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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]

With the rise of GeoAI research, streetscape imagery has received extensive attention due to its comprehensive coverage, abundant information, and accessibility. However, obtaining a holistic spatial–temporal scene representation is difficult because places are often composed of multiple images from different angles, times and locations. This problem also exists in other types of geo-tagged imagery. To solve it, we propose a purely visual, robust, and reliable method for urban function identification at the street scale. We introduce a method based on a two-layer spatially dependent graph neural network structure, which handles sequential street view imagery as input (typically available in services such as Google Street View, Baidu Maps, and Mapillary), with full consideration of the spatial dependencies among road networks. In this paper, we construct an urban topological map network using OpenStreetMap data in Wuhan, China, and compute a semantic representation of the scene as a whole at the street scale using a large-scale pre-trained model. We construct the graph network with streets as nodes based on 28,693 mapping relationships constructed from 75,628 street view images and 5,458 streets. Only 5.3% of the node labels were required to obtain 10 categories of functions for all nodes in the study area. The results demonstrate that by using appropriate spatial weights, street encoder, and graph structure, our novel method achieves high accuracy of P@1 46.2%, P@3 73.0%, P@5 82.4%, and P@10 89.9%, fully demonstrating the effectiveness of the introduced approach. We also use the model to sense urban spatial–temporal renewal by computing time series street images. The model is also applicable to the prediction of other attributes, where only a small number of labels are required to obtain valid and reliable scene perception results. The example data and code is shared at: https://github.com/yemanzhongting/Knowledge-and-Topology. [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]

Around the end of 2019, in Wuhan, Hubei Province, China, the first confirmed cases of COVID-19 were identified, and from then on, the world we were used to knowing changed globally. The role of population density, in relation to the spread of the pandemic, has been widely scrutinised in urban studies, believed to be the triggering variable. However, the results so far are inconclusive. This paper suggests instead to shift the focus to socio-spatial vulnerabilities, as the effects of the pandemic's spread have been more severe in urban units which feature long-standing inequalities. The paper's aim is, therefore, twofold: on the one hand it aims at contributing to the debate on population density and COVID-19 in urban areas, and, on the other hand, to analyse the pandemic's spread in relation to socio-spatial vulnerabilities. Different cities across the globe are drawn into a comparative project, where the pandemic's spread is analysed in relation to variables of Population Density (PD) and a Social Vulnerability Index (SVI), by employing correlation matrices. The results suggest that there is no significant correlation between density and the spread of COVID-19. Instead, a positive correlation is in place when analysing the pandemic's diffusion with socio-spatial inequalities. • COVID-19 spread is not associated with population density while social vulnerability is found to be positively correlated. • The study invites to undertake a more critical understanding of how density and vulnerability are intertwined. • Measures based on physical concentration should be supplemented by topological density, made up of relational factors. [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]

Time Reliability (TR) affects the accessibility of an Urban Rail Transit Network (URTN), which refers to the reachability of destination stations in this paper. However, existing studies do not incorporate TR into accessibility metrics when measuring a URTN's accessibility. Here, the Buffer Time Index (BTI), which is the ratio of extra "buffer" time needed for arriving reliably to the average Travel Time (TT), is used to measure TR. Time-based, cost-based, and timebound-destination-reachability metrics which incorporate TR are proposed to measure the accessibility of a URTN. A Monte Carlo simulation is applied to estimate TR according to the probability distributions of TT components, which overcomes the difficulty of obtaining many passengers' data. The accessibility of Wuhan's URTN during weekday morning peak hours indicates that the metrics which do not consider TR overestimate a URTN's accessibility. The evaluation results can effectively guide decisions on improvements to a URTN's accessibility. The advantage of the measures of accessibility incorporating TR for a URTN is that they not only improve the reasonableness of evaluation but also reflect an aspect of great concern to passengers. [ABSTRACT FROM AUTHOR]

Scene classification is a means to interpret high-resolution remote sensing (HRS) imagery, to obtain the high-level semantic information, which can provide a reliable reference for urban planning and monitoring. The traditional scene classification methods based on HRS imagery take uniform grid cells as the scene units, thereby missing the geographic boundaries and leading to the mosaicking effect. Therefore, in this paper, the urban scene is defined as a geographic unit bordered by the road network. The task of urban scene understanding is to recognize the socioeconomic or natural semantics of the urban scene. However, due to the complexity of the urban environment, the effectiveness of the traditional scene classification methods is limited on account of three problems when applied in urban scenes: 1) The lack of socioeconomic information in HRS images, with which it is difficult to discriminate diverse urban scenes with similar exteriors. 2) The large discrepancy in the sizes and shapes of urban land parcels affects the scene feature extraction and representation. 3) Urban scene understanding frameworks that can embed various scene classification models have rarely been studied. In this paper, to solve these problems, a universal urban scene understanding framework based on multi-source geographic data (USUMG) is proposed. In the USUMG framework, road network and water channel data from OpenStreetMap (OSM) are used for generating the urban scene units. For each irregular unit, a scene decomposition method based on a morphological skeleton is employed to represent the urban scene unit by unified processing patches. To integrate the different data sources, the high-level semantic features extracted from the HRS imagery and the socioeconomic features extracted from point of interest (POI) data are fused to determine the urban scene category. Finally, the USUMG framework with various scene classification methods was tested in urban districts of Wuhan and Macao in China to verify the universality and feasibility of the proposed framework. The experimental performances are provided in this paper as a benchmark for urban scene understanding based on multi-source geographic data. [ABSTRACT FROM AUTHOR]

• The solar potential in different residential blocks is simulated. • The influences of block morphological parameters on solar potential are varified. • Four parameters that impact on the block solar potential more than 70% are verified. • The facade PV capacity in high-rise and hybrid residential blocks is more than 66%. • The average PV installation rate of roof, south and west facade is 96%, 32% and 39%. Block-scale application of photovoltaic technology in city is becoming a viable solution for renewable energy utilization. Rapid urbanization process has made urban buildings have huge development potential for solar energy in China, especially in residential areas that occupy large parts of the city. However, the residential blocks in China show differences in the morphological layout and parameters, which means they have diverse potential for solar energy development. This paper studies the effect of quantified morphological parameters on solar potential for residential blocks in Wuhan, China. The distribution characteristics of solar potential in different block environments are discussed. Various strategies are evaluated in terms of the installation position and height of PV modules. The results showed that floor area ratio, building density, average building height and building interval have significant impact on solar potential for residential block, and their correlation can reach 75%, 71%, 78% and 72% respectively. The average PV installation ratio of roof, south facade and west facade can reach 96%, 32% and 39% respectively. The results of this paper not only provide a reference to the block-scale application of PV technology in urban environment, but also stimulate the implementation of photovoltaic policies on various residential blocks in China. [ABSTRACT FROM AUTHOR]

