Objective To evaluate the efficacy and safety of Ding Xin Recipe(DXR)combined with amiodarone in patients with PVCs.Methods A total of360patients with PVCs across7centers in China were randomly assigned in a1:1:1ratio...Objective To evaluate the efficacy and safety of Ding Xin Recipe(DXR)combined with amiodarone in patients with PVCs.Methods A total of360patients with PVCs across7centers in China were randomly assigned in a1:1:1ratio to receive up to8weeks of amiodarone combined with DXR placebo(amiodarone group),DXR combined with amiodarone placebo(DXR group),or DXR combined with amiodarone(DCA group)from July2012to December2013.Randomization was conducted according to a centralized randomization schedule prepared by an independent steering committee.Staff and patients at all sites were masked to treatment allocation.All patients received best-evidence advice.The primary outcome was the efficacy for treating PVCs,with efficacy assessed by the reduction of premature ventricular contractions.Other outcome measures included PVCs-related symptom scores.All data were analyzed by intention to treat.Results The efficacy for treating PVCs in the DCA group(90.7%)significantly increased compared with that in the amiodarone group(72.3%)and the DXR group(73.9%).The frequency,the degree,and the duration per week of heart palpitations,chest tightness,shortness of breath and fatigue improved significantly in the DCA group in comparison with the amiodarone group and the DXR group(P<0.05),while no significant difference was observed in the improvement of insomnia among the three groups(P>0.05).With regard to laboratory parameters for safety,there were no clinically relevant changes in the three groups.Conclusion The present study demonstrates that DXR combined with amiodarone is significantly more effective than DXR or amiodarone alone for treating PVCs.展开更多
电池健康状态(State of Health,SOH)和剩余使用寿命(Remaining Useful Life,RUL)是电池健康管理的重要评价指标。针对锂电池在使用过程中受较多复杂因素影响难以准确预测其剩余使用寿命问题,文中提出了一种基于IDBO-CNN-BiLSTM(Improved...电池健康状态(State of Health,SOH)和剩余使用寿命(Remaining Useful Life,RUL)是电池健康管理的重要评价指标。针对锂电池在使用过程中受较多复杂因素影响难以准确预测其剩余使用寿命问题,文中提出了一种基于IDBO-CNN-BiLSTM(Improved Dung Beetle Optimizer-Convolutional Neural Networks-Bi-directional Long Short-Term Memory)的混合预测模型。通过分析锂电池充电过程中的状态来提取9种健康因子(Health Factor,HF),通过皮尔逊相关系数筛选强相关性健康因子,并将其作为模型输入。采用混沌初始化Tent映射生成蜣螂的初始位置,采用正余弦策略优化偷窃蜣螂位置,解决了DBO(Dung Beetle Optimizer)算法初始化导致的局部收敛问题以及优化了DBO算法的平衡性,提高了预测的稳定性。基于NASA(National Aeronautics and Space Administration)提供的公开锂电池老化数据集进行实验,并使用不同模型预测NASA锂电池SOH,结果表明所提方法误差更小,具有一定应用价值。展开更多
文摘全球气候变暖严重影响冰川的稳定性,南极多条冰川表面发生塌陷。由于缺少高空间和高时间分辨率的南极地表高程模型DEM(Digital Elevation Model),目前单支冰川表面时空变化的研究不充分。利用2011年—2016年11期南极参考高程模型REMA(The Reference Elevation Model of Antarctica)数据,开展东南极达尔克冰川表面塌陷区域的高程变化监测,并利用Landsat 7/8和Worldview-2光学影像等数据分析塌陷过程和原因。结果表明,达尔克冰川在2013年发生了一起严重的塌陷事件,塌陷深度最大约45.29 m,造成了约26.29×10^(6)m^(3)的水体损失;塌陷发生后,该区表面高程不断增加,于2016年恢复至塌陷前的高程。塌陷区具有明显的整体性沉降特征,并存在融水聚集,推测塌陷和达尔克冰川冰下湖的排水过程存在密切的联系。本研究证明达尔克冰川存在较大的不稳定性,同时验证了REMA数据监测冰川表面塌陷的可行性,为未来精细化监测南极冰盖/冰架响应气候变化提供技术参考。
基金funding support from the National Natural Sciences Foundation of China (No. 81373574 and No. 81774213)the Guangdong 211 key Disciplines
文摘Objective To evaluate the efficacy and safety of Ding Xin Recipe(DXR)combined with amiodarone in patients with PVCs.Methods A total of360patients with PVCs across7centers in China were randomly assigned in a1:1:1ratio to receive up to8weeks of amiodarone combined with DXR placebo(amiodarone group),DXR combined with amiodarone placebo(DXR group),or DXR combined with amiodarone(DCA group)from July2012to December2013.Randomization was conducted according to a centralized randomization schedule prepared by an independent steering committee.Staff and patients at all sites were masked to treatment allocation.All patients received best-evidence advice.The primary outcome was the efficacy for treating PVCs,with efficacy assessed by the reduction of premature ventricular contractions.Other outcome measures included PVCs-related symptom scores.All data were analyzed by intention to treat.Results The efficacy for treating PVCs in the DCA group(90.7%)significantly increased compared with that in the amiodarone group(72.3%)and the DXR group(73.9%).The frequency,the degree,and the duration per week of heart palpitations,chest tightness,shortness of breath and fatigue improved significantly in the DCA group in comparison with the amiodarone group and the DXR group(P<0.05),while no significant difference was observed in the improvement of insomnia among the three groups(P>0.05).With regard to laboratory parameters for safety,there were no clinically relevant changes in the three groups.Conclusion The present study demonstrates that DXR combined with amiodarone is significantly more effective than DXR or amiodarone alone for treating PVCs.
文摘电池健康状态(State of Health,SOH)和剩余使用寿命(Remaining Useful Life,RUL)是电池健康管理的重要评价指标。针对锂电池在使用过程中受较多复杂因素影响难以准确预测其剩余使用寿命问题,文中提出了一种基于IDBO-CNN-BiLSTM(Improved Dung Beetle Optimizer-Convolutional Neural Networks-Bi-directional Long Short-Term Memory)的混合预测模型。通过分析锂电池充电过程中的状态来提取9种健康因子(Health Factor,HF),通过皮尔逊相关系数筛选强相关性健康因子,并将其作为模型输入。采用混沌初始化Tent映射生成蜣螂的初始位置,采用正余弦策略优化偷窃蜣螂位置,解决了DBO(Dung Beetle Optimizer)算法初始化导致的局部收敛问题以及优化了DBO算法的平衡性,提高了预测的稳定性。基于NASA(National Aeronautics and Space Administration)提供的公开锂电池老化数据集进行实验,并使用不同模型预测NASA锂电池SOH,结果表明所提方法误差更小,具有一定应用价值。
