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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]

The estimation of spatial heterogeneity within real estate markets holds significant importance in house price modelling. However, employing a single or straightforward distance to measure spatial proximity is probably insufficient in complex urban areas, thereby resulting in an inadequate modelling of spatial heterogeneity. To address this issue, this paper incorporates multiple distance measures within a neural network framework to achieve an optimized measure of spatial proximity (OSP). Consequently, a geographically neural network weighted regression model with optimized measure of spatial proximity (osp-GNNWR) is devised for the purpose of spatially heterogeneous modeling. Trained as a unified model, osp-GNNWR obviates the need for separate pretraining of OSP. This enables OSP to delineate the modeled spatial process through a post hoc calculated value. Through simulation experiments and a real-world case study on house prices, the proposed model reaches more accurate descriptions of diverse spatial processes and exhibits better overall performance. The interpretable results of the case study in Wuhan demonstrate the efficacy of the osp-GNNWR model in addressing spatial heterogeneity within real estate markets, suggesting its potential for modelling and predicting complex geographical phenomena. [ABSTRACT FROM AUTHOR]

Customers' hotel location evaluations are critical to hotel location decisions, future investment, visitor experience and urban tourism development. In this paper, spatial autocorrelation analysis, GeoDetection and geographically weighted models are used to explore the characteristics of the location satisfaction pattern of 1126 hotels in Wuhan and its causes.The findings suggest that there are obvious spatial clustering distribution characteristics of hotel location satisfaction in Wuhan. Distance from highway entrances/exit, scenic spot accessibility, and free parking services have the strongest positive influence on the spatial differentiation pattern of hotel location satisfaction, while hotel transportation services, the percentage of water area, and the number of universities have significant spatial differentiation characteristics on the influence of hotel location satisfaction. This study contributes to a better understanding of hotel location satisfaction pattern and its causes through multi-source data and spatial analysis techniques. The results of the study are of practical significance to the planning and development of urban hotels in Wuhan, as well as to other cities similar to Wuhan in terms of city scale, spatial layout of hotels, and tourism development. [ABSTRACT FROM AUTHOR]

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

In this paper, the mathematical modeling of the novel corona virus (COVID-19) is considered. A brief relationship between the unknown hosts and bats is described. Then the interaction among the seafood market and peoples is studied. After that, the proposed model is reduced by assuming that the seafood market has an adequate source of infection that is capable of spreading infection among the people. The reproductive number is calculated and it is proved that the proposed model is locally asymptotically stable when the reproductive number is less than unity. Then, the stability results of the endemic equilibria are also discussed. To understand the complex dynamical behavior, fractal-fractional derivative is used. Therefore, the proposed model is then converted to fractal-fractional order model in Atangana-Baleanu (AB) derivative and solved numerically by using two different techniques. For numerical simulation Adam-Bash Forth method based on piece-wise Lagrangian interpolation is used. The infection cases for Jan-21, 2020, till Jan-28, 2020 are considered. Then graphical consequences are compared with real reported data of Wuhan city to demonstrate the efficiency of the method proposed by us. [ABSTRACT FROM AUTHOR]

An outbreak of coronavirus disease 2019 (COVID-19) occurred in Wuhan and it has rapidly spread to almost all parts of the world. In the field of Medical Science, concerning the definition of the topological index on the molecular structure and corresponding medical, biological, chemical, pharmaceutical properties of drugs can be studied by the topological index calculation. In this paper, we compute some of the general topological properties of chloroquine and hydroxychloroquine used to inhibit the outbreak of coronavirus disease-19. The results in this paper may be useful in finding new drug and vaccine for the treatment and prevention of COVID-19. [ABSTRACT FROM AUTHOR]

The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) infections that were first detected in Wuhan, China, at the end of 2019 led to the state of the global COVID-19 pandemic declared by the World Health Organization in March 2020. Thus, the world faces a new challenge to control the spread of the virus. The pandemic highlighted the need for rapid detection of infections that would be able to accurately screen the population much more rapidly and economically than the gold standard molecular biology-based tools such as quantitative real-time reverse transcription polymerase chain reaction. Such screening measures would be able to isolate infected individuals, including those showing no symptoms, and provide early treatments before more serious complications develop, thus allowing for the spread of infections to be better controlled without the implementation of socioeconomically crippling lockdowns. In this review, we present a comprehensive overview of the major advances in the use of electrolyte-gated field effect transistor-based biosensors for the detection of viruses, including their use for SARS-CoV-2 detection. We describe the main types of bioreceptors used for the detection of viruses in general and those used for COVID-19 in detail. [ABSTRACT FROM AUTHOR]