Observing traffic flow is of great significance to contemporary urban management. Overhead images, as represented by remote sensing images, provide a major source of information about traffic flow. However, the spatial resolutions of most common high-resolution remote sensing images are often limited to 0.5 m and even below, which makes it unrealistic to count vehicles by means of widely used object detection methods. Therefore, to explore the potential of remote sensing data for studying global urban development and management, this paper introduces a density map-based vehicle counting method for remote sensing imagery with limited resolution. Density map-based models regard the vehicle counting task as estimating the density of vehicle targets in terms of pixel values. We propose an improved CNN-based network, called Congested Scene Recognition Network Minus (CSRNet—), that generates a density map of vehicles from the input remote sensing imagery. A new dataset, RSVC2021, which was generated from the public DOTA and ITCVD datasets, is also introduced for network training and testing. A benchmark on the RSVC2021 dataset is accordingly established and CSRNet— is selected as the baseline model for subsequent experiments. A set of GF-2 time series images with a resolution of 1 m taken before, during and after the COVID-19 epidemic lockdown covering Wuhan city are applied for real-world application testing. The testing results on both the RSVC2021 dataset and real satellite images confirm that, in terms of both the counting values and the visualized density maps, the proposed method achieves good performance and exhibits considerable application potential in this task. The generating codes of RSVC2021 dataset will be publicly available at https://github.com/YinongGuo/RSVC2021-Dataset. [ABSTRACT FROM AUTHOR]

Outdated or sketchy inventory of street furniture may misguide the planners on the renovation and upgrade of transportation infrastructures, thus posing potential threats to traffic safety. Previous studies have taken their steps using point clouds or street-view imagery (SVI) for street furniture inventory, but there remains a gap to balance semantic richness, localization accuracy and working efficiency. Therefore, this paper proposes an effective pipeline that combines SVI and point clouds for the inventory of street furniture. The proposed pipeline encompasses three steps: (1) Off-the-shelf street furniture detection models are applied on SVI for generating two-dimensional (2D) proposals and then three-dimensional (3D) point cloud frustums are accordingly cropped; (2) The instance mask and the instance 3D bounding box are predicted for each frustum using a multi-task neural network; (3) Frustums from adjacent perspectives are associated and fused via multi-object tracking, after which the object-centric instance segmentation outputs the final street furniture with 3D locations and semantic labels. This pipeline was validated on datasets collected in Shanghai and Wuhan, producing component-level street furniture inventory of nine classes. The instance-level mean recall and precision reach 86.4%, 80.9% and 83.2%, 87.8% respectively in Shanghai and Wuhan, and the point-level mean recall, precision, weighted coverage all exceed 73.7%. [ABSTRACT FROM AUTHOR]

With the establishment of carbon peaking and carbon neutrality goals in China, developing low-carbon cities has become crucial. Previous studies primarily focus on the quantitative structure of land use to achieve low carbon optimization, while ignoring the spatial integration between urban form and carbon emissions. To fulfill this gap, this paper proposed a dual-scale low carbon oriented multi-objective land use allocation (DOLA) optimization model, utilizing idea-point multi-objective linear programming (IMLP) and non-dominated sorting genetic algorithm II (NSGA-II) to optimize the low-carbon-emission pattern of the city at both the structural and spatial scales respectively. By innovatively linking the urban shape index with carbon emissions, this model aims to minimize urban carbon emissions, while maximizing economic development, urban development suitability, and the compactness of city. The proposed method is applied to Wuhan's main urban area in China, the DOLA model shows significant improvements in target values across all scenarios. Among them, the scenario that prioritized low carbon emissions at both the structural and spatial scales achieved the most significant reduction of 6.43 % in carbon emissions. The DOLA effectively contributes to achieving multiple spatial targets, thereby aiding in reducing carbon emissions. It offers technical support for developing pathways towards China's "dual carbon" target. • A dual-scale low carbon oriented multi-objective land use allocation (DOLA) optimization model is proposed. • The DOLA model innovatively incorporates urban form indices to carbon emissions. • The DOLA model achieves spatial aggregation targets, thereby facilitating significant reductions in carbon emissions. • The DOLA model generates a series of land use optimization scenarios, offering a valuable reference for land use planning. [ABSTRACT FROM AUTHOR]

• The coupled heat transfer process of rooftop photovoltaic shading units and indoor heat gain are analyzed. • The energy-saving potential of photovoltaic rooftops compared to traditional rooftops is revealed. • The energy-saving performance of photovoltaic and traditional rooftops under different roof reflectivity are summarized. • The impact of the optimal tilt angle on the power generation of the photovoltaic rooftop are discussed. • An energy-saving scheme for applying rooftop photovoltaic systems in hot summer areas is proposed. Rooftop photovoltaic panels can serve as external shading devices on buildings, effectively reducing indoor heat gain caused by sunlight. This paper uses a numerical model to analyze rooftop photovoltaic panels' thermal conduction, convection, and radiation in hot summer areas as shading devices. The researcher builds an experimental platform to verify the model, exploring the potential for energy savings of photovoltaic rooftop units in the Wuhan area. The results show that after installing photovoltaic panels, the delay performance of the roof increases by 0.5 h, the roof heat flux is reduced by 41.7%, the peak temperature of the roof is reduced by 22.9 °C, and the daily heat gain is reduced by 74.84%. Finally, this paper discusses the impact of high-reflectance and low-reflectance roofs on the shading effect. The study finds that low-reflectance roofs are more energy-efficient in the hot summer, as high-reflectance roofs lead to a 10.8% increase in indoor heat gain when the photovoltaic panel is installed. Finally, a quantitative method for evaluating the comprehensive potential for energy savings is proposed, considering the electricity generation gain of photovoltaic panels and the comprehensive energy-saving efficiency of photovoltaic roofs, which generates a total potential for energy savings rate of 61.06%. The model presented in this paper provides theoretical guidance for analyzing the comprehensive energy-saving effects of photovoltaic rooftop systems and reveals the potential for energy savings of rooftop photovoltaic panels as external shading devices. [ABSTRACT FROM AUTHOR]

