Set to set broadcasting in communication networks

Suppose G = (V,E) is a graph whose vertices represent people and edges represent telephone lines between pairs of people. Each person knows a unique message and is ignorant of the messages of other people at the beginning. These messages are then spread by telephone calls. In each call, two people exchange all information they have so far in exactly one unit of time. Suppose A and B are two nonempty subsets of V. The main purpose of this paper is to study the minimum number b(A,B,G) of telephone calls by which A broadcasts to B; and the minimum time t(A,B,G) such that A broadcasts to B. In particular, we give an exact formula for b(A,B,K_n) and linear-time algorithms for computing b(A,B,T) and t(A,B,T) of a tree T.     

轮图和多轮图的生成树的计数

本文应用计算生成树个数的有向图方法、分块矩阵的行列式计算法以及常系数线性递归方程的解法 ,计算得到轮图和多轮图的生成树个数的表达式 (显式或递推式 )     

图的L(3,2,1)-标号

无向图G的L(3,2,1)-标号是指从顶点集V(G)到非负整数集Z*的一个映射,满足:对i=1,2,3,只要dG(x,y)=i,则f(x)-f(y)|≥4-i.若一个L(3,2,1)-标号中的所有像元素都不超过整数k,则称之为k-L(3,2,1)-标号.图G的L(3,2,1)-标号数,记作3λ(G),是使得图G存在k-L(3,2,1)-标号的最小整数k.文中给出了路、圈、树等特殊图的L(3,2,1)-标号数,并给出了一般图的L(3,2,1)-标号数的一个上界.     

关于图的基本群的正规子群的指数

本文主要研究图的基本群的正规子群的指数何时成为任意正整数的条件．主要讨论的群类有:有限生成的无扭幂零群、自由群、多重无限循环群等．     

Interruptible Exact Sampling in the Passive Case

We establish, for various scenarios, whether or not interruptible exact stationary sampling is possible when a finite-state Markov chain can only be viewed passively. In particular, we prove that such sampling is not possible using a single copy of the chain. Such sampling is possible when enough copies of the chain are available, and we provide an algorithm that terminates with probability one.     

关于图的若干介值问题

对连通图G,以C_i(G),■(G)分别表G的有i条边的连通支撑子图之集与连通子图之集,以C~i(G),(?)(G)分别表G的顶点数为i的子树集与连通子图之集.本文讨论了这四类子图簇对若干基本参数及端点数的介值性,从而对已有的一些结果作了若干有意义的拓广.     

具有最小反对称分割指数的化学树

设图$G$,其中边集为$E(G)$,顶点集$V(G)$.反对称分割指数被定义为$ISDD(G)=\sum_{uv \in E(G)}\dfrac{d_ud_v}{d_u^2+d_v^2}$,其中$d_u$, $d_v$分别为顶点$u,v$的度.化学树就是顶点的度不超过4的树.在本文中,我们刻画出具有最小反对称分割指数的$n$阶化学树.     

树和单圈图的斜谱矩

给定图$G$,对图$G$的每条边确定一个方向,称为$G$的定向图$G^\sigma$, $G$称为$G^\sigma$的基础图. $G^\sigma$的斜邻接矩阵$S(G^\sigma)$是反对称矩阵,其特征值是0或纯虚数. $S(G^\sigma)$所有特征值的$k$次幂之和称为$G^\sigma$的$k$阶斜谱矩,其中$k$是非负整数.斜谱矩序列可用于对图进行排序.本文主要研究定向树和定向单圈图的斜谱矩,并对这两类图的斜谱矩序列依照字典序进行排序.首先确定了直径为$d$的树作为基础图的所有定向树中,斜谱矩序最大的$2\lfloor\frac{d}{4}\rfloor$个图; 然后确定以围长为$g$的单圈图作为基础图的所有定向单圈图中, 斜谱矩序最大的$2\lfloor\frac{g}{4}\rfloor+1$个图.     

Scaling effect on the tensile properties of [±45/0/±45/0/±45] Polypropylene/Twaron laminates

The effects of scaling on the mechanical response under tension of balanced nonsymmetrical laminates were investigated for a thermoplastic composite: Polypropylene reinforced with Twaron^® fibers. The composite baseline was an 8-ply laminate which consisted of unidirectional plies arranged in the sequence [±45/0/±45/0/±45]. The influence of specimen size on the tensile properties was studied for one (thickness), two (in-plane) and three (volume) dimensional scaling. The stress-strain curves suggested some variation in laminate behavior owing to the dimensional scaling; nevertheless, a further analysis with the classical lamination theory demonstrated that the observed effect was due to small variations in the fiber volume fraction of the laminates. It was concluded that the mechanical properties of these thermoplastic laminates do not exhibit scaling effects. The failure mechanism of the laminates was studied at macroscopic level; a scale effect of the fracture mechanism was observed.     

Classification of nasopharyngeal cell lines (C666-1, CNE2, NP69) via Raman spectroscopy and decision tree

Researchers have demonstrated that Raman spectroscopy can be used for characterization of tumor cells with excellent spatial resolution. However, performance evaluation of different algorithms in classifying multiclass of Raman spectra has not been reported yet. In this work, we present Raman spectra of nasopharyngeal carcinoma and nasopharyngeal normal cell lines. Combined with student’s t-test and several multivariate approaches, including decision tree, support vector classification, and linear discriminant analysis, our work shows that the relative content of two histological abnormality sensitive bands at 1449 and 1658 cm⁻¹ in tumor cells is significantly different from that of normal cells (p = 0.0132), and can be a biomarker to classify these cells. This difference is confirmed by importance analyses in the decision tree model. Furthermore, performances of statistical methods are compared with one another to explore the ability in classification. Results show that the decision tree can be more capable for classification between tumorous and normal cell lines with sensitivity and specificity of 99.0% and 96.9%, respectively. Findings of this work further support our previous work and indicate that the decision tree performs more robustly in cell classification. Our work will prove helpful to the early diagnosis of nasopharyngeal carcinoma, and will indicate the decision tree to be the primary algorithm in tumor-cell classification.