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• To solve the forecasting problems of time-lead and nonlinearity in a system, a novel grey multivariable model is proposed. • To reduce the morbidity of the model, the solution of the proposed model is deduced through a derivation method. • The grey prediction evolution algorithm is employed to find the optimal value of parameters. • The validity of the new model was verified by three examples and applied to house price forecasting. Time lags or leads and non-linearity of complex systems are critical to address in accurate scientific prediction. However, few studies have focused on time-lead and nonlinear models with limited information. Therefore, this paper aims to develop a novel grey multivariate model with time-lead operator τ and nonlinear factor r from the perspective of this problem. Next, the solution of the proposed model is deduced through a derivation method to reduce the morbidity of the model. Then, the parameter identification of the model is given on the basis of the least-squares method. The grey prediction evolution algorithm is employed to find the optimal value of parameters τ and r to meet the minimum errors. Finally, house prices of Wuhan, Beijing and Shanghai in China are performed to verify the validity and reliability of the new model. The results demonstrate the model we proposed has satisfactory prediction precision, demonstrating that time-lead and nonlinear factor in the model is reasonable. At the end of the paper, the house prices of these three cities from 2022 to 2028 are also predicted. The average annual growth rate in the three regions is 2.49%, 3.57% and 2.97%, respectively. These results are useful to policy analysis regarding house price correlations. They provide insights for market investors and policy makers. [ABSTRACT FROM AUTHOR]

In the elicitation of decision makers' fuzzy and uncertain assessments, linguistic terms are natural and efficient as the preference modeling tools. Although the linguistic variables are available, they would not be operational without any detailed quantification. Motivated by the flexibility of information granularity, this paper develops information granules to represent linguistic terms in the form of intervals and interval type-2 fuzzy sets (IT2FSs) in best worst method (BWM). The development is aimed at minimizing inconsistency in the decision making (DM) process to ensure the rationality of the assessments provided by decision makers. Furthermore, the input and output based consistencies of BWM are considered. The granulation of entries of pairwise comparison vectors are the foundation of BWM to formulate an optimization problem where particle swarm optimization (PSO) algorithm serves as the optimization framework. Both individual and group decision making (GDM) scenarios are taken into consideration. For the GDM process, a performance index for measuring the group consensus is also proposed. Several examples and validity analysis are covered to illustrate the major ideas of this study. Finally, as a case study, a recommendation of the sequence of visiting tourist attractions in Wuhan and the corresponding comparative analysis are represented. • A granular computing-based method in BWM framework is proposed. • Construction of granular linguistic information to represent preference information of DMs is given. • A novel Euclidean distance function to measure the agreement among the group is proposed. • A case study of scenic spots recommendation in Wuhan with online reviews is exhibited. [ABSTRACT FROM AUTHOR]

• Study of longitudinal ionospheric variations over Asian regions during the intense geomagnetic storms of May and September 2017. • Positive and negative changes were observed during the main and recovery phases of the storms. • Local time variations of the electric field ensure the longitude difference of the TEC variations. This paper presents the longitudinal dependence of ionospheric responses from Global Navigation Satellite System (GNSS) derived Total Electron Content (TEC) during two intense geomagnetic storms of May and September 2017. The GNSS-TEC is retrieved from four stations installed at the verge of low to mid-latitude Asian regions of Pakistan and China. Two ionospheric enhancements were observed during the storm of May 2017. The first one at local noon–afternoon during the storm main phase on 28 May was due to the southward turning of Interplanetary Magnetic Field (IMF-Bz) and eastward Prompt Penetration Electric Field (PPEF), with the maximum TEC enhancement at Wuhan. The second one at nighttime during the recovery phase of the storm on 29 May triggered ionospheric variations, mainly due to the later southward turning of the IMF-Bz as the Asian regions, were on the nightside with the westward PPEF. Negative storm time ionospheric responses were observed on 30 May, related to the change of the thermospheric composition as O/N 2 depletion. Moreover, a significant increase in TEC was recorded during the main phase of the storm on 8 September 2017. This enhancement corresponded with the eastward PPEF and an increase in the O/N 2. The TEC increment was also observed during the recovery phase on 9 September in the Pakistani stations. A minor storm on 7 September also gave rise to TEC enhancements, especially in western regions. However, the negative phase was registered from 9 to 10 September at each station because of the changes in the thermospheric composition as O/N 2 depletion. [ABSTRACT FROM AUTHOR]

The newest infection is a novel coronavirus named COVID-19, that initially appeared in December 2019, in Wuhan, China, and is still challenging to control. The main focus of this paper is to investigate a novel fractional-order mathematical model that explains the behavior of COVID-19 in Ethiopia. Within the proposed model, the entire population is divided into nine groups, each with its own set of parameters and initial values. A nonlinear system of fractional differential equations for the model is represented using Caputo fractional derivative. Legendre spectral collocation method is used to convert this system into an algebraic system of equations. An inexact Newton iterative method is used to solve the model system. The effective reproduction number (R 0) is computed by the next-generation matrix approach. Positivity and boundedness, as well as the existence and uniqueness of solution, are all investigated. Both endemic and disease-free equilibrium points, as well as their stability, are carefully studied. We calculated the parameters and starting conditions (ICs) provided for our model using data from the Ethiopian Public Health Institute (EPHI) and the Ethiopian Ministry of Health from 22 June 2020 to 28 February 2021. The model parameters are determined using least squares curve fitting and MATLAB R2020a is used to run numerical results. The basic reproduction number is R 0 = 1.4575. For this value, disease free equilibrium point is asymptotically unstable and endemic equilibrium point is asymptotically stable, both locally and globally. [ABSTRACT FROM AUTHOR]

With the expansion of the city, the number of subway lines has increased, and the subway stations have become more and more crowded. Whether it is possible to transfer quickly and conveniently has become a concern of subway passengers. Especially in big cities, for the convenience of residents' travel, there are many transfer stations between subway lines, and the transfer layout and connection method between subway stations affect the transfer efficiency of passengers. Jiedaokou Station of Wuhan Metro Line 2 is located in the center of Wuhan and is one of the busiest transfer stations in Wuhan. AnyLogic builds a simulation model of passenger entrances and exits and transfers, and observes whether the design of subway station halls is reasonable from the perspective of regional density, regional density, average passenger flow speed and time distribution of entrances and exits. Then, according to the location of congestion and the degree of congestion, it will be designed, modified, optimized and upgraded. After the analysis, a comparison of various schemes and a new round of simulation analysis are carried out. On the basis of summarizing the analysis results of each strategy, the design optimization strategy of the subway station hall is comprehensively proposed. Finally, the feasibility of the optimization strategy is proved again through simulation experiments. This optimization can enable passengers to walk faster in the station and reduce the occurrence of problems such as stagnation and congestion. This paper aims to provide a certain degree of guidance for the design of subway station halls in the future by using AnyLogic simulation method. [ABSTRACT FROM AUTHOR]

