Results 51 to 60 of about 13,572 (222)

Laplacian integral signed graphs with few cycles

open access: yesAIMS Mathematics, 2023
A connected graph with n vertices and m edges is called k-cyclic graph if k=m−n+1. We call a signed graph is Laplacian integral if all eigenvalues of its Laplacian matrix are integers.
Dijian Wang, Dongdong Gao
doaj   +1 more source

A bound for the permanent of the Laplacian matrix

open access: yesLinear Algebra and its Applications, 1986
Let L(G) be D-A where D is the diagonal matrix of vertex degrees and A the adjacency matrix. The author proves by an explicit formula that the permanent of L is at least 2(n-1)k where k, the complexity, is the number of spanning trees.
openaire   +2 more sources

Signless Laplacian determinations of some graphs with independent edges

open access: yesKarpatsʹkì Matematičnì Publìkacìï, 2018
Let $G$ be a simple undirected graph. Then the signless Laplacian matrix of $G$ is defined as $D_G + A_G$ in which $D_G$ and $A_G$ denote the degree matrix and the adjacency matrix of $G$, respectively.
R. Sharafdini, A.Z. Abdian
doaj   +1 more source

Data‐Driven Distributed Safe Control Design for Multi‐Agent Systems

open access: yesInternational Journal of Adaptive Control and Signal Processing, EarlyView.
This paper presents a data‐driven control barrier function (CBF) technique for ensuring safe control of multi‐agent systems (MASs) with uncertain linear dynamics. A data‐driven quadratic programming (QP) optimization is first developed for CBF‐based safe control of single‐agent systems using a nonlinear controller. This approach is then extended to the
Marjan Khaledi, Bahare Kiumarsi
wiley   +1 more source

Unimodular congruence of the Laplacian matrix of a graph

open access: yesLinear Algebra and its Applications, 1994
Let \(Q= Q(G)\) be an oriented vertex-edge incidence matrix of the graph \(G\). Then \(L(G)= QQ^ t\) is the Laplacian matrix of \(G\) and \(K(G)= Q^ t Q\) is an edge version of the Laplacian. In this note it is shown that Laplacian matrices \(L(G)\) and \(L(H)\) of graphs \(G\) and \(H\), respectively, are unimodularly congruent if and only if \(G ...
openaire   +3 more sources

Nonlocal Conduction in a Metawire

open access: yesAdvanced Materials, Volume 37, Issue 13, April 2, 2025.
A 1D metawire composed of twisted copper wires is designed and realized. This metamaterial exhibits pronounced effects of nonlocal electric conduction according to Ohm's law. The current at one location not only depends on the electric field at that location but also on other locations.
Julio Andrés Iglesias Martínez   +3 more
wiley   +1 more source

The sum of the largest and smallest signless laplacian eigenvalues and some Hamiltonian properties of graphs

open access: yesCommunications in Advanced Mathematical Sciences, 2018
The signless Laplacian eigenvalues of a graph $G$ are eigenvalues of the matrix $Q(G) = D(G) + A(G)$, where $D(G)$ is the diagonal matrix of the degrees of the vertices in $G$ and $A(G)$ is the adjacency matrix of $G$.
Rao Li
doaj   +1 more source

Spectral properties of edge Laplacian matrix

open access: yes, 2023
Let $N(X)$ be the Laplacian matrix of a directed graph obtained from the edge adjacency matrix of a graph $X.$ In this work, we study the bipartiteness property of the graph with the help of $N(X).$ We computed the spectrum of the edge Laplacian matrix for the regular graphs, the complete bipartite graphs, and the trees.
Chauhan, Shivani   +1 more
openaire   +2 more sources

Promoting Electrochemical Reactions with Dual‐Atom Catalysts for High‐Rate Lithium–Sulfur Batteries

open access: yesAdvanced Materials, EarlyView.
A scalable strategy for synthesizing transition metal–bismuth atomic pairs on carbon nitride to accelerate sulfur redox reactions in lithium–sulfur batteries is presented. Nickel‐bismuth and cobatl‐bismuth catalysts improve rate performance by promoting direct electrochemical transitions and rapid Li2S nucleation, minimizing sulfur loss, and enhancing ...
Jing Yu   +19 more
wiley   +1 more source

Applications of QSPR and Machine Learning in Molecular Photonics

open access: yesAdvanced Optical Materials, EarlyView.
Quantitative structureproperty relationships (QSPR) and machine learning (ML) are transforming photochemistry by enabling pre‐synthetic screening of photoactive molecules. This review outlines advances in data‐driven discovery of optical materials and functional dyes, identifies effective descriptors and models for photophysical processes, and provides
Andrey A. Buglak   +2 more
wiley   +1 more source

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