Mechano luminescence(ML),which involves the emission of light under mechanical stimuli,shows great potential in various applications such as sensing,imaging,and energy harvesting.Current research suggests that the lum...Mechano luminescence(ML),which involves the emission of light under mechanical stimuli,shows great potential in various applications such as sensing,imaging,and energy harvesting.Current research suggests that the luminescence mechanism of ML is typically connected to specific defects present within the material.In this study,we focus on the investigation of ML defects in Pr^(3+)-doped NaNbO_(3)/LiNbO_(3)heterojunctions,employing a combination of experimental and theoretical approaches.Through experimental analysis,we confirmed the presence of the heterojunction and its influence on ML intensity,and the optimal doping ratio for the heterojunction in ML was established.Furthermore,we examined the influence of varying Pr^(3+)doping concentrations on ML behavior and a proof-of-concept was demonstrated using the X-rays charged heterostructural phosphor as a stress sensor for biological applications.The position and concentration of internal defects in the ML material were scrutinized through thermo luminescence tests employing the variable heating rate method and positron annihilation.Complementing the experimental findings,theoretical simulations were conducted to elucidate the underlying mechanisms responsible for the observed ML defects.Density functional theory calculations were employed to investigate the energy levels,charge transfer processes,and lattice distortions within the heterojunctions under mechanical stress.Theoretical predictions were compared and validated against the experimental results.The integration of experimental and theoretical approaches provides a comprehensive understanding of the ML behavior of Pr^(3+)-doped NaNbO_(3)/LiNbO_(3)heterojunctions.The insights gained from this research contribute to the development of novel ML materials and pave the way for their applications in next-generation sensing and energy conversion devices.展开更多
The significant threat of wildfires to forest ecology and biodiversity,particularly in tropical and subtropical regions,underscores the necessity for advanced predictive models amidst shifting climate patterns.There i...The significant threat of wildfires to forest ecology and biodiversity,particularly in tropical and subtropical regions,underscores the necessity for advanced predictive models amidst shifting climate patterns.There is a need to evaluate and enhance wildfire prediction methods,focusing on their application during extended periods of intense heat and drought.This study reviews various wildfire modelling approaches,including traditional physical,semi-empirical,numerical,and emerging machine learning(ML)-based models.We critically assess these models’capabilities in predicting fire susceptibility and post-ignition spread,highlighting their strengths and limitations.Our findings indicate that while traditional models provide foundational insights,they often fall short in dynamically estimating parameters and predicting ignition events.Cellular automata models,despite their potential,face challenges in data integration and computational demands.Conversely,ML models demonstrate superior efficiency and accuracy by leveraging diverse datasets,though they encounter interpretability issues.This review recommends hybrid modelling approaches that integrate multiple methods to harness their combined strengths.By incorporating data assimilation techniques with dynamic forecasting models,the predictive capabilities of ML-based predictions can be significantly enhanced.This review underscores the necessity for continued refinement of these models to ensure their reliability in real-world applications,ultimately contributing to more effective wildfire mitigation and management strategies.Future research should focus on improving hybrid models and exploring new data integration methods to advance predictive capabilities.展开更多
This paper proposes a novel less-conservative non-monotonic Lyapunov-Krasovskii stability approach for stability analysis of discrete time-delay systems.In this method,monotonically decreasing requirements of the Lyap...This paper proposes a novel less-conservative non-monotonic Lyapunov-Krasovskii stability approach for stability analysis of discrete time-delay systems.In this method,monotonically decreasing requirements of the Lyapunov-Krasovskii method are replaced with non-monotonic ones.The Lyapunov-Krasovskii functional is allowed to increase in some steps,but the overall trend should be decreasing.The model of practical systems used for stability analysis usually contain uncertainty.Therefore,firstly a non-monotonic stability condition is derived for certain discrete time-delay systems,then robust non-monotonic stability conditions are proposed for uncertain systems.Finally,a novel stabilization algorithm is derived based on the introduced non-monotonic stability condition.The Lyapunov-Krasovskii functional and the controller are obtained by solving a set of linear matrix inequalities(LMI)or iterative LMI based nonlinear minimization.The proposed theorems are first evaluated by some numerical examples,and then by simulation and implementation on the pH neutralizing process plant.展开更多
