In the era of Corona Virus Disease 2019 (COVID-19), wearing a mask can effectively protect people from infection risk and largely decrease the spread in public places, such as hospitals and airports. This brings a demand for the monitoring instruments that are required to detect people who are wearing masks. However, this is not the objective of existing face detection algorithms. In this article, we propose a two-stage approach to detect wearing masks using hybrid machine learning techniques. The first stage is designed to detect candidate wearing mask regions as many as possible, which is based on the transfer model of Faster_RCNN and InceptionV2 structure, while the second stage is designed to verify the real facial masks using a broad learning system. It is implemented by training a two-class model. Moreover, this article proposes a data set for wearing mask detection (WMD) that includes 7804 realistic images. The data set has 26403 wearing masks and covers multiple scenes, which is available at "https://github.com/BingshuCV/WMD." Experiments conducted on the data set demonstrate that the proposed approach achieves an overall accuracy of 97.32% for simple scene and an overall accuracy of 91.13% for the complex scene, outperforming the compared methods. Index Terms-Broad learning system (BLS), Corona Virus Disease 2019 (COVID-19), transfer learning, wearing mask detection (WMD). I. INTRODUCTION S INCE the first patient infected by Corona Virus Disease 2019 (COVID-19) has been identified in 2019, the virus spread the world very fast. It is quickly declared as a global Manuscript

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Shadow detection and removal is an important task for digitized document applications. It is hard for many methods to distinguish shadow from printed text due to the high darkness similarity. In this paper, we propose a local water-filling method to remove shadows by mapping a document image into a structure of topographic surface. Firstly, we design a local water-filling approach including a flooding and effusing process to estimate the shading map, which can be used to detect umbra and penumbra. Then, the umbra is enhanced using Retinex Theory. For penumbra, we propose a binarized water-filling strategy to correct illumination distortions. Moreover, we build up a dataset called optical shadow removal (OSR dataset), which includes hundreds of shadow images. Experiments performed on OSR dataset show that our method achieves an average ErrorRatio of 0.685 with a computation time of 0.265 s to process an image size of 960×544 pixels on a desktop. The proposed method can remove the shading artifacts and outperform some state-of-the-art methods, especially for the removal of shadow boundaries.

The cationic group distribution along the polymeric backbones of anion exchange membranes (AEMs) has significant influence on their microscopic morphology and anion conductivity. To develop high-performance AEMs for vanadium redox flow batteries (VRFBs), a series of poly (fluorenyl ether) samples bearing di-and tri-quaternary ammonium side chains with similar ion exchange capacities (IECs) were synthesized by grafting cationic alkyl chains with tertiary amine-containing poly(fluorenyl ether) precursors. The experimental results indicate that the introduction of the multi-cationic side chains facilitates the formation of microphase-separated morphologies and enhances anion conductivity. Moreover, the number of spacer atoms between the quaternary ammonium groups on the side chains affects the water uptake of the membranes, thus complicating the relationship between the density of cationic group distribution and anion conductivity. The poly(fluorenyl ether)s with dicationic side chains and six spacing atoms (DQA-PFE-C6) showed the highest anion conductivity. A VRFB assembled with DQA-PFE-C6 exhibited a maximum power density of 239.80 mW cm −2 at 250 mA cm −2 , which is significantly higher than a VRFB assembled with Nafion 212. Therefore, side chain engineering is an effective chemical approach to enhance the properties of AEMs for VRFB applications.

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