As an important plant species with high protein contents,wild soybean(Glycine soja),has drawn much attention and appeared to be useful for the genetic improvement of soybean germplasms.Since temperature is one of the ...As an important plant species with high protein contents,wild soybean(Glycine soja),has drawn much attention and appeared to be useful for the genetic improvement of soybean germplasms.Since temperature is one of the numerous environmental factors affecting the germination of most plants,an experimental study was carried out to determine the effect of temperature on germination of wild soybean(G.soja)seeds.Germination test was conducted by setting up thirty-six constant and alternating temperature regimes,ranging from 5 to 40 ℃(16 h night/8 h day).Responses in germination rate to these temperature regimes were then used to construct a quadratic response surface,giving estimated germination rates with confidence intervals at P ≤ 0.05.The results showed that germination capacity was significantly greater while exposed to constant temperatures of 25 ℃,and under the alternating temperature regime the optimum temperature occurred at the 20/25,25/25,25/30 ℃ regime(16 h/8 h)with the amplitude widened from 0 to 5 ℃.Together with regional monthly climate data,these results could be used to improve and promote the cultivation of wild soybean(G.soja),making it available to develop the location-specific optimum seeding time and to apply weed-control treatments.展开更多
In recent years,growing attention has been paid to the interval investigation of uncertainty problems.However,the contradiction between accuracy and efficiency always exists.In this paper,an iterative interval analysi...In recent years,growing attention has been paid to the interval investigation of uncertainty problems.However,the contradiction between accuracy and efficiency always exists.In this paper,an iterative interval analysis method based on Kriging-HDMR(IIAMKH)is proposed to obtain the lower and upper bounds of uncertainty problems considering interval variables.Firstly,Kriging-HDMR method is adopted to establish the meta-model of the response function.Then,the Genetic Algorithm&Sequential Quadratic Programing(GA&SQP)hybrid optimization method is applied to search for the minimum/maximum values of the meta-model,and thus the corresponding uncertain parameters can be obtained.By substituting them into the response function,we can acquire the predicted interval.Finally,an iterative process is developed to improve the accuracy and stability of the proposed method.Several numerical examples are investigated to demonstrate the effectiveness of the proposed method.Simulation results indicate that the presented IIAMKH can obtain more accurate results with fewer samples.展开更多
In this paper, we provide a framework of fuzzy landscape theory and discuss an application to alliance analysis. The fuzzy landscape theory may allow us to analyses a variety of aggregation processes in political, eco...In this paper, we provide a framework of fuzzy landscape theory and discuss an application to alliance analysis. The fuzzy landscape theory may allow us to analyses a variety of aggregation processes in political, economic, and social problems in a more flexible manner. The simulation results for the problems of the international alignment of the Second World War in Europe and the coalition formation in standard-setting alliances in the case of the UNIX operating system are compared with those given by the original theory.展开更多
基金supported by the fund of Jinhua Science Technology Foundation of China(2009-2-02)
文摘As an important plant species with high protein contents,wild soybean(Glycine soja),has drawn much attention and appeared to be useful for the genetic improvement of soybean germplasms.Since temperature is one of the numerous environmental factors affecting the germination of most plants,an experimental study was carried out to determine the effect of temperature on germination of wild soybean(G.soja)seeds.Germination test was conducted by setting up thirty-six constant and alternating temperature regimes,ranging from 5 to 40 ℃(16 h night/8 h day).Responses in germination rate to these temperature regimes were then used to construct a quadratic response surface,giving estimated germination rates with confidence intervals at P ≤ 0.05.The results showed that germination capacity was significantly greater while exposed to constant temperatures of 25 ℃,and under the alternating temperature regime the optimum temperature occurred at the 20/25,25/25,25/30 ℃ regime(16 h/8 h)with the amplitude widened from 0 to 5 ℃.Together with regional monthly climate data,these results could be used to improve and promote the cultivation of wild soybean(G.soja),making it available to develop the location-specific optimum seeding time and to apply weed-control treatments.
基金supported by the National Natural Science Foundation of China(Grant No.11472137)the Fundamental Research Funds for the Central Universities(Grant No.309181A8801 and 30919011204).
文摘In recent years,growing attention has been paid to the interval investigation of uncertainty problems.However,the contradiction between accuracy and efficiency always exists.In this paper,an iterative interval analysis method based on Kriging-HDMR(IIAMKH)is proposed to obtain the lower and upper bounds of uncertainty problems considering interval variables.Firstly,Kriging-HDMR method is adopted to establish the meta-model of the response function.Then,the Genetic Algorithm&Sequential Quadratic Programing(GA&SQP)hybrid optimization method is applied to search for the minimum/maximum values of the meta-model,and thus the corresponding uncertain parameters can be obtained.By substituting them into the response function,we can acquire the predicted interval.Finally,an iterative process is developed to improve the accuracy and stability of the proposed method.Several numerical examples are investigated to demonstrate the effectiveness of the proposed method.Simulation results indicate that the presented IIAMKH can obtain more accurate results with fewer samples.
基金This research is supported by CAS,NSFC and the National Basic Research Programme in Natural Sciences of Vietnam.
文摘In this paper, we provide a framework of fuzzy landscape theory and discuss an application to alliance analysis. The fuzzy landscape theory may allow us to analyses a variety of aggregation processes in political, economic, and social problems in a more flexible manner. The simulation results for the problems of the international alignment of the Second World War in Europe and the coalition formation in standard-setting alliances in the case of the UNIX operating system are compared with those given by the original theory.
