Matching Preclusion Number of Radix Triangular Mesh with Odd Vertices

2018 ◽  
Vol 18 (02n03) ◽  
pp. 1850010
Author(s):  
CHUNXIA WANG ◽  
YALAN LI ◽  
SHUMIN ZHANG ◽  
CHENGFU YE ◽  
XIA WANG
Keyword(s):  
Perfect Matching ◽  
Triangular Mesh ◽  
Minimum Number

The matching preclusion number of graph G is the minimum number of edges whose deletion leaves the resulting graph without a perfect matching or almost-perfect matching. The strong matching preclusion number of a graph G is the minimum number of vertices and edges whose deletion leaves the resulting graph without a perfect matching or an almost-perfect matching. The conditional matching preclusion number of G is the minimum number of edges whose deletion leaves the resulting graph with no isolated vertices and without a perfect matching or almost-perfect matching. In this paper, we study the matching preclusion number of radix triangular mesh with an odd number of vertices, and strong matching preclusion number and conditional matching preclusion number of radix triangular mesh. Also, we obtained the radix triangular mesh with an even number of vertices is super strongly matched and conditionally super matched.

Fractional matching preclusion for radix triangular mesh

2019 ◽  
Vol 11 (04) ◽  
pp. 1950048
Author(s):  
Xia Wang ◽  
Tianlong Ma ◽  
Jun Yin ◽  
Chengfu Ye
Keyword(s):  
Perfect Matching ◽  
Triangular Mesh ◽  
Perfect Matchings ◽  
Minimum Number ◽  
Fractional Perfect Matching

The matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has neither perfect matchings nor almost perfect matchings. As a generalization, Liu and Liu recently introduced the concept of fractional matching preclusion number. The fractional matching preclusion number (FMP number) of [Formula: see text], denoted by [Formula: see text], is the minimum number of edges whose deletion leaves the resulting graph without a fractional perfect matching. The fractional strong matching preclusion number (FSMP number) of [Formula: see text], denoted by [Formula: see text], is the minimum number of vertices and edges whose deletion leaves the resulting graph without a fractional perfect matching. In this paper, we study the fractional matching preclusion number and the fractional strong matching preclusion number for the radix triangular mesh [Formula: see text], and all the optimal fractional matching preclusion sets and fractional strong matching preclusion sets of these graphs are categorized.

Fractional Matching Preclusion for (n, k)-Star Graphs

2018 ◽  
Vol 28 (04) ◽  
pp. 1850017 ◽  
Author(s):  
Tianlong Ma ◽  
Yaping Mao ◽  
Eddie Cheng ◽  
Jinling Wang
Keyword(s):  
Perfect Matching ◽  
Perfect Matchings ◽  
Star Graphs ◽  
Minimum Number ◽  
Fractional Perfect Matching

The matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has neither perfect matchings nor almost perfect matchings. As a generalization, Liu and Liu introduced the concept of fractional matching preclusion number in 2017. The Fractional Matching Preclusion Number (FMP number) of G is the minimum number of edges whose deletion leaves the resulting graph without a fractional perfect matching. The Fractional Strong Matching Preclusion Number (FSMP number) of G is the minimum number of vertices and/or edges whose deletion leaves the resulting graph without a fractional perfect matching. In this paper, we obtain the FMP number and the FSMP number for (n, k)-star graphs. In addition, all the optimal fractional strong matching preclusion sets of these graphs are categorized.

scholarly journals Fractional Strong Matching Preclusion of Split-Star Networks

2019 ◽  
Vol 11 (4) ◽  
pp. 32
Author(s):  
Ping Han ◽  
Yuzhi Xiao ◽  
Chengfu Ye ◽  
He Li
Keyword(s):  
Perfect Matching ◽  
Minimum Size ◽  
Star Networks ◽  
Minimum Number

The matching preclusion number of graph G is the minimum size of edges whose deletion leaves the resulting graph without a perfect matching or an almost perfect matching. Let F be an edge subset and F′ be a subset of edges and vertices of a graph G. If G − F and G − F′ have no fractional matching preclusion, then F is a fractional matching preclusion (FMP) set, and F ′is a fractional strong matching preclusion (FSMP) set of G. The FMP (FSMP) number of G is the minimum number of FMP (FSMP) set of G. In this paper, we study fractional matching preclusion number and fractional strong matching preclusion number of split-star networks. Moreover, We categorize all the optimal fractional strong matching preclusion sets of split-star networks.

