scholarly journals Corrigendum to “Factoring, into edge transpositions of a tree, permutations fixing a terminal vertex” [J. Combin. Theory Ser. A 85 (1) (1999) 92–95]

2011 ◽  
Vol 118 (2) ◽  
pp. 726-727
Author(s):  
John H. Smith
Keyword(s):  
Author(s):  
Samvel Darbinyan

Let D be a 2-strongly connected directed graph of order p ≥ 3. Suppose that d(x) ≥ p for every vertex x ∈ V (D) \ {x0}, where x0 is a vertex of D. In this paper, we show that if D is Hamiltonian or d(x0) > 2(p − 1)/5, then D contains a Hamiltonian path, in which the initial vertex dominates the terminal vertex.


2019 ◽  
Vol 256 ◽  
pp. 11-37 ◽  
Author(s):  
D. Cornaz ◽  
Y. Magnouche ◽  
A.R. Mahjoub ◽  
S. Martin

Mathematics ◽  
2022 ◽  
Vol 10 (2) ◽  
pp. 267
Author(s):  
Richard Schweickert ◽  
Xiaofang Zheng

A Multinomial Processing Tree (MPT) is a directed tree with a probability associated with each arc and partitioned terminal vertices. We consider an additional parameter for each arc, a measure such as time. Each vertex represents a process. An arc descending from a vertex represents selection of a process outcome. A source vertex represents processing beginning with stimulus presentation and a terminal vertex represents a response. An experimental factor selectively influences a vertex if changing the factor level changes parameter values on arcs descending from that vertex and no others. Earlier work shows that if each of two factors selectively influences a different vertex in an arbitrary MPT it is equivalent to one of two simple MPTs. Which applies depends on whether the two selectively influenced vertices are ordered by the factors or not. A special case, the Standard Binary Tree for Ordered Processes, arises if the vertices are ordered and the factor selectively influencing the first vertex changes parameter values on only two arcs. We derive necessary and sufficient conditions, testable by bootstrapping, for this case. Parameter values are not unique. We give admissible transformations for them. We calculate degrees of freedom needed for goodness of fit tests.


Author(s):  
Ryuichi Kitamura ◽  
Toshiyuki Yamamoto ◽  
Keiko Kishizawa ◽  
Ram M. Pendyala

A methodology to estimate the location and size of space-time prisms that govern individuals’ activity and travel is presented. Because the vertices of a prism are unobservable, stochastic frontier models are formulated to locate prism vertices along the time axis using observable trip starting or ending times as the dependent variable and commute characteristics, personal and household attributes, and area characteristics as explanatory variables. Models are estimated successfully with coherent behavioral indications. A mean difference of 1.46 h is found between the observed trip ending time and the expected location of the terminal vertex for workers’ evening prisms. The estimation results aid in enhancing the understanding of prism constraints by identifying the determinants of prism vertex locations.


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