中国人民解放军陆军勤务学院

Epoxy resins (EPs) have gained widespread industrial adoption due to their exceptional mechanical properties, chemical resistance, and electrical insulation characteristics. However, their inherent brittleness caused by highly cross-linked networks significantly limits their application under impact loading conditions. This study demonstrates a solvent-free strategy for significantly enhancing the toughness of epoxy resins while maintaining their mechanical strength and thermal stability through incorporation of hyperbranched epoxy resins (HPEs). At an optimal 15 wt% loading, the HPE-modified epoxy system exhibits a remarkable 120% improvement in impact strength with simultaneous enhancement of tensile and flexural properties, accompanied by only a minimal 3°C reduction in glass transition temperature. Comprehensive characterization reveals that this exceptional performance stems from three synergistic mechanisms: free volume expansion (from 0.15 nm³ to 0.21 nm³), stress-induced microvoid formation, and optimized crosslinking via terminal group interactions, offering new insights for developing high-performance epoxy composites through sustainable processing.

The article is based on language model, through the cue word engineering and agent thinking method, automatic knowledge extraction, with China accounting standards support to complete the corresponding knowledge map construction. Through the way of extracting the accounting entities and their connections in the pattern layer, the data layer is provided for the fine-tuning and optimization of the large model. Studies found that, through the reasonable application of language model, knowledge can be realized in massive financial data neural five effective extracted tuples, and complete accounting knowledge map construction.

Urinary calculi and renal cancer are significant urological conditions that may share overlapping etiologies. Despite common links to metabolic dysfunction and chronic inflammation, their co-occurrence and shared molecular mechanisms remain underexplored. This study aimed to evaluate clinical, biochemical, and molecular associations between these conditions. A cohort of 526 patients was analyzed for demographic data, clinical features, and laboratory markers. Molecular analyses were performed on key renal cancer–related genes: VHL, PBRM1, and MET. Structural models were generated using RCSB PDB data, and bioinformatics techniques were employed to assess protein expression and mutation frequency. Obesity (OR = 2.1), hypertension (OR = 1.8), diabetes (OR = 1.7), and hypercalciuria (OR = 2.3) were all identified as significant risk factors (p < 0.05). Molecular analysis revealed frequent mutations in VHL (23.6%), PBRM1 (20.9%), and MET (18.6%). A pro-inflammatory environment was indicated by elevated oxidative stress markers (ROS, MDA) and inflammatory biomarkers (CRP, IL-6). Structural studies of VHL and MET proteins revealed conformational changes that may affect their biological activity. The co-occurrence of urinary calculi and renal cancer suggests a shared pathogenic mechanism involving chronic inflammation, metabolic dysfunction, and genetic alterations. These findings underscore the need for comprehensive clinical management and early genetic screening to reduce risk and improve outcomes.

Based on the massive applications of FRP-reinforced structure technology,the time-varying patterns of interfacial stresses of FRP-strengthened RC beams under sustained load are investigated in this study.A finite element(FE)model was developed to analyze the external FRP strains,and the correctness and usability of the FE method were verified by comparing the predicted results with the test results.For the FRP end area,both the FE method and the analytical method were applied to conduct investigations considering the spew fillet of adhesive and the secondary loading of the reinforced structures.For the area near the intermediate concrete crack,the analytical method was used to conduct calculations.The results show that the concrete creep leads to the increase of interfacial stresses with time,and the adhesive creep leads to the relief of stresses.This conclusion is applicable to both areas mentioned above and is not affected by the spew fillet of the adhesive and the secondary loading of the reinforced structures.In the FRP end region,the FE results and analytical results are in good agreement.

To enhance the resilient protection of underground engineering,real-time monitoring of cracks in the whole engineering is of great significance.In the present paper,a real-time localization method for structural cracking is proposed.Through the method of transfer learning,the picking model of structural cracking direct acoustic wave and reflected acoustic wave was established after training the deep learning model of seismic phase-pick with 1 847 sets of training data.Automatic recognition and extraction of cracking acoustic signals is realized in this way.To solve the problem that the localization accuracy of broadband multi-signal classification(MUSIC)algorithm is reduced due to spatial aliasing,the equation between the direction of sound source and inter channel phase difference is established through the relationship of time delay in spatial and frequency domain.The narrow bands with two specified positive phase differences are used to calculate spatial spectral in localization after further analysis of the relationship between phase difference and spatial aliasing.The in-situ lining cracking simulation test verifies that the improved algorithm effectively reduces the spatial aliasing and improves the positioning accuracy.The average error distance between the estimated cracking point and the actual cracking point is 0.05 m.The monitoring of the extending direction and length of crack can be realized after connecting each cracking point,which can meet the engineering needs.