Highlights • This paper describes the epidemiology of influenza viruses among children in Wuhan, China during nine influenza seasons (2007–2015) and predicts the positive rate of different types of influenza virus in the future. • In contrast to previous research at home and abroad, this paper is the first study that explores the application of time series analysis using the positive rate of influenza virus in children to predict the future incidence. • The ARIMA model is a good instrument that forecasts the positive rate of different types of influenza virus. • The wide use of influenza vaccines, especially for influenza B(Yamagata) and B(Victoria), can potentially reduce the effects of influenza on the children of China. Abstract Objective Influenza is a common childhood disease and protecting children by predicting the positive rate of influenza virus is important as vaccines are not routinely administered in China. Our study aims to describe the epidemiology of influenza viruses among children in Wuhan, China during the past nine influenza seasons (2007–2015) and to predict the positive rate of different types of influenza virus in the future. Methods During the last nine influenza seasons (2007–2015), a total of 10,232 nasopharyngeal swabs collected from pediatric outpatients (age <15 years) with influenza-like illness (ILI) infections in two sentinel children’s hospitals were examined for influenza A and B by real-time one step RT-PCR. An autoregressive integrated moving average (ARIMA) model was used to fit the time series and to predict the future (first half of 2016) positive rates of different types of influenza virus. Results A total of 1,341 specimens were positive for influenza A and 490 for influenza B. The majority of infected patients were 1–11 years old (87.7%). The ARIMA model could effectively predict the positive rate of influenza virus in a short time. ARIMA(0,0,11), SARIMA(1,0,0)(0,1,1) 12 , ARIMA(0,0,1) and SARIMA(0,0,1)(1,0,1) 12 were suitable for B(Victoria), B(Yamagata), A(H1N1)pdm09, and A(H3N2), respectively. Conclusion Additional policies must be formulated to prevent and control influenza. The wide use of influenza vaccines, especially for influenza B, especially for influenza B(Yamagata) and B(Victoria), can potentially reduce the effects of influenza on children of China. [ABSTRACT FROM AUTHOR]

City water utilities are increasingly integrating climate uncertainties into planning process. Considering our current scientific understanding of climate change have been developed into a new level, traditional water utility planning methods, based on an assumption of stationarity, are being fundamentally challenged. It is encouraging that many water utilities, recognizing the limitations of stationarity, are broadening their perspective on climate issues and deliberating new planning methods to deal with conditions of pervasive climatic uncertainty. This paper elaborates steps and processes of a water utility planning approach which is incorporated into water sector (water supply and drainage) of Wuhan. With the Asian Development Bank's financial fund in 2010, the city water utility planners developed a framework and a series of investment decisions through a “bottom-up” method involving Wuhan staff and stakeholders in workshops. An essential lesson draw from this case study is that initiative to increase climate resilience does not need to wait for advancements of future climate conditions study or detailed climate scenarios to be developed or for those scenarios to be incorporated into utility plans. The framework for ranking and identifying impacts and vulnerabilities was derived from widely accepted international methodologies and tailored to suit local conditions. In this way, it takes account of the situation on the ground, including the local perceptions of both conditions and needs, the existing plans, and the need to achieve consensus for investments in climate change adaptation. Finally this paper emphasizes that in order to meet specific needs, decision makers have to select appropriate methodology to suit the circumstances. [ABSTRACT FROM AUTHOR]

• 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]

• Numerical simulation software was used to establish the numerical model of the ancient pagoda under the blasting load of the underpass tunnel. • The maximum primary stress and vibration velocity were analyzed by numerical modeling in order to safeguard historic structures. • The reason why the vibration velocity and stress of each part of irregular tower vary greatly is analyzed. • The safety of the ancient pagoda is assessed according to the relevant norms. In the blasting excavation project of the subway tunnel in the urban area, ensuring the safety of the surface buildings (structures) above the crossing area is the key concern. This paper is based on the blasting project of the ancient pagoda (Shengxiang Pagoda) under the tunnel between Pengliuyang Road Station and Simenkou Station of Wuhan Metro Line 5. To protect the integrity of the ancient buildings, this paper mainly adopts the numerical simulation method to analyze the dynamic response characteristics of the tower above the subway and realizes the safety assessment of the tower based on the maximum principal stress and vibration velocity control. The results show that the maximum error rate between the measured data and the numerical simulation results is only 11.9 %, which verifies the reliability of the numerical model and parameters. In the process of blasting vibration propagation, the vibration velocity of the tower body is affected by the whiplash effect and the elevation amplification effect. The vibration velocity of the tower bottom is 0.29 cm / s, and the vibration velocity of the tower tip reaches 4.29 cm / s. The vibration velocity of the upper part of the tower is much larger than that of the lower part. The stress distribution of the tower body is accompanied by stress concentration, with the maximum reaching 0.0442 MPa and the minimum only 0.014 MPa. The safety assessment of the ancient pagoda from the aspects of structural strength and vibration velocity can determine that the structure is safe under this condition. The research results are of great significance to the analysis of dynamic response characteristics and safety evaluation of ancient buildings under blasting load in urban complex environments. [ABSTRACT FROM AUTHOR]

Energy efficiency enhancement and carbon reduction are essential means by which to promote carbon neutralization. However, is the constant pursuit of improving energy efficiency always conducive to urban development? The collaborative innovation development pattern has become the focus of regional competition. Thus, this paper establishes a new paradigm for analyzing the driving effect of energy efficiency change from the perspective of collaborative innovation development pattern. Using panel data from cities in the middle reaches of the Yangtze River urban agglomeration in China from 2009 to 2019, the DEA-Malmquist and projection pursuit models calculate the energy efficiency change and collaborative innovation city construction level. Then, we use the Tobit model to analyze the reverse forcing effect of energy efficiency change on collaborative innovation city construction level from the dual perspectives of energy efficiency changes at different levels and geographical locations. The results show that the driving effect of energy efficiency change is U-shaped. In addition, there are some city differences, and the U-shaped relationship only exists in high-efficiency cities, the Wuhan Metropolitan Area and the Poyang Urban Agglomeration. This paper also finds that industrial structure, government interaction, development level, and population density impact collaborative innovation development pattern. Finally, these findings provide policy-makers with new ideas to promote sustainable urban development. • Explore the driving effect of energy efficiency change. • Quantitatively measure the development pattern of collaborative innovation. • Find the reverse forcing effect of energy efficiency on urban development. • Find the heterogeneous effect of energy efficiency change on collaborative innovation city construction. [ABSTRACT FROM AUTHOR]

The paper takes design of the tenth China (Wuhan) International Garden EXPO as an example, concludes the development direction of low-carbon parks in China, and highlights the use of sponge city and green architecture design. Firstly, the paper introduces overview and design cases of the EXPO. Then, the paper elaborates the goals of low-carbon design. It describes the low-carbon technical application of the EXPO park, i.e. comprehensive design based on overall coordination and avoidance of conflicts, intelligent city planning, processing technology of existing landfill, conducting onsite balance work for earthwork, water recycling system planning, building ecological low-carbon architecture, building and repairing of ecological darning bridge, etc. Besides, the park also adopts design principle of sponge city. As an urban oasis, Wuhan International Garden EXPO darns the divided city; as a public space, it provides an ecological space for more citizens to visit and enjoy. Its example is a reference for design of low-carbon parks in China in the future. [ABSTRACT FROM AUTHOR]

