As the proportion of newenergy increases,the traditional cumulant method(CM)produces significant errorswhen performing probabilistic load flow(PLF)calculations with large-scale wind power integrated.Considering the wi...As the proportion of newenergy increases,the traditional cumulant method(CM)produces significant errorswhen performing probabilistic load flow(PLF)calculations with large-scale wind power integrated.Considering the wind speed correlation,a multi-scenario PLF calculation method that combines random sampling and segmented discrete wind farm power was proposed.Firstly,based on constructing discrete scenes of wind farms,the Nataf transform is used to handle the correlation between wind speeds.Then,the random sampling method determines the output probability of discrete wind power scenarios when wind speed exhibits correlation.Finally,the PLF calculation results of each scenario areweighted and superimposed following the total probability formula to obtain the final power flow calculation result.Verified in the IEEE standard node system,the absolute percent error(APE)for the mean and standard deviation(SD)of the node voltages and branch active power are all within 1%,and the average root mean square(AMSR)values of the probability curves are all less than 1%.展开更多
Since China announced its goal of becoming carbon-neutral by 2060, carbon neutrality has become a major target in the development of China's urban agglomerations. This study applied the Future Land Use Simulation(...Since China announced its goal of becoming carbon-neutral by 2060, carbon neutrality has become a major target in the development of China's urban agglomerations. This study applied the Future Land Use Simulation(FLUS) model to predict the land use pattern of the ecological space of the Beibu Gulf urban agglomeration, in 2060 under ecological priority, agricultural priority and urbanized priority scenarios. The Integrated Valuation of Ecosystem Services and Trade-offs(In VEST) model was employed to analyse the spatial changes in ecological space carbon storage in each scenario from 2020 to 2060. Then, this study used a Geographically Weighted Regression(GWR) model to determine the main driving factors that influence the changes in land carbon sinking capacity. The results of the study can be summarised as follows: firstly, the agricultural and ecological priority scenarios will achieve balanced urban expansion and environmental protection of resources in an ecological space. The urbanized priority scenario will reduce the carbon sinking capacity. Among the simulation scenarios for 2060, carbon storage in the urbanized priority scenario will decrease by 112.26 × 10^(6) t compared with that for 2020 and the average carbon density will decrease by 0.96 kg/m^(2) compared with that for 2020. Carbon storage in the agricultural priority scenario will increase by 84.11 × 10^(6) t, and the average carbon density will decrease by 0.72 kg/m^(2). Carbon storage in the ecological priority scenario will increase by 3.03 × 10^(6) t, and the average carbon density will increase by 0.03 kg/m^(2). Under the premise that the population of the town will increases continuously, the ecological priority development approach may be a wise choice.Secondly, slope, distance to river and elevation are the most important factors that influence the carbon sink pattern of the ecological space in the Beibu Gulf urban agglomeration, followed by GDP, population density, slope direction and distance to traffic infrastructure.At the same time, urban space expansion is the main cause of the changes of this natural factors. Thirdly, the decreasing trend of ecological space is difficult to reverse, so reasonable land use policy to curb the spatial expansion of cities need to be made.展开更多
Building the Yangtze River Economic Belt(YREB)is one of China’s three national development policies in the new era.The ecological environment of the Yangtze River Economic Belt must be protected not only for regional...Building the Yangtze River Economic Belt(YREB)is one of China’s three national development policies in the new era.The ecological environment of the Yangtze River Economic Belt must be protected not only for regional economic development but also for regional ecological security and ecological progress in this region.This paper takes the ecological space of the Yangtze River Economic Belt as the research object,based on land use data in 2010 and 2015,and uses the FLUS model to simulate and predict the ecological space of the research area in 2035.The variation of the research area’s ecological space area and its four sub-zones has remarkable stability under diverse situations.Both the production space priority scenarios(S1)and living space priority scenarios(S2)saw a fall in ecological space area,with the former experiencing the highest reduction(a total reduction of 25,212 km^(2)).Under the ecological space priority scenarios(S3)and comprehensive space optimization scenario(S4),the ecological space area increased,and the ecological space area expanded even more under the former scenario(a total growth of 23,648 km^(2)).In Yunnan-Guizhou,the ecological space is relatively stable,with minimal signs of change.In Sichuan-Chongqing,the Sichuan Basin,Zoige Grassland,and Longmen Mountains were significant regions of area changes in ecological space.In the middle reaches of the Yangtze River,the ecological space changes mainly occur in the Wuyi Mountains,Mufu Mountains,and Dabie Mountains,as well as the surrounding waters of Dongting Lake.The Yangtze River Delta’s changes were mainly observed in the eastern Dabie Mountains and Jianghuai Hills.展开更多
