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To control the rapid spread of COVID-19, we consider deploying a set of Unmanned Aerial Vehicles (UAVs) to form a quarantine barrier such that anyone crossing the barrier can be detected. We use a charging pile to recharge UAVs. The problem is scheduling UAVs to cover the barrier, and, for any scheduling strategy, estimating theminimum number of UAVs needed to cover the barrier forever. We propose breaking the barrier into subsegments so that each subsegment can be monitored by a single UAV. We then analyze two scheduling strategies, where the first one is simple to implement and the second one requires fewer UAVs. The first strategy divides UAVs into groups with each group covering a subsegment. For this strategy, we derive a closed-form formula for the minimum number of UAVs. In the case of insufficient UAVs, we give a recursive function to compute the exact coverage time and give a dynamic-programming algorithm to allocate UAVs to subsegments to maximize the overall coverage time. The second strategy schedules all UAVs dynamically. We prove a lower and an upper bound on the minimum number of UAVs. We implement a prototype system to verify the proposed coverage model and perform simulations to investigate the performance.
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PurposeWhile researchers recognize the significance of philanthropic donations in disaster relief and recovery, the benefits that firms derive from such donations remain unclear, particularly when firms are adversely impacted by the disaster. To address this gap, this study seeks to elucidate the impact of various donation strategies on firm resilience in the context of the COVID-19 pandemic.Design/methodology/approachBased on the hand-collected data on donations, the authors employ ordinary least squares regressions to investigate the effectiveness of various donation strategies - including type, timing and location - in enhancing firm resilience in terms of the severity of stock price losses during the pandemic. To address potential endogeneity concerns, the authors use a two-stage least squares regression with instrumental variables.FindingsThis study finds robust evidence that certain donation strategies are more effective at mitigating stock price losses during the pandemic. Specifically, the authors find that in-kind donations (compared to monetary ones), earlier donations (compared to later ones) and donations targeting severely impacted areas (Hubei province vs. other places) are more effective methods to reduce the severity of stock price losses.Originality/valueThis study points out an alternative mechanism through which donations influence firm resilience during a crisis context and provides important managerial implications for firms to better engage in disaster donations.
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Global pandemic due to the spread of COVID-19 has post challenges in a new dimension on facial recognition, where people start to wear masks. Under such condition, the authors consider utilizing machine learning in image inpainting to tackle the problem, by complete the possible face that is originally covered in mask. In particular, autoencoder has great potential on retaining important, general features of the image as well as the generative power of the generative adversarial network (GAN). The authors implement a combination of the two models, context encoders and explain how it combines the power of the two models and train the model with 50,000 images of influencers faces and yields a solid result that still contains space for improvements. Furthermore, the authors discuss some shortcomings with the model, their possible improvements, as well as some area of study for future investigation for applicative perspective, as well as directions to further enhance and refine the model. © 2023 SPIE.
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Graphical The three-layer surgical mask was recognized by the World Health Organization as an effective-protection tool for reducing SARS-CoV-2 transmission during the COVID-19 pandemic;however, the contribution of each layer of this mask to the particle size–dependent filtration performance resistance remains unclear. Here, both experimental work and numerical simulation were conducted to study the role of each mask layer in particle size–dependent filtration and respiratory resistance. By using scanning electron microscopy images of a commercial three-layer mask, composed of two spun-bond and one melt-blown nonwoven polypropylene fabric layers, four representative models were constructed, in which the computational fluid dynamics of multiphase flow were performed. The pressure drop of all models under different flow conditions was measured next. Numerical simulation was then verified by comparing the experimental results in the present study and other theoretical works. The filtration efficiency of the spun-bond polypropylene nonwoven fabric layer was much lower than that of the melt-blown nonwoven polypropylene fabric layer for the particle diameter in the range of 0.1–2.0 μm. Both the spun-bond and melt-blown nonwoven polypropylene fabric layers demonstrated extremely low filtration efficiency for particles was less than 0.3 μm in diameter, with the maximum filtration efficiency being only 30%. The present results may facilitate rational design of mask products in terms of layer number and structural design.
