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• Propose a new nonsmooth two-stage stochastic equilibrium model of medical supplies in epidemic management. • Transform to a monotone two-stage stochastic variational inequality model that is computationally tractable. • Decisions with fairness, including storage and dynamic distribution by competitions among hospitals. • Efficient and convergent progressive hedging method enrolling semismooth Newton method for subproblems. • Case study in Wuhan suffered from the COVID-19 pandemic. The storage and distribution of medical supplies are important parts of epidemic prevention and control. This paper first proposes a new nonsmooth two-stage stochastic equilibrium model of medical supplies in epidemic management. The first stage addresses the storage in the pre-disaster phase, and the second stage focuses on the dynamic distribution by enrolling competitions among multiple hospitals over a period of time in the post-disaster phase. The uncertainties are the numbers of infected people treated in multiple hospitals during the period of time, which are time-varying around a nominal distribution predicted by historical experience. The two-stage stochastic equilibrium model is further approximated and transformed to a monotone two-stage stochastic variational inequality (SVI) model that is computationally tractable, with the aid of a smooth approximation technique. We employ the progressive hedging method (PHM) to solve a case study in the city of Wuhan in China suffered from the COVID-19 pandemic. Numerical results are presented to demonstrate the effectiveness of the proposed model in planning the storage and dynamic distribution of medical supplies in epidemic management. [ABSTRACT FROM AUTHOR]

• The latest summary of the COVID-19 outbreak in China; • There might be an oral-fecal transmission of the virus; • Aggregates and consolidates the epidemiology, clinical manifestations, diagnosis, treatments and preventions of this new type of coronavirus. There is a current worldwide outbreak of the novel coronavirus Covid-19 (coronavirus disease 2019; the pathogen called SARS-CoV-2; previously 2019-nCoV), which originated from Wuhan in China and has now spread to 6 continents including 66 countries, as of 24:00 on March 2, 2020. Governments are under increased pressure to stop the outbreak from spiraling into a global health emergency. At this stage, preparedness, transparency, and sharing of information are crucial to risk assessments and beginning outbreak control activities. This information should include reports from outbreak site and from laboratories supporting the investigation. This paper aggregates and consolidates the epidemiology, clinical manifestations, diagnosis, treatments and preventions of this new type of coronavirus. [ABSTRACT FROM AUTHOR]

• Trade-offs in compensated cropland quality and ecosystem services exist. • Lower cropland quality yet higher ecological cost as compensated cropland increase. • Strict cropland protection is not suitable for rapidly urbanizing areas. Urban expansion is taking in cropland at unprecedented rates worldwide, especially in China. In order to guarantee national food security, China has been implementing the strictest cropland protection policies in the world. The purpose of cropland protection policies in China is to keep the quantity and quality of cropland from declining. The loss of cropland resulted from urbanization is required to be compensated by occupying natural or semi natural land, which leads to a sharp degradation of the terrestrial ecosystem services. As a result, it is crucial to explore the relationship between cropland quality and ecosystem services with different amounts of compensated cropland required by cropland protection policies. In this paper, we evaluated the impacts of the combination of urban expansion and cropland protection policies on trade-offs between cropland quality and ecosystem services in Wuhan, China. Specifically, we simulated land use changes with different amounts of cropland compensation in order to reveal the relationship between cropland quality and ecosystem services of marginal compensated cropland. Results show that, with more cropland are compensated, the quality of marginal compensated cropland decreases while the marginal loss of ecosystem services increases. Our study indicates that, an increasing amount of compensated cropland would result in relative lower quality of compensated cropland with relatively higher ecological cost. [ABSTRACT FROM AUTHOR]

An outbreak of coronavirus disease (COVID-19) in Wuhan, China caused by SARS-CoV-2 has led to a serious epidemic in China and other countries, resulting in worldwide concern. With active efforts of prevention and control, more and more patients are being discharged. However, how to manage these patients normatively is still challenging. This paper reports an asymptomatic discharged patient with COVID-19 who retested positive for SARS-CoV-2, which arouses concern regarding the present discharge standards of COVID-19. [ABSTRACT FROM AUTHOR]

