The mechanisms of systolic anterior motion(SAM)of the mitral valve in hypertrophic obstructive cardiomyopathy(HOCM)remain unclear.To investigate the angle of attack between blood flow and mitral valve leaflets at pre-...The mechanisms of systolic anterior motion(SAM)of the mitral valve in hypertrophic obstructive cardiomyopathy(HOCM)remain unclear.To investigate the angle of attack between blood flow and mitral valve leaflets at pre-SAM time point,patient-specific CT-based computational models were constructed for 5 patients receiving septal myectomy surgery to obtain pre-and post-operative 2D vector flow mapping.The comparisons between pre-and post-operative angles of attack based on 2D vector flow mapping of 5 patients were performed.It was found that there was no statistically significant difference between pre-and post-operative angles of attack(61.1±t wa o vs.56.2±56.o,p=0.306,n=5).Therefore,we propose that the angle of attack might not play an important role in the initiation of SAM.展开更多
The blast furnace is a highly energy-intensive,highly polluting,and extremely complex reactor in the ironmaking process.Soft sensors are a key technology for predicting molten iron quality indices reflecting blast furn...The blast furnace is a highly energy-intensive,highly polluting,and extremely complex reactor in the ironmaking process.Soft sensors are a key technology for predicting molten iron quality indices reflecting blast furnace energy consumption and operation stability,and play an important role in saving energy,reducing emissions,improving product quality,and producing economic benefits.With the advancement of the Internet of Things,big data,and artificial intelligence,data-driven soft sensors in blast furnace ironmaking processes have attracted increasing attention from researchers,but there has been no systematic review of the data-driven soft sensors in the blast furnace ironmaking process.This review covers the state-of-the-art studies of data-driven soft sensors technologies in the blast furnace ironmaking process.Specifically,wefirst conduct a comprehensive overview of various data-driven soft sensor modeling methods(multiscale methods,adaptive methods,deep learning,etc.)used in blast furnace ironmaking.Second,the important applications of data-driven soft sensors in blast furnace ironmaking(silicon content,molten iron temperature,gas utilization rate,etc.)are classified.Finally,the potential challenges and future development trends of data-driven soft sensors in blast furnace ironmaking applications are discussed,including digital twin,multi-source data fusion,and carbon peaking and carbon neutrality.展开更多
基金The authors would like to acknowledge the research support from Natural Science Foundation of Fujian Province of China(Grant No.2017J01009)Fundamental Research Funds for the Central Universities(Grant No.20720180004)National Heart,Lung and Blood Institute grants R01 HL089269,and National Sciences Foundation of China(Grant No.11672001,81571691).
文摘The mechanisms of systolic anterior motion(SAM)of the mitral valve in hypertrophic obstructive cardiomyopathy(HOCM)remain unclear.To investigate the angle of attack between blood flow and mitral valve leaflets at pre-SAM time point,patient-specific CT-based computational models were constructed for 5 patients receiving septal myectomy surgery to obtain pre-and post-operative 2D vector flow mapping.The comparisons between pre-and post-operative angles of attack based on 2D vector flow mapping of 5 patients were performed.It was found that there was no statistically significant difference between pre-and post-operative angles of attack(61.1±t wa o vs.56.2±56.o,p=0.306,n=5).Therefore,we propose that the angle of attack might not play an important role in the initiation of SAM.
基金Project supported by the National Natural Science Founda-tion of China(Nos.62003301,61933013,and 61833014)the Natural Science Foundation of Zhejiang Province,China(No.LQ21F030018)the Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang Univer-sity,China(Nos.ICT2022B30 and ICT2022B08)。
文摘The blast furnace is a highly energy-intensive,highly polluting,and extremely complex reactor in the ironmaking process.Soft sensors are a key technology for predicting molten iron quality indices reflecting blast furnace energy consumption and operation stability,and play an important role in saving energy,reducing emissions,improving product quality,and producing economic benefits.With the advancement of the Internet of Things,big data,and artificial intelligence,data-driven soft sensors in blast furnace ironmaking processes have attracted increasing attention from researchers,but there has been no systematic review of the data-driven soft sensors in the blast furnace ironmaking process.This review covers the state-of-the-art studies of data-driven soft sensors technologies in the blast furnace ironmaking process.Specifically,wefirst conduct a comprehensive overview of various data-driven soft sensor modeling methods(multiscale methods,adaptive methods,deep learning,etc.)used in blast furnace ironmaking.Second,the important applications of data-driven soft sensors in blast furnace ironmaking(silicon content,molten iron temperature,gas utilization rate,etc.)are classified.Finally,the potential challenges and future development trends of data-driven soft sensors in blast furnace ironmaking applications are discussed,including digital twin,multi-source data fusion,and carbon peaking and carbon neutrality.
