In this paper, we derive some lower bounds for the minimum Meigenvalue of elasticity M-tensors, these bounds only depend on the elements of the elasticity M-tensors and they are easy to be verified. Comparison theorems for elasticity M-tensors are also given.
Irrelevant aspects of the environment or irrelevant attributes of task-relevant stimuli can have important and reliable effects on behavior. When the specific values of an irrelevant attribute are correlated with different responses, a correlational-cuing effect is observed: faster and more accurate responses when the correlation is positive. Previous work has shown that this effect is not due to simple differences in how often the specific stimuli or attributes are being presented, and most explanations of the effect have stressed the clear parallels with classical associative learning. There are alternative explanations, however, that center on instances, episodes, or events, instead of associative learning. One such model posits that transient bindings between irrelevant stimulus attributes and responses (i.e., most-recent-pairings) may be responsible for the correlation-cuing effect and some recent work has found no evidence of correlational cuing when most-recent-pairings are taken into account. However, the experimental conditions that were employed previously may not have been optimized for associative learning. A new experiment that was designed to emphasize associative learning was conducted and produced reliable evidence of correlational cuing even when controlling for most-recent-pairing effects.
a b s t r a c t. The domination number, denoted by γ(G), is the minimum cardinality of a dominating set. For the generalized Petersen graph G(n), Behzad et al. [A. Behzad, M. Behzad, C.E. Praeger, On the domination number of the generalized Petersen graphs, Discrete Mathematics 308 (2008) 603-610] proved that γ(G(n)) ≤ 3n 5 and conjectured that the upper bound 3n 5is the exact domination number. In this paper we prove this conjecture.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.