Assessing spatiotemporal variation in global soil erosion is essential for identifying areas that require greater attention and management under the effects of anthropogenic activities and climate change.Soil erosion ...Assessing spatiotemporal variation in global soil erosion is essential for identifying areas that require greater attention and management under the effects of anthropogenic activities and climate change.Soil erosion can be modelled using the universal soil loss equation(USLE),which includes rainfall erosivity(R-factor),vegetation cover(C-factor),topography(LS-factor),soil erodibility(K-factor),and management practices(P-factor).However,global soil erosion modeling faces numerous challenges,including data acquisition,calculation processes,and parameter calibration under different climatic and topographic backgrounds.Thus,we presented an improved USLE-based model using highly distributed parameters.The R-,C-,and P-factors were modified by the climate zone,country,and topography.This distributed model was applied to estimate the intensity and variations in global soil erosion from 1992 to 2015.We validated the accuracy of this model by comparing simulations with measurements from 11,439 plot years of erosion data.The results showed that i)the average global erosion rate was 5.78 t ha^(-1)year^(-1),with an increase rate of 4.26×10^(-3)t ha^(-1)year^(-1);ii)areas with significantly increasing erosion accounted for 16%of the land with water erosion,whereas those with significantly decreasing erosion accounted for 7%;and iii)areas with severe erosion included the western Ghats,Abyssinian Plateau,Brazilian Plateau,south and east of the Himalayas,and western coast of South America.Intensified erosion occurred mainly on the Amazon Plain and the northern coast of the Mediterranean.This study provides an improved water erosion prediction model and accurate information for researchers and policymakers to identify the drivers underlying changes in water erosion in different regions.展开更多
Economic globalization has transformed many manufacturing enterprises from a single-plant production mode to a multi-plant cooperative production mode.The distributed flexible job-shop scheduling problem(DFJSP)has bec...Economic globalization has transformed many manufacturing enterprises from a single-plant production mode to a multi-plant cooperative production mode.The distributed flexible job-shop scheduling problem(DFJSP)has become a research hot topic in the field of scheduling because its production is closer to reality.The research of DFJSP is of great significance to the organization and management of actual production process.To solve the heterogeneous DFJSP with minimal completion time,a hybrid chemical reaction optimization(HCRO)algorithm is proposed in this paper.Firstly,a novel encoding-decoding method for flexible manufacturing unit(FMU)is designed.Secondly,half of initial populations are generated by scheduling rule.Combined with the new solution acceptance method of simulated annealing(SA)algorithm,an improved method of critical-FMU is designed to improve the global and local search ability of the algorithm.Finally,the elitist selection strategy and the orthogonal experimental method are introduced to the algorithm to improve the convergence speed and optimize the algorithm parameters.In the experimental part,the effectiveness of the simulated annealing algorithm and the critical-FMU refinement methods is firstly verified.Secondly,in the comparison with other existing algorithms,the proposed optimal scheduling algorithm is not only effective in homogeneous FMUs examples,but also superior to existing algorithms in heterogeneous FMUs arithmetic cases.展开更多
基金This work was funded by the National Natural Science Foundation of China(U2102209).
文摘Assessing spatiotemporal variation in global soil erosion is essential for identifying areas that require greater attention and management under the effects of anthropogenic activities and climate change.Soil erosion can be modelled using the universal soil loss equation(USLE),which includes rainfall erosivity(R-factor),vegetation cover(C-factor),topography(LS-factor),soil erodibility(K-factor),and management practices(P-factor).However,global soil erosion modeling faces numerous challenges,including data acquisition,calculation processes,and parameter calibration under different climatic and topographic backgrounds.Thus,we presented an improved USLE-based model using highly distributed parameters.The R-,C-,and P-factors were modified by the climate zone,country,and topography.This distributed model was applied to estimate the intensity and variations in global soil erosion from 1992 to 2015.We validated the accuracy of this model by comparing simulations with measurements from 11,439 plot years of erosion data.The results showed that i)the average global erosion rate was 5.78 t ha^(-1)year^(-1),with an increase rate of 4.26×10^(-3)t ha^(-1)year^(-1);ii)areas with significantly increasing erosion accounted for 16%of the land with water erosion,whereas those with significantly decreasing erosion accounted for 7%;and iii)areas with severe erosion included the western Ghats,Abyssinian Plateau,Brazilian Plateau,south and east of the Himalayas,and western coast of South America.Intensified erosion occurred mainly on the Amazon Plain and the northern coast of the Mediterranean.This study provides an improved water erosion prediction model and accurate information for researchers and policymakers to identify the drivers underlying changes in water erosion in different regions.
基金This work was supported by the National Natural Science Foundation of China(Nos.61973120,62076095,61673175,and 61573144).
文摘Economic globalization has transformed many manufacturing enterprises from a single-plant production mode to a multi-plant cooperative production mode.The distributed flexible job-shop scheduling problem(DFJSP)has become a research hot topic in the field of scheduling because its production is closer to reality.The research of DFJSP is of great significance to the organization and management of actual production process.To solve the heterogeneous DFJSP with minimal completion time,a hybrid chemical reaction optimization(HCRO)algorithm is proposed in this paper.Firstly,a novel encoding-decoding method for flexible manufacturing unit(FMU)is designed.Secondly,half of initial populations are generated by scheduling rule.Combined with the new solution acceptance method of simulated annealing(SA)algorithm,an improved method of critical-FMU is designed to improve the global and local search ability of the algorithm.Finally,the elitist selection strategy and the orthogonal experimental method are introduced to the algorithm to improve the convergence speed and optimize the algorithm parameters.In the experimental part,the effectiveness of the simulated annealing algorithm and the critical-FMU refinement methods is firstly verified.Secondly,in the comparison with other existing algorithms,the proposed optimal scheduling algorithm is not only effective in homogeneous FMUs examples,but also superior to existing algorithms in heterogeneous FMUs arithmetic cases.