To understand the roles of different transport modes in the spread of COVID-19 pandemic across Chinese cities, this paper looks at the factors influencing the number of imported cases from Wuhan and the spread speed and pattern of the pandemic. We find that frequencies of air flights and high-speed train (HST) services out of Wuhan are significantly associated with the number of COVID-19 cases in the destination cities. The presence of an airport or HST station at a city is significantly related to the speed of the pandemic spread, but its link with the total number of confirmed cases is weak. The farther the distance from Wuhan, the lower number of cases in a city and the slower the dissemination of the pandemic. The longitude and latitude coordinates do not have a significant relationship with the number of total cases but can increase the speed of the COVID-19 spread. Specifically, cities in the higher longitudinal region tended to record a COVID-19 case earlier than their counterparties in the west. Cities in the north were more likely to report the first case later than those in the south. The pandemic may emerge in large cities earlier than in small cities as GDP is a factor positively associated with the spread speed. • This paper explores the factors influencing the COVID-19 spread speed and spatial pattern. • Frequencies of flights and HSTs are significantly associated with the number of cases. • The farther the distance from Wuhan, the lower the number of cases and dissemination speed. [ABSTRACT FROM AUTHOR]

The pandemic caused by coronavirus disease 2019(COVID-19) continues to disrupt the global supply chain system, bringing new risks and challenges. The uncertainty created by COVID-19 makes it is difficult for various industries to deal with the pandemic. Since the pandemic, the supply chain's resilience has been discussed and examined in some studies. However, most existing works start from a single industry perspective or pay more attention to the disturbance caused by changes in the production side. Supply chain networks of different industries, mainly transport networks, are relatively limited under the epidemic's impact. In this paper, from the perspective of highway freight transport, a comprehensive competitiveness evaluation framework was proposed to reveal and the disruption and resilience of the supply chain under the outbreak based on nine indexes with five dimensions, including efficiency, capacity, activity, connectivity, and negotiability. Based on the availability of the data(Large-scale truck trajectory), we sorted out seven categories of Chinese industries(related to highway transport) and divided them into four categories respectively: (a) Slight disruption and worse resilience; (b) Slight disruption and remarkable resilience; (c) Serious disruption and worse resilience; (d) Serious disruption and remarkable resilience. The measurement results of supply chain network performance show that the industries (cold-chain, general products, and other industries) dominated by "Efficiency - Negotiability - Connectivity" are slightly disrupted (about 33%), forming a spatial diffusion with Wuhan(the city where the pandemic first broke out) as the disrupted center, spreading outward in a circle structure. Simultaneously, five urban agglomerations surrounding it have been impacted. By contrast, due to the strict isolation measures, the industries (building materials, construction, engineering, and high-value products industry) more vulnerable to be disrupted seriously (about 82%) tend to be the pattern of "Capacity - Activity". However, a large-scale centralized disruption was observed in the Triangle of Central China urban agglomeration was presented, resulting in almost stagnation of industry development. Meanwhile, as the future of the pandemic remains uncertain, the supply chain represented by the engineering industry, construction industry, etc are deserved to be paid more attention in line with they are prone to large-scale centralized damage due to the disruption of a single city node. • Disruption and resilience of multi-industry supply chain under COVID-19 epidemic. • Multi-dimensional comprehensive competitiveness evaluation framework for supply chain performance. • Supply chain destruction and repair performance of seven industries in China under COVID-19 epidemic. • Characteristic differences of supply chain between different industries from the perspective of highway freight transport. [ABSTRACT FROM AUTHOR]

The global outbreak of COVID-19 has further exposed deficiencies in city logistics based on human and ground roads, such as poor emergency response capacity and high risk of infection during transportation. Metro-based underground logistics system (M-ULS) may be an innovative approach to deal with this city-level disaster due to its efficient operation, contactless and driverless characteristics. However, the market evolution process and the quantitative calculation framework of comprehensive benefits after the application of M-ULS are still unclear, which has become a problem of mutual restriction with the extensive application of M-ULS. This paper attempts to use the system dynamics method, based on the real-world simulation, to analyze the quantitative relationship between the M-ULS implementation and the city logistics performance under epidemic outbreaks. Wuhan city in China was selected as the empirical background, and five simulation scenarios were set under different implementation strategies of M-ULS in response to the epidemic. Six variables were selected to measure city logistics performance and M-ULS operation status, including demand fill-rate, unit delivery time, total deprivation cost, total transportation cost, total number of susceptible people, and utilization rate of M-ULS. The results show that M-ULS is effective in improving the performance of city logistics and responding to the epidemic. The delivery time and transportation cost have a strong impact on the market share of M-ULS. Finally, a set of incentive policies was designed to promote the adoption of M-ULS. The findings not only provide a method for evaluating the overall performance of M-ULS, but also provide a unique perspective for promoting the implementation of M-ULS and responding to the transportation challenges brought by the epidemic. • M-ULS is introduced to break through the plight of city logistics. • A set of effective macro-micro incentive policies are established to promote the implementation of M-ULS. • The delivery time, transportation cost and government subsidies play different roles in the market penetration of M-ULS. • The benefits of M-ULS have been analyzed. [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]