Urbanization with mismanagement has seriously threatened regional ecology and cultivated land security. At present, the territorial spatial usage regulations often isolate functions from a single aspect, which makes it difficult to meet demands of regional economic development, ecological protection, and sustainable development. This paper attempted to construct a zoning system of territorial spatial usages that comprehensively considers local development demands, resource endowment conditions, and sustainable development, and proposed a method to regulate sustainable development intensity. Wuhan City was selected as the study area. At first, the land resource function was zoned by the modified multi-objective land allocation (MOLA) model considering the resource endowment conditions and social development demands. The study area was divided into ecological, agricultural, and construction land resources. Also, the functions of land, water, and geological resources are integrated into the zonation framework of territorial spatial usages. Then, the construction and agricultural development intensity in different usage areas were quantitatively regulated by comprehensively considering the current basic demands, sustainable development, land use status, and relevant policies. The calculation results show that the development intensity of construction land in the construction space should be limited to 43%–53%, while the development intensity in the general farmland area should be limited to 10%. The agricultural development intensity should be greater than 65% in the general farmland, while it should be lower than 64% in the general ecological function area. Additionally, by comparing with the land-use status, the construction in the study area is within a reasonable scope (about 44%). The north and south of Huangpi District, the north of Xinzhou District, Hannan District, and the east of Caidian District still have great potential for development. Finally, based on territorial spatial usage and development intensity regulation, this paper proposed some effective suggestions for territorial spatial development. This paper can provide a reference for the territorial spatial planning and ecological environment protection of Wuhan City. At the same time, it provides a new idea for urban planning research considering the resource, environment, and economic development. [ABSTRACT FROM AUTHOR]

Actually, COVID-19 and its variants present a big challenge for the public health security. COVID-19 is a new form of the coronaviruses characterized by a set of symptoms like laboratory and radiological symptoms, when the first case has confirmed in December 2019 in Wuhan City, as well as a new variant of this form has appeared in December 2020 in the United Kingdom. Internet of things (IoT) is a technological revolution employed in different areas in the aim to serve the asked purposes. The implementation of IoT solutions in healthcare area has several benefits such as reducing the cost of services and improving treatment results. In this paper, we present a review on the impact of IoT on this new health challenge (COVID-19 and its variants), we will focus this study on the impact of the use of IoT devices to reduce transmissions of COVID-19 and its variants. [ABSTRACT FROM AUTHOR]

Since the genesis case was confirmed in Wuhan, China in late 2019, the Novel Coronavirus Disease 2019 (COVID-19) has been spreading all over the world at an accelerating rate. Almost immediately, IoT technologies were deployed in various surveillance scenarios as part of an effort to combat the pandemic. Among the emerging solutions, contact tracing mobile applications have been playing an effective role to help stem the spread of the virus by tracking individuals and those they come into exposure with. This paper aims at providing a panoramic view of the digital tracking technologies that have been utilized so far in response to the pandemic. We particularly provide a detailed analysis of 47 contact tracing mobile applications that emerged in response to COVID-19. We accompany our analysis with a discussion on the privacy and the technology / social constraints that may challenge the deployment of these applications as digital surveillance platforms. [ABSTRACT FROM AUTHOR]

PV windows are considered to be a promising building-integrated PV technology due to their excellent electrical, daylighting and thermal performance. However, the shading of the PV cells increases the heating load in winter and the high temperature of PV cells exacerbates the cooling load in summer. Moreover, a significant portion of the heat absorbed by the PV modules is usually wasted. To address these issues, this paper proposed a multi-functional PV/T window. This system could recover the heat on PV cells for producing warm air in winter and hot water in other seasons. As a result, the proposed system can realize the combined PV/T utilization of solar energy and reduce the seasonal thermal needs of the building. First, the mathematical model of the proposed window was developed and validated. Next, the developed model was integrated into building energy software to evaluate the overall energy performance of the proposed window in four different cities of Yangtze River region: Shanghai, Nanjing, Wuhan, and Chongqing in China. These cities experience the subtropical humid climate (Cfa in the Köppen-Geiger classification) and belong to the zones with medium global horizontal irradiation. The sensitivity analysis revealed that the optimal slat angles are between 0o and 40o, while the optimal orientation angles range from 30o to 60o. Selecting low-iron glass as the inner glass resulted in the lowest overall energy consumption. By adopting the MFPV/T window, the annual operation cost of the studied case could be reduced by 34% (6.4 $/m2), 36% (7.0 $/m2), 28% (5.7 $/m2), and 16% (2.9 $/m2) in these four cites, respectively. Additionally, the annual CO 2 emissions could be curtailed by 53.5, 58.5, 47, and 22.3 kg/m2, respectively. • A novel PV/T window was proposed for addressing the common issues of PV windows. • The mathematical model of proposed window was developed and validated. • The parameter sensitivity was analyzed. • The overall energy performance was predicted in Yangtze River region. [ABSTRACT FROM AUTHOR]

Outbreaks of the COVID-19 pandemic caused by the SARS-CoV-2 infection that started in Wuhan, China, have quickly spread worldwide. The current situation has contributed to a dynamic rate of hospital admissions. Global efforts by Artificial Intelligence (AI) and Machine Learning (ML) communities to develop solutions to assist COVID-19-related research have escalated ever since. However, despite overwhelming efforts from the AI and ML community, many machine learning-based AI systems have been designed as black boxes. This paper proposes a model that utilizes Formal Concept Analysis (FCA) to explain a machine learning technique called Long-short Term Memory (LSTM) on a dataset of hospital admissions due to COVID-19 in the United Kingdom. This paper intends to increase the transparency of decision-making in the era of ML by using the proposed LSTM-FCA explainable model. Both LSTM and FCA are able to evaluate the data and explain the model to make the results more understandable and interpretable. The results and discussions are helpful and may lead to new research to optimize the use of ML in various real-world applications and to contain the disease. [ABSTRACT FROM AUTHOR]

Mathematical tools called differential and integral operators are used to model real world problems in all fields of science as they are able to replicate some behaviors observed in real world like fading memory, long-range dependency, power law, random walk and many others. Very recently the world has faced a serious challenge since the breakout of corona-virus started in Wuhan, China. The deathly disease has killed about 1720000 and infected more than 2 millions humans around the globe since December 2019 to 21 of April 2020. In this paper, we analyzed a mathematical model for the spread of COVID-19, we first start with stability analysis, present the optimal control for the system. The model was extended to the concept of non-local operators for each case, we presented the positiveness of the system solutions. We presented numerical solutions are presented for different scenarios. [ABSTRACT FROM AUTHOR]

COVID-19 is a virus causing pneumonia, also known as Corona Virus Disease. The first outbreak was found in Wuhan, China, in the province of Hubei on December 2019. The objective of this paper is to predict the death and infected COVID-19 in Indonesia using Savitzky Golay Smoothing and Long Short Term Memory Neural Network model (LSTM-NN). The dataset is obtained from Humanitarian Data Exchange (HDX), containing daily information on death and infected due to COVID-19. In Indonesia, the total data collected ranges from 2 March 2020 and by 26 July 2020, with a total of 147 records. The results of these two models are compared to determine the best fitted model. The curve of LSTM-NN shows an increase in death and infected cases and the Time Series also increases, however the smoothing shows a tendency to decrease. In conclusion, LSTM-NN prediction produce better result than the Savitzky Golay Smoothing. The LSTM-NN prediction shows a distinct rise and align with the actual Time Series data. [ABSTRACT FROM AUTHOR]

