Greater Bay Area mainland residents’ evaluation of COVID-19 prevention and city impression of Hong Kong and Macau

Co-founded by Lingnan University and South China University of Technology, “Joint Research Centre for Greater Bay Area - Social Policy & Governance” released a recent survey entitled “Greater Bay Area mainland residents’ evaluation of COVID-19 prevention and city impression of Hong Kong and Macau” on April 16 2020. The result found that mainland residents in Great Bay Area (GBA) believed that their local governments’ epidemic prevention was done better than the Hong Kong and Macau governments

Deformable non-local network for video super-resolution

The video super-resolution (VSR) task aims to restore a high-resolution (HR) video frame by using its corresponding low-resolution (LR) frame and multiple neighboring frames. At present, many deep learning-based VSR methods rely on optical flow to perform frame alignment. The final recovery results will be greatly affected by the accuracy of optical flow. However, optical flow estimation cannot be completely accurate, and there are always some errors. In this paper, we propose a novel deformable nonlocal network (DNLN) which is a non-optical-flow-based method. Specifically, we apply the deformable convolution and improve its ability of adaptive alignment at the feature level. Furthermore, we utilize a nonlocal structure to capture the global correlation between the reference frame and the aligned neighboring frames, and simultaneously enhance desired fine details in the aligned frames. To reconstruct the final highquality HR video frames, we use residual in residual dense blocks to take full advantage of the hierarchical features. Experimental results on benchmark datasets demonstrate that the proposed DNLN can achieve state-of-the-art performance on VSR task

Multimodal Traffic Speed Monitoring: A Real-Time System Based on Passive Wi-Fi and Bluetooth Sensing Technology

SED-000080This manuscript was originally printed in the IEEE Transactions on Intelligent Transportation Systems, Volume 9, Issue 14.Traffic speed is one of the critical indicators reflecting traffic status of roadway networks. The abnormality and sudden changes of traffic speed indicate the occurrence of traffic congestions, accidents, and events. Traffic control and management systems usually take the spatiotemporal variations of traffic speed as the critical evidence to dynamically adjust the traffic signal timing plan, broadcast traffic accidents, and form a management strategy. Meanwhile, transport is multimodal in most cities, including vehicles, pedestrians, and bicyclists. Traffic states of different traffic modes are usually used simultaneously as the significant input of advanced traffic control systems, e.g., multiobjective traffic signal control system, connected vehicles, and autonomous driving. In previous studies, Wi-Fi and Bluetooth passive sensing technology was demonstrated as an effective method for obtaining traffic speed data. However, there are some challenges that greatly affect the accuracy the estimated traffic speed, e.g., traffic mode uncertainty and the errors caused by sensors\u2019 detection range. Thus, this study develops a real-time method for estimating the multimodal traffic speed of road networks covered by Wi-Fi and Bluetooth passive sensors. To address the two identified challenges, an algorithm is developed to correct the biased estimated traffic speed based on the received signal strength indicator of Wi-Fi and Bluetooth signals, and a novel semisupervised Possibilistic Fuzzy C-Means clustering algorithm is proposed for identifying traffic modes of Wi-Fi and Bluetooth device owners. The performance of the proposed algorithms is evaluated by comparing with the selected baseline algorithms. The experimental results indicate the superiority of the proposed algorithm. The proposed method of this study can provide accurate and real-time multimodal traffic speed information for supporting traffic control and management, and, thus, improving the operational performance of the whole road network

Latent membrane protein 1 encoded by Epstein-Barr virus modulates directly and synchronously cyclin D1 and p16 by newly forming a c-JunJun B heterodimer in nasopharyngeal carcinoma cell line

Recently we confirmed that latent membrane protein 1 (LMP1) encoded by Epstein-Barr virus (EBV) accelerates a newly forming active c-Jun/Jun B heterodimer, a transcription factor, but little is known about the target gene regulated by it. In this paper, results indicated that a c-Jun/Jun B heterodimer induced by LMP1 upregulated cyclin D1 promoters activity and expression, on the contrary, downregulated p16, and maladjustment of cyclin D1 and p16 expression accelerated progression of cell cycle. Firstly, we found a c-Jun/Jun B heterodimer regulated synchronously and directly cyclin D1 and p16 in the Tet-on-LMP1-HNE2 cell line, in which LMP1 expression is regulated by Tet-on system. This paper investigated in depth function of the newly forming active c-Jun/Jun B heterodimer, and built new connection between environmental pathogenic factor, signal transduction and cell cycle. (c) 2005 Elsevier B.V. All rights reserved

Reconciling discrepancies in the source characterization of VOCs between emission inventories and receptor modeling

Emission inventory (EI) and receptor model (RM) are two of the three source apportionment (SA) methods recommended by Ministry of Environment of China and used widely to provide independent views on emission source identifications. How to interpret the mixed results they provide, however, were less studied. In this study, a cross-validation study was conducted in one of China's fast-developing and highly populated city cluster- the Pearl River Delta (PRD) region. By utilizing a highly resolved speciated regional EI and a region-wide gridded volatile organic compounds (VOCs) speciation measurement campaign, we elucidated underlying factors for discrepancies between EI and RM and proposed ways for their interpretations with the aim to achieve a scientifically plausible source identification. Results showed that numbers of species, temporal and spatial resolutions used for comparison, photochemical loss of reactive species, potential missing sources in EI and tracers used in RM were important factors contributed to the discrepancies. Ensuring the consensus of species used in EIs and RMs, utilizing a larger spatial coverage and longer time span, addressing the impacts of photochemical losses, and supplementing emissions from missing sources could help reconcile the discrepancies in VOC source characterizations acquired using both approaches. By leveraging the advantages and circumventing the disadvantages in both methods, the EI and RM could play synergistic roles to obtain robust SAs to improve air quality management practices