Showing papers by "Hong Kong Polytechnic University published in 2020"
TL;DR: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates, and there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries.
5,802 citations
Christopher J L Murray1, Christopher J L Murray2, Christopher J L Murray3, Aleksandr Y. Aravkin2 +2269 more•Institutions (286)
TL;DR: The largest declines in risk exposure from 2010 to 2019 were among a set of risks that are strongly linked to social and economic development, including household air pollution; unsafe water, sanitation, and handwashing; and child growth failure.
3,059 citations
TL;DR: The FCV-19S, a seven-item scale, has robust psychometric properties and is reliable and valid in assessing fear of COVID-19 among the general population and will also be useful in allaying CO VID-19 fears among individuals.
Abstract: Background: The emergence of the COVID-19 and its consequences has led to fears, worries, and anxiety among individuals worldwide. The present study developed the Fear of COVID-19 Scale (FCV-19S) t ...
2,546 citations
TL;DR: The 2019 novel coronavirus (2019-nCoV) pneumonia, believed to have originated in a wet market in Wuhan, Hubei province, China at the end of 2019, has gained intense attention nationwide and globally and a range of measures has been urgently adopted.
2,447 citations
TL;DR: The early outbreak data largely follows the exponential growth and indicates the potential of 2019-nCoV to cause outbreaks, as well as the impact of the variations in disease reporting rate, modelled through theonential growth.
1,561 citations
TL;DR: The fundamentals of HER are summarized and the recent state-of-the-art advances in the low-cost and high-performance catalysts based on noble and non-noble metals, as well as metal-free HER electrocatalysts are reviewed.
Abstract: Hydrogen fuel is considered as the cleanest renewable resource and the primary alternative to fossil fuels for future energy supply. Sustainable hydrogen generation is the major prerequisite to realize future hydrogen economy. The electrocatalytic hydrogen evolution reaction (HER), as the vital step of water electrolysis to H2 production, has been the subject of extensive study over the past decades. In this comprehensive review, we first summarize the fundamentals of HER and review the recent state-of-the-art advances in the low-cost and high-performance catalysts based on noble and non-noble metals, as well as metal-free HER electrocatalysts. We systemically discuss the insights into the relationship among the catalytic activity, morphology, structure, composition, and synthetic method. Strategies for developing an effective catalyst, including increasing the intrinsic activity of active sites and/or increasing the number of active sites, are summarized and highlighted. Finally, the challenges, perspectives, and research directions of HER electrocatalysis are featured.
1,387 citations
TL;DR: A conceptual models for the COVID-19 outbreak in Wuhan with the consideration of individual behavioural reaction and governmental actions is proposed, and it successfully captures the course of the COIDs, and thus sheds light on understanding the trends of the outbreak.
925 citations
TL;DR: Experts have reached a consensus on the admission of patients with severe mental illness during the CO VID-19 outbreak in mental health institutions, and the rapid transmission of the COVID-19 has emerged to mount a serious challenge to the mental health service in China.
Abstract: The novel coronavirus disease (COVID-19) has been rapidly transmitted in China, Macau, Hong Kong, and other Asian and European counterparts. This COVID-19 epidemic has aroused increasing attention nationwide. Patients, health professionals, and the general public are under insurmountable psychological pressure which may lead to various psychological problems, such as anxiety, fear, depression, and insomnia. Psychological crisis intervention plays a pivotal role in the overall deployment of the disease control. The National Health Commission of China has summoned a call for emergency psychological crisis intervention and thus, various mental health associations and organizations have established expert teams to compile guidelines and public health educational articles/videos for mental health professionals and the general public alongside with online mental health services. In addition, mental health professionals and expert groups are stationed in designated isolation hospitals to provide on-site services. Experts have reached a consensus on the admission of patients with severe mental illness during the COVID-19 outbreak in mental health institutions. Nevertheless, the rapid transmission of the COVID-19 has emerged to mount a serious challenge to the mental health service in China.
771 citations
TL;DR: Using a large computed Tomography database from 4,154 patients, an AI system is developed that can diagnose NCP and differentiate it from other common pneumonia and normal controls and is made available globally to assist the clinicians to combat COVID-19.