Increased human activities in China's coastal zone have resulted in the depletion of ecological land resources.Thus,conducting current and future multi-scenario simulation research on land use and land cover chang...Increased human activities in China's coastal zone have resulted in the depletion of ecological land resources.Thus,conducting current and future multi-scenario simulation research on land use and land cover change(LUCC)is crucial for guiding the healthy and sustainable development of coastal zones.System dynamic(SD)-future land use simulation(FLUS)model,a coupled simulation model,was developed to analyze land use dynamics in China's coastal zone.This model encompasses five scenarios,namely,SSP1-RCP2.6(A),SSP2-RCP4.5(B),SSP3-RCP4.5(C),SSP4-RCP4.5(D),and SSP5-RCP8.5(E).The SD model simulates land use demand on an annual basis up to the year 2100.Subsequently,the FLUS model determines the spatial distribution of land use for the near term(2035),medium term(2050),and long term(2100).Results reveal a slowing trend in land use changes in China's coastal zone from 2000–2020.Among these changes,the expansion rate of construction land was the highest and exhibited an annual decrease.By 2100,land use predictions exhibit high accuracy,and notable differences are observed in trends across scenarios.In summary,the expansion of production,living,and ecological spaces toward the sea remains prominent.Scenario A emphasizes reduced land resource dependence,benefiting ecological land protection.Scenario B witnesses an intensified expansion of artificial wetlands.Scenario C sees substantial land needs for living and production,while Scenario D shows coastal forest and grassland shrinkage.Lastly,in Scenario E,the conflict between humans and land intensifies.This study presents pertinent recommendations for the future development,utilization,and management of coastal areas in China.The research contributes valuable scientific support for informed,long-term strategic decision making within coastal regions.展开更多
The Yellow River Delta(YRD), a critical economic zone along China's eastern coast, also functions as a vital ecological reserve in the lower Yellow River. Amidst rapid industrialization and urbanization, the regio...The Yellow River Delta(YRD), a critical economic zone along China's eastern coast, also functions as a vital ecological reserve in the lower Yellow River. Amidst rapid industrialization and urbanization, the region has witnessed significant land use/cover changes(LUCC), impacting ecosystem services(ES) and ecological security patterns(ESP). Investigating LUCC's effects on ES and ESP in the YRD is crucial for ecological security and sustainable development. This study utilized the PLUS model to simulate 2030 land use scenarios, including natural development(NDS), economic development(EDS), and ecological protection scenarios(EPS). Subsequently, the InVEST model and circuit theory were applied to assess ES and ESP under varying LUCC scenarios from 2010 to 2030. Findings indicate:(1) Notable LUCC from 2010 to 2030, marked by decreasing cropland and increasing construction land and water bodies.(2) From 2010 to 2020, improvements were observed in carbon storage,water yield, soil retention, and habitat quality, whereas 2020–2030 saw increases in water yield and soil retention but declines in habitat quality and carbon storage. Among the scenarios, EPS showed superior performance in all four ES.(3) Between 2010 and 2030, ecological sources, corridors, and pinchpoints expanded, displaying significant spatial heterogeneity. The EPS scenario yielded the most substantial increases in ecological sources,corridors, and pinchpoints, totaling 582.89 km^(2), 645.03 km^(2),and 64.43 km^(2), respectively. This study highlights the importance of EPS, offering insightful scientific guidance for the YRD's sustainable development.展开更多
【目的】为保护并优化高度城镇化地区的碳汇空间,有必要系统研究其时空演变特征及规律。【方法】本研究聚焦苏南地区“城镇尺度”的碳汇空间,在研究其时空演变特征的基础上,结合斑块生成土地利用变化模拟(patch-generating land use sim...【目的】为保护并优化高度城镇化地区的碳汇空间,有必要系统研究其时空演变特征及规律。【方法】本研究聚焦苏南地区“城镇尺度”的碳汇空间,在研究其时空演变特征的基础上,结合斑块生成土地利用变化模拟(patch-generating land use simulation,PLUS)模型和聚类分析法研判不同城镇综合响应状态,并提出差异化的碳汇空间管控策略。【结果】1)2000—2020年苏南地区碳汇空间面积大幅减少,减少区域高度集中于高价值碳汇空间。碳汇空间格局在城镇尺度上未因城镇化而全面瓦解,表现出较强的稳定性。2)通过对自然增长情景、碳汇保护情景、碳汇强化情景3种情景的模拟,发现加大碳汇空间保护力度能够实现高质量碳汇空间扩张,但需要警惕生态功能单一化风险,避免盲目追求“高碳汇系数”。3)在3种模拟情景下,大部分城镇碳汇空间结构较稳定,建议通过存量挖潜与功能置换等方式优化碳汇空间;而部分敏感型城镇则呈现差异化演变路径,需根据其具体风险类型,实施更具针对性的管控策略。【结论】快速城镇化地区碳汇空间面积虽然呈现缩减趋势,但在城镇尺度表现出稳定性与敏感性共存的特征。这一特性可通过多情景模拟研判,从而为制定差异化的城镇碳汇空间管控策略提供科学依据。展开更多
Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method f...Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers.展开更多
Radiative cooling textiles with spectrally selective surfaces offer a promising energy-efficient approach for sub-ambient cooling of outdoor objects and individuals.However,the spectrally selective mid-infrared emissi...Radiative cooling textiles with spectrally selective surfaces offer a promising energy-efficient approach for sub-ambient cooling of outdoor objects and individuals.However,the spectrally selective mid-infrared emission of these textiles significantly hinders their efficient radiative heat exchange with self-heated objects,thereby posing a significant challenge to their versatile cooling applicability.Herein,we present a bicomponent blow spinning strategy for the production of scalable,ultra-flexible,and healable textiles featuring a tailored dual gradient in both chemical composition and fiber diameter.The gradient in the fiber diameter of this textile introduces a hierarchically porous structure across the sunlight incident area,thereby achieving a competitive solar reflectivity of 98.7%on its outer surface.Additionally,the gradient in the chemical composition of this textile contributes to the formation of Janus infrared-absorbing surfaces:The outer surface demonstrates a high mid-infrared emission,whereas the inner surface shows a broad infrared absorptivity,facilitating radiative heat exchange with underlying self-heated objects.Consequently,this textile demonstrates multi-scenario radiative cooling capabilities,enabling versatile outdoor cooling for unheated objects by 7.8℃ and self-heated objects by 13.6℃,compared to commercial sunshade fabrics.展开更多
Edge computation offloading has made some progress in the fifth generation mobile network(5G).However,load balancing in edge computation offloading is still a challenging problem.Meanwhile,with the continuous pursuit ...Edge computation offloading has made some progress in the fifth generation mobile network(5G).However,load balancing in edge computation offloading is still a challenging problem.Meanwhile,with the continuous pursuit of low execution latency in 5G multi-scenario,the functional requirements of edge computation offloading are further exacerbated.Given the above challenges,we raise a unique edge computation offloading method in 5G multi-scenario,and consider user satisfaction.The method consists of three functional parts:offloading strategy generation,offloading strategy update,and offloading strategy optimization.First,the offloading strategy is generated by means of a deep neural network(DNN),then update the offloading strategy by updating the DNN parameters.Finally,we optimize the offloading strategy based on changes in user satisfaction.In summary,compared to existing optimization methods,our proposal can achieve performance close to the optimum.Massive simulation results indicate the latency of the execution of our method on the CPU is under 0.1 seconds while improving the average computation rate by about 10%.展开更多
The accurate estimation of the remaining charge time(RCT)is essential in a battery management system(BMS),because it guarantees the safety and dependability of the power battery systems of new energy vehicles.However,...The accurate estimation of the remaining charge time(RCT)is essential in a battery management system(BMS),because it guarantees the safety and dependability of the power battery systems of new energy vehicles.However,the direct estimation of RCT is challenging because of the variability of actual charging scenarios and the complex charging process,which complicates the estimation of RCT in actual scenarios.Hence,this paper proposes an estimation framework based on deep learning for multi-scenario charging data to estimate the remaining charging times.Through an in-depth analysis of multi-scenario charging data,the RCT of the charging process is estimated using the temporal convolutional network(TCN)model,which has a strong generalization ability.Additionally,a dynamic learning rate(DLR)mechanism and an early stopping strategy(ES)are designed in the TCN model(DLR-ES TCN)for the nonlinear characteristics of the battery system to balance the relationship between model convergence speed and accuracy.Finally,compared with the traditional TCN model and four common deep learning models under three different scenarios,the experimental results show the mean absolute percentage error(MAPE)of the proposed method is less than 2%,indicating better accuracy and stability.This research can improve the safety monitoring of power batteries when applied to various target domains.展开更多
基金supported by Basic Science Research Program through the National Natural Science Foundation of China(Grant No.61867003).
