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The 2019 outbreak of coronavirus disease (COVID‐19) in Wuhan (Hubei province of China) has given rise to a pandemic spread of virus, more than 240 million incidences and a death toll larger than 5 million people. COVID‐19 has set off large efforts in research, therapy and patient care, as well as public and private debates in every imaginable form. A number of scientists used the publication platforms provided by the Federation of the European Biochemical Societies (FEBS) to present their research data, reviews, opinions and other contributions relating to COVID‐19 and severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Here, I highlight the recent COVID‐19 papers which have been published and collected in a Virtual Issue in FEBS Letters, and discuss their implications towards understanding the molecular, biochemical and cellular mechanisms of SARS‐CoV‐2 infections, vaccine development and antiviral discovery strategies. [ABSTRACT FROM AUTHOR]

Infectious diseases are among the leading causes of mortality worldwide. A new coronavirus named severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) was identified in Wuhan, China in 2019, and the World Health Organization (WHO) declared its outbreak, coronavirus disease 2019 (COVID-19), as a global pandemic in 2020. COVID-19 can spread quickly from person to person. One of the most challenging issues is to identify the infected individuals and prevent potential spread of SARS-CoV-2. Recently, anti-SARS-CoV-2 immunoglobulin M (IgM) and immunoglobulin G (IgG) antibody tests using immunochromatographic methods have been used as a complement to current detection methods and have provided information of the approximate course of COVID-19 infection. However, blood sampling causes pain and poses risks of infection at the needle puncture site. In this study, a novel patch sensor integrating porous microneedles and an immunochromatographic assay (PMNIA) was developed for the rapid detection of anti-SARS-CoV-2 IgM/IgG in dermal interstitial fluid (ISF), which is a rich source of protein biomarkers, such as antibodies. Biodegradable porous microneedles (MNs) made of polylactic acid were fabricated to extract ISF from human skin by capillary effect. The extracted ISF was vertically transported and flowed into the affixed immunoassay biosensor, where specific antibodies could be detected colorimetrically on-site. Anti-SARS-CoV-2 IgM/IgG antibodies were simultaneously detected within 3 min in vitro. Moreover, the limit of detection of anti-SARS-CoV-2 IgM and IgG concentrations was as low as 3 and 7 ng/mL, respectively. The developed device integrating porous MNs and immunochromatographic biosensors is expected to enable minimally invasive, simple, and rapid anti-SARS-CoV-2 IgM/IgG antibody testing. Furthermore, the compact size of the MN and biosensor-integrated device is advantageous for its widespread use. The proposed device has great potential for rapid screening of various infectious diseases in addition to COVID-19 as an effective complementary method with other diagnostic tests. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic originating in the Wuhan province of China in late 2019 has impacted global health, causing increased mortality among elderly patients and individuals with comorbid conditions. During the passage of the virus through affected populations, it has undergone mutations, some of which have recently been linked with increased viral load and prognostic complexities. Several of these variants are point mutations that are difficult to diagnose using the gold standard quantitative real-time PCR (qRT-PCR) method and necessitates widespread sequencing which is expensive, has long turn-around times, and requires high viral load for calling mutations accurately. Here, we repurpose the high specificity of Francisella novicida Cas9 (FnCas9) to identify mismatches in the target for developing a lateral flow assay that can be successfully adapted for the simultaneous detection of SARS-CoV-2 infection as well as for detecting point mutations in the sequence of the virus obtained from patient samples. We report the detection of the S gene mutation N501Y (present across multiple variant lineages of SARS-CoV-2) within an hour using lateral flow paper strip chemistry. The results were corroborated using deep sequencing on multiple wild-type (n = 37) and mutant (n = 22) virus infected patient samples with a sensitivity of 87% and specificity of 97%. The design principle can be rapidly adapted for other mutations (as shown also for E484K and T716I) highlighting the advantages of quick optimization and roll-out of CRISPR diagnostics (CRISPRDx) for disease surveillance even beyond COVID-19. This study was funded by Council for Scientific and Industrial Research, India. [ABSTRACT FROM AUTHOR]

What started as a cluster of patients with a mysterious respiratory illness in Wuhan, China, in December 2019, was later determined to be coronavirus disease 2019 (COVID-19). The pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel Betacoronavirus, was subsequently isolated as the causative agent. SARS-CoV-2 is transmitted by respiratory droplets and fomites and presents clinically with fever, fatigue, myalgias, conjunctivitis, anosmia, dysgeusia, sore throat, nasal congestion, cough, dyspnea, nausea, vomiting, and/or diarrhea. In most critical cases, symptoms can escalate into acute respiratory distress syndrome accompanied by a runaway inflammatory cytokine response and multiorgan failure. As of this article's publication date, COVID-19 has spread to approximately 200 countries and territories, with over 4.3 million infections and more than 290,000 deaths as it has escalated into a global pandemic. Public health concerns mount as the situation evolves with an increasing number of infection hotspots around the globe. New information about the virus is emerging just as rapidly. This has led to the prompt development of clinical patient risk stratification tools to aid in determining the need for testing, isolation, monitoring, ventilator support, and disposition. COVID-19 spread is rapid, including imported cases in travelers, cases among close contacts of known infected individuals, and community-acquired cases without a readily identifiable source of infection. Critical shortages of personal protective equipment and ventilators are compounding the stress on overburdened healthcare systems. The continued challenges of social distancing, containment, isolation, and surge capacity in already stressed hospitals, clinics, and emergency departments have led to a swell in technologically-assisted care delivery strategies, such as telemedicine and web-based triage. As the race to develop an effective vaccine intensifies, several clinical trials of antivirals and immune modulators are underway, though no reliable COVID-19-specific therapeutics (inclusive of some potentially effective single and multi-drug regimens) have been identified as of yet. With many nations and regions declaring a state of emergency, unprecedented quarantine, social distancing, and border closing efforts are underway. Implementation of social and physical isolation measures has caused sudden and profound economic hardship, with marked decreases in global trade and local small business activity alike, and full ramifications likely yet to be felt. Current state-of-science, mitigation strategies, possible therapies, ethical considerations for healthcare workers and policymakers, as well as lessons learned for this evolving global threat and the eventual return to a "new normal" are discussed in this article. [ABSTRACT FROM AUTHOR]

Achieving effective integration of urban–rural relationships and promoting the flow of resources between urban and rural areas in megacities are a key priority in the development of China's new urbanization efforts. As a transitional zone between urban and rural areas, the urban fringe is the frontier of urban–rural integration. The specific research object of this paper is the urban fringe areas of Wuhan City. This paper quantifies the neighborhood vitality of the fringe areas by the short-stay visitors in the fringe areas and selects the 5D elements of the built environment and social media data from multiple sources to construct the indicator system assessing the neighborhood vitality of the urban fringe areas. This paper analyzes the spatial distribution characteristics of neighborhood vitality and its influencing factors in urban fringe areas and investigates the connection between neighborhood vitality and its influencing factors through the application of the multi-scale geographically weighted regression (MGWR) model. Based on the regression results, relevant planning recommendations are made on how to enhance the vitality of neighborhoods in urban fringe areas. The results show that the index system constructed by "5D" elements of built environment and social media data can well explain the spatial distribution of neighborhood vitality in urban fringe areas. Among the influencing factors, the absolute value of the correlation coefficient of network exposure is the largest, followed by road density and functional density. Thanks to the different bandwidths given by MGWR to the influencing factors, the global influencing factors are only two indicators—development intensity and functional mixing degree—while the other influencing factors are all local, and the influence degree of different regions is different, so it is necessary to analyze and put forward different planning suggestions accordingly. [ABSTRACT FROM AUTHOR]

