In the past decade, the concept of high-entropy alloys (HEAs) or multi-principal element alloys (MPEAs), which are composed of at least four principal elements, significantly expands the compositional space for alloy design. This concept can also be employed in the design of superelastic alloys to promote the development of this functional material field. Here, we report the orientation-dependent superelasticity of a metastable Fe-27.5Ni-16.5Co-10Al-2.2Ta-0.04B (at.%) HEA through in situ micropillar compression tests along ⟨001⟩, ⟨011⟩, and ⟨111⟩ orientations. Our results show that considerable superelastic strains can be achieved along the three orientations in the metastable HEA via a reversible martensitic transformation. Thermoelastic martensite with thin-plate morphology was observed under cryogenic conditions. This work demonstrates that the maximum superelastic strains vary with different orientations, and the ⟨001⟩-oriented specimen shows the largest superelastic strain. The superelastic strains along specific orientations are compared with theoretical values calculated from the lattice deformation method and the energy minimization theory, respectively. The limited number of martensite variants under compression testing may be responsible for the discrepancy that exists in the experimental and the two theoretically predicted transformation strains. This study may provide a feasible strategy for the design of superelastic HEAs with specific orientation for applications in microsystems.
Electrocardiogram (ECG) signal plays a key role in the diagnosis of arrhythmia, which will pose a great threat to human health. As an effective feature extraction method, deep learning has shown excellent results in processing ECG signals. However, most of these methods neglect the cooperation between the multi-lead ECG series correlation and intra-series temporal patterns. In this work, a multi-domain collaborative analysis and decision approach is proposed, which makes the classification and diagnosis of arrhythmia more accurate. With this decision, we can realize the transition from the spatial domain to the spectral domain, and from the time domain to the frequency domain, and make it possible that ECG signals can be more clearly detected by convolution and sequential learning modules. Moreover, instead of the prior method, the self-attention mechanism is used to learn the relation matrix between the sequences automatically in this paper. We conduct extensive experiments on eight advanced models in the same field to demonstrate the effectiveness of our method.
Abstract-To manage and retain talent has apparently become important factors influencing the enterprise's survival and development. Compared with other talents, enterprise operation and management talents have a small quantity, but their intellectual capital contribution rate is higher and they are more sensitive to organizational justice. On the premise of related theory, this study takes enterprise operation and management talents of Jiangsu province as research object, researching on the effect of organizational justice and job satisfaction to reduce turnover intention.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.