In recent years,urban floods have increased in frequency and severity due to intensified extreme rainfall events exacerbated by rapid urbanization.This study integrates a Markov-PLUS model and a rainfall-runoff-flood ...In recent years,urban floods have increased in frequency and severity due to intensified extreme rainfall events exacerbated by rapid urbanization.This study integrates a Markov-PLUS model and a rainfall-runoff-flood hydraulic numerical model to establish a scenario-based research framework for identifying interactions between land use dynamics and urban flood risk,using the Jialu River basin in Zhengzhou,China,as a case study.Future land use changes under three scenarios were forecast:Natural Development(ND),Economic Development(ED),and Ecological Protection(EP),alongside rainfall scenarios occurring every 10,50,and 100 years.There were expansions and decreases in construction land under the ED and EP scenarios,respectively,emphasizing the importance of prioritizing ecological conservation.Economic scenarios showed the highest risks under the increased surface runoff and flood risk driven by higher rainstorm intensity.Over the next 15 years,the Economic Development scenario is projected to increase flood hazard areas,whereas the intensified Ecological Protection scenario is expected to reduce these risks.This underscores the contribution of prioritizing ecological conservation to mitigating disaster risks,calling for enhanced drainage systems and elevated flood protection standards to promote resilient urban development in the face of increasingly severe urban flood challenges.展开更多
In this paper,the Taixin Integrated Economic Zone in Shanxi Province is taken as the research object,and the coupling coordination degree model and bivariate spatial autocorrelation model are used to judge the couplin...In this paper,the Taixin Integrated Economic Zone in Shanxi Province is taken as the research object,and the coupling coordination degree model and bivariate spatial autocorrelation model are used to judge the coupling coordination and spatial-temporal correlation between urbanization and ecosystem service,and the hotspot analysis is used to judge the spatial-temporal trend of urbanization and ecosystem service.The results show that:(1)The urbanization level from 2000 to 2020 continued to rise,the areas with relatively high urbanization were concentrated in the central part of the study area,and the relatively high terrain areas on both sides of the study area,the urbanization was relatively slow,and the hotspot areas with highly significant and significant urbanization level from 2000 to 2020 were distributed as bands in the central part of the study area and the area was rising,and there was no Cold spot area distribution;between 2000 and 2020,the ecosystem service value in the study area increased by 2.6800×10^(8) yuan.Over these two decades,it exhibited a development trend that first rose and then declined.The woodland and grassland agglomeration areas were located on the two sides of the study area,forming highly significant and significant hotspots.Conversely,the central and northeastern parts of the study area were characterized by concentrated man-made land surfaces and croplands,resulting in the formation of highly significant and significant cold spots.(2)In the central part of the study area where man-made land surface and cultivated land are concentrated,the coupling coordination between urbanization and ecosystem service is in the intermediate dislocation and mild dislocation interval;the woodland and grassland concentration areas on both sides of the study area are ecologically fragile,and the coupling coordination between the two is in the level of less than intermediate dislocation.(3)From 2000 to 2020,urbanization and the value of ecosystem services were both negatively correlated,although the correlation coefficient was low.In the central and northeastern parts,urbanization and ecosystem service exhibited patterns of high-low,high-high,and low-low clustering.Conversely,on both sides of the study area,most of the clusters showed a low-high pattern.展开更多
On-time mapping dynamics of crop area,yield,and production is important for global food security.Such information,however,is often not available.Here,we used satellite information,a spectral-phenology integration appr...On-time mapping dynamics of crop area,yield,and production is important for global food security.Such information,however,is often not available.Here,we used satellite information,a spectral-phenology integration approach for mapping crop area,and a machine learning model for predicting yield in the war-stricken Ukraine.We found that in Ukraine crop area and production declined in 2022 relative to 2017–2021 and 2021 for wintertriticeae crops,which was invaded before the cropping season in February of that year.At the same time,crop area and production for rapeseed increased in Ukraine,with yields consistently lower by 6.5%relative to 2021.The low precipitation and the Russian-Ukrainian conflict-related factors contributed to such yield variations by-1.3%and-0.9%for winter-triticeae crops and-4.2%and-0.5%for rapeseed in 2022.We demonstrate a robust framework for monitoring country-wide crop production dynamics in near real-time,serving as an early-foodsecurity-warning system.展开更多