In this review paper efforts are made to present the Impact of COVID-19 on various economic activities like production, consumption and distribution. This is known to everyone that COVID-19 (Corona Virus Disease) spreadout in December 2019 from Wuhan in China and effected a lot of countries resulting in death of many. Therefore, to minimise the risk, many countries opted lockdown as a precaution to control its spread which effected the lives of ordinary people as well as the economy as a whole, GDP, Functions of Economy and a lot of activities were effected very badly. It had a huge impact on the consumption pattern of population as well as supply chains and production of various goods and services were also impacted as transportation was at halt and movement of people, products, services etc. was not possible from one place to another. There was a heavy down in employment, production, distribution and other activities as well. Hence, this paper will help the readers know the collective impact along with the sector wise effect of COVID-19 on various economic activities. For better understanding we have taken three major economic activities that is Production, Consumption and distribution. Along with these the basic introduction of COVID-19, lockdown and its impact on economy is stated. [ABSTRACT FROM AUTHOR]

Background: In December of 2019, a new disease which is caused by SARS-CoV-2, as an epidemic disease out of Wuhan, China, began to circulate. On March 11, 2020, the Republic of Turkey Ministry of Health had announced the first case from Turkey. The aim of this study is to analyze the scientific publications in the field of COVID-19 included in Science Citation Index Expanded (SCIE) from Turkey and to establish a theoretical background for future studies in the health literature with obtained valuable information about the publications. We searched all papers published in the field of COVID-19 by using the terms of "COVID-19," "2019-n-CoV," "SARS-CoV-2," "Coronavirus disease 19," and "2019 novel coronavirus" as scientific nomenclatures of COVID-19 in the topic search section of the software. Results: Overall, 47,368 papers, indexed by SCI-E, were found related to COVID-19 between January 1, 2020, and December 13, 2020. Of these, 931 were from Turkey. In terms of specialities, the most contribution was from the Medicine General Internal followed by Dermatology. Most of the publications were article. English was the most preferred language in papers. Dermatological Theraphy published the most paper. Conclusions: Applying this kind of analysis on an intermittent basis gives a general perspective for contribution of a countries to scientific publications and useful for the further studies. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic, a significant public health emergency, has underscored the criticality of jobs–housing proximity. Static statistical research, however, struggles to uncover the mechanisms underlying jobs–housing balance, providing limited guidance for urban management. This paper adopts the concept of jobs–housing dynamic balance, analyzing the trends in jobs–housing balance in the metropolitan development area of Wuhan in the early and later period of the pandemic from the perspective of individual jobs–housing migration. Using mobile phone signaling data, we identified a stable population of 161,698 residents in June 2019, June 2021, and June 2023, and calculated jobs–housing synchronization and migration impact indices across seven regions. The study finds the following: (1) there is a pronounced misalignment of jobs–housing in the new cities of Wuhan's suburbs, with clear asynchronous in-migration and out-migration; (2) COVID-19 initially led to a unidirectional exodus of the local population for job purposes, significantly contributing to regional jobs–housing imbalance, followed by a partial rebound in the later stages; and (3) the stability of jobs–housing balance in suburban new cities lacking policy support and comprehensive urban functions is worse, primarily due to insufficient employment resilience and the out-migration of the employed population. This paper puts forward a set of recommendations for the sustainable development of suburban new cities. It offers insights into the theoretical advancement of jobs–housing balance and the dynamic, refined transformation of urban studies, enhancing urban managers' understanding of human–place interactions and new city construction. [ABSTRACT FROM AUTHOR]

Different choices of living space for urban residents are concrete reflections of a social class divide. Emerging residential space differentiation is a critical issue in Chinese cities. This paper aims to explore the current situations and mechanisms of residential spatial differentiation, and reveal its social responses in Chinese cities. Taking Wuhan, the largest city in central China, as an example, this research divides the residential space based on social resource structures. It analyzes and compares spatial differentiation and influencing factors of different residential areas and houses with different prices in Wuhan by spatial differentiation indicators and geographic detector. The results show that residential areas are divergent due to differences in urban resource spatial structure, with few and concentrated high-quality residential areas. The spatial differentiation level of different residential areas also varies, with central location, landscape, educational resources, and other dominant scarce resources being the key to residential spatial differentiation in most of China's big cities. In addition, residential spatial differentiation can lead to issues including housing wealth inequality, resource deprivation, and class identity solidification. In the end, this paper puts forward policy implications on alleviating residential spatial differentiation and optimizing allocation of residential resources. [ABSTRACT FROM AUTHOR]

Global climate change, driven by increasing carbon emissions, has posed a significant challenge to both human survival and development, becoming a major issue for the sustainable progress of human society. This paper examines the characteristics and differences in the spatio-temporal distribution of carbon emissions and sinks across Wuhan Region that is stratified into three spatial levels: Hubei Province, the Wuhan Metropolitan Area, and Wuhan City. On a finer spatial scale within Wuhan, it uncovers the spatial correlations and disparities between carbon emissions and sinks. Accordingly, the paper proposes strategies and suggestions for reducing carbon emissions and enhancing carbon sinks at the provincial, metropolitan, and municipal levels. These findings are valuable for sustainable regional development and construction of low-carbon eco-cities. [ABSTRACT FROM AUTHOR]

Background: Since the COVID-19 pandemic outbreak, diabetes mellitus (DM) has been at the core of the confirmed risk factors for fatal or critical care unittreated COVID-19 and COVID-19 related complications. Although relevant studies on DM have developed rapidly during the COVID-19 pandemic. However, the aforementioned research results have not been systematically quantified by means of bibliometric analysis. Purpose: The purpose of this study is to provide a comprehensive analysis of the current status and trends of publications related to DM research during the COVID19 epidemic. Methods: A bibliometric analysis was performed using the Web of Science database. In this study, we used citespace, R software and R-Bibliometrix to analyze keywords, most-cited authors, most-cited countries, most-cited global documents, and co-occurrence and co-citation networks. Results: A total of 1688 publications was included in this study. Investigators from the United States contributed the most publications. The United States, China and Europe have the most collaboration with the other countries/regions. A total of 3355 institutions made contributions to this study. Of the top 10 institutions with the most publications, N8 Research Partnership showed the most centrality. Among the top 10 journals, Diabetes Research and Clinical Practice published the most articles. Among authors included, Khunti Kamlesh is rated first with 27 papers and has the highest centrality. The most frequently co-cited article is entitled "Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study". The most popular keywords included diabetes, mortality, diabetes, outcome, occurrences, risk, and type 1 diabetes. Conclusion: This bibliometric study provides an overall picture of DM research and research trends during the COVID-19 pandemic and provides a basis for researchers to develop their next research strategies. [ABSTRACT FROM AUTHOR]