Undersized fraction from aged municipal solid waste (UFAMSW), as a kind of soil-like material, has been proved effective in providing a large amount of organic matter and nutrients for soil and plants. The characteristics and effectiveness of heavy metal pollution removal in UFAMSW attracted tremendous research interest from scientists recently. In this study, the heavy metal removal efficiencies and bioavailability of washing on contaminated UFAMSW were evaluated with three washing reagents including ethylene diamine tetra acetic acid (EDTA), citric acid (CA), and humic acid (HA). The effects of chelating agent concentration, pH, and washing time on metal removal were investigated and response surface methodology (RSM) was employed to optimize the washing conditions. The results indicated that the removal efficiencies of Cu, Zn, and Mn could be 53.68%, 52.12%, and 30.63% by EDTA/HA washing and 42.36%, 39.67% and 28.49% by CA/HA washing, respectively. The European Community Bureau of Reference (BCR) sequential extraction was applied to analyze the fraction change of heavy metals in UFAMSW before and after washing, and it was found that chelating agent combined with HA could contribute to the removal of the exchangeable fraction. Physical and chemical properties of UFAMSW were improved to some extent after washing with mixed HA and chelating agent and could achieve the quality standard of landscape gardening soil. Accordingly, the mixture of HA and other chelating agents could be a promising washing process for preparation of landscape gardening soil using UFAMSW. Implications: Our manuscript studies the removal of heavy metals from the contaminated undersized fraction from aged municipal solid waste (UFAMSW). UFAMSW, as a kind of soil-like material, has been proved effective in providing a large amount of organic matter and nutrients for soil and plants however often limited by heavy metal pollution. The UFAMSW used in this experiment was collected after the excavation and screening-sorting of aged refuse from Changshankou Domestic Waste Sanitary Landfill in Wuhan City, Hubei Province, Southern China. This study investigated the effects of EDTA, CA, HA, mixed EDTA/HA, and mixed CA/HA washing on heavy metal removal (Cu, Zn, and Mn), bioavailability of residual heavy metal and properties. The effects of chelating agent concentration, pH, and washing time on metal removal were investigated and then response surface methodology was employed to optimize the washing conditions. The results showed that washing by CA/HA and EDTA/HA, had a higher removal efficiency of heavy metals (Cu, Zn, and Mn) in UFAMSW compared to single HA. Meanwhile, HA has a higher removal for exchangeable fraction of heavy metals, the exchangeable concentration of Cu, Zn, and Mn in CA/HA and EDTA/HA washed UFAMSW were lower compared with UFAMSW washed by single CA and EDTA. Thus, mixing HA with EDTA or CA makes a less risk to environmental and the removal efficiency is acceptable. Additionally, CA/HA and EDTA/HA washing tend to improve soil physicochemical properties and soil fertility. Thus, mixing HA with different washing agent are potential methods for preparation of landscape gardening soil using UFAMSW. [ABSTRACT FROM AUTHOR]

COVID-19 is a disease caused by infection with the virus 2019-nCoV, a single-stranded RNA virus. During the infection and transmission processes, the virus evolves and mutates rapidly, though the disease has been quickly controlled in Wuhan by 'Fangcang' hospitals. To model the virulence evolution, in this paper, we formulate a new age structured epidemic model. Under the tradeoff hypothesis, two special scenarios are used to study the virulence evolution by theoretical analysis and numerical simulations. Results show that, before 'Fangcang' hospitals, two scenarios are both consistent with the data. After 'Fangcang' hospitals, Scenario I rather than Scenario II is consistent with the data. It is concluded that the transmission pattern of COVID-19 in Wuhan obey Scenario I rather than Scenario II. Theoretical analysis show that, in Scenario I, shortening the value of L (diagnosis period) can result in an enormous selective pressure on the evolution of 2019-nCoV. [ABSTRACT FROM AUTHOR]

The corona virus disease 2019 (COVID-19) pandemic, which originated in the city of Wuhan, China, has rapidly spread to various countries, with many cases having been reported. Some existing antiviral agents showed positive results to inhibit the infection and transmission of the novel corona virus 2019 (2019-nCoV) in vitro. Covaxin is India's first indigenous COVID-19 vaccine. In this paper, we introduce the Gourava and hyper-Gourava polynomials of a graph. We compute the Gourava and hyper-Gourava polynomials of some specific class of graphs. Further, we obtain the Gourava and hyper-Gourava polynomials of some chemical structures viz., chloroquine, hydroxychloroquine, remdesivir and theaflavin which were used in the treatment of COVID-19. The Gourava and hyper-Gourava indices are investigated for the aforesaid antiviral drugs using polynomial approach. The Indian Council of Medical Research and Bharat Biotech announced that Phase 3 results of covaxin, developed by them, have shown an interim vaccine efficacy of 81 percent. So, the results obtained may be useful for finding the design of new medicine for the treatment of COVID-19. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to ignite a conversation on the challenges of the ongoing 'novel' surrounding the coronavirus (SARS-CoV-2) pandemic in the Southern Africa region. The global COVID-19 pandemic started with the outbreak in Wuhan, China, in December 2019. It is understood that different countries, regions and territories will have different, context-specific challenges in fighting the pandemic. These will differ from country to country and therefore necessitates contextual research. By sharing African perspectives on swift action against this coronavirus, this paper is intended to appraise the nexus between the coronavirus and tourism in order to prepare tourism practitioners for the recovery strategies that will be needed to address the damages caused by the pandemic. This paper points to tourism researchers focusing on the Southern African region and possible key areas for further research as the coronavirus situation unfolds. It draws from published material and document analysis, including from academic research, media reports and social media platforms. [ABSTRACT FROM AUTHOR]

Population mobility plays an essential role in the diffusion of knowledge. To the best of our knowledge, the existing literature focuses on the migration of workers between regions. Still, we employ a temporary policy of human mobility restriction in China to estimate the effect of COVID-19 on innovation. We find that the number of application invention patents decreases remarkably 1.813 using RD design and remains declined even if we rule out the Spring Festival and the viral effects using RD-DID. Moreover, the mechanism research suggests that the lockdown causes a decline in the population mobility within Wuhan city and inflow from other cities and we rule out the mechanism that skilled labours flow out Wuhan. In addition, we further find that institutional innovations have a significant reduction, especially invention patents in universities. This study highlights the enormous negative impact of human mobility restrictions on innovation due to COVID-19. [ABSTRACT FROM AUTHOR]

Though mesh generated by UAV-based oblique photogrammetry has revolutionized the large urban scene data representation, its semantic segmentation still challenging due to structural irregularities. In this paper, a deep learning model is proposed for the mesh semantic segmentation. To efficiently capture local geometry and neighbourhood context within the mesh, graph concept and transformer blocks are employed for mesh representation and feature learning. Based on these components, a hierarchical network architecture is presented. Experimental results demonstrate that the proposed network performs well on the self-made Wuhan dataset. The ablation study shows the improvement brought by each component in the network. By comparison, both mIoU and mF1 of our network are much higher than the prior work on the public SUM dataset, achieving 83.7% and 73.7%. [ABSTRACT FROM AUTHOR]

