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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]

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]

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]

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]

How to maintain the balanced stability and resilient development of rural systems is an important issue that needs urgent attention in the field of sustainable rural development at present. In this paper, the entropy method, spatial autocorrelation model, and Geodetector were used to explore the rural resilience level, spatial distribution characteristics, and driving factors of 31 cities in the urban agglomerations in the middle reaches of the Yangtze River (UAMRYR), and to put forward corresponding policy suggestions. The results are as follows: (1) From 2005 to 2020, rural resilience in the UAMRYR showed an upward trend with an average annual growth rate of 8.26%. The ranking of the three major urban agglomerations is Changsha-Zhuzhou-Xiangtan urban agglomerations > Wuhan urban agglomerations > Poyang Lake urban agglomerations. (2) During the study period, the spatial distribution of rural resilience gradually developed from a negative correlation to a strong positive correlation and generally showed the characteristics of high in the west and low in the east. (3) The urbanization rate, the size and structure of the economy, the difference in consumption and income between urban and rural areas, the local fiscal revenue, and the number of village committees are the key factors affecting the level of rural resilience. On this basis, we proposed policy recommendations to improve the economic, social, and ecological resilience of rural areas in the UAMRYR. The findings of this paper are expected to provide insights into the policy formulation of China's rural revitalization strategy. [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]

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]

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]

Purpose: Building on the theory of brand psychological ownership, this paper aims to explore the mediating role of brand psychological ownership in the relationship between brand personality (innocence/coolness) and consumers' preferences, as well as identify the boundary conditions of this relationship. Design/methodology/approach: To test the hypotheses, a series of four experiments were conducted in Wuhan, a city in southern China, using questionnaires administered at two universities and two supermarkets. Hypotheses were tested using PLS-SEM in SmartPLS 4. Findings: The results indicate that brand personality, specifically the dimensions of innocence and coolness, has a significant impact on consumers' brand preferences. Brands with a cool personality are preferred over those with an innocent personality. Moreover, the relationship between brand personality and consumers' brand preferences is moderated by power motivation and identity centrality. Originality/value: This study contributes to the literature by differentiating between brand personality of innocence and coolness as two separate constructs and proposing brand psychological ownership as a mechanism through which brand personality affects brand preferences. The study's samples were drawn from universities and supermarkets in southern China, providing evidence for the significant moderating effects of power motivation and identity centrality on consumers' brand preferences. [ABSTRACT FROM AUTHOR]

Municipal solid waste (MSW) management is a crucial issue in socioeconomic metabolism and requires multicategory and high‐resolution data, and data on the physical composition of municipal solid waste (PCMSW) are fundamental in MSW research. Extensive financial resources have been invested in the research on field investigations of PCMSW; however, it is time‐consuming and sometimes not truly representative of the studied case. In this work, two bottom‐up and two top‐down approaches were applied for analyzing the PCMSW, namely, field investigation (FI), BP neural network (BPNN), material flow analysis (MFA), and inversion algorithm based on electricity generation of waste incinerator (IAEI). Wuhan City, China, was chosen as the studied case for analyzing and comparing the PCMSW results. The PCMSW values obtained by applying those four methods showed acceptable differences, and the standard deviations of organic fraction, ash and stone, paper, plastic and rubber, textile, wood, metal, glass, and others were 3.94%, 2.77%, 6.57%, 2.22%, 2.49%, 1.36%, 0.53%, 1.19%, and 0.28%, respectively. Use of the MFA, BPNN, and IAEI methods could reduce time and labor spent on manual sampling, sorting, and weighing of MSW, compared to the FI method. BPNN algorithm advances in providing PCMSW data in history and by trajectory throughout the year, whereas IAEI contributes PCMSW data with much higher temporal resolution. Data quality and applicability of different methods were discussed, considering the availability of time, labor, and reference data. [ABSTRACT FROM AUTHOR]

In the era of stock planning, the renovation of old residential communities has become an important part of urban development in China. However, the renovation planning is difficult to implement. The paper points out three main factors that affect the implementation of old residential community renovation under the current system: complex property right structure, multiple stakeholders, and lack of funds. Based on the concept of Low Impact Development, and with minimizing the impact as the goal, the paper proposes a new mode for the renovation of old residential communities: through an analysis on property rights and interest game in old residential communities, and according to cost estimation, priority should be given to renovating the public elements that neither involve property rights or core interests nor require too many funds, including public space, roads, supporting facilities, etc., with community management to be properly improved. In the end, with the renovation of Quanyechang Community of Wuhan City as an example, the paper concludes that this renovation mode can not only meet the urgent needs for renovation, but is also easy to implement. [ABSTRACT FROM AUTHOR]

Urban logistics is important to a city's sustainable growth and development. With the increase in population and the economic growth in urban areas, the issue of congestion and the negative influence of transport of goods on people and the environment is one of the most important factors in the development of urban logistics. By determining the optimal location of urban logistics centres, total transport costs of logistics, the flow of goods in urban areas and the greenhouse gas emissions will be reduced. However, the traditional methods are easily influenced by the ambiguity of objective data, which makes it difficult to accurately describe the logistics demand in the urban area. To address this issue, the improved location–allocation model for urban logistics centres based on truck trajectory data is proposed. After extracting the origin–destination points, the logistics service demand can be estimated by the DBSCAN (density-based spatial clustering of applications with noise) clustering method. Then, the location–allocation of logistics centres is determined by an improved P-median method with the supply capacity limitation for simultaneous delivery of goods in the logistics centres. To validate the model, taking Wuhan, a central logistics city in China, as an example, the model can effectively ensure the equitable distribution of logistics facilities, minimize freight expenses, achieve more uniformity in logistics center services, and foster sustainable development of the city's logistics sector. [ABSTRACT FROM AUTHOR]