668 citations
TL;DR: This review discussed remediation of PTEs contaminated soils through immobilization techniques using different soil amendments with respect to type of element, soil, and amendment, immobilization efficiency, underlying mechanisms, and field applicability.
630 citations
TL;DR: It is shown that a recombinant vaccine that comprises residues 319–545 of the RBD of the spike protein induces a potent functional antibody response in immunized mice, rabbits and non-human primates as early as 7 or 14 days after the injection of a single vaccine dose.
Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes a respiratory disease called coronavirus disease 2019 (COVID-19), the spread of which has led to a pandemic. An effective preventive vaccine against this virus is urgently needed. As an essential step during infection, SARS-CoV-2 uses the receptor-binding domain (RBD) of the spike protein to engage with the receptor angiotensin-converting enzyme 2 (ACE2) on host cells1,2. Here we show that a recombinant vaccine that comprises residues 319-545 of the RBD of the spike protein induces a potent functional antibody response in immunized mice, rabbits and non-human primates (Macaca mulatta) as early as 7 or 14 days after the injection of a single vaccine dose. The sera from the immunized animals blocked the binding of the RBD to ACE2, which is expressed on the cell surface, and neutralized infection with a SARS-CoV-2 pseudovirus and live SARS-CoV-2 in vitro. Notably, vaccination also provided protection in non-human primates to an in vivo challenge with SARS-CoV-2. We found increased levels of RBD-specific antibodies in the sera of patients with COVID-19. We show that several immune pathways and CD4 T lymphocytes are involved in the induction of the vaccine antibody response. Our findings highlight the importance of the RBD domain in the design of SARS-CoV-2 vaccines and provide a rationale for the development of a protective vaccine through the induction of antibodies against the RBD domain.
TL;DR: In this paper, the authors proposed a three-phase pilot-based channel estimation framework for IRS-assisted uplink multiuser communications, in which the user-BS direct channels and the users-IRS-BS reflected channels of a typical user were estimated in Phase I and Phase II, respectively, while the users reflected channels were estimated with low overhead in Phase III via leveraging their strong correlation with those of the typical user under the case without receiver noise at the BS.
Abstract: In intelligent reflecting surface (IRS) assisted communication systems, the acquisition of channel state information is a crucial impediment for achieving the beamforming gain of IRS because of the considerable overhead required for channel estimation Specifically, under the current beamforming design for IRS-assisted communications, in total $KMN+KM$ channel coefficients should be estimated, where $K$ , $N$ and $M$ denote the numbers of users, IRS reflecting elements, and antennas at the base station (BS), respectively For the first time in the literature, this paper points out that despite the vast number of channel coefficients that should be estimated, significant redundancy exists in the user-IRS-BS reflected channels of different users arising from the fact that each IRS element reflects the signals from all the users to the BS via the same channel To utilize this redundancy for reducing the channel estimation time, we propose a novel three-phase pilot-based channel estimation framework for IRS-assisted uplink multiuser communications, in which the user-BS direct channels and the user-IRS-BS reflected channels of a typical user are estimated in Phase I and Phase II, respectively, while the user-IRS-BS reflected channels of the other users are estimated with low overhead in Phase III via leveraging their strong correlation with those of the typical user Under this framework, we analytically prove that a time duration consisting of $K+N+\max (K-1,\lceil (K-1)N/M \rceil)$ pilot symbols is sufficient for perfectly recovering all the $KMN+KM$ channel coefficients under the case without receiver noise at the BS Further, under the case with receiver noise, the user pilot sequences, IRS reflecting coefficients, and BS linear minimum mean-squared error channel estimators are characterized in closed-form
TL;DR: It is highlighted that large emissions reduction in transportation and slight reduction in industrial would not help avoid severe air pollution in China, especially when meteorology is unfavorable and more efforts should be made to completely avoid severeAir pollution.
