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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]

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]

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]

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]

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]

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]

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]

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 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]

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]

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]

Logistics industry in Wuhan city under the influence of economic development and related logistics policies, consumer demand is increasing, achieving a steady and rapid sustainable development. However, the ensuing high-energy consumption and high pollution have caused great pressure and negative impact on the environment of Wuhan city, and the pollution control problem has gradually aroused the common concern of the government, people, and relevant scholars, and the future development trend of low-carbon logistics has become a research hotspot of this new industry of logistics. In recent years, Wuhan city has been deeply affected by haze, and low-carbon and sustainable development is an important driving force for activities related to energy saving and emission reduction in Wuhan city at present. The logistics industry, as a high potential industry with high-speed development, is a key industry for environmental management and supervision, and the contradiction between the high-speed development of logistics industry and environmental pollution problem needs to be properly handled. This paper measures and analyzes the comprehensive efficiency of the logistics industry in Wuhan city from the perspective of low-carbon development, describes the current situation of the development of low-carbon logistics in Wuhan city and the problems that exist, analyzes them, and proposes feasible policies and establishments to make a contribution to the future development of low-carbon logistics in Wuhan city. This paper analyzes and organizes a large amount of literature, defines the definition of low-carbon logistics and performance evaluation, and selects the performance evaluation method that meets the research topic of this paper. On the basis of theoretical support, combined with the current situation of the development of low-carbon logistics industry in Wuhan city, reasonable indicators are selected for regression analysis of impact factors, and this paper's low-carbon logistics efficiency evaluation index system is established to evaluate the performance of the low-carbon logistics industry in Wuhan city from 2010 to 2019, and finally put forward targeted policy recommendations. [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 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]

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]

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]

After the outbreak of COVID-19 pandemic, devising an effective reverse logistics supply chain to clean up disaster medical waste is conducive to controlling and containing novel coronavirus transmission. Thus, the focus of this paper concentrates on multi-period multi-type disaster medical waste location-transportation integrated optimization problem with the concern of sustainability, which is formulated as a tri-objective mixed-integer programming model with the goals of maximizing total economic benefits, minimizing total carbon emissions and total potential social risks. Then, a real-world case from Wuhan using CPLEX solver is used to validate the developed model. Results indicate that constructing DMWTTSs with flexible capacity in different periods is encouraged to handle the sharply increasing disaster medical waste. The multi-period decision model outperforms the single-period one in disaster medical waste supply chains because the former has the capability of handling the uncertainties in the future periods. Increasingly, since the increase of budget doesn't always work well and social resources are limited, the estimation of minimum budget to obtain optimum overall performance is of great importance. [ABSTRACT FROM AUTHOR]

The rise of e-commerce, bolstered by advances in information and communication technology (ICT), has made it possible for consumers to shop online without the need to physically travel. The unexpected emergence of COVID-19 further accelerated this shift towards online shopping. This paper compares virtual versus physical access to goods, drawing from dual access theory. It aims to offer a comprehensive understanding of the disparities in accessibility between digital and brick-and-mortar shopping experiences. Our results indicate that, when considering the complete private costs — including the intrinsic costs of shopping and those incurred en-route like travel and delivery fees — online shopping typically offers greater accessibility and is more cost-effective than its in-store counterpart. While physical access to shopping displays a pattern where the central city has a distinct advantage over the suburbs, virtual access presents a more uniform distribution throughout the city. [ABSTRACT FROM AUTHOR]

Remote sensing provides us with an approach for the rapid identification and monitoring of spatiotemporal changes in the urban ecological environment at different scales. This study aimed to construct a remote sensing assessment index for urban ecological livability with continuous fine spatiotemporal resolution data from Landsat and MODIS to overcome the dilemma of single image-based, single-factor analysis, due to the limitations of atmospheric conditions or the revisit period of satellite platforms. The proposed Ecological Livability Index (ELI) covers five primary ecological indicators – greenness, temperature, dryness, water-wetness, and atmospheric turbidity – which are geometrically aggregated by non-equal weights based on an entropy method. Considering multisource time-series data of each indicator, the ELI can quickly and comprehensively reflect the characteristics of the Ecological Livability Quality (ELQ) and is also comparable at different time scales. Based on the proposed ELI, the urban ecological livability in the central urban area of Wuhan, China, from 2002 to 2017, in the different seasons was analyzed every 5 years. The ELQ of Wuhan was found to be generally at the medium level (ELI ≈0.6) and showed an initial trend of degradation but then improved. Moreover, the ecological livability in spring and autumn and near rivers and lakes was found to be better, whereas urban expansion has led to the outward ecological degradation of Wuhan, but urban afforestation has enhanced the environment. In general, this paper demonstrates that the ELI has an exemplary embodiment in urban ecological research, which will support urban ecological protection planning and construction. [ABSTRACT FROM AUTHOR]

