In the analysis and design for linear systems in the form of state space,it is undisputed that state responses play a fundamentally important role.For continuous-time linear time-invariant(CT-LTI)systems,the well-know...In the analysis and design for linear systems in the form of state space,it is undisputed that state responses play a fundamentally important role.For continuous-time linear time-invariant(CT-LTI)systems,the well-known result is that the state responses are given in terms of matrix exponential functions[1].For discrete-time linear time-invariant(DT-LTI)systems,the state responses are expressed in terms of matrix power functions[1].展开更多
Maize yield is critically endangered by diseases throughout its growth cycle,posing significant risks to food security.The spatial and temporal dynamics of maize yield loss and the rate of yield loss attributable to t...Maize yield is critically endangered by diseases throughout its growth cycle,posing significant risks to food security.The spatial and temporal dynamics of maize yield loss and the rate of yield loss attributable to these threats on a regional scale have been challenging to ascertain due to scarce continuous observation data.This study compiled county-level data on maize yield and yield loss across China's six primary cropping regions over twenty years from 1999 to 2018.These include the Spring-sown area of Northern China(1-NC),the Summer-sown Huang-Huai-Hai Plain(2-HHP),the Southwest Mountain(3-SM),the Southern Hilly(4-SH),the Northwest Irrigated(5-NI),and the Qinghai-Tibet Plateau Maize Regions(6-QTP).We identified 15 major diseases affecting these regions.The annual average yield loss due to maize diseases in the regions 1-NC,2-HHP,3-SM,4-SH,5-NI,and 6-QTP were 0.40,0.58,0.12,0.05,0.04 and<0.01 million tons,respectively,and the corresponding average yield loss rate(the ratio of yield loss to total yield)in these regions was 0.63,0.90,0.65,0.63,0.44,and 0.05.The yield loss due to all diseases increased for three regions in 3-SM,4-SH and 5-NI.The yield loss rate due to diseases significantly increased in region 4-SH and 5-NI.Predominantly,maize leaf blight has become the most significant threats.In region 1-NC,maize head smut(D1)and maize leaf blight(D2)were the primary diseases.In region 2-HHP,maize leaf blight(D2),maize rust(D3),maize brown spot(D5),Curvularia leaf spot(D7),and maize virus disease(D14)were the key pathogens.Bivariate trend analysis(joint analysis of yield loss and loss rate trends)indicated that maize head smut(D1)decreased significantly in 1-NC,while in 2-HHP,six diseases showed a significant decrease in both yield loss and loss rate,namely sheath blight(D4),brown spot(D5),root rot(D11),downy mildew(D12)and virus disease(D14).By providing a long-term,national-scale perspective,this study not only supports the development of broad management strategies but also guides the creation of precise,region-specific control protocols to safeguard maize production.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
A joint statistical model of wind speed and wind shear is critical for height-dependent wind resource characteristic analysis.However,given the different atmospheric conditions that may be involved,the statistical dis...A joint statistical model of wind speed and wind shear is critical for height-dependent wind resource characteristic analysis.However,given the different atmospheric conditions that may be involved,the statistical distribution of the two variables may show multimodal characteristics.In this work,a finite mixture bivariate statistical model was designed to describe the statistical properties,which is composed of several components,each with a Weibull distribution and a normal distribution for wind speed and wind shear,respectively,with a Gaussian copula to describe the dependency structure between the two variables.To confirm the developed model,reanalysis data from six positions in the coastal sea areas of China were used.Our results disclosed that the developed joint statistical model can accurately capture the different multimodal structures presented in all the bivariate samples under mixed atmospheric conditions,giving acceptable predictions of the joint probability distributions.Proper consideration of wind shear coefficient variation is crucial in estimating height-dependent wind resource characteristics.Importantly,unlike traditional methods that are limited to specific hub heights,the model developed here can estimate wind energy potential across different hub heights,enhancing the economic viability assessment of wind power projects.展开更多
基金supported by the Preeminent Youth Team Project of Guangdong Provincial Natural Science Foundation(Grant No.2024B1515040008)the National Natural Science Foundation of China(Grant No.62173112)+3 种基金the Shenzhen Science and Technology Program(Grant No.RCJC20210609104400005)the Science Center Program of National Natural Science Foundation of China(Grant No.62188101)the Joint Funds of the National Natural Science Foundation of China(Grant No.U2013203)the Fundamental Research Funds for the Central Universities(Grant No.HIT.OCEF.2023051)。
文摘In the analysis and design for linear systems in the form of state space,it is undisputed that state responses play a fundamentally important role.For continuous-time linear time-invariant(CT-LTI)systems,the well-known result is that the state responses are given in terms of matrix exponential functions[1].For discrete-time linear time-invariant(DT-LTI)systems,the state responses are expressed in terms of matrix power functions[1].