Abstract: Due to the pandemic of coronavirus disease 2019 in China, almost all avoidable activities in China are prohibited since Wuhan announced lockdown on January 23, 2020. With reduced activities, severe air pollution events still occurred in the North China Plain, causing discussions regarding why severe air pollution was not avoided. The Community Multi-scale Air Quality model was applied during January 01 to February 12, 2020 to study PM2.5 changes under emission reduction scenarios. The estimated emission reduction case (Case 3) better reproduced PM2.5. Compared with the case without emission change (Case 1), Case 3 predicted that PM2.5 concentrations decreased by up to 20% with absolute decreases of 5.35, 6.37, 9.23, 10.25, 10.30, 12.14, 12.75, 14.41, 18.00 and 30.79 μg/m3 in Guangzhou, Shanghai, Beijing, Shijiazhuang, Tianjin, Jinan, Taiyuan, Xi'an, Zhengzhou, Wuhan, respectively. In high-pollution days with PM2.5 greater than 75 μg/m3, the reductions of PM2.5 in Case 3 were 7.78, 9.51, 11.38, 13.42, 13.64, 14.15, 14.42, 16.95 and 22.08 μg/m3 in Shanghai, Jinan, Shijiazhuang, Beijing, Taiyuan, Xi'an, Tianjin, Zhengzhou and Wuhan, respectively. The reductions in emissions of PM2.5 precursors were ~2 times of that in concentrations, indicating that meteorology was unfavorable during simulation episode. A further analysis shows that benefits of emission reductions were overwhelmed by adverse meteorology and severe air pollution events were not avoided. This study highlights that large emissions reduction in transportation and slight reduction in industrial would not help avoid severe air pollution in China, especially when meteorology is unfavorable. More efforts should be made to completely avoid severe air pollution.
TL;DR: Several industrial sectors such as shipping, manufacturing, automotive, aviation, finance, technology, energy, healthcare, agriculture and food, e-commerce, and education among others are examined that can be successfully revamped with blockchain based technologies through enhanced visibility and business process management.
Abstract: Blockchain is a technology with unique combination of features such as decentralized structure, distributed notes and storage mechanism, consensus algorithm, smart contracting, and asymmetric encryption to ensure network security, transparency and visibility. Blockchain has immense potential to transform supply chain (SC) functions, from SC provenance, business process reengineering to security enhancement. More and more studies exploring the use of blockchain in SCs have appeared in recent years. In this paper, we consider a total of 178 articles and examine all the relevant research done in the field associated with the use of blockchain integration in SC operations. We highlight the corresponding opportunities, possible societal impacts, current state-of-the-art technologies along with major trends and challenges. We examine several industrial sectors such as shipping, manufacturing, automotive, aviation, finance, technology, energy, healthcare, agriculture and food, e-commerce, and education among others that can be successfully revamped with blockchain based technologies through enhanced visibility and business process management. A future research agenda is established which lays the solid foundation for further studies on this important emerging research area.
07 Jun 2020
TL;DR: In this paper, the authors proposed a client-edge-cloud hierarchical federated learning system, supported with a HierFAVG algorithm that allows multiple edge servers to perform partial model aggregation.
Abstract: Federated Learning is a collaborative machine learning framework to train a deep learning model without accessing clients' private data. Previous works assume one central parameter server either at the cloud or at the edge. The cloud server can access more data but with excessive communication overhead and long latency, while the edge server enjoys more efficient communications with the clients. To combine their advantages, we propose a client-edge-cloud hierarchical Federated Learning system, supported with a HierFAVG algorithm that allows multiple edge servers to perform partial model aggregation. In this way, the model can be trained faster and better communication-computation trade-offs can be achieved. Convergence analysis is provided for HierFAVG and the effects of key parameters are also investigated, which lead to qualitative design guidelines. Empirical experiments verify the analysis and demonstrate the benefits of this hierarchical architecture in different data distribution scenarios. Particularly, it is shown that by introducing the intermediate edge servers, the model training time and the energy consumption of the end devices can be simultaneously reduced compared to cloud-based Federated Learning.
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TL;DR: The proposed aggregation scheme is permutation-invariant and consists of three modules, node embedding, structural neighborhood, and bi-level aggregation, and an implementation of the scheme in graph convolutional networks, termed Geom-GCN, to perform transductive learning on graphs.