• A deep learning COVID-19 diagnostic system is developed using state-of-the-art deep learning architectures. • A detailed analysis of different methods used for detecting COVID-19 is presented in this paper. • Chest X-rays from different sources are collected to build a robust classification model. • The developed diagnostic model provided efficient results, even with a limited number of input images. • The results produced in this work are validated by an expert radiologist. In recent months, a novel virus named Coronavirus has emerged to become a pandemic. The virus is spreading not only humans, but it is also affecting animals. First ever case of Coronavirus was registered in city of Wuhan, Hubei province of China on 31st of December in 2019. Coronavirus infected patients display very similar symptoms like pneumonia, and it attacks the respiratory organs of the body, causing difficulty in breathing. The disease is diagnosed using a Real-Time Reverse Transcriptase Polymerase Chain reaction (RT-PCR) kit and requires time in the laboratory to confirm the presence of the virus. Due to insufficient availability of the kits, the suspected patients cannot be treated in time, which in turn increases the chance of spreading the disease. To overcome this solution, radiologists observed the changes appearing in the radiological images such as X-ray and CT scans. Using deep learning algorithms, the suspected patients' X-ray or Computed Tomography (CT) scan can differentiate between the healthy person and the patient affected by Coronavirus. In this paper, popular deep learning architectures are used to develop a Coronavirus diagnostic systems. The architectures used in this paper are VGG16, DenseNet121, Xception, NASNet, and EfficientNet. Multiclass classification is performed in this paper. The classes considered are COVID-19 positive patients, normal patients, and other class. In other class, chest X-ray images of pneumonia, influenza, and other illnesses related to the chest region are included. The accuracies obtained for VGG16, DenseNet121, Xception, NASNet, and EfficientNet are 79.01%, 89.96%, 88.03%, 85.03% and 93.48% respectively. The need for deep learning with radiologic images is necessary for this critical condition as this will provide a second opinion to the radiologists fast and accurately. These deep learning Coronavirus detection systems can also be useful in the regions where expert physicians and well-equipped clinics are not easily accessible. [ABSTRACT FROM AUTHOR]

China's Green Building (GB) evaluation standard has always emphasized the use of Recycled Building Materials (RBM). However, the quality of RBM is a major obstacle to its promotion. Unlike previous studies, this study innovatively developed a four-way evolutionary game model including Local Government (LG), RBM suppliers, developers and homebuyers, and examined their strategy changes from the production of building materials to the purchase and use of homes. This paper uses MATLAB 2021b software to simulate and verify the model and uses the real market data of Wuhan city and Sanya city to analyze the influence of some key factors among 23 factors such as the price of purchasing natural building materials P 2 and the basic utility obtained by purchasing a house B. The results showed the following: (1) Among the four evolutionary stable strategies in this system, the optimal strategy combination lies when LG does not plan, manufacturers perform upcycling and when developers and homebuyers choose RBM. (2) High fines can force suppliers to perform upcycling, and developers will build houses according to homebuyers' preferences. If homebuyers are in immediate demand for homes, they will more willing to purchase homes built with RBM. (3) Considering the financial pressure of LG, it should promote the system in parallel with rewards and penalties in the early stage of development, while after a period of time, LG can stop making any subsidies and only retain the regulation of suppliers. This study helps to better understand the behaviors of various stakeholders in the full life cycle of RBM, and provides some reference for the development of RBM and GB in China. [ABSTRACT FROM AUTHOR]

