Channel avulsion is a natural phenomenon that occurs abruptly on alluvial river deltas,which can affect the channel stability.The causes for avulsion could be generally categorized as topography-and flood-driven facto...Channel avulsion is a natural phenomenon that occurs abruptly on alluvial river deltas,which can affect the channel stability.The causes for avulsion could be generally categorized as topography-and flood-driven factors.However,previous studies on avulsion thresholds usually focused on topography-driven factors due to the centurial or millennial avulsion timescales of the world’s most deltas,but neglected the impacts of flood-driven factors.In the current study,a novel demarcation equation including the two driven factors was proposed,with the decadal timescale of avulsion being considered in the Yellow River Estuary(YRE).In order to quantify the contributions of different factors in each category,an entropy-based methodology was used to calculate the contributing weights of these factors.The factor with the highest weight in each category was then used to construct the demarcation equation,based on avulsion datasets associated with the YRE.An avulsion threshold was deduced according to the demarcation equation.This avulsion threshold was then applied to conduct the risk assessment of avulsion in the YRE.The results show that:two dominant factors cover respectively geomorphic coefficient representing the topography-driven factor and fluvial erosion intensity representing the flood-driven factor,which were thus employed to define a two dimensional mathematical space in which the demarcation equation can be obtained;the avulsion threshold derived from the equation was also applied in the risk assessment of avulsion;and the avulsion threshold proposed in this study is more accurate,as compared with the existing thresholds.展开更多
In this study,the entropy weight method was used to comprehensively evaluate the eight sweet-glutinous maize varieties in the regional trials in Shanxi Province in 2016. The results showed that the order of the perfor...In this study,the entropy weight method was used to comprehensively evaluate the eight sweet-glutinous maize varieties in the regional trials in Shanxi Province in 2016. The results showed that the order of the performance of comprehensive traits of the tested varieties was Heitiannuo 631,Heitiannuo No. 2,Cainuo No. 5,Xinnuo 8601,FYN 1601,Xinnuo 8608,Jindannuo 41,Caitiannuo 1958( from good to poor). Among them,Heitiannuo 631,Heitiannuo No. 2 and Cainuo No. 5 are excellent varieties,and the rest are good varieties.展开更多
Considering the difficulty of fuzzy synthetic evaluation method in calculation of the multiple factors and ignorance of the relationship among evaluating objects, a new weight evaluation process using entropy method w...Considering the difficulty of fuzzy synthetic evaluation method in calculation of the multiple factors and ignorance of the relationship among evaluating objects, a new weight evaluation process using entropy method was introduced. This improved method for determination of weight of the evaluating indicators was applied in water quality assessment of the Three Gorges reservoir area. The results showed that this method was favorable for fuzzy synthetic evaluation when there were more than one evaluating objects. One calculation was enough for calculating every monitoring point. Compared with the original evaluation method, the method predigested the fuzzy synthetic evaluation process greatly and the evaluation results are more reasonable.展开更多
In complex networks,identifying influential spreader is of great significance for improving the reliability of networks and ensuring the safe and effective operation of networks.Nowadays,it is widely used in power net...In complex networks,identifying influential spreader is of great significance for improving the reliability of networks and ensuring the safe and effective operation of networks.Nowadays,it is widely used in power networks,aviation networks,computer networks,and social networks,and so on.Traditional centrality methods mainly include degree centrality,closeness centrality,betweenness centrality,eigenvector centrality,k-shell,etc.However,single centrality method is onesided and inaccurate,and sometimes many nodes have the same centrality value,namely the same ranking result,which makes it difficult to distinguish between nodes.According to several classical methods of identifying influential nodes,in this paper we propose a novel method that is more full-scaled and universally applicable.Taken into account in this method are several aspects of node’s properties,including local topological characteristics,central location of nodes,propagation characteristics,and properties of neighbor nodes.In view of the idea of the multi-attribute decision-making,we regard the basic centrality method as node’s attribute and use the entropy weight method to weigh different attributes,and obtain node’s combined centrality.Then,the combined centrality is applied to the gravity law to comprehensively identify influential nodes in networks.Finally,the classical susceptible-infected-recovered(SIR)model is used to simulate the epidemic spreading in six real-society networks.Our proposed method not only considers the four topological properties of nodes,but also emphasizes the influence of neighbor nodes from the aspect of gravity.It is proved that the new method can effectively overcome the disadvantages of single centrality method and increase the accuracy of identifying influential nodes,which is of great significance for monitoring and controlling the complex networks.展开更多