Vehicles have been increasingly equipped with GPS receivers to record their trajectories, which we call floating car data. Compared with other data sources, these data are characterized by low cost, wide coverage, and rapid updating. The data have become an important source for road network extraction. In this paper, we propose a novel approach for mining road networks from floating car data. First, a Gaussian model is used to transform the data into bitmap, and the Otsu algorithm is utilized to detect road intersections. Then, a clothoid-based method is used to resample the GPS points to improve the clustering accuracy, and the data are clustered based on a distance-direction algorithm. Last, road centerlines are extracted with a weighted least squares algorithm. We report on experiments that were conducted on floating car data from Wuhan, China. To conclude, existing methods are compared with our method to prove that the proposed method is practical and effective. [ABSTRACT FROM AUTHOR]

The unprecedented urbanization in China has dramatically changed the urban spatial structure of cities. With the proliferation of individual-level geospatial big data, previous studies have widely used the network abstraction model to reveal the underlying urban spatial structure. However, the construction of network abstraction models primarily focuses on the topology of the road network without considering individual travel flows along with the road networks. Individual travel flows reflect the urban dynamics, which can further help understand the underlying spatial structure. This study therefore aims to reveal the intra-urban hierarchical spatial structure by integrating the road network abstraction model and individual travel flows. To achieve this goal, we 1) quantify the spatial interaction relatedness of road segments based on the Word2Vec model using large volumes of taxi trip data, then 2) characterize the road abstraction network model according to the identified spatial interaction relatedness, and 3) implement a community detection algorithm to reveal sub-regions of a city. Our results reveal three levels of hierarchical spatial structures in the Wuhan metropolitan area. This study provides a data-driven approach to the investigation of urban spatial structure via identifying traffic interaction patterns on the road network, offering insights to urban planning practice and transportation management. [ABSTRACT FROM AUTHOR]

This study examined the association between sense of community and neighbourhood governance. Based on residents' evaluation of the service efficacies of the government, market and resident agencies, three types of neighbourhood governance are identified: tripartite balance governance, weak-market governance and government-dominated governance. The tripartite balance governance does not necessarily lead to a higher sense of community than the government-dominated governance, but the latter significantly promotes a higher sense of community than the weak-market governance. The diversity of agencies is not necessarily conducive to sense of community, whilst those agencies' horizontal cooperation based on even power relations promotes sense of community. [ABSTRACT FROM AUTHOR]

During the ongoing Covid-19 pandemic, telemedicine has provided people with a technical solution to alleviate chronic disease management without the fear of cross-contamination of Covid-19. This research adopted semi-structured interviews to understand Wuhan senior citizens' intentions to use digital devices for chronic disease management during and after the lockdown. The research illustrated how citizens in Wuhan managed their medical requirements by the 'grid-managed' online community. This paper also revealed different emphases on telemedicine between middle-aged and senior citizens. In addition, the findings suggested that cultural background, medical literacy, and socio-economic infrastructures play a pivotal role in influencing senior citizens' perceptions and use of telemedicine and telecare services. The findings also identified the key role of social networks which we term as a new form of acquaintance society. [ABSTRACT FROM AUTHOR]

This paper investigates, for the first time, the presence of financial contagion among several important Chinese coronavirus concept-based stock indices during the recent COVID-19 global pandemic. We utilize a regime-switching skew-normal (RSSN) methodology to test for contagion through the correlation and coskewness channels while considering structural breaks in the different moments. Our results present evidence of contagion effects, which are robust across identified crisis and non-crisis periods, including that of the Wuhan lockdown. Our empirical results offer investors and policy-makers an additional layer of information when evaluating response mechanisms to major crises through the use of concept-based indices. [ABSTRACT FROM AUTHOR]

COVID-19 has posed a serious threat to more than 200 countries, causing over one million deaths worldwide (as of December 2020) and leading to lockdowns that are unprecedented in modern times. Given the physical restrictions, social media platforms have become crucial for people to maintain contact and share ideas during the pandemic. In this paper, I examine the discursive representations of evaders of the Wuhan lockdown. Specifically, I investigate how social media users employed animal metaphors to portray the identities of people who fled Wuhan during this time. More than 250 posts with over 15,000 comments were collected from the Chinese microblogging site Weibo; data were thematically analyzed, and metaphors were identified. The results demonstrated that various kinds of animal metaphors were used to discredit the evaders and to highlight their objectionable behavior and moral standards. The use of violent expressions associated with animal metaphors also revealed the issue of dehumanization vis-à-vis all the residents in Wuhan, which has various theoretical and ideological implications. The findings suggest that, while dehumanizing the evaders by likening their health status to that of infected animals, the users unconsciously revealed the evaders' helplessness and inability to control their situation, reflecting ideological ambivalence. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) remains a global pandemic at present. Although the human-to-human transmission route for this disease has been well established, its transmission mechanism is not fully understood. In this paper, we propose a mathematical model for COVID-19 which incorporates multiple transmission pathways and which employs time-dependent transmission rates reflecting the impact of disease prevalence and outbreak control. Applying this model to a retrospective study based on publicly reported data in China, we argue that the environmental reservoirs play an important role in the transmission and spread of the coronavirus. This argument is supported by our data fitting and numerical simulation results for the city of Wuhan, for the provinces of Hubei and Guangdong, and for the entire country of China. [ABSTRACT FROM AUTHOR]