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Purpose: The purpose of this paper is to assesses whether supervisor justice is linked to COVID-19 negative and positive impacts directly and indirectly through the mechanisms of stress and resiliency among auxiliary police in China. Design/methodology/approach: This study utilized survey data from more than 300 auxiliary police in a large Chinese provincial capital city in 2020. Structural equation modeling was conducted to analyze the direct and indirect relationships between supervisor justice and COIVD-19 impacts. Findings: Results indicate that supervisor justice connects to COVID-19 negative impacts indirectly through stress. Supervisor justice is also indirectly related to positive impact through resiliency. Research limitations/implications: The findings' generalizability is limited due to using a nonrandom sample of officers. Officers' emotional states in the forms of stress and resiliency are important in mediating the association between supervisory justice and COVID-19 impacts. Originality/value: The present study represents one of the first attempts to empirically investigate the occupational experiences of a vital group of frontline workers in Chinese policing. This study also generates evidence to support the importance of officers' emotional conditions in reducing negative COVID-19 impacts in an authoritarian country. © 2023, Emerald Publishing Limited.
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The COVID-19 pandemic has created significant disruptions in both demand and supply. Our study makes use of such dramatic changes in demand and supply during the pandemic to examine resource dependence and power balancing/unbalancing issues in buyer-supplier relationships. Specifically, we investigate the effect of customer and supplier concentrations on firm resilience during the pandemic. Drawing on resource-dependence theory (RDT), we theorize that shifts in demand and supply in different pandemic stages influence the effect of customer and supplier concentrations on firm resilience by altering the power dynamics between focal firms and their concentrated customers and suppliers. Central to our theorizing is that the worsening power imbalance is more detrimental. Measuring firm resilience by loss and recovery (i.e., change) in productivity, our analysis of 23,440 Chinese listed firms' quarter observations from 2019 to 2020 shows that customer concentration is negatively related to firm resilience in the disruption stage but has no effect in the restoration stage. Supplier concentration is positively related to firm resilience in the disruption stage but undermines firm resilience in the restoration stage. These findings largely confirm our theoretical propositions. We discuss the theoretical and managerial implications.
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Previous pneumonia classification algorithms have succeeded in the clinic under closed and static environments. However, in the real world, the emergence of new categories (e.g., COVID-19) and changes in data distribution will cause the existing methods to lose their robustness. In this paper, we formalize this problem as medical open-set domain adaptation under open and dynamic environments. The critical challenge of this problem is to accurately detect the open class samples with subtle differences from the common class. To achieve that, we propose transferable discriminative learning that remarkably achieves robust pneumonia classification with distribution shift and open class emerging. First, we propose the transferable high-density clustering module to detect open class samples and obtain reliable common class samples by considering the density degree. Secondly, we present the transferable triplet loss to enlarge the semantic feature difference between common class and open class samples. Finally, we design the transferable scoring function to detect open class samples effectively. A series of empirical studies show that our algorithm remarkably outperforms state-of-the-art methods. This result demonstrates its potential as a clinical tool for medical open-set domain adaptation. © 2022 IEEE.
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The coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has now entered a critical stage worldwide.Patients typically show fever and severe respiratory symptoms, and some others also present gastrointestinal symptoms, like diarrhea, vomiting, nausea, anorexia and also abdominal pain.At present, it is still lack of effective antiviral medicines for this disease, and now clinics mainly focus on symptomatic treatment. The classical theory of traditional Chinese medicine "exterior and interior relationship between lung and large intestine" which coincides with the "gut-lung axis" in modern medicine, this theory indicate alternatives related to gut microbiota might help to control this viral infection. Therefore, this review focus on discusses the relationship between gut microbiota and respiratory viral diseases, the use of probiotics and nutritional therapies to balance the gut microbiota, modulate the immune response and inhibit viral replication. These might be promising alternative pathways in the treatment of COVID-19. Copyright © 2022, Editorial Board of Pharmaceutical Biotechnology. All right reserved.
