In this paper, a direct probabilistic approach（DPA） is presented to formulate and solve moment equations for nonlinear systems excited by environmental loads that can be either a stationary or nonstationary random p...In this paper, a direct probabilistic approach（DPA） is presented to formulate and solve moment equations for nonlinear systems excited by environmental loads that can be either a stationary or nonstationary random process.The proposed method has the advantage of obtaining the response＇s moments directly from the initial conditions and statistical characteristics of the corresponding external excitations. First, the response＇s moment equations are directly derived based on a DPA, which is completely independent of the It？/filtering approach since no specific assumptions regarding the correlation structure of excitation are made.By solving them under Gaussian closure, the response＇s moments can be obtained. Subsequently, a multiscale algorithm for the numerical solution of moment equations is exploited to improve computational efficiency and avoid much wall-clock time. Finally, a comparison of the results with Monte Carlo（MC） simulation gives good agreement.Furthermore, the advantage of the multiscale algorithm in terms of efficiency is also demonstrated by an engineering example.展开更多
The relationship between environmental degradation and poverty has increasingly become the focus of national strategic decision-making in recent years.However,despite several theoretical explorations on the nexus,a de...The relationship between environmental degradation and poverty has increasingly become the focus of national strategic decision-making in recent years.However,despite several theoretical explorations on the nexus,a dearth of empirical literature on the poverty-environmental degradation nexus,specifically on Sub-Saharan Africa(SSA),still exists.In this study,we investigated the poverty-environmental degradation nexus in SSA.We hypothesized that poverty is both a cause and effect of environmental degradation,and this relationship is explained as a vicious cycle.Unlike previous studies,we employed several alternative indicators of environmental degradation to examine the poverty-environmental degradation nexus in this study.We used data from 41 countries of SSA between 1996 and 2019 and employed the generalized method of moments(GMM)approach.The findings suggest a cyclical relationship between poverty and environmental degradation in SSA,which confirms that an increase in poverty leads to an increase in environmental degradation,especially in deforestation and PM2.5 emissions.Similarly,the increase in environmental degradation positively affects poverty in SSA.We also confirmed that exogenous conditioning factors such as population growth rate,education,industrialization,and income inequality,institutional quality indicators such as governance effectiveness,control of corruption,freedom ad civil liberty,and democracy,and endogenous factors including fossil fuel energy use,fuelwood energy use,household health expenditure,infant mortality rate,and agriculture productivity influence the nexus between poverty and environmental degradation.The findings on the relationship between poverty and environmental degradation in SSA are a testimonial evidence that both poverty and environmental degradation are significant issues in SSA.Hence,poverty alleviation policies in SSA should not lead to PM2.5 emissions and deforestation,which may as well force people into a poverty-environmental degradation trap.Instead,poverty reduction policies should simultaneously achieve environmental conservation.展开更多
In uncertainty analysis and reliability-based multidisciplinary design and optimization(RBMDO)of engineering structures,the saddlepoint approximation(SA)method can be utilized to enhance the accuracy and efficiency of...In uncertainty analysis and reliability-based multidisciplinary design and optimization(RBMDO)of engineering structures,the saddlepoint approximation(SA)method can be utilized to enhance the accuracy and efficiency of reliability evaluation.However,the random variables involved in SA should be easy to handle.Additionally,the corresponding saddlepoint equation should not be complicated.Both of them limit the application of SA for engineering problems.The moment method can construct an approximate cumulative distribution function of the performance function based on the first few statistical moments.However,the traditional moment matching method is not very accurate generally.In order to take advantage of the SA method and the moment matching method to enhance the efficiency of design and optimization,a fourth-moment saddlepoint approximation(FMSA)method is introduced into RBMDO.In FMSA,the approximate cumulative generating functions are constructed based on the first four moments of the limit state function.The probability density function and cumulative distribution function are estimated based on this approximate cumulative generating function.Furthermore,the FMSA method is introduced and combined into RBMDO within the framework of sequence optimization and reliability assessment,which is based on the performance measure approach strategy.Two engineering examples are introduced to verify the effectiveness of proposed method.展开更多
基金supported by the Defense Industrial Technology Development Program (Grant JCKY2013601B)the "111" Project (Grant B07009)the National Natural Science Foundation of China (Grants 11372025, 11432002)
文摘In this paper, a direct probabilistic approach（DPA） is presented to formulate and solve moment equations for nonlinear systems excited by environmental loads that can be either a stationary or nonstationary random process.The proposed method has the advantage of obtaining the response＇s moments directly from the initial conditions and statistical characteristics of the corresponding external excitations. First, the response＇s moment equations are directly derived based on a DPA, which is completely independent of the It？/filtering approach since no specific assumptions regarding the correlation structure of excitation are made.By solving them under Gaussian closure, the response＇s moments can be obtained. Subsequently, a multiscale algorithm for the numerical solution of moment equations is exploited to improve computational efficiency and avoid much wall-clock time. Finally, a comparison of the results with Monte Carlo（MC） simulation gives good agreement.Furthermore, the advantage of the multiscale algorithm in terms of efficiency is also demonstrated by an engineering example.