This paper attempts to evaluate the impact of massive infectious and contagious diseases and its final impact on the economic performance anywhere and anytime. We are considering to evaluate the case of Wuhan, China. We are taking in consideration the case of COVID-19 to be evaluated under a domestic, national, and international level impact. In this paper, we also propose a new simulator to evaluate the impact of massive infections and contagious diseases on the economic performance subsequently. This simulator is entitled "The Impact of Pandemics on the Economic Performance Simulator (IPEP-Simulator)" Hence, this simulator tries to show a macro and micro analysis with different possible scenarios simultaneously. Finally, the IPEP-Simulator was applied to the case of Wuhan-China respectively. [ABSTRACT FROM AUTHOR]

This paper addresses the reactions of domestic helpers to the Wuhan (Hubei Province) lockdown that began on January 23, 2020. We use a novel dataset containing the information of over 40,000 Chinese domestic helpers registered on a leading professional website from November 2019 to June 2020. The results indicate a declining pattern of short-term labor supply of domestic helpers across 11 major Chinese cities, which shows an increase in the expected monthly wage of domestic helpers in these cities. More importantly, using a difference-in-difference (DID) model, this paper provides some evidence on the existence of labor market discrimination against domestic helpers born in Hubei Province due to employers' fear of infection. [ABSTRACT FROM AUTHOR]

When COVID-19 broke out in Wuhan in 2019, the world looked at it as a 'Chinese' disease and no international efforts were made to assist China. Countries that supported China just offered solidarity messages. China adopted a raft of measures that included prescribing a lockdown on Wuhan and construction of infrastructure such as hospitals. In 2020, COVID-19 extended its tentacles across the globe. A majority of countries adopted the lockdown as a mitigatory measure. The lockdown activated an extraordinary instant emergency in the education sector as schools, colleges and universities shut down. What worsened the situation was that no solution was in sight as the medical researchers dithered from one suggestion to the other. This paper examines possible ways to deal with the emergency in the education sector by suggesting alternative learning solutions. The major argument of this paper is that countries should not simply copy and paste solutions that are not in sync with their local settings. Using the multistage designs sampling technique, three universities from a target of eighteen were selected. Convenience sampling was used to select the three universities and analytic rubrics were used to analyse clarity of policy and disaster preparedness by universities in Zimbabwe. For comparative purposes, four international webinars on education and COVID-19 were selected. This paper contributes towards addressing the lacunae created by global lockdown and subsequent shutdown of learning institutions due to COVID-19. The findings were that the lockdown approach was adopted and implemented without adaptation. Learning institutions were closed indefinitely despite the economic environment, the digital divide and the rural-urban divide militating against lockdown's entire adoption. Key proposals to deal with the lockdown include scaling up distance education based on mixed technologies, a paradigm shift on perceptions on digital education and resuscitation of postal services. [ABSTRACT FROM AUTHOR]

The ground surface deformation induced by shield tunnels passing through enclosure structures of existing tunnels is a particular underground construction scenario that has been encountered in Wuhan Metro Line 12 engineering cases in China. Timely ground deformation prediction is important to keep shield tunneling safe. However, the classic ground deformation theory is difficult to accurately predict for this ground deformation. This paper develops a semi-analytical method to predict ground heave considering the space effect in this engineering condition. Based on the improved ground deformation theory, a novel deformation prediction method for the ground and enclosure structure is derived and combined with Kirchhoff plate theory. Comparing with field deformation measurements, the maximum difference between the measured and calculated deformation is 14.6%, which demonstrates that the proposed method can be used to predict the ground heave induced by shield tunnels passing through the enclosure structure of existing tunnels. The parameters of the underground diaphragm wall used in Wuhan Metro Line 12 are further studied in detail. The results show that the ground heaves have a positive correlation with the embedded ratio of the diaphragm wall, but a negative correlation with its elastic modulus and thickness. However, the thickness and embedded ratio have a limited effect on ground heaves. This study provides a technical reference for optimizing the setting of enclosure structures in order to protect existing buildings. [ABSTRACT FROM AUTHOR]

The general remit of this paper is the role of wildlife trade in pathogen spillover. Its underlying assumption is that, so far from being the exclusive domain of the life sciences, the study of pathogen spillover will be greatly enhanced by multi-perspectival approaches, including One Health and those employed here, namely, non-speciesist green criminology and critical animal studies. The paper moves from discussions of zoonosis, anthroponosis and wildlife trade to the emergence of COVID-19 in Wuhan, China. The paper recommends the abolition of all wildlife trade and the reclamation of wildlife habitat and broaches discussion of the extension of legal personhood to wild animals. [ABSTRACT FROM AUTHOR]