A global pandemic on March 11th of 2020, which was initially renowned by the World Health Organization (WHO) revealed the coronavirus (the COVID-19 epidemic). Coronavirus was flown in -December 2019 in Wuhan, Hubei region in China. Currently, the situation is enlarged by the infection in more than 200 countries all over the world. In this situation it was rising into huge forms in Bangladesh too. Modulated with a public dataset delivered by the IEDCR health authority, we have produced a sustainable prognostic method of COVID-19 outbreak in Bangladesh using a deep learning model. Throughout the research, we forecasted up to 30 days in which per day actual prediction was confirmed, death and recoveries number of people. Furthermore, we illustrated that long short-term memory (LSTM) demands the actual output trends among time series data analysis with a controversial study that exceeds random forest (RF) regression and support vector regression (SVR), which both are machine learning (ML) models. The current COVID-19 outbreak in Bangladesh has been considered in this paper. Here, a well-known recurrent neural network (RNN) model in order to referred by the LSTM network that has forecasted COVID-19 cases on per day infected scenario of Bangladesh from May 15th of 2020 till June 15th of 2020. Added with a comparative study that drives into the LSTM, SVR, RF regression which is processed by the RMSE transmission rate. In all respects, in Bangladesh the gravity of COVID-19 has become excessive nowadays so that depending on this situation public health sectors and common people need to be aware of this situation and also be able to get knowledge of how long self-lockdown will be maintained. So far, to the best of our knowledge LSTM based time series analysis forecasting infectious diseases is a well-done formula [ABSTRACT FROM AUTHOR]

The Coronavirus epidemic first appeared in Wuhan-China on December, 31st, 2019. This has put the world's hospitals, clinics, testing laboratories and health administrations under pressure. As of April, 04th, 2020, the World Health Organization reported more than 167515 confirmed cases in more than 100 countries worldwide. The diagnosis of the epidemic will increase the burden on overburdened testing laboratories. Several screening methods have been proposed in parallel in order to facilitate and, above all, to make rapid diagnosis easier. At this level, X-Ray images seem to be a good accompanying solution for emerging countries to help rapid screening. The solution proposed in this paper consists on a collaborative and smart platform based on the Convolutional Neural Network for Classification and Detection namely VGG16. The platform ensures the fast download of the X-Ray image, with the entry of the patient's personal information followed by the launch of a 5 seconds test. The platform generates, as a result, a PDF file containing all patient information. [ABSTRACT FROM AUTHOR]

• Almost all currently infected COVID-19 patients in Henan province were analyzed. • COVID-19 patients in Henan province show gender and age preferences, migrant workers or college students are at high risk. • The incubation period was statistically estimated. • The state transition diagram can reveal the time-phased nature of the COVID-19 epidemic. • Network analysis reveals the aggregate outbreak phenomena of COVID-19. COVID-19 is spreading quickly all over the world. Publicly released data for 1212 COVID-19 patients in Henan of China were analyzed in this paper. Various statistical and network analysis methods were employed. We found that COVID-19 patients show gender (55% vs 45%) and age (81% aged between 21 and 60) preferences; possible causes were explored. The estimated average, mode and median incubation periods are 7.4, 4 and 7 days. Incubation periods of 92% of patients were no more than 14 days. The epidemic in Henan has undergone three stages and has shown high correlations with the numbers of patients recently returned from Wuhan. Network analysis revealed that 208 cases were clustering infected, and various People's Hospitals are the main force in treating COVID-19. The incubation period was statistically estimated, and the proposed state transition diagram can explore the epidemic stages of emerging infectious disease. We suggest that although the quarantine measures are gradually working, strong measures still might be needed for a period of time, since ∼7.45% of patients may have very long incubation periods. Migrant workers or college students are at high risk. State transition diagrams can help us to recognize the time-phased nature of the epidemic. Our investigations have implications for the prevention and control of COVID-19 in other regions of the world. [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]

With the rapid development of the economy, PM2.5 severely harms human health and social development. In this paper, a novel hybrid hourly PM2.5 prediction model, named CEEMDAN-PE-GWO-VMD-MIF-BiLSTM-AT, is proposed. Firstly, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose the hourly PM2.5 sequence. Secondly, the grey wolf optimizer (GWO) algorithm is utilized to optimize the parameters of variational mode decomposition (VMD). GWO-VMD is employed to decompose the subsequence with the first decomposition's largest permutation entropy (PE). Then, combining multiple impact factors (MIF) including air pollution factors, meteorological parameters, and Global Navigation Satellite System-derived Zenith Total Delay (GNSS-ZTD), a Bidirectional Long Short-Term Memory network based on attention mechanism (BiLSTM-AT) is established for hourly PM2.5 concentration prediction. Finally, The proposed model predicts hourly PM2.5 in Beijing, Wuhan, Urumqi, and Lhasa to verify its performance. Compared with other models, the present model can predict hourly PM2.5 more accurately. • Propose a new secondary decomposition method CEEMDAN-PE-GWO-VMD to decompose the PM2.5 sequence. • Consider the effect of multiple impact factors including air pollution factors, meteorological parameters, and GNSS-ZTD to predict PM2.5. • Propose a novel hybrid model CEEMDAN-PE-GWO-VMD-MIF-BiLSTM-AT for hourly PM2.5 concentration prediction. [ABSTRACT FROM AUTHOR]

Abstract: Urban heat island effect has obtained more and more attention because environmental problems caused by the urban heat island have affected people''s normal life. In this paper, qualitative and quantitative analyses have been used to study the relationship between normalized difference vegetated index (NDVI) and surface radiation temperature. The relationship of NDVI and surface radiation temperature has been studied in spatial profile. On that basis, the paper has studied the spatial distribution of heat island in Wuhan city and explained the features of the Wuhan spatial distribution of heat island. Research results has showed that there is obvious negative correlation between NDVI and the surface radiation Heating island strength is higher in industrial and commercial areas than others; Water and green space perform a distinct role to divide and mitigate the heat island effect. [Copyright &y& Elsevier]

Abstract: Spectral Change Vector Analysis (CVA) is based on multi-temporal images. In this paper, a dichotomy search which can be used on detecting changes in the threshold vector is adopted. Meanwhile, a supervised classification technique is used in the direction cosine space with the type of central point in the initial assay vector remote sensing images. Results are discussed in the last part of this paper, which show that CVA can extract change information effectively in our study area of Wuhan city. [Copyright &y& Elsevier]

Urbanization alters the supply of ecosystem services that are vital for human well-being. The loss of ecosystem services is particularly challenging in rapid urbanization areas where economic development needs to consume substantial natural resources. The quantitative and spatial optimization of land use provides an effective tool for rationally allocating land use structure and pattern to ensure the provision of expected ecosystem services. In this paper, we combine the Multi-Objective Programming and the Dyna-CLUE model to project land use changes in 2030 for Wuhan city under three scenarios, i.e., Business As Usual (BAU), Rapid Economic Development (RED), and Ecological Land Protection (ELP). The coupled model that integrates “top-down” and “bottom-up” processes is capable of obtaining the optimized land use patterns under different scenarios and examining the potential impacts of land use changes on ecosystem services in a spatially explicit way. We find that built-up land will continue its remarkable growth during 2015-2030 under the BAU scenario (grows by 96%) at the expense of ecological lands (decreases by 18%). Meanwhile, the predicted losses of ecological lands are 11% and 6% under the RED and ELP scenarios, respectively. Projected land use changes result in varying magnitudes of declines in ecosystem service values for BAU (11%), RED (6%) and ELP (2%) scenarios from 2015 to 2030. The ELP scenario, which incorporates ecological protection policies and spatial restrictions, plays a positive role in altering land use trends and mitigating ecosystem degradation. Finally, we establish an ecosystem service value change matrix to explain how interactions between land use types give rise to trade-offs among multiple ecosystem services. We find that conversions between ecological land use types can trigger trade-offs among ecosystem services, but the conversion from ecological lands towards urban land leads to a net loss of all individual ecosystem services. By linking land and ecological systems, the coupled modeling framework in this study can be useful for obtaining optimal ecosystem-based land use allocation strategies and provide scientific support for sustainable land use management. [ABSTRACT FROM AUTHOR]