Classification-based methods for estimating water quality parameter (WQP) using remote sensing have shown great application potential in inland waters. Water classification algorithms have seen progress in water remote sensing. In this paper, we conducted the Secchi Depth value (ZSD) estimation based on a global water typology for the Wuhan area. Firstly, we classified the water into seven types using the Sentinel-2 data. The procedure was based on spectral angle mapping (SAM) of the 13 inland water optical types (IWOTs). Afterwards, seven IWOTs were summarized into four categories for Wuhan area. We then developed empirical models for each water category by stepwise multiple linear regression, generalized regression neural network (GRNN), and sparse spectrum Gaussian process regression (SSGPR), and applied the better approaches (GRNN and SSGPR) to three full satellite images (27 October 2018, 10 May 2019, and 29 July 2019). Finally, the retrieved results were validated using in situ-satellite match-ups and compared with the results based on unclassified imagery. With root-mean-square error (RMSE) of three satellite-derived results reduced from 0.32 m (without classification) to 0.16 m (with classification), and mean absolute percentage error (MAPE) reduced from 52% to 18%, from 0.51 m (MAPE = 57%) to 0.19 m (MAPE = 21%), and from 0.18 m (MAPE = 35%) to 0.09 m (MAPE = 17%), ZSD estimations over optically complex waters were improved based on this water classification. Due to its low cost and ease of operation, the SAM – derived classification applied in this paper provides a possibility for dynamic and high-precision monitoring for water management. [ABSTRACT FROM AUTHOR]

Daily spatial distributions of water quality parameters (WQPs) are a crucial indicator in delicacy management of lake water environment in China. However, because of the high cost of construction and maintenance, there are a few automatic monitoring stations (AMSs) for continuous water sampling with only ultra-sparse spatial sampling data can be obtained. Meanwhile, although WQPs from satellite inversion are spatially continuous, they are missing on most days due to severe affected by weather and satellite revisit cycle, besides the high purchase cost. Thus, the model called MLP-GPR is formulated in this study to address the issue of predicting the spatial distribution of WQPs from ultra-sparse sampling data of AMSs combined with the spatial correlation information based on ultra-sparse satellite inversion data. This model includes the multilayer perceptron model (MLP) and Gaussian process regression model (GPR). MLP is trained to learn the interaction factors of WQPs between different locations through the satellite inversion data for predicting WQPs at more locations based on the data measured by AMSs. Then, GPR regresses the spatial distribution of WQPs at all locations on the entire lake from the predicted data by MLP. Based on the above model, we applied the satellite inversion, AMSs and artificial sampling data to predict the WQP spatial distribution of Shahu Lake in Wuhan, China. The experiment results show that the spatial resolution of WQP distribution predicted by MLP-GPR trained by satellite inversion data can reach 15 m by sampling data from artificial stations on the lake with 3.197 km2. Moreover, for each of these WQPs, compared with the satellite inversion data, the average MRE, R, and NSE of prediction results are below 3.95%, above 0.85, and above 0.69, respectively. [ABSTRACT FROM AUTHOR]

The global ecosystem is facing a severe situation, with increasing ecological risks threatening ecosystem protection. The research is conducted based on land use data and socio-economic data of Wuhan during the three periods of 2000, 2010, and 2020. Various models including the ecosystem service value model, ecosystem risk model, bivariate spatial autocorrelation model, and CA-Markov model are employed to calculate the spatial and temporal evolution as well as the correlation of ecosystem service values and ecological risks in Wuhan City over the past 20 years. Moreover, the study aims to predict the ecosystem service values and ecological risks for the year 2030 and establish an ecological zoning framework accordingly. The results show that: (1) The value of Wuhan's ecosystem services show a trend of first decreasing and then increasing in the past 20 years. (2) The ecological risks of Wuhan show an overall upward trend, and the ecological risks in the study area are generally characterized by "high in the middle and low around". (3) There is a significant positive spatial correlation between the value of ecosystem services and ecological risks in Wuhan. The overall ecological risk in the study area in 2030 is relatively alleviated compared with the previous two periods, and the ecological zones with a larger proportion are "low-low" and "low-high" zones. This study can provide critical information for building resilient cities. [ABSTRACT FROM AUTHOR]

This article examines China's emotional governance by analysing the emotional politics behind the wide circulation of the Fang Fang diaries during the early stages of the COVID-19 outbreak. By highlighting the flexible and creative reorientation of online emotions in the Chinese state's governance approach, the article investigates how the diaries' reverberation of negative feelings – such as grief and indignation – engendered contentious affective publics and amounted to a moment of dissent. The article also explores how the state used multi-layered strategies to demobilise and reorient public sentiment and opinion into regime-supportive nationalism. In doing so, the article offers a more nuanced understanding of state–society relations, online expression and authoritarian resilience in China than interpretations that emphasise suppression and censorship. [ABSTRACT FROM AUTHOR]

Since February 2020, COVID-19 has spread rapidly to more than 200 countries in the world. During the pandemic, local governments in China have implemented different interventions to efficiently control the spread of the epidemic. Characterizing transmission of COVID-19 under some typical interventions is essential to help countries develop appropriate interventions. Based on the pre-symptomatic transmission patterns of COVID-19, we established a novel compartmental model: Susceptible-Infectious-Confirmed-Removed (SICR) model, which allowed the effective reproduction number to change over time, thus the effects of policies could be reasonably estimated. Using the epidemic data of Wuhan, Wenzhou, and Shenzhen, we migrated the corresponding estimated policy modes to South Korea, Italy, and the United States and simulated the potential outcomes for these countries when they adopted similar policy strategies to China. We found that the mild interventions implemented in Shenzhen were effective in controlling the epidemic in the early stage, while more stringent policies which were implemented in Wuhan and Wenzhou were necessary if the epidemic became severe and needed to be controlled in a short time. [ABSTRACT FROM AUTHOR]

