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At high cutting speeds, the internal microstructure of the difficult-to-machine material Inconel 718 will change. Alterations in the internal microstructure lead to macroscopic deformations, which in turn result in the formation of adiabatic shear bands. In order to better study the formation of adiabatic shear bands during cutting Inconel 718, cutting experiments, SHPB experiments, and finite element simulations were carried out. The stored energy model for adiabatic shear bands was developed by combining the overall dislocation density and the variation of the stress field in this paper. Based on the strain gradient theory, the subroutine was compiled for secondary development to achieve multi-scale finite element simulation that can visualize the stored energy. The experiment and simulation results were combined to derive the change in stress, temperature, and overall dislocation density and to analyze the adiabatic shear bands storage energy based on the change. The analytical results are also combined with dynamic recrystallization and periodic fracture theories to characterize the formation mechanism of adiabatic shear bands. It was found that the overall dislocation density, stress, and adiabatic shear bands storage energy follow the same trend, while the temperature and shear zone storage energy follow opposite trends. [ABSTRACT FROM AUTHOR]

Deep learning has demonstrated notable success in mechanical signal processing with a large amount labelled data. However, the systems of the Jiaolong deep‐sea submersible prone to malfunction are typically diverse, due to the high complexity of its structure and operational environment. Consequently, this diversity gives rise to variations in the types of sensor signals and their associated data distributions that require analysis. Unsupervised domain adaptation (UDA) uses transferable knowledge derived from the source domain and applies it to an unlabelled target domain in order to improve the reusability of pre‐existing models and data. Inspired by the diffusion models that have the robust capabilities to transform data distributions across a large gap, we propose a novel diffusion‐based unsupervised domain adaptation (diffusion‐UDA) model, which leverages contrastive learning to alleviate the challenges of cross‐domain analysis for fault diagnosis within different systems of the deep‐sea submersible. Experimental results show the proposed method achieves state‐of‐the‐art performance on various benchmarks. [ABSTRACT FROM AUTHOR]

Diabetes mellitus (DM) is a disease that affects millions of individuals worldwide and is characterized by abnormal glucose metabolism that can induce severe damage to numerous organs throughout the body. Sex differences have been demonstrated in a number of factors associated with diabetes and its complications, such as diabetic kidney disease and diabetic liver disease. To investigate the sex differences in DM further, the changes in the weight, food and water intake, and blood sugar of mice were recorded for 8 weeks in the present study. Hematoxylin and eosin staining, Masson's trichrome staining and transmission electron microscopy were used to observe the pathological changes of liver and kidney tissues. There is no significant difference in the water intake and blood glucose concentration between db/db female and male mice was observed. However, sex differences in liver and kidney damage including glomerular injury and hepatic fibrosis were found. In conclusion, the present study characterized the features of liver and kidney damage in db/db mice and indicated that sex differences should be taken into account in experiments using female and male experimental animals. Furthermore, sex differences should be taken into account in the selection of drug interventions in experiments and in clinical drug therapy. [ABSTRACT FROM AUTHOR]