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In this paper, a new evolving model with tunable attractiveness is presented. Based on the Barabasi—Albert (BA) model, we introduce the attractiveness of node which can change with node degree. Using the mean-field theory, we obtain the analytical expression of power-law degree distribution with the exponent γ ∈ (3,∞). The new model is more homogeneous and has a lower clustering coefficient and bigger average path length than the BA model. 相似文献
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Using adaptive control techniques, we investigate the multi-switching synchronization of chaotic systems with parameters unknown. Based upon the Lyapunov stability theory, we design the controllers and updating laws of different switching, and it is extended to investigate the synchronization problems with different combinations of slave states with master systems. We take the Lorenz system and the Chen system as an example to analyze the multi-switching synchronization process of different structures of chaotic systems. Finally, numerical simulations have shown the effectiveness of the method. 相似文献
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本文采用基于密度泛函理论的广义梯度近似方法和赝势平面波法的第一性原理计算及化学势的热力学平衡原理,对BiXO3 (X= Cr, Mn, Fe, Ni)的结构稳定性进行了仔细的研究. 结果表明,这四种多铁化合物中, BiFeO3最稳定, BiCrO3次之, 而BiMnO3和BiNiO3则很难在热平衡条件下稳定,因此在样品制备中要多考虑热平衡之外的手段.
关键词:
3')" href="#">BiXO3
结构稳定性
第一性原理 相似文献
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单分子荧光共振能量转移(smFRET)和磁镊(MT)技术目前广泛应用于研究分子马达.相较于常规技术,其具有高精度及动态观测的优点.本文研究对象为T7解旋酶,是六聚体解旋酶的典型代表.研究表明,这种解旋酶主要消耗脱氧胸苷三磷酸(dTTP)提供能量,且仅能沿着5′-3′单向进行行走和解旋工作.目前对于六聚体解旋酶的解旋和换链机制的认知仍然存在着诸多问题,因此本文主要以此作为切入点开展研究.首先通过运用smFRET技术研究T7解旋酶在不同DNA底物上的解旋现象,发现其需要3′-尾链参与到解旋工作中,但其为单链或双链结构并无明显区别;通过改变脱氧核糖核酸(DNA)序列中的GC含量,发现T7解旋酶随着序列中GC含量的升高会更容易在解旋过程中发生回退现象,导致解旋长度明显缩短;通过进一步分析发生回退先的实验数据,发现T7解旋酶除了可以瞬时回退到叉形DNA岔口或脱落外,还可以缓慢回退到叉形DNA岔口;运用MT技术研究该解旋酶,同样发现这种缓慢回退现象的存在.根据T7解旋酶解旋DNA遵循的单向性和极性,其只能沿着5′到3′方向进行行走和解旋.因此,本文推测这种缓慢回退的现象可能是解旋酶从5′-链转移至3′-链上,即发生换链过程;最后,本文提出了T7解旋酶在解旋过程中进行换链的模型,将有助于进一步理解环状六聚体解旋酶行使其功能的分子机制. 相似文献
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We propose a novel neural network based on a diagonal recurrent neural network and chaos,and its structure and learning algorithm are designed.The multilayer feedforward neural network,diagonal recurrent neural network,and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map.The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks. 相似文献