Accurate forecasting of vessel traffic flow is crucial for establishing a smart inland waterway transportation system, ensuring real-time safety supervision, and enabling prompt emergency response. While existing methods mainly focus on temporal correlation, there is a need for research on utilizing spatiotemporal features for area vessel traffic forecasting. The study proposes a novel spatiotemporal vessel traffic fluxes prediction model called STA-BiLSTM. The model combines the Graph Attention (GAT) network and Long Short-Term Memory (LSTM) network within the GAT-LSTM framework to capture the spatial and temporal aspects of vessel traffic patterns. The proposed model incorporates the attention mechanism and Bi-directional Long Short-Term Memory (Bi-LSTM) model to enhance the accuracy and robustness of ship traffic flow prediction by capturing the dependencies between time steps and waterway segments. Experimental results based on three actual Yangtze River vessel traffic flow datasets demonstrate the superiority of the proposed method over traditional approaches. Furthermore, ablation experiments validate the effective extraction of spatiotemporal dependency features between river segments by the STA-BiLSTM model. Finally, the paper showcases the significance of the proposed method through practical examples. • A new graph organizational form is used to inland waterway transport network. • GAT-LSTM framework performs the LSTM and GAT methods in inland waterway transport network. • STA-BiLSTM model is presented to deal with the vessel traffic flow forecasting problem. • The performance of the STA-BiLSTM model is higher than other competitors, which demonstrates the superiority of our model. [ABSTRACT FROM AUTHOR]

The popularity of electric vehicles (EVs) brings environmental benefits, but their hard-to-estimate stochastic charging behaviors places additional diversity on grid load management. This paper proposes a procedure to identify typical charging load profiles (CLPs) via large scale charging session data from charging stations (CSs). The daily CLPs are computed from charging sessions, and a comprehensive similarity metric based on the weighting of Euclidean and Pearson correlation coefficient is proposed to achieve better clustering. The clustering is performed using the Clustering LARge Applications (CLARA) algorithm to accommodate large sample scenarios. Subsequently, hierarchical clustering of CSs is performed based on possible CLPs，and their CLPs are estimated by Monte Carlo simulation. The performance of the proposed method is tested and evaluated with over 340,000 charging sessions from 109 CSs in Wuhan at central China, and seasonal differences in CLPs are explored. The results show that the method of mining typical CLPs from charging sessions is effective, 17 typical CLPs are identified in different seasons, which provide effective information on the fluctuation and magnitude of daily power demand, the charging power demand also shows significant seasonal differences, and good accuracy is achieved by dividing the CSs into different groups for load estimation. • A method for identifying typical charging load profiles from charging session data is proposed. • 17 typical charging load profiles for charging stations in Wuhan are reported. • Monte Carlo simulation combined with typical charging load profiles enables good estimation of charging loads. • Seasonal differences in charging loads are compared. [ABSTRACT FROM AUTHOR]

Road detection based on convolutional neural networks (CNNs) has achieved remarkable performances for very high resolution (VHR) remote sensing images. However, this approach relies on massive annotated samples, and the problem of limited generalization for unseen images still remains. The manual pixel-level labeling process is also extremely time-consuming, and the performance of CNNs degrades significantly when there is a domain gap between the training and test images. In this paper, to address this problem, a global-local adversarial learning (GOAL) framework is proposed for cross-domain road detection. On the one hand, considering the spatial information similarities between the source and target domains, feature space driven adversarial learning is applied to explore the shared features across domains. On the other hand, the complex background of VHR remote sensing images, such as the occlusions and shadows of trees and buildings, makes some roads easy to recognize, while others are much more difficult. However, the traditional global adversarial learning approach cannot guarantee local semantic consistency. Therefore, a local alignment operation is introduced, which adaptively adjusts the weight of the adversarial loss according to the road recognition difficulty. Extensive experiments were conducted on different road datasets, including two public competition road datasets—SpaceNet and DeepGlobe—and our own large-scale annotated images from four cities: Boston, Birmingham, Shanghai, and Wuhan. The experimental results show that the proposed GOAL framework can clearly improve the cross-domain road detection performance, without any annotation of the target domain images. For instance, taking SpaceNet road dataset as the source domain, compared with the no adaptation method, the IOU performance of GOAL framework is increased by 14.36%, 5.49%, 4.51%, 5.63% and 15.14% on DeepGlobe, Boston, Birmingham, Shanghai, and Wuhan images, respectively, which demonstrates its strong generalization capability. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 in 2020 has had drastic impacts on urban economies and activities, with transit systems around the world witnessing an unprecedented decline in ridership. This paper attempts to estimate the effect of COVID-19 on the daily ridership of urban rail transit (URT) using the Synthetic Control Method (SCM). Six variables are selected as the predictors, among which four variables unaffected by the pandemic are employed. A total of 22 cities from Asia, Europe, and the US with varying timelines of the pandemic outbreak are selected in this study. The effect of COVID-19 on the URT ridership in 11 cities in Asia is investigated using the difference between their observed ridership reduction and the potential ridership generated by the other 11 cities. Additionally, the effect of the system closure in Wuhan on ridership recovery is analyzed. A series of placebo tests are rolled out to confirm the significance of these analyses. Two traditional methods (causal impact analysis and straightforward analysis) are employed to illustrate the usefulness of the SCM. Most Chinese cities experienced about a 90% reduction in ridership with some variation among different cities. Seoul and Singapore experienced a minor decrease compared to Chinese cities. The results suggest that URT ridership reductions are associated with the severity and duration of restrictions and lockdowns. Full system closure can have severe impacts on the speed of ridership recovery following resumption of service, as demonstrated in the case of Wuhan with about 22% slower recovery. The results of this study can provide support for policymakers to monitor the URT ridership during the recovery period and understand the likely effects of system closure if considered in future emergency events. • The Synthetic Control Method helps evaluate COVID-19's impact on public transit. • A reduction effect of COVID-19 on ridership is indicated in most Chinese cities. • Ridership reduction varies between cities and is not guided by the infection rate. • Ridership reduction is influenced by the severity and duration of lockdowns. • Full system closure delayed the ridership recovery following service resumption. [ABSTRACT FROM AUTHOR]

