(Z)-Ethyl 3-(2,4-difluoro­anilino)-2-(4-methoxy­phen­yl)acrylate

The title compound, C18H17F2NO3, consists of three individually planar subunits, namely two benzene rings and one amino­acrylate group. The amino­acrylate group forms dihedral angles of 5.92 (7) and 50.21 (6)° with the difluoro and methoxy benzene rings, respectively. The dihedral angle between the two benzene rings is 55.25 (7)°. The mol­ecules exhibit intra­molecular N—H⋯O and N—H⋯F inter­actions and form a three-dimensional network via inter­molecular C—H⋯O and C—H⋯π hydrogen bonds

General stationary charged black holes as charged particle accelerators

We study the possibility of getting infinite energy in the center of mass frame of colliding charged particles in a general stationary charged black hole. For black holes with two-fold degenerate horizon, it is found that arbitrary high center-of-mass energy can be attained, provided that one of the particle has critical angular momentum or critical charge, and the remained parameters of particles and black holes satisfy certain restriction. For black holes with multiple-fold degenerate event horizons, the restriction is released. For non-degenerate black holes, the ultra-high center-of-mass is possible to be reached by invoking the multiple scattering mechanism. We obtain a condition for the existence of innermost stable circular orbit with critical angular momentum or charge on any-fold degenerate horizons, which is essential to get ultra-high center-of-mass energy without fine-tuning problem. We also discuss the proper time spending by the particle to reach the horizon and the duality between frame dragging effect and electromagnetic interaction. Some of these general results are applied to braneworld small black holes.Comment: 23 pages, no figures, revised version accepted for publication in Phys. Rev.

(E)-2-(4-Methoxy­phen­yl)-N-(2-pyrid­yl)-3-(2-pyridylamino)acrylamide

In the title compound, C20H18N4O2, the amino­acrylamide group makes a dihedral angles of 4.0 (1)° with the amino-bound pyridyl ring and 15.66 (12)° with the amide-bound pyridyl ring. The dihedral angle between the amino­acrylamide group and the pendant 4-methoxy­phenyl group is 71.22 (9)°. In the crystal structure, N—H⋯N hydrogen bonds and C—H⋯O and C—H⋯N inter­actions help to establish the packing. Intra­molecular C—H⋯O and C—H⋯(N,O) contacts also occur

Speech Enhancement with Multi-granularity Vector Quantization

With advances in deep learning, neural network based speech enhancement (SE) has developed rapidly in the last decade. Meanwhile, the self-supervised pre-trained model and vector quantization (VQ) have achieved excellent performance on many speech-related tasks, while they are less explored on SE. As it was shown in our previous work that utilizing a VQ module to discretize noisy speech representations is beneficial for speech denoising, in this work we therefore study the impact of using VQ at different layers with different number of codebooks. Different VQ modules indeed enable to extract multiple-granularity speech features. Following an attention mechanism, the contextual features extracted by a pre-trained model are fused with the local features extracted by the encoder, such that both global and local information are preserved to reconstruct the enhanced speech. Experimental results on the Valentini dataset show that the proposed model can improve the SE performance, where the impact of choosing pre-trained models is also revealed

1-(2-Hydr­oxy-3,4-dimethoxy­phen­yl)-2-(4-methoxy­phen­yl)ethanone

In the title compound, C17H18O5, the pyrogallol group is almost coplanar with the mean plane of the attached carbonyl group [dihedral angle of 1.95 (13)°] and makes a dihedral angle of 56.01 (10)° with the other benzene ring. Of the three meth­oxy groups, only one is significantly twisted relative to its attached benzene ring [C—O—C—C torsion angles of 4.0 (5), 3.9 (6) and −106.3 (4)°]. Intra­molecular O—H⋯O and C—H⋯O hydrogen bonds help to establish the conformation, and the packing is consolidated by C—H⋯O inter­actions and π–π stacking interactions [centroid–centroid separation = 3.735 (2) Å]

Effects of Compound Danshen tablets on spatial cognition and expression of brain β-amyloid precursor protein in a rat model of alzheimer's disease

AbstractObjectiveTo observe the effects of Compound Danshen Tablets (CDST) on spatial cognition and expression of brain b-amyloid precursor protein (β-APP) in a rat model of Alzheimer's disease.MethodsThe rat model of Alzheimer's disease (AD) was established using D-galactose to cause subacute aging combined with Meynert nucleus damage. Rat behavior was monitored using the Morris water maze, and the expression of β-APP in rat brain tissue was detected via immunohistochemistry.ResultsCDST significantly improved spatial cognition and decreased β-APP expression in the cortex and hippocampus (P<0.05, P<0.01).ConclusionsCDST can significantly improve spatial cognition in a rat model of AD. This observation is possibly related to a reduction in β-APP expression in the rat brain

Memory augment is All You Need for image restoration

Image restoration is a low-level vision task, most CNN methods are designed as a black box, lacking transparency and internal aesthetics. Although some methods combining traditional optimization algorithms with DNNs have been proposed, they all have some limitations. In this paper, we propose a three-granularity memory layer and contrast learning named MemoryNet, specifically, dividing the samples into positive, negative, and actual three samples for contrastive learning, where the memory layer is able to preserve the deep features of the image and the contrastive learning converges the learned features to balance. Experiments on Derain/Deshadow/Deblur task demonstrate that these methods are effective in improving restoration performance. In addition, this paper's model obtains significant PSNR, SSIM gain on three datasets with different degradation types, which is a strong proof that the recovered images are perceptually realistic. The source code of MemoryNet can be obtained from https://github.com/zhangbaijin/MemoryNe