The dockless bike-sharing system has rapidly expanded worldwide and has been widely used as an intermodal transport to connect with public transportation. However, higher flexibility may cause an imbalance between supply and demand during daily operation, especially around the metro stations. A stable and efficient rebalancing model requires spatio-temporal usage patterns as fundamental inputs. Therefore, understanding the spatio-temporal patterns and correlates is important for optimizing and rescheduling bike-sharing systems. This study proposed a dynamic time warping distance-based two-dimensional clustering method to quantify spatio-temporal patterns of dockless shared bikes in Wuhan and further applied the multiclass explainable boosting machine to explore the main related factors of these patterns. The results found six patterns on weekdays and four patterns on weekends. Three patterns show the imbalance of arrival and departure flow in the morning and evening peak hours, while these phenomena become less intensive on weekends. Road density, living service facility density and residential density are the top influencing factors on both weekdays and weekends, which means that the comprehensive impact of built-up environment attraction, facility suitability and riding demand leads to the different usage patterns. The nonlinear influence universally exists, and the probability of a certain pattern varies in different value ranges of variables. When the densities of living facilities and roads are moderate and the relationship between job and housing is relatively balanced, it can effectively promote the balanced usage of dockless shared bikes while maintaining high riding flow. The spatio-temporal patterns can identify the associated problems such as imbalance or lack of users, which could be mitigated by corresponding solutions. The relative importance and nonlinear effects help planners prioritize strategies and identify effective ranges on different patterns to promote the usage and efficiency of the bike-sharing system. [ABSTRACT FROM AUTHOR]

Previous studies generally used land use and travel flow to investigate the efficiency of the railway system in Transit-Oriented Development (TOD) cities. Furthermore, to study the association of land use and jobs–housing distribution of commuters, we can find out the potential development of rail commuting. In this research, four railway lines in Wuhan, China, were selected to explore the land use in promoting practical commuting population according to the smart-card data obtained. For land use issues, except the road density and building density, a Normalized Location-Weighted Landscape Index (NLWLI) based on the source – sink theory in landscape ecology was established to assess the jobs–housing land use around rail transit stations. Meanwhile, employment and housing details of commuters around rail transit stations were identified using smart-card data. We found that the generation of commuting flow was affected by building density and the land use of employment in the immediate vicinity of rail transit stations. The distribution of building density and commuters in a mature rail line was roughly a normal distribution. However, due to the inconsistency of land use and jobs – housing distribution around stations, the requirement of the balance of land use and jobs – housing distribution should be reduced at the scale of rail stations in the TOD planning systems. This study is the application of massive smart-card data in the field of urban research. It identifies land use issues that affect rail transit commuting flow, and can help urban planners improve the efficiency of rail transit through planning and design. [ABSTRACT FROM AUTHOR]

Most countries and territories worldwide are affected by coronavirus disease 2019 (COVID-19), and some cities have become known as epicenters owing to high outbreaks. Because of the changeable and unknown nature of the virus, managers of different cities could learn from the experiences of cities that have been successful in controlling COVID-19 instead of wasting time exploring different methods. It would be even more beneficial if they analyzed the experiences of similar cities. The similarity of such cities could be examined within a geographic information system based on various criteria. This study investigated the similarities among eight cities – Wuhan, Tehran, Bergamo, Madrid, Paris, Daegu, New York, and Berlin – in terms of the COVID-19 situation (target) in these locations based on proximity factors, weather, and demographic criteria. First, the factor and target layers were prepared, and then similar cities were identified using a similarity model and different distance metrics. The results were aggregated using the Copeland method because of the different outcomes for each metric. The most similar city was identified for each selected city, and its similarity level was determined based on these criteria. The results suggested the following pairs of similar cities: Wuhan–Berlin, Tehran–Berlin, Daegu–Wuhan, Bergamo–Madrid, Paris–Madrid, and New York–Paris based on COVID-19 related data up to 15 April 2020 (target T1), and Daegu–Wuhan, Tehran–Madrid, Bergamo–Paris, Berlin–Paris, and New York–Madrid up to 8 December 2021 (target T2) with a minimum and maximum similarity rate of 82.85% and 92.36%, respectively. For similar cities, the most similar factors among the proximity criteria are the distance from bus and metro stations; among weather, the criteria are humidity and pressure; and among demographics, the criteria are male and female population ratios, literacy ratio, and death ratio from asthma and cancer, with a minimum and maximum difference of 0% and 64.94%, respectively. In addition, according to the random forests ranking results (with root mean squared error = 0.23), temperature, distance from the bank, and gender were the most important criteria for the eight studied cities. Identifying these important factors helps to determine hotspots or places of future outbreaks to choose control strategies according to the cultural and ecological conditions of each city. [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]

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]

The outbreak of COVID-19 was first experienced in Wuhan City, China, during December 2019 before it rapidly spread over globally. This paper has proposed a mathematical model for studying its transmission dynamics in the presence of face mask wearing and hospitalization services of human population in Tanzania. Disease-free and endemic equilibria were determined and subsequently their local and global stabilities were carried out. The trace-determinant approach was used in the local stability of disease-free equilibrium point while Lyapunov function technique was used to determine the global stability of both disease-free and endemic equilibrium points. Basic reproduction number, R 0 , was determined in which its numerical results revealed that, in the presence of face masks wearing and medication services or hospitalization as preventive measure for its transmission, R 0 = 0.698 while in their absence R 0 = 3.8. This supports its analytical solution that the disease-free equilibrium point E 0 is asymptotically stable whenever R 0 < 1 , while endemic equilibrium point E ∗ is globally asymptotically stable for R 0 > 1. Therefore, this paper proves the necessity of face masks wearing and hospitalization services to COVID-19 patients to contain the disease spread to the population. [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]