In December 2019, a group of people in Wuhan city of Hubei province of China were found to be affected by an infection called dark etiology pneumonia. The outbreak of this pneumonia infection was declared a deadly disease by the China Center for Disease Control and Prevention on January 9, 2020, named Novel Coronavirus 2019 (nCoV-2019). This nCoV- 2019 is now known as COVID-19. There is a big list of infections of this coronavirus which is present in the form of a big family. This virus can cause several diseases that usually develop with a serious problem. According to the World Health Organization (WHO), 2019-nCoV has been placed as the modern generation of Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) coronaviruses, so COVID-19 can repeatedly change its internal genome structure to extend its existence. Understanding and accurately predicting the mutational properties of the genome structure of COVID-19 can forma good leadership role in preventing and fighting against coronavirus. In this research paper, an analytical approach has been presented which is based on the k-means cluster technique of machine learning to find the clusters over the mutational properties of the COVID-19 viruses' complete genome. This method would be able to act as a promising tool to monitor and track pathogenic infections in their stable and local genetics/hereditary varieties. This paper identifies five main clusters of mutations with k = 5 as best in most cases in the coronavirus that could help scientists and researchers develop disease control vaccines for the transformation of coronaviruses. [ABSTRACT FROM AUTHOR]

Thee coronavirus which appeared in December 2019 in Wuhan has spread out worldwide and caused the death of more than 330,000 people (as of May 29, 2020, submission date for the present article). Since February 2020, doubts were raised about the numbers of confirmed cases and deaths reported by the Chinese government. In this paper, we examine data available from China at the city and provincial levels and we compare them with Canadian provincial data, US state data and French regional data. We consider cumulative and daily numbers of confirmed cases and deaths and examine these numbers through the lens of their first two digits and in particular we measure departures of these first two digits to the Newcomb-Benford distribution, often used to detect frauds. Our finding is that there is no evidence that cumulative and daily numbers of confirmed cases and deaths for all these countries have different first or second digit distributions. We also show that the Newcomb-Benford distribution cannot be rejected for these data. [ABSTRACT FROM AUTHOR]

A bacterial infection in the lungs can cause viral pneumonia, a disease. Later the middle of December 2019, there have been multiple episodes of pneumonia in Wuhan City, China, with no known cause; it has since been discovered that this pneumonia is actually a new respiratory condition brought on by coronavirus infection. Humans who have lung abnormalities are more likely to develop high-risk conditions; this risk can be decreased with much quicker and more effective therapy. The symptoms of Covid-19 pneumonia are similar to those of viral pneumonia; they are not distinctive. X-ray or Computed Tomography (CT) scan images are used to identify lung abnormalities. Even for a skilled radiologist, it might be challenging to identify Covid-19/Viral pneumonia by looking at the X-ray images. For prompt and effective treatment, accurate diagnosis is essential. In this epidemic condition, delayed diagnosis can cause the number of cases to double, hence a suitable tool is required is necessary for the early identification of Covid-19. This paper highlights various AI techniques as a part of our contribution to swift identification and curie Covid-19 to front-line corona. The safety of Covid-19 people who have viral pneumonia is a concern. Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), two AI technologies from Deep Learning (DL), were utilized to identify Covid-19/Viral pneumonia. The Algorithm is taught utilizing non-public local hospitals or Covid-19 wards, as well as X-ray images of healthy lungs, fake lungs from viral pneumonia, and ostentatious lungs from Covid-19 that are all publicly available. The model is also validated over a lengthy period of time using the transfer learning technique. The results correspond with clinically tested positive Covid-19 patients who underwent Swap testing conducted by medical professionals, giving us an accuracy of 78 to 82 percent. We discovered that each DL model has a unique expertise after testing the various models. [ABSTRACT FROM AUTHOR]

Objective: This paper used meta-regression to analyze the heterogenous factors contributing to the prevalence rate of mental health symptoms of the general and frontline healthcare workers (HCWs) in China under the COVID-19 crisis. Method: We systematically searched PubMed, Embase, Web of Science, and Medrxiv and pooled data using random-effects meta-analyses to estimate the prevalence rates, and ran meta-regression to tease out the key sources of the heterogeneity. Results: The meta-regression results uncovered several predictors of the heterogeneity in prevalence rates among published studies, including severity (e.g., above severe vs. above moderate, p < 0.01; above moderate vs. above mild, p < 0.01), type of mental symptoms (PTSD vs. anxiety, p = 0.04), population (frontline vs. general HCWs, p < 0.01), sampling location (Wuhan vs. Non-Wuhan, p = 0.04), and study quality (p = 0.04). Conclusion: The meta-regression findings provide evidence on the factors contributing to the prevalence rate of mental health symptoms of the general and frontline healthcare workers (HCWs) to guide future research and evidence-based medicine in several specific directions. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display%5frecord.php?RecordID=220592, identifier: CRD42020220592. [ABSTRACT FROM AUTHOR]

The world is urgently looking for ways to flatten the coronavirus disease 2019 (COVID-19) curve, and many governments have resorted to implementing strict lockdowns, as researchers show the effectiveness of China's approaches in containing the virus. However, this paper argues that the draconian lockdowns instituted in Wuhan, Hubei, China, may have actually contributed to intensifying patient surges and incapacitating local health systems. Medical aids were rushed to Hubei and new hospitals were rapidly built, however, the healthcare system was still unable to match the staggering increase of patients in the early stages of the lockdowns. The paper proposes using patient evacuation to enhance sustainable COVID-19 mitigation during lockdowns. It demonstrates that patients in Hubei could have been transported to other Chinese provinces where hospitals were under-utilized. This could have theoretically saved thousands of lives by reducing inequities between Hubei and the rest of China in healthcare capacity for treating COVID-19 patients. [ABSTRACT FROM AUTHOR]