scholarly journals Fractional strong matching preclusion of some Cartesian product graphs

2021 ◽  
Vol 2132 (1) ◽  
pp. 012033
Author(s):  
Bo Zhu ◽  
Shumin Zhang ◽  
Chenfu Ye
Keyword(s):  
Perfect Matching ◽  
Cartesian Product ◽  
Product Graphs ◽  
Minimum Number ◽  
Cartesian Product Graphs ◽  
Torus Networks ◽  
Fractional Perfect Matching

Abstract The fractional strong matching preclusion number of a graph is the minimum number of edges and vertices whose deletion leaves the resulting graph without a fractional perfect matching. In this paper, we obtain the fractional strong matching preclusion number for the Cartesian product of a graph and a cycle. As an application, the fractional strong matching preclusion number for torus networks is also obtained.

Conditional Matching Preclusion for Folded Hypercubes

2019 ◽  
Vol 19 (03) ◽  
pp. 1940011
Author(s):  
RUIZHI LIN ◽  
HEPING ZHANG
Keyword(s):  
Perfect Matching ◽  
Link Failure ◽  
Minimum Number ◽  
Important Variant ◽  
Folded Hypercube

Let G be a graph with an even number of vertices. The matching preclusion number of G is the minimum number of edges whose deletion leaves the resulting graph without a perfect matching, and the conditional matching preclusion number of G is the minimum number of edges whose deletion results in a graph with no isolated vertices and without a perfect matching. Matching preclusion number was introduced for measuring the robustness of a network when there is a link failure. In this paper, we focus on conditional matching preclusion for folded hypercube FQn, an important variant of hypercube. We show that conditional matching preclusion number of FQn is 2n and all optimal conditional matching preclusion sets are trivial for n ⩾ 5.

scholarly journals Fractional Matching Preclusion for Restricted Hypercube-Like Graphs

2019 ◽  
Vol 19 (03) ◽  
pp. 1940010
Author(s):  
HUAZHONG LÜ ◽  
TINGZENG WU
Keyword(s):  
Interconnection Networks ◽  
Perfect Matching ◽  
Parallel Systems ◽  
Perfect Matchings ◽  
Minimum Number ◽  
Fractional Perfect Matching

The restricted hypercube-like graphs, variants of the hypercube, were proposed as desired interconnection networks of parallel systems. The matching preclusion number of a graph is the minimum number of edges whose deletion results in the graph with neither perfect matchings nor almost perfect matchings. The fractional perfect matching preclusion and fractional strong perfect matching preclusion are generalizations of the matching preclusion. In this paper, we obtain fractional matching preclusion number and fractional strong matching preclusion number of restricted hypercube-like graphs, which extend some known results.

scholarly journals On the Paired-Domination Subdivision Number of Trees

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1135
Author(s):  
Shouliu Wei ◽  
Guoliang Hao ◽  
Seyed Mahmoud Sheikholeslami ◽  
Rana Khoeilar ◽  
Hossein Karami
Keyword(s):  
Perfect Matching ◽  
Dominating Set ◽  
Domination Number ◽  
Induced Subgraph ◽  
Minimum Cardinality ◽  
Paired Domination Number ◽  
Minimum Number ◽  
Paired Domination ◽  
Domination Subdivision Number

A paired-dominating set of a graph G without isolated vertices is a dominating set of vertices whose induced subgraph has perfect matching. The minimum cardinality of a paired-dominating set of G is called the paired-domination number γpr(G) of G. The paired-domination subdivision number sdγpr(G) of G is the minimum number of edges that must be subdivided (each edge in G can be subdivided at most once) in order to increase the paired-domination number. Here, we show that, for each tree T≠P5 of order n≥3 and each edge e∉E(T), sdγpr(T)+sdγpr(T+e)≤n+2.

scholarly journals Analogies between the Crossing Number and the Tangle Crossing Number

10.37236/7581 ◽  
2018 ◽  
Vol 25 (4) ◽  
Author(s):  
Robin Anderson ◽  
Shuliang Bai ◽  
Fidel Barrera-Cruz ◽  
Éva Czabarka ◽  
Giordano Da Lozzo ◽  
...  
Keyword(s):  
Perfect Matching ◽  
Crossing Number ◽  
Binary Trees ◽  
Biologically Relevant ◽  
Line Drawings ◽  
Crossing Numbers ◽  
Parallel Lines ◽  
Straight Line ◽  
Minimum Number ◽  
Number Of Leaves