andslide risk analysis is one of the primary studies providing essential instructions to the subsequent risk management process. The quantification of tangible and intangible potential losses is a critical step becau...andslide risk analysis is one of the primary studies providing essential instructions to the subsequent risk management process. The quantification of tangible and intangible potential losses is a critical step because it provides essential data upon which judgments can be made and policy can be formulated. This study aims at quantifying direct economic losses from debris flows at a medium scale in the study area in Italian Central Alps. Available hazard maps were the main inputs of this study. These maps were overlaid with information concerning elements at risk and their economic value. Then, a combination of both market and construction values was used to obtain estimates of future economic losses. As a result, two direct economic risk maps were prepared together with risk curves, useful to summarize expected monetary damage against the respective hazard probability. Afterwards, a qualitative risk map derived using a risk matrix officially provided by the set of laws issued by the regional government, was prepared. The results delimit areas of high economic as well as strategic importance which might be affected by debris flows in the future. Aside from limitations and inaccuracies inherently included in risk analysis process, identification of high risk areas allows local authorities to focus their attention on the “hot-spots”, where important consequences may arise and local （large） scale analysis needs to be performed with more precise cost-effectiveness ratio. The risk maps can be also used by the local authorities to increase population’s adaptive capacity in the disaster prevention process.展开更多
This paper investigates the structure of the payment card market, with consumers and merchants basing their subscription decisions on different information sets. We find that the market structure depends crucially on ...This paper investigates the structure of the payment card market, with consumers and merchants basing their subscription decisions on different information sets. We find that the market structure depends crucially on the information set on which consumers and merchants base their subscription decisions. In the studied case, we observe that a market with few cards dominating only emerges when decisions are based on very limited information. Under the same conditions using a complete information set, all cards survive in the long run. The use of an agent-based model, focusing on the interactions between merchants and consumers, as a basis for subscription decisions allows us to investigate the dynamics of the market and the effect of the indirect network externalities rather than investigating only equilibrium outcomes.展开更多
1 The Global Polycrisis The world is in the midst of a polycrisis,where multiple,interconnected crises are unfolding simultaneously,amplifying one another in unpredictable ways.This is not merely a confluence of indep...1 The Global Polycrisis The world is in the midst of a polycrisis,where multiple,interconnected crises are unfolding simultaneously,amplifying one another in unpredictable ways.This is not merely a confluence of independent crises,but rather an intricate web of interconnected challenges that collectively pose an unprecedented threat to human civilization and ecological systems.The interconnections between these risks,their geographical reach,and ability to exacerbate one another,have created a world of global systemic risk that is more serious,in terms of scale and severity,than risks we have encountered before.展开更多
The concept of systemic resilience,as it is understood in the context of climate change adaptation addressing systemic risks and polycrisis,is an inherently normative notion that carries ethical weight.To account for ...The concept of systemic resilience,as it is understood in the context of climate change adaptation addressing systemic risks and polycrisis,is an inherently normative notion that carries ethical weight.To account for these implications,systemic resilience needs to be supplemented with ethical reflections on a system’s function,why it should be made resilient,and who the resilience serves.Crucially,considerations surrounding various forms of justice,such as participatory,procedural,distributive,and historical,need to be accounted for when making decisions about a community’s resilience in the face of increasing climate hazards.Resilience in the context of systemic risks and climate adaptation currently does not account for its ethical implications.This investigation builds on complexity science research and specifically the expanded concept of systemic resilience.In this article,the concept of systemic resilience is applied to the local level,highlighting its ethical underpinnings in the process.Specifically,a case-study explores the application of the ethically informed version of systemic climate resilience,exploring how the Rhine-Erft catchment in Germany could be assessed on this basis.展开更多
