RESUMEN
Aging and deterioration of building structures have been persistent concerns in the field of engineering. To address these challenges while supporting modern green and sustainable development goals, this study introduces an innovative reinforcement system that employs steel textiles as the primary reinforcing material. The steel textiles were engineered by optimizing the compatibility between steel fibers and a hot melt adhesive (HMA). These textiles were then used to reinforce concrete structures, creating a steel textile-reinforced mortar (STRM)-reinforced concrete system. The study also examined the durability of the steel textiles and the interfacial bonding performance within the STRM-reinforced concrete system. The results showed that the compatibility between steel fibers and ethylene vinyl acetate copolymer (HMA-2) is better, and the steel textiles prepared with it have superior hydrothermal and corrosion resistance. After the system had been maintained for 28 days, the overall flexural strength of the STRM-reinforced concrete system was increased by 100.03%, the interfacial shear load by 49.54%, the interfacial shear stiffness by 61.2%, and the positive tensile bond performance by 26.1%. This proves that STRM has a good reinforcement effect on the concrete reinforcement system.
RESUMEN
Steel fiber textile, which is composed of steel fibers and glass fibers, has a support layer impregnated with hot melt adhesive (HMA). During long-term service, the bonding force between the steel fiber/HMA system interfaces is poor. In order to improve the bond strength and durability of the interface, this paper introduced sandblasting, acid-etching, and phosphating treatments on the surface of the steel fibers. Also, the effects of these three pretreatment methods on the bond strength of the steel fiber/HMA interface were investigated. The results indicate that the interfacial bond strength of composites made from steel fibers is improved via surface treatment. Under a hydrothermal and simulated concrete pore solution environment, the durability of the steel fiber/HMA interface after sandblasting and acid-etching pretreatment is not as good as that after phosphating pretreatment. The mechanical properties of the phosphating/HMA composite were maintained at 4.56 and 2.24 times compared to those of the untreated/HMA composite, respectively. This is because the pinning effect formed by the phosphating film on the surface of steel fibers at the interface of steel fiber/HMA can serve as a physical barrier against corrosion, preventing the invasion of chloride ions and water vapor and improving the durability of the interface.
RESUMEN
PURPOSE: We aimed to assess the diagnostic performance of radiomics using machine learning algorithms to predict the methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter in glioma patients. METHODS: A comprehensive literature search of PubMed, EMBASE, and Web of Science until 27 July 2021 was performed to identify eligible studies. Stata SE 15.0 and Meta-Disc 1.4 were used for data analysis. RESULTS: A total of fifteen studies with 1663 patients were included: five studies with training and validation cohorts and ten with only training cohorts. The pooled sensitivity and specificity of machine learning for predicting MGMT promoter methylation in gliomas were 85% (95% CI 79%-90%) and 84% (95% CI 78%-88%) in the training cohort (n=15) and 84% (95% CI 70%-92%) and 78% (95% CI 63%-88%) in the validation cohort (n=5). The AUC was 0.91 (95% CI 0.88-0.93) in the training cohort and 0.88 (95% CI 0.85-0.91) in the validation cohort. The meta-regression demonstrated that magnetic resonance imaging sequences were related to heterogeneity. The sensitivity analysis showed that heterogeneity was reduced by excluding one study with the lowest diagnostic performance. CONCLUSION: This meta-analysis demonstrated that machine learning is a promising, reliable and repeatable candidate method for predicting MGMT promoter methylation status in glioma and showed a higher performance than non-machine learning methods.