The coronavirus outbreak in Wuhan, China has sparked a global epidemic, which the World Health Organization declared a public health emergency of international concern on 31st January 2020 (Beijing time). This crisis has attracted intense media attention. Recently, some media outlets inappropriately labelled the coronavirus by race, using such headlines as "Chinese virus pandemonium" and even suggesting "China kids stay home." The biased and misleading coverage presented via Western media channels has incited anger throughout the Chinese community and has placed undue stress upon Chinese individuals living outside China. This post-published review takes a tourism-focused perspective to examine findings from a quantitative study (Rodriguez-Seijas, Stohl, Hasin, & Eaton, 2015) published in 2015 in JAMA Psychiatry. The current paper highlights the potential impacts of misleading and biased media coverage on Chinese individuals' mental health. Specifically, this work considers perceived racial discrimination stemming from coronavirus as a public health crisis and the effects of such discrimination on individuals of Chinese heritage. Similarly imperative are pertinent effects on country image and destination image with respect to tourism marketing and tourist behaviour during times of crisis. By considering racism in the context of the coronavirus outbreak, this paper identifies potential avenues for relevant research in tourism and hospitality. [ABSTRACT FROM AUTHOR]

Coronaviruses (CoVs) are a large family of viruses and are endemic in humans and animals, causing respiratory and intestinal infections. Coronavirus has become a challenge in China region due to its recent outbreak at the start of the year 2020. The current outbreak of coronavirus disease (COVID) has resulted in many fatalities and has forced the people of Wuhan Province in China to remain confined in their homes. Two other two forms of CoV were epidemic in 2003 when Severe Acute Respiratory Syndrome (SARS-CoV) spread in Hong Kong and Middle East Respiratory Syndrome (MERS-CoV) spread in the Middle East region. This bibliometric study is an attempt to trace the trends of research associated with "Coronavirus" for a period of 50 years using the SCOPUS database. The study was carried out using the keyword Coronavirus and analyzed for annual growth, productive countries, institutes, authors, journals, highly cited papers, and research focus using keywords. There were two peaks when a large number of publications appeared. Most of the research publications were from the USA (31.67%), and the University of Hong Kong was the most productive institute. The Journal of Virology has published the most number of articles on CoV. [ABSTRACT FROM AUTHOR]

This paper provides a study of the changes in land use in urban environments in two cities, Wuhan, China and western Sydney in Australia. Since mixed pixels are a characteristic of medium resolution images such as Landsat, when used for the classification of urban areas, due to changes in urban ground cover within a pixel, Multiple Endmember Spectral Mixture Analysis (MESMA) together with Super-Resolution Mapping (SRM) are employed to derive class fractions to generate classification maps at a higher spatial resolution using an Artificial Neural Network (ANN) predicted Wavelet method. Landsat images over the two cities for a 30-year period, are classified in terms of vegetation, buildings, soil and water. The classifications are then processed using Indifrag software to assess the levels of fragmentation caused by changes in the areas of buildings, vegetation, water and soil over the 30 years. The extents of fragmentation of vegetation, buildings, water and soil for the two cities are compared, while the percentages of vegetation are compared with recommended percentages of green space for urban areas for the benefit of health and well-being of inhabitants. Changes in Ecosystem Service Values (ESVs) resulting from the urbanization have been assessed for Wuhan and Sydney. The UN Sustainable Development Goals (SDG) for urban areas are being assessed by researchers to better understand how to achieve the sustainability of cities. [ABSTRACT FROM AUTHOR]

As a key component of electronic commerce, online shopping logistics service quality (OSLSQ) has received extensive attention in recent years. Many studies have focused on how to appraise and improve logistics service quality, while little research has been done on the potential value of improving OSLSQ. Based on 1236 questionnaires completed in Beijing, Wuhan, Changsha and Zhoukou, this paper contributes to the study of customer perceived value of OSLSQ by using an ordinal logit regression model. The results indicate that customer perceived value of OSLSQ varies across customers according to their age, education and location, but no difference is found between males and females. In addition, customer perceived value is closely linked with customer satisfaction. [ABSTRACT FROM AUTHOR]

The satellite visibility number of GNSS is an important indicator for evaluating its availability for positioning and navigation. In urban areas, urban canyons cause serious satellite signals block, resulting in positioning uncertainty. Many studies used 3D city models to evaluate the visible satellites in some areas at a certain time. Nevertheless, this kind of method is difficult to apply because 3D models are not widely available. This paper thus proposes an easy method to evaluate the visibility of satellites with widely available street view panoramic imagery and GNSS ephemeris. The proposed method utilizes the locations of street view panoramic imagery and the associated GNSS ephemeris to calculate the visible satellite number at different times. Hence, the visible satellite number at a specific time can be mapped. Moreover, the visibility of satellites can be predicted according to its orbit parameters. To evaluate the effectiveness of the proposed method, Wuhan and Shanghai were taken to map post-event, real-time and forecast GNSS visibility. The experiments demonstrated that the proposed method provides a light weighted and easy to use solution to map the spatio-temporal visibility of satellites in urban areas, which is an important reference for GNSS stations layout and positioning qualities evaluation. [ABSTRACT FROM AUTHOR]

Urban waterlogging probability assessment is critical to emergency response and policymaking. Remote Sensing (RS) is a rich and reliable data source for waterlogging monitoring and evaluation through water body extraction derived from the pre- and post-disaster RS images. However, RS images are usually limited to the revisit cycle and cloud cover. To solve this issue, social media data have been considered as another data source which are immune to the weather such as clouds and can reflect the real-time public response for disaster, which leads itself a compensation for RS images. In this paper, we propose a coarse-to-fine waterlogging probability assessment framework based on multisource data including real-time social media data, near real-time RS image and historical geographic information, in which a coarse waterlogging probability map is refined by using the real-time information extracted from social media data to acquire a more accurate waterlogging probability. Firstly, to generate a coarse waterlogging probability map, the historical inundated areas are derived from Digital Elevation Model (DEM) and historical waterlogging points, then the geographic features are extracted from DEM and RS image, which will be input to a Random Forest (RF) classifier to estimate the likelihood of hazards. Secondly, the real-time waterlogging-related information is extracted from social media data, where the Convolutional Neural Network (CNN) model is applied to exploit the semantic information of sentences by capturing the local and position-invariant features using convolution kernel. Finally, fine waterlogging probability map scan be generated based on morphological method, in which real-time waterlogging-related social media data are taken as isolated highlight point and used to refine the coarse waterlogging probability map by a gray dilation pattern considering the distance-decay effect. The 2016 Wuhan waterlogging and 2018 Chengdu waterlogging are taken as case studies to demonstrate the effectiveness of the proposed framework. It can be concluded from the results that by integrating RS image and social media data, more accurate waterlogging probability maps can be generated, which can be further applied for inundated areas identification and disaster monitoring. [ABSTRACT FROM AUTHOR]