Due to various factors such as aging, natural environment erosion, and man-made destruction, architectural heritage has formed various diseases and cracks, especially in pathology cracks, which are the most typical masonry–timber architectural heritages, directly affecting the structural stability of masonry–timber buildings. This paper uses artificial intelligence and architecture and other multi-disciplinary research methods, taking James Jackson Gymnasium, a famous masonry–timber architectural heritage in Wuhan, as an example, using 3D laser scanning technology to obtain disease details and crack data of architectural heritage, using a Mask R-CNN model to detect crack area, using an FCN model to identify and calculate single cracks, and finally summarizing the type, location, and characteristics of cracks, analyzing the causes of cracks, and then putting forward corresponding hierarchical restoration strategies. The research results build a set of detection and repair systems of masonry–timber architectural heritage pathology cracks, which provide a set of accurate and objective pathology cracks data for architectural heritage protection and repair, and provide a reference for architectural heritage repair. [ABSTRACT FROM AUTHOR]

First described in December 2019 in Wuhan (China), COVID-19 disease rapidly spread worldwide, constituting the biggest pandemic in the last 100 years. Even if SARS-CoV-2, the agent responsible for COVID-19, is mainly associated with pulmonary injury, evidence is growing that this virus can affect many organs, including the heart and vascular endothelial cells, and cause haemostasis, CNS, and kidney and gastrointestinal tract abnormalities that can impact in the disease course and prognosis. In fact, COVID-19 may affect almost all the organs. Hence, SARS-CoV-2 is essentially a systemic infection that can present a large number of clinical manifestations, and it is variable in distribution and severity, which means it is potentially life-threatening. The goal of this comprehensive review paper in the series is to give an overview of non-pulmonary involvement in COVID-19, with a special focus on underlying pathophysiological mechanisms and clinical presentation. [ABSTRACT FROM AUTHOR]

The urban agglomeration in the middle reaches of the Yangtze River holds a crucial strategic position in China's economic and social development landscape. Exploring the coordinated development effects within the digital economy and innovation in this area is conducive to promoting the development of the central region of China and the Yangtze River Economic Belt. This paper uses the 28 sample cities in urban agglomeration in the middle reaches of the Yangtze River as the study sample, and constructs a digital economy indicator system and an urban innovation efficiency indicator system. Based on the coupling coordination degree model, we use kernel density estimation, the Markov chain algorithm, and the Dagum Gini coefficient decomposition method to empirically investigate the dynamic trends in coupling coordination development between the digital economy and urban innovation efficiency from 2012 to 2021. The research results reveal an overall upward trend in the digital economy, innovation efficiency, and coupling coordination development stages. However, the upward trend is accompanied by the risk of recession. Moreover, there are significant differences between cities, as highlighted by the differences between Wuhan and other cities. In the light of the findings, it is recommended that government departments take measures, including being alert to the risk of regressive development, developing cities in a realistic manner, and drawing on the experiences of outstanding cities in development. This research can provide new insights and empirical references for government entities to take measures for a more coordinated development of the digital economy and innovation efficiency in the urban agglomeration in the middle reaches of the Yangtze River. [ABSTRACT FROM AUTHOR]

Economic growth has not only made life more comfortable for people but has also increased the demands and expectations of the living environment. Urban planning, layout, construction, transportation, environmental protection, etc. are increasingly attracting the attention of the government. Due to the increase in residents, there are many problems in cities, such as traffic congestion, smog, etc. The relevant departments hope to use intelligent means to effectively alleviate these problems, and many cities have achieved good results. As a part of urban economics research, analyzing the economic benefits of a city helps to study how to develop the city. Smart sensor and communication technology is an advanced technology for smart manufacturing and the Internet of Things. As an advanced sensor tool, it is of great significance for exploring the economic benefits of smart cities. This paper discusses the calculation method of urban economic benefit value based on smart cities and evaluates the calculation method. The purpose of this paper is to find out the calculation method applicable to the urban economic benefit and to propose an effective evaluation method for the urban economic benefit, so as to have a more complete and effective calculation and evaluation of the urban economic benefit. This paper introduces the smart city, combining intelligent sensing and communication technology, and designs the economic benefit calculation method and evaluation model. Finally, this paper designs an experiment of economic benefit calculation and evaluation. First, 18 cities in China are used as examples to explore the relationship between indicators and GDP. It is concluded that there is a long-term and stable relationship between GDP and household savings balance at the end of the year and that this does not exist with the average number of on-the-job workers. Then we took Wuhan Metro Line 5 as an example to evaluate its economic benefits. The experiment shows that the largest weight value is the investment return period, with a weight of 0.54, and the evaluation level of Wuhan Metro Line 5 is 4. [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 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]

