[Objective] This study aimed to investigate the endodormancy release in nectarine bud treated by short-term freezing. [Method] Through short-term freezing at seven different temperatures for three different periods on...[Objective] This study aimed to investigate the endodormancy release in nectarine bud treated by short-term freezing. [Method] Through short-term freezing at seven different temperatures for three different periods on bud, the livability, burst, ratio of free water to bound water and membrane permeability of ＇Shuguang＇ nec- tarine bud were studied. [Result] On November 10, compared with non-freezing treatment （CK）, the bud burst, ratio of free water to bound water and membrane permeability treated by freezing at -5 and -8 ℃ were almost the same as CK. But the rest freezing treatments advanced the date of endodormancy release, while the bud burst, ratio of free water to bound water and membrane permeability were high- er than CK. on November 20 and 30, the effects of the freezing treatment on en- dodormancy release were the same when the treatment on November 10, and the effect was better as the treatment was later. [Conclusion] The correlation of the rate of bud burst, ratio of free water to bound water, and membrane permeability of the different freezing treatments indicated that the change from bound water to free wa- ter and the increase of membrane permeability were probably the signal of endodor- mancy release.展开更多
In order to achieve an intelligent and automated self-management network,dynamic policy configuration and selection are needed.A certain policy only suits to a certain network environment.If the network environment ch...In order to achieve an intelligent and automated self-management network,dynamic policy configuration and selection are needed.A certain policy only suits to a certain network environment.If the network environment changes,the certain policy does not suit any more.Thereby,the policy-based management should also have similar "natural selection" process.Useful policy will be retained,and policies which have lost their effectiveness are eliminated.A policy optimization method based on evolutionary learning was proposed.For different shooting times,the priority of policy with high shooting times is improved,while policy with a low rate has lower priority,and long-term no shooting policy will be dormant.Thus the strategy for the survival of the fittest is realized,and the degree of self-learning in policy management is improved.展开更多
基金Supported by the Special Fund of Modern System of Agricultural Industry Technology(CARS-30-6)the Special Fund of Department of Science and Technology,Liaoning Province(2011204001)~~
文摘[Objective] This study aimed to investigate the endodormancy release in nectarine bud treated by short-term freezing. [Method] Through short-term freezing at seven different temperatures for three different periods on bud, the livability, burst, ratio of free water to bound water and membrane permeability of ＇Shuguang＇ nec- tarine bud were studied. [Result] On November 10, compared with non-freezing treatment （CK）, the bud burst, ratio of free water to bound water and membrane permeability treated by freezing at -5 and -8 ℃ were almost the same as CK. But the rest freezing treatments advanced the date of endodormancy release, while the bud burst, ratio of free water to bound water and membrane permeability were high- er than CK. on November 20 and 30, the effects of the freezing treatment on en- dodormancy release were the same when the treatment on November 10, and the effect was better as the treatment was later. [Conclusion] The correlation of the rate of bud burst, ratio of free water to bound water, and membrane permeability of the different freezing treatments indicated that the change from bound water to free wa- ter and the increase of membrane permeability were probably the signal of endodor- mancy release.
基金National Natural Science Foundation of China(No.60534020)Cultivation Fund of the Key Scientific and Technical Innovation Project from Ministry of Education of China(No.706024)International Science Cooperation Foundation of Shanghai,China(No.061307041)
文摘In order to achieve an intelligent and automated self-management network,dynamic policy configuration and selection are needed.A certain policy only suits to a certain network environment.If the network environment changes,the certain policy does not suit any more.Thereby,the policy-based management should also have similar "natural selection" process.Useful policy will be retained,and policies which have lost their effectiveness are eliminated.A policy optimization method based on evolutionary learning was proposed.For different shooting times,the priority of policy with high shooting times is improved,while policy with a low rate has lower priority,and long-term no shooting policy will be dormant.Thus the strategy for the survival of the fittest is realized,and the degree of self-learning in policy management is improved.