The accurate knowledge of demographic, signs and symptoms, imaging characteristics of coronavirus disease 2019 (COVID-19) is essential for the accurate management of these patients. However, the claims between the previous papers are not always consistent and may even contradict each other, for example, some claims the virus infects more men than women in Wuhan. In this large-scale cohort study, we aimed to update the demographic, signs and symptoms, imaging characteristics of patients with COVID-19 in the whole quarantine of Wuhan, China. A cohort of 2126 patients with a diagnosis of COVID-19 pneumonia (confirmed by real-time reverse transcriptase-polymerase chain reaction, RT-PCR) who were admitted to one hospital in Wuhan were retrospectively enrolled. Data were collected between January 13, 2020, and April 8, 2020, the end of Wuhan quarantine. Demographic, signs and symptoms, imaging characteristics were analyzed. CT imaging characteristics associated with respiratory failure or death were identified. Of the 2126 patients with COVID-19, 1051 (49.44%) were men and 1075 (50.56%) were women, 1933 (90.92%) have fever and 1328 (62.46%) have dry cough. The mean age was 57.43 years of age (range 1–95). The CT imaging findings were bilateral pneumonia (1883[88.57%]), unilateral pneumonia (243[11.43%]), ground-glass opacity (GGO) or consolidation (1175[55.27%]), pleural effusion (69[3.25%]). Patients with respiratory failure or death were more likely to have pleural effusion on CT than patients without respiratory failure or death (p < 0.05). Men and women have been infected by SARS-CoV-2 in roughly equal numbers. Fever and cough are the most prevalent symptoms at disease onset in patients. Other prevalent symptoms include fatigue, and sputum production. COVID-19 patients with bilateral pneumonia and pleural effusion are more likely to develop respiratory failure or death. • Men and women have been infected by SARS-CoV-2 in roughly equal numbers. • Fever and cough are the most prevalent symptoms at disease onset in patients. Other prevalent symptoms include fatigue, and sputum production. • COVID-19 patients with bilateral pneumonia and pleural effusion are more likely to develop respiratory failure or death. [ABSTRACT FROM AUTHOR]

This paper demonstrates that transportation networks may be used to assess and predict the regional risk of COVID-19 infection from the outbreak. We use China's high-speed rail (HSR) network at the scale of prefecture level to assess, based on a probabilistic risk model, the risk of COVID-19 infection from Wuhan to the country's 31 province-level regions at the early stage of domestic spread. We find that the high-risk regions are mainly distributed along the southern half of Beijing-Hong Kong HSR line, where a large number of infection cases have been confirmed at the early stage. Furthermore, the two components of the infection risk, namely, the probability (proxied by the region's correlation with Wuhan through HSR) and the impact (proxied by the region's population with mobility), can play different roles in the risk ranking for different regions. For public health administrators, these findings may be used for better decision making, including the preparation of emergency plans and supplies, and the allocation of limited resources, before the extensive spread of the epidemic. Moreover, the administrators should adopt different intervention measures for different regions, so as to better mitigate the epidemic spread according to their own risk scenarios with respect to the probability of occurring and, once occurred, the impact. [ABSTRACT FROM AUTHOR]

• There is a similarity in PV potential within similar urban block types in the same types. • A significant difference exists between the three different types of urban blocks in regards to PV potential. • The highest PV potential is exhibited by commercial blocks, followed by residential and then industrial blocks. • Different block types exhibit different proportions of PV potential in terms of rooftops and facades due to variation in urban form. Due to the ever increasing energy demand, PV utilisation in the urban scale has become an attractive solution. The purpose of this paper is to demonstrate the effect of urban block type on solar potential. There exists a requirement to analyse solar potential in the real urban context of Wuhan based on block function and type. Therefore, this paper studies a real case in Wuhan's urban context by selecting 15 cases of real urban industrial, commercial and residential blocks. The PV output was calculated for roofs and façades respectively. The results obtained showed obvious differences between the three type of blocks, where commercial blocks received the most total solar irradiation, followed by residential blocks, and then industrial blocks. Similarly, the distribution of solar irradiation on roofs and façades for each block was vividly different based on the block type, owing to differences in building forms based on block function. [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]

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]

While urban rail transit has gained increasing popularity, there are still many problems related to obtaining financial resources for constructing it in China. It is proved that the Land Value Capture (LVC) theory can provide theoretical support for exploring new financing mode of urban rail transit to solve these problems. This paper reviews the concept of LVC and the existing LVC finance mechanisms, in particular, Joint Development (JD) . It is revealed that JD can’t be directly copied and reproduced in mainland China. The characteristics of land acquisition policy as well as the practice of rail transit construction in mainland China are summarized, and based on the findings, the Predetermined Land Reserve Mode (PLR) is proposed. The essence of this proposed mode is to link the reserve of specific land parcels with the relevant rail transit project and ensure it benefits from the predetermined land reserve. Rail transit companies, with the authorizations from the government, can reserve suitable land parcels prior to the planning and construction of the rail transit system so that they can capture the increased land value after the land transfer. PLR is a new finance mechanism based on LVC and it is different from JD under comparative analysis. Analytical results show that PLR has unique advantages. To demonstrate the performance of PLR , a case study of constructing the urban rail transit system in Wuhan City, China, is presented in this paper, and it shows that the PLR is well suited for financing the urban railway systems in cities of mainland China. [ABSTRACT FROM AUTHOR]

When 2020 began, we had no idea what was to unfold globally as we learnt about the Novel-Coronavirus in Wuhan, in the Hubei province of China. As this virus spread rapidly, it became a matter of time before many countries began to implement measures to try and contain the spread of the disease. COVID-19 as it is referred to, resulted in two main approaches to fighting the viral pandemic, either through a progressive set of measures to slow down the number of identified cases designed to 'flatten the curve' over time (anticipated to be at least six months), or to attack it by the severest of measures including a total lock-down and/or herding exposure to fast track 'immunisation' while we await a vaccine. The paper reports the findings from the first phase of an ongoing survey designed to identify the changing patterns in travel activity of Australian residents as a result of the stage 2 restrictions imposed by the Australian government. The main restrictions, in addition to social distancing of at least 1.5 m, are closure of entry to Australia (except residents returning), and closure of non-essential venues such as night clubs, restaurants, mass attendee sporting events, churches, weddings, and all social gatherings in any circumstance. With some employers encouraging working from home and others requiring it, in addition to job losses, and many children attending school online from home, the implications on travel activity is extreme. We identify the initial impacts associated with the first month of stricter social distancing measures introduced in Australia. • General support for the actions of government and business. • Widespread suppression of travel demand for all trip purposes across all modes. • Sizeable shift to working from home by those who can. • Public transport will face the largest hurdles in regaining confidence. • Retail and food logistics will need to examine distribution processes • The aviation sector will likely need to restart with a heavy focus on domestic travel. • Public transport will need to take overt measures to restore confidence. • The dominance of the car is further enforced in the context of biosecurity concerns. • Active transport is a viable option for short inner-city trips. • Infrastructure investment should be carefully considered. • Flexible working arrangements are perhaps the biggest policy lever available to governments. [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]

