The COVID-19 pandemic has had a tremendous impact on the transportation and tourism sectors.As vaccines were being administered and lockdowns implemented,owing to the significant social and economic impacts,travel bub...The COVID-19 pandemic has had a tremendous impact on the transportation and tourism sectors.As vaccines were being administered and lockdowns implemented,owing to the significant social and economic impacts,travel bubbles were proposed as a gradual and intermediate open policy to balance risks and economic recovery.However,the travel bubble framework and associated costs and benefits were not well-established during policy implementation.In this study,we propose a travel bubble transportation framework using a metapopulation epidemic and mobility model,and conduct a cost-benefit analysis for the decision-making process during the border reopening phase.Our model focuses on the control of domestic and international long-distance travel inside the bubble to maximize monetary returns derived from tourism benefits and pandemic costs.A sequential decision problem is proposed to make multiple decisions based on real-time observations.We conduct a case study on travel bubbles in Australia,New Zealand,and Japan to test the feasibility of the model.The simulation results show that controlled intra-bubble transportation can generate positive economic benefits while keeping the epidemic within an acceptable range of control.Our framework can assist policymakers in making informed,open decisions by considering multiple attributes during a global pandemic.展开更多
Deep learning models demonstrate impressive performance in rapidly predicting urban floods,but there are still limitations in enhancing physical connectivity and interpretability.This study proposed an innovative mode...Deep learning models demonstrate impressive performance in rapidly predicting urban floods,but there are still limitations in enhancing physical connectivity and interpretability.This study proposed an innovative modeling approach that integrates convolutional neural networks with weighted cellular automaton(CNN-WCA)to achieve the precise and rapid prediction of urban pluvial flooding processes and enhance the physical connectivity and reliability of modeling results.The study began by generating a rainfall-inundation dataset using WCA and LISFLOOD-FP,and the CNN-WCA model was trained using outputs from LISFLOOD-FP and WCA.Subsequently,the pre-trained model was applied to simulate the flood caused by the 20 July 2021 rainstorm in Zhengzhou City.The predicted inundation spatial distribution and depth by CNN-WCA closely aligned with those of LISFLOOD-FP,with the mean absolute error concentrated within 5 mm,and the prediction time of CNN-WCA was only 0.8%that of LISFLOOD-FP.The CNN-WCA model displays a strong capacity for accurately predicting changes in inundation depths within the study area and at susceptible points for urban flooding,with the Nash-Sutcliffe e fficiency values of most flood-prone points exceeding 0.97.Furthermore,the physical connectivity of the inundation distribution predicted by CNN-WCA is better than that of the distribution obtained with a CNN.The CNN-WCA model with additional physical constraints exhibits a reduction of around 34%in instances of physical discontinuity compared to CNN.Our results prove that the CNN model with multiple physical constraints has signifi cant potential to rapidly and accurately simulate urban flooding processes and improve the reliability of prediction.展开更多
Gasification technology can effectively realize energy recovery from municipal solid waste(MSW)to reduce its negative impact on the environment.However,ammonia,as a pollutant derived from MSW gasification,needs to be ...Gasification technology can effectively realize energy recovery from municipal solid waste(MSW)to reduce its negative impact on the environment.However,ammonia,as a pollutant derived from MSW gasification,needs to be treated because its emission is considered harmful to mankind.This work aims to decompose the NH3 pollutant from MSW gasification by an in-situ catalytic method.The MSW sample is composed of rice,paper,polystyrene granules,rubber gloves,textile and wood chips.Ni–M(M=Co,Fe,Zn)bimetallic catalysts supported on sewage sludge-derived biochar(SSC)were prepared by co-impregnation method and further characterized by X-ray diffraction,N2 isothermal adsorption,scanning electron microscopy,transmission electron microscopy and NH3 temperature programmed desorption.Prior to the experiments,the catalysts were first homogeneously mixed with the MSW sample,and then in-situ catalytic tests were conducted in a horizontal fixed-bed reactor.The effect of the second metal(Co,Fe,Zn)on the catalytic performance was compared to screen the best Ni-M dual.It was found that the Ni–Co/SSC catalyst had the best activity toward NH3 decomposition,whose decomposition rate reached 40.21%at 650℃.The best catalytic performance of Ni–Co/SSC can be explained by its smaller Ni particle size that facilitates the dispersion of active sites as well as the addition of Co reducing the energy barrier for the associative decomposition of NH species during the NH3 decomposition process.Besides,the activity of Ni–Co/SSC increased from 450℃to 700℃as the NH3 decomposition reaction was endothermic.展开更多
文摘The COVID-19 pandemic has had a tremendous impact on the transportation and tourism sectors.As vaccines were being administered and lockdowns implemented,owing to the significant social and economic impacts,travel bubbles were proposed as a gradual and intermediate open policy to balance risks and economic recovery.However,the travel bubble framework and associated costs and benefits were not well-established during policy implementation.In this study,we propose a travel bubble transportation framework using a metapopulation epidemic and mobility model,and conduct a cost-benefit analysis for the decision-making process during the border reopening phase.Our model focuses on the control of domestic and international long-distance travel inside the bubble to maximize monetary returns derived from tourism benefits and pandemic costs.A sequential decision problem is proposed to make multiple decisions based on real-time observations.We conduct a case study on travel bubbles in Australia,New Zealand,and Japan to test the feasibility of the model.The simulation results show that controlled intra-bubble transportation can generate positive economic benefits while keeping the epidemic within an acceptable range of control.Our framework can assist policymakers in making informed,open decisions by considering multiple attributes during a global pandemic.
