FEDformer model, the GCformer model is found to achieve the lowest error in both total displacement and vertical displacement.

Basalt laterite weathering profile is very suitable for studying the geochemical behavior of elements under extreme weathering. A laterite weathering profile developed on the Middle Pleistocene Duowen Formation basalt in Lingao County, northwestern Hainan Island was reported. Detailed analysis of main-trace elements, pH, Eh and cation exchange capacity (CEC) were carried out on 84 profile samples. The migration and redistribution behavior of elements in the profile was studied by mass balance calculation. The laterite weathering profile of Lingao in Hainan Island has high Fe₂O₃(17.0%~41.6%) and Al₂O₃(15.3%~28.4%), low SiO₂(10.6%~43.6%), and very high chemical index of alteration (CIA) (average 99.3). It reflects that the weathering profile has experienced strong chemical weathering with Fe and Al enrichment, and desiliconization under extreme weathering conditions. The mass balance calculation results show that alkali metals and alkaline earth metals are mostly lost along the whole pofile with a high degree. Among the transition metals, Sc, Cu and Zn are leached to a high degree in the section, V and Ni are enriched in the top and Ⅳ layer of the saprolite, respectively, and high field strength element (HFSE) are leached with different degrees in the profile. Among the redox sensitive elements, Fe mainly precipitates and accumulates in the form of Fe (OH)₃ at the top of the saprolite. Cr exists as water-insoluble Cr₂O₃ in the profile and is enriched at the top of the saprolite. Mn and Co exist in the form of soluble Mn²⁺ and Co²⁺, and their enrichment is caused by the dissolution of oxides containing Mn²⁺ and Co²⁺ during weathering. U precipitates and accumulates in the form of UO₂ at the bottom of the saprolite, while U in other layers exists in the form of soluble UO₂CO₃ and $\mathrm{UO}_{2}^{2+}$. The enrichment behavior is related to the adsorption of iron hydroxide in the profile. The slight enrichment of uranium throughout the profile may be due to groundwater introduction. It is found that the formation of ferrite laterite in Lingao section should be caused by the obvious leaching of Al and the enrichment of Fe at the top of the saprolite, while the ferrite laterite in Wenchang section is the product of both Fe and Al enrichment, which is helpful to understand the difference between the laterite weathering products of basalt in northeast and northwest Hainan Island, and has certain indicative significance for the development and utilization of mineral resources in the future.

The conflict between coal resource extraction and ecological environmental protection is particularly pronounced in the Gaojialiang coal mine of Inner Mongolia. To accurately characterize the deformation extent and evolutionary patterns of mining-induced ground subsidence within the study area, small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology combined with Sentinel-1 radar remote sensing data were utilized to obtain the annual average deformation velocity and time-series cumulative deformation over three primary panels. Additionally, the Kriging interpolation method was employed to predict and supplement data in decoherence regions, ensuring comprehensive coverage of the deformation field. The results show that three distinct subsidence zones are identified, spatially correlated with the mined-out areas of panels 203, 301, and 401, respectively. The subsidence is characterized by slow deformation, with a peak annual average deformation velocity of approximately -34 mm/a. The temporal initiation and spatial propagation of subsidence in the three panels align closely with the actual mining sequence and operational conditions. Among these, panel 401 exhibited the largest subsidence area, covering approximately 4.56 km², with a maximum cumulative deformation of -189 mm, followed by panels 301 and 203 in descending order. Ground fractures identified through high-resolution optical remote sensing imagery are consistent with field investigations, predominantly distributed in the zones of maximum deformation intensity. Based on the deformation characteristics and fractures distribution, three high-risk geohazard zones are delineated within the study area. The primary driver of ground subsidence is attributed to longwall mining activities, while geological structures and precipitation infiltration also contributed to the deformation process. SBAS-InSAR technology has good application effects in monitoring large-scale mining-induced ground subsidence, and can provide crucial technical and data support for geological disaster prevention and ecological environment restoration in Gaojialiang mining area.

In the coal-water slurry gasification system, a reduction in the temperature of the syngas pipeline can cause acid gases to condense, which may lead to corrosion of the pipeline's inner surface and potentially result in perforation leaks. To enable prompt detection and precise localization of any leakage or damage within the syngas pipeline, The techniques were explored for identifying and locating such issues through distributed temperature sensing(DTS). An algorithm based on an adaptive variance threshold was proposed for DTS detection and localization. Initially, hierarchical clustering was utilized to recognize detected signals, facilitating differentiation between normal operating conditions and those indicative of leaks or damages. Following this, identified leak signals undergo processing via variance analysis combined with adaptive threshold settings to accurately determine leak or damage locations. This approach shows improved accuracy in pinpointing leak or damage sites compared to fixed threshold methods as well as selective average threshold methods, enhancing positioning precision by 0.32 m and 0.17 m respectively. A temperature measurement experiment conducted at a coal gasification facility successfully confirmed accurate identification of leakage or damage points.