Abstract: Message-passing neural networks (MPNNs) have been successfully applied to representation learning on graphs in a variety of real-world applications. However, two fundamental weaknesses of MPNNs' aggregators limit their ability to represent graph-structured data: losing the structural information of nodes in neighborhoods and lacking the ability to capture long-range dependencies in disassortative graphs. Few studies have noticed the weaknesses from different perspectives. From the observations on classical neural network and network geometry, we propose a novel geometric aggregation scheme for graph neural networks to overcome the two weaknesses. The behind basic idea is the aggregation on a graph can benefit from a continuous space underlying the graph. The proposed aggregation scheme is permutation-invariant and consists of three modules, node embedding, structural neighborhood, and bi-level aggregation. We also present an implementation of the scheme in graph convolutional networks, termed Geom-GCN (Geometric Graph Convolutional Networks), to perform transductive learning on graphs. Experimental results show the proposed Geom-GCN achieved state-of-the-art performance on a wide range of open datasets of graphs. Code is available at this https URL.
TL;DR: It can be concluded that biochar technology represents a new, cost effective, and environmentally-friendly solution for the treatment of wastewater.
01 Jan 2020
TL;DR: Following a wave of fear and worry in the society, several communities seem to develop a new by-product of discrimination, that is, mutual discrimination within the Asian/Chinese societies.
Abstract: With the number of people infected by the 2019 novel coronavirus (COVID-19), which is rapidly increasing worldwide,[1] public anxieties and worries are elevated in many regions. As the COVID-19 outbreak is ongoing, a wave of fear and worry in the society has arisen. Following this wave of fear and worry, several communities seem to develop a new by-product of discrimination, that is, mutual discrimination within the Asian/Chinese societies. For example, people who reside in Taiwan are afraid of interaction with those living in Hong Kong; people in Hong Kong avoid interaction with China mainlanders; and people from southeastern or southern region of Asia are afraid of contacts with Chinese ethnic people. More recently, people in Hong Kong and Taiwan feel scared when interacting with Koreans and Japanese due to their recent community outbreak.
TL;DR: In this paper, the authors highlight the recent advances in Zn anode and outline future opportunities for the development of high-performance zinc metal anodes in aqueous ZIBs.
14 Jun 2020
TL;DR: An auxiliary network is designed which converts the convolutional features in the backbone network back to point-level representations and an efficient part-sensitive warping operation is developed to align the confidences to the predicted bounding boxes.
Abstract: 3D object detection from point cloud data plays an essential role in autonomous driving. Current single-stage detectors are efficient by progressively downscaling the 3D point clouds in a fully convolutional manner. However, the downscaled features inevitably lose spatial information and cannot make full use of the structure information of 3D point cloud, degrading their localization precision. In this work, we propose to improve the localization precision of single-stage detectors by explicitly leveraging the structure information of 3D point cloud. Specifically, we design an auxiliary network which converts the convolutional features in the backbone network back to point-level representations. The auxiliary network is jointly optimized, by two point-level supervisions, to guide the convolutional features in the backbone network to be aware of the object structure. The auxiliary network can be detached after training and therefore introduces no extra computation in the inference stage. Besides, considering that single-stage detectors suffer from the discordance between the predicted bounding boxes and corresponding classification confidences, we develop an efficient part-sensitive warping operation to align the confidences to the predicted bounding boxes. Our proposed detector ranks at the top of KITTI 3D/BEV detection leaderboards and runs at 25 FPS for inference.
TL;DR: The programmable nature of smart textiles makes them an indispensable part of an emerging new technology field and a timely overview and comprehensive review of progress of this field in the last five years are provided.
Abstract: The programmable nature of smart textiles makes them an indispensable part of an emerging new technology field. Smart textile-integrated microelectronic systems (STIMES), which combine microelectronics and technology such as artificial intelligence and augmented or virtual reality, have been intensively explored. A vast range of research activities have been reported. Many promising applications in healthcare, the internet of things (IoT), smart city management, robotics, etc., have been demonstrated around the world. A timely overview and comprehensive review of progress of this field in the last five years are provided. Several main aspects are covered: functional materials, major fabrication processes of smart textile components, functional devices, system architectures and heterogeneous integration, wearable applications in human and nonhuman-related areas, and the safety and security of STIMES. The major types of textile-integrated nonconventional functional devices are discussed in detail: sensors, actuators, displays, antennas, energy harvesters and their hybrids, batteries and supercapacitors, circuit boards, and memory devices.
TL;DR: The under-reporting of 2019-nCoV cases was likely to have occurred during the first half of January 2020 and should be considered in future investigation.