Rationally relieving the population of urban centers in large cities, such as megacities and supercities, is one of the current goals of population development in China. The fringe area of a large city is a potential area to undertake the population of the central area. Studying the relationship between the population and the built environment in this area can help urban planners formulate targeted construction strategies to attract the population of the city center to move to the fringe areas. This paper takes the fringe areas of Wuhan in 2010 and 2020 as its specific research object and puts forward the "5D" index system of built environments that affects the spatial distribution of population based on population data and built environment data. The OLS model is used to screen the influencing factors. This paper analyzes the correlation between population and built environment using a multi-scale geographic weighted regression model as well. According to the results of the regression analysis combined with the development and construction of the fringe areas of remote urban areas in Wuhan over the past 20 years, some suggestions are put forward for the planning and construction of remote urban areas. The results show that the "5D" index system of the built environment covers the influencing factors of the spatial distribution of the population. MGWR reveals the correlation between the influencing factors and the spatial distribution of population in the marginal areas on the global scale and the local scale, respectively, which provides a clear direction for the development of planning and construction to improve the attractiveness of the non-central areas to the population. [ABSTRACT FROM AUTHOR]

Since 2019n-CoV has swept the whole world, people's daily life is affected seriously and the public space system is facing to major challenges. Community public space should be re-evaluated and optimized as people change the way they use it. This research conducted a nationwide questionnaire survey in China, which collected the opinions of residents on community public space from the perspective of epidemic prevention and social needs to select research indicators. The questionnaire data were processed through the frequency analysis method, precedence chart and TOPSIS. We found: (1) under the background of the epidemic, people pay more attention to the satisfaction of leisure activities in community public space and the control of social spacing during activities; (2) the current assessment result of community public space risk of 2019-nCoV exposure is 0.386, which is relatively high; and (3) the exposure risk of the community can be effectively reduced by controlling for the two indicators of social distance and social facility sterilization. When the social distance is 1.8–3 m and the facility sterilization is once a day, the community public space minimizes exposure risk. In view of the above results, this paper selects the Wuhan start-up area of Wuhan, China, as an example for carrying out the optimization design of community public space based on the epidemic and the design the space optimization model for the two indicators of social distance and facility disinfection from both software and hardware aspects. [ABSTRACT FROM AUTHOR]

The safety risk analysis of urban elevated bridge construction is an important management method to reduce the loss of safety accidents, and it has significant scientific research value and engineering application value. Therefore, this study proposes a novel analysis method to address these challenges. Firstly, this paper constructs a Work Breakdown Structure (WBS)–Risk Breakdown Structure (RBS) matrix for the safety risk of urban elevated bridge construction in order to achieve a comprehensive and complete identification of the indicator system. Then, a combination of static weights and dynamic weights calculation methods is developed. The static weights are obtained using the analytic hierarchy process, while the dynamic weights are obtained based on the relationship between the dynamic scores of construction safety risk indicators in different construction stages and the preset evaluation levels. Finally, a case study of the Longlingshan elevated bridge project in Wuhan, China, is conducted to validate the feasibility of the proposed model and its potential application in projects. The case analysis for the first time reveals that with the progress of construction, the weights of each indicator continuously change, and the secondary indicators related to environmental factors, such as extreme high-temperature weather, undergo the greatest changes. A comparison of different dynamic weight calculation methods is conducted to highlight the advancement of the proposed model. The research findings of this paper will provide new insights and guidance for improving the construction safety of urban elevated bridge projects. [ABSTRACT FROM AUTHOR]

The onset of the COVID-19 pandemic in late 2019 triggered an unprecedented global health crisis. Originating in Wuhan, China, the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a worldwide upheaval, affecting every aspect of human life. As countries grappled with rising case counts, hospitalizations, and fatalities, the role of geospatial information and visualization in managing and understanding the pandemic became increasingly vital. This special issue of the Cartography and Geographic Information Science (CaGIS) Journal sought to explore the innovative and impactful ways in which geovisualization techniques have been employed to confront and elucidate the complexities of COVID-19. We describe significance and contribution of each paper that is part of the special issue, and avenues for new, exciting research around infectious diseases, keeping in mind that the research needs to be reproducible. [ABSTRACT FROM AUTHOR]

In this paper, the urban signal propagation characteristics based on the location of blind sources are investigated. To address the issue of blind electromagnetic radiation sources in complex urban environments, intelligent methods for propagation channel path classification, and model determination are brought forth based on field test data. The intelligent classification method distinguishes between the Line-of-Sight (LoS) path channel and a direct path, the LoS multipath channel with a direct path and other multiple paths, and the Non-Line-of-Sight (NLoS) multipath channel without a direct path from the source to the test point. The modeling aspect determines the model type to which the received signal belongs based on the statistical model derived from the tested data of a specific source. A validation measurement system was constructed for the FM broadcasting band, and validation campaigns were conducted in the city of Wuhan. The process and analysis of the data using this method demonstrate the accurate distinction of the different propagation path channels and models and involve the construction of a statistical model for the FM band in Wuhan's urban area. [ABSTRACT FROM AUTHOR]

Introduction: The stress-strain relationship of clays characterized by strain hardening exhibits varying curves under different confining pressures and dry densities. Methods: Considering the viscoelastic properties of clays, a normalized viscoelastic model of strain-hardening clay was established based on fractional derivatives, and normalization factors were proposed. Results: The experimental results showed that the stress-strain relationship of the clay was strain hardening. It shows that Chengdu clay has better normalization conditions. Furthermore, the normalized analysis of this clay through the viscoelastic normalization model revealed that the straight line of normalized data displayed a goodness-of-fit of over 0.98. The obtained values were consistent with experimental results, suggesting the reasonability of the normalized strain-hardening parameters and elastic moduli. Discussion: In addition, the superiority of the developed model was verified by testing the strain-hardening clays in Wuhan, China and Bangkok, Thailand. After analyzing the strain-hardening parameters and normalization factors of our model, it was found that the slope of the normalized line can accurately reflect the strain-hardening ability of the clay. These findings demonstrated that the proposed normalization factor is preferred for a normalized viscoelastic model. It shows that the model proposed in this paper has clearer physical meaning and advancement. [ABSTRACT FROM AUTHOR]

The visual quality and spatial distribution of architectural styles represent a city's image, influence inhabitants' living conditions, and may have positive or negative social consequences which are critical to urban sensing and designing. Conventional methods of identifying architectural styles rely on human labor and are frequently time-consuming, inefficient, and subjective in judgment. These issues significantly affect the large-scale management of urban architectural styles. Fortunately, deep learning models have robust feature expression abilities for images and have achieved highly competitive results in object detection in recent years. They provide a new approach to supporting traditional architectural style recognition. Therefore, this paper summarizes 22 architectural styles in a study area which could be used to define and describe urban architectural styles in most Chinese urban areas. Then, this paper introduced a Faster-RCNN general framework of architectural style classification with a VGG-16 backbone network, which is the first machine learning approach to identifying architectural styles in Chinese cities. Finally, this paper introduces an approach to constructing an urban architectural style dataset by mapping the identified architectural style through continuous street view imagery and vector map data from a top-down building contour map. The experimental results show that the architectural style dataset created had a precision of 57.8%, a recall rate of 80.91%, and an F1 score of 0.634. This dataset can, to a certain extent, reflect the geographical distribution characteristics of a wide variety of urban architectural styles. The proposed approach could support urban design to improve a city's image. [ABSTRACT FROM AUTHOR]