The global healthcare sector has been dealing with a situation known as a novel severe acute respiratory syndrome (SARS-CoV-2) since the end of 2019. Covid-19 is an acronym for Covid-19 (Coronavirus Disease- 2019). It causes a respiratory infection that includes cold, sneezing and coughing, and pneumonia. In the case of an animal, it causes diarrhea and upper respiratory diseases. Covid-19 transmitted human to human via airborne droplets. First Covid-19 emerged in Wuhan market China and it spread rapidly throughout the World. As we know nanoparticles are a novel drug delivery system. They have various advantageous effects like increasing the efficacy of the drug, safety, etc. In this review, we study about the nanoparticles and summarize how it is effective during drug delivery system in Covid-19. Chitosan is a much focused biopolymeric nanoparticle. It delivers drugs to the specific target site. In a recent health crisis, chitosan nanoparticles are one of the ways to release drugs of Covid-19, and specifically in the lungs of the affected patients. We studied and extracted our data from various research papers, review papers, and some other articles. The main goal is to study the nanoparticles and their future aspects which is an effective drug delivery system in Covid-19. The bibliographic search was done through a systematic search. The terms "Nanoparticles", "Covid-19 ", "Drug delivery" etc. were used to search the databases/search engines like "Google Scholar", "NCBI", "PubMed", "Science Direct" etc. These databases and search engines used here perform the limited criteria of search to conduct a systematic literature survey for the study and report writing. All the text from the articles and research papers were studied and analyzed. The various articles and research papers were used in writing this report and all of which are mentioned in the reference section of this report. Our current studies reveal that nanoparticles may prove very helpful in the delivery of drugs for Covid-19 treatment. Many cases showed that patients, where drugs are delivered with the help of nanoparticles, produced very few side effects. [Display omitted] • Almost every antiviral pharmaceutical system can be nano-scaled to improve its properties. • Nanoparticles as delivery vehicles can improve the effectiveness of drug delivery. • Nanomedicines and Nano- drug delivery system have shown promising effectiveness against the Covid 19 as treatments. • Numerous subsidies to advance the nanoparticular antiviral field are provided for public health and financially. [ABSTRACT FROM AUTHOR]

Water leakage typically infiltrates through bolt holes and segment joints, posing a threat to the stability of tunnel structures. In this paper, we develop a copula-based Bayesian network (CBN) method for dependency modeling of complex systems to conduct risk analysis and management of water leakage in operational tunnels. Thirteen potential factors are used to construct the water leakage risk system, and the permeate water volume is selected as the observation index of water leakage risk. An application to a realistic cross-river tunnel of Wuhan Rail Transit Line 3 is implemented to verify the effectiveness of the developed model. Through correlation analysis, the bolt failure rate and seal breakage and aging rate are diagnosed as critical factors with the greatest impact on the water leakage risk. Forward and backward reasoning enables the model to be dynamically updated to provide decision support for water leakage risk management. This research contributes to the following: (a) expressing the dependence of the factors affecting water leakage; (b) solving the problem of modeling uncertainty and information fusion; and (c) updating the model to realize the safety control of tunnel water leakage. [ABSTRACT FROM AUTHOR]

Nexus approaches, aiming at the complex connections among the correlative systems, provide an effective method for analyzing coevolution processes. In the context of urbanization, water, energy, and environment (W-E-E) systems are increasingly connected thus can be profiled as a W-E-E nexus. However, research on urban water-energy interactions and their impact on the environment is still in its infancy. In addition, a generic framework for modeling W-E-E nexus that includes dynamic feedbacks between human and natural systems remains elusive. In an attempt to fill this gap, this paper proposes a sensitivity-based framework consisting of four components: the water balance model, the energy balance model, the environmental response model, and the urbanization model. Community sensitivity serves as the missing link, tying together two feedback loops driven by economy and environment respectively. To demonstrate how such a framework could be applied, Wuhan city was selected as a case study and analyzed for its coevolution under three scenarios: extensive development, common development, and sustainable development. The results show that the proposed framework is competent in modeling the W-E-E nexus in Wuhan. The coevolution process is broadly divided into four cyclic stages (expansion, degeneration, convalescence, and stabilization) and environmental recovery consumes decades or more regardless of the development pattern. However, development patterns exert a salient effect on community sensitivity, with +294.4%, +5.5%, and −17.4% for extensive, common, and sustainable development patterns during the simulation period respectively. And only under sustainable development, can Wuhan anticipate achieving the goal of carbon neutrality by the 2060s. It is hoped that this framework will aid our understanding of the fundamental interactions in this complex relationship and allow researchers to improve W-E-E nexus modeling through better capture of the key elements in the feedback loops. • Present a sensitivity-based framework for the water-energy-environment nexus. • Build the missing link that connects 2 key loops driven by economics and environment. • Provide urban development pattern decisions for achieving resource sustainability. • Assess Wuhan's development trajectory over the next 2 centuries quantitatively. • Propose a development pattern to achieve carbon neutrality in Wuhan in 2060s. [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]

• Use Machine Learning models to predict the mortality of COVID19 patients. • Analyse patient case studies which may help in better personalised treatment. • Apply Shapley Values to interpret the Machine Learning models predictions. • Perform what-if analysis to see how patient mortality outcomes change. In this paper we apply a series of Machine Learning models to a recently published unique dataset on the mortality of COVID19 patients. We use a dataset consisting of blood samples of 375 patients admitted to a hospital in the region of Wuhan, China. There are 201 patients who survived hospitalisation and 174 patients who died whilst in hospital. The focus of the paper is not only on seeing which Machine Learning model is able to obtain the absolute highest accuracy but more on the interpretation of what the Machine Learning models provides. We find that age , days in hospital , Lymphocyte and Neutrophils are important and robust predictors when predicting a patients mortality. Furthermore, the algorithms we use allows us to observe the marginal impact of each variable on a case-by-case patient level, which might help practicioneers to easily detect anomalous patterns. This paper analyses the global and local interpretation of the Machine Learning models on patients with COVID19. [ABSTRACT FROM AUTHOR]

This paper develops a new method for interactive multi-criteria group decision-making (MCGDM) with probabilistic linguistic information and applies to the emergency assistance area selection of COVID-19 for Wuhan. First, a new possibility degree for PLTSs is defined and a new possibility degree algorithm is devised to rank a series of probabilistic linguistic term sets (PLTSs). Second, some new operational laws of PLTSs based on the Archimedean copulas and co-copulas are defined. A generalized probabilistic linguistic Choquet (GPLC) operator and a generalized probabilistic linguistic hybrid Choquet (GPLHC) operator are developed and their desirable properties are discussed in details. Third, a tri-objective nonlinear programming model is constructed to determine the weights of DMs. This model is transformed into a linear programming model to solve. The fuzzy measures of criterion subsets are derived objectively by establishing a goal programming model. Fourth, using the probabilistic linguistic Gumbel weighted average (PLGWA) operator, the collective normalized decision matrix is obtained by aggregating all individual normalized decision matrices. The overall evaluation values of alternatives are derived by the probabilistic linguistic Gumbel hybrid Choquet (PLGHC) operator. The ranking order of alternatives is generated. Finally, an emergency assistance example is illustrated to validate the proposed method of this paper. • A new possibility degree of PLTSs is defined. • A new similarity degree of PLTSs is defined. • Define new operational laws of PLTSs based on Archimedean copulas and co-copulas. • Construct programming models to determine DMs' weights and fuzzy measures of criteria. • A new method for the interactive MCGDM with PLTSs is put forward. [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]

