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Grain boundary (GB) precipitation-induced cracking is a significant issue for S31254 super austenitic stainless steel during hot working. Investigating the deformation behavior based on precipitate morphology and distribution is essential. In this study, continuous smaller and intermittent larger precipitates were obtained through heat treatments at 950 °C and 1050 °C. The microstructure evolution and mechanical properties influenced by precipitates were experimentally investigated using an in situ tensile stage inside a scanning electron microscope (SEM) combined with electron backscatter diffraction (EBSD). The results showed that continuous precipitates at 950 °C had a stronger pinning effect on the GB, making grain rotation difficult and promoting slip deformation in the plastic interval. Continuous precipitates caused severe stress concentration near GB and reduced coordinated deformation ability. Additionally, the crack propagation path changed from transcrystalline to intercrystalline. Furthermore, internal precipitates were a crucial factor affecting the initial crack nucleation position. Interconnected precipitates led to an intergranular fracture tendency and severe deterioration of the material's plasticity, as observed in fracture morphology. [ABSTRACT FROM AUTHOR]

The identification of prototypical waveforms, such as sleep spindles and epileptic spikes, is crucial for the diagnosis of neurological disorders. These prototypical waveforms are usually recurrently presented in certain brain states, serving as potential biomarkers for clinical evaluations. Convolutional sparse coding (CSC) approaches have demonstrated strength in identifying recurrent patterns in time-series. However, existing CSC approaches do not explicitly explore state-specific patterns, making it difficult to identify state-related biomarkers. To address this problem, we propose state-sensitive CSC to learn state-specific prototypical waveforms. Specifically, we model signals of a certain state with specific waveforms that only appear frequently in this state and background waveforms that are independent of states. Based on this, state-sensitive CSC separates state-specific waveforms from background ones explicitly by incorporating incoherence constraints into optimizations. Experiments with epilepsy brain signals demonstrate that our approach can effectively identify prototypical waveforms in pre-ictal states, providing potential biomarkers for seizure prediction. Our approach provides a promising tool for automatic biomarker candidate identification. [ABSTRACT FROM AUTHOR]