As a hot research topic in urban geography, spatiotemporal interaction analysis has been used to detect the hotspot mobility patterns of crowds and urban structures based on the origin-destination (OD) flow data, which provide useful information for urban planning and traffic management applications. However, existing methods mainly focus on the detection of explicit spatial interaction patterns (such as spatial flow clusters) in OD flow data, with less attention to the discovery of underlying crowd travel demands. Therefore, this paper proposes a framework to discover the crowd travel demands by associating the dynamic spatiotemporal interaction patterns and the contextual semantic features of the geographical environment. With urban functional zones (UFZs) as the basic units of human mobility in urban spaces, this paper gives a case study in Wuhan, China, to detect and interpret the human mobility patterns based on the characteristics of spatiotemporal interaction between UFZs. Firstly, we build the spatiotemporal interaction matrix based on the OD flows of different UFZs and analyze the characteristics of the interaction matrix. Then, hotspot poles, defined as the local areas where people gather significantly, are extracted using the Gi-statistic-based spatial hotspot detection algorithm. Next, we develop a frequent interaction pattern mining method to detect the frequent interaction patterns of the hotspot poles. Finally, based on the detected frequent interaction patterns, we discover the travel demands of crowds with semantic features of corresponding urban functional zones. The characteristics of crowd travel distance and travel time are further discussed. Experiments with floating car data, road networks, and POIs in Wuhan were conducted, and results show that the underlying travel demands can be better discovered and interpreted by the proposed framework and methods in this paper. This study helps to understand the characteristics of human movement and can provide support for applications such as urban planning and facility optimization. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of air quality and climate variability during the first wave of COVID-19 associated with accelerated transmission and lethality in Wuhan in China and four European metropolises (Milan, Madrid, London, and Bucharest). For the period 1 January–15 June 2020, including the COVID-19 pre-lockdown, lockdown, and beyond periods, this study used a synergy of in situ and derived satellite time-series data analyses, investigating the daily average inhalable gaseous pollutants ozone (O3), nitrogen dioxide (NO2), and particulate matter in two size fractions (PM2.5 and PM10) together with the Air Quality Index (AQI), total Aerosol Optical Depth (AOD) at 550 nm, and climate variables (air temperature at 2 m height, relative humidity, wind speed, and Planetary Boundary Layer height). Applied statistical methods and cross-correlation tests involving multiple datasets of the main air pollutants (inhalable PM2.5 and PM10 and NO2), AQI, and aerosol loading AOD revealed a direct positive correlation with the spread and severity of COVID-19. Like in other cities worldwide, during the first-wave COVID-19 lockdown, due to the implemented restrictions on human-related emissions, there was a significant decrease in most air pollutant concentrations (PM2.5, PM10, and NO2), AQI, and AOD but a high increase in ground-level O3 in all selected metropolises. Also, this study found negative correlations of daily new COVID-19 cases (DNCs) with surface ozone level, air temperature at 2 m height, Planetary Boundary PBL heights, and wind speed intensity and positive correlations with relative humidity. The findings highlight the differential impacts of pandemic lockdowns on air quality in the investigated metropolises. [ABSTRACT FROM AUTHOR]

Objective: Coronavirus disease 19 (COVID-19) is a highly contagious disease characterized by severe respiratory infection by SARS-CoV-2 virus. COVID-19 was first appeared in Wuhan, China, in December 2019 and then rapidly became a global pandemic from a local outbreak. The present study aims to present the rapid increase of scientific productivity on COVID-19 by comparing publications of the first quarter with the first half of 2020. Materials and Methods: Web of Science (WoS) software was used for the search and the analysis. To compare scientific productivity of two periods as the first quarter and the first half of the pandemic era, all scientific papers published about COVID-19 included in Science Citation Index Expanded (SCI-E) from January 1st to April 5th and from January 1st to July 9th of 2020 were searched using the following terms: "COVID-19","2019-n-CoV","SARS-CoV-2","Coronavirus disease 19" and "2019 novel coronavirus" as nomenclatures of COVID-19. Results: Overall, 337 and 11.704 scientific papers related to COVID-19, indexed by SCI-E, were found in the first quarter and the first half of 2020, respectively. While the biggest contribution for publications was from People's Republic of China (PRC) in the first quarter and was from the USA in the first half of 2020 for COVID-19. Conclusion: We found a close correlation between the rapid acceleration of scientific papers and turning the disease from a local outbreak to a global pandemic. Since sharing experiences is as important as struggling with these kinds of novel diseases, we believe that encouraging researchers to make scientific publications for others is more important than ever in the circumstances like this. [ABSTRACT FROM AUTHOR]

Background In present study, we aimed to identify and evaluate the trends of the 100 most-cited research articles on Covid-19 using bibliometric techniques. Methods Scopus online database hosted by Elsevier was used to extract relevant articles for this study. We have identified the top 100 research papers on Covid-19 published across the globe based on their citations data. Further analysis was made to find the countries of origin, journal wise distribution, author cluster network, keyword analysis and inter-citation map to understand and to establish the links among them. Results The T100 most cited articles were published between January-August, 2020 and their citations ranged from 304 to 5295. The T100 articles were contributed by 24 countries, with more than half is originated from China (n=63). Scientific publications originated from china received highest citations (55,688) followed by USA (13,996) and Hong Kong (9,501). Although 'New England Journal of Medicine' published the most papers (n=15) with the highest impact factor value of (74.699) but studies published in 'The Lancet' has received highest citations (n=18,431). The top five journals hold 44% of these influential studies. The Tongji medical college, Huazhong University of science & technology, Wuhan, China is the top institution with the most T100 articles in the field of Covid-19. Conclusions We analyzed the 100 most-cited articles in the field of Covid-19. China and USA are the dominant countries in terms of the number of T100 articles, scientists and institutions. Scientific publications originated from China also had the highest mean number of citations. The leading institutions with the most productive articles were Tongji medical college, Jin yin-tan hospital, Tsinghua university school of medicine (China) and University of North Carolina (US). The study authored by Huang C on "Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China" has received maximum citations (5295). [ABSTRACT FROM AUTHOR]

