Results 191 to 200 of about 57,585 (308)
Decoupling Size from Shape: Cellular Sheaf Laplacians as Ligand Geometry Descriptors for Binding Affinity Prediction. [PDF]
Akgüller Ö, Balcı MA, Cioca G.
europepmc +1 more source
Synthesis and Thermolysis of Ylidyl‐Substituted Stannylenes
The synthesis of an amino(ylidyl)stannylene is reported. The title compound was characterized via X‐ray diffraction and features a Sn2C2 heterocycle. Thermal treatment of the stannylene leads to the elimination of the amine. The products of the thermolysis are discussed.
Pascal Weisenburger +3 more
wiley +1 more source
Dy2 Schiff Base Single Molecule Magnet Forming a Halogen Bonding Network: Experiment and Theory
A major challenge to improve SMMs is control over phonon‐based relaxation mechanisms, such as the Raman process. We approach this challenge through the incorporation of intermolecular interactions, which can restrict acoustic phonon modes. For this, we engineer the crystal lattice of a Dy2 SMM using halogen bonding and investigate its properties using ...
Jonas Braun +7 more
wiley +1 more source
Anomaly detection in smart power grids with graph-regularized MS-SVDD: a multimodal subspace learning approach. [PDF]
Debelle T +3 more
europepmc +1 more source
The isolation and solid‐state structures of the two disulfate‐containing pnictogenates [SeCl3][Pn(S2O7)2] (Pn = Sb, Bi), as well as the mixed‐anionic As2[S2O7]2[S3O10] are presented. The former two consist of complex ∞1{[Pn(S2O7)1/1t(S2O7)2/2e]‐}$$ {\infty }^{1}\left\{{\left[\mathrm{Pn}{\left({\mathrm{S}}_{2}{\mathrm{O}}_{7}\right)}_{1/1}^{\mathrm{t}}{\
Jan Langwald +5 more
wiley +1 more source
Decomposable and Essentially Univariate Mass-Action Systems: Extensions of the Deficiency One Theorem. [PDF]
Deshpande A, Müller S.
europepmc +1 more source
High-frequency energy fusion (HFEF) for nuclei segmentation with boundary-aware loss. [PDF]
Yin W, He W, Shang B, Zhao B, Wu X.
europepmc +1 more source
The Laplacian and the Kohn Laplacian for the sphere
openaire +3 more sources
Abstract Graph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph‐structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification.
Amit Sharma +4 more
wiley +1 more source
On Shape Optimization with Large Magnetic Fields in Two Dimensions. [PDF]
Lotoreichik V, Morin L.
europepmc +1 more source

