Results 211 to 220 of about 4,485 (249)

Reducing Variability in Deep Gray Matter QSM Using Differential ROI Referencing: A Phantom and In Vivo Evaluation

open access: yesNMR in Biomedicine, Volume 39, Issue 6, June 2026.
Differential ROI referencing in QSM reduces subject‐specific offsets and improves group‐discrimination sensitivity, enabling detection of PD‐related susceptibility differences (SN–PU). ABSTRACT Quantitative susceptibility mapping (QSM) measures the intrinsic magnetic susceptibility of tissues.
Tae Hyun Hwang   +3 more
wiley   +1 more source

Robustness Assessment of Public Transport Networks in Various Graph Representations: Systematic Review, Decision Support, and Case Study

open access: yesNetworks, Volume 87, Issue 4, Page 343-371, June 2026.
ABSTRACT The analysis of certain properties of the underlying graph of a public transport network generates insights about the network's structure. Hereby, the choice of the graph representation depends on a trade‐off between complexity reduction and information preservation to adequately model a public transport network.
Michael Palk   +2 more
wiley   +1 more source

Direct Evidence for a Carbon–Carbon One‐Electron σ‐Bond, or a Weak Carbon–Carbon Interaction?

open access: yesChemistryOpen, Volume 15, Issue 6, June 2026.
Experiments reporting the existence of a single‐electron C─C σ bond in a synthesized stable radical cation open up new perspectives in the study of the existence of chemical bonds. However, the supporting theoretical results at density functional theory cost do not seem to fully support this evidence.
Costantino Zazza   +4 more
wiley   +1 more source

Comparative Density Functional Theory Insights Into B16C16 and Si16C16 Nanocages for Sensing Oil‐Derived Fault Gases in Energy and Industrial Systems

open access: yesChemistryOpen, Volume 15, Issue 6, June 2026.
B16C16 and Si16C16 nanocages were investigated via DFT for sensing transformer oil fault gases (C2H4, CO, H2S). BC nanocages exhibit stronger adsorption, reduced energy gaps, enhanced electrical conductivity, and positive sensing responses compared to SiC. NCI and QTAIM analyses reveal covalent and partially covalent interactions.
Khalid Abdullah Alrashidi   +3 more
wiley   +1 more source

The perturbed laplacian matrix of a graph [PDF]

open access: yesLinear and Multilinear Algebra, 2001
For a graph G, we define its perturbed Laplacian matrix as D−A(G) where A(G) is the adjacency matrix of G and D is an arbitrary diagonal matrix. Both the Laplacian matrix and the negative of the adjacency matrix are special instances of the perturbed Laplacian.
R B Bapat, Sukanta Pati
exaly   +4 more sources

On Determinant of Laplacian Matrix and Signless Laplacian Matrix of a Simple Graph

Lecture Notes in Computer Science, 2017
In a simple graph, Laplacian matrix and signless Laplacian matrix are derived from both adjacency matrix and degree matrix. Although, determinant of Laplacian matrix is always zero, yet we express it using only the adjacency matrix and square of its adjacency matrix.
Olayiwola Babarinsa   +2 more
exaly   +2 more sources

Hermitian normalized Laplacian matrix for directed networks

Information Sciences, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Guihai Yu   +2 more
exaly   +3 more sources

Hermitian Laplacian matrix and positive of mixed graphs

Applied Mathematics and Computation, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Guihai Yu
exaly   +3 more sources

The Laplacian matrix in chemistry

Journal of Chemical Information and Computer Sciences, 1994
The Laplacian matrix, its spectrum, and its polynomial are discussed. An algorithm for computing the number of spanning trees of a polycyclic graph, based on the corresponding Laplacian spectrum, is outlined. Also, a technique using the Le Verrier-Faddeev-Frame method for computing the Laplacian polynomial of a graph is detailed.
Nenad Trinajstic   +5 more
openaire   +3 more sources

Orthogonal Eigenvector Matrix of the Laplacian

2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2015
The orthogonal eigenvector matrix Z of the Laplacian matrix of a graph with N nodes is studied rather than its companion X of the adjacency matrix, because for the Laplacian matrix, the eigenvector matrix Z corresponds to the adjacency companion X of a regular graph, whose properties are easier.
Xiangrong Wang 0002, Piet Van Mieghem
openaire   +1 more source

Home - About - Disclaimer - Privacy