The coronavirus disease 2019(COVID-19)and its mutant viruses are still wreaking global havoc over the last two years,but the impact of human activity on the transmission of the pandemic is difficult to ascertain.Estim...The coronavirus disease 2019(COVID-19)and its mutant viruses are still wreaking global havoc over the last two years,but the impact of human activity on the transmission of the pandemic is difficult to ascertain.Estimating human dynamic spatiotemporal distribution can help in our understanding of how to mitigate COVID-19 spread,which can help in maintaining urban health within a county and between counties within a country.This distribution can be computed using the Volunteered Geographic Information(VGI)of the citizens in conjunction with other variables,such as climatic conditions,and used to analyze how human’s daily density distribution quantitatively affects COVID-19 transmission.Based on the estimated population density,when the population density increases daily by 1 person/km^(2) in a county or prefectural-level administrative unit with an average size of 26,000 km^(2),the county would have an additional 3.6 confirmed cases and 0.054 death cases after 5 days,which is the illness onset time for a new COVID-19 case.After 14 days,which is the maximum incubation period of the COVID-19 virus,there would be 5 new confirmed cases and 0.092 death cases.However,in neighboring regions,there can be 0.96 fewer people infected with COVID-19 on average per day as a result of strong intervention of local and neighboring authorities.The primary innovation and contribution are that this is the first quantitative assessment of the impacts of dynamic population density on the COVID-19 pandemic.Additionally,the direct and indirect effects of the impact are estimated using spatial panel models.The models that control the unobserved factors improve the reliability of the estimation,as validated by random experiments and the use of the Baidu migration dataset.展开更多
In mountainous rural settlements facing the threat of geohazards,local adaptation is a self-organizing process influenced by individual and group behaviors.Encouraging a wide range of local populations to embrace geoh...In mountainous rural settlements facing the threat of geohazards,local adaptation is a self-organizing process influenced by individual and group behaviors.Encouraging a wide range of local populations to embrace geohazard adaptation strategies emerges as a potent means of mitigating disaster risks.The purpose of this study was to investigate whether neighbors influence individuals'adaptation decisions,as well as to unravel the mechanisms through which neighborhood effects exert their influence.We employed a spatial Durbin model and a series of robustness checks to confirm the results.The dataset used in this research came from a face-to-face survey involving 516 respondents residing in 32 rural settlements highly susceptible to geohazards.Our empirical results reveal that neighborhood effects are an important determinant of adaptation to geohazards.That is,a farmer's adaptation decision is influenced by the adaptation choices of his/her neighbors.Furthermore,when neighbors adopt adaptation strategies,the focal individuals may also want to adapt,both because they learn from their neighbors'choices(social learning)and because they tend to abide by the majority's choice(social norms).Incorporating neighborhood effects into geohazard adaptation studies offers a new perspective for promoting disaster risk reduction decision making.展开更多
East Asian evergreen broadleaved forests(EBFLs) harbor high species richness,but these ecosystems are severely impacted by global climate change and deforestation.Conserving and managing EBLFs requires understanding d...East Asian evergreen broadleaved forests(EBFLs) harbor high species richness,but these ecosystems are severely impacted by global climate change and deforestation.Conserving and managing EBLFs requires understanding dominant tree distribution dynamics.In this study,we used 29 species in Quercus section Cyclobalanopsis-a keystone lineage in East Asian EBLFs-as proxies to predict EBLF distribution dynamics using species distribution models(SDMs).We examined climatic niche overlap,similarity,and equivalency among seven biogeographical regions’ species using’ecospat’.We also estimated the effectiveness of protected areas in the predicted range to elucidate priority conservation regions.Our results showed that the climatic niches of most geographical groups differ.The western species under the Indian summer monsoon regime were mainly impacted by temperature factors,whereas precipitation impacted the eastern species under the East Asian summer monsoon regime.Our simulation predicted a northward range expansion of section Cyclobalanopsis between 2081 and 2100,except for the ranges of the three Himalayan species analyzed,which might shrink significantly.The greatest shift of highly suitable areas was predicted for the species in the South Pacific,with a centroid shift of over 300 km.Remarkably,only 7.56% of suitable habitat is currently inside protected areas,and the percentage is predicted to continue declining in the future.To better conserve Asian EBLFs,establishing nature reserves in their northern distribution ranges,and transplanting the populations with predicted decreasing numbers and degraded habitats to their future highly suitable areas,should be high-priority objectives.展开更多
