Ultra-wideband linear-to-circular polarizer realized by bi-layer metasurfaces.

We propose a bi-layer transmissive metasurface to obtain linear-to-circular polarization conversion in a wideband. The unit cell of each metasurface layer, which has identical configuration, consists of a Jerusalem-cross-like resonator and a metal strip. A universal equivalent circuit model is employed to guide the design of the polarizer. By analyzing the circuit parameters of the equivalent circuit, four metal strips are symmetrically inserted in each unit cell to broaden the bandwidth of linear-to-circular polarization conversion. Numerical and experimental results show that the polarizer can convert a linearly polarized wave into a circularly polarized wave in a wideband from 6.1 GHz to 12.6 GHz. Compared to the reported designs, the proposed polarization converter has advantages of ultrathin thickness and ultrawideband, and hence can be used in many applications, such as antennas and remote sensors.

FAR-Net: Feature-Wise Attention-Based Relation Network for Multilabel Jujube Defect Classification.

In production, due to natural conditions or process peculiarities, a single product often may exhibit more than one type of defect. The accurate identification of all defects has an important guiding significance and practical value to improve the planting and production processes. Concerning the surface defect classification task, convolutional neural networks can be implemented as a powerful instrument. However, a typical convolutional neural network tends to consider an image as an inseparable entity and a single instance when extracting features; moreover, it may overlook semantic correlations between different labels. To address these limitations, in the present paper, we proposed a feature-wise attention-based relation network (FAR-Net) for multilabel jujube defect classification. The network included four different modules designed for (1) image feature extraction, (2) label-wise feature aggregation, (3) feature activation and deactivation, and (4) correlation learning among labels. To evaluate the proposed method, a unique multilabel jujube defect dataset was constructed as a benchmark for the multilabel classification task of the jujube defect images. The results of experiments show that owing to the relation learning mechanism, the average precision of the three main composite defects in the dataset increases by 5.77%, 4.07%, and 3.50%, respectively, compared to the backbone of our network, namely Inception v3, which indicated that the proposed FAR-Net effectively facilitated the learning of correlation between labels and eventually, improved the multilabel classification accuracy.

Digital Forensics of Scanned QR Code Images for Printer Source Identification Using Bottleneck Residual Block.

With the rapid development of information technology and the widespread use of the Internet, QR codes are widely used in all walks of life and have a profound impact on people's work and life. However, the QR code itself is likely to be printed and forged, which will cause serious economic losses and criminal offenses. Therefore, it is of great significance to identify the printer source of QR code. A method of printer source identification for scanned QR Code image blocks based on convolutional neural network (PSINet) is proposed, which innovatively introduces a bottleneck residual block (BRB). We give a detailed theoretical discussion and experimental analysis of PSINet in terms of network input, the first convolution layer design based on residual structure, and the overall architecture of the proposed convolution neural network (CNN). Experimental results show that the proposed PSINet in this paper can obtain extremely excellent printer source identification performance, the accuracy of printer source identification of QR code on eight printers can reach 99.82%, which is not only better than LeNet and AlexNet widely used in the field of digital image forensics, but also exceeds state-of-the-art deep learning methods in the field of printer source identification.

'Shi Han Quan' natural soda water prevents development of gout.

PURPOSE: This study aimed to explore the regulatory mechanism of the natural soda Shi Han Quan (SHQ) in the development of gout. METHODS: Human umbilical vein endothelial cells (HUVECs) were stimulated with monosodium urate (MSU) for 24 h to induce acute gouty inflammation in vitro. HUVECs were divided into four groups: ddH2 O group, ddH2 O + MSU group, 1/2 ddH2 O +1/2 SHQ + MSU group, and SHQ + MSU group. The effects of SHQ on cell viability, concentration and expression of intercellular adhesion molecule-1 (ICAM-1), and MSU-induced release of interleukin (IL)-1ß and IL-6 were investigated. Additionally, cell viability and ICAM-1 concentration and expression were detected after HUVECs were incubated with the culture supernatant of THP-1 cells that had been treated with phorbol 12-myristate 13-acetate (PMA), MSU and SHQ for 24 h. RESULTS: The viability of HUVECs was significantly decreased with increasing transfection concentrations of MSU, and MSU treatment resulted in a significant increase of the concentration and expression of ICAM-1. In addition, SHQ improved the MSU-induced decrease in cell viability and alleviated MSU-mediated increase in ICAM-1 levels. Moreover, SHQ decreased MSU-induced release of IL-1ß and IL-6. After HUVECs were incubated with the culture supernatant of THP-1 cells that had been treated with PMA, MSU and SHQ for 24 h, SHQ also markedly alleviated the effects of MSU, such as by increasing cell viability and decreasing ICAM-1 levels. CONCLUSIONS: Drinking natural soda water may have a significant role in preventing gouty inflammation.