Drawing on a recent online survey combined with city-level data, this paper examines the impact of the COVID-19 on consumers' online food purchase behavior in the short term. To address the potential endogeneity issues, we adopt an instrumental variable (IV) strategy, using the distance from the surveyed city to Wuhan as the instrumental variable. We show that our IV method is effective in minimizing potential bias. It is found that the share of confirmed COVID-19 cases increases the possibility of consumers purchasing food online. This is more likely to be the case for young people having a lower perceived risk of online purchases and living in large cities. Despite some limitations, this paper has policy implications for China and other countries that have been influenced by the COVID-19 epidemic. Specifically, government support and regulation should focus on (i) ensuring the safety of food sold on the internet, (ii) protecting the carrier from becoming infected, and (iii) providing financial support to the poor since they may have difficulties in obtaining access to food living in small cities. Moreover, how to help those who are unable to purchase food online because of their technical skills (e.g., the elderly who are not familiar with smart phones or the internet) also deserves more attention for the government and the public. [ABSTRACT FROM AUTHOR]

The generation of construction and demolition waste1 (CDW) in China has increased dramatically in recent decades due to the rapid urbanization. Yet there is a very limited utilization of this waste, meaning that there is an urgent need to address this issue in order to reduce the reliance on virgin materials and improve the lived environment in China. This problem contrasts with the EU experience where many member states already exceed an 80% utilization rate of CDW due to extensive policy measures. We argue that the supervision of CDW is an important and underestimated element in addressing this waste stream, and that China and other rapidly developing states can build on the EU experience to address this issue rapidly and efficiently. This paper took a comparative policy analytical approach to summarize advanced experiences promoting CDW utilization, highlighting 14 advanced policy measures. We then conducted a case study approach applied to a Chinese metropolitan city, Wuhan, to identify the key measures to promote CDW utilization in the local area by questionnaire. On this basis, we then proposed a whole process supervision framework for the six most important policy measures, to cover the whole process of the generation, transport and recycling of this waste. This approach provides a mode for policymakers to prioritize the most important policy measures to address CDW problem in China through a timely and data-driven process. [ABSTRACT FROM AUTHOR]

The health organizations around the world are currently facing one of the greatest challenges, to overcome the current global pandemic, COVID-19. It erupted in December 2019, in Wuhan City, China. It spreads rapidly throughout the world within couple of months. In this paper, the data of the COVID-19 have been collected, organized, analyzed and interpreted using the discrete-time model of SIR epidemic model. Moreover, results for several countries from different regions of the world have been obtained. Furthermore, comparative study has been carried out for the countries under consideration. The comparison was performed for the data of different countries on same dates of each month. However, the calculations are carried out for thirteen consecutive weeks, to investigate the rate of spread and the control of the disease in these countries. This guides us to some important concepts like factors favoring the spread of virus and those resisting the spread. Different regions are studied and their data have been evaluated to know which regions are the most effected. This study helps to know the important factors about the behavior of the coronavirus in different environments, such as lockdowns, temperatures, humidity and other restrictions. The proposed concepts and equations can be used to project the upcoming behavior of the pandemic. [ABSTRACT FROM AUTHOR]

Purpose: The public's trust in the authorities has a great impact on people's perception and cognition on development of different types of urban transport infrastructure projects (UTIPs). Given the importance of public acceptance for the efficient construction and operation of UTIPs, this study aims at investigating the personal and environmental factors that influence public acceptance behavior from the perspective of stakeholder management. Design/methodology/approach: Based on social cognitive theory (SCT), this study explores the multiple dimensions of social trust on public acceptance in the development of UTIPs by a comparative case study. Two types of UTIPs, a metro railway and a bridge in the Wuhan City, China, were selected as cases, with a questionnaire distributed among the public to collect their sense of trust towards the development of these projects. The data were analyzed through structural equation modeling (SEM). Findings: This study reveals that social trust positively influences public acceptance, directly or indirectly through perceived benefit and -risks and self-efficacy. However, the emphasis on social trust about competence and integrity of the authorities varies with the types of projects. Self-efficacy worked as the "mirror of trust" reflecting people's attitude towards social trust in the authorities on their ability and morality. Originality/value: The value of the paper lies in discussing social trust from multiple dimensions in the field of urban infrastructures, which provides new insights into specific mechanisms for shaping public acceptance in project management towards the development of UTIPs. [ABSTRACT FROM AUTHOR]