基金supported by the General Program of National Natural Science Foundation of China(Grant No.42377467)。
文摘Deep learning models demonstrate impressive performance in rapidly predicting urban floods,but there are still limitations in enhancing physical connectivity and interpretability.This study proposed an innovative modeling approach that integrates convolutional neural networks with weighted cellular automaton(CNN-WCA)to achieve the precise and rapid prediction of urban pluvial flooding processes and enhance the physical connectivity and reliability of modeling results.The study began by generating a rainfall-inundation dataset using WCA and LISFLOOD-FP,and the CNN-WCA model was trained using outputs from LISFLOOD-FP and WCA.Subsequently,the pre-trained model was applied to simulate the flood caused by the 20 July 2021 rainstorm in Zhengzhou City.The predicted inundation spatial distribution and depth by CNN-WCA closely aligned with those of LISFLOOD-FP,with the mean absolute error concentrated within 5 mm,and the prediction time of CNN-WCA was only 0.8%that of LISFLOOD-FP.The CNN-WCA model displays a strong capacity for accurately predicting changes in inundation depths within the study area and at susceptible points for urban flooding,with the Nash-Sutcliffe e fficiency values of most flood-prone points exceeding 0.97.Furthermore,the physical connectivity of the inundation distribution predicted by CNN-WCA is better than that of the distribution obtained with a CNN.The CNN-WCA model with additional physical constraints exhibits a reduction of around 34%in instances of physical discontinuity compared to CNN.Our results prove that the CNN model with multiple physical constraints has signifi cant potential to rapidly and accurately simulate urban flooding processes and improve the reliability of prediction.
基金supported by the National Key R&D Program of China(No.2019YFC1906803)Key R&D Program of Jiangsu Province(No.BE2021701).
文摘Gasification technology can effectively realize energy recovery from municipal solid waste(MSW)to reduce its negative impact on the environment.However,ammonia,as a pollutant derived from MSW gasification,needs to be treated because its emission is considered harmful to mankind.This work aims to decompose the NH3 pollutant from MSW gasification by an in-situ catalytic method.The MSW sample is composed of rice,paper,polystyrene granules,rubber gloves,textile and wood chips.Ni–M(M=Co,Fe,Zn)bimetallic catalysts supported on sewage sludge-derived biochar(SSC)were prepared by co-impregnation method and further characterized by X-ray diffraction,N2 isothermal adsorption,scanning electron microscopy,transmission electron microscopy and NH3 temperature programmed desorption.Prior to the experiments,the catalysts were first homogeneously mixed with the MSW sample,and then in-situ catalytic tests were conducted in a horizontal fixed-bed reactor.The effect of the second metal(Co,Fe,Zn)on the catalytic performance was compared to screen the best Ni-M dual.It was found that the Ni–Co/SSC catalyst had the best activity toward NH3 decomposition,whose decomposition rate reached 40.21%at 650℃.The best catalytic performance of Ni–Co/SSC can be explained by its smaller Ni particle size that facilitates the dispersion of active sites as well as the addition of Co reducing the energy barrier for the associative decomposition of NH species during the NH3 decomposition process.Besides,the activity of Ni–Co/SSC increased from 450℃to 700℃as the NH3 decomposition reaction was endothermic.
