Thermally Responsive Selenide-containing Materials Based on Transalkylation of Selenonium Salts

Covalent adaptable networks (CANs) possess unique properties as a result of their internal dynamic bonds, such as self-healing and reprocessing abilities. In this study, we report a thermally responsive C−Se dynamic covalent chemistry (DCC) that relies on the transalkylation exchange between selenonium salts and selenides, which undergo a fast transalkylation reaction in the absence of any catalyst. Additionally, we demonstrate the presence of a dissociative mechanism in the absence of selenide groups. After incorporation of this DCC into selenide-containing polymer materials, it was observed that the cross-linked networks display varying dynamic exchange rates when using different alkylation reagents, suggesting that the reprocessing capacity of selenide-containing materials can be regulated. Also, by incorporating selenonium salts into polymer materials, we observed that the materials exhibited good healing ability at elevated temperatures as well as excellent solvent resistance at ambient temperature. This novel dynamic covalent chemistry thus provides a straightforward method for the healing and reprocessing of selenide-containing materials.     

On the optimum capacity of capacity expansion problems

In this paper we consider problems of the following type: Let E = { e ₁, e ₂,..., e _n } be a finite set and be a family of subsets of E. For each element e _i in E, c _i is a given capacity and _i is the cost of increasing capacity c _i by one unit. It is assumed that we can expand the capacity of each element in E so that the capacity of family can be expanded to a level r. For each r, let f (r) be the efficient function with respect to the capacity r of family , and be the cost function for expanding the capacity of family to r. The goal is to find the optimum capacity value r ^* and the corresponding expansion strategy so that the pure efficency function is the largest. Firstly, we show that this problem can be solved efficiently by figuring out a series of bottleneck capacity expansion problem defined by paper (Yang and Chen, Acta Math Sci 22:207–212, 2002) if f (r) is a piecewise linear function. Then we consider two variations and prove that these problems can be solved in polynomial time under some conditions. Finally the optimum capacity for maximum flow expansion problem is discussed. We tackle it by constructing an auxiliary network and transforming the problem into a maximum cost circulation problem on the auxiliary network.     

A time-series approach to measuring node similarity in networks and its application to community detection

A central concept in network analysis is that of similarity between nodes. In this paper, we introduce a dynamic time-series approach to quantifying the similarity between nodes in networks. The problem of measuring node similarity is exquisitely embedded into the framework of time series for state evolution of nodes. We develop a deterministic parameter-free diffusion model to drive the dynamic evolution of node states, and produce a unique time series for each source node. Then we introduce a measure quantifying how far all the other nodes are located from each source one. Following this measure, a quantity called dissimilarity index is proposed to signify the extent of similarity between nodes. Thereof, our dissimilarity index gives a deep and natural integration between the local and global perspectives of topological structure of networks. Furthermore, we apply our dissimilarity index to unveil community structure in networks, which verifies the proposed dissimilarity index.     

Topological properties of phylogenetic trees in evolutionary models

The extent to which evolutionary processes affect the shape of phylogenetic trees is an important open question. Analyses of small trees seem to detect non-trivial asymmetries which are usually ascribed to the presence of correlations in speciation rates. Many models used to construct phylogenetic trees have an algorithmic nature and are rarely biologically grounded. In this article, we analyze the topological properties of phylogenetic trees generated by different evolutionary models (populations of RNA sequences and a simple model with inheritance and mutation) and compare them with the trees produced by known uncorrelated models as the backward coalescent, paying special attention to large trees. Our results demonstrate that evolutionary parameters as mutation rate or selection pressure have a weak influence on the scaling behavior of the trees, while the size of phylogenies strongly affects measured scaling exponents. Within statistical errors, the topological properties of phylogenies generated by evolutionary models are compatible with those measured in balanced, uncorrelated trees.     

基于SRLG约束和资源共享的交迭段保护机制研究

通过对共享风险链路组约束机制和交迭段保护机制的分析,将两者相结合并根据工作和保护路径分别采用动态链路权重调整,提出了一种基于共享风险链路组不相关的交迭段共享保护算法.交迭段共享保护算法在共享风险链路组不相关的约束条件下,为整个工作路由提供了多个交迭的保护段,并给出了一种能够合理有效选择这些交迭保护段的方法.对交迭段共享保护算法的分析和仿真结果表明,与以往保护算法相比,交迭段共享保护算法不但大大提高了网络连接的可靠性,而且还通过不同交迭保护段间资源的合理共享,有效地提高了网络资源的利用率.     

多层网络中的联合资源配置方案

在多层网络的资源配置中,为了实现备份资源在各网络层的最大共享,在综合考虑网络各层的资源使用信息和拓扑信息的基础上,提出了一种联合的资源配置方案.在建立资源分配方案的整数线性规划模型的同时,提出了适合解决大规模网络的遗传算法.数值结果表明:联合资源配置方案可以更好地共享各网络层的备份资源,从而使多层网络具有更高的带宽利用率和更低的运营成本.由于引入了各网络层的协调,从而防止了回路问题.     

基于实时样本采集的个性化手写汉字输入系统设计

手写汉字识别是手写汉字输入的基础。目前智能设备中的手写汉字输入法无法根据用户的汉字书写习惯,动态调整识别模型以提升手写汉字的正确识别率。通过对最新深度学习算法及训练模型的研究,提出了一种基于用户手写汉字样本实时采集的个性化手写汉字输入系统的设计方法。该方法将采集用户的手写汉字作为增量样本,通过对服务器端训练生成的手写汉字识别模型的再次训练,使识别模型能够更好地适应该用户的书写习惯,提升手写汉字输入系统的识别率。最后,在该理论方法的基础上,结合新设计的深度残差网络,进行了手写汉字识别的对比实验。实验结果显示,通过引入实时采集样本的再次训练,手写汉字识别模型的识别率有较大幅度的提升,能够更有效的满足用户在智能设备端对手写汉字输入系统的使用需求。     

On network form and function

Moving from the observation that drainage network configurations minimizing total energy dissipation are stationary solutions of the general equation describing landscape evolution, we review theoretical and observational evidence on river patterns and their scale-invariant structure. Exact results complemented by numerical annealing of the basic equation in the presence of additive noise suggest that configurations at (or very close to) the global minimum of energy dissipation differ from dynamically accessible states, which have rather different scaling properties and conform much better to natural forms. Thus we argue that, at least in the fluvial landscape, Nature works through imperfect searches for dynamically accessible optimal configurations. We also show that optimal networks are spanning loopless configurations only under precise physical requirements. This is stated in a form applicable to generic networks, suggesting that other branching structures occurring in Nature (e.g. scale-free and looping) may possibly arise through optimality to selective pressures. Indeed, we show that this is the case.     

Prosocial norms and degree heterogeneity in social networks

We provide empirical evidence to support the claims that social diversity promotes prosocial behavior. We elicit a real-life social network and its members’ adherence to a social norm, namely inequity aversion. The data reveal a positive relationship between subjects’ prosociality and several measures of centrality. This result is in line with the theoretical literature that relates the evolution of social norms to the structure of social interactions and argues that central individuals are crucial for the emergence of prosocial behavior.