Abstract: Background: In December 2019, an outbreak of respiratory illness caused by a novel coronavirus (2019-nCoV) emerged in Wuhan, China and has swiftly spread to other parts of China and a number of foreign countries. The 2019-nCoV cases might have been under-reported roughly from 1 to 15 January 2020, and thus we estimated the number of unreported cases and the basic reproduction number, R0, of 2019-nCoV. Methods: We modelled the epidemic curve of 2019-nCoV cases, in mainland China from 1 December 2019 to 24 January 2020 through the exponential growth. The number of unreported cases was determined by the maximum likelihood estimation. We used the serial intervals (SI) of infection caused by two other well-known coronaviruses (CoV), Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) CoVs, as approximations of the unknown SI for 2019-nCoV to estimate R0. Results: We confirmed that the initial growth phase followed an exponential growth pattern. The under-reporting was likely to have resulted in 469 (95% CI: 403–540) unreported cases from 1 to 15 January 2020. The reporting rate after 17 January 2020 was likely to have increased 21-fold (95% CI: 18–25) in comparison to the situation from 1 to 17 January 2020 on average. We estimated the R0 of 2019-nCoV at 2.56 (95% CI: 2.49–2.63). Conclusion: The under-reporting was likely to have occurred during the first half of January 2020 and should be considered in future investigation.
TL;DR: In this paper, the authors reviewed the overall impacts of coronavirus disease 2019 (COVID-19) pandemic on China's hotel industry and proposed a management framework to address the anti-pandemic phases, principles, and strategies.
TL;DR: In this article, the authors advocate a new set of design guidelines for wireless communication in edge learning, collectively called learning-driven communication, and provide examples to demonstrate the effectiveness of these design guidelines.
Abstract: The recent revival of AI is revolutionizing almost every branch of science and technology. Given the ubiquitous smart mobile gadgets and IoT devices, it is expected that a majority of intelligent applications will be deployed at the edge of wireless networks. This trend has generated strong interest in realizing an "intelligent edge" to support AI-enabled applications at various edge devices. Accordingly, a new research area, called edge learning, has emerged, which crosses and revolutionizes two disciplines: wireless communication and machine learning. A major theme in edge learning is to overcome the limited computing power, as well as limited data, at each edge device. This is accomplished by leveraging the mobile edge computing platform and exploiting the massive data distributed over a large number of edge devices. In such systems, learning from distributed data and communicating between the edge server and devices are two critical and coupled aspects, and their fusion poses many new research challenges. This article advocates a new set of design guidelines for wireless communication in edge learning, collectively called learning- driven communication. Illustrative examples are provided to demonstrate the effectiveness of these design guidelines. Unique research opportunities are identified.
01 Jul 2020
TL;DR: In this paper, the authors examine processes that can lead to the contamination of agricultural land with heavy metal(loid)s, which range from mine tailings runoff entering local irrigation channels to the atmospheric deposition of incinerator and coal-fired power-plant emissions.
Abstract: Agricultural soil is a non-renewable natural resource that requires careful stewardship in order to achieve the United Nations’ Sustainable Development Goals However, industrial and agricultural activity is often detrimental to soil health and can distribute heavy metal(loid)s into the soil environment, with harmful effects on human and ecosystem health In this Review, we examine processes that can lead to the contamination of agricultural land with heavy metal(loid)s, which range from mine tailings runoff entering local irrigation channels to the atmospheric deposition of incinerator and coal-fired power-plant emissions We discuss the relationship between heavy metal(loid) biogeochemical transformations in the soil and their bioavailability We then review two biological solutions for remediation of contaminated agricultural land, plant-based remediation and microbial bioremediation, which offer cost-effective and sustainable alternatives to traditional physical or chemical remediation technologies Finally, we discuss how integrating these innovative technologies with profitable and sustainable land use could lead to green and sustainable remediation strategies, and conclude by identifying research challenges and future directions for the biological remediation of agricultural soils Contamination of agricultural soils by heavy metals and metalloids has severe consequences on human and ecosystem health This Review discusses the sources of heavy metal(loid) contamination, the mechanisms by which these contaminants interact with biological and geochemical soil elements, and plant-based and microorganism-based remediation strategies
TL;DR: Residents' perceptions of the risks posed by tourism activity are described, and their willingness to pay to reduce public health risks based on hypothetical scenarios are estimated using the triple-bounded dichotomous choice contingent valuation method.