Extreme hazard events can severely threaten the well-being of society.To understand their impacts on the well-being of people,social scientists have proposed various indicators related to individuals’sufering,and ana...Extreme hazard events can severely threaten the well-being of society.To understand their impacts on the well-being of people,social scientists have proposed various indicators related to individuals’sufering,and analyzed them mainly via post-disaster surveys.Social media has shown its value in capturing people’s perceptions of disasters,but few scholars have investigated individuals’sufering levels based on social media data.Accordingly,this study used social media data and developed a hybrid model that combines machine learning classifers and lexicon-based approaches to estimate the wellbeing impacts of disasters,which are measured by individuals’physical,emotional,and social sufering levels.Six machine learning models were trained to categorize the sufering as refected by disaster-related posts on Weibo.Convolutional neural networks(CNN)were found to be the most accurate model,and was selected to classify all posts into four groups(no suffering,physical sufering,emotional sufering,and social sufering).In each classifed group of posts,word co-occurrence analysis was then applied to construct sufering lexicons.By combining the classifcation results from CNN and sufering lexicons,this study proposed optimization-based algorithms to estimate sufering levels for posts across space and time.The proposed model was applied to the 2023 Beijing-Tianjin-Hebei extreme rainfall event.The temporal analysis revealed that individuals’physical sufering levels declined more rapidly than mental and social sufering levels.Spatial analysis revealed that individuals’sufering presented high spatial heterogeneity,and that the hazard-afected regions experienced signifcantly greater levels of sufering.This hybrid model provides an analytical tool for timely and human-centered disaster emergency management.展开更多
Interactions among zoonotic pathogens play a critical role in shaping disease transmission,severity,and public health responses.However,the mechanisms and population-level consequences of these interactions remain und...Interactions among zoonotic pathogens play a critical role in shaping disease transmission,severity,and public health responses.However,the mechanisms and population-level consequences of these interactions remain underexplored in current modelling frameworks.This review aims to synthesize emerging evidence and address key scientific challenges in understanding how pathogen interactions influence transmission dynamics and mathematical modelling,with a focus on zoonotic and other cocirculating pathogens.In this review,we synthesize current evidence on synergistic,antagonistic,and neutral interactions between zoonotic and other cocirculating pathogens.We explore the underlying mechanisms of these interactions,such as transmission enhancement,immune modulation,and resource competition,at both the individual and population levels.We further review mathematical models to illustrate how these interaction features,such as transmission pathways,coinfection histories,cross-immunity,and superspreading potential,could be incorporated into epidemiological frameworks to increase our understanding of the community transmission of infections.Particular attention is given to the challenges of parameter estimation,incomplete surveillance data,and the difficulty of modelling interactions across scales and pathogen types.Understanding and modelling these interactions is essential for predicting outbreak trajectories,designing effective vaccination strategies,and improving early-warning systems.We conclude by calling for enhanced integration of empirical data and mechanistic modelling,especially in the context of emerging zoonoses and postpandemic preparedness.This review provides a structured perspective to support future interdisciplinary efforts aimed at managing cocirculating pathogens and mitigating their public health impact.展开更多
Soils represent the largest contributor to global nitrous oxide(N_(2)O)emissions.However,current research efforts predominantly focus on N_(2)O emissions from agricultural soils,while studies on N_(2)O emissions from ...Soils represent the largest contributor to global nitrous oxide(N_(2)O)emissions.However,current research efforts predominantly focus on N_(2)O emissions from agricultural soils,while studies on N_(2)O emissions from natural soils,another significant source,remain limited.In this study,we explored the magnitude,spatiotemporal dynamics,and drivers of N_(2)O emissions from China's natural soils over the period 1980–2022 via machine learning and a compiled novel dataset of 319 field observations.Our results revealed that the N_(2)O flux per unit area was generally higher in the southeast compared to the northwest,and