In December 2019, the SARS-CoV-2 virus was first detected in Wuhan, China. Soon after, China became the first country in the world to enforce strict lockdown protocols in an effort to mitigate the spread of the disease. This study examines the perceptions of pandemic rules enforcement during the first COVID-19 lockdown in 2020 among 600 Chinese police officers. The analyses reveal that police departments' prompt and adequate adjustment to the pandemic, police officers' procedural justice in interacting with citizens, and police perceptions of police trustworthiness among the citizens all had direct and indirect effects on public compliance to lockdown enforcement during the pandemic. These findings affirm the role of police procedural justice in public compliance to law enforcement in China and stress the importance of organizational adjustment and public trust during an emergency situation such as the COVID-19 pandemic. Implications to pandemic-related policymaking and enforcement are also discussed. [ABSTRACT FROM AUTHOR]

The coronavirus pandemic had a tremendous impact on the hotel industry worldwide. The present study highlights strategic directions for quality improvements and customer satisfaction enhancement in Wuhan hotels by applying Multiple Criteria Satisfaction Analysis to online user-generated satisfaction ratings. In total, 21.247 guest satisfaction ratings on four aspects of hotel stay (location, cleanliness, value for money and service) as well as on overall satisfaction for the 406 hotels listed for Wuhan on TripAdvisor were retrieved. The analysis revealed that convenient location is the most important satisfaction criterion for hotel guests' satisfaction and that hotels perform well regarding this criterion. Also, cleanliness, value for moneyand service can be potential threats for customer satisfaction. [ABSTRACT FROM AUTHOR]

In the Silurian, the suborder Caryocaridina of the phyllocarids was replaced by the suborder Ceratiocaridina as the dominant group. The latter did not achieve a global distribution until the late Silurian. In the early Silurian, ceratiocarids were a little-diversified group and palaeogeographically restricted mainly to Laurussia. Although previous studies have mentioned phyllocarid fossils from the Silurian of the South China plate, these have never been systematically described. This study describes three genera and four species (including one new genus, two new species, and two undetermined species), i.e. Cugocaris future gen. et sp. nov., Gonatocaris wuhanensis sp. nov., Gonatocaris sp. and Warneticaris sp., based on 38 phyllocarid specimens from the Fentou Formation in Wuhan. Cugocaris future gen. et sp. nov. is distinguished by an elliptic carapace ornamented by sinuous and anastomosing longitudinal striae, a styliform telson, and slender furcal rami. Gonatocaris wuhanensis sp. nov. is characterized by anastomosing longitudinal striae on the carapace, greatest carapace width in the centre of the carapace, and three different types of ornamentations on the surface of the abdominal segments. A phylogenetic analysis of 41 Palaeozoic phyllocarids reveals that Gonatocaris, Rolfecaris and Cugocaris gen. nov. form a monophyletic group. Thus, a new family, Gonatocarididae fam. nov., is proposed. This family is characterized by a carapace lacking median dorsal plate, rostral plate, a well-developed anterior carapace horn, and ornamented with prominent raised, wavy, anastomosing longitudinal striae. A palaeoecological analysis suggests that G. wuhanensis was a fairly active swimmer, while C. future was necto-benthic with relatively weak swimming abilities. This study not only extends the known morphological range of phyllocarids, but also has implications for the functional morphology, taxonomy, and evolution of archaeostracans as a whole. [ABSTRACT FROM AUTHOR]

Urban logistics is vital to the development and operation of cities, and its optimization is highly beneficial to economic growth. The increasing customer needs and the complexity of urban systems are two challenges for current logistics optimization. However, little research considers both, failing to balance efficiency and cost. In this study, we propose a hybrid sparrow search algorithm (SA-SSA) by combining the sparrow search algorithm with fast computational speed and the simulated annealing algorithm with the ability to get the global optimum solution. Wuhan city was selected for logistics optimization experiments. The results show that the SA-SSA can optimize large-scale urban logistics with guaranteed efficiency and solution quality. Compared with simulated annealing, sparrow search, and genetic algorithm, the cost of SA-SSA was reduced by 17.12, 18.62, and 14.72%, respectively. Although the cost of SS-SSA was 11.50% higher than the ant colony algorithm, its computation time was reduced by 99.06%. In addition, the simulation experiments were conducted to explore the impact of spatial elements on the algorithm performance. The SA-SSA can provide high-quality solutions with high efficiency, considering the constraints of many customers and complex road networks. It can support realizing the scientific scheduling of distribution vehicles by logistics enterprises. [ABSTRACT FROM AUTHOR]

Spatial heterogeneity is an essential feature of land use change. It is important for cellular automata (CA)-based urban expansion modeling to consider the spatial heterogeneity of neighborhood effect. According to the first law of geography, there exists a distance decay effect in the neighborhood space of the urban CA models. But no research has ever been able to accurately quantify the neighborhood decay effect and apply it to models. In this study, we propose a methodology to accurately quantify the neighborhood decay effect of CA models with large neighborhood size, including the design of neighborhood configuration and the selection and optimization of decay strategies. Taking Wuhan and Guangzhou as examples, we show the implementation process of the methodology and simulate urban expansion of two areas for the periods 2000–2010 and 2010–2015 by CA models with distance decay neighborhood to reveal the advantages and characteristics of modeling the neighborhood decay effect. The results show that the simulation accuracy of CA models with the spatial heterogeneous neighborhood and the hybrid neighborhood (HYN) is higher than that of CA models with homogeneous neighborhood, and in most cases, the HYN can more accurately describe the neighborhood decay effect. [ABSTRACT FROM AUTHOR]