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By using WRF-Chem coupled with a heterogeneous reaction mechanism for sulfate formation, this study investigated the impact of meteorological condition and emission changes on chemical species, atmospheric oxidizing capacity (AOC), and secondary aerosol formation during the COVID-19 lockdown period from 23 January to April 8, 2020, focusing on a severe haze event on 7-14 February. The model with the new sulfate formation scheme reasonably reproduces the spatial-temporal distribution of meteorological variables and chemical species, and significantly improves predictions for both sulfate and SO2 concentrations, as well as for PM2.5, ammonium, and nitrate to some extent. It is found that the adverse meteorological conditions were the main cause for the haze event formation, whereas emission reduction due to the lockdown somewhat decreased PM2.5 concentration on average in the Beijing-Tianjin-Hebei (BTH) region. Compared with the same period in 2019, increased surface air temperature and relative humidity (RH) and decreased planetary boundary layer height (PBLH) facilitated accumulation of pollutants and formation of secondary aerosols during the haze episode in 2020, whereas the emission reduction due to the lockdown led to decreases in SO2, NO2, primary PM2.5 (PPM2.5), black carbon (BC), primary organic aerosols (POA), nitrate and ammonium concentrations, but increases in O3, sulfate and secondary organic aerosol (SOA) concentrations, due to the combined effect of changes in emissions and AOC. Gas and aqueous phase oxidation of SO2 accounted for approximately 24% of sulfate formation, while the heterogeneous reaction of Mn-catalytic oxidation of SO2 on aerosol surfaces dominated sulfate formation (76%) during the haze episode in the BTH region. Both adverse meteorological conditions and emission reductions increased heterogeneous sulfate formation rate mainly through altering aerosol surface area (ASA), pH, and Mn2+ concentration. Chemical species varied diversely during the three subperiods before (Period-1, 15-22 January) and during the lockdown (Period-2, 23 January to 5 March and Period-3, 6 March to 8 April) over the BTH. NO2 concentration firstly decreased and then rebounded, whereas O3 concentration increased gradually from the Period-1 to Period-3. All aerosols except SOA decreased throughout the lockdown period, whereas SOA peaked in the Period-2 due to its strong sensitivity to increasing AOC. Sulfate concentration decreased from the Period-1 to Period-2, mainly due to more adverse meteorological conditions in the Period-1, although sulfate increased slightly due to increasing AOC in the Period-2. The large difference in the direction and magnitude of species variations during the COVID-19 lockdown indicates the complex interplay among meteorology, emission, and chemistry. Copyright © 2022 Elsevier Ltd
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Li–air batteries have received significant attention for their ultrahigh theoretical energy density. However, the byproducts induced by attacking air hinder the conversion of Li–O2 batteries to Li–air batteries. Humidity is one of the main obstacles, not only causing side reactions with the discharge products but also leading to rapid corrosion of the lithium anode. Here, we fabricated a novel composite hydrophobic catalyst by loading RuO2 and graphene on N-doped porous carbon. The catalyst was endowed with hydrophobicity and showed superior catalytic performance and low affinity to water in the air. A Li–air battery equipped with this novel composite catalyst exhibited eminent cycling performance in pure oxygen (over 470 h), humid oxygen [∼40% relative humidity (RH), over 310 h], and ambient air (∼42% RH, over 330 h) at a current density of 500 mA g−1, and the discharge specific capacity increased from 13122.1 to 19358.6 mAh g−1. © 2022
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Big Traffic data [1] is cross-border multi-source data for multiple industries, but traffic roads have brought significant economic and social benefits, the number of traffic accidents and casualties is on the rise. Among them, traffic accidents are related to many factors, such as weather and population density. The data set used in this article is open source in Barcelona. The Random Forest algorithm is used to screen essential risk factors, establish a traffic risk prediction model, and compare traffic risks before and after COVID-19. It is concluded that the outbreak of the new crown virus -19-19 has a great impact on people's travel and transportation. Finally, the R square of the model established by Random Forest is 0.9. The K-means clustering algorithm is used to determine the location of the accident handling centre. Moreover, the scope of each accident risk management centre can cover more than 85 percent of traffic accident sites from 2016 to 2020. © 2021 IEEE.