For intelligent risk prediction and optimization in the shield tunnel excavation under uncertainty, this paper proposes a data-driven decision support framework based on the integration of deep neural network and gradient descent technique (DNN-GDO). More specifically, the DNN architecture for modeling non-linear relationships between influential factors and potential risk is superior in concurrently extracting and learning features from multi-variable inputs, while the GDO technique as a solution for the multi-objective optimization problem can be directly applied in the established DNN model to provide suggestions for reducing or preventing potential risks in the tunneling environment. To reveal the effectiveness of the developed approach, it is performed in a practical tunnel construction case in the Wuhan metro system, China. Sixteen decision variables belong to four categories of tunnel design, geology, the tunnel boring machine (TBM) operation, and surrounding building are taken into account, which are closely relevant to two significant risks named the accumulative ground settlement and building tilt rate. It has been found that the end-to-end learning pipeline based on DNN can yield high accuracy in the multi-output regression task, resulting in the mean-square error (MSE) of 0.737 and 0.002 for the two target risks, respectively. GDO is proven useful in not only finding the optimal solutions for effective risk control but also uncovering the key features for a better explanation of the optimization process. It has been proved that optimal solutions derived from GDO can flexibly guide to adjust input variables of interest to achieve the minimal risk degree. Besides, cutter rotating speed and ground volume of TBM along with the relative horizontal distance of buildings turn out to be the top 3 critical features contributing more to the tunnel-induced risk mitigation, which deserve more attention. In brief, the novel DNN-GDO decision tool supports to formulate meaningful strategies objectively and efficiently for tunneling safety assurance, which no longer largely relies on time-consuming and laborious manual assessment. • A data-driven approach based on deep neural network and gradient descent technique is proposed. • Gradient descent technique aims to provide suggestions for preventing potential risks in tunnels. • A practical tunnel construction case in China is applied for demonstration purposes. • Deep learning model can yield a high accuracy in the multi-output regression prediction task. • The derived optimal solutions can flexibly guide to adjust input variables to achieve minimal risk. [ABSTRACT FROM AUTHOR]

Exploring how rapid urbanization affects ecological functional vulnerability is a key issue in regional ecological risk assessment. This paper proposes a complex network approach to explore the response of ecological functional vulnerability to different urbanization scenarios from the perspective of stock and flow for the case study area of the Wuhan City in China. The paper aims to (1) establish a theoretical framework to explain how the urbanization process degrades ecological functions of the land system from a flow and stock perspective, (2) evaluate stock and flow changes of ecosystem service value triggered by land use change, and (3) explore the impact of different urbanization scenarios and different management costs on ecological functional vulnerability by combining two indicators, i.e., ecosystem service value change and ecological load entropy. Our results for Wuhan reveal that (1) non-ecological flows or degraded ecological flows are responsible for the increase in ecological functional vulnerability, (2) the total ecosystem service stock of the land system had slightly increased during 1990–2015, contributing to an enhanced ecological flow, (3) the critical points for maintaining the proper functioning of the land system are 80% or higher for cropland and 40% or higher for lake. These findings can provide critical references for the sustainable management of urban land system in Wuhan and other major cities in China. This study also suggest that the combination of ecosystem service value change and ecological load entropy can be used to comprehensively assess ecological functional vulnerability. [ABSTRACT FROM AUTHOR]

Abstract: The Ionospheric Laboratory of Wuhan University is interested in upgrading its instrumentation replacing the WIOBSS (Wuhan Ionospheric Oblique Backscattering Sounding System) with a more flexible and automatic ionosonde. Then the updated ionosonde WMI (Wuhan Multifunctional Ionosonde) with automatic ionogram scaling has been developed to satisfy future research. The new system not only inherited some classic techniques from WIOBSS, such as the pulse compression and the coherent integration techniques, but also made great improvements in hardware design and software updating. This paper mainly introduces evolutions of this new ionosonde in both digital transceiver and digital signal processing domain, and also presents some observations. [Copyright &y& Elsevier]

