Systematic investigation of the rotational bands in nuclei with Z≈100Z \approx 100 using a particle-number conserving method based on a cranked shell model

The rotational bands in nuclei with $Z \approx 100$ are investigated systematically by using a cranked shell model (CSM) with the pairing correlations treated by a particle-number conserving (PNC) method, in which the blocking effects are taken into account exactly. By fitting the experimental single-particle spectra in these nuclei, a new set of Nilsson parameters ($\kappa$ and $\mu$) and deformation parameters ($\varepsilon_2$ and $\varepsilon_4$) are proposed. The experimental kinematic moments of inertia for the rotational bands in even-even, odd-$A$ and odd-odd nuclei, and the bandhead energies of the 1-quasiparticle bands in odd-$A$ nuclei, are reproduced quite well by the PNC-CSM calculations. By analyzing the $\omega$-dependence of the occupation probability of each cranked Nilsson orbital near the Fermi surface and the contributions of valence orbitals in each major shell to the angular momentum alignment, the upbending mechanism in this region is understood clearly.Comment: 21 pages, 24 figures, extended version of arXiv: 1101.3607 (Phys. Rev. C83, 011304R); added refs.; added Fig. 4 and discussions; Phys. Rev. C, in pres

To investigate the reasons and nursing strategies of postoperative return to ICU in patients with severe valvular disease

目的  探讨重症瓣膜置换病人术后再次非计划性返回ICU的重返率及原因。根据原因分析建立最佳护理模式及干预策略，通过护理干预手段降低重返率，提高治愈率及患者术后康复质量。方法  回顾性分析2010年6月—2015年6月间150例重症瓣膜置患者术后重返ICU患者的临床资料、分析重返的原因。结果  150例重症瓣膜置换患者术后非计划重返ICU的患者有17例，重返率为11.3%。重返的主要原因是呼吸困难、恶性心律失常、低心排、循环负荷过重、家属照护压力大主动要求重返ICU等。结论  制定合理的转出标准；合理配置人力资源，动态监测，密切观察患者生命体征、出入量平衡、电解质平衡；提供个性化健康指导提高患者及家属的遵医行为是降低ICU重返率的有效措施。Objective: To investigate the return rate and causes of postoperative non - planned return to ICU in patients with severe valve replacement. According to the analysis of the reasons, establish the best nursing model and intervention strategies, through the nursing intervention to reduce the return rate, and improve the cure rate and postoperative rehabilitation quality. Methods: Retrospective analysis of clinical data of 150 cases of severe valvular replacement patients who returned to ICU in June, June 2010, and the reasons for the return of ~2015. Results: 150 cases of patients with severe heart valve replacement patients, among them 150 cases had no plan to return to ICU, the return rate was 11.3%. The main reason to return to ICU is the difficulty of breathing, malignant arrhythmia, low cardiac output, overweight, family care pressure and so on n. Conclusion: To establish a reasonable standard of transfer; the rational allocation of human resources, dynamic monitoring, close observation of vital signs of patients, access to balance, electrolyte balance; providing personalized health guidance to improve the compliance behavior of patients and their families are the effective measures to reduce the ICU readmission rate.

Improving Continual Relation Extraction through Prototypical Contrastive Learning

Continual relation extraction (CRE) aims to extract relations towards the continuous and iterative arrival of new data, of which the major challenge is the catastrophic forgetting of old tasks. In order to alleviate this critical problem for enhanced CRE performance, we propose a novel Continual Relation Extraction framework with Contrastive Learning, namely CRECL, which is built with a classification network and a prototypical contrastive network to achieve the incremental-class learning of CRE. Specifically, in the contrastive network a given instance is contrasted with the prototype of each candidate relations stored in the memory module. Such contrastive learning scheme ensures the data distributions of all tasks more distinguishable, so as to alleviate the catastrophic forgetting further. Our experiment results not only demonstrate our CRECL's advantage over the state-of-the-art baselines on two public datasets, but also verify the effectiveness of CRECL's contrastive learning on improving CRE performance