In response to uncertainty in remotely sensed land cover products, there is continuing research on accuracy assessment and analysis. Given reference sample data, accuracy indicators are commonly estimated based on error matrices, from which areal extents of different cover types are also estimated. There are merits to explore the ways utilities of land cover products may be further enhanced beyond map face values and conventional area estimation. This paper presents an integrative method (CCAErrMat) for uncertainty characterization and utility enhancement. This works through reference-map cover type co-occurrence analyses based on error matrices localized in canonical correspondence analysis (CCA) ordination space rather than in geographic space to overcome the sparsity of reference sample data. The aforementioned co-occurrence analyses facilitate quantification of accuracy indicators, identification of correctly classified and perfectly misclassified pixels, and prediction of reference class probabilities, all at individual pixels. Moreover, these predicted reference class probabilities are used as auxiliary variables to formulate model-assisted area estimation, further enhancing map utilities. Extensions to CCAErrMat are also investigated as a way to bypass the pre-computing of map class occurrence pattern indices as candidate explanatory variables for CCAErrMat, leading to two variant methods: CCACCAErrMat and CNNCCAErrMat. A case study based in Wuhan municipality, central China was undertaken to compare the proposed method against alternative methods, including CCA-separate and CNN-separate. The advantages of CCAErrMat and CCACCAErrMat were confirmed. The proposed method is recommendable for characterizing uncertainty and enhancing utilities in land cover maps by analyzing locally constrained error matrices. The method is also cost-effective in terms of reference sample data, as requirements for them are similar to those for conventional accuracy assessments. [ABSTRACT FROM AUTHOR]

In the context of the global pandemic of COVID-19, with the epidemic epicenter located in the Wuhan City, China, patients with severe mental illness have also been deeply affected by the epidemic. In this paper, two patients with schizophrenia who recovered from COVID-19 were reported. Because of the long-term positive results of the SARS-CoV-2 serum antibody IgM test, they had to undergo medical isolation and social restrictions for a long time. After the situation was effectively identified by the medical staff and they were eliminated as a potential virus carrier and released from the medical isolation center. Since psychiatrists often lack systematic knowledge of infectious diseases, the authors hope that this paper can provide a reference to avoid unnecessary wastage of medical resources and prevent placing serious mental burden on such patients in the future. [ABSTRACT FROM AUTHOR]

Novel coronavirus (COVID-19) is started from Wuhan (City in China), and is rapidly spreading among people living in other countries. Today, around 215 countries are affected by COVID-19 disease. WHO announced approximately number of cases 11,274,600 worldwide. Due to rapidly rising cases daily in the hospitals, there are a limited number of resources available to control COVID-19 disease. Therefore, it is essential to develop an accurate diagnosis of COVID-19 disease. Early diagnosis of COVID-19 patients is important for preventing the disease from spreading to others. In this paper, we proposed a deep learning based approach that can differentiate COVID- 19 disease patients from viral pneumonia, bacterial pneumonia, and healthy (normal) cases. In this approach, deep transfer learning is adopted. We used binary and multi-class dataset which is categorized in four types for experimentation: (i) Collection of 728 X-ray images including 224 images with confirmed COVID-19 disease and 504 normal condition images (ii) Collection of 1428 X-ray images including 224 images with confirmed COVID-19 disease, 700 images with confirmed common bacterial pneumonia, and 504 normal condition images. (iii) Collections of 1442 X- ray images including 224 images with confirmed COVID-19 disease, 714 images with confirmed bacterial and viral pneumonia, and 504 images of normal conditions (iv) Collections of 5232 X- ray images including 2358 images with confirmed bacterial and 1345 with viral pneumonia, and 1346 images of normal conditions. In this paper, we have used nine convolutional neural network based architecture (AlexNet, GoogleNet, ResNet-50, Se-ResNet-50, DenseNet121, Inception V4, Inception ResNet V2, ResNeXt-50, and Se-ResNeXt-50). Experimental results indicate that the pre trained model Se-ResNeXt-50 achieves the highest classification accuracy of 99.32% for binary class and 97.55% for multi-class among all pre-trained models. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak initiated from the Chinese city of Wuhan and eventually affected almost every nation around the globe. From China, the disease started spreading to the rest of the world. After China, Italy became the next epicentre of the virus and witnessed a very high death toll. Soon nations like the USA became severely hit by SARS-CoV-2 virus. The World Health Organisation, on 11th March 2020, declared COVID-19 a pandemic. To combat the epidemic, the nations from every corner of the world has instituted various policies like physical distancing, isolation of infected population and researching on the potential vaccine of SARS-CoV-2. To identify the impact of various policies implemented by the affected countries on the pandemic spread, a myriad of AI-based models have been presented to analyse and predict the epidemiological trends of COVID-19. In this work, the authors present a detailed study of different artificial intelligence frameworks applied for predictive analysis of COVID-19 patient record. The forecasting models acquire information from records to detect the pandemic spreading and thus enabling an opportunity to take immediate actions to reduce the spread of the virus. This paper addresses the research issues and corresponding solutions associated with the prediction and detection of infectious diseases like COVID-19. It further focuses on the study of vaccinations to cope with the pandemic. Finally, the research challenges in terms of data availability, reliability, the accuracy of the existing prediction models and other open issues are discussed to outline the future course of this study. [ABSTRACT FROM AUTHOR]