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The Chinese government has curbed the rapid transmission of COVID-19 through a population flow control rarely seen in history. What is the effect of population flow control on pandemic prevention and control? How does it affect China's population mobility and short-term population distribution? In this paper, an SEIR model of virus transmission dynamics is used to evaluate the effectiveness of the control measures, and mobile location data are employed to track the temporal and spatial changes of population mobility in China, in order to review the positive and negative effects of population flow control during the major outbreaks of COVID-19: (1) Population flow control has significantly stabilized the daily new infection, serving as an essential part of China's non-pharmacological intervention measures in response to major public emergencies of COVID-19. Population flow control postponed the arrival of the peak day of daily new infections in China by 1.9 times, and reduced the number of newly infected people on that day by 63.4%. In the selected 5 provinces, 5 cities in Hubei, and 6 cities outside Hubei, the peak days were postponed by 1.4-8 times, 5.6-16.7 times, and 2.3-7.2 times, respectively, and the number of newly infected people on that day was reduced by 56.9%-85.5%, 62.2%-89.2%, and 67.1%-86.2%, respectively. Therefore, population flow control bought valuable buffer time for the prevention and control of the pandemic, and greatly weakened the impact of concentrated transmissions on medical facilities. (2) Population flow control limited intercity population flow. From January to April 2020, the average daily population flow intensity in China decreased by 40.18% compared with the same period in 2019. In particular, the coming-back-to-work flow after the Spring Festival travel rush in 2020 (from January 25 to February 18) decreased by 66.4% on average. (3) Population flow control and people's fear of the pandemic greatly affected the Spring Festival travel rush in 2020, and the spatial and temporal and distribution of China's population was changed for a short period. This paper helps the understanding of the impact of the population flow control strategy introduced by the government on major public emergencies, as well as the influences of geographical characteristics upon on the population flow and distribution. © 2022, Science Press. All right reserved.
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In April 2020, with the development of the nationwide epidemic prevention and control work, the epidemic situation of New Coronavirus has entered a stable stage. However, the resumption of production and recovery is crucial to maintain the stable development of economy and society. Imminent. Therefore, how to co-ordinate the epidemic prevention and control and return to work has become another major challenge for governments at all levels. The joint prevention and control mechanism of the State Council issued a document requiring all localities to 'conduct accurate prevention and control in different regions and levels, and coordinate the prevention and control of epidemic situation and the restoration of economic and social order'. In this context, China Unicom gives full play to the unique advantages of multi-source, massive and integrated big data of operators, and helps enterprises to resume work and production from four aspects: real-time insight of regional return to work rate, grid risk index assessment, risk analysis of regional population inflow, and risk analysis of employees' travel mode, so as to provide support for enterprise decision makers and formulate scientific policies and means, gradually realize the full return to work. © 2021 IEEE.
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Dr. Deborah Birx, the White House Coronavirus Task Force coordinator, told NBC News on "Meet the Press" that "[T]he U.S. needs a 'breakthrough' in coronavirus testing to help screen Americans and get a more accurate picture of the virus' spread." We have been involved with biopathogen detection since the 2001 anthrax attacks and were the first to detect anthrax in real-time. A variation on the laser spectroscopic techniques we developed for the rapid detection of anthrax can be applied to detect the Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2 virus). In addition to detecting a single virus, this technique allows us to read its surface protein structure. In particular, we have been conducting research based on a variety of quantum optical approaches aimed at improving our ability to detect Corona Virus Disease-2019 (COVID-19) viral infection. Indeed, the detection of a small concentration of antibodies, after an infection has passed, is a challenging problem. Likewise, the early detection of disease, even before a detectible antibody population has been established, is very important. Our team is researching both aspects of this problem. The paper is written to stimulate the interest ofboth physical and biological scientists in this important problem. It is thus written as a combination of tutorial (review) and future work (preview). We join Prof. Federico Capasso and Editor Dennis Couwenberg in expressing our appreciation to all those working so heroically on all aspects of the COVTD-19 problem. And we thank Drs. Capasso and Couwenberg for their invitation to write this paper. © 2021 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved.
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The Chinese government has curbed the outbreak of COVID-19 through a population flow control rarely seen in history. The COVID-19 pandemic has greatly impacted the recreation industry. Using mobile location data, this study quantitatively analyzed the impact of the COVID-19 pandemic on population heat map in the leisure areas within the Third Ring Road of Beijing City on the Qingming Festival and Labor Day. The results showed that: 1) The COVID-19 pandemic significantly impacted population heat map in leisure areas in Beijing on holidays, and the population heat map values of the three types of leisure areas investigated in this study declined by 54.2% and 53.0% on the Qingming Festival and Labor Day in 2020 as compared to the 2019 values, respectively. To be specific, the population heat map values of famous scenery, shopping services, and hotel accommodation decreased by 53.6%, 57.5%, and 52.9% on the Qingming Festival, and by 48.5%, 52.0%, and 55.6% on Labor Day, respectively. 2) There were differences in the degree of the impact on population heat map in different types of areas in famous scenery. The impact on the three major segments of famous scenery can be ranked in ascending order as follows: temples and churches (41.7%, 50.3%), parks and squares (53.1%, 47.1%), and scenic spots (61.1%, 51.2%). Wilcoxon rank sum test showed that the hourly variation of population heat map in temples and churches was smaller, and the overall demand can be ranked in ascending order as follows: sightseeing, daily leisure, and religious activities. 3) The 2020 population heat map of the leisure areas within the Third Ring Road of Beijing City was significantly negatively and positively correlated with the population heat map before the pandemic and area of these leisure areas, respectively. This can be attributed to the risk perception of the leisure crowds and the spatial and environmental factors of the disease prevention and control measures. This study provides a scientific basis for assessing the impact of the COVID-19 pandemic on leisure forms in big cities of China. © 2021, Editorial office of PROGRESS IN GEOGRAPHY. All rights reserved.
