2,4-Dichloro-7-fluoro­quinazoline

The mol­ecule of the title compound, C8H3Cl2FN2, is essentially planar, with a maximum deviation of 0.018 (2) Å. In the crystal, π–π stacking is observed between parallel quinazoline moieties of adjacent mol­ecules, the centroid–centroid distance being 3.8476 (14) Å

Light Coupling Between a Singlemode-Multimode-Singlemode (SMS) Fiber Structure and a Long Period Fiber Gating

We propose a novel optical coupling technique based on evanescent field coupling between a singlemode-multimode-singlemode (SMS) fiber structure and a long period fiber grating (LPFG). By parallel placement of the two fiber sections in close proximity to each other, the excited multi-cladding modes from the SMS fiber section can be selectively coupled to the guided mode in the LPFG, and vice versa. A theoretical analysis based for such a structure is undertaken and the simulated results are verified by experiments demonstrating a maximum coupling efficiency of up to 1.66% (which could be improved to 27.5% in theory) over a broadband resonance (42 nm with a 3 dB bandwidth)

Multi-nanolayered VO2/Sapphire Thin Film via Spinodal Decomposition

Abstract Coating of VO2-based thin film has been extensively studied for fabricating energy-saving smart windows. One of the most efficient ways for fabricating high performance films is to create multi-nanolayered structure. However, it has been highly challenge to make such layers in the VO2-based films using conventional methods. In this work, a facile two-step approach is established to fabricate multilayered VO2-TiO2 thin films. We first deposited the amorphous thin films upon sputtering, and then anneal them to transform the amorphous phase into alternating Ti- and V-rich multilayered nanostructure via a spinodal decomposition mechanism. In particular, we take advantage of different sapphire substrate planes (A-plane (11–20), R-plane (1–102), C-plane (0001), and M-plane (10-10)) to achieve different decomposition modes. The new approach has made it possible to tailoring the microstructure of the thin films for optimized performances by controlling the disorder-order transition in terms of both kinetic and thermodynamic aspects. The derived thin films exhibit superior optical modulation upon phase transition, significantly reduced transition temperature and hysteresis loop width, and high degradation resistance, these improvements indicate a high potential to be used for fabricating the next generation of energy saving smart windows

trans-5-(4-Chloro­phen­yl)-N-cyclo­hexyl-4-methyl-2-oxo-1,3-thia­zolidine-3-carboxamide

The title pesticide, C17H21ClN2O2S, has a trans arrangement of the 4-chloro­phenyl and 4-methyl substituents of the thia­zolidine ring; the structure features an intra­molecular amide–ring carbonyl N—H⋯O hydrogen bond. The thia­zolidine ring is almost planar, the largest deviation being 0.199 (1) Å for the methyl-substitued C atom, and the cyclohexane ring has a chair conformation

Multi-Dimensional Refinement Graph Convolutional Network with Robust Decouple Loss for Fine-Grained Skeleton-Based Action Recognition

Graph convolutional networks have been widely used in skeleton-based action recognition. However, existing approaches are limited in fine-grained action recognition due to the similarity of inter-class data. Moreover, the noisy data from pose extraction increases the challenge of fine-grained recognition. In this work, we propose a flexible attention block called Channel-Variable Spatial-Temporal Attention (CVSTA) to enhance the discriminative power of spatial-temporal joints and obtain a more compact intra-class feature distribution. Based on CVSTA, we construct a Multi-Dimensional Refinement Graph Convolutional Network (MDR-GCN), which can improve the discrimination among channel-, joint- and frame-level features for fine-grained actions. Furthermore, we propose a Robust Decouple Loss (RDL), which significantly boosts the effect of the CVSTA and reduces the impact of noise. The proposed method combining MDR-GCN with RDL outperforms the known state-of-the-art skeleton-based approaches on fine-grained datasets, FineGym99 and FSD-10, and also on the coarse dataset NTU-RGB+D X-view version