Quantum spin Hall effect induced by electric field in silicene

We investigate the transport properties in a zigzag silicene nanoribbon in the presence of an external electric field. The staggered sublattice potential and two kinds of Rashba spin-orbit couplings can be induced by the external electric field due to the buckled structure of the silicene. A bulk gap is opened by the staggered potential and gapless edge states appear in the gap by tuning the two kinds of Rashba spin-orbit couplings properly. Furthermore, the gapless edge states are spin-filtered and are insensitive to the non-magnetic disorder. These results prove that the quantum spin Hall effect can be induced by an external electric field in silicene, which may have certain practical significance in applications for future spintronics device.Comment: 4 pages, 5 figure

Implementation of hospital level evaluation specification management to realize sustainable development

目的  通过医院等级评审，提高医院综合实力与整体水平。方法  通过对评审标准的分解，严格规范化管理，制定相应措施，在临床工作中认真实施。结果  以评促建，以评促改，促进医院规范化建设，提升管理、诊疗和服务水平，使患者利益更大限度地得到保障。结论  通过医院等级评审，可促进医院可持续发展。Objective: To improve the comprehensive strength and overall level of hospital through the hospital grade evaluation. Methods: Through decomposing the standards of evaluation, we achieved strict standardized management, drawn up corresponding measures, and then put them into practice seriously in the clinical work. Results: Assessing the purpose of promoting construction, assessing the purpose of reform, promoted standardization construction of hospital, improved the level of management, diagnosis and service, and protected the patients’ interests as much as possible. Conclusion: The grade evaluation of hospital promoted sustainable development of the hospital

Depth-agnostic Single Image Dehazing

Single image dehazing is a challenging ill-posed problem. Existing datasets for training deep learning-based methods can be generated by hand-crafted or synthetic schemes. However, the former often suffers from small scales, while the latter forces models to learn scene depth instead of haze distribution, decreasing their dehazing ability. To overcome the problem, we propose a simple yet novel synthetic method to decouple the relationship between haze density and scene depth, by which a depth-agnostic dataset (DA-HAZE) is generated. Meanwhile, a Global Shuffle Strategy (GSS) is proposed for generating differently scaled datasets, thereby enhancing the generalization ability of the model. Extensive experiments indicate that models trained on DA-HAZE achieve significant improvements on real-world benchmarks, with less discrepancy between SOTS and DA-SOTS (the test set of DA-HAZE). Additionally, Depth-agnostic dehazing is a more complicated task because of the lack of depth prior. Therefore, an efficient architecture with stronger feature modeling ability and fewer computational costs is necessary. We revisit the U-Net-based architectures for dehazing, in which dedicatedly designed blocks are incorporated. However, the performances of blocks are constrained by limited feature fusion methods. To this end, we propose a Convolutional Skip Connection (CSC) module, allowing vanilla feature fusion methods to achieve promising results with minimal costs. Extensive experimental results demonstrate that current state-of-the-art methods. equipped with CSC can achieve better performance and reasonable computational expense, whether the haze distribution is relevant to the scene depth