Abstract: Ecological footprint (EF) forecasting is essential for dynamically evaluating human impact on earth as well as for planning for a sustainable future. In this paper, a radial basis function neural network (RBFNN) model was developed to forecast the total ecological footprint (TEF) from 2006 to 2015. For a case study of Wuhan city, Hubei province in central China, per capita ecological footprint (EF) and biological capacity (BC) were calculated from 1988 to 2005. Partial least square (PLS) was used to select the important impact factors. We put the selected socio-economic factors as input and the TEF as output together to build RBFNN model and predict the development trends of the TEF in the following 10 years. Five-fold cross-validation was conducted to validate the model in the process of input selection and RBFNN model training. From the results, continuous increase of per capita EF (1988–2005) indicated stronger and stronger human effect on the district and Wuhan''s ecological state is in the ecological deficit. Up to 2015, the district would have been bearing accumulative TEF of 24.782 million gha, which is near 2.5 times of that in 1988. Although the increase rate of gross domestic product (GDP) would be restricted in a lower level from 2006 to 2015, the urban ecological environmental burden could only respond to the socio-economic circumstances moderately. [Copyright &y& Elsevier]

Abstract: This paper examines the characteristics of historical floods and associated monsoon precipitation in the Yangtze Catchment, with specific attention given to the middle and lower Yangtze basin. Based on in situ field observations and historical discharge, flood duration, and land-use information, etc, we propose a number of flood characteristics of the basin: 1) floods have occurred more frequently after 1950s, however, changes in discharge can only partially explain the occurrence of the floods; 2) spectral analysis on the annual mean water discharge during 1860–1985 in Hankou hydrological gauging station of the middle Yangtze River reveals 16.4-year and 2.5-year flood recurrences, which become shortened from what has been recorded in the Chinese historical literature on millennial time scale; 3) floods after 1950s tend to have higher water levels (both mean and peak) and longer duration than before, but with remarkably limited inundation area; and 4) a series of flood waves generated by monsoon precipitation in 1998, initiated from the middle Yangtze, highlights the basin-wide climate hazards with a recurrence of around 60 years. Intensifying anthropogenic activity in the last century, including deforestation, dyking, and lake–coast reclamation etc. that results directly in riverbeds aggradation and shrinkage of lake water area in the middle Yangtze basin, are the key causes for recently human-induced floods in the basin. Threatened land property in the basin reminds us to harmonize with the river and provide more space for flooding. [Copyright &y& Elsevier]

Abstract: Based on observations of two ionosondes at Wuhan and Kokubunji, this paper presents effects of TADs on the daytime mid-latitude ionosphere during the intense geomagnetic storm on March 31, 2001. During a positive ionospheric storm, the start of the enhancement of the foF2 (F2 peak plasma frequency) at Wuhan lags that at Kokubunji by 15min, which corresponds to the time interval of traveling atmospheric disturbances (TADs’) propagation from Kokubunji to Wuhan. Associated with the uplifting of the hmF2 (height of F2 peak) caused by TADs, it is observed by the two ionosondes that the F1 cusp becomes better developed. Therefore, during a geomagnetic storm, TADs originating from the auroral oval may have a strong influence on the shape of the electron density profile in the F1 region ionosphere at middle latitudes. It is highly likely that TADs are responsible for the evolution of the F1 cusp. [Copyright &y& Elsevier]

Abstract: Neural networks (NNs) have been applied to ionospheric predictions recently. This paper uses radial basis function neural network (RBF-NN) to forecast hourly values of the ionospheric F 2 layer critical frequency(foF 2), over Wuhan (30.5N, 114.3E), China. The false nearest neighbor method is used to determine the embedding dimension, and the principal component analysis (PCA) is used to reduce noise and dimension. The whole study is based on a sample of about 26,000 observations of foF 2 with 1-h time resolution, derived during the period from January 1981 to December 1983. The performance of RBF-NN is estimated by calculating the normalized root-mean-squared (NRMSE) error, and its results show that short-term predictions of foF 2 are improved. [Copyright &y& Elsevier]

