Background:Stem hardness is one of the major influencing factors for plant architecture in upland cotton(Gossypium hirsutum L.).Evaluating hardness phenotypic traits is very important for the selection of elite lines ...Background:Stem hardness is one of the major influencing factors for plant architecture in upland cotton(Gossypium hirsutum L.).Evaluating hardness phenotypic traits is very important for the selection of elite lines for resistance to lodging in Gossypium hirsutum L.Cotton breeders are interested in using diverse genotypes to enhance fiber quality and high-yield.Few pieces of research for hardness and its relationship with fiber quality and yield were found.This study was designed to find the relationship of stem hardness traits with fiber quality and yield contributing traits of upland cotton.Results:Experiments were carried out to measure the bending,acupuncture,and compression properties of the stem from a collection of upland cotton genotypes,comprising 237 accessions.The results showed that the genotypic difference in stem hardness was highly significant among the genotypes,and the stem hardness traits(BL,BU,AL,AU,CL,and CU)have a positive association with fiber quality traits and yield-related traits.Statistical analyses of the results showed that in descriptive statistics result bending(BL,BU)has a maximum coefficient of variance,but fiber length and fiber strength have less coefficient of variance among the genotypes.Principal component analysis(PCA)trimmed quantitative characters into nine principal components.The first nine principal components(PC)with Eigenvalues>1 explained 86%of the variation among 237 accessions of cotton.Both 2017 and 2018,PCA results indicated that BL,BU,FL,FE,and LI contributed to their variability in PC1,and BU,AU,CU,FD,LP,and FWPB have shown their variability in PC2.Conclusion:We describe here the systematic study of the mechanism involved in the regulation of enhancing fiber quality and yield by stem bending strength,acupuncture,and compression properties of G.hirsutum.展开更多
A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural n...A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy.展开更多
基金National Key Technology R&D Program,Ministry of Science and Technology(2016YFD0100306,2016YFD0100203)National Natural Science Foundation of China(grants 31671746).
文摘Background:Stem hardness is one of the major influencing factors for plant architecture in upland cotton(Gossypium hirsutum L.).Evaluating hardness phenotypic traits is very important for the selection of elite lines for resistance to lodging in Gossypium hirsutum L.Cotton breeders are interested in using diverse genotypes to enhance fiber quality and high-yield.Few pieces of research for hardness and its relationship with fiber quality and yield were found.This study was designed to find the relationship of stem hardness traits with fiber quality and yield contributing traits of upland cotton.Results:Experiments were carried out to measure the bending,acupuncture,and compression properties of the stem from a collection of upland cotton genotypes,comprising 237 accessions.The results showed that the genotypic difference in stem hardness was highly significant among the genotypes,and the stem hardness traits(BL,BU,AL,AU,CL,and CU)have a positive association with fiber quality traits and yield-related traits.Statistical analyses of the results showed that in descriptive statistics result bending(BL,BU)has a maximum coefficient of variance,but fiber length and fiber strength have less coefficient of variance among the genotypes.Principal component analysis(PCA)trimmed quantitative characters into nine principal components.The first nine principal components(PC)with Eigenvalues>1 explained 86%of the variation among 237 accessions of cotton.Both 2017 and 2018,PCA results indicated that BL,BU,FL,FE,and LI contributed to their variability in PC1,and BU,AU,CU,FD,LP,and FWPB have shown their variability in PC2.Conclusion:We describe here the systematic study of the mechanism involved in the regulation of enhancing fiber quality and yield by stem bending strength,acupuncture,and compression properties of G.hirsutum.
文摘A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy.
