Traumatic brain hemorrhage(TBH) often results in persistent cognitive and behavioral dysfunctions, with traditional rehabilitation models failing to adequately address self-efficacy and environmental interactions. Stage-based nursing interventions grounded in social cognitive theory(SCT) synergize cognitive restructuring, behavioral reinforcement, and environmental support. However, their application in TBH remains understudied. Aiming to examine the impact of SCT-based stage nursing interventions on cognitive function, self-management, self-efficacy, and daily living ability in TBH patients, a total of 105 TBH patients were randomly assigned to an intervention group(n=53) and a control group(n=52) by using a random number method. The control group received conventional rehabilitation therapy, while the intervention group received stage-based nursing interventions based on SCT in addition to conventional therapy. Changes in cognitive function, self-management ability, self-efficacy, and activities of daily living were compared between the two groups before and after the intervention. Cognitive function was assessed by using the Montreal cognitive assessment-basic(MoCA-Basic), self-management ability was evaluated by using the appraisal of self-care agency scale-revised(ASAS-R-C), self-efficacy was measured by using the general self-efficacy scale(GSES), and activities of daily living were assessed by using the Barthel index. The results reveal that after the SCT-based stage nursing intervention, the intervention group show significantly higher scores on MoCA-Basic, ASAS-R-C, GSES, and the Barthel index compared to the control group(P<0.05), with substantial improvements observed across all indicators. Stage-based nursing interventions grounded in SCT can significantly enhance cognitive function, self-management, self-efficacy, and activities of daily living in TBH patients, demonstrating considerable clinical value.

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.

To investigate the contribution and temporal variability of lake-groundwater interactions in the water balance of flood-controlled lakes, Honghu Lake in the middle reaches of the Yangtze River was selected as a case study. Based on the analysis of water level dynamics of Honghu Lake and surrounding groundwater, the recharge-discharge relationship between Honghu Lake and groundwater were identified and calculated by a water balance equation. The contribution of groundwater-lake interactions to the water balance of Honghu Lake and its temporal variability were examined. The results indicate that there is a significant water level difference between Honghu Lake and the adjacent phreatic groundwater, with dynamic changes in both showing a significant positive correlation, correlation coefficient r=0.729. The aquifer in the Honghu Lake area is permeable, which indicates that there are interaction conditions between Honghu Lake and groundwater. On an inter-annual scale from 2017 to 2022, Honghu Lake generally infiltrate into the groundwater, with an average annual discharge volume of 6.43×10⁸ m³, accounting for 14.11% of the lake's outflow. On a multi-year average monthly scale from 2017 to 2022, Honghu Lake infiltrates into the groundwater during the dry season (September to February of the following year), with an average monthly discharge volume of 0.8×10⁸ m³. During the rainy season (April to August), groundwater-lake interactions are dynamic, with an average monthly exchange volume of -1.17×10⁸~0.88×10⁸ m³. Honghu Lake presents an opposite seasonal variation characteristic of the lake water-groundwater interaction compared with the reported Yangtze-connected lakes such as Poyang Lake and Dongting Lake. This is mainly because the water level of Honghu Lake, affected by water conservancy regulation, has altered the natural interaction process between the lake and groundwater. These findings provide new insights into groundwater-lake interactions in flood-controlled lake systems and hold significance for the management of water resources and ecological protection in the Honghu Lake region.

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.

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.

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.

In order to solve the problems of large heat leakage and unclear stress of the adiabatic support structure in the cryogenic storage tank, a finite element model of a 37.4 m³ storage tank was established by the method of thermal-solid interaction, and the heat transfer, stress and deformation of the tank were analyzed, and the supporting structure was optimized. The results show that the daily evaporation rate of liquid nitrogen is 0.10%/d when the heat leakage through the supporting structure is 62.18 W, and the heat leakage of the supporting structure decreases with the decrease of ambient temperature. The influence of liquid temperature on the storage tank is mainly concentrated in the support structure and the inner tank, and the stress and deformation of the support structure increase greatly after considering the influence of temperature, and the maximum stress of the inner tank is less affected, and the deformation is increased by 11.81 times. When storing liquid hydrogen, the heat transfer of the support structure increases by 26% compared with liquid nitrogen. The topology of the supporting structure under the sliding end was optimized with the minimum heat transfer as the optimization goal. The heat transfer of the “Y” type support structure is reduced by 27.20% and the maximum stress is reduced by 7.73%.

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.

In the process of coal seam mining, it is easy to cause problems such as large roof overhang area and long collapse step, which affects the failure form of the surrounding rock of the roadway and the deterioration and failure of the supporting body. In view of the occurrence of thick and hard roofs in the 113105 working face of the Bojiang Haizi Mine and the instability of the narrow coal pillars along the empty roadway, the use of roof cutting and pressure relief is an effective way to effectively solve the problem of roof overhang, and the design of its key parameters has an important impact on the pressure relief effect. In order to explore the influence of different roof cutting pressure relief heights on the roof stress evolution law above the narrow coal pillar along the empty roadway, the effect of lateral roof cutting of roadway was studied by combining theoretical analysis, numerical simulation and field observation. Based on the slip-revolve stability theory of masonry beams, the mechanical model of the roof of the coal pillar under different roof cutting heights was constructed, and the bearing stress distribution equation of the coal pillar after different roof cutting heights was obtained. FLAC3D software was used to simulate the stress distribution and displacement evolution characteristics of the coal pillar roof under different roof cutting heights. The simulation results show that the slitting surface formed after the implementation of roof cutting technology effectively blocks the stress propagation path, and the vertical stress peak value in the coal body on both sides of the roadway decreases, and the pressure relief effect gradually increases with the increase of the roof cutting height, when the roof cutting height reaches 19 m, the stress of the coal pillar roof decreases by 17.22 MPa, and the pressure relief rate is 43.4%. The displacement of the roof of the coal pillar gradually decreases with the increase of the roof cutting height, and the reduction rate gradually decreases. The field test shows that according to the designed roof cutting height, the stress of the surrounding rock is significantly reduced, and the deformation degree of the roadway meets the normal mining requirements.

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.