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Micro/nano-structure is one of the important types of multilayer construction, which can make nanocrystalline metals and alloys with excellent comprehensive mechanical properties. By means of Aluminothermic Reaction (AR) and subsequent rolling with deformation of 50% at room temperature (RT) and followed by annealed at 973 K for 1 h, a heterogeneous composite structure of micro/nano-structured 304 stainless steel (SS) is obtained. Such a stainless steel specimen exhibits a tensile strength as high as 1023 MPa and an elongation-to-failure of about 27.3%. In the process, the much elevated strength originates from martensite strengthening, austenite grain refinement and dislocation density increasing, while ductility promotes from the recrystallization of deformed austenite and recovery of strain induced martensite. Superior strength–ductility combination achieves in micro/nano-structured 304 SS, which demonstrates a novel approach for optimizing the mechanical properties in engineering materials.

The prevalence of diabetes is increasing rapidly and becoming a major public health issue worldwide. We aimed to develop a novel nomogram model for long-term diabetic risk prediction in a Chinese population. A prospective cohort study was performed on 687 nondiabetic individuals who underwent routine physical examination in 1992 and 2007. Using the least absolute shrinkage and selection operator model to optimize feature selection. Multiple Cox regression analysis was performed, and a simple nomogram was constructed. The area under receiver operating characteristic curve (AUC) and calibration plot were conducted to assess the predictive accuracy of the model. The model was subjected to bootstrap internal validation. Of the 687 participants without diabetes at baseline, 74 developed diabetes during the follow-up time. This simple nomogram model was constructed by family history of diabetes, height, waist circumference, triglycerides, fasting plasma glucose and white blood cell count. The AUCs were 0.812 (95% CI: 0.729–0.895) and 0.794 (95% CI: 0.734–0.854) for 10-year and 15-year diabetic risk. The bootstrap corrected c-index was 0.771 (95% CI: 0.721–0.821). The calibration plot also achieved good agreement between observational and actual diabetic incidence. The stratification into different risk groups by optimal cut-off value of 12.8 allowed significant distinction between cumulative diabetic incidence curves in the whole cohort and several subgroups. We established and internally validated a novel nomogram which can provide individual diabetic risk prediction for Chinese population and this practical screening model may help clinicians to identify individuals at high risk of diabetes.