From a process perspective, a flood includes several phases with distinguishable features. Fine-grained multisource data for different flood phases can be used to inform decision-making as flooding progresses. Therefore, the aim of this study was to develop an integrated framework based on human perceptions to progressively profile floods, including flood process segmentation rules (FPSR), flood severity index (FSI) and flood process perception ontology (FPPO). FPSR identifies flood phases based on specific signals in multisource data and provides spatiotemporal process information to FPPO consistent with flood perception. FSI follows FPSR to evaluate flooding throughout its evolution process. The comparison between FPSR and the flood monitoring index (IF) demonstrates that FPSR can detect flood events and segment the flooding process into latency, onset, development and recovery phases. The correlations between the standardized antecedent precipitation index (SAPI) and FSI show that FSI can assess flood severity with both natural and social effects in every flooding phase (R2 = 0.726 and 0.673 for the 2016 and 2020 floods, respectively). An experiment finds that flood events in Wuhan, China, usually begin in mid-to-late June and are the most severe in July, when more caution is needed for flood prevention and mitigation. [ABSTRACT FROM AUTHOR]

By exploiting the lockdown of Wuhan on January 23, 2020, during the COVID-19 pandemic and the disclosure of public firms' top five suppliers, we examine the impact of a supply chain disruption on stock returns. Our findings suggest that firms with major suppliers in Wuhan experience significantly worse cumulative abnormal returns than those whose suppliers are not located in Wuhan. The results are robust to alternative estimation methods, event windows, and supply chain disruption metrics. Our findings suggest that supply chain disruption contributes to negative stock returns and highlight the importance of supply chain disruption on firm value. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has been a worldwide crisis. During the pandemic, social workers have proactively participated in providing support. The Good Companions Response Team is a volunteer team started by social workers in Wuhan, China and serving people living in China and Chinese communities around the world. This paper introduces the experiences of the team in the makeshift hospitals in Wuhan and the '4 + 1 Online-Offline Remote Support Model' developed by the team. Our goal is to inspire social workers and other professionals in the Asian Pacific regions to work collaboratively during a similar public health emergency in the future. [ABSTRACT FROM AUTHOR]

The lifecycle of COVID-19 pandemic curves requires timely decisions to protect public health while minimizing the impact to global economy. New models are necessary to predict the effect of mobility suppression/reactivation decisions at a global scale. This research presents an approach to understand such tensions by modeling air travel restrictions during the new coronavirus outbreak. The paper begins with an updated review on the impact of air mobility in infectious disease progression, followed by the adoption of complex networks based on semi-supervised statistical learning. The model can be used to (1) determine the early identification of infectious disease spread via air travel and (2) align the need to keep the economy working with open connections and the different dynamic of national pandemic curves. The approach takes advantage of open data and machine self-supervised statistical learning to develop knowledge networks visualization. Test cases using Hong Kong and Wuhan aerial mobility are discussed in the decisions to (1) restrict and (2) increase mobility. The approach may also be of governments use in their international cooperation policy and commercial companies that need to choose how to prioritize the re-opening of international trade routes. [ABSTRACT FROM AUTHOR]

Improving the digital citizenship of secondary vocational students is critical for cultivating vocational talents in education. It is an inevitable requirement for modernizing vocational education. Focusing on social media competence, this study explores possible ways to improve the digital citizenship of secondary vocational students. A total of 469 secondary vocational students in Wuhan city, Hubei province, were investigated using questionnaires. The data analysis shows that the four dimensions of social media competence (technical use, content interpretation, content generation, and anticipatory reflection) significantly predict digital citizenship. On this basis, we propose four paths for improving the digital citizenship of secondary vocational students: creating a suitable environment for secondary vocational students to apply their skills in information technology; enhancing the ability of secondary vocational students to discriminate about information on the Internet; improving students' interpersonal communication ability when dealing with technical support; building an early warning system for secondary vocational students' behavioral perception. [ABSTRACT FROM AUTHOR]

Accurate traffic flow prediction on the urban road network is an indispensable function of Intelligent Transportation Systems (ITS), which is of great significance for urban traffic planning. However, the current traffic flow prediction methods still face many challenges, such as missing values and dynamic spatial relationships in traffic flow. In this study, a dynamic temporal graph neural network considering missing values (D-TGNM) is proposed for traffic flow prediction. First, inspired by the Bidirectional Encoder Representations from Transformers (BERT), we extend the classic BERT model, called Traffic BERT, to learn the dynamic spatial associations on the road structure. Second, we propose a temporal graph neural network considering missing values (TGNM) to mine traffic flow patterns in missing data scenarios for traffic flow prediction. Finally, the proposed D-TGNM model can be obtained by integrating the dynamic spatial associations learned by Traffic BERT into the TGNM model. To train the D-TGNM model, we design a novel loss function, which considers the missing values problem and prediction problem in traffic flow, to optimize the proposed model. The proposed model was validated on an actual traffic dataset collected in Wuhan, China. Experimental results showed that D-TGNM achieved good prediction results under four missing data scenarios (15% random missing, 15% block missing, 30% random missing, and 30% block missing), and outperformed ten existing state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Production fragmentation across multiple regions can result in a regional shock propagating along value chains to a wider array of regions. We propose a methodological framework to measure the economic exposure to regional value chain disruptions due to city lockdown during Covid-19. The exposure index is evaluated by applying a hypothetical extraction method to a regionally extended inter-country input–output framework incorporating China's interregional input–output table. Our methodology can be adapted to conduct disaster impact analyses at city, state and country levels. It provides a tool for the immediate assessment of the economic risks of value chain disruptions, enabling quick policy responses. [ABSTRACT FROM AUTHOR]