4,9,12,15-Tetra­oxa-3,5,8,10,14,16-hexa­aza­tetra­cyclo­[11.3.0.02,6.07,11]hexa­deca-1(16),2,5,7,10,13-hexaen-3-ium-3-olate monohydrate

The organic mol­ecule in the title monohydrate, C6N6O5·H2O, presents an almost planar configuration, the greatest deviation from the least-squares plane through the atoms being 0.061 (1) Å for the O atom within the seven-membered ring. Each water H atom is bifurcated, one forming two O—H⋯N hydrogen bonds and the other forming O—H⋯N,O hydrogen bonds. The result of the hydrogen bonding is the formation of supra­molecular layers with a zigzag topology that stack along [001]

Implementation and Study of a Novel Doubly Salient Structure Starter/Generator System

AbstractA new type of double salient starter/generator is presented, which can be used in aircraft Low Voltage Direct Current (LVDC), Variable Speed Constant Frequency (VSCF) and High Voltage Direct Current (HVDC) systems. The operational theory of the motor and generator is analyzed, and corresponding control strategies are given. An 18kW prototype has been implemented to verify the system performance. It is shown that the DSM S/G system possesses simple structure, high efficiency and flexible control. It is appropriate to be used for aircraft application

Observation on intravitreal injection of ranibizumab combined with laser photocoagulation for the treatment of macular edema in BRVO

AIM: To observe the safety and efficacy of intravitreal injection of ranibizumab combined with laser photocoagulation for the treatment of macular edema in branch retinal vein occlusion(BRVO).<p>METHODS: According to fundus fluorescein angiography(FFA), 30 eyes of 30 patients with BRVO were randomized into three groups: group 1(10 eyes)received grid laser treatment alone, group 2 received a single dose of intravitreal injection of ranibizumab(0.05mL/0.5mg)followed by grid laser treatment on 7d following injection, while group 3(10 eyes)received three loading doses of intravitreal ranibizumab with 0.05mL/0.5mg for three times. At 1mo interval, grid laser treatment was performed after 7d of the 1st injection. After 6mo follow-up, the best-corrected visual acuity and optical coherence tomography(OCT)and central macular thickness were observed. <p>RESULTS:After 6mo, the visual acuity of patients were improved significantly. There was an average increase of 11 letters, 17 letters and 18 letters in group 1, 2, and 3, respectively, with the average decrease in OCT being 208.7μm, 312.9μm and 326.8μm, respectively, in these groups. Gain in visual acuity more than 3 lines was 1 case(10%)in group 1. There were 3 cases(30%)in group 2 and 4 cases(40%)in group 3.<p>CONCLUSION:Combined therapy is better than laser therapy alone. Single dose of intravitreal ranibizumab with grid laser for macular edema in BRVO seems to be effective

SAMFlow: Eliminating Any Fragmentation in Optical Flow with Segment Anything Model

Optical Flow Estimation aims to find the 2D dense motion field between two frames. Due to the limitation of model structures and training datasets, existing methods often rely too much on local clues and ignore the integrity of objects, resulting in fragmented motion estimation. Through theoretical analysis, we find the pre-trained large vision models are helpful in optical flow estimation, and we notice that the recently famous Segment Anything Model (SAM) demonstrates a strong ability to segment complete objects, which is suitable for solving the fragmentation problem. We thus propose a solution to embed the frozen SAM image encoder into FlowFormer to enhance object perception. To address the challenge of in-depth utilizing SAM in non-segmentation tasks like optical flow estimation, we propose an Optical Flow Task-Specific Adaption scheme, including a Context Fusion Module to fuse the SAM encoder with the optical flow context encoder, and a Context Adaption Module to adapt the SAM features for optical flow task with Learned Task-Specific Embedding. Our proposed SAMFlow model reaches 0.86/2.10 clean/final EPE and 3.55/12.32 EPE/F1-all on Sintel and KITTI-15 training set, surpassing Flowformer by 8.5%/9.9% and 13.2%/16.3%. Furthermore, our model achieves state-of-the-art performance on the Sintel and KITTI-15 benchmarks, ranking #1 among all two-frame methods on Sintel clean pass

High-frequency structural-acoustic analysis using an unstructured zero-order energy FEM formulation

Based on the governing equations of energy flow analysis in plate and acoustic domain, an unstructured zero-order energy finite element method (uEFEM0) formulation is presented to simulate the high-frequency behavior of plate structures in contact with acoustic cavities. The new formulation is derived using EFEM0, in which the energy primary variable is conserved in each element. The bending, longitudinal, and shear wave fields in the plates are all included. By meshing plates with triangular grids and cavities with tetrahedral grids, this new formulation can be easily used for modeling structures with arbitrary shape. The formulation is validated by comparing the results obtained from uEFEM0 with those from statistical energy analysis and literature. Good correlations are observed, and the advantages of the uEFEM0 formulation are identified

Learning Parallax Transformer Network for Stereo Image JPEG Artifacts Removal

Under stereo settings, the performance of image JPEG artifacts removal can be further improved by exploiting the additional information provided by a second view. However, incorporating this information for stereo image JPEG artifacts removal is a huge challenge, since the existing compression artifacts make pixel-level view alignment difficult. In this paper, we propose a novel parallax transformer network (PTNet) to integrate the information from stereo image pairs for stereo image JPEG artifacts removal. Specifically, a well-designed symmetric bi-directional parallax transformer module is proposed to match features with similar textures between different views instead of pixel-level view alignment. Due to the issues of occlusions and boundaries, a confidence-based cross-view fusion module is proposed to achieve better feature fusion for both views, where the cross-view features are weighted with confidence maps. Especially, we adopt a coarse-to-fine design for the cross-view interaction, leading to better performance. Comprehensive experimental results demonstrate that our PTNet can effectively remove compression artifacts and achieves superior performance than other testing state-of-the-art methods.Comment: 11 pages, 12 figures, ACM MM202