Rapid peri-urbanization has become a new challenge for sustainable urban-rural development worldwide. To clarify how unprecedented urban sprawl at the metropolitan fringe impacts urban-rural landscape, this study took...Rapid peri-urbanization has become a new challenge for sustainable urban-rural development worldwide. To clarify how unprecedented urban sprawl at the metropolitan fringe impacts urban-rural landscape, this study took the Beijing-Tianjin corridor of Beijing-Tianjin-Hebei metropolitan area, one of the largest urban clusters in China, as a typical example. By using Landsat-based landscape metrics and a practical methodology, we investigated the landscape changes and discussed the potential reasons in the context of rapid peri-urbanization of China. Specifically, multi-temporal land use maps derived from Landsat images were used to calculate landscape metrics and analyze their characteristics along the urban-rural gradients. The practical methodology was used to monitor spatio-temporal characteristics of landscape change in large metropolitan areas. The results showed that landscape patterns in the area had changed greatly from 2000 to 2015 with characteristics of construction land sprawl and arable land shrinkage. The intensity and scale of landscape changes varied along the urban-rural gradients. Sampled plots in urbanized areas and rural areas demonstrated distinguishable landscape patterns and significant differences. Urban areas had more heterogeneous and fragmented landscapes than rural areas. Peri-urban areas in general experienced higher levels of land diversification than rural areas. Rural residential land appeared to be more aggregated near Beijing and Tianjin cities. Besides, our findings also indicated that urban expansion was largely responsible for landscape patterns.The findings of this study potentially provide strategical insights into landscape planning around mega cities and sustainable coordinated urban-rural development.展开更多
Unmanned aerial vehicles(UAVs)are widely used in situations with uncertain and risky areas lacking network coverage.In natural disasters,timely delivery of first aid supplies is crucial.Current UAVs face risks such as...Unmanned aerial vehicles(UAVs)are widely used in situations with uncertain and risky areas lacking network coverage.In natural disasters,timely delivery of first aid supplies is crucial.Current UAVs face risks such as crashing into birds or unexpected structures.Airdrop systems with parachutes risk dispersing payloads away from target locations.The objective here is to use multiple UAVs to distribute payloads cooperatively to assigned locations.The civil defense department must balance coverage,accurate landing,and flight safety while considering battery power and capability.Deep Q-network(DQN)models are commonly used in multi-UAV path planning to effectively represent the surroundings and action spaces.Earlier strategies focused on advanced DQNs for UAV path planning in different configurations,but rarely addressed non-cooperative scenarios and disaster environments.This paper introduces a new DQN framework to tackle challenges in disaster environments.It considers unforeseen structures and birds that could cause UAV crashes and assumes urgent landing zones and winch-based airdrop systems for precise delivery and return.A new DQN model is developed,which incorporates the battery life,safe flying distance between UAVs,and remaining delivery points to encode surrounding hazards into the state space and Q-networks.Additionally,a unique reward system is created to improve UAV action sequences for better delivery coverage and safe landings.The experimental results demonstrate that multi-UAV first aid delivery in disaster environments can achieve advanced performance.展开更多
The worldwide slum population currently stands at over one billion,with substantial growth expected in the coming decades.Traditionally,slums have been mapped using information derived mainly from either physical indi...The worldwide slum population currently stands at over one billion,with substantial growth expected in the coming decades.Traditionally,slums have been mapped using information derived mainly from either physical indicators using remote sensing data,or socio-economic indicators using census data.Each data source on its own provides only a partial view of slums,an issue further compounded by data poverty in less-developed countries.To overcome such issues,this paper explores the fusion of traditional with emerging open data sources and data mining tools to identify additional indicators that can be used to detect and map the presence of slums,map their footprint,and map their evolution.Towards this goal,we develop an indicator database for slums using open sources of physical and socio-economic data that can be used to characterize slum settlements.Using this database,we then leverage data mining techniques to identify the most suitable combination of these indicators for mapping slums.Using three cities in Kenya as test cases,results show that the fusion of these data can improve the mapping accuracy of slums.These results suggest that the proposed approach can provide a viable solution to the emerging challenge of monitoring the growth of slums.展开更多
We present a tensor-structured algorithm for efficient large-scale density functional theory(DFT)calculations by constructing a Tucker tensor basis that is adapted to the Kohn–Sham Hamiltonian and localized in real-s...We present a tensor-structured algorithm for efficient large-scale density functional theory(DFT)calculations by constructing a Tucker tensor basis that is adapted to the Kohn–Sham Hamiltonian and localized in real-space.The proposed approach uses an additive separable approximation to the Kohn–Sham Hamiltonian and an L1 localization technique to generate the 1-D localized functions that constitute the Tucker tensor basis.Numerical results show that the resulting Tucker tensor basis exhibits exponential convergence in the ground-state energy with increasing Tucker rank.Further,the proposed tensor-structured algorithm demonstrated sub-quadratic scaling with system-size for both systems with and without a gap,and involving many thousands of atoms.This reduced-order scaling has also resulted in the proposed approach outperforming plane-wave DFT implementation for systems beyond 2000 electrons.展开更多