Terminal airspace is the convergence area of air traffic flow, which is the bottleneck of air traffic management. With the rapid growth of air traffic volume, the impact of convective weather on flight operations is becoming more and more serious. To change the conditions and improve the utilization of terminal area airspace, a convective weather terminal area capacity (CWTAC) model is developed to quantify the effect of convective weather on the capacity of the terminal area in this paper. The airspace of the terminal area is divided into major airspace, minor airspace and no-impact airspace according to the distribution of the air traffic flow. Under convective weather, their permeabilities are calculated and used as input features, and the actual availability rate is set to label. Three machine learning algorithms, support vector regression (SVR), random forest (RF), and artificial neural network (ANN), are used to predict the availability rate. Then, the terminal airspace capacity under convective weather can be calculated. The historical operation data of the Guangzhou terminal area and the Wuhan terminal area are taken to test machine learning algorithms and verify the CWTAC model. It shows that all three machine learning algorithms are practical, and ANN is the best one based on mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The predicted capacity of the CWTAC model accords well with the actual flight number in the terminal airspace under convective weather. The reasons why they are not entirely consistent are also analyzed. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has dramatically influenced urban mobility patterns. This paper explores the impact of the pandemic on the travel patterns and flow structure of bike-sharing, in Wuhan, China. We find that during the pandemic, bike-sharing became a major mode of transport and helped for the travel to hospitals. In post-pandemic Wuhan, more cycling neighbourhoods emerged in suburban areas, and the percentage of bike-sharing ridership in denser areas tends to decrease, especially in workplaces, indicating there is a decentralised trend of bike-sharing flow structure. These findings imply that bike-sharing plays a significant role in promoting urban resilience, and are helpful to build bike-friendly cities and communities in the post-pandemic era. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic was an unprecedented global event in recent history. Beginning with an initial outbreak in Wuhan, China, in December 2019, the virus spread rapidly across the globe, causing millions of deaths and triggering an unprecedented health, economic, and social crisis. The initial response to the outbreak in many countries was the implementation of social distancing measures, including the closure of schools and businesses, the cancellation of mass events, and the banning of travel. These measures were aimed at reducing the virus' spread and preventing health systems from being overwhelmed by the numerous severe COVID-19 cases. However, these measures also had a devastating economic impact, especially on precarious workers and freelancers, as well as those who were unable to work from home. As the pandemic (also considered a syndemic or synergistic epidemic) dragged on, countries adopted more flexible approaches to dealing with the virus, adopting mitigation measures rather than social distancing measures. These included the use of masks, testing and contact tracing, and the opening of businesses and schools with the implementation of additional safety measures. This paper highlights the social consequences of the pandemic, ultimately arguing that it is a total social fact (from the French fait social total), based on Marcel Mauss' categorization, since it encompassed and impacted all facets of human life. [ABSTRACT FROM AUTHOR]

Urban wetlands represent a significant ecosystem type within urban landscapes. The quantitative assessment of their ecological service value holds great significance in guiding and improving the urban habitat. However, due to the insufficient spatial resolution of traditional low-to-medium resolution remote sensing imagery for surface monitoring, previous studies have conducted relatively limited research on the ecosystem services of urban wetlands. In this paper, based on multi-source data including multi-scale remote sensing data, a spatial-temporal fusion model and multiple ecological parameter inversion models were employed to invert three key ecological parameters at high spatial resolution, thereby assessing the ecosystem service values (ESVs) of urban wetlands. Taking the East Lake Scenic Area (ELSA) in Wuhan as an example, the dynamics of its ecosystem services' value components were comparatively analyzed. The results indicate that, while the total value of ecosystem services declined slightly in 2015 compared to 2011, there was a notable increase in their value to CNY 3.219 billion by 2019, which represents a doubling of the total value relative to 2011. This trend could be primarily attributed to a significant rise in cultural services within the region. Specifically, the value of tourism services reached CNY 2.090 billion in 2019, representing a threefold increase compared to 2011. This demonstrates that ecosystem services in the ELSA have been significantly optimized and enhanced through associated ecological projects. Further research should investigate the mechanisms by which urbanization affects these crucial ecosystem services, particularly the characterization of cultural services in urban wetlands, and develop more effective strategies to enhance urban resilience and sustainable development. [ABSTRACT FROM AUTHOR]

Background: In early infected or severe coronavirus disease 2019 (COVID-19) patients, circulating NK cells are consistently reduced, despite being highly activated or exhausted. The aim of this paper was to establish whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein (SP) may directly trigger NK cells and through which receptor(s). Methods: SP-stimulated human NK cells have been evaluated for the expression of activation markers, cytokine release, and cytotoxic activity, as well as for gene expression profiles and NF-κB phosphorylation, and they have been silenced with specific small interfering RNAs. Results: SPs from the Wuhan strain and other variants of concern (VOCs) directly bind and stimulate purified NK cells by increasing activation marker expression, cytokine release, and cytolytic activity, prevalently in the CD56brightNK cell subset. VOC-SPs differ in their ability to activate NK cells, G614, and Delta-Plus strains providing the strongest activity in the majority of donors. While VOC-SPs do not trigger ACE2, which is not expressed on NK cells, or other activating receptors, they directly and variably bind to both Toll-like receptor 2 (TLR2) and TLR4. Moreover, SP-driven NK cell functions are inhibited upon masking such receptors or silencing the relative genes. Lastly, VOC-SPs upregulate CD56dimNK cell functions in COVID-19 recovered, but not in non-infected, individuals. Conclusions: TLR2 and TLR4 are novel activating receptors for SP in NK cells, suggesting a new role of these cells in orchestrating the pathophysiology of SARS-CoV-2 infection. The pathogenic relevance of this finding is highlighted by the fact that free SP providing NK cell activation is frequently detected in a SARS-CoV-2 inflamed environment and in plasma of infected and long-COVID-19 subjects. [ABSTRACT FROM AUTHOR]

The construction of new towns is one of the main measures to evacuate urban populations and promote regional coordination and urban–rural integration in China. Mining the spatio-temporal pattern of new town hot spots based on multivariate data and analyzing the influencing factors of new town construction hot spots can provide a strategic basis for new town construction, but few researchers have extracted and analyzed the influencing factors of new town internal hot spots and their classification. In order to define the key points of Wuhan's new town construction and promote the construction of new cities in an orderly and efficient manner, this paper first constructs a space-time cube based on the luminous remote sensing data from 2010 to 2019, extracts hot spots and emerging hot spots in Wuhan New City, selects 14 influencing factor indicators such as population density, and uses bivariate Moran's index to analyze the influencing factors of hot spots, indicating that the number of bus stops and vegetation coverage rate are the most significant. Secondly, the disorderly multivariate logistic regression model is used to analyze the influencing factors of emerging hot spots. The results show that population density, vegetation coverage, road density, distance to water bodies, and distance to train stations are the most significant factors. Finally, based on the analysis results, some relevant suggestions for the construction of Wuhan New City are proposed, providing theoretical support for the planning and policy guidance of new cities, and offering reference for the construction of new towns in other cities, promoting the construction of high-quality cities. [ABSTRACT FROM AUTHOR]

An adequate provision of medical supplies is critical in the battle against pandemics, such as the ongoing one against COVID-19. First, this paper proposes a generalized analysis based on the fluctuation period of emergency material demand. Second, the nonlinear problem in the low-dimensional space is transformed into a linear problem in the high-dimensional feature space by using the support vector machine method, constructing a combined forecasting model of time series and support vector machines. Lastly, the daily demand of specific protective masks donated by the Wuhan COVID-19 Epidemic Prevention and Control Headquarters in the period from February 1 to March 16, 2020 is predicted through the use of data from the Wuhan Red Cross. Compared with traditional linear time series forecasting models, the proposed forecasting model sees its accuracy increased by 37.55%, with the relative errors of mean square error, average absolute error, and average absolute error percentage being respectively reduced by 37.57%, 60.88%, and 37.86%. It transpires that the ARIMA–SVM combined model is able to make full use of the potential information implied in the original data. The decision-making process provides a reference point for the forecast of the demand of medical emergency materials in future major public health emergencies. [ABSTRACT FROM AUTHOR]

