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The strong matching preclusion number of a graph is the minimum number of vertices and edges whose deletion results in a graph that has neither perfect matchings nor almost-perfect matchings. This is an extension of the matching preclusion problem and has recently been introduced by Park and Ihm.15 In this paper, we examine properties of strong matching preclusion for alternating group graphs, by finding their strong matching preclusion numbers and categorizing all optimal solutions. More importantly, we prove a general result on taking a Cartesian product of a graph with K2 (an edge) to obtain the corresponding results for split-stars. [ABSTRACT FROM AUTHOR]

Data logging is the process of collecting and storing data from an experiment. Traditionally, data from science experiments was collected manually by hand and graphs were produced using pen and paper. By the introduction of cheap and powerful microcontrollers, all data logging operations are currently carried out using microcontroller-based data logging devices. This article describes the design of an educational microcontroller-based data logger device, which can be used to collect and save data for both offline and online analysis. © 2009 Wiley Periodicals, Inc. Comput Appl Eng Educ 18: 397–405, 2010; Published online in Wiley InterScience (www.interscience.wiley.com); DOI 10.1002/cae.20281 [ABSTRACT FROM AUTHOR]

In this paper, we extend the notion of similarity matrix, which has been used to define similarity between nodes of two graphs, to the case of colored graphs, where the coloring is either on the nodes or on the edges of both graphs. The proposed method tries to find the optimal matching between the nodes or edges of both graphs but only performs the comparison when their colors are the same. The proposed cost function nevertheless uses the connectivity between all nodes and edges of both graphs. We then also show how to extend this to the notion of low rank similarity matrix, by defining it as a constrained optimization problem. [ABSTRACT FROM AUTHOR]

A graph is called a weighted graph when each edge e is assigned a nonnegative number w( e), called the weight of e. For a vertex v of a weighted graph, d w( v) is the sum of the weights of the edges incident with v. For a subgraph H of a weighted graph G, the weight of H is the sum of the weights of the edges belonging to H. In this paper, we give a new sufficient condition for a weighted graph to have a heavy cycle. A 2-connected weighted graph G contains either a Hamilton cycle or a cycle of weight at least c, if G satisfies the following conditions: In every induced claw or induced modified claw F of G, (1) max{ d w( x), d w( y)}≥ c/2 for each non-adjacent pair of vertices x and y in F, and (2) all edges of F have the same weight. [ABSTRACT FROM AUTHOR]