higher in forests than in grasslands.This spatial and biome heterogeneity was strongly regulated by local hydrothermal conditions and nitrogen availability.The averaged N_(2)O emissions over the study period were 646.2±27 Gg N_(2)O yr^(-1),with approximately equivalent contributions from forests and grasslands.Moreover,there exhibited a significant and increasing trend from 1980 to 2022,with a rate of 2.7 Gg N_(2)O yr^(-2).Such an increase was primarily driven by the expansion of forested area and elevated nitrogen deposition.Our data-driven study presents a long-term and gridded estimate of N_(2)O emissions from China's natural soils,emphasizing the need for future research on changes of greenhouse gas emissions induced by land use change and nitrogen deposition.展开更多
The application of ensemble learning models has been continuously improved in recent landslide susceptibility research,but most studies have no unified ensemble framework.Moreover,few papers have discussed the applica...The application of ensemble learning models has been continuously improved in recent landslide susceptibility research,but most studies have no unified ensemble framework.Moreover,few papers have discussed the applicability of the ensemble learning model in landslide susceptibility mapping at the township level.This study aims at defining a robust ensemble framework that can become the benchmark method for future research dealing with the comparison of different ensemble models.For this purpose,the present work focuses on three different basic classifiers:decision tree(DT),support vector machine(SVM),and multi-layer perceptron neural network model(MLPNN)and two homogeneous ensemble models such as random forest(RF)and extreme gradient boosting(XGBoost).The hierarchical construction of deep ensemble relied on two leading ensemble technologies(i.e.,homogeneous/heterogeneous model ensemble and bagging,boosting,stacking ensemble strategy)to provide a more accurate and effective spatial probability of landslide occurrence.The selected study area is Dazhou town,located in the Jurassic red-strata area in the Three Gorges Reservoir Area of China,which is a strategic economic area currently characterized by widespread landslide risk.Based on a long-term field investigation,the inventory counting thirty-three slow-moving landslide polygons was drawn.The results show that the ensemble models do not necessarily perform better;for instance,the Bagging based DT-SVM-MLPNNXGBoost model performed worse than the single XGBoost model.Amongst the eleven tested models,the Stacking based RF-XGBoost model,which is a homogeneous model based on bagging,boosting,and stacking ensemble,showed the highest capability of predicting the landslide-affected areas.Besides,the factor behaviors of DT,SVM,MLPNN,RF and XGBoost models reflected the characteristics of slow-moving landslides in the Three Gorges reservoir area,wherein unfavorable lithological conditions and intense human engineering activities(i.e.,reservoir water level fluctuation,residential area construction,and farmland development)are proven to be the key triggers.The presented approach could be used for landslide spatial occurrence prediction in similar regions and other fields.展开更多
Developing a regional damage function to quickly estimate direct economic losses(DELs) caused by heavy rain and floods is crucial for providing scientific supports in effective disaster response and risk reduction. Th...Developing a regional damage function to quickly estimate direct economic losses(DELs) caused by heavy rain and floods is crucial for providing scientific supports in effective disaster response and risk reduction. This study investigated the factors that influence regional rainfall-induced damage and developed a calibrated regional rainfall damage function(RDF) using data from the 2016 extreme rainfall event in Hebei Province, China. The analysis revealed that total precipitation, asset value exposure, per capita GDP, and historical geological disaster density at both the township and county levels significantly affect regional rainfall-induced damage. The coefficients of the calibrated RDF indicate that doubling the values of these factors leads to varying increases or decreases in rainfall-induced damage. Furthermore, the study demonstrated a spatial scale dependency in the coefficients of the RDF, with increased elasticity values for asset value exposure and per capita GDP at the county level compared to the township level. The findings emphasize the challenges of applying RDFs across multiple scales and highlight the importance of considering socioeconomic factors in assessing rainfall-induced damage. Despite the limitations and uncertainties of the RDF developed, this study contributes to our understanding of the relationship between physical and socioeconomic factors and rainfall-induced damage. Future research should prioritize enhancing exposure estimation and calibrating RDFs for various types of rainfall-induced disasters to improve model accuracy and performance.The study also acknowledges the variation in RDF performance across different physical environments, especially concerning geological disasters and slope stability.展开更多
