Soil Moisture Rather than Soil Nutrient Regulates the Belowground Bud Bank of Rhizomatous Species Psammochloa villosa in Arid Sand Dunes

In arid and semi-arid sand dune ecosystems,belowground bud bank plays an important role in population regeneration and vegetation restoration.However,the responses of belowground bud bank size and composition to sand burial and its induced changes in soil environmental factors have been rarely studied.In arid sand dunes of Northwestern China,we investigated belowground bud bank size and composition of the typical rhizomatous psammophyte Psammochloa villosa as well as three key soil environmental factors(soil moisture,total carbon and total nitrogen)under different depths of sand burial.Total buds and rhizome buds increased significantly with increasing burial depth,whereas tiller buds first increased and then decreased,with a peak value at the depth of 20-30 cm.Soil moisture increased significantly with sand burial depth,and was positively correlated with the number of all buds and rhizome buds.Soil total carbon concentration first increased and then decreased with sand burial depth,and total nitrogen concentration was significantly lower under deep sand burial than those at shallow depths,and only the number of tiller buds was positively correlated with soil total nitrogen concentration.These results indicate that soil moisture rather than soil nutrient might regulate the belowground bud bank of P.villosa,and that clonal psammophytes could regulate their belowground bud bank in response to sand burial and the most important environmental stress(i.e.,soil moisture).These responses,as the key adaptive strategy,may ensure clonal plant population regeneration and vegetation restoration in arid sand dunes.

Trajectory Rotation Privacy Protection Algorithm Based on <i>k</i>Anonymity

The frequent use of location query services in location-based services will come out a large amount of space-time data related to users. Attackers infer information of location or track based on these rich background knowledge. Therefore, aiming at the problem of trajectory privacy, the context adds instant traffic monitoring based on user behavior patterns, trajectory similarity and other background information. According to the idea of k anonymity, proposed a method combined with traffic condition to protect the trajectory privacy. First, the user randomly selects a time point of the real trajectory to rotate to generate dummy trajectory, and then repeat the above process on the real trajectory and dummy trajectory. Up to the generation of k &minus;1 dummy trajectory, and according to the actual road conditions and trajectory leakage probability, traversing dummy trajectory to adjust. Finally, it is further proved through experiments that the method will be more efficient and protect privacy well.

A Dynamic Social Network Data Publishing Algorithm Based on Differential Privacy

Social network contains the interaction between social members, which constitutes the structure and attribute of social network. The interactive relationship of social network contains a lot of personal privacy information. The direct release of social network data will cause the disclosure of privacy information. Aiming at the dynamic characteristics of social network data release, a new dynamic social network data publishing method based on differential privacy was proposed. This method was consistent with differential privacy. It is named DDPA (Dynamic Differential Privacy Algorithm). DDPA algorithm is an improvement of privacy protection algorithm in static social network data publishing. DDPA adds noise which follows Laplace to network edge weights. DDPA identifies the edge weight information that changes as the number of iterations increases, adding the privacy protection budget. Through experiments on real data sets, the results show that the DDPA algorithm satisfies the user’s privacy requirement in social network. DDPA reduces the execution time brought by iterations and reduces the information loss rate of graph structure.

考虑塑性应变记忆恢复的循环棘轮本构模型研究

论文在统一粘塑性循环本构理论框架下,以Ohno-Abdel-Karim非线性随动硬化模型为基础,建立了一个循环本构模型.模型通过引入塑性应变幅值记忆效应,并在塑性应变记忆项中加入恢复系数,提高了对循环硬化材料单轴行为的预言能力.多轴模型中通过引入Tanaka提出的非比例度效应,描述多轴加载中因路径不同产生非比例附加硬化,提高模型对多轴棘轮行为预测能力.通过和实验结果的对比,可以发现：论文模型可以合理的描述316L不锈钢的单、多轴棘轮变形,模拟数据均与实验数据吻合较好,论文模型可以合理地描述循环硬化材料的棘轮变形.

Uniaxial Ratcheting Behaviors of Metals with Different Crystal Structures or Values of Fault Energy: Macroscopic Experiments

The uniaxial ratcheting behaviors of several metals with different crystal structures or values of fault energy were observed by the stress-controlled cyclic tests at room temperature. The prescribed metals included 316L stainless steel, pure copper, pure aluminum, and ordinary 20# carbon steel. The effects of applied mean stress, stress amplitude and stress ratio on the uniaxial ratcheting were also investigated. The observations show that different crystal structures or values of fault energy result in more or less different ratcheting behaviors for the prescribed metals. The different ratcheting behaviors are partially caused by the variation of dislocation mobility.