• The pressure fluctuation occurred during the tunnel construction. • Numerical simulation and experiments are both carried out to analyze the causes. • The breakdown of the filter cake is the main cause of the pressure fluctuations. • Compressed air breaks through filter cake, which causes pressure fluctuations. The Wuhan Metro Line 8 Crossing River Tunnel Project suffered manifest pressure fluctuations when opening the excavation chamber of the slurry shield. In this paper, numerical simulation and air pressure test are both carried out to analyze the causes of the pressure fluctuation. The analysis results show that the air pressure applied on the excavation face during the opening of chamber lies in the safe region, excluding the possibilities of active and/or passive excavation face failures. Then, calculated by empirical formula, the pressure on site is lower than the hydraulic fracture pressure, which means the pressure fluctuation is not related to the hydraulic fracture. At last, by air pressure tests, the break through pressure of the filter cake is found much smaller than the air pressure on site. Therefore, it is concluded in this paper that the quality of the filter cake does not meet the design standard, and the breakdown of the filter cake is the main cause of the observed pressure fluctuations on the excavation face. Furthermore, the mechanism of filter cake breakdown is investigated experimentally. [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]

• 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]

In this paper, three kinds of models, including support vector machine-firefly algorithm (SVM-FFA), copula-base nonlinear quantile regression (CNQR) and empirical models were developed for daily diffuse radiation (H d) estimation. The meteorological data during 1981–2000 and 2001–2010 of Lhasa, Urumqi, Beijing and Wuhan in China were used for model training and validation, respectively. Five combinations of meteorological data: (a) clearness index (K t); (b) sunshine ratio (S); (c) K t and S ; (d) K t , S and average temperature (T a); (e) K t , S , T a and average relative humidity, were considered for simulation. The results showed that for the training phases, SVM-FFA outperformed the corresponding models while empirical models performed slightly better than corresponding CNQR models. For validation phases, CNQR and SVM-FFA models performed much better than empirical models. Compared CNQR and SVM-FFA, SVM-FFA performed slightly better than CNQR models with average MABE decreased by 0.67% (0.01 MJm−2d−1) and average R2 increased by 0.43% (0.004). For the training time, SVM-FFA (1.68 s) showed less computational costs than CNQR (6.68 s); but the parameter optimization time of SVM-FFA (4.9 × 105) were 105 times as much as CNQR. Thus, the overall computational costs of SVM-FFA during training phases were much higher than CNQR. Considering the trade-off between accuracy and computational costs, CNQR were highly recommended for the daily H d estimation. • CNQR, SVM and empirical models were developed for daily diffuse radiation. • The models were trained and compared using data from China as a case study. • SVM and CNQR models showed comparable estimation accuracy. • CNQR showed much more efficient than SVM models. [ABSTRACT FROM AUTHOR]

Oblique photogrammetry is an emerging technology in unmanned aerial vehicle (UAV)-based low-altitude remote sensing, and oblique digital cameras are widely used for data acquisition. Moreover, panoramic cameras are becoming popular with their capacities of comprehensive range of imaging compared with original oblique cameras, a spherical panorama can be transformed to several perspective images which are similar to normal oblique images. In this paper, a UAV-based panoramic oblique photogrammetry (POP) approach is proposed to achieve georeferenced panoramic images and real 3D models together by using panorama image projection algorithms. First, a UAV-based panoramic imaging system with six fish-eye cameras is presented to obtain multiangle and large-field-of-view images to generate two-dimensional (2D) spherical panorama images. Second, two panorama image projection models from a 2D panorama to perspectives are proposed to regenerate small distortion images toward different directions as traditional oblique photogrammetry images. Third, aerial triangulation, dense matching and three-dimensional (3D) modeling are presented with these small distortion images to achieve panoramic oblique photogrammetry processing. Two real scenes in the city of Wuhan are taken to verify the feasibility and precision of the proposed approach. Experimental results show that the panoramic oblique photogrammetry for 3D modeling can achieve cm-level accuracy. The proposed approach can obtain aerial panoramas and georeferenced 3D models together, which can be an effective method for virtual geography environment visualization. [ABSTRACT FROM AUTHOR]

• Using subsidies and carbon trading jointly is more effective than their sole use. • A proper carbon quota is vital for firms to obey emission reduction constraints. • The subsidy limits for carbon emission reduction is significant on its reduction. • The subsidy rate can adjust the total carbon emissions and emission reduction. With the increasing demand for low-carbon cold chain logistics, corresponding subsidy policy and carbon emissions trading policy are needed to guide cold chain logistics enterprises in energy-saving and emission reduction transformation of cold storage. However, current research mainly focuses on energy-saving and emission reduction through improving the energy efficiency of equipment or optimizing the operation and management of cold chain logistics enterprises, rarely considering the government policy. This paper constructs a decision-making model of cold chain logistics system by using the method of bilevel programming. The upper level is the government's goal, pursuing the total cost minimization of the whole cold chain logistics system. The lower level is the goal of the cold chain logistics enterprise, pursuing the cost minimization of the enterprise. Chaotic particle swarm optimization (CPSO) was developed to improve this model, and this method was applied to the decision-making of cold chain logistics system in Wuhan City, Hubei Province, China. The study reveals: (1) The joint use of carbon emission reduction subsidies and carbon emission quota optimization measures is more effective in promoting energy-saving and emission reduction of enterprises than using them separately; (2) The carbon emission quota set by the government should not be too high or too low; (3) The government subsidy limits for carbon emission reduction have a significant impact on the carbon emission reduction of enterprises; (4) The subsidy rate per unit carbon emission reduction can adjust the total carbon emissions and total carbon emission reduction of enterprises. This study provides a scientific basis and inspires the government and enterprise in decision-making to improve the energy-saving and emission reduction of the whole society. [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]