基金supported by the National Key Research and Development Plan of China(Grants No.2022YFC3004404 and 2023YFF1305303)。
文摘In recent years,urban floods have increased in frequency and severity due to intensified extreme rainfall events exacerbated by rapid urbanization.This study integrates a Markov-PLUS model and a rainfall-runoff-flood hydraulic numerical model to establish a scenario-based research framework for identifying interactions between land use dynamics and urban flood risk,using the Jialu River basin in Zhengzhou,China,as a case study.Future land use changes under three scenarios were forecast:Natural Development(ND),Economic Development(ED),and Ecological Protection(EP),alongside rainfall scenarios occurring every 10,50,and 100 years.There were expansions and decreases in construction land under the ED and EP scenarios,respectively,emphasizing the importance of prioritizing ecological conservation.Economic scenarios showed the highest risks under the increased surface runoff and flood risk driven by higher rainstorm intensity.Over the next 15 years,the Economic Development scenario is projected to increase flood hazard areas,whereas the intensified Ecological Protection scenario is expected to reduce these risks.This underscores the contribution of prioritizing ecological conservation to mitigating disaster risks,calling for enhanced drainage systems and elevated flood protection standards to promote resilient urban development in the face of increasingly severe urban flood challenges.
基金supported by the Natural Science Foundation of Shanxi Province(Grant No.20210302124437)the Graduate Student Research and Innovation Project of Shanxi Province(Grant No.2023KY551).
文摘In this paper,the Taixin Integrated Economic Zone in Shanxi Province is taken as the research object,and the coupling coordination degree model and bivariate spatial autocorrelation model are used to judge the coupling coordination and spatial-temporal correlation between urbanization and ecosystem service,and the hotspot analysis is used to judge the spatial-temporal trend of urbanization and ecosystem service.The results show that:(1)The urbanization level from 2000 to 2020 continued to rise,the areas with relatively high urbanization were concentrated in the central part of the study area,and the relatively high terrain areas on both sides of the study area,the urbanization was relatively slow,and the hotspot areas with highly significant and significant urbanization level from 2000 to 2020 were distributed as bands in the central part of the study area and the area was rising,and there was no Cold spot area distribution;between 2000 and 2020,the ecosystem service value in the study area increased by 2.6800×10^(8) yuan.Over these two decades,it exhibited a development trend that first rose and then declined.The woodland and grassland agglomeration areas were located on the two sides of the study area,forming highly significant and significant hotspots.Conversely,the central and northeastern parts of the study area were characterized by concentrated man-made land surfaces and croplands,resulting in the formation of highly significant and significant cold spots.(2)In the central part of the study area where man-made land surface and cultivated land are concentrated,the coupling coordination between urbanization and ecosystem service is in the intermediate dislocation and mild dislocation interval;the woodland and grassland concentration areas on both sides of the study area are ecologically fragile,and the coupling coordination between the two is in the level of less than intermediate dislocation.(3)From 2000 to 2020,urbanization and the value of ecosystem services were both negatively correlated,although the correlation coefficient was low.In the central and northeastern parts,urbanization and ecosystem service exhibited patterns of high-low,high-high,and low-low clustering.Conversely,on both sides of the study area,most of the clusters showed a low-high pattern.
基金supported by the National Natural Science Foundation of China(Grant No.42061144003).
文摘On-time mapping dynamics of crop area,yield,and production is important for global food security.Such information,however,is often not available.Here,we used satellite information,a spectral-phenology integration approach for mapping crop area,and a machine learning model for predicting yield in the war-stricken Ukraine.We found that in Ukraine crop area and production declined in 2022 relative to 2017–2021 and 2021 for wintertriticeae crops,which was invaded before the cropping season in February of that year.At the same time,crop area and production for rapeseed increased in Ukraine,with yields consistently lower by 6.5%relative to 2021.The low precipitation and the Russian-Ukrainian conflict-related factors contributed to such yield variations by-1.3%and-0.9%for winter-triticeae crops and-4.2%and-0.5%for rapeseed in 2022.We demonstrate a robust framework for monitoring country-wide crop production dynamics in near real-time,serving as an early-foodsecurity-warning system.