文摘As the proportion of newenergy increases,the traditional cumulant method(CM)produces significant errorswhen performing probabilistic load flow(PLF)calculations with large-scale wind power integrated.Considering the wind speed correlation,a multi-scenario PLF calculation method that combines random sampling and segmented discrete wind farm power was proposed.Firstly,based on constructing discrete scenes of wind farms,the Nataf transform is used to handle the correlation between wind speeds.Then,the random sampling method determines the output probability of discrete wind power scenarios when wind speed exhibits correlation.Finally,the PLF calculation results of each scenario areweighted and superimposed following the total probability formula to obtain the final power flow calculation result.Verified in the IEEE standard node system,the absolute percent error(APE)for the mean and standard deviation(SD)of the node voltages and branch active power are all within 1%,and the average root mean square(AMSR)values of the probability curves are all less than 1%.
基金Under the auspices of National Natural Science Foundation of China (No. 52268008, 51768001)。
文摘Since China announced its goal of becoming carbon-neutral by 2060, carbon neutrality has become a major target in the development of China's urban agglomerations. This study applied the Future Land Use Simulation(FLUS) model to predict the land use pattern of the ecological space of the Beibu Gulf urban agglomeration, in 2060 under ecological priority, agricultural priority and urbanized priority scenarios. The Integrated Valuation of Ecosystem Services and Trade-offs(In VEST) model was employed to analyse the spatial changes in ecological space carbon storage in each scenario from 2020 to 2060. Then, this study used a Geographically Weighted Regression(GWR) model to determine the main driving factors that influence the changes in land carbon sinking capacity. The results of the study can be summarised as follows: firstly, the agricultural and ecological priority scenarios will achieve balanced urban expansion and environmental protection of resources in an ecological space. The urbanized priority scenario will reduce the carbon sinking capacity. Among the simulation scenarios for 2060, carbon storage in the urbanized priority scenario will decrease by 112.26 × 10^(6) t compared with that for 2020 and the average carbon density will decrease by 0.96 kg/m^(2) compared with that for 2020. Carbon storage in the agricultural priority scenario will increase by 84.11 × 10^(6) t, and the average carbon density will decrease by 0.72 kg/m^(2). Carbon storage in the ecological priority scenario will increase by 3.03 × 10^(6) t, and the average carbon density will increase by 0.03 kg/m^(2). Under the premise that the population of the town will increases continuously, the ecological priority development approach may be a wise choice.Secondly, slope, distance to river and elevation are the most important factors that influence the carbon sink pattern of the ecological space in the Beibu Gulf urban agglomeration, followed by GDP, population density, slope direction and distance to traffic infrastructure.At the same time, urban space expansion is the main cause of the changes of this natural factors. Thirdly, the decreasing trend of ecological space is difficult to reverse, so reasonable land use policy to curb the spatial expansion of cities need to be made.
基金The Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan),No.CUG2018123。
文摘Building the Yangtze River Economic Belt(YREB)is one of China’s three national development policies in the new era.The ecological environment of the Yangtze River Economic Belt must be protected not only for regional economic development but also for regional ecological security and ecological progress in this region.This paper takes the ecological space of the Yangtze River Economic Belt as the research object,based on land use data in 2010 and 2015,and uses the FLUS model to simulate and predict the ecological space of the research area in 2035.The variation of the research area’s ecological space area and its four sub-zones has remarkable stability under diverse situations.Both the production space priority scenarios(S1)and living space priority scenarios(S2)saw a fall in ecological space area,with the former experiencing the highest reduction(a total reduction of 25,212 km^(2)).Under the ecological space priority scenarios(S3)and comprehensive space optimization scenario(S4),the ecological space area increased,and the ecological space area expanded even more under the former scenario(a total growth of 23,648 km^(2)).In Yunnan-Guizhou,the ecological space is relatively stable,with minimal signs of change.In Sichuan-Chongqing,the Sichuan Basin,Zoige Grassland,and Longmen Mountains were significant regions of area changes in ecological space.In the middle reaches of the Yangtze River,the ecological space changes mainly occur in the Wuyi Mountains,Mufu Mountains,and Dabie Mountains,as well as the surrounding waters of Dongting Lake.The Yangtze River Delta’s changes were mainly observed in the eastern Dabie Mountains and Jianghuai Hills.
