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Journal of Zhejiang University SCIENCE B

ISSN 1673-1581(Print), 1862-1783(Online), Monthly

Kidney function change after transcatheter aortic valve replacement in patients with diabetes and/or hypertension

Abstract: Aortic stenosis (AS) is a progressive heart valve disease occurring predominantly in older patients. According to a survey in a western country, the prevalence of AS is nearly 6.4% in patients over 75 years old (Carabello and Paulus, 2009). Transcatheter aortic valve replacement (TAVR) is an alternative method for AS patients. Previous studies have described how up to 66% of TAVR patients have concomitant baseline kidney dysfunction (Ferro et al., 2015; Gargiulo et al., 2015). The majority of patients can benefit from the TAVR procedure with the recovery of kidney function. The TAVR procedure releases the obstruction of the left ventricle caused by severe AS, and the increased cardiac output may be reasonably responsible for recovery of the kidney function (Ewe et al., 2010; Dauerman et al., 2016). Kidney dysfunction is most commonly attributed to diabetes and hypertension (HTN) (Chen et al., 2019). A few studies have reported kidney function change after TAVR in baseline chronic kidney disease (CKD) patients (Beohar et al., 2017; Azarbal et al., 2019; Okoh et al., 2019). However, no study has focused on kidney function change after TAVR in the diabetic or hypertensive population. Therefore, we aimed to investigate kidney function change during the TAVR procedure in patients with diabetes mellites (DM) and/or HTN.

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Chinese Summary  <23> 面向变化用户家居环境的服务机器人云辅助认知适应

王祺1,樊臻1,盛卫华2,张森林1,刘妹琴1,3
1浙江大学电气工程学院,中国杭州市,310027
2俄克拉荷马州立大学电气与计算机工程学院,美国俄克拉荷马州斯蒂尔沃特,74078
3西安交通大学人工智能与机器人研究院,中国西安市,710049
摘要:机器人需要更强的智能以胜任家居环境中的认知任务。本文提出一种新的云辅助家居服务机器人认知适应机制,它可以从其他机器人处学习新知识。在该机制中,在机器人处部署一种变化检测方法以检测用户家居环境变化,并触发认知适应过程,实现经云端从其他机器人处学习新知识。而认知适应是通过模型融合方法将知识从云端全局模型迁移至机器人本地模型得以实现。首先,提出3种不同模型融合方法执行认知适应过程,并给出影响模型融合方法的两个关键因素。其次,确定最适合云端至机器人知识转移的模型融合方法及其设置。再次,在一个变化的用户家居环境中进行案例研究,,实验结果验证了所提方案的效率和有效性。基于实验结果,提出一种云端至机器人知识转移模型融合的经验准则。

关键词组:家居服务机器人;云端至机器人知识迁移;模型融合


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DOI:

10.1631/jzus.B2000431

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On-line Access:

2021-03-12

Received:

2020-08-10

Revision Accepted:

2020-12-09

Crosschecked:

2021-02-23

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