基金supported by the Marie Curie Research and Training Network "Mountain Risks" funded by the European Commission (2007–2010, Contract MCRTN-35098).
文摘andslide risk analysis is one of the primary studies providing essential instructions to the subsequent risk management process. The quantification of tangible and intangible potential losses is a critical step because it provides essential data upon which judgments can be made and policy can be formulated. This study aims at quantifying direct economic losses from debris flows at a medium scale in the study area in Italian Central Alps. Available hazard maps were the main inputs of this study. These maps were overlaid with information concerning elements at risk and their economic value. Then, a combination of both market and construction values was used to obtain estimates of future economic losses. As a result, two direct economic risk maps were prepared together with risk curves, useful to summarize expected monetary damage against the respective hazard probability. Afterwards, a qualitative risk map derived using a risk matrix officially provided by the set of laws issued by the regional government, was prepared. The results delimit areas of high economic as well as strategic importance which might be affected by debris flows in the future. Aside from limitations and inaccuracies inherently included in risk analysis process, identification of high risk areas allows local authorities to focus their attention on the “hot-spots”, where important consequences may arise and local （large） scale analysis needs to be performed with more precise cost-effectiveness ratio. The risk maps can be also used by the local authorities to increase population’s adaptive capacity in the disaster prevention process.
文摘This paper investigates the structure of the payment card market, with consumers and merchants basing their subscription decisions on different information sets. We find that the market structure depends crucially on the information set on which consumers and merchants base their subscription decisions. In the studied case, we observe that a market with few cards dominating only emerges when decisions are based on very limited information. Under the same conditions using a complete information set, all cards survive in the long run. The use of an agent-based model, focusing on the interactions between merchants and consumers, as a basis for subscription decisions allows us to investigate the dynamics of the market and the effect of the indirect network externalities rather than investigating only equilibrium outcomes.
文摘1 The Global Polycrisis The world is in the midst of a polycrisis,where multiple,interconnected crises are unfolding simultaneously,amplifying one another in unpredictable ways.This is not merely a confluence of independent crises,but rather an intricate web of interconnected challenges that collectively pose an unprecedented threat to human civilization and ecological systems.The interconnections between these risks,their geographical reach,and ability to exacerbate one another,have created a world of global systemic risk that is more serious,in terms of scale and severity,than risks we have encountered before.
基金funded by the European Union(Grant No.101073978).
文摘The concept of systemic resilience,as it is understood in the context of climate change adaptation addressing systemic risks and polycrisis,is an inherently normative notion that carries ethical weight.To account for these implications,systemic resilience needs to be supplemented with ethical reflections on a system’s function,why it should be made resilient,and who the resilience serves.Crucially,considerations surrounding various forms of justice,such as participatory,procedural,distributive,and historical,need to be accounted for when making decisions about a community’s resilience in the face of increasing climate hazards.Resilience in the context of systemic risks and climate adaptation currently does not account for its ethical implications.This investigation builds on complexity science research and specifically the expanded concept of systemic resilience.In this article,the concept of systemic resilience is applied to the local level,highlighting its ethical underpinnings in the process.Specifically,a case-study explores the application of the ethically informed version of systemic climate resilience,exploring how the Rhine-Erft catchment in Germany could be assessed on this basis.
基金supported by the National Natural Science Foundation of China(Grant Nos.72304039,52394232)the Fundamental Research Funds for the Central Universities(Grant Nos.2243300007,x2ggC2250200)the National Key R&D Program of China(Grant No.2024YFC3808601).