基金Under the auspices of National Natural Science Foundation of China (No.42176221,41901133)Strategic Priority Research Program of the Chinese Academy of Sciences (No.XDA19060205)Seed project of Yantai Institute of Coastal Zone Research,Chinese Academy of Sciences (No.YIC-E3518907)。
文摘Increased human activities in China's coastal zone have resulted in the depletion of ecological land resources.Thus,conducting current and future multi-scenario simulation research on land use and land cover change(LUCC)is crucial for guiding the healthy and sustainable development of coastal zones.System dynamic(SD)-future land use simulation(FLUS)model,a coupled simulation model,was developed to analyze land use dynamics in China's coastal zone.This model encompasses five scenarios,namely,SSP1-RCP2.6(A),SSP2-RCP4.5(B),SSP3-RCP4.5(C),SSP4-RCP4.5(D),and SSP5-RCP8.5(E).The SD model simulates land use demand on an annual basis up to the year 2100.Subsequently,the FLUS model determines the spatial distribution of land use for the near term(2035),medium term(2050),and long term(2100).Results reveal a slowing trend in land use changes in China's coastal zone from 2000–2020.Among these changes,the expansion rate of construction land was the highest and exhibited an annual decrease.By 2100,land use predictions exhibit high accuracy,and notable differences are observed in trends across scenarios.In summary,the expansion of production,living,and ecological spaces toward the sea remains prominent.Scenario A emphasizes reduced land resource dependence,benefiting ecological land protection.Scenario B witnesses an intensified expansion of artificial wetlands.Scenario C sees substantial land needs for living and production,while Scenario D shows coastal forest and grassland shrinkage.Lastly,in Scenario E,the conflict between humans and land intensifies.This study presents pertinent recommendations for the future development,utilization,and management of coastal areas in China.The research contributes valuable scientific support for informed,long-term strategic decision making within coastal regions.
基金financially supported by the National Natural Science Foundation of China (Grant No. 41461011)。
文摘The Yellow River Delta(YRD), a critical economic zone along China's eastern coast, also functions as a vital ecological reserve in the lower Yellow River. Amidst rapid industrialization and urbanization, the region has witnessed significant land use/cover changes(LUCC), impacting ecosystem services(ES) and ecological security patterns(ESP). Investigating LUCC's effects on ES and ESP in the YRD is crucial for ecological security and sustainable development. This study utilized the PLUS model to simulate 2030 land use scenarios, including natural development(NDS), economic development(EDS), and ecological protection scenarios(EPS). Subsequently, the InVEST model and circuit theory were applied to assess ES and ESP under varying LUCC scenarios from 2010 to 2030. Findings indicate:(1) Notable LUCC from 2010 to 2030, marked by decreasing cropland and increasing construction land and water bodies.(2) From 2010 to 2020, improvements were observed in carbon storage,water yield, soil retention, and habitat quality, whereas 2020–2030 saw increases in water yield and soil retention but declines in habitat quality and carbon storage. Among the scenarios, EPS showed superior performance in all four ES.(3) Between 2010 and 2030, ecological sources, corridors, and pinchpoints expanded, displaying significant spatial heterogeneity. The EPS scenario yielded the most substantial increases in ecological sources,corridors, and pinchpoints, totaling 582.89 km^(2), 645.03 km^(2),and 64.43 km^(2), respectively. This study highlights the importance of EPS, offering insightful scientific guidance for the YRD's sustainable development.
文摘【目的】为保护并优化高度城镇化地区的碳汇空间,有必要系统研究其时空演变特征及规律。【方法】本研究聚焦苏南地区“城镇尺度”的碳汇空间,在研究其时空演变特征的基础上,结合斑块生成土地利用变化模拟(patch-generating land use simulation,PLUS)模型和聚类分析法研判不同城镇综合响应状态,并提出差异化的碳汇空间管控策略。【结果】1)2000—2020年苏南地区碳汇空间面积大幅减少,减少区域高度集中于高价值碳汇空间。碳汇空间格局在城镇尺度上未因城镇化而全面瓦解,表现出较强的稳定性。2)通过对自然增长情景、碳汇保护情景、碳汇强化情景3种情景的模拟,发现加大碳汇空间保护力度能够实现高质量碳汇空间扩张,但需要警惕生态功能单一化风险,避免盲目追求“高碳汇系数”。3)在3种模拟情景下,大部分城镇碳汇空间结构较稳定,建议通过存量挖潜与功能置换等方式优化碳汇空间;而部分敏感型城镇则呈现差异化演变路径,需根据其具体风险类型,实施更具针对性的管控策略。【结论】快速城镇化地区碳汇空间面积虽然呈现缩减趋势,但在城镇尺度表现出稳定性与敏感性共存的特征。这一特性可通过多情景模拟研判,从而为制定差异化的城镇碳汇空间管控策略提供科学依据。
基金National Natural Science Foundation of China,No.42301470,No.52270185,No.42171389Capacity Building Program of Local Colleges and Universities in Shanghai,No.21010503300。
文摘Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers.