In recent years, with the acceleration of urbanization and the frequent occurrence of extreme weather globally, the risk of urban flood disasters has gradually increased, and its potential consequences are immeasurable. Therefore, conducting risk assessment of urban flood disasters is of great significance, as it is one of the foundations and decision-making means for Disaster Prevention and Mitigation, and has become a hot topic and trend in current research. This paper starts by exploring the concept and formation mechanism of urban flood disasters, taking Hazard Factors, Disaster-prone Environment sensitivity, Vulnerability of Exposed Bodies, and Disaster Prevention and Mitigation Capabilities as primary indicators. Based on this, a risk assessment index system is established with 14 secondary indicators, such as annual average rainfall, distance to water systems, elevation, and terrain undulation. The spatialization of each indicator data point is processed through ArcGIS10.7, and the importance of hazard and sensitivity indicators is ranked using the Random Forest algorithm. The indicators are then weighted using a combination of the Analytic Hierarchy Process (AHP) and the entropy method, and the combined weights of each assessment indicator are calculated. Taking Wuhan City as the research area, the weights of each indicator are input into the established risk assessment model. ArcGIS spatial analysis techniques and raster calculation functions are utilized to solve the fuzzy comprehensive evaluation of the assessment model, obtaining zoning maps of risk levels for hazard, sensitivity, vulnerability, disaster prevention, and mitigation capabilities, as well as the distribution of comprehensive risk levels. The validity and rationality of the model results are verified by actual disaster data, providing important reference for urban flood disaster prevention in the future. [ABSTRACT FROM AUTHOR]

According to piloti design, the outdoor thermal environment can be improved in cities with hot summer conditions. Taking Chinese cities with a hot summer and cold winter as the research object, this paper discusses the improvement of the outdoor thermal environment of residential districts in summer by considering piloti design factors. In this article, according to our investigation of piloti design in Wuhan, a basic model of the overhead layer in the Wuhan residential area is presented, along with the effects of different piloti ratios (0–80%), piloti heights (2–6 m), and greening rates (30–35%) on the outdoor thermal environment of buildings. The average air temperature and average wind speed at the pedestrian level are used as outdoor thermal environment indicators, the average PET is used as the outdoor thermal comfort indicator, and the comfort wind ratio is used as the outdoor wind comfort indicator. The results show that increasing the ratio of corridor columns has the greatest thermal comfort enhancement effect in the corridor area, and when the piloti ratio increases from 20% to 80%, the PET in piloti areas reduces by 2.926 °C. Improving the greening rate has the greatest thermal comfort enhancement effect in the passageway area, and when the greening rate increases from 20% to 80%, the PET in piloti areas reduces by 0.9 °C. Furthermore, the increases in both the piloti ratio and piloti height have an enhancement effect on the outdoor wind environment and wind comfort, with thresholds of a piloti ratio over 60% and a piloti height over 5 m. In contrast, the increase in the greening rate will deteriorate the outdoor wind environment and wind comfort. The conclusions of this study are of great significance for the planning and design of overhead layers in residential areas in hot and humid areas in summer. [ABSTRACT FROM AUTHOR]

The urban fringe is the transitional area from rural form to urban form, and it is also the urban space reserve land in the Territorial Spatial Plan. However, few researchers predict its overall evolution and guide the implementation of the Territorial Spatial Plan. This study attempts to explore the dynamic evolution law of urban fringe, analyze its driving factors, predict its future development, and put forward management suggestions for the implementation of the Territorial Spatial Plan. In this paper, the land use data of Wuhan in 2000, 2010 and 2020 are applied to delimit the urban fringe area of Wuhan by means of a sliding t-test. Fifteen driving factors are selected from three dimensions, natural factors, socio-economic factors and traffic accessibility, and brought into the Logistic model to explore the driving factors of its spatial evolution. The CA–Markov model is used to predict the fringe area of Wuhan in 2035. The results show that the transformation of rural hinterland into urban fringe is obviously affected by the distance from railway stations, highways, commercial centers and urban main roads. It is predicted that the outer boundary of Wuhan's fringe area in 2035 will be basically the same as the planned urban development boundary. In order to realize the intention of land space planning, the development and construction of the northwest of the Huangpi District, the East Lake Scenic Area, and the west side of the Jiangxia District should be restricted. From the perspective of the evolution of the fringe area, this paper puts forward some management suggestions for the implementation of the Territorial Spatial Plan and makes a beneficial attempt in theory and method to understand the development characteristics of the fringe area and promote the implementation of the Territorial Spatial Plan. [ABSTRACT FROM AUTHOR]

Purpose: Anti-epidemic Emergency Projects (AEEPs) have unique characteristics such as a short construction period, high-quality requirements, complex construction environment, many construction participants and many uncertain affecting factors. The purpose of this paper was to propose the establishment method for the Construction Community (CC) of AEEPs (CC-AEEPs) by analyzing the management features of AEEPs, to establish the method of the Social Network Analysis (SNA) for CC-AEEPs, and to apply this method and the framework to Wuhan Huoshenshan Hospital for their verification. Design/methodology/approach: According to the CC theory, this paper explored the member composition and the establishment method for CC-AEEPs. The optimal management factors of CC-AEEPs were proposed by combining the management features of AEEPs and the SNA method for CC-AEEPs was further established. Finally, the applicability of the method was verified through a case study, and some countermeasures for the CC-AEEP social networks were proposed. Findings: The establishment of CC-AEEPs is an important guarantee to complete AEEPs with top speed and high quality. Ten types of CC-AEEP members all played different but irreplaceable roles in cooperative construction, among which the Government, the Contractor, and the Supervisor had outstanding performances. The SNA method could effectively analyze the complexity and cooperative relationship among the members in four aspects. The case study of Huoshenshan Hospital validated the important role of CC-AEEP and its social network in the AEEP research providing beneficial enlightenment for the cooperative optimization path of the AEEP construction participants. Originality/value: The new establishment method for CC-AEEPs was proposed from the perspective of "cooperation among human, society, and engineering" according to the theories of the engineering sociology and the CC. In this paper, the SNA method was applied to the research on the AEEP construction for the first time and the SNA method for CC-AEEPs was purposed. The optimal management factors of CC-AEEPs and the expansion path of the CC-AEEP social networks were proposed according to the whole-process tracking of AEEPs in Wuhan. [ABSTRACT FROM AUTHOR]

This paper summarizes the methods and approaches of using mobile positioning data to estimate and monitor urban population size. It starts with the necessity of using big data to monitor urban population size in territorial spatial planning. Then it elaborates on the difference between the definition of "population size" reflected by mobile positioning data and the common concept of urban population size, and the necessity of verifying the logic and measurement of sample expansion at four levels. Finally, taking Wuhan city as a case, this paper proposes the technical approach of monitoring the size of urban permanent population through multi-source data verification. The study finds that when it comes to monitoring urban population size, mobile positioning data have the advantage of monitoring short-period changes in population size and spatial distribution, yet special attention must be paid to the three technical links of definition, sample expansion and verification. [ABSTRACT FROM AUTHOR]