文摘Extreme hazard events can severely threaten the well-being of society.To understand their impacts on the well-being of people,social scientists have proposed various indicators related to individuals’sufering,and analyzed them mainly via post-disaster surveys.Social media has shown its value in capturing people’s perceptions of disasters,but few scholars have investigated individuals’sufering levels based on social media data.Accordingly,this study used social media data and developed a hybrid model that combines machine learning classifers and lexicon-based approaches to estimate the wellbeing impacts of disasters,which are measured by individuals’physical,emotional,and social sufering levels.Six machine learning models were trained to categorize the sufering as refected by disaster-related posts on Weibo.Convolutional neural networks(CNN)were found to be the most accurate model,and was selected to classify all posts into four groups(no suffering,physical sufering,emotional sufering,and social sufering).In each classifed group of posts,word co-occurrence analysis was then applied to construct sufering lexicons.By combining the classifcation results from CNN and sufering lexicons,this study proposed optimization-based algorithms to estimate sufering levels for posts across space and time.The proposed model was applied to the 2023 Beijing-Tianjin-Hebei extreme rainfall event.The temporal analysis revealed that individuals’physical sufering levels declined more rapidly than mental and social sufering levels.Spatial analysis revealed that individuals’sufering presented high spatial heterogeneity,and that the hazard-afected regions experienced signifcantly greater levels of sufering.This hybrid model provides an analytical tool for timely and human-centered disaster emergency management.
基金National Natural Science Foundation of China(82304207)Science and Technology Projects of Xizang Autonomous Region,China(XZ202501JD0012)+4 种基金Beijing Natural Science Foundation(L232014)Research on Key Technologies of Plague Prevention and Control in Inner Mongolia Autonomous Region(2021ZD0006)Fundamental Research Funds for the Central Universities(2233300001)National Key Research and Development Program of China(2023YFC2307500)supported by the Beijing Research Center for Respiratory Infectious Diseases Project(BJRID2025-001).
文摘Interactions among zoonotic pathogens play a critical role in shaping disease transmission,severity,and public health responses.However,the mechanisms and population-level consequences of these interactions remain underexplored in current modelling frameworks.This review aims to synthesize emerging evidence and address key scientific challenges in understanding how pathogen interactions influence transmission dynamics and mathematical modelling,with a focus on zoonotic and other cocirculating pathogens.In this review,we synthesize current evidence on synergistic,antagonistic,and neutral interactions between zoonotic and other cocirculating pathogens.We explore the underlying mechanisms of these interactions,such as transmission enhancement,immune modulation,and resource competition,at both the individual and population levels.We further review mathematical models to illustrate how these interaction features,such as transmission pathways,coinfection histories,cross-immunity,and superspreading potential,could be incorporated into epidemiological frameworks to increase our understanding of the community transmission of infections.Particular attention is given to the challenges of parameter estimation,incomplete surveillance data,and the difficulty of modelling interactions across scales and pathogen types.Understanding and modelling these interactions is essential for predicting outbreak trajectories,designing effective vaccination strategies,and improving early-warning systems.We conclude by calling for enhanced integration of empirical data and mechanistic modelling,especially in the context of emerging zoonoses and postpandemic preparedness.This review provides a structured perspective to support future interdisciplinary efforts aimed at managing cocirculating pathogens and mitigating their public health impact.
基金supported by the National Natural Science Foundation of China(Grant Nos.42471116 and 41988101)。
文摘Soils represent the largest contributor to global nitrous oxide(N_(2)O)emissions.However,current research efforts predominantly focus on N_(2)O emissions from agricultural soils,while studies on N_(2)O emissions from natural soils,another significant source,remain limited.In this study,we explored the magnitude,spatiotemporal dynamics,and drivers of N_(2)O emissions from China's natural soils over the period 1980–2022 via machine learning and a compiled novel dataset of 319 field observations.Our results revealed that the N_(2)O flux per unit area was generally higher in the southeast compared to the northwest,and higher in forests than in grasslands.This spatial and biome heterogeneity was strongly regulated by local hydrothermal conditions and nitrogen availability.The averaged N_(2)O emissions over the study period were 646.2±27 Gg N_(2)O yr^(-1),with approximately equivalent contributions from forests and grasslands.Moreover,there exhibited a significant and increasing trend from 1980 to 2022,with a rate of 2.7 Gg N_(2)O yr^(-2).Such an increase was primarily driven by the expansion of forested area and elevated nitrogen deposition.Our data-driven study presents a long-term and gridded estimate of N_(2)O emissions from China's natural soils,emphasizing the need for future research on changes of greenhouse gas emissions induced by land use change and nitrogen deposition.