基金financially supported by the National Key Research and Development Program of China(Grant No.2023YFC3200026)the National Natural Science Foundation of China(Grant No.U2243238)。
文摘Channel avulsion is a natural phenomenon that occurs abruptly on alluvial river deltas,which can affect the channel stability.The causes for avulsion could be generally categorized as topography-and flood-driven factors.However,previous studies on avulsion thresholds usually focused on topography-driven factors due to the centurial or millennial avulsion timescales of the world’s most deltas,but neglected the impacts of flood-driven factors.In the current study,a novel demarcation equation including the two driven factors was proposed,with the decadal timescale of avulsion being considered in the Yellow River Estuary(YRE).In order to quantify the contributions of different factors in each category,an entropy-based methodology was used to calculate the contributing weights of these factors.The factor with the highest weight in each category was then used to construct the demarcation equation,based on avulsion datasets associated with the YRE.An avulsion threshold was deduced according to the demarcation equation.This avulsion threshold was then applied to conduct the risk assessment of avulsion in the YRE.The results show that:two dominant factors cover respectively geomorphic coefficient representing the topography-driven factor and fluvial erosion intensity representing the flood-driven factor,which were thus employed to define a two dimensional mathematical space in which the demarcation equation can be obtained;the avulsion threshold derived from the equation was also applied in the risk assessment of avulsion;and the avulsion threshold proposed in this study is more accurate,as compared with the existing thresholds.
基金Biological Breeding Project of Shanxi Academy of Agricultural Sciences(17yzgc057)
文摘In this study,the entropy weight method was used to comprehensively evaluate the eight sweet-glutinous maize varieties in the regional trials in Shanxi Province in 2016. The results showed that the order of the performance of comprehensive traits of the tested varieties was Heitiannuo 631,Heitiannuo No. 2,Cainuo No. 5,Xinnuo 8601,FYN 1601,Xinnuo 8608,Jindannuo 41,Caitiannuo 1958( from good to poor). Among them,Heitiannuo 631,Heitiannuo No. 2 and Cainuo No. 5 are excellent varieties,and the rest are good varieties.
基金The National Natural Science Foundation of China (No. 50378008)
文摘Considering the difficulty of fuzzy synthetic evaluation method in calculation of the multiple factors and ignorance of the relationship among evaluating objects, a new weight evaluation process using entropy method was introduced. This improved method for determination of weight of the evaluating indicators was applied in water quality assessment of the Three Gorges reservoir area. The results showed that this method was favorable for fuzzy synthetic evaluation when there were more than one evaluating objects. One calculation was enough for calculating every monitoring point. Compared with the original evaluation method, the method predigested the fuzzy synthetic evaluation process greatly and the evaluation results are more reasonable.
基金Project support by the National Key Research and Development Program of China(Grant No.2018YFF0301000)the National Natural Science Foundation of China(Grant Nos.71673161 and 71790613)。
文摘In complex networks,identifying influential spreader is of great significance for improving the reliability of networks and ensuring the safe and effective operation of networks.Nowadays,it is widely used in power networks,aviation networks,computer networks,and social networks,and so on.Traditional centrality methods mainly include degree centrality,closeness centrality,betweenness centrality,eigenvector centrality,k-shell,etc.However,single centrality method is onesided and inaccurate,and sometimes many nodes have the same centrality value,namely the same ranking result,which makes it difficult to distinguish between nodes.According to several classical methods of identifying influential nodes,in this paper we propose a novel method that is more full-scaled and universally applicable.Taken into account in this method are several aspects of node’s properties,including local topological characteristics,central location of nodes,propagation characteristics,and properties of neighbor nodes.In view of the idea of the multi-attribute decision-making,we regard the basic centrality method as node’s attribute and use the entropy weight method to weigh different attributes,and obtain node’s combined centrality.Then,the combined centrality is applied to the gravity law to comprehensively identify influential nodes in networks.Finally,the classical susceptible-infected-recovered(SIR)model is used to simulate the epidemic spreading in six real-society networks.Our proposed method not only considers the four topological properties of nodes,but also emphasizes the influence of neighbor nodes from the aspect of gravity.It is proved that the new method can effectively overcome the disadvantages of single centrality method and increase the accuracy of identifying influential nodes,which is of great significance for monitoring and controlling the complex networks.