基金financial support from the National Natural Science Foundation of China(Grant No.52273067,52233006)the Fundamental Research Funds for the Central Universities(Grant No.2232023A-03)+3 种基金the Shuguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission(Grant No.23SG29)the Natural Science Foundation of Shanghai(Grant No.24ZR1402400)the Shanghai Scientific and Technological Innovation Project(Grant No.24520713000)Innovation Program of Shanghai Municipal Education Commission(Grant No.2021-01-07-00-03-E00108).
文摘Radiative cooling textiles with spectrally selective surfaces offer a promising energy-efficient approach for sub-ambient cooling of outdoor objects and individuals.However,the spectrally selective mid-infrared emission of these textiles significantly hinders their efficient radiative heat exchange with self-heated objects,thereby posing a significant challenge to their versatile cooling applicability.Herein,we present a bicomponent blow spinning strategy for the production of scalable,ultra-flexible,and healable textiles featuring a tailored dual gradient in both chemical composition and fiber diameter.The gradient in the fiber diameter of this textile introduces a hierarchically porous structure across the sunlight incident area,thereby achieving a competitive solar reflectivity of 98.7%on its outer surface.Additionally,the gradient in the chemical composition of this textile contributes to the formation of Janus infrared-absorbing surfaces:The outer surface demonstrates a high mid-infrared emission,whereas the inner surface shows a broad infrared absorptivity,facilitating radiative heat exchange with underlying self-heated objects.Consequently,this textile demonstrates multi-scenario radiative cooling capabilities,enabling versatile outdoor cooling for unheated objects by 7.8℃ and self-heated objects by 13.6℃,compared to commercial sunshade fabrics.
基金This work was supported in part by the Science and Technology Project of North China University of Science and Technology under Grant ZD-YG-202317-23。
文摘Edge computation offloading has made some progress in the fifth generation mobile network(5G).However,load balancing in edge computation offloading is still a challenging problem.Meanwhile,with the continuous pursuit of low execution latency in 5G multi-scenario,the functional requirements of edge computation offloading are further exacerbated.Given the above challenges,we raise a unique edge computation offloading method in 5G multi-scenario,and consider user satisfaction.The method consists of three functional parts:offloading strategy generation,offloading strategy update,and offloading strategy optimization.First,the offloading strategy is generated by means of a deep neural network(DNN),then update the offloading strategy by updating the DNN parameters.Finally,we optimize the offloading strategy based on changes in user satisfaction.In summary,compared to existing optimization methods,our proposal can achieve performance close to the optimum.Massive simulation results indicate the latency of the execution of our method on the CPU is under 0.1 seconds while improving the average computation rate by about 10%.
基金supported in part by the National Natural Science Foundation of China(Grant No.5217051006)the Shandong Province Natural Science Foundation(Grant No.ZR2021ME223)+1 种基金the Yantai Science and Technology Planning Project(Grant No.2022GCCRC158)the Graduate Innovation Foundation of Yantai University,GIFYTU(Grant No.GGIFYTU2349).
文摘The accurate estimation of the remaining charge time(RCT)is essential in a battery management system(BMS),because it guarantees the safety and dependability of the power battery systems of new energy vehicles.However,the direct estimation of RCT is challenging because of the variability of actual charging scenarios and the complex charging process,which complicates the estimation of RCT in actual scenarios.Hence,this paper proposes an estimation framework based on deep learning for multi-scenario charging data to estimate the remaining charging times.Through an in-depth analysis of multi-scenario charging data,the RCT of the charging process is estimated using the temporal convolutional network(TCN)model,which has a strong generalization ability.Additionally,a dynamic learning rate(DLR)mechanism and an early stopping strategy(ES)are designed in the TCN model(DLR-ES TCN)for the nonlinear characteristics of the battery system to balance the relationship between model convergence speed and accuracy.Finally,compared with the traditional TCN model and four common deep learning models under three different scenarios,the experimental results show the mean absolute percentage error(MAPE)of the proposed method is less than 2%,indicating better accuracy and stability.This research can improve the safety monitoring of power batteries when applied to various target domains.