基金This research was funded by the National Natural Science Foundation of China(Grant No.41877525)the National Natural Science Foundation of China(Grant No.41601563)。
文摘The application of ensemble learning models has been continuously improved in recent landslide susceptibility research,but most studies have no unified ensemble framework.Moreover,few papers have discussed the applicability of the ensemble learning model in landslide susceptibility mapping at the township level.This study aims at defining a robust ensemble framework that can become the benchmark method for future research dealing with the comparison of different ensemble models.For this purpose,the present work focuses on three different basic classifiers:decision tree(DT),support vector machine(SVM),and multi-layer perceptron neural network model(MLPNN)and two homogeneous ensemble models such as random forest(RF)and extreme gradient boosting(XGBoost).The hierarchical construction of deep ensemble relied on two leading ensemble technologies(i.e.,homogeneous/heterogeneous model ensemble and bagging,boosting,stacking ensemble strategy)to provide a more accurate and effective spatial probability of landslide occurrence.The selected study area is Dazhou town,located in the Jurassic red-strata area in the Three Gorges Reservoir Area of China,which is a strategic economic area currently characterized by widespread landslide risk.Based on a long-term field investigation,the inventory counting thirty-three slow-moving landslide polygons was drawn.The results show that the ensemble models do not necessarily perform better;for instance,the Bagging based DT-SVM-MLPNNXGBoost model performed worse than the single XGBoost model.Amongst the eleven tested models,the Stacking based RF-XGBoost model,which is a homogeneous model based on bagging,boosting,and stacking ensemble,showed the highest capability of predicting the landslide-affected areas.Besides,the factor behaviors of DT,SVM,MLPNN,RF and XGBoost models reflected the characteristics of slow-moving landslides in the Three Gorges reservoir area,wherein unfavorable lithological conditions and intense human engineering activities(i.e.,reservoir water level fluctuation,residential area construction,and farmland development)are proven to be the key triggers.The presented approach could be used for landslide spatial occurrence prediction in similar regions and other fields.
基金funded by the National Key R&D Program of China(Grant No.2022YFC3004404)the Key Research and Development Project of Science and Technology Department of Hebei Province(No.21375410D and No.22375421D).
文摘Developing a regional damage function to quickly estimate direct economic losses(DELs) caused by heavy rain and floods is crucial for providing scientific supports in effective disaster response and risk reduction. This study investigated the factors that influence regional rainfall-induced damage and developed a calibrated regional rainfall damage function(RDF) using data from the 2016 extreme rainfall event in Hebei Province, China. The analysis revealed that total precipitation, asset value exposure, per capita GDP, and historical geological disaster density at both the township and county levels significantly affect regional rainfall-induced damage. The coefficients of the calibrated RDF indicate that doubling the values of these factors leads to varying increases or decreases in rainfall-induced damage. Furthermore, the study demonstrated a spatial scale dependency in the coefficients of the RDF, with increased elasticity values for asset value exposure and per capita GDP at the county level compared to the township level. The findings emphasize the challenges of applying RDFs across multiple scales and highlight the importance of considering socioeconomic factors in assessing rainfall-induced damage. Despite the limitations and uncertainties of the RDF developed, this study contributes to our understanding of the relationship between physical and socioeconomic factors and rainfall-induced damage. Future research should prioritize enhancing exposure estimation and calibrating RDFs for various types of rainfall-induced disasters to improve model accuracy and performance.The study also acknowledges the variation in RDF performance across different physical environments, especially concerning geological disasters and slope stability.