In this paper we evaluate the operational efficiency of the Brazilian airlines considering the novel coronavirus Covid-19 outbreak. This novel coronavirus was first reported end of 2019 in Wuhan, China, however the powerful contamination spread among people forced the World Health Organization to characterize the Covid-19 as a pandemic in March of 2020. Here we analyze the main Brazilian airlines operations response due to lower demand because Covid-19 outbreak in first quarter of 2020 comparing with first quarter of 2019. The analysis here aims to verify the efficiency of airlines in domestic air transport market in Brazil through Multicriteria Data Envelopment Analysis (MCDEA) model. We used MCDEA to avoid limitations of classical DEA models for the case, especially the numbers of decision units and variable. In this paper we used an improvement of the MCDEA model to seek benchmarks considering a dual model all objective functions of MCDEA. The results highlight the challenges for the airlines, due to flight restriction and demand dropping. And also, the evaluation exposes the different company configuration of aircrafts age and network reconfiguration which was reflected by the efficiency difference on the period. The assessment shows the company with a better mix of aircraft models has a leverage on efficiency response due to unpredictable period as the pandemic Covid-19 outbreak. • Air transport largely facilitates diseases spread worldwide. • Analysis of Brazilian airlines during the Covid-19 outbreak. • Assessment considers the Multicriteria Data Envelopment Analysis (MCDEA) model. • Improvement of the MCDEA model to seek benchmarks based in a duality formulation. • Efficiency challenges for the airlines to outbrave the Covid-19. [ABSTRACT FROM AUTHOR]

This paper presents the energy and exergy studies of a ground source heat pump (GSHP) system for a public building in Wuhan, China under five different control strategies to improve the system performance. Original system was under manual operation with constant frequency heat pump, constant speed circulation pumps, and constant water flow rate. The five control strategies were: 1) Automatic ON/OFF control based on Building Load Ratio (BLR); 2) Optimum circulation flow rate control; 3) Optimum ON/OFF control with variable speed heat pump units; 4) Variable flow control by adjusting valve position; 5) Variable flow control by variable speed pumps. The results of the system exergy efficiency, exergy loss, COP, and energy consumption under different operation scenarios are presented. By comparing original operation with the best case (scenario five), it is found that during cooling and heating season, the system exergy efficiency was improved from 9.0% to 10.4% and from 6.1% to 6.9%; exergy loss dropped by 31% and 51%; COP increased from 3.2 to 3.7 and from 2.7 to 3.8; and energy consumption was reduced by 37% and 60%, respectively. [ABSTRACT FROM AUTHOR]

Building information modeling (BIM) maturity models occupy a crucial role in guiding BIM-reliant stakeholders and enterprises to identify BIM capabilities and facilitate process improvements. Nevertheless, few quantitative BIM maturity models are available for the measurement and improvement of BIM utilization performance. This study designs a refined assessment system for the maturity measurement of BIM-based projects during the design and construction stages. The advocated BIM maturity model combines a probability distribution function aggregation paradigm and a large-scale group decision-making framework to provide an expert-based assessment system for evaluating project-based BIM performance. The case study of the Corning Gen 10.5 glass substrate production line workshop in Wuhan demonstrates the feasibility and effectiveness of the proposed model. This paper establishes a generalizable structural framework that can potentially facilitate BIM maturity analysis in a portfolio of projects or the industry as a whole and will generate fresh insight into designing quantitative BIM maturity models across various contexts. • We design a refined assessment system for BIM maturity measurement. • We redefine the BIM maturity levels and provide corresponding descrpitions. • We propose a BIM maturity model based on PDF aggregation and LSGDM. [ABSTRACT FROM AUTHOR]

COVID-19 is the pandemic caused by one of the coronaviruses. This virus was not known before the outbreak in Wuhan, China, in December 2019. By January of 2020 it was declared to be a global human health crisis. The deaths and illnesses caused by the virus caused extensive fear and anxiety among people in all societies. The pandemic slowed economic activities nearly to a halt. The challenges of how companies should respond to the disruptions in their supply chains and how they can build more resilient systems, must be systematically addressed. The authors of this paper highlighted essential factors which can help companies to overcome this crisis and other types of crises, by learning from the approaches taken in India, which has a unique and diverse economic system. The Analytical Hierarchy Process (AHP) technique was used to identify the essential factors which can help companies to improve their resilience so they can recover during and after the COVID-19 pandemic era and potentially in other similar complex crises. The results of the AHP evaluation were prioritized by performing a sensitivity analysis to prioritise the essential factors. The "Role of governance" was found to be the most important factor that can be used to help in rebuilding industries and societies and in helping them to become more resilient to future severe shocks. The results of this research were used to develop recommendations for company managers, practitioners and policy-makers. The authors hope that this advice will help India to become a stronger nation with more resilient companies, which are better prepared to anticipate and to respond to future crises. We hope people in other nations will also benefit from the finding presented in this paper. Image 1 • Factors influencing rebuilding resilient industries and societies are proposed. • Focus is on measures to avoid disruption in supply chain in Indian context. • The ranking framework is developed using AHP followed by sensitivity analysis. • Role of governance reported highest rank indicating its importance in post COVID era. • Policy implications are derived to build resilient nation, which can face shocks. [ABSTRACT FROM AUTHOR]

COVID-19 infection was reported in December 2019 at Wuhan, China. This virus critically affects several countries such as the USA, Brazil, India and Italy. Numerous research units are working at their higher level of effort to develop novel methods to prevent and control this pandemic scenario. The main objective of this paper is to propose a medical decision support system using the implementation of a convolutional neural network (CNN). This CNN has been developed using EfficientNet architecture. To the best of the authors' knowledge, there is no similar study that proposes an automated method for COVID-19 diagnosis using EfficientNet. Therefore, the main contribution is to present the results of a CNN developed using EfficientNet and 10-fold stratified cross-validation. This paper presents two main experiments. First, the binary classification results using images from COVID-19 patients and normal patients are shown. Second, the multi-class results using images from COVID-19, pneumonia and normal patients are discussed. The results show average accuracy values for binary and multi-class of 99.62% and 96.70%, respectively. On the one hand, the proposed CNN model using EfficientNet presents an average recall value of 99.63% and 96.69% concerning binary and multi-class, respectively. On the other hand, 99.64% is the average precision value reported by binary classification, and 97.54% is presented in multi-class. Finally, the average F1-score for multi-class is 97.11%, and 99.62% is presented for binary classification. In conclusion, the proposed architecture can provide an automated medical diagnostics system to support healthcare specialists for enhanced decision making during this pandemic scenario. • An automated system to support the diagnosis of COVID-19 patients using EfficientNet. • The proposed method has been validated using 10-fold cross-validation. • An external dataset also has been used for validation. • An accuracy of 99.62% and 96.70% is reported for binary and multi-class classification. [ABSTRACT FROM AUTHOR]