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The existing face recognition datasets usually lack occlusion samples, which hinders the development of face recognition. Especially during the COVID-19 coronavirus epidemic, wearing a mask has become an effective means of preventing the virus spread. Traditional CNN-based face recognition models trained on existing datasets are almost ineffective for heavy occlusion. To this end, we pioneer a simulated occlusion face recognition dataset. In particular, we first collect a variety of glasses and masks as occlusion, and randomly combine the occlusion attributes (occlusion objects, textures,and colors) to achieve a large number of more realistic occlusion types. We then cover them in the proper position of the face image with the normal occlusion habit. Furthermore, we reasonably combine original normal face images and occluded face images to form our final dataset, termed as Webface-OCC. It covers 804,704 face images of 10,575 subjects, with diverse occlusion types to ensure its diversity and stability. Extensive experiments on public datasets show that the ArcFace retrained by our dataset significantly outperforms the state-of-the-arts. Webface-OCC is available at https://github.com/Baojin-Huang/Webface-OCC.
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The coronavirus pandemic has been declared a world health emergency by the World Health Organization, which has raised the importance of an accurate epidemiological model to predict the evolution of COVID-19. In this paper, we propose mean field evolutionary dynamics (MFEDs), inspired by optimal transport theory and mean field games on graphs, to model the evolution of COVID-19. In the MFEDs, we derive the payoff functions for different individual states from the commonly used replicator dynamics (RDs) and employ them to govern the evolution of epidemics. We also compare epidemic modeling based on MFEDs with that based on RDs through numerical experiments. Moreover, we show the efficiency of the proposed MFED-based model by fitting it to the COVID-19 statistics of Wuhan, China. Finally, we analyze the effects of one-time social distancing as well as the seasonality of COVID19 through the post-pandemic period.
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Custom-made, point-of-care PCR platforms are a necessary tool for rapid, point-of-care diagnostics in situations such as the current Covid-19 pandemic. However, a common issue faced by them is noisy fluorescence signals, which consist of a drifting baseline or noisy sigmoidal curve. This makes automated detection difficult and requires human verification. In this paper, we have tried to use nonlinear fitting for automated classification of PCR waveforms to identify whether amplification has taken place or not. We have presented several novel signal reconstruction techniques based on nonlinear fitting which will enable better pre-processing and automated differentiation of a valid or invalid PCR amplification curve. We have also tried to perform this classification at lower PCR cycles to reduce decision times in diagnostic tests.
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Public health emergencies always lead to serious consequences which affect a lot on human health and socioeconomic progress. It is essential that governments and regional health commissions guide the public toward self-protection and better arranged social production during epidemic outbreaks and spreads. According to the need of risk communication and information disclosure, existing studies for COVID-19 maps and visualization applications are conducive to predicting the future trend of the pandemic, mitigating the harmful effect on public wellbeing by leading to effective intervention and policy measures. However, unsettled tasks remain on comprehensive organization of risk information, effective expression of data for public requirement, and systematic theoretical framework as a standard of map design for public health emergencies. To close the research gaps, this paper proposes a conceptual framework with a three-dimensional spatiotemporal-logic structure as a theoretical foundation for map thematic content selection, which is also a good basis for determining the effective visualization approaches of map design. It enhances the validity and legibility of the map expression by leading maps' thematic content couple with features and processes of an epidemic. Then, using the COVID-19 outbreak in Shenzhen, China, as an example, this paper illustrates how to apply the conceptual framework for selecting the thematic content of COVID-19 maps, and explains the specific ways to transform epidemic data into objects for cartographic representation with proper principles and modes. To our knowledge, this paper is the very first study to bring the thematic content of maps for public health emergencies to the fore, and it is thus believed to shed fresh lights into thematic map design.