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]

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]

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 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]

The outbreak of COVID-19 posed a challenge to global governance, residents' happiness, and economic systems around the world. Since the crux of previous research centers on the reactions of both local and national governments, studies on how governance arrangement at the neighborhood level influences people's happiness during the crisis response remain insufficient. This paper aims to explore the relationship between neighborhood governance and residents' happiness based on first-hand data collected during Wuhan's first lockdown. This study highlights the significance of neighborhood governance in crisis response, which includes providing diverse public services, ensuring access to life's necessities, and offering prompt medical treatment. All of these factors are essential for maintaining overall satisfaction with governance and contributing to the happiness of individuals within the community. However, active governance actions do not always lead to favorable results. For example, increased group participation may lead to social conflicts among those involved, ultimately diminishing one's happiness. Furthermore, the COVID-19 pandemic has acted as a risk 'amplifier', exposing and exacerbating pre-existing hukou-based social inequalities in the governance process. The impact of the pandemic on citizen happiness is the cumulative effect of both the immediate social crisis brought on by the pandemic and long-standing structural inequalities. To improve people's happiness and establish inclusive policies, this paper advocates for a 'people-centered' urban governance that enhances public satisfaction and addresses the needs and priorities of migrant populations. [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]

China has experienced significant urbanization during the past 40 years, which exerts impacts on regional climates through changing land surface properties. Previous studies mainly focused on the Pearl River Delta, the Yangtze River Delta, and the Beijing-Tianjin-Hebei areas, while less attention has been paid to central China. In this paper, the regional climate effects of urbanization around the greater Wuhan area were investigated using the WRF model. High resolution, satellite-derived, impervious datasets were used to generate two realistic scenarios representing urban surface states of the years 1986 and 2018. By comparing the simulation results of two sensitivity experiments from 1 July 2015 to 12 July 2015, the spatial and diurnal changes in surface air temperature, surface skin temperature, and surface energy budget were analyzed. Our results reveal that urban expansion leads to 2 m air temperature and surface skin temperature increases by approximate 0.63 °C and 0.83 °C, respectively. Surface sensible heat flux increases, while latent heat flux decreases, with much greater effects in daytime than nighttime. The planetary boundary layer height (PBLH) increases with its maximum value over 100 m, and a 2 m water vapor mixing ratio decreases with a peak value around −2 g/kg. These findings provide knowledge to improve the understanding of land–atmospheric interactions and pave the way to studying urban expansion effects under future climate change scenarios. [ABSTRACT FROM AUTHOR]

Since the outbreak of the global COVID-19 pandemic in Wuhan, China, in 2019, its impact has been seen worldwide. Early identification of COVID-19 is very crucial, as it keeps the infected people isolated from other people, thus minimizing the risk of further transmission. The standard diagnostic approach is based on RT-PCR. However, due to the scarcity of PCR kits in some regions and the costs associated with this technique, there is a growing demand for alternative solutions. Recently, diagnosis of COVID-19 by medical imaging has been recognized as a valid clinical practice. Meanwhile, the massive increase in COVID-19 cases has put considerable pressure on radiologists responsible for interpreting these scans. This paper introduces an automated detection approach as a rapid alternative for COVID-19 diagnosis. We present a deep CNN model to differentiate between normal and pneumonia cases, as well as patients with COVID-19. Our approach is based on EfficientNet-B7 architecture and improved with Squeeze and Excitation block as an attention mechanism. In addition, we propose an innovative architecture that combines CNN with SVM to achieve the best performance. Experimental results show that the proposed framework provides better performance than existing SOTA methods, with an average accuracy of 97.50%, while the precision and recall of COVID-19 are both 100%. [ABSTRACT FROM AUTHOR]