Based on the fact that the electronic throttle angle effect performs well in the traditional car following model,this paper attempts to introduce the electronic throttle angle into the smart driver model(SDM)as an acc...Based on the fact that the electronic throttle angle effect performs well in the traditional car following model,this paper attempts to introduce the electronic throttle angle into the smart driver model(SDM)as an acceleration feedback control term,and establish an extended smart driver model considering electronic throttle angle changes with memory(ETSDM).In order to show the practicability of the extended model,the next generation simulation(NGSIM)data was used to calibrate and evaluate the extended model and the smart driver model.The calibration results show that,compared with SDM,the simulation value based on the ETSDM is better fitted with the measured data,that is,the extended model can describe the actual traffic situation more accurately.Then,the linear stability analysis of ETSDM was carried out theoretically,and the stability condition was derived.In addition,numerical simulations were explored to show the influence of the electronic throttle angle changes with memory and the driver sensitivity on the stability of traffic flow.The numerical results show that the feedback control term of electronic throttle angle changes with memory can enhance the stability of traffic flow,which shows the feasibility and superiority of the proposed model to a certain extent.展开更多
As the major source of air pollution,sulfur dioxide(S0_(2))emissions have become the focus of global attention.However,existing studies rarely consider spatial effects when discussing the relationship between foreign ...As the major source of air pollution,sulfur dioxide(S0_(2))emissions have become the focus of global attention.However,existing studies rarely consider spatial effects when discussing the relationship between foreign direct investment(FDI)and S0_(2) emissions.This study took the Yangtze River Delta as the research area and used the spatial panel data of 26 cities in this region for 2004-2017.The study investigated the spatial agglomeration effects and dynamics at work in FDI and S0_(2) emissions by using global and local measures of spatial autocorrelation.Then,based on regression analysis using a results of traditional ordinary least squares(OLS)model and a spatial econometric model,the spatial Durbin model(SDM)with spatial-time effects was adopted to quantify the impact of FDI on S0_(2) emissions,so as to avoid the regression results bias caused by ignoring the spatial effects.The results revealed a significant spatial autocorrelation between FDI and S0_(2) emissions,both of which displayed obvious path dependence characteristics in their geographical distribution.A series of agglomeration regions were observed on the spatial scale.The estimation results of the SDM showed that FDI inflow promoted S0_(2) emissions,which supports the pollution haven hypothesis.The findings of this study are significant in the prevention and control of air pollution in the Yangtze River Delta.展开更多
目的基于视觉的前车防碰撞预警技术是汽车主动安全领域的一个重要研究方向,其中对前车进行快速准确检测并建立稳定可靠的安全距离模型是该技术亟待解决的两个难点。为此,本文提出车路视觉协同的高速公路防碰撞预警算法。方法将通过图像...目的基于视觉的前车防碰撞预警技术是汽车主动安全领域的一个重要研究方向,其中对前车进行快速准确检测并建立稳定可靠的安全距离模型是该技术亟待解决的两个难点。为此,本文提出车路视觉协同的高速公路防碰撞预警算法。方法将通过图像处理技术检测出来的视频图像中的车道线和自车的行驶速度作为输入,运用安全区实时计算算法构建安全距离模型,在当前车辆前方形成一块预警安全区域。采用深度神经网络YOLOv3(you only look once v3)对前车进行实时检测,得到车辆的位置信息。根据图像中前车的位置和构建的安全距离模型,对可能发生的追尾碰撞事故进行预测。结果实验结果表明,重新训练的YOLOv3算法车辆检测准确率为98.04%,提出算法与马自达CX-4的FOW(forward obstruction warning)前方碰撞预警系统相比,能够侧向和前向预警,并提前0.8 s发出警报。结论本文方法与传统的车载超声波、雷达或激光测距的防碰撞预警方法相比,具有较强的适用性和稳定性,预警准确率高,可以帮助提高司机在高速公路上的行车安全性。展开更多
基金funding from the National Science and Technology Major Project of the Ministry of Science and Technology of China[grant number 2017YFB0503605]the National Natural Science Foundation of China[grant number 41771478]+3 种基金the Fundamental Research Funds for the Central Universities[grant number 2019B02514]Natural Science Foundation of Beijing,China[grant number 8172046]the China Scholarship Council(CSC)Queen Mary University of London.