基金supported by the National Natural Science Foundation of China(52201008,52372003)Natural Science Foundation of Heilongjiang Province of China(ZD2023E004)+1 种基金Fundamental Research Funds for the Central Universities(3072020CF2515,3072022CFJ2504)the State Key Laboratory of Particle Detection and Electronics(SKLPDE-KF-202311)。
文摘Mechano luminescence(ML),which involves the emission of light under mechanical stimuli,shows great potential in various applications such as sensing,imaging,and energy harvesting.Current research suggests that the luminescence mechanism of ML is typically connected to specific defects present within the material.In this study,we focus on the investigation of ML defects in Pr^(3+)-doped NaNbO_(3)/LiNbO_(3)heterojunctions,employing a combination of experimental and theoretical approaches.Through experimental analysis,we confirmed the presence of the heterojunction and its influence on ML intensity,and the optimal doping ratio for the heterojunction in ML was established.Furthermore,we examined the influence of varying Pr^(3+)doping concentrations on ML behavior and a proof-of-concept was demonstrated using the X-rays charged heterostructural phosphor as a stress sensor for biological applications.The position and concentration of internal defects in the ML material were scrutinized through thermo luminescence tests employing the variable heating rate method and positron annihilation.Complementing the experimental findings,theoretical simulations were conducted to elucidate the underlying mechanisms responsible for the observed ML defects.Density functional theory calculations were employed to investigate the energy levels,charge transfer processes,and lattice distortions within the heterojunctions under mechanical stress.Theoretical predictions were compared and validated against the experimental results.The integration of experimental and theoretical approaches provides a comprehensive understanding of the ML behavior of Pr^(3+)-doped NaNbO_(3)/LiNbO_(3)heterojunctions.The insights gained from this research contribute to the development of novel ML materials and pave the way for their applications in next-generation sensing and energy conversion devices.
基金funding enabled and organized by CAUL and its Member Institutions.
文摘The significant threat of wildfires to forest ecology and biodiversity,particularly in tropical and subtropical regions,underscores the necessity for advanced predictive models amidst shifting climate patterns.There is a need to evaluate and enhance wildfire prediction methods,focusing on their application during extended periods of intense heat and drought.This study reviews various wildfire modelling approaches,including traditional physical,semi-empirical,numerical,and emerging machine learning(ML)-based models.We critically assess these models’capabilities in predicting fire susceptibility and post-ignition spread,highlighting their strengths and limitations.Our findings indicate that while traditional models provide foundational insights,they often fall short in dynamically estimating parameters and predicting ignition events.Cellular automata models,despite their potential,face challenges in data integration and computational demands.Conversely,ML models demonstrate superior efficiency and accuracy by leveraging diverse datasets,though they encounter interpretability issues.This review recommends hybrid modelling approaches that integrate multiple methods to harness their combined strengths.By incorporating data assimilation techniques with dynamic forecasting models,the predictive capabilities of ML-based predictions can be significantly enhanced.This review underscores the necessity for continued refinement of these models to ensure their reliability in real-world applications,ultimately contributing to more effective wildfire mitigation and management strategies.Future research should focus on improving hybrid models and exploring new data integration methods to advance predictive capabilities.
文摘This paper proposes a novel less-conservative non-monotonic Lyapunov-Krasovskii stability approach for stability analysis of discrete time-delay systems.In this method,monotonically decreasing requirements of the Lyapunov-Krasovskii method are replaced with non-monotonic ones.The Lyapunov-Krasovskii functional is allowed to increase in some steps,but the overall trend should be decreasing.The model of practical systems used for stability analysis usually contain uncertainty.Therefore,firstly a non-monotonic stability condition is derived for certain discrete time-delay systems,then robust non-monotonic stability conditions are proposed for uncertain systems.Finally,a novel stabilization algorithm is derived based on the introduced non-monotonic stability condition.The Lyapunov-Krasovskii functional and the controller are obtained by solving a set of linear matrix inequalities(LMI)or iterative LMI based nonlinear minimization.The proposed theorems are first evaluated by some numerical examples,and then by simulation and implementation on the pH neutralizing process plant.
