Results 171 to 180 of about 3,095,070 (339)
Proteins: Structure, Function, and Bioinformatics, EarlyView.ABSTRACT Protein–receptor interactions play a critical role in viral entry and pathogenesis. While ACE2 is the primary receptor for SARS‐CoV, the role of DPP4 as potential coreceptor remains underexplored. This study investigates the binding mechanisms and dissociation dynamics of the SARS‐CoV/DPP4, SARS‐CoV/ACE2 and MERS‐CoV/DPP4 complexes using ...
Patrícia Pereira Duzi Carvalho +1 more
wiley +1 more source
Flows that are sums of hamiltonian cycles in Cayley graphs on abelian groups [PDF]
, 2005 Dave Witte Morris +2 more
openalex +1 more source
A conservative numerical scheme for the multilayer shallow‐water equations on unstructured meshes
Quarterly Journal of the Royal Meteorological Society, EarlyView.An energy‐conserving scheme is derived for the multilayer shallow water model, making use of the direct connection between energy conservation and the skew symmetry of the Poisson bracket for the model. A new mechanism is proposed to prevent layer interface outcropping.
Qingshan Chen
wiley +1 more source
SOLVING THE HAMILTONIAN CYCLE PROBLEM USING SYMBOLIC DETERMINANTS [PDF]
, 2006 Vladimir Ejov +3 more
openalex +1 more source
Quarterly Journal of the Royal Meteorological Society, EarlyView.Our article presents a new vertical‐slice test case for benchmarking atmospheric dynamical cores. The test case is based on the Eady frontogenesis problem, producing sharp fronts that provide a challenge for numerical models. This was not previously possible in a 2D vertical‐slice configuration unless the model is incompressible, so our test case ...
Hiroe Yamazaki, Colin J. Cotter
wiley +1 more source
Distributed algorithms for building Hamiltonian cycles in k-ary n-cubes and hypercubes with faulty links [PDF]
, 2006 Iain A. Stewart
openalex +1 more source
What can we Learn from Quantum Convolutional Neural Networks?
Advanced Quantum Technologies, EarlyView.Quantum Convolutional Neural Networks have been long touted as one of the premium architectures for quantum machine learning (QML). But what exactly makes them so successful for tasks involving quantum data? This study unlocks some of these mysteries; particularly highlighting how quantum data embedding provides a basis for superior performance in ...
Chukwudubem Umeano +3 more
wiley +1 more source
Characterization of the Spin and Crystal Field Hamiltonian of Erbium Dopants in Silicon
Advanced Quantum Technologies, EarlyView.Erbium in silicon is a promising platform for scalable quantum information processing, as it combines optically addressable spins in the telecom regime with the mature, wafer‐scale nanofabrication techniques available for silicon. In this work, the point symmetry and magnetic interaction of two particularly promising erbium sites are investigated.
Adrian Holzäpfel +5 more
wiley +1 more source
Computing and Sampling Restricted Vertex Degree Subgraphs and Hamiltonian Cycles
, 2000 Scott Sheffield⋆
openalex +2 more sources
Distribution system state estimation using the Hamiltonian cycle theory
IEEE Power & Energy Society General Meeting, 2016 Jonatas Boas Leite +1 more
semanticscholar +1 more source