基金National Key Research and Development Program of China,No.2017YFC0504701
文摘Rapid peri-urbanization has become a new challenge for sustainable urban-rural development worldwide. To clarify how unprecedented urban sprawl at the metropolitan fringe impacts urban-rural landscape, this study took the Beijing-Tianjin corridor of Beijing-Tianjin-Hebei metropolitan area, one of the largest urban clusters in China, as a typical example. By using Landsat-based landscape metrics and a practical methodology, we investigated the landscape changes and discussed the potential reasons in the context of rapid peri-urbanization of China. Specifically, multi-temporal land use maps derived from Landsat images were used to calculate landscape metrics and analyze their characteristics along the urban-rural gradients. The practical methodology was used to monitor spatio-temporal characteristics of landscape change in large metropolitan areas. The results showed that landscape patterns in the area had changed greatly from 2000 to 2015 with characteristics of construction land sprawl and arable land shrinkage. The intensity and scale of landscape changes varied along the urban-rural gradients. Sampled plots in urbanized areas and rural areas demonstrated distinguishable landscape patterns and significant differences. Urban areas had more heterogeneous and fragmented landscapes than rural areas. Peri-urban areas in general experienced higher levels of land diversification than rural areas. Rural residential land appeared to be more aggregated near Beijing and Tianjin cities. Besides, our findings also indicated that urban expansion was largely responsible for landscape patterns.The findings of this study potentially provide strategical insights into landscape planning around mega cities and sustainable coordinated urban-rural development.
基金supported by the Committee of Science of the Ministry of Education and Science of the Republic of Kazakhstan under Grant No.249015/0224.
文摘Unmanned aerial vehicles(UAVs)are widely used in situations with uncertain and risky areas lacking network coverage.In natural disasters,timely delivery of first aid supplies is crucial.Current UAVs face risks such as crashing into birds or unexpected structures.Airdrop systems with parachutes risk dispersing payloads away from target locations.The objective here is to use multiple UAVs to distribute payloads cooperatively to assigned locations.The civil defense department must balance coverage,accurate landing,and flight safety while considering battery power and capability.Deep Q-network(DQN)models are commonly used in multi-UAV path planning to effectively represent the surroundings and action spaces.Earlier strategies focused on advanced DQNs for UAV path planning in different configurations,but rarely addressed non-cooperative scenarios and disaster environments.This paper introduces a new DQN framework to tackle challenges in disaster environments.It considers unforeseen structures and birds that could cause UAV crashes and assumes urgent landing zones and winch-based airdrop systems for precise delivery and return.A new DQN model is developed,which incorporates the battery life,safe flying distance between UAVs,and remaining delivery points to encode surrounding hazards into the state space and Q-networks.Additionally,a unique reward system is created to improve UAV action sequences for better delivery coverage and safe landings.The experimental results demonstrate that multi-UAV first aid delivery in disaster environments can achieve advanced performance.
文摘The worldwide slum population currently stands at over one billion,with substantial growth expected in the coming decades.Traditionally,slums have been mapped using information derived mainly from either physical indicators using remote sensing data,or socio-economic indicators using census data.Each data source on its own provides only a partial view of slums,an issue further compounded by data poverty in less-developed countries.To overcome such issues,this paper explores the fusion of traditional with emerging open data sources and data mining tools to identify additional indicators that can be used to detect and map the presence of slums,map their footprint,and map their evolution.Towards this goal,we develop an indicator database for slums using open sources of physical and socio-economic data that can be used to characterize slum settlements.Using this database,we then leverage data mining techniques to identify the most suitable combination of these indicators for mapping slums.Using three cities in Kenya as test cases,results show that the fusion of these data can improve the mapping accuracy of slums.These results suggest that the proposed approach can provide a viable solution to the emerging challenge of monitoring the growth of slums.
基金We gratefully adknowladge the support of the Air Force Ofice df Sciantific Rsearch though grant numbar FA-9550-170172 undar the aspices of which ths work was conducted VG also gadully adnowladgs the support of the Amy Resmach ofce trough the DURP gan W911NF1810242which providad camputatianal resources for ths wark。
文摘We present a tensor-structured algorithm for efficient large-scale density functional theory(DFT)calculations by constructing a Tucker tensor basis that is adapted to the Kohn–Sham Hamiltonian and localized in real-space.The proposed approach uses an additive separable approximation to the Kohn–Sham Hamiltonian and an L1 localization technique to generate the 1-D localized functions that constitute the Tucker tensor basis.Numerical results show that the resulting Tucker tensor basis exhibits exponential convergence in the ground-state energy with increasing Tucker rank.Further,the proposed tensor-structured algorithm demonstrated sub-quadratic scaling with system-size for both systems with and without a gap,and involving many thousands of atoms.This reduced-order scaling has also resulted in the proposed approach outperforming plane-wave DFT implementation for systems beyond 2000 electrons.