The acceleration of urbanization has significantly impacted the changing regional thermal environment, leading to a series of ecological and environment-related problems. A scientific evaluation of the urban thermal security pattern (TSPurban) strongly benefits the planning and layout of sustainable development and the construction of comfortable human settlements. To analyze the characteristics of the TSPurban under cross-regional differences and provide targeted solutions to mitigate the urban heat island effect in later stages, the logical system research framework of the TSPurban based on the "construction–evaluation–optimization" model was explored using reverse thinking. This study selected the Wuhan metropolitan area in China as the research object. First, a morphological spatial pattern analysis (MSPA) model was used to extract the top 30 core heat island patches, and Conefor 2.6 software was used for connection analysis to evaluate their importance. Second, based on the characteristics of various land cover types, the friction (cost) map of surface urban heat island (SUHI) diffusion was simulated. The spatial attributes of the heat island resistance surface were examined using a standard deviation ellipse and hot spot analysis. Finally, this paper used circuit theory to find 56 low-cost heat island links (corridors) and circuit scape software to find widely distributed vital nodes. The optimization of the TSPurban network was then investigated using a reverse thinking process. Heat island patches, corridors, and vital nodes are among the crucial components of the TSPurban. By obstructing corridor links and disturbing important nodes, it is possible to appropriately and proficiently reduce the TSPurban network's connection efficiency and stability, which will have a positive influence on regional climate mitigation and the heat island effect. [ABSTRACT FROM AUTHOR]

With the continuous acceleration of urbanization and agricultural modernization in China, the trend of concentration of rural land transfer is irreversible. For landless farmers, the absence of land guaranteed function inevitably gives rise to the substitution effect of other guaranteed methods. And the subjective preferences exhibited by farmers in making guaranteed behavior decisions can be quantitatively described as guaranteed behavioral responses (GBRs). Based on the analytical framework of distributed cognitive theory, this paper adopts the validated factor analysis method of structural equation modeling to quantitatively study the cognitive basis and behavioral responses of landless farmers' guaranteed behavior by combining the survey data of rural households in typical rural areas of Wuhan urban area. The study shows that the GBRs of landless farmers are significantly influenced by the cognitive level. "Locality power," "cultural power," and "personal power" are the main, important, and effective cognitive levels that influence farmers' GBRs, respectively. Policy-based protection occupies a dominant position in the rural social guaranteed system, savings-based protection still plays an important function in rural areas, and market-based protection has greater development potential. [ABSTRACT FROM AUTHOR]

To explore companion movement laws of different age groups and transfer rules of panic emotion during evacuation at subway stations, evacuation groups of different ages were distinguished in this study through differences in movement rate. Moreover, a double-exit subway cellular automaton evacuation model considering companion behaviours was constructed. A case study of evacuation of dense crowds at the Hukou Subway in Wuhan City of China was simulated, and effects of personnel structure, companion behaviours, and panic emotion on evacuation efficiency were analysed. Results show that, (1) the overall crowd evacuation time present a linear growth trend with the increase of pedestrian density. (2) The evacuation time initially decreases and then increases with the increase of companion ratio. The shortest evacuation time is achieved when the companion cells account for 10 %. (3) People are more reasonable and the general evacuation efficiency is higher if they can control emotions better during evacuation. The obtained conclusions provide great references to analyse companion behaviours of different age groups during evacuation. [ABSTRACT FROM AUTHOR]

For decades, urbanization has been a major topic of discussion among academics and professionals when investigating issues such as social segregation, spatial inequality, and urban sustainability. However, little attention has been given to the relationship between social characteristics and the ever-changing development of urban spaces. This paper seeks to investigate progressive changes in the social stratification structure in Wuhan's urban development zone (UDZ) while focusing on their associated spatial transformations, based on census data of Wuhan in 1990, 2000, and 2010. To obtain this objective, the study will answer two key questions: (1) What are the characteristics of urban social spatial evolutions of Wuhan the second-tier city in China? (2) What are the mechanisms of social spatial evolution? And (3) what role does the social spatial evolution play in urban social planning and management? We used varimax rotated principal component analysis (PCA) to extract social dimensions along with ArcMap to enhance spatial analysis while visualizing the distribution of social factor scores. The results show that since 1990, the local population (particularly the aging population) has reflected a hollowing trend. Industrial relocation and population mobility have become the main social dimensions in the 1990s and 2000s respectively. The characteristics of socio-spatial transformations are from concentric rings as well as combinations of concentric rings, axial aggregations, and multiple clusters. These transformations are formed by the push–pull force of the large area and the centripetal and centrifugal forces inside the city. We have concluded that top-down systems dominate socio-spatial transformations in the early stages, while public participatory management plays a more important role in the later stages, which will become inclusive governance. [ABSTRACT FROM AUTHOR]

Urbanization has caused severe negative impacts on intra-urban river water bodies. In this paper, 22 physicochemical parameters were measured at 20 locations in the level period and 29 locations in the wet and dry period using nearby urban area surface water samples from a medium-sized polluted river (a lake–river continuum, i.e., Xunsi River, Qingling River and Tangxun Lake) in the Yangtze River watershed in Wuhan, China. In this study, the environmental quality standards for surface water of China (GB3838-2002) and the WHO drinking water standard (2011) were used as reference standards. After the basic physicochemical parameters were evaluated, the possible sources of pollution in the study area under urban pressure were examined by applying multivariate statistical techniques. The factor analysis method and entropy weighting method identified three critical factors describing the type of surface water pollution in the watershed: (1) pollution from nutrients, mainly nitrogen pollution, (2) trace metals pollution, mainly from Fe and Pb, and (3) organic pollution. Based on multivariate analysis, the weighted arithmetic water quality index (WQI) was used to assess the water quality of each period. The WQI results show that the water near the urban area of the watershed temporally shows light to moderate pollution overall in the level and wet periods, and sever pollution in the dry period. The worst water quality indicating water that is not suitable for any practical use occurs in the dry period. Spatially distributed in clusters and strips visualized in kriging method, the water quality of the lake source and upstream is better than that of the downstream. In addition, Escherichia coli was detected at consistently high levels, and although the river is not a source of drinking water, it could pose a threat to downstream waterworks. [ABSTRACT FROM AUTHOR]