文摘The coronavirus disease 2019(COVID-19)and its mutant viruses are still wreaking global havoc over the last two years,but the impact of human activity on the transmission of the pandemic is difficult to ascertain.Estimating human dynamic spatiotemporal distribution can help in our understanding of how to mitigate COVID-19 spread,which can help in maintaining urban health within a county and between counties within a country.This distribution can be computed using the Volunteered Geographic Information(VGI)of the citizens in conjunction with other variables,such as climatic conditions,and used to analyze how human’s daily density distribution quantitatively affects COVID-19 transmission.Based on the estimated population density,when the population density increases daily by 1 person/km^(2) in a county or prefectural-level administrative unit with an average size of 26,000 km^(2),the county would have an additional 3.6 confirmed cases and 0.054 death cases after 5 days,which is the illness onset time for a new COVID-19 case.After 14 days,which is the maximum incubation period of the COVID-19 virus,there would be 5 new confirmed cases and 0.092 death cases.However,in neighboring regions,there can be 0.96 fewer people infected with COVID-19 on average per day as a result of strong intervention of local and neighboring authorities.The primary innovation and contribution are that this is the first quantitative assessment of the impacts of dynamic population density on the COVID-19 pandemic.Additionally,the direct and indirect effects of the impact are estimated using spatial panel models.The models that control the unobserved factors improve the reliability of the estimation,as validated by random experiments and the use of the Baidu migration dataset.
基金supported by the National Natural Science Foundation of China(Grant No.42071222)the Sichuan Science and Technology Program(No.2022JDJQ0015)+1 种基金the Fundamental Research Funds for the Central Universities(No.2023CDSKXYGG006)the Tianfu Qingcheng Program(No.ZX20220027)。
文摘In mountainous rural settlements facing the threat of geohazards,local adaptation is a self-organizing process influenced by individual and group behaviors.Encouraging a wide range of local populations to embrace geohazard adaptation strategies emerges as a potent means of mitigating disaster risks.The purpose of this study was to investigate whether neighbors influence individuals'adaptation decisions,as well as to unravel the mechanisms through which neighborhood effects exert their influence.We employed a spatial Durbin model and a series of robustness checks to confirm the results.The dataset used in this research came from a face-to-face survey involving 516 respondents residing in 32 rural settlements highly susceptible to geohazards.Our empirical results reveal that neighborhood effects are an important determinant of adaptation to geohazards.That is,a farmer's adaptation decision is influenced by the adaptation choices of his/her neighbors.Furthermore,when neighbors adopt adaptation strategies,the focal individuals may also want to adapt,both because they learn from their neighbors'choices(social learning)and because they tend to abide by the majority's choice(social norms).Incorporating neighborhood effects into geohazard adaptation studies offers a new perspective for promoting disaster risk reduction decision making.
基金supported by the National Scientific Foundation of China(NSFC)(Grants nos.31972858,31700174)Yunnan Key Laboratory for Integrative Conservation of Plant Species with Extremely Small Populations(Grant No.PSESP2021)+1 种基金the Southeast Asia Biodiversity Research Institute,Chinese Academy of Sciences(Grant No.Y4ZK111B01)the project of the Yunnan Academy of Forestry and Grassland(Grant No.KFJJ21-05)。
文摘East Asian evergreen broadleaved forests(EBFLs) harbor high species richness,but these ecosystems are severely impacted by global climate change and deforestation.Conserving and managing EBLFs requires understanding dominant tree distribution dynamics.In this study,we used 29 species in Quercus section Cyclobalanopsis-a keystone lineage in East Asian EBLFs-as proxies to predict EBLF distribution dynamics using species distribution models(SDMs).We examined climatic niche overlap,similarity,and equivalency among seven biogeographical regions’ species using’ecospat’.We also estimated the effectiveness of protected areas in the predicted range to elucidate priority conservation regions.Our results showed that the climatic niches of most geographical groups differ.The western species under the Indian summer monsoon regime were mainly impacted by temperature factors,whereas precipitation impacted the eastern species under the East Asian summer monsoon regime.Our simulation predicted a northward range expansion of section Cyclobalanopsis between 2081 and 2100,except for the ranges of the three Himalayan species analyzed,which might shrink significantly.The greatest shift of highly suitable areas was predicted for the species in the South Pacific,with a centroid shift of over 300 km.Remarkably,only 7.56% of suitable habitat is currently inside protected areas,and the percentage is predicted to continue declining in the future.To better conserve Asian EBLFs,establishing nature reserves in their northern distribution ranges,and transplanting the populations with predicted decreasing numbers and degraded habitats to their future highly suitable areas,should be high-priority objectives.
