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

Prediction of Stable Ground-State Lithium Polyhydrides under High Pressures

Hydrogen-rich compounds are important for understanding the dissociation of dense molecular hydrogen, as well as searching for room temperature Bardeen-Cooper-Schrieffer (BCS) superconductors. A recent high pressure experiment reported the successful synthesis of novel insulating lithium polyhydrides when above 130 GPa. However, the results are in sharp contrast to previous theoretical prediction by PBE functional that around this pressure range all lithium polyhydrides (LiHn (n = 2-8)) should be metallic. In order to address this discrepancy, we perform unbiased structure search with first principles calculation by including the van der Waals interaction that was ignored in previous prediction to predict the high pressure stable structures of LiHn (n = 2-11, 13) up to 200 GPa. We reproduce the previously predicted structures, and further find novel compositions that adopt more stable structures. The van der Waals functional (vdW-DF) significantly alters the relative stability of lithium polyhydrides, and predicts that the stable stoichiometries for the ground-state should be LiH2 and LiH9 at 130-170 GPa, and LiH2, LiH8 and LiH10 at 180-200 GPa. Accurate electronic structure calculation with GW approximation indicates that LiH, LiH2, LiH7, and LiH9 are insulative up to at least 208 GPa, and all other lithium polyhydrides are metallic. The calculated vibron frequencies of these insulating phases are also in accordance with the experimental infrared (IR) data. This reconciliation with the experimental observation suggests that LiH2, LiH7, and LiH9 are the possible candidates for lithium polyhydrides synthesized in that experiment. Our results reinstate the credibility of density functional theory in description H-rich compounds, and demonstrate the importance of considering van der Waals interaction in this class of materials.Comment: 34 pages, 15 figure

Dual Long Short-Term Memory Networks for Sub-Character Representation Learning

Characters have commonly been regarded as the minimal processing unit in Natural Language Processing (NLP). But many non-latin languages have hieroglyphic writing systems, involving a big alphabet with thousands or millions of characters. Each character is composed of even smaller parts, which are often ignored by the previous work. In this paper, we propose a novel architecture employing two stacked Long Short-Term Memory Networks (LSTMs) to learn sub-character level representation and capture deeper level of semantic meanings. To build a concrete study and substantiate the efficiency of our neural architecture, we take Chinese Word Segmentation as a research case example. Among those languages, Chinese is a typical case, for which every character contains several components called radicals. Our networks employ a shared radical level embedding to solve both Simplified and Traditional Chinese Word Segmentation, without extra Traditional to Simplified Chinese conversion, in such a highly end-to-end way the word segmentation can be significantly simplified compared to the previous work. Radical level embeddings can also capture deeper semantic meaning below character level and improve the system performance of learning. By tying radical and character embeddings together, the parameter count is reduced whereas semantic knowledge is shared and transferred between two levels, boosting the performance largely. On 3 out of 4 Bakeoff 2005 datasets, our method surpassed state-of-the-art results by up to 0.4%. Our results are reproducible, source codes and corpora are available on GitHub.Comment: Accepted & forthcoming at ITNG-201