This paper examines the gendered impacts of the COVID-19 pandemic on the Chinese migrants who had returned to their hometowns for the Spring Festival before the Wuhan lockdown, using data from a recent nationally representative survey. The study finds that women migrants were less likely than men migrants to return to the cities and also less likely to return to paid work after the pandemic outbreak. It also finds that having a preschool-age child had a strong negative effect on women migrants' employment decisions, but it had no effect on men migrants' decisions. These results expand the literature on the economic vulnerabilities of Chinese migrant workers. More importantly,~the findings indicate that the COVID-19 pandemic has caused a setback in the~progress made in pre-pandemic times in advancing Chinese rural women's position in the labor market. HIGHLIGHTS The COVID-19 pandemic had negative employment consequences for Chinese migrant workers. Women migrants were the hardest hit and more likely to withdraw from migration flows and paid work. Women's caregiving roles and employment in face-to-face services are the main causes of their withdrawal. The pandemic has reinforced traditional gender roles and heightened labor market inequalities. [ABSTRACT FROM AUTHOR]

Coronavirus outbreak in Wuhan, China, turned into a pandemic in record time. Communication of disease presentation and mechanism of spread remain keys to getting ahead of the virus and limiting its spread beyond the capacity of management. Owing to huge academic focus and pandemic concern around the globe, this bibliometric analysis investigated research productivity related to coronavirus disease (COVID-19) pandemic using the Web of Science database. The relevant data were harvested, and search query was further refined by publication years (2020 OR 2019) and document types (article, book chapter, and proceedings paper). Finally, 6694 records were imported and downloaded in Plaintext and BibTeX formats on August 1, 2020. The data analysis was performed using MS Excel, VOS viewer, and Biblioshiny software. Of the 6694 publications that appeared in that period, the USA and Chinese research institutions topped the numbers. At the same time, the Journal of Medical Virology and CUREUS (Cureus Journal of Medical Science), remained favorite journals for publications. The pattern of multi-author publications has outstripped that of single-authors. Apart from COVID-19 and the novel coronavirus, the important keywords mentioned included pandemic, pneumonia, epidemiology, public health, outbreak, epidemic, China, infection, and treatment. The analysis shows a strong local research response from China, with large teams reporting on the disease outbreak. Subsequent studies will document a global response as the virus spreads worldwide. The initial research related to the current coronavirus outbreak was reported from within China. The data and patterns were supposed to alter as the virus spread globally. [ABSTRACT FROM AUTHOR]

Purpose: The content of heavy metals in the soil is directly related to the control of soil pollution, but due to the limitations of manpower and material resources, it is difficult to detect them in detail; researchers usually need to predict the content of soil heavy metals in unknown areas based on existing data. Therefore, how to choose an effective method to complete this process has become a challenging problem. Materials and methods: In this paper, a deep composite model (DCM) is proposed. The model is based on radial basis function neural network (RBFNN), then, uses self-adaptive learning based particle swarm optimization algorithm (SLPSO) to generate the weight and bias of the output layer of RBFNN and employs adaptive adjustment based root mean square back-propagation (ARMSProp) to optimize all variables of RBFNN, so as to improve the prediction accuracy of the model for soil heavy metal content. When using this model to predict soil heavy metal content, the Pearson coefficient is used as a comparison index to compare the correlation between different heavy metals and heavy metals to be predicted, and finally the content of heavy metals with a Pearson coefficient greater than 0.5 is selected as the input of the model variable. Results and discussion: First in the validation of the proposed SLPSO algorithm, the effectiveness of SLPSO and the feasibility of being applied to the DCM model have been proved. Then, the DCM was applied to the prediction of soil heavy metal content in six new urban areas of Wuhan in China, the experimental results show that the predicted value of soil heavy metal content of DCM is closer to the actual value than other comparison models, and the four error indicator values of DCM are also significantly lower than other comparison models, especially when compared with RBFNN, the MAPE and SMAPE of DCM have dropped by 8.6% and 3.9%, respectively. Conclusions: We can conclude that the deep composite model proposed in this paper obtains a good prediction accuracy when predicting soil heavy metal content; it has certain feasibility and can be used as an effective method for soil heavy metal content prediction. [ABSTRACT FROM AUTHOR]

In this paper, we study how China's stock market reacts to the sudden outbreak of COVID-19 in 2020, particularly to the announcement of the pandemic lockdown. In general, we observe reversals both at the industry level and at the firm level due to investors' overreactions to the pandemic lockdown. For industry- and firm-level stocks with positive cumulative abnormal returns (CARs) in the event window when Wuhan was locked down, the reversals are stronger. Thus, the reversal effects are mostly driven by industries and stocks that positively overreact to COVID-19 than do others. Further investigation shows that overreactions are stronger for stocks with lower institutional ownership, which means that retail investors react more strongly to COVID-19. Among stocks with positive CARs in the event window, those with higher idiosyncratic volatilities and lower book-to-market ratios tend to have worse performance after one month. [ABSTRACT FROM AUTHOR]

