采用共形天线的反辐射导弹探测系统在对大入射角信号测向时,由于信号多路径、折射、绕射等情况产生的干扰,会直接被引入到极化敏感阵列空间谱估计理论模型中,从而导致测向误差偏大。针对该问题,基于均匀共形圆阵天线模型,提出了一种基...采用共形天线的反辐射导弹探测系统在对大入射角信号测向时,由于信号多路径、折射、绕射等情况产生的干扰,会直接被引入到极化敏感阵列空间谱估计理论模型中,从而导致测向误差偏大。针对该问题,基于均匀共形圆阵天线模型,提出了一种基于空间域补偿的极化敏感阵列多重信号分类(multiple signal classification,MUSIC)测向算法。该算法用误差补偿方法对空间域分量进行修正,结合MUSIC测向算法,构造四维极化敏感阵列空间谱函数,通过降维谱峰搜索实现目标信号的二维测向。经仿真验证,与经典极化敏感阵列MUSIC测向算法相比,所提算法修正了系统的前端误差,避免了测向过程中的空间域分量与算法理论模型失配问题,可实现对目标信号的高精度测向及跟踪。展开更多
Background Heart failure(HF)is a chronic,impactful condition on individuals and healthcare systems,requiring management that goes beyond inpatient care.This study investigated the impact of cardiovascular specialist n...Background Heart failure(HF)is a chronic,impactful condition on individuals and healthcare systems,requiring management that goes beyond inpatient care.This study investigated the impact of cardiovascular specialist nurses on long-term health outcomes and patient satisfaction in HF outpatient care.Methods In a year-long observational study from July 2021 to July 2022,230 HF patients at our hospital were divided into an experimental group(n=115)receiving care from cardiovascular specialist nurses and a control group(n=115)managed by general nursing staff.The interventions included comprehensive care such as patient education,medication management,lifestyle guidance,symptom monitoring,and psychological support.The major adverse cardiovascular events(MACEs)and rehospitalization rates were applied as the primary endpoints.Results The experimental group demonstrated significantly fewer MACEs and lower rehospitalization rates compared to the control group(P<0.001).Higher patient satisfaction was observed in the experimental group,with 81.7%reporting high satisfaction vs.53.9%in the control group(P<0.001).Differences in incidence of myocardial infarction and death rates between the groups were not statistically significant.Conclusions The involvement of cardiovascular specialist nurses in HF outpatient care significantly enhances clinical outcomes and patient satisfaction.These nurses play a key role in bridging the transition from hospital to home care,improving adherence to treatment regimens,and reducing healthcare system burdens.展开更多
文摘采用共形天线的反辐射导弹探测系统在对大入射角信号测向时,由于信号多路径、折射、绕射等情况产生的干扰,会直接被引入到极化敏感阵列空间谱估计理论模型中,从而导致测向误差偏大。针对该问题,基于均匀共形圆阵天线模型,提出了一种基于空间域补偿的极化敏感阵列多重信号分类(multiple signal classification,MUSIC)测向算法。该算法用误差补偿方法对空间域分量进行修正,结合MUSIC测向算法,构造四维极化敏感阵列空间谱函数,通过降维谱峰搜索实现目标信号的二维测向。经仿真验证,与经典极化敏感阵列MUSIC测向算法相比,所提算法修正了系统的前端误差,避免了测向过程中的空间域分量与算法理论模型失配问题,可实现对目标信号的高精度测向及跟踪。
文摘Background Heart failure(HF)is a chronic,impactful condition on individuals and healthcare systems,requiring management that goes beyond inpatient care.This study investigated the impact of cardiovascular specialist nurses on long-term health outcomes and patient satisfaction in HF outpatient care.Methods In a year-long observational study from July 2021 to July 2022,230 HF patients at our hospital were divided into an experimental group(n=115)receiving care from cardiovascular specialist nurses and a control group(n=115)managed by general nursing staff.The interventions included comprehensive care such as patient education,medication management,lifestyle guidance,symptom monitoring,and psychological support.The major adverse cardiovascular events(MACEs)and rehospitalization rates were applied as the primary endpoints.Results The experimental group demonstrated significantly fewer MACEs and lower rehospitalization rates compared to the control group(P<0.001).Higher patient satisfaction was observed in the experimental group,with 81.7%reporting high satisfaction vs.53.9%in the control group(P<0.001).Differences in incidence of myocardial infarction and death rates between the groups were not statistically significant.Conclusions The involvement of cardiovascular specialist nurses in HF outpatient care significantly enhances clinical outcomes and patient satisfaction.These nurses play a key role in bridging the transition from hospital to home care,improving adherence to treatment regimens,and reducing healthcare system burdens.