The coordination between regional ecological and economic development has become a crucial topic in current environmental and development research due to the establishment of sustainable development goals and the acceleration of urbanization. In this study, an improved eco-economy coordination (EEC) model is proposed to evaluate the coordination of regional ecological and economic development. This study focuses on Wuhan, China, and utilizes socioeconomic and remote sensing data from 2000 to 2015 to measure both static and dynamic ecosystem service values (ESV). ESVs are the direct and indirect benefits produced by ecosystems that support human survival and development. The calculated ESVs are then integrated into the ecological–economic coordination (EEC) evaluation. This study aims to conduct a comparative analysis of the ecological-economic coordination across various districts of Wuhan, with a focus on spatiotemporal factors. In addition, this paper highlights the role of environmental adjustment coefficients in enhancing the EEC assessment. The results show that (1) the total static ESV experienced a loss of RMB 1.2 billion (approximately USD 169 million) and showed a decreasing trend, while the dynamically corrected ESV reversed this trend; and (2) EEC based on static ESV showed a low degree of conflict between ecological and economic reconciliation compared with a significant increase in EEC based on dynamic ESV. Based on static ESV, the EEC showed a low degree of conflict between ecological and economic reconciliation compared to a significant increase in EEC based on dynamic ESV. After being corrected by environmental adjustment coefficients, the EEC values showed a more differentiated distribution. Most regions demonstrated an overall upward trend in EEC, with a few, such as Wuchang District and Jiang'an District, being notable examples. However, Jianghan District presented a high conflict status. Finally, a series of decision-making suggestions are given based on relevant study results, and an important policy implication is that the coordination of economic growth and ecological protection, under large downward pressure from EEC values, needs to be paid special attention in policy decision-making. [ABSTRACT FROM AUTHOR]

BACKGROUND: Coronaviruses (CoVs) belong to the RNA viruses family. The viruses in this family are known to cause mild respiratory disease in humans. The origin of the novel SARSCOV2 virus that caused the coronavirus-19 disease (COVID-19) is the Wuhan city in China from where it disseminated to cause a global pandemic. Although lungs are the predominant target organ for Coronavirus Disease-19 (COVID-19), since its outbreak, the disease is known to affect heart, blood vessels, kidney, intestine, liver and brain. This review aimed to summarize the catastrophic impacts of Coronavirus disease-19 on heart and liver along with its mechanisms of pathogenesis. METHODS: The information used in this review was obtained from relevant articles published on PubMed, Google Scholar, Google, WHO website, CDC and other sources. Key searching statements and phrases related to COVID-19 were used to retrieve information. Original research articles, review papers, research letters and case reports were used as a source of information. RESULTS: Besides causing severe lung injury, COVID-19 has also been reported to affect and cause dysfunction of many other organs. COVID-19 infection can affect people by downregulating membrane-bound active angiotensin-converting enzyme (ACE). People who have deficient ACE2 expression are more vulnerable to COVID-19 infection. The patients' pre-existing co-morbidities are major risk factors that predispose individuals to severe COVID-19. CONCLUSION: The disease severity and its broad spectrum phenotype is a result of combined direct and indirect pathogenic factors. Therefore, protocols that harmonize many therapeutic preferences should be the best alternatives to de-escalate the disease and obviate deaths caused as a result of multiple organ damage and dysfunction induced by the disease. [ABSTRACT FROM AUTHOR]

The road transportation of explosives is highly concerning due to its substantial impact on social safety. For the safety management of explosive transportation, e.g., transport route planning and emergency rescue, explosion consequence evaluation is of paramount importance. The consequence evaluation of explosion accidents is affected by many factors, especially spatial features, such as the location of transport vehicles, the distribution of buildings, and the presence of individuals around the road, etc. However, there is still a lack of quantification methods for building damage evaluation, human casualty evaluation that considers real-time population density, and efficient interactive damage evaluation methods. In this paper, we formalize three typical scenarios of damage evaluation for explosive road transportation accidents, i.e., explosion point-based, road segment-based, and route-based damage evaluation. For each scenario, we propose a Height-aware Hierarchical Building Damage (HHBD) model and a Shelter-aware Human Casualty (SHC) model for building damage evaluation and human casualty evaluation, respectively. We also develop a GIS-based interactive visualization platform that integrates multi-source geospatial data and that enables efficient geospatial computation. In addition, a case study of liquefied natural gas (LNG) transportation in Wuhan is demonstrated in order to verify the effectiveness and efficiency of the proposed system. The research results can support the decision-making process of explosive transportation safety warnings and emergency rescue. [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]