The ventilation corridor is an essential element in urban planning and design to improve the climate and environment. In this paper, four forms of two ventilation corridors were set up in the southeast of Wuhan City based on its urban planning outline to quantitatively study the influences of different ventilation corridor forms on the urban thermal environment in summer. The urban micro-meteorological environment was simulated using the next-generation mesoscale Weather Research and Forecast (WRF) model coupled with an urban canopy model (UCM). Critical time-dependent meteorological values were extracted and plotted, including temperature and wind difference fields, average temperature at 2-m height, average wind speed at 10-m height, and surface energy flux. By conducting a comparative analysis of the quantitative results, the ventilation corridors in construction land are arranged at intervals in the upwind position of the city in summer, which can slightly adjust the thermal environment of the central area of the city, and have an effective regulation on the temperature of the corridors and surrounding areas during the daytime. Especially at 15:00 pm when the temperature is at its highest during the day, the temperature can be reduced by 0.8 °C. Compared to other corridors, the wind speed in and around the corridor is the strongest 11 h a day. Due to the internal arrangement of construction land, this corridor form is more advantageous in the urban land utilization. After considering comprehensively, the ventilation corridor form with construction land arranged at intervals is the preferred corridor form in the southeast of Wuhan. The experimental results can provide quantitative reference for the layout of ventilation corridors in hot inland cities located in central China. [ABSTRACT FROM AUTHOR]

The runoff change in a large river is significant for hydrological event monitoring, such as floods and droughts. In this paper, we attempt to invert the river flow change based on the surface displacements observed by the continuous global navigation satellite system (GNSS) stations in the Wuhan section of the Yangtze River (WYR). We establish a two‐dimensional model accounting for deformation from a line load in an elastic half‐space to estimate the annual water load change from the GNSS data. The results show that the inverted line load from the annual maximum displacement change observed by the GNSS is Ne=2.30×108N·m−1 ${N}_{e}=2.30\times {10}^{8}\mathrm{N}\cdot {\mathrm{m}}^{-1}$, which is in good agreement with the river load from the hydrological station. This paper demonstrates a low‐cost and practical method to river flow research in local areas, which is also a creative application for the GNSS. Plain Language Summary: The runoff change in large rivers is an important element for flood and drought monitoring. Hydrological stations are used to monitor the water load change. Because of the high cost of construction and maintenance, there are few hydrological stations in most areas, and the unpredictable noise of hydrological stations influences the stability and precision of the results. We use the vertical displacements observed by the global navigation satellite system (GNSS) to estimate the river load and find that the results agree with the calculated water load change from hydrological stations. Thus, the GNSS can be an alternative for runoff change research, with low cost and high efficiency. In this study, we find a new application for the GNSS to water runoff change in local areas. Key Points: We propose a new method for estimate the river load change using the continuous global navigation satellite system (GNSS) dataA simple two‐dimensional line load on Elastic half‐space can invert the river load with considerable accuracy in local areasThe forward modeling prove that the modeled displacement agrees with the observed displacement from the GNSS [ABSTRACT FROM AUTHOR]

One of the crucial methods for adapting distributed PV generation is the microgrid. However, solar resources, load characteristics, and the essential microgrid system components are all directly tied to the optimal planning scheme for microgrids. This article conducts a collaborative planning study of grid-connected PV-storage microgrids under electric vehicle integration in various scenarios using HOMER 1.8.9 software. To be more specific, in multiple scenarios, we built capacity optimization models for PV modules, energy storage, and converters in microgrids, with several scenarios each accounting for the cleanliness, economic performance, and overall performance of microgrids. For multiple scenarios, this paper used the net present value cost and levelized cost of electricity as indicators of microgrid economics, and carbon dioxide emissions and the fraction of renewable energy were used as indicators of microgrid cleanliness. The optimal capacity allocation for economy, cleanliness, and a combination of economy and cleanliness were separately derived. Finally, on a business park in Wuhan, China, we conducted thorough case studies to compare and debate the planning performance under various scenarios and to undertake sensitivity analyses on the cases. The sensitivity analyses were conducted for the optimal configuration of microgrids in terms of the EV charging scale, carbon dioxide emissions, PV module unit cost, and storage unit cost. The results of the simulation and optimization show that the optimization approach could determine the ideal configuration for balancing economy and cleanliness. As the EV charging demand increased, the energy storage capacity required in the microgrid gradually increased, while the carbon dioxide emission limit was negatively correlated with the energy storage capacity demand. The unit investment cost of PV module units had a greater impact on the optimal system configuration than the cost of batteries. [ABSTRACT FROM AUTHOR]

Based on the combination of landscape ecology theory and GIS technology, this paper selects the landscape pattern index at the patch type level and landscape level from the two aspects of unit characteristics and landscape heterogeneity, and quantitative analysis of the landscape pattern of parks and green spaces in the main urban area of Wuhan City and suggestions for optimization are put forward. The research results show that: from the patch level, the park green space in the main urban area mainly presents a landscape pattern dominated by comprehensive parks, supplemented by specialized parks, and supplemented by community parks and strip parks. Among them, Hongshan District Park has the largest green space patch area, and Jianghan District has the smallest area; in terms of the average patch area of park green space, the strip park is the largest and the community park is the smallest. From the perspective of landscape level, the diversity index and evenness index of park green space in each district in the main urban area are generally low, among which Jianghan District is the lowest and Hongshan District is the highest; the degree of fragmentation is relatively high in Jianghan District and the lowest in Hongshan District.The patch boundary shape of comprehensive park is complex and has the highest fractal dimension, while that of community park is regular and has the lowest fractal dimension. On this basis, this paper puts forward targeted optimization suggestions based on the research results. [ABSTRACT FROM AUTHOR]

The law of urban scaling implies that there is a universally applicable nonlinear scaling relationship between population size and urban indicators, which is a method of quantitative analysis that can reflect the growth law and internal logic of the urban system. However, most present research is conducted at the municipal scale, and studies of scaling law in the inner-city system are scarce, especially from the perspective of compact urban form development. The goal of this paper is to discover the scaling law within urban systems from a multi-scale perspective. Through the empirical analysis of Wuhan, this paper examines the internal scale law of the urban system from the municipal and district scales. Moreover, we use the landscape expansion index to perform spatial autocorrelation analysis. In this way, we assess the relationship between the compactness of urban morphological development and the urban scaling law. The results indicate that the temporal scaling law on the city scale has a more significant linear law than the single-year scaling law. The analysis also shows the scaling law relationship within the inner-city system. Nevertheless, there is a deviation between the temporal scaling law and the cross-section scaling law. Namely, the time series development of a district does not follow the section scaling law of the urban system. Furthermore, the urban scaling law shows a negative correlation with the compactness of the urban form development. It is crucial to understand the current economic development and resource endowment of an urban system in the urbanization process, as it significantly contributes to urban development and regional coordinated planning. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic triggered the first global "Infodemic" in the era of social media. Understanding how governments deal with the negative impacts of the infodemic (e.g., public panic) has become a priority. This paper uses the theoretical framework of the Elaboration Likelihood Model (ELM) to explore mechanisms for alleviating panic associated with the infodemic. It considers, in particular, the quality of information circulated on Government Social Media (GSM) as the central route and local government trust as the peripheral route. An empirical study was conducted using data from a focus group interview and a questionnaire survey collected within the first three weeks following the citywide lockdown of Wuhan, China. The results show that as: (1) Quality of GSM information does not significantly reduce public panic, but local government trust significantly increases people's pandemic prevention knowledge; (2) Pandemic prevention knowledge is a critical mediator between information quality of GSM and public panic, as well as local government trust and public panic; and (3) Information quality of GSM significantly increases people's trust in local governments. This paper contributes to the literature on infodemic and government social media and provides implications for practice. [ABSTRACT FROM AUTHOR]