Abstract: In this paper, we use canonical correlation analysis (CCA) method to investigate the semi-diurnal tidal winds in mesosphere and low thermosphere (MLT) region, observed by a newly installed meteor radar at Wuhan (30.6°N, 114.4°E), during the year 2002. In general, 4(3) effective semi-diurnal tidal pairs of patterns are obtained, which represent ∼2/3 total variances of the origin data set. These patterns are expected to be corresponding to the atmospheric oscillations within the semi-diurnal frequency band excited or modulated by different sources, i.e., the seasonal variations, the modulations by the planetary wave oscillations or the solar 27-day activity. Among all the patterns, the 1st pattern, which represents ∼1/3 of total variances, is the most notable. Its amplitudes show maximum values in spring and autumn, and the vertical wavelengths are longer in summer and shorter in winter, which is in line with the results obtained from traditional harmonic analysis. The vertical wavelengths of the higher order patterns (∼50km) suggest the classic semi-tidal mode S(2,4)/S(2,5) is dominant. [Copyright &y& Elsevier]

Green space within residential areas is an important part of urban green space. However, in the past, these areas have not been completely open to the public and are only available to residents of the community within gated fences. At present, China has proposed to gradually open these closed communities, but quantitative assessment of the impacts of implementation on equity of green accessibility has not been explored. This paper selected Wuhan as a research case and used a Gaussian-based two-step floating catchment (2SFCA) and genetic algorithm to analyze and simulate whether the opened community policy would improve the equity of green accessibility, and how to best implement it. The main conclusions were: 1) The average green accessibility of the community within 1 km, 2.5 km, 5 km, and 10 km increased from 76.08, 75.50, 85.01, and 64.28 in the closed state to 102.43, 103.83, 114.39, and 94.87 in the fully open state; 2) After opening the gates, green equity was lower than before within the 1 km and 2.5 km thresholds, while opening the gates at distance thresholds over 3.6 km, green equity was promoted; 3) After optimization by the genetic algorithm, Gini coefficients for green equity were 0.39 and 0.28 within the 1 km and 2.5 km thresholds, and equity clearly increased compared to full community closure or full opening. This study quantified the spatial relationship between residents and green space and can be used to improve equitable spatial allocation of urban green space. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 is a kind of systemic virus, and lungs are not the only target organ. SARS-CoV-2 attacks digestive system, urogenital system, nervous system and circulatory system. Some new mechanisms about SARS-CoV-2 invading human cell. SARS-CoV-2 plays an important role in "Inflammatory Cascade". Since early December 2019, a number of pneumonia cases associated with unknown coronavirus infection were identified in Wuhan, China, and many additional cases were identified in other regions of China and in other countries within 3 months. Currently, more than 80,000 cases have been diagnosed in China, including more than 3000 deaths. The epidemic is spreading to the rest of the world, posing a grave challenge to prevention and control. On February 12, 2020, the International Committee on Taxonomy of Viruses and the World Health Organization officially named the novel coronavirus and associated pneumonia as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease 2019 (COVID-19), respectively. According to the recent research on SARS-CoV-2, the virus mainly infects the respiratory system but may cause damage to other systems. In this paper, we will systematically review the pathogenic features, transmission routes, and infection mechanisms of SARS-CoV-2, as well as any adverse effects on the digestive system, urogenital system, central nervous system, and circulatory system, in order to provide a theoretical and clinical basis for the diagnosis, classification, treatment, and prognosis assessment of SARS-CoV-2 infection. [ABSTRACT FROM AUTHOR]

