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Carbide-free bainitic steels with the bainitic ferrite (BF) as matrix constituent have been widely used and manufactured. The mixed microstructure of pre-formed martensite (M), BF, and retained austenite (RA) was obtained by quenching for a short time below the martensitic transformation temperature followed by austempering. A similar amount of fresh martensite was obtained by interrupted austempering at the same temperature. Results show that the carbon distribution among these constituents in the mixed microstructure plays a major role in controlling mechanical properties. The carbon content of M and BF in the M-BF-RA microstructure is lower than that of BF-M-RA microstructure, while that of RA was reversed. Mechanical properties of the M-BF-RA microstructure exhibit higher toughness, higher product of strength and elongation as well as higher yield ratio, rather than strength, compared to that of the BF-M-RA microstructure. It can be attributed to toughened pre-formed martensite with low carbon content and mechanical stable RA with high carbon content. In addition, the pre-formed martensite decreases the block size, leading to the increase of high angle grain boundaries, which further improves toughness. The high carbon fresh martensite BF increased the strength of the BF-M-RA microstructure, but sharply decreases ductility and toughness.

Susceptibility analysis is important in any study of debris flows. This paper presents a model for debris flow susceptibility analysis using a combination weighting and an improved fuzzy C-means algorithm. Twelve factors of influence were acquired by 3S technologies. Analytic hierarchy process (AHP) and entropy method were performed to obtain subjective and objective weighting of the factors, respectively. Game theory was carried out to determine the combination weighting since it provides analytical tools to model interactions among factors. An improved fuzzy C-means clustering analysis was applied to determine the susceptibility level of debris flows. This method is based on a particle swarm optimization algorithm, which is an evolutionary algorithm that can achieve global optimization, and is not sensitive to the initial cluster centers. Results showed that the susceptibility levels for one of the debris flow catchments was high, four were moderate, and five were low. Our quantitative assessments based on these nonlinear methods were consistent with field investigations.

Context: MicroRNAs (miRNAs) are small, noncoding RNAs that negatively regulate gene expression post-transcriptionally. Whether differently expressed miRNAs contribute to promoting granulosa cell proliferation in polycystic ovarian syndrome disease (PCOS) remains unknown., Objective: We explored whether certain miRNAs are involved in the ovarian dysfunction of PCOS and the mechanism of increased granulosa cells proliferation. Patients and Cells: miRNA expression was analyzed in excised ovarian cortexes from 16 women with PCOS and 8 non-PCOS. An immortalized human granulosa (KGN) cell was used for the mechanism study., Main Outcome Measures: Expressions of miRNAs in ovarian cortexes were measured using qRT-PCR and KGN granulosa cells were cultured for proliferation assays after overexpression or inhibition of miR-93 or after insulin treatment. Bioinformatics were used to identify the potential miRNA targets. Protein expression analysis, luciferase assays, and rescue assays were used to confirm the substrate of miR-93., Results: MiR-93 expression was higher in PCOS ovarian cortex and its identified target, CDKN1A, was downregulated. MiR-93 overexpression promoted cell proliferation and G1 to S transition. Knocking down CDKN1A promoted cell growth and cell cycle progression in granulosa cells, and CDKN1A re-introduction reversed the promotional role of miR-93. High concentrations of insulin induced upregulation of miR-93, stimulated KGN cells proliferation and reduced CDKN1A expression., Conclusions: miR-93 was increased in PCOS granulosa cells and targeted CDKN1A to promote proliferation and cell cycle progression. Insulin could upregulate the expression of miR-93 and stimulate cell proliferation. This might provide a new insight into the dysfunction of granulosa cells in PCOS.