TL;DR: Initial results of serological surveillance in China provide valuable data for estimation of the cumulative prevalence of SARS-CoV-2 infection in the general population and whether these results are generalizable to other populations and geographic locations.
Abstract: Detection of asymptomatic or subclinical novel human coronavirus SARS-CoV-2 infection is critical for understanding the overall prevalence and infection potential of COVID-19. To estimate the cumulative prevalence of SARS-CoV-2 infection in China, we evaluated the host serologic response, measured by the levels of immunoglobulins M and G in 17,368 individuals, in the city of Wuhan, the epicenter of the COVID-19 pandemic in China, and geographic regions in the country, during the period from 9 March 2020 to 10 April 2020. In our cohorts, the seropositivity in Wuhan varied between 3.2% and 3.8% in different subcohorts. Seroposivity progressively decreased in other cities as the distance to the epicenter increased. Patients who visited a hospital for maintenance hemodialysis and healthcare workers also had a higher seroprevalence of 3.3% (51 of 1,542, 2.5-4.3%, 95% confidence interval (CI)) and 1.8% (81 of 4,384, 1.5-2.3%, 95% CI), respectively. More studies are needed to determine whether these results are generalizable to other populations and geographic locations, as well as to determine at what rate seroprevalence is increasing with the progress of the COVID-19 pandemic. Serologic surveillance has the potential to provide a more faithful cumulative viral attack rate for the first season of this novel SARS-CoV-2 infection.
TL;DR: The three-dimensional model of the SARS-CoV-2 (aka 2019-nCoV) 3C-like protease (3CL pro) is prepared using the crystal structure of the highly similar (96% identity) ortholog from the Sars- CoV.
Abstract: We prepared the three-dimensional model of the SARS-CoV-2 (aka 2019-nCoV) 3C-like protease (3CL pro) using the crystal structure of the highly similar (96% identity) ortholog from the SARS-CoV. All residues involved in the catalysis, substrate binding and dimerisation are 100% conserved. Comparison of the polyprotein PP1AB sequences showed 86% identity. The 3C-like cleavage sites on the coronaviral polyproteins are highly conserved. Based on the near-identical substrate specificities and high sequence identities, we are of the opinion that some of the previous progress of specific inhibitors development for the SARS-CoV enzyme can be conferred on its SARS-CoV-2 counterpart. With the 3CL pro molecular model, we performed virtual screening for purchasable drugs and proposed 16 candidates for consideration. Among these, the antivirals ledipasvir or velpatasvir are particularly attractive as therapeutics to combat the new coronavirus with minimal side effects, commonly fatigue and headache. The drugs Epclusa (velpatasvir/sofosbuvir) and Harvoni (ledipasvir/sofosbuvir) could be very effective owing to their dual inhibitory actions on two viral enzymes.
23 Jan 2020
TL;DR: A thorough survey on the historical process and status quo of V2X technologies, as well as demonstration of emerging technology developing directions toward IoV can provide beneficial insights and inspirations for both academia and the IoV industry.
Abstract: To enable large-scale and ubiquitous automotive network access, traditional vehicle-to-everything (V2X) technologies are evolving to the Internet of Vehicles (IoV) for increasing demands on emerging advanced vehicular applications, such as intelligent transportation systems (ITS) and autonomous vehicles. In recent years, IoV technologies have been developed and achieved significant progress. However, it is still unclear what is the evolution path and what are the challenges and opportunities brought by IoV. For the aforementioned considerations, this article provides a thorough survey on the historical process and status quo of V2X technologies, as well as demonstration of emerging technology developing directions toward IoV. We first review the early stage when the dedicated short-range communications (DSRC) was issued as an important initial beginning and compared the cellular V2X with IEEE 802.11 V2X communications in terms of both the pros and cons. In addition, considering the advent of big data and cloud-edge regime, we highlight the key technical challenges and pinpoint the opportunities toward the big data-driven IoV and cloud-based IoV, respectively. We believe our comprehensive survey on evolutionary V2X technologies toward IoV can provide beneficial insights and inspirations for both academia and the IoV industry.