Tanglegrams are special graphs that consist of a pair of rooted binary trees with the same number of leaves, and a perfect matching between the two leaf-sets. These objects are of use in phylogenetics and are represented with straight-line drawings where the leaves of the two plane binary trees are on two parallel lines and only the matching edges can cross. The tangle crossing number of a tanglegram is the minimum number of crossings over all such drawings and is related to biologically relevant quantities, such as the number of times a parasite switched hosts.Our main results for tanglegrams which parallel known theorems for crossing numbers are as follows. The removal of a single matching edge in a tanglegram with $n$ leaves decreases the tangle crossing number by at most $n-3$, and this is sharp. Additionally, if $\gamma(n)$ is the maximum tangle crossing number of a tanglegram with $n$ leaves, we prove $\frac{1}{2}\binom{n}{2}(1-o(1))\le\gamma(n)<\frac{1}{2}\binom{n}{2}$. For an arbitrary tanglegram $T$, the tangle crossing number, $\mathrm{crt}(T)$, is NP-hard to compute (Fernau et al. 2005). We provide an algorithm which lower bounds $\mathrm{crt}(T)$ and runs in $O(n^4)$ time. To demonstrate the strength of the algorithm, simulations on tanglegrams chosen uniformly at random suggest that the tangle crossing number is at least $0.055n^2$ with high probabilty, which matches the result that the tangle crossing number is $\Theta(n^2)$ with high probability (Czabarka et al. 2017).

Information capacity and bandwidth limitations in the SEM

1989 ◽  
Vol 47 ◽  
pp. 84-85
Author(s):  
D. C. Joy ◽  
R. D. Bunn
Keyword(s):  
Signal To Noise Ratio ◽  
Critical Dimension ◽  
Information Capacity ◽  
Acquisition Time ◽  
Signal To Noise ◽  
Sem Image ◽  
Probe Size ◽  
Minimum Number ◽  
Noise Ratio ◽  
Signal Channel

The information available from an SEM image is limited both by the inherent signal to noise ratio that characterizes the image and as a result of the transformations that it may undergo as it is passed through the amplifying circuits of the instrument. In applications such as Critical Dimension Metrology it is necessary to be able to quantify these limitations in order to be able to assess the likely precision of any measurement made with the microscope.The information capacity of an SEM signal, defined as the minimum number of bits needed to encode the output signal, depends on the signal to noise ratio of the image - which in turn depends on the probe size and source brightness and acquisition time per pixel - and on the efficiency of the specimen in producing the signal that is being observed. A detailed analysis of the secondary electron case shows that the information capacity C (bits/pixel) of the SEM signal channel could be written as :

Applying Generalizability Theory to Optimize Analysis of Spontaneous Teacher Talk in Elementary Classrooms

2020 ◽  
Vol 63 (6) ◽  
pp. 1947-1957
Author(s):  
Alexandra Hollo ◽  
Johanna L. Staubitz ◽  
Jason C. Chow
Keyword(s):  
Special Education Teachers ◽  
Generalizability Theory ◽  
Child Outcomes ◽  
Elementary Classrooms ◽  
Teacher Talk ◽  
Group Instruction ◽  
Data Set ◽  
School Year ◽  
Minimum Number ◽  
Representative Samples

Purpose Although sampling teachers' child-directed speech in school settings is needed to understand the influence of linguistic input on child outcomes, empirical guidance for measurement procedures needed to obtain representative samples is lacking. To optimize resources needed to transcribe, code, and analyze classroom samples, this exploratory study assessed the minimum number and duration of samples needed for a reliable analysis of conventional and researcher-developed measures of teacher talk in elementary classrooms. Method This study applied fully crossed, Person (teacher) × Session (samples obtained on 3 separate occasions) generalizability studies to analyze an extant data set of three 10-min language samples provided by 28 general and special education teachers recorded during large-group instruction across the school year. Subsequently, a series of decision studies estimated of the number and duration of sessions needed to obtain the criterion g coefficient ( g > .70). Results The most stable variables were total number of words and mazes, requiring only a single 10-min sample, two 6-min samples, or three 3-min samples to reach criterion. No measured variables related to content or complexity were adequately stable regardless of number and duration of samples. Conclusions Generalizability studies confirmed that a large proportion of variance was attributable to individuals rather than the sampling occasion when analyzing the amount and fluency of spontaneous teacher talk. In general, conventionally reported outcomes were more stable than researcher-developed codes, which suggests some categories of teacher talk are more context dependent than others and thus require more intensive data collection to measure reliably.

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