Covid-19 pandemic started in Wuhan in December 2019 and then spread to the rest of China and the world. This paper examines the relationship between inter-provincial migration and the diffusion of covid-19 outbreak in Mainland China from 26 January to 6 March 2020. Statistical analysis shows that the migrants who moved into Hubei in 2010–2015 are a good indicator of the population flow from Hubei to other provinces that caused the spread of covid-19 in early 2020. Many migrants traveled back to hometown before the traffic ban of Wuhan city for the spring holiday. Thus, the migration flow from a province to Hubei in the period 2010–2015 had the highest correlation coefficient with the number of covid-19 cases in four selected dates. The population flow based on the big data from the Baidu map on 20 January 2020 was also highly correlated with the number of cases, but not as good as the above migration data. The finding demonstrates the role of established migrant networks in the spread of covid-19. [ABSTRACT FROM AUTHOR]

Using the meteor radar data at the Mohe (53.5° N, 122.3° E) and Wuhan (30.5° N, 114.2° E) regions over China, this paper conducts a study on the diurnal and seasonal variation of high-frequency (within 2 h) gravity waves (GWs) activity in the mesosphere and the lower thermosphere (MLT). On the basis of the composite day analysis and Hocking's technique, the variance and momentum flux of the high-frequency GWs are derived from the radial velocities of individual meteor trails. Spectral results demonstrate that the high-frequency GWs activity shows 12 and 24 h periodicity, which may be due to the tidal modulation on the high-frequency GWs. The spectra of the variance and momentum flux also show 6 and 8 h periodicity. In addition to the diurnal variation, the high-frequency GWs activity shows the annual and semiannual oscillations. Additionally, the quasi-4-month oscillation is found at Mohe. [ABSTRACT FROM AUTHOR]

On 31 December 2019, WHO received inputs of pneumonia cases in Wuhan city of China, but the cause of infection could not be known initially. However, after some time, the medical researchers of China disclosed a novel coronavirus as the cause on 7 January 2020, named "2019-nCoV." The coronavirus (CoV) belongs to Severe Acute Respiratory Syndrome (SARS) species that leads to infection, causing illness, which ranges from the common cold to serious sickness. Finally, on 11 March 2020, the WHO Director-General Dr. Tedros Adhanom Ghebreyesus announced the outbreak as a pandemic. As the fear and ambiguity rose among companies and firms, the profit rate seemed to be lower due to the COVID-19 global impact. Nearly US$6 trillion in wealth from 24th to 28 February 2020 of the stock market has been wiped out. There was a great decrease in value over the S&P index, which abolished over $5 trillion in the same week. However, the largest 10 companies of S&P faced a loss of $1.4 trillion. The investors make an analytical prediction that firms' profits may drop in response to the impact of coronavirus. The prime focus is on the importance of digital business practices and how different sectors have been affected in terms of economic loss during this pandemic outbreak in this paper. [ABSTRACT FROM AUTHOR]

Long-span cable-supported bridge is one of crucial transportation infrastructures. Structural health monitoring (SHM) system that is used to assess structural service conditions and detect early damage has drawn a growing attention in engineering structural society. It provides a real-time monitoring of various structural changes under operational and extreme conditions. Yangluo Bridge over Yangtze River in Wuhan city of China is a 1280 m central span suspension bridge with an orthotropic steel box girder. Taking the SHM system of Yangluo Yangtze River Bridge as an example, this paper presents an implementation of SHM system on the bridge in terms of sensor layout, data acquisition strategy, data transmission, data processing, and abnormal alarm framework. With such an SHM system, the typical monitoring results are demonstrated such as operational environments, loadings, structural deformation, strains, dynamic properties, and structural responses. The SHM system of Yangluo Yangtze River Bridge is continuously gathering data and monitoring structural behavior. It is anticipated that it will provide either scientific or technological supports to the service comfort and safety of the bridge. [ABSTRACT FROM AUTHOR]

In this paper, we present a challenging stereo-inertial dataset collected onboard a sports utility vehicle (SUV) for the tasks of visual-inertial odometry (VIO), simultaneous localization and mapping (SLAM), autonomous driving, object detection, and other computer vision techniques. We recorded a large set of time-synchronized stereo image sequences (2 × 1280 × 720 @ 30 fps RGB) and corresponding inertial measurement unit (IMU) readings (400 Hz) from a Stereolabs ZED2 camera, along with centimeter-level-accurate six-degree-of-freedom ground truth (100 Hz) from a u-blox GNSS-IMU navigation device with real-time kinematic correction signals. The dataset comprises 34 sequences recorded during November 2020 in Wuhan, the largest city of Central China. Further, the dataset contains abundant unique urban scenes and features of a complex modern metropolis, which have rarely appeared in previously released benchmarks. Results from milestone VIO/SLAM algorithms reveal that methods exhibiting excellent performance on established datasets such as KITTI and EuRoC perform unsatisfactorily when moved outside the laboratory to the real world. We expect our dataset to reduce this limitation by providing more challenging and diverse scenarios to the research community. The full dataset with raw and calibrated data is publicly available along with a lightweight MATLAB/Python toolbox for preprocessing and evaluation. The dataset can be downloaded in its entirety from the uniform resource locator (URL) we provide in the main text. [ABSTRACT FROM AUTHOR]