This research presents an experimental study of analysis of stress strain state SSS of X-60 pipe weld joints employing magnetic anisotropy indicator of mechanical stresses Stress Vision (IMS) using of “before and af...This research presents an experimental study of analysis of stress strain state SSS of X-60 pipe weld joints employing magnetic anisotropy indicator of mechanical stresses Stress Vision (IMS) using of “before and after” comparison approach taking readings on pipe base metal, weld area and heat affected zone (HAZ) before and after hydrotest. Test results were compared with X-ray testing results for welded joints and with metallographic testing. Test results demonstrate the relevance of applied test conditions and redistribution of residual stresses. A new equation was established for estimating the residual (technological) and operating stresses in other pipelines with a tolerance of 15% in the field of elastic deformation (up to the yield point), according to Hooke law.展开更多
Neurological disorders such as Alzheimer’s disease,Parkinson’s disease,and epilepsy pose significant challenges to public health owing to their complex pathophysiology.Early detection and accurate diagnosis are crit...Neurological disorders such as Alzheimer’s disease,Parkinson’s disease,and epilepsy pose significant challenges to public health owing to their complex pathophysiology.Early detection and accurate diagnosis are critical for effective intervention;however,traditional diagnostic methods often fall short in terms of sensitivity and specificity.Machine learning(ML)has shown great promise for overcoming these limitations by analyzing large-scale datasets,including neuroimaging and genomic data,to enhance diagnostic and predictive accuracy.This review explored the applications of ML in the prediction and diagnosis of Alzheimer’s disease,Parkinson’s disease,and epilepsy.We reviewed the principles of the ML algorithms,their performance in multimodal data analysis,and their potential for streamlining diagnostic workflows.Additionally,we discuss key challenges,such as model interpretability,data integration,and clinical adoption of ML technologies.Our goal was to highlight how ML can transform the prediction,diagnosis,and management of neurological disorders and ultimately improve patient outcomes.展开更多
文摘This research presents an experimental study of analysis of stress strain state SSS of X-60 pipe weld joints employing magnetic anisotropy indicator of mechanical stresses Stress Vision (IMS) using of “before and after” comparison approach taking readings on pipe base metal, weld area and heat affected zone (HAZ) before and after hydrotest. Test results were compared with X-ray testing results for welded joints and with metallographic testing. Test results demonstrate the relevance of applied test conditions and redistribution of residual stresses. A new equation was established for estimating the residual (technological) and operating stresses in other pipelines with a tolerance of 15% in the field of elastic deformation (up to the yield point), according to Hooke law.
基金supported by Beijing Natural Science Foundation Youth Project(No.7244427).
文摘Neurological disorders such as Alzheimer’s disease,Parkinson’s disease,and epilepsy pose significant challenges to public health owing to their complex pathophysiology.Early detection and accurate diagnosis are critical for effective intervention;however,traditional diagnostic methods often fall short in terms of sensitivity and specificity.Machine learning(ML)has shown great promise for overcoming these limitations by analyzing large-scale datasets,including neuroimaging and genomic data,to enhance diagnostic and predictive accuracy.This review explored the applications of ML in the prediction and diagnosis of Alzheimer’s disease,Parkinson’s disease,and epilepsy.We reviewed the principles of the ML algorithms,their performance in multimodal data analysis,and their potential for streamlining diagnostic workflows.Additionally,we discuss key challenges,such as model interpretability,data integration,and clinical adoption of ML technologies.Our goal was to highlight how ML can transform the prediction,diagnosis,and management of neurological disorders and ultimately improve patient outcomes.
