3,881 research outputs found
Advances in clinical application of optical coherence tomography in vitreomacular interface disease
Vitreous macular interface disease mainly includes vitreomacular traction syndrome, idiopathic macular epiretinal membrane and idiopathic macular hole. Optical coherence tomography(OCT)as a new tool that provides high resolution biopsy cross section image non traumatic imaging inspection, has a unique high resolution, no damage characteristics, and hence clinical widely used, vitreous macular interface for clinical disease diagnosis, differential diagnosis and condition monitoring and quantitative evaluation, treatment options, <i>etc</i> provides important information and reference value. Vitreous macular interface disease in OCT image of anatomical morphology characteristics, improve the clinical on disease occurrence and development of knowledge. We reviewed the advances in the application of OCT in vitreomacular interface disease
A minimal two-band model for the superconducting Fe-pnictides
Following the discovery of the Fe-pnictide superconductors, LDA band
structure calculations showed that the dominant contributions to the spectral
weight near the Fermi energy came from the Fe 3d orbitals. The Fermi surface is
characterized by two hole surfaces around the point and two electron
surfaces around the M point of the 2 Fe/cell Brillouin zone. Here, we describe
a 2-band model that reproduces the topology of the LDA Fermi surface and
exhibits both ferromagnetic and spin density wave (SDW)
fluctuations. We argue that this minimal model contains the essential low
energy physics of these materials.Comment: 5 figures, 5 page
A Kohn-Sham Scheme Based Neural Network for Nuclear Systems
A Kohn-Sham scheme based multi-task neural network is elaborated for the
supervised learning of nuclear shell evolution. The training set is composed of
the single-particle wave functions and occupation probabilities of 320 nuclei,
calculated by the Skyrme density functional theory. It is found that the
deduced density distributions, momentum distributions, and charge radii are in
good agreements with the benchmarking results for the untrained nuclei. In
particular, accomplishing shell evolution leads to a remarkable improvement in
the extrapolation of nuclear density. After a further charge-radius-based
calibration, the network evolves a stronger predictive capability. This opens
the possibility to infer correlations among observables by combining
experimental data for nuclear complex systems
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