This paper presents the pollution levels, variation characteristics, chemical reactivity, source apportionment and potential source regions of 102 volatile organic compounds (VOCs) observed from 2017.4.26 to 2017.6.6 in urban Wuhan. The concentration of total VOCs (TVOCs) was 28.92 ± 20.10 ppbv, and the VOC composition was dominated by alkanes (51.14%). Propane, ethane and acetone were the most abundant VOC species. The results for the OH radical loss rate (L OH) and ozone formation potential (OFP) indicated that alkenes were responsible for much of the chemical reactivity and O 3 formation, accounting for 47.02% and 49.52%, respectively, and the dominant VOC species were mainly associated with anthropogenic sources, such as vehicle emissions, the use of natural gas (NG) and liquefied petroleum gas (LPG), solvent usage and fuel evaporation. Ambient ratio data showed that VOCs were greatly affected by the pollution sources (such as emissions of NG and LPG, liquid gasoline and fuel evaporation sources, vehicle exhaust emissions, and biogenic sources), photochemical reaction processes and regional transport. Industrial sources (11.84%), industrial sources + coal burning (13.09%), LPG/NG usage + solvent usage in painting/coating (37.28%), fuel evaporation (12.04%), vehicle exhaust (13.65%), biogenic sources (5.95%) and biomass burning (6.15%) were determined by positive matrix factorization (PMF). Furthermore, LPG/NG usage, solvent usage in painting/coating and vehicle exhaust were the most important VOC sources, and their various contributions were affected by a combination of factors, including the emission strength of pollution sources, meteorological conditions and photochemical reaction processes. Backward trajectory analysis showed that VOC pollution was mainly derived from the south via short-distance transport. The potential source contribution function (PSCF) results indicated that the main sources affecting VOC pollution were the potential sources to the south and local sources within the city. This study provides evidence for local governments to formulate VOC control strategies during the transition from spring to summer. Image 1 • Alkenes were the key components of chemical reactivity and O 3 formation. • The effects of sources, photochemical reactions and air mass on VOCs were analysed. • LPG/NG usage, solvent usage in painting/coating and vehicle exhaust were important. • Potential sources from the south and local sources within the city were important. [ABSTRACT FROM AUTHOR]

Groundwater heat pump (GWHP) systems are central heating and cooling systems that utilize groundwater heat. With the rapid development of this technology in China, it has become imperative to study the changes in the ground environment, including the flow features, heat transfer characteristics and clogging caused by long-term recharge, during the operation of GWHP systems. In this paper, a mathematical model considering the effect of particle deposition was proposed to describe a GWHP system based on the typical geological and hydrogeological conditions (i.e., a dual-structure strata) in Wuhan, China. During research on a GWHP engineering project in the Baibuting Garden community, four groundwater flow conditions (including no flow, forward flow, reverse flow and cross flow) and two typical flow modes (continuous and flushing modes) were considered. The groundwater temperature and flow rate were measured through measuring wells, and a comparison between theoretical and actual values shows that this model can provide an easy yet effective way to describe GWHP engineering. The seepage and temperature fields both display periodic trends under the continuous flow mode and can be significantly affected by well flushing. The central area is marginally influenced by groundwater flow when compared with the surrounding areas; for instance, the temperature fluctuation at MW3 can reach 8.2 °C, while the fluctuation reaches 2.9 °C at MW1. Particle deposition can affect the parameters of the ground environment; we find considerable correspondence between the concentration of deposited particles and the porosity. Moreover, both the concentration of deposited particles and the porosity change by a large margin after flushing, especially in regions around pumping wells (the concentration of deposited particles can fluctuate by 108 g/L, and the porosity can change by 0.055). [ABSTRACT FROM AUTHOR]

Quality management in shield tunneling projects is a challenging problem. To improve the quality of segment erection, the shield driver must constantly adjust the shield's attitude and position to reduce a snakelike motion for fitting the design tunnel axis based on the driver's experience, which is untimely and less reliable during excavation. Considering the disadvantage of this control method, this paper presents a predictive framework for the attitude and position in shield tunneling by applying a hybrid deep learning model. This framework contains a wavelet transform noise filter, convolutional neural network feature extractor, and long short-term memory predictor for determining the attitude and position of the shield machine in the future. The prediction framework is tested with the collected data of Mixshield operated in the river-crossing tunnel project of Yangtze Sanyang Road, Wuhan, China. Six variables characterizing the shield attitude and position are selected to validate the feasibility and performance of our method. Results reveal that the proposed model outperforms the other three similar models in predictive accuracy and provides decision support for adjusting the attitude and position in shield tunneling. • A new method for predicting the attitude and position of shield machine was proposed. • A hybrid deep learning model consisting of wavelet transform, CNN, and LSTM was constructed for the prediction task. • The on-site data collected from the largest river-crossing tunnel in China has been tested. • The prediction results comparing with other three models and the potential practice value were discussed. [ABSTRACT FROM AUTHOR]