The novel Coronavirus disease of 2019 (nCOV-19) is a viral outbreak noted first in Wuhan, China. This disease is caused by Severe Acute Respiratory Syndrome (SARS) Coronavirus (CoV)-2. In the past, other members of the coronavirus family, such as SARS and Middle East Respiratory Syndrome (MERS), have made an impact in China and the Arabian peninsula respectively. Both SARS and COVID-19 share similar symptoms such as fever, cough, and difficulty in breathing that can become fatal in later stages. However, SARS and MERS infections were epidemic diseases constrained to limited regions. By March 2020 the SARS-CoV-2 had spread across the globe and on March 11th, 2020 the World Health Organization (WHO) declared COVID-19 as pandemic disease. In severe SARS-CoV-2 infection, many patients succumbed to pneumonia. Higher rates of deaths were seen in older patients who had co-morbidities such as diabetes mellitus, hypertension, cardiovascular disease (CVD), and dementia. In this review paper, we discuss the effect of SARS-CoV-2 on CNS diseases, such as Alzheimer's-like dementia, and diabetes mellitus. We also focus on the virus genome, pathophysiology, theranostics, and autophagy mechanisms. We will assess the multiorgan failure reported in advanced stages of SARS-CoV-2 infection. Our paper will provide mechanistic clues and therapeutic targets for physicians and investigators to combat COVID-19. Unlabelled Image • SARS-CoV-2 and Dementia, Multiple Sclerosis, Encephalitis, and Parkinson's disease • SARS-CoV-2 and Diabetes Mellitus Coronaviruses and Autophagy • Protease Inhibitors and Repurposed Drugs for SARS-CoV-2 USFDA approved drugs-HCQ and Remdesivir. • SARS-CoV-2 diagnostics [ABSTRACT FROM AUTHOR]

Taxicabs play significant roles in public transport systems in large cities. To meet repetitive demands of daily intra-urban travels, cabdrivers acquire self-organized habitual operation behaviors in space and time, largely with assistance of their longitudinal operating experience. Recognizing those collective operation behavior patterns of taxicabs will enable us to better design and implement public transport services and urban development plan. In this paper, we systematically study patterns of the spatial supply of 6000 + taxicabs in Wuhan, China based on a monthly collection of their digital traces and the non-negative matrix factorization method. We successfully identify a set of high-level statistical features of the spatial operation behaviors of taxicabs in Wuhan, providing valuable insights to our knowledge of the demand and supply of taxicabs in (similar) large cities. First, we decouple several spatially cohesive regions with intensive internal taxicab travels (termed as demand regions ), which intuitively reveal the well-known multi-sectored urban configuration of Wuhan. Second, by applying the non-negative matrix factorization to taxicab's longitudinal traces, we uncover remarkably self-organized operation patterns of cabdrivers in space (termed as supply regions ) as reactions to the sectored distribution of daily travel behaviors. We find that a large proportion of cabdrivers frequently operate within single specific service area and a small proportion of taxicabs works as shifting tools between different service areas. Last, we focus on performances of taxicabs with distinct spatial operation behaviors and unveil their statistical characteristics in terms of frequency, duration and distance with passenger on board. Our work demonstrates the great potential to understand and improve urban mobility and public transport system from cabdrivers' collective intelligence. [ABSTRACT FROM AUTHOR]

COVID-19 is an evolving respiratory transmittable disease, and it holds all daily activity worldwide as a global pandemic. It appeared in the city of Wuhan (China) in November 2019 and slowly started spreading to the rest of the world. The number of cases keeps increasing drastically, leading to a shortage of medical resources and testing kids worldwide. As the physicians facing this problem, several scientists and specialists in Artificial Intelligent (AI) are rendering their support to healthcare professionals in the early detection of COVID-19 using chest X-ray image samples to determine the level of severity at a low cost. This paper proposed Genetic Deep Learning Convolutional Neural Network (GDCNN) architecture that includes Huddle Particle Swarm Optimization as an alternative to Gradient descent. Huddle PSO performs better when clubbed with GDCNN architecture. Based on publicly available datasets, trained chest X-ray images are used to predict and identify various pneumonia diseases. The proposed model performed better with an accuracy of 97.23%, a sensitivity of 98.62%, specificity of 97.0%, and precision of 93.0%. The proposed model act as a tool for earlier detection of COVID-19. In the future, we plan to apply the proposed model for the larger dataset and to predict various lung diseases. [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]

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]

Metro shield construction will inevitably cause changes in the stress and strain state of the surrounding soil, resulting in stratum deformation and surface settlement (SS), which will seriously endanger the safety of nearby buildings, roads and underground pipe networks. Therefore, in the design and construction stage, optimizing the shield construction parameters (SCP) is the key to reducing the SS rate and increasing the safe driving speed (DS). However, optimization of existing SCP are challenged by the need to construct a unified multiobjective model for optimization that are efficient, convenient, and widely applicable. This paper innovatively proposes a hybrid intelligence framework that combines random forest (RF) and non-dominant classification genetic algorithm II (NSGA-II), which overcomes the shortcomings of time-consuming and high cost for the establishment and verification of traditional prediction models. First, RF is used to rank the importance of 10 influencing factors, and the nonlinear mapping relationship between the main SCP and the two objectives is constructed as the fitness function of the NSGA-II algorithm. Second, a multiobjective optimization framework for RF-NSGA-II is established, based on which the optimal Pareto front is calculated, and reasonable optimized control ranges for the SCP are obtained. Finally, a case study in the Wuhan Rail Transit Line 6 project is examined. The results show that the SS is reduced by 12.5% and the DS is increased by 2.5% with the proposed framework. Meanwhile, the prediction results are compared with the back-propagation neural network (BPNN), support vector machine (SVM), and gradient boosting decision tree (GBDT). The findings indicate that the RF-NSGA-II framework can not only meet the requirements of SS and DS calculation, but also used as a support tool for real-time optimization and control of SCP. [ABSTRACT FROM AUTHOR]

This study investigates how the built environment influences pedestrian distribution in housing estates and whether its influence differs between gated and non-gated housing types. This study is built upon the theoretical framework of the movement economy, initially proposed by scholars from the Space Syntax Laboratory and recently extended by others. The extended movement economy framework conceptualises the interrelationship between street configuration, functional uses, physical structures, and pedestrian movement. To evaluate the hypothesised associations within this theoretical framework, we constructed structural equation modelling using pedestrian movement data of 606 locations from six pairs of gated and non-gated housing estates in Wuhan, China. We assessed the direct, indirect, and total effects of street configuration, functional uses, and physical structures on pedestrian movement. We then conducted a multigroup analysis to determine whether statistically significant differences exist between gated and non-gated housing areas regarding how the three spatial factors affect pedestrian movement. The findings indicated that the structural model could explain over 60 % of variances on pedestrian movement data, regardless of housing types. Although pedestrian movement demonstrated statistically significant correlations with street configuration, functional use, and physical structure, the street configuration was the most powerful and reliable predictor of all. This paper also evidenced that functional use and physical structure were two effective mediators between street configuration and pedestrian movement, albeit with small mediating effect sizes. More importantly, the results also uncovered several statistically significant differences in the builtenvironmental influences between gated and non-gated models. To the best of our knowledge, this is the first study that (i) verifies the direct, indirect, and total effects of the built environment on pedestrian movement in Chinese housing estates; and (ii) confirms statistically significant differences between gated and non-gated housing types in their built-environmental impacts on pedestrian movement. • The movement economy theory is applicable to explaining pedestrian movement patterns in China's housing estates. • Street configuration, functional uses, and physical structures have direct effects on pedestrian movement patterns. • Street configuration has indirect effects on pedestrian movement pattern, mediated by functional use and physical structure. • Influences of configuration, function, and physical structure on pedestrian flows significantly differ between housing types. • Street configuration is the strongest and most reliable predictor of pedestrian movement, regardless of housing type. [ABSTRACT FROM AUTHOR]