文摘The relationship between environmental degradation and poverty has increasingly become the focus of national strategic decision-making in recent years.However,despite several theoretical explorations on the nexus,a dearth of empirical literature on the poverty-environmental degradation nexus,specifically on Sub-Saharan Africa(SSA),still exists.In this study,we investigated the poverty-environmental degradation nexus in SSA.We hypothesized that poverty is both a cause and effect of environmental degradation,and this relationship is explained as a vicious cycle.Unlike previous studies,we employed several alternative indicators of environmental degradation to examine the poverty-environmental degradation nexus in this study.We used data from 41 countries of SSA between 1996 and 2019 and employed the generalized method of moments(GMM)approach.The findings suggest a cyclical relationship between poverty and environmental degradation in SSA,which confirms that an increase in poverty leads to an increase in environmental degradation,especially in deforestation and PM2.5 emissions.Similarly,the increase in environmental degradation positively affects poverty in SSA.We also confirmed that exogenous conditioning factors such as population growth rate,education,industrialization,and income inequality,institutional quality indicators such as governance effectiveness,control of corruption,freedom ad civil liberty,and democracy,and endogenous factors including fossil fuel energy use,fuelwood energy use,household health expenditure,infant mortality rate,and agriculture productivity influence the nexus between poverty and environmental degradation.The findings on the relationship between poverty and environmental degradation in SSA are a testimonial evidence that both poverty and environmental degradation are significant issues in SSA.Hence,poverty alleviation policies in SSA should not lead to PM2.5 emissions and deforestation,which may as well force people into a poverty-environmental degradation trap.Instead,poverty reduction policies should simultaneously achieve environmental conservation.
基金support from the Key R&D Program of Shandong Province(Grant No.2019JZZY010431)the National Natural Science Foundation of China(Grant No.52175130)+1 种基金the Sichuan Science and Technology Program(Grant No.2022YFQ0087)the Sichuan Science and Technology Innovation Seedling Project Funding Projeet(Grant No.2021112)are gratefully acknowledged.
文摘In uncertainty analysis and reliability-based multidisciplinary design and optimization(RBMDO)of engineering structures,the saddlepoint approximation(SA)method can be utilized to enhance the accuracy and efficiency of reliability evaluation.However,the random variables involved in SA should be easy to handle.Additionally,the corresponding saddlepoint equation should not be complicated.Both of them limit the application of SA for engineering problems.The moment method can construct an approximate cumulative distribution function of the performance function based on the first few statistical moments.However,the traditional moment matching method is not very accurate generally.In order to take advantage of the SA method and the moment matching method to enhance the efficiency of design and optimization,a fourth-moment saddlepoint approximation(FMSA)method is introduced into RBMDO.In FMSA,the approximate cumulative generating functions are constructed based on the first four moments of the limit state function.The probability density function and cumulative distribution function are estimated based on this approximate cumulative generating function.Furthermore,the FMSA method is introduced and combined into RBMDO within the framework of sequence optimization and reliability assessment,which is based on the performance measure approach strategy.Two engineering examples are introduced to verify the effectiveness of proposed method.