[Display omitted] • A novel concept of the operational stability for the buried pipeline robot and its control strategies are presented. • An improved PSO-PID, whose parameters are online tuned, is utilized to improve the operational stability. • The proposed controller with a simple structure and reliable function can improve the operational stability effectively. Ensuring the structural safety of buried pipelines is critical for urban areas due to their capacity for transporting important liquid supplies and wastes. Due to the dangerous (i.e., harmful gas) and narrow environment of pipelines, using robots instead of manual work has become an excellent solution to maintain the pipelines. However, because of the poor perception (i.e., vision and global positioning system signal), the robots controlled manually are easily yawed or even overturned, resulting in an unstable and dangerous maintaining process. Therefore, our research aims to address the following research question: How can we evaluate and improve the operational stability of pipeline maintenance robots in the poor-perception pipeline environment? Aiming to address this question, this paper (i) proposes a framework of operational stability and its control strategies to evaluate and reduce the robot's overturning probability, (ii) analyzes qualitatively the operational stability and its influential factors using zero-point moments and fuzzy theories, and (iii) implements a hybrid genetic algorithm-particle swarm optimization (GA-PSO) and proportional integral derivative (PID) controller to improve the operational stability of the buried pipeline robot. The proposed framework is simulated by the case of the Dadong Lake deep sewage tunnel system in Wuhan and its maintenance robot. Compared with the ZN-PID and BPSO-PID methods, this method can improve tracking accuracy by 88.9% and 78.6% during verification, respectively. Furthermore, laboratory-level experiments using a scaled-down robot are carried out to validate the effectiveness of the controller by using it in practice. The results of simulations and experiments show that the buried pipeline robots can be effectively controlled by the proposed framework. [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]

The proposed framework. [Display omitted] • Images from social media can be timely collected. • The deep learning-based detection model can automatically detect human body parts. • Computer vision technology can automatically calculate water depth. • The water depth of urban flood can be spatially visualized. Urban flooding disasters are the most frequent natural disasters in cities, which broadly affects people's livelihood. Prompt emergency response is the key to alleviating such impact, however, that would rely on the timely mapping of flooding according to waterlogging severity. Compared with traditional approaches, crowdsourcing images from social media has emerged as a more efficient way to obtain this type of information. Therefore, this paper aims to explore the approach based on computer vision technology to automatically extract water depth from social media images, which can be utilized to construct flooding maps during urban flooding. Firstly, the images related to urban flooding were retrieved from social media and then filtered, leaving only images containing people. Secondly, a specific objective detection model based on YOLO was trained to detect the human body parts which are divided into crus, thigh, shoulder, and head from bottom to top. The experiment shows that the mAP of the trained human body parts detection model in the test dataset is 0.967. Afterward, an algorithm for extracting water depth was proposed based on the ratio of bounding boxes of detected body parts in images to the actual length of human body parts. Next, these models were verified by comparing them with manually estimated depth range and manually measured depth. The experiment shows that the accuracy of water depth range is 0.959 and the mean absolute error of the actual depth detection of water is 10.22 cm in the measurement dataset. Finally, the proposed models were applied to map the 2016 flooding that occurred in Wuhan as an illustrative case. This approach is helpful to broaden the source of flooding severity information and improve the efficiency of flood mapping in urban flood emergency management. [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]

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]

This paper proposes a novel grey spatiotemporal model and quantitatively analyzes the spillover and momentum effects of the COVID-19 lockdown policy on the concentration of PM2.5 (particulate matter of diameter less than 2.5 μm) in Wuhan during the COVID-19 pandemic lockdown from 23 January to 8 April 2020 inclusive, and the post-pandemic period from 9 April 2020 to 17 October 2020 inclusive. The results suggest that the stringent lockdowns lead to a reduction in PM2.5 emissions arising from a momentum effect (9.57–18.67%) and a spillover effect (7.07–27.60%). • This paper proposes a novel spatiotemporal grey model for PM2.5 trend forecasting. • This paper considers the momentum effect of historical policies on PM2.5 emission. • Comparing the forecast of PM2.5 with actual values, this paper quantifies the spillover effect of lockdown policies. • A lockdown has a positive knock-on effect on urban air quality. [ABSTRACT FROM AUTHOR]

Economic growth and eco-environmental protection are largely incompatible in the optimization of the urban land-use structure (ULUS) in rapidly developing regions. Here, we attempted to construct a model for optimizing ULUS with the inclusion of uncertain interval values, simultaneously considering the economic growth and eco-environmental protection in rapidly developing regions so as to estimate the elastic interval value of ULUS. The corresponding urban economic and environmental benefits were analyzed. Furthermore, an empirical study of Wuhan city revealed that there are certain counterbalance relationships among commercial land, industrial land and green land: the area of residential land, green land and public land is increasing year by year, and that of industrial land remains basically unchanged, while that of commercial land is decreasing gradually. Therefore, in rapidly developing regions, greater economic benefits from the ULUS are usually obtained at the cost of environmental and ecological benefits. • This paper is to optimize the ULUS by using interval uncertainty method, considering the eco-environmental constraints. • An uncertain interval linear optimization model was developed with the aim to simulate the ULUS of Wuhan city. • Greater economic benefits from the ULUS are usually obtained at the cost of environmental and ecological benefits. [ABSTRACT FROM AUTHOR]

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]

Abstract Identifying and monitoring urban land is essential for sprawl management. The use of nighttime lights (NTL) data has been reported as a suitable approach for identifying urban land across large regions, but the accuracy of urban land classification using these data is seldom discussed, particularly in small- and mid-sized cities. This paper provides a hierarchical framework for analyzing the accuracy of several DMSP/OLS- and NPP VIIRS-based NTL metrics at three nested levels (the overall Wuhan metropolis [WHM], nine cities comprising WHM, and 36 counties comprising the nine cities) using threshold approaches. Comparative analyses show mixed results, ranging [59.72%, 99.79%] and [0%, 83.96%] for map- and class-level accuracies, respectively, at the three nested levels. Moreover, NPP VIIRS is generally superior to DMSP/OLS for classifying urban land across the entire WHM and most cities/counties. Findings suggest map-level accuracy (over 95%) may be overinflated for certain NTL-based metrics, as the metrics produced relatively low class-level accuracies, around 60%. Pass time, spatial resolution of the data product, and certain situations (toll and railway stations, construction sites, less developed urban areas, and reflective surfaces near urban areas) are demonstrated as notable factors impacting NTL-based urban land identification. The findings from this study contribute to a better understanding of the appropriateness of using these metrics for urban land identification in different cities/scenarios and the development of more formalized frameworks for assessing applications in large-scale regions. Highlights • A framework is provided to analyze NTL metric accuracies for urban identification. • Low class-level and overinflated map-level accuracies are common for NTL metrics. • Metric efficiency varies at global, regional, local levels in an urban hierarchy. • Complex situations and features of NTL data are confusing urban land extracting. • Consistently misclassified land uses motivate need for additional NTL research. [ABSTRACT FROM AUTHOR]