Given China's rapid urbanization process, the study of water resource carrying capacity (WRCC) has important theoretical and practical significance for urban sustainable development. In this paper, a "human–water–city" (HWC) framework was proposed that offers a comprehensive consideration of the development of human society, water quality and quantity, and the progress of urban construction. Based on this framework, we established an HWC evaluation indicator system and constructed the WRCC-HWC approach, to assess, compare, and forecast the WRCC. Taking Wuhan, China, as an example, principal component analysis (PCA) was adopted to evaluate the current (2009–2019) and future (2020–2030) WRCC-HWC. Specifically, global climate models (GCMs) and a grey model, GM (1,1), were used to forecast the value of the future index system. The results show that the WRCC-HWC in Wuhan gradually increased from −3.04 to 3.23 during 2009–2019, being mainly affected by the income of urban residents, per capita GDP, reuse ratio of water used by industry, built-up area, and the output value of new and high-tech industry. With a rapid growth rate from 2011 to 2016, the WRCC-HWC reached the Grade I (excellent) state in 2016. In addition, the forecast results show that, with the exceptions of precipitation and good air quality rate, all HWC indicators show upward trends from 2020 to 2030 in Wuhan. In particular, the per capita disposable income of urban residents (N 1), per capita GDP (N 2), and the output value of new and high-tech industry (N 11) have faster growth rates, with relative growth rates from 2020 to 2030 exceeding 100%. Correspondingly, the WRCC-HWC from 2020 to 2030 were predicted to grow steadily each year, with a higher absolute growth rate starting in 2025. This study supports a comprehensive understanding of the current and future situation for WRCC-HWC in Wuhan and can provide a reference for the formulation of scientific and sustainable development strategies. [Display omitted] • A novel HWC framework was proposed to evaluate WRCC-HWC, with a stride from the "poor" to "excellent" stage during 2009–2019. • The mainly influencing factors of the regional WRCC-HWC were identified as the human welfare and reuse ratio of water. • The development trend of the future WRCC-HWC was predicted to be steady growth from 2020 to 2030. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and has led to the most severe global pandemic, which began in Wuhan, China. Angiotensin-converting enzyme 2 (ACE2) combines with the spike protein of SARS-CoV-2, allowing the virus to cross the membrane and enter the cell. SARS-CoV-2 is modified by the transmembrane protease serine 2 (TMPRSS2) to facilitate access to cells. Accordingly, ACE2 and TMPRSS2 are targets of vital importance for the avoidance of SARS-CoV-2 infection. Sanghuangporus sanghuang (SS) is a traditional Chinese medicine that has been demonstrated to have antitumor, antioxidant, anti-inflammatory, antidiabetic, hepatoprotective, neuroprotective and immunomodulatory properties. In this paper, we demonstrated that SS decreased ACE2 and TMPRSS2 expression in cell lines and a mouse model without cytotoxicity or organ damage. Liver and kidney sections were confirmed to have reduced expression of ACE2 and TMPRSS2 by immunohistochemistry (IHC) assessment. Then, hispidin, DBA, PAC, PAD and CA, phenolic compounds of SS, were also tested and verified to reduce the expression of ACE2 and TMPRSS2. In summary, the results indicate that SS and its phenolic compounds have latent capacity for preventing SARS-CoV-2 infection in the future. [ABSTRACT FROM AUTHOR]

[Display omitted] • A multi-scenario ecological space simulation based on Markov-FLUS for WMA was proposed. • Human activities complicate the landscape structure of ecological space. • Water and forest land play a major role in ecosystem services in the ecological space of WMA. • The optimal ecological space change is under the BCD scenario. The contradiction between the ecological environment and economic development restricts the sustainable development of Wuhan Metropolitan Area (WMA). As an important carrier of the ecosystem in WMA, the change of ecological space can effectively reflect the ecological environment of WMA. Accordingly, based on various development needs and the Markov-FLUS model, this paper simulated the distribution of ecological space in WMA in 2035 under five scenarios, and discussed the characteristics of landscape structure and ecosystem service value of ecological space under five different scenarios. The results show that the Markov-FLUS model can properly simulate the future distribution of ecological space in WMA. Compared with 2020, the scale of ecological space under the natural development scenario (ND), the balanced and coordinated development scenario (BCD) and the ecological space preference development scenario (ESPD) increases, while the scale of the production space preference development scenario (PSPD) and the living space preference development scenario (LSPD) decreases. In the five scenarios, the areas of ecological spatial change are predominantly concentrated in the central urban area of Wuhan, the urban construction area of Ezhou, Huangshi and Huanggang, and the Dabie Mountain area in the northeast of Huanggang. Under the influence of human activities, the landscape structure of ecological space in WMA tended to be smaller in size, simplersize, simpler in boundary, more dispersed in distribution, and more uneven. Water and forest land play a major role in the ecosystem service value of ecological space in WMA, accounting for more than 95% of the value, and mostly play hydrological and climate regulation functions. The sustainable development and protection of ecological space under the BCD mode can provide reference for the policy implementation of sustainable development of territorial space and regional ecological security in WMA in the future. [ABSTRACT FROM AUTHOR]

• Evaluation of proposed COVDC-Net method for both 3-class and 4-class classification. • A novel hybrid fusion-based Deep Convolutional Neural Network model is presented. • The proposed model helps radiologists to distinguish between types of pneumonia and COVID infections. • The performance of COVDC-Net method is better than several other methods proposed for detection of COVID-19. Coronavirus disease is a viral infection caused by a novel coronavirus (CoV) which was first identified in the city of Wuhan, China somewhere in the early December 2019. It affects the human respiratory system by causing respiratory infections with symptoms (mild to severe) like fever, cough, and weakness but can further lead to other serious diseases and has resulted in millions of deaths until now. Therefore, an accurate diagnosis for such types of diseases is highly needful for the current healthcare system. In this paper, a state of the art deep learning method is described. We propose COVDC-Net, a Deep Convolutional Network-based classification method which is capable of identifying SARS-CoV-2 infected amongst healthy and/or pneumonia patients from their chest X-ray images. The proposed method uses two modified pre-trained models (on ImageNet) namely MobileNetV2 and VGG16 without their classifier layers and fuses the two models using the Confidence fusion method to achieve better classification accuracy on the two currently publicly available datasets. It is observed through exhaustive experiments that the proposed method achieved an overall classification accuracy of 96.48% for 3-class (COVID-19, Normal and Pneumonia) classification tasks. For 4-class classification (COVID-19, Normal, Pneumonia Viral, and Pneumonia Bacterial) COVDC-Net method delivered 90.22% accuracy. The experimental results demonstrate that the proposed COVDC-Net method has shown better overall classification accuracy as compared to the existing deep learning methods proposed for the same task in the current COVID-19 pandemic. [ABSTRACT FROM AUTHOR]