This study presents a neural network-based model for predicting linear quadratic regulator(LQR)weighting matrices for achieving a target response reduction.Based on the expected weighting matrices,the LQR algorithm is...This study presents a neural network-based model for predicting linear quadratic regulator(LQR)weighting matrices for achieving a target response reduction.Based on the expected weighting matrices,the LQR algorithm is used to determine the various responses of the structure.The responses are determined by numerically analyzing the governing equation of motion using the state-space approach.For training a neural network,four input parameters are considered:the time history of the ground motion,the percentage reduction in lateral displacement,lateral velocity,and lateral acceleration,Output parameters are LQR weighting matrices.To study the effectiveness of an LQR-based neural network(LQRNN),the actual percentage reduction in the responses obtained from using LQRNN is compared with the target percentage reductions.Furthermore,to investigate the efficacy of an active control system using LQRNN,the controlled responses of a system are compared to the corresponding uncontrolled responses.The trained neural network effectively predicts weighting parameters that can provide a percentage reduction in displacement,velocity,and acceleration close to the target percentage reduction.Based on the simulation study,it can be concluded that significant response reductions are observed in the active-controlled system using LQRNN.Moreover,the LQRNN algorithm can replace conventional LQR control with the use of an active control system.展开更多
Hepatitis C virus(HCV)chronic infection is associated with fibrosis progression,end-stage liver complications and HCC.Not surprisingly,HCV infection is a leading cause of liver-related morbidity and mortality worldwid...Hepatitis C virus(HCV)chronic infection is associated with fibrosis progression,end-stage liver complications and HCC.Not surprisingly,HCV infection is a leading cause of liver-related morbidity and mortality worldwide.After sustained virological response(SVR),the risk of developing hepatocellular carcinoma is not completely eliminated in patients with established cirrhosis or with advanced fibrosis.Therefore,lifelong surveillance is currently recommended.This strategy is likely not universally cost-effective and harmless,considering that not all patients with advanced fibrosis have the same risk of developing HCC.Factors related to the severity of liver disease and its potential to improve after SVR,the molecular and epigenetic changes that occur during infection and other associated comorbidities might account for different risk levels and are likely essential for identifying patients who would benefit from screening programs after SVR.Efforts to develop predictive models and risk calculators,biomarkers and genetic panels and even deep learning models to estimate the individual risk of HCC have been made in the direct-acting antiviral agents era,when thousands of patients with advanced fibrosis and cirrhosis have reached SVR.These tools could help to identify patients with very low HCC risk in whom surveillance might not be justified.In this review,factors affecting the probability of HCC development after SVR,the benefits and risks of surveillance,suggested strategies to estimate individualized HCC risk and the current evidence to recommend lifelong surveillance are discussed.展开更多
基金Dean Research&Consultancy under Grant No.Dean (R&C)/2020-21/1155。
文摘This study presents a neural network-based model for predicting linear quadratic regulator(LQR)weighting matrices for achieving a target response reduction.Based on the expected weighting matrices,the LQR algorithm is used to determine the various responses of the structure.The responses are determined by numerically analyzing the governing equation of motion using the state-space approach.For training a neural network,four input parameters are considered:the time history of the ground motion,the percentage reduction in lateral displacement,lateral velocity,and lateral acceleration,Output parameters are LQR weighting matrices.To study the effectiveness of an LQR-based neural network(LQRNN),the actual percentage reduction in the responses obtained from using LQRNN is compared with the target percentage reductions.Furthermore,to investigate the efficacy of an active control system using LQRNN,the controlled responses of a system are compared to the corresponding uncontrolled responses.The trained neural network effectively predicts weighting parameters that can provide a percentage reduction in displacement,velocity,and acceleration close to the target percentage reduction.Based on the simulation study,it can be concluded that significant response reductions are observed in the active-controlled system using LQRNN.Moreover,the LQRNN algorithm can replace conventional LQR control with the use of an active control system.
文摘Hepatitis C virus(HCV)chronic infection is associated with fibrosis progression,end-stage liver complications and HCC.Not surprisingly,HCV infection is a leading cause of liver-related morbidity and mortality worldwide.After sustained virological response(SVR),the risk of developing hepatocellular carcinoma is not completely eliminated in patients with established cirrhosis or with advanced fibrosis.Therefore,lifelong surveillance is currently recommended.This strategy is likely not universally cost-effective and harmless,considering that not all patients with advanced fibrosis have the same risk of developing HCC.Factors related to the severity of liver disease and its potential to improve after SVR,the molecular and epigenetic changes that occur during infection and other associated comorbidities might account for different risk levels and are likely essential for identifying patients who would benefit from screening programs after SVR.Efforts to develop predictive models and risk calculators,biomarkers and genetic panels and even deep learning models to estimate the individual risk of HCC have been made in the direct-acting antiviral agents era,when thousands of patients with advanced fibrosis and cirrhosis have reached SVR.These tools could help to identify patients with very low HCC risk in whom surveillance might not be justified.In this review,factors affecting the probability of HCC development after SVR,the benefits and risks of surveillance,suggested strategies to estimate individualized HCC risk and the current evidence to recommend lifelong surveillance are discussed.