The subway shield tunnel of Sanyang Road in Wuhan, China, is a Yangtze River-crossing tunnel project with a mega diameter of 15.7 m, which crosses several soil layers with sharply different properties. To investigate the seismic characteristics of this special subway tunnel under spatially varying earthquake ground motions (SVEGMs) with high efficiency, the concept of response displacement method was utilized and realized with a refined free-field model and a simplified soil-tunnel model. Two sets of borehole accelerograms, denoting near-field and far-field ground motions, respectively, were selected and taken as the reference to generate fully non-stationary SVEGMs. A lot of responses along the longitudinal direction under multi-support excitation (MSE), including intersegment opening width, inner forces, and acceleration, were observed and compared with those under identical support excitation (ISE). The comparative results showed that the responses of subway shield tunnel under MSE are generally higher than those under ISE. The longitudinal distributions of intersegment opening width and bending moment response of lining are dominantly affected by the site conditions. Excitation methods, spectral characteristic and PGA of input earthquake can also have effect on the longitudinal distribution of tunnel response. The results can contribute to revealing the difference between seismic response characteristics of shield tunnel under MSE and those under ISE and predicting the underlying location of seepage or severe damage of shield tunnel. The analysis procedure established in this paper possesses highly potential for application to the aseismic design of practical shield tunnel project. [ABSTRACT FROM AUTHOR]

Risk assessment and collision avoidance are extremely important for the safe navigation of surface ships. In this paper, we propose a regulation aware dynamic path planning method based on uncertain velocity obstacles for intelligent ships. Specifically, we first use the fuzzy logic theory to evaluate the real-time collision risk of the own ship. Based on the closest point of approach criterion, considering the influences of the relative bearing, the distance, and ship speed ratio, the collision risk is used to activate the path planning process. Moreover, we consider the various ship encountering scenarios including crossing, overtaking and head-on. To deal with the uncertainties in the target ship information, the relative cone angle of the traditional velocity obstacles are systematically expanded using a robust tube idea. The reachable velocity set of the own ship is accordingly divided into three subsets that meet the rules specified by COLREGs in crossing, overtaking and head-on scenarios. Real automatic identification system (AIS) data in an area of the Yangtze River in Wuhan, China, is used to demonstrate the effectiveness of the proposed approach. • A novel dynamic path planning method is proposed in compliance with COLREGs. • A collision risk assessment algorithm is used to activate the path planning process. • An UVO approach with a robust tube idea is proposed to handle the uncertainties. • Realistic AIS data is used to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

COVID-19 has spread to many cities and countries in the world since the major outbreak in Wuhan city in later 2019. Population flow is the main channel of COVID-19 transmission between different cities and countries. This study recognizes that the flows of different population groups such as visitors and migrants returning to hometown are different in nature due to different length of stay and exposure to infection risks, contributing to the spatial diffusion of COVID-19 differently. To model population flows and the spatial diffusion of COVID-19 more accurately, a population group based SEIR (susceptible-exposed-infectious-recovered) metapopulation model is developed consisting of 32 regions including Wuhan, the rest of Hubei and other 30 provinces in Mainland China. The paper found that, in terms of the total export, Wuhan residents as visitors and Wuhan migrants returned to hometown were the first and second largest contributors in the simulation period. In terms of the net export, Wuhan migrants returned to hometown were the largest contributor, followed by Wuhan residents as visitors. • Population flow is the main channel of COVID-19 transmission over space. • Different population groups contribute to spatial diffusion of COVID-19 differently. • A population group based SEIR metapopulation model is developed. • Wuhan residents as visitors are the largest exporter of COVID-19 in early 2020. • Wuhan migrants returned to hometown are the largest net exporter of COVID-19. [ABSTRACT FROM AUTHOR]

The outbreak of Coronavirus disease 2019 (COVID-19) highlights the importance of sufficient medical supplies stockpiling at the pre-event stage. In contrast, the potential disadvantages of maintaining adequate items at strategic locations (i.e., reserves) are considerable inventory-related costs. Unpredicted demand leads to a high degree of uncertainty. Efforts to mitigate the uncertainty should rely not only on prepositioning supplies at reserves but also on integrating various channels of medical materials. This paper proposes multi-mitigation strategies in medical supplies to ensure uninterrupted supply for hospitals and significant savings by introducing two-type suppliers, reserving and manufacturing suppliers. Thus, each hospital with uncertain demand is enabled to be served by various channels during pandemics: prepositioning in reserves, backups served by reserving suppliers, and medical commodities produced by manufacturing suppliers. Stochasticity is also incorporated into the raw materials available to produce. This research aims to develop an emergency response application that integrates preparedness action (reserve location, inventory level, and contract supplier's selection) with post-event operations (allocating medical materials from various channels). We formulate a two-stage stochastic mixed integer program to determine prepositioning strategy, including two-type suppliers' selection, and post-event allocation of multiple sources. A branch-and-Benders-cut method is developed for this problem and significantly outperforms both the classical Benders decomposition and Gurobi in the solution time. Different-sized test instances also verify the robustness of the proposed method. Based on a realistic and typical case study (inspired by the COVID-19 pandemic in Wuhan, China), significant savings, an increase in inventory utilization and an increase in demand fulfilment are obtained by our approach. • We study multi-mitigation strategies in medical supplies for epidemic outbreaks. • A two-stage stochastic mixed integer program is proposed for the problem. • We incorporate the stochasticity of raw materials used to produce and demand. • We develop a branch-and-Benders-cut method for the model and test its robustness. • We design test instances to evaluate the model and the exact algorithm. [ABSTRACT FROM AUTHOR]