Residents' activities have a significant interaction with urban socioeconomic environment. Taxi trajectory data has been widely used to mine human activity patterns to identify urban functional districts. However, previous studies merely chose several spatiotemporal statistics of taxi pick-up and drop-off points. This paper compares seven time series statistics of taxi pick-up and drop-off points, and selects the best combination to identify urban functional districts. The basic analysis units are not only constructed based on the OpenStreetMap data, but also optimized with the fine-grained clean rasterized pixels, generated from preprocessed taxi trajectory data through the improved head/tail breaks method. The experiment conducted in Wuchang District, Wuhan, shows that the combination of the average statistics of pick-up points, the average statistics of drop-off points, and the ratio statistics of pick-up and drop-off difference achieves the best identification precision of 83.65%, the F1-score of 82.2%, and the recall score of 81.48%. The proposed approach has good scalability and can be transplant to other identification applications. [ABSTRACT FROM AUTHOR]

The 2019 novel coronavirus (SARS-CoV-2 / COVID-19), with a point of origin in Wuhan, China, has spread rapidly all over the world. It turned into a raging pandemic wrecking havoc on health care facilities, world economy and affecting everyone's life to date. With every new variant, rate of transmission, spread of infections and the number of cases continues to rise at an international level and scale. There are limited reliable researches that study microdroplets spread and transmissions from human sneeze or cough in the airborne space. In this paper, we propose an intelligent technique to visualize, detect, measure the distance of spread in a real-world settings of microdroplet transmissions in airborne space, called "COVNET45". In this paper, we investigate the microdroplet transmission and validate the measurements accuracy compared to published researches, by examining several microscopic and visual images taken to investigate the novel coronavirus (SARS-CoV-2 / COVID-19). The ultimate contribution is to calculate the spread of the microdroplets, measure it precisely and provide a graphical presentation. Additionally, the work employs machine learning and five algorithms for image optimization, detection and measurement. [ABSTRACT FROM AUTHOR]

In December 2019, COVID-19 appeared for the first time in Wuhan (Hubei Province, China), after which it quickly spread over the entire earth. The World Health Organization quickly designated COVID-19 a pandemic because of the high number of deaths and rapid global spread of the disease. Because of this, many facets of society have been impacted, and those effects may last for years to come. Therefore, COVID-19 detection methods were the focus of numerous research projects in the past. This has led to the development of the COVID-19 AI Detector, a specialized area of artificial intelligence-based research. In this paper, we survey all significant current efforts that have taken advantage of machine learning to COVID-19 detection and prediction. We first surveyed all datasets used in relevant research, and then summarized them in a table containing the link to that data. Then, we mention all the methodologies employed to detect the presence of COVID-19 using such datasets. Later, the challenges and difficulties that facing the concerned researches are reported, while the results of all interesting related work that lay ahead in this field before concluding this paper were discussed fairly. [ABSTRACT FROM AUTHOR]

Summary: The wind direction in the atmospheric boundary layer (ABL) twists with height due to the Coriolis force; this phenomenon is called the Ekman spiral. However, this phenomenon is generally not considered in the present wind load estimation of super high‐rise buildings, which may lead to an incorrect estimation and affect the safety of structures. Therefore, this study considers and analyzes the influence of the Ekman spiral phenomenon in the wind direction reduction effect (WDRE) of the wind load of super high‐rise buildings. First, this paper proposes an empirical fitting equation for the twisted wind direction angle for a height of 100–800 m according to the classical Ekman spiral theory model (CE model). Subsequently, on the basis of twisted wind, this paper proposes a method for the correction of the wind direction reduction factors (WDRFs) of strong winds considering the influence of the Ekman spiral phenomenon in the design wind load estimation of super high‐rise buildings with heights of 400–800 m. A high‐frequency balance force measurement test of a square‐section super high‐rise building model was performed to analyze the influence of the Ekman spiral phenomenon on the WDRE of the aerodynamic force and wind‐induced response. Three Chinese cities (i.e., Beijing, Wuhan, and Kunming) are selected as case studies to illustrate the importance and necessity of the correction method. The results demonstrate that the proposed empirical fitting equation accurately determines the twisted wind direction angle at different latitudes and altitudes. Furthermore, estimating the design wind load while considering the WDRE and neglecting the influence of the Ekman spiral phenomenon may lead to a significant underestimation of the wind load of super high‐rise buildings, rendering the designed building structure more dangerous. [ABSTRACT FROM AUTHOR]

A close relationship exists between ecological landscape and housing prices. Taking Wuhan as the research object, this paper establishes a series of spatial generalized additive models based on the hedonic method to explore the effects of different ecological landscapes such as lakes, rivers, mountains, and parks on house prices. Results are as follows: (1) Overall, lakes, parks, and mountains have price elasticity values of approximately 0.62%, 1.30%, and 0.81%, respectively. However, the price elasticity values of 2-km inner and outer lakes are 0.18% and 3.39% respectively, with similar results in other landscapes. The existence of lakes, parks, and mountains can trigger the increase in housing price percentages by 34.61%, 43.12%, and decrease by 25.09%, respectively. (2) The nonparametric form shows that the influence of lakes and parks on housing prices within 2 km constantly changes around zero. The influence of rivers is always positive, whereas the influence of mountains is always negative. (3) Interaction occurs between landscapes. In the situation of interaction with lakes, the elasticity values of the distance among rivers, mountains, and parks become 0.50%, − 0.32% and 0.90%, respectively. Future research should focus on the nonlinear characteristics and interactive effects of landscape premium. [ABSTRACT FROM AUTHOR]

In recent years, epidemic disasters broke through frequently around the world, posing a huge threat to economic and social development, as well as human health. A fair and accurate distribution of emergency supplies during an epidemic is vital for improving emergency rescue efficiency and reducing economic losses. However, traditional emergency material allocation models often focus on meeting the amount of materials requested, and ignore the differences in the importance of different emergency materials and the subjective urgency demand of the disaster victims. As a result, it is difficult for the system to fairly and reasonably match different scarce materials to the corresponding areas of greatest need. Consequently, this paper proposes a material shortage adjustment coefficient based on the entropy weight method, which includes indicators such as material consumption rate, material reproduction rate, durability, degree of danger to life, and degree of irreplaceability, to enlarge and narrow the actual shortage of material supply according to the demand urgency. Due to the fact that emergency materials are not dispatched in one go during epidemic periods, a multi-period integer programming model was established to minimize the adjusted total material shortage based on the above function. Taking the cases of Wuhan and Shanghai during the lockdown and static management period, the quantitative analysis based on material distribution reflected that the model established in this paper was effective in different scenarios where there were significant differences in the quantity and structure of material demand. At the same time, the model could significantly adjust the shortage of emergency materials with higher importance and improve the satisfaction rate. [ABSTRACT FROM AUTHOR]