图中相互独立的4圈和含4个点的路

设k是一个正整数，G是一个顶点数为|G|=4k的图. 假设σ\-2(G)≥4k-1, 则G有一个支撑子图含k-1个4圈和一条顶点数为4的路，使得所有这些圈和路都是相互独立的. 设G=(V\-1,V \-2;E)是一个二分图使得|V\-1|=|V\-2|=2k. 如果对G中每一对满足x∈V\-1和y∈V\-2的不 相邻的顶点x和y 都有d(x)+d(y)≥2k+1, 则G包含k-1个相互独立的4圈和一条顶点数为4的路，使得所有这些圈和路都是相互独立的，并且此度条件是最好的.     

一种基于Spearman秩相关系数与熵的多属性决策方法

本以方案间单个指标的关联系数为基础，引入相关度概念，对以关联系数平均值作为测度的评价方法进行了改进，提出了以方案相关度为测度指标的新的决策方法。     

正系数多项式倒数逼近的点态和整体估计

利用Ditzian-Totik光滑模对于[0,1]上定义的非角连续函数f(x)，且f(x)≠0，文中证明存在正系数多项式Pn(x)及常数C，使得|f9x)-1/Pn(x)|≤Cωψ^λ（f,n^-1/2(ψ(x) 1/√n)^1-λ)。当λ＝1时，上述结果导出已有的整体估计，而当0≤λ＜1时，得到倒数逼近一个新的点态局部估计。     

度和与图中具有给定阶数的点不交的路

设G是一个n阶图，n=∑^ki=1ni，其中,ni≥2(i=1,2,…，k）是整数。我们利用度和给出图G中存在n1,n2，…，nk阶点不交路的充分条件。     

台阶爆破破碎度的分布规律

本文采用摄影方法在海州露天煤矿对台阶爆破破碎块度进行了测量。根据现场试验所获得的数据,通过多元曲线拟合处理,给出了二种岩石爆破破碎块度的分布规律。所得结论对控制和提高爆破破碎质量具有重要意义。     

异质结构光子晶体的能带特性研究

运用光学传输矩阵理论,研究了异质结构光子晶体的能带特性。结果表明,与周期结构相比异质结构光子晶体可以显著地拓宽光子晶体的光子带隙;在该文提出的结构中引入缺陷,与(1H1L)m(1H)n(1L1H)m光子晶体相比,产生缺陷模式光子带隙范围明显拓宽,适当调整缺陷层的数目、位置和厚度可得到多通道滤波;并研究了实际操作中随机误差带来的影响,随无序度的增大,其带宽基本不变。     

金激光等离子体冕区电离态特性研究

 提出一种测量金激光等离子体电荷态分布与平均电离度的X射线光谱学诊断方法。该方法基于稳态碰撞-辐射近似,考虑电子离子直接碰撞激发与双电子复合两种激发态布居方式,建立了金M带5f-3d跃迁组辐射总强度与离子态分布的耦合方程。根据实验测量的金平面靶激光等离子体冕区辐射的5f-3d跃迁线系的强度分布,诊断得到了金激光等离子体的电荷态分布与平均电离度。此外,还分析了电子温度、电子密度以及双电子复合过程对电荷态分布及平均电离度诊断的影响,并将实验诊断结果与辐射流体力学理论模拟结果及离化平衡动力学计算结果进行了对比分析。结果表明：实验诊断结果与基于CRE近似的离化平衡动力学计算结果近似;当电子温度高于1.5 keV时,双电子复合过程对电离度的诊断结果影响较小。     

A Mixture Model of Truncated Zeta Distributions with Applications to Scientific Collaboration Networks

The degree distribution has attracted considerable attention from network scientists in the last few decades to have knowledge of the topological structure of networks. It is widely acknowledged that many real networks have power-law degree distributions. However, the deviation from such a behavior often appears when the range of degrees is small. Even worse, the conventional employment of the continuous power-law distribution usually causes an inaccurate inference as the degree should be discrete-valued. To remedy these obstacles, we propose a finite mixture model of truncated zeta distributions for a broad range of degrees that disobeys a power-law behavior in the range of small degrees while maintaining the scale-free behavior. The maximum likelihood algorithm alongside the model selection method is presented to estimate model parameters and the number of mixture components. The validity of the suggested algorithm is evidenced by Monte Carlo simulations. We apply our method to five disciplines of scientific collaboration networks with remarkable interpretations. The proposed model outperforms the other alternatives in terms of the goodness-of-fit.     

Evolution mechanism of Weibo top news competition

In a certain period, some news will compete for the top news to gain the most attention and influence, and more news will be submerged in the ocean of news and become mediocre. This article deeply studies the evolution process and competition mechanism of the dissemination of Weibo news. In this paper, we innovatively propose a pre-processing scheme for traditional small-world networks and scale-free networks and divide nodes into three roles:fans, passersby, and anti-fans. The competition mechanism of Weibo top news is defined from the aspects of node role and node aggregation degree. A network evolution model is established based on the competition mechanism. The propagation characteristics of the network evolution model are deeply analyzed, and simulation experiments are performed on the small-world network and the scale-free network. Finally, the validity and rationality of the new model are verified through comparative experiments, and a feasible scheme for the propagation of top news on Weibo is given.     

A Novel Conflict Management Method Based on Uncertainty of Evidence and Reinforcement Learning for Multi-Sensor Information Fusion

Dempster–Shafer theory (DST), which is widely used in information fusion, can process uncertain information without prior information; however, when the evidence to combine is highly conflicting, it may lead to counter-intuitive results. Moreover, the existing methods are not strong enough to process real-time and online conflicting evidence. In order to solve the above problems, a novel information fusion method is proposed in this paper. The proposed method combines the uncertainty of evidence and reinforcement learning (RL). Specifically, we consider two uncertainty degrees: the uncertainty of the original basic probability assignment (BPA) and the uncertainty of its negation. Then, Deng entropy is used to measure the uncertainty of BPAs. Two uncertainty degrees are considered as the condition of measuring information quality. Then, the adaptive conflict processing is performed by RL and the combination two uncertainty degrees. The next step is to compute Dempster’s combination rule (DCR) to achieve multi-sensor information fusion. Finally, a decision scheme based on correlation coefficient is used to make the decision. The proposed method not only realizes adaptive conflict evidence management, but also improves the accuracy of multi-sensor information fusion and reduces information loss. Numerical examples verify the effectiveness of the proposed method.