基金the Natural Science Foundation of Zhejiang Province,China(Grant No.LY20G010004)the the Program of Humanities and Social Science of Education Ministry of China(Grant No.20YJA630008)the K.C.Wong Magna Fund in Ningbo University,China.
文摘Based on the fact that the electronic throttle angle effect performs well in the traditional car following model,this paper attempts to introduce the electronic throttle angle into the smart driver model(SDM)as an acceleration feedback control term,and establish an extended smart driver model considering electronic throttle angle changes with memory(ETSDM).In order to show the practicability of the extended model,the next generation simulation(NGSIM)data was used to calibrate and evaluate the extended model and the smart driver model.The calibration results show that,compared with SDM,the simulation value based on the ETSDM is better fitted with the measured data,that is,the extended model can describe the actual traffic situation more accurately.Then,the linear stability analysis of ETSDM was carried out theoretically,and the stability condition was derived.In addition,numerical simulations were explored to show the influence of the electronic throttle angle changes with memory and the driver sensitivity on the stability of traffic flow.The numerical results show that the feedback control term of electronic throttle angle changes with memory can enhance the stability of traffic flow,which shows the feasibility and superiority of the proposed model to a certain extent.
基金Under the auspices of National Natural Science Foundation of China(No.41771140)National Key R&D Program of China(No.2018YFE0105900)。
文摘As the major source of air pollution,sulfur dioxide(S0_(2))emissions have become the focus of global attention.However,existing studies rarely consider spatial effects when discussing the relationship between foreign direct investment(FDI)and S0_(2) emissions.This study took the Yangtze River Delta as the research area and used the spatial panel data of 26 cities in this region for 2004-2017.The study investigated the spatial agglomeration effects and dynamics at work in FDI and S0_(2) emissions by using global and local measures of spatial autocorrelation.Then,based on regression analysis using a results of traditional ordinary least squares(OLS)model and a spatial econometric model,the spatial Durbin model(SDM)with spatial-time effects was adopted to quantify the impact of FDI on S0_(2) emissions,so as to avoid the regression results bias caused by ignoring the spatial effects.The results revealed a significant spatial autocorrelation between FDI and S0_(2) emissions,both of which displayed obvious path dependence characteristics in their geographical distribution.A series of agglomeration regions were observed on the spatial scale.The estimation results of the SDM showed that FDI inflow promoted S0_(2) emissions,which supports the pollution haven hypothesis.The findings of this study are significant in the prevention and control of air pollution in the Yangtze River Delta.
文摘目的基于视觉的前车防碰撞预警技术是汽车主动安全领域的一个重要研究方向,其中对前车进行快速准确检测并建立稳定可靠的安全距离模型是该技术亟待解决的两个难点。为此,本文提出车路视觉协同的高速公路防碰撞预警算法。方法将通过图像处理技术检测出来的视频图像中的车道线和自车的行驶速度作为输入,运用安全区实时计算算法构建安全距离模型,在当前车辆前方形成一块预警安全区域。采用深度神经网络YOLOv3(you only look once v3)对前车进行实时检测,得到车辆的位置信息。根据图像中前车的位置和构建的安全距离模型,对可能发生的追尾碰撞事故进行预测。结果实验结果表明,重新训练的YOLOv3算法车辆检测准确率为98.04%,提出算法与马自达CX-4的FOW(forward obstruction warning)前方碰撞预警系统相比,能够侧向和前向预警,并提前0.8 s发出警报。结论本文方法与传统的车载超声波、雷达或激光测距的防碰撞预警方法相比,具有较强的适用性和稳定性,预警准确率高,可以帮助提高司机在高速公路上的行车安全性。