基金supported by the National Key Research and Development Program of China,China(2022YFF1301801)Agricultural scientific and technological innovation project of Shandong Academy of Agricultural Sciences,China(333 Project)(06202214442066)+1 种基金Beijing Natural Science Foundation,China(5232018)Technology Innovation Project of Shandong Academy of Agricultural Sciences,China(06202214442062).
文摘Maize yield is critically endangered by diseases throughout its growth cycle,posing significant risks to food security.The spatial and temporal dynamics of maize yield loss and the rate of yield loss attributable to these threats on a regional scale have been challenging to ascertain due to scarce continuous observation data.This study compiled county-level data on maize yield and yield loss across China's six primary cropping regions over twenty years from 1999 to 2018.These include the Spring-sown area of Northern China(1-NC),the Summer-sown Huang-Huai-Hai Plain(2-HHP),the Southwest Mountain(3-SM),the Southern Hilly(4-SH),the Northwest Irrigated(5-NI),and the Qinghai-Tibet Plateau Maize Regions(6-QTP).We identified 15 major diseases affecting these regions.The annual average yield loss due to maize diseases in the regions 1-NC,2-HHP,3-SM,4-SH,5-NI,and 6-QTP were 0.40,0.58,0.12,0.05,0.04 and<0.01 million tons,respectively,and the corresponding average yield loss rate(the ratio of yield loss to total yield)in these regions was 0.63,0.90,0.65,0.63,0.44,and 0.05.The yield loss due to all diseases increased for three regions in 3-SM,4-SH and 5-NI.The yield loss rate due to diseases significantly increased in region 4-SH and 5-NI.Predominantly,maize leaf blight has become the most significant threats.In region 1-NC,maize head smut(D1)and maize leaf blight(D2)were the primary diseases.In region 2-HHP,maize leaf blight(D2),maize rust(D3),maize brown spot(D5),Curvularia leaf spot(D7),and maize virus disease(D14)were the key pathogens.Bivariate trend analysis(joint analysis of yield loss and loss rate trends)indicated that maize head smut(D1)decreased significantly in 1-NC,while in 2-HHP,six diseases showed a significant decrease in both yield loss and loss rate,namely sheath blight(D4),brown spot(D5),root rot(D11),downy mildew(D12)and virus disease(D14).By providing a long-term,national-scale perspective,this study not only supports the development of broad management strategies but also guides the creation of precise,region-specific control protocols to safeguard maize production.
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 Key R&D Program of Shandong Province,China(No.2021ZLGX04)the National Natural Science Foundation of China(No.52171284)。
文摘A joint statistical model of wind speed and wind shear is critical for height-dependent wind resource characteristic analysis.However,given the different atmospheric conditions that may be involved,the statistical distribution of the two variables may show multimodal characteristics.In this work,a finite mixture bivariate statistical model was designed to describe the statistical properties,which is composed of several components,each with a Weibull distribution and a normal distribution for wind speed and wind shear,respectively,with a Gaussian copula to describe the dependency structure between the two variables.To confirm the developed model,reanalysis data from six positions in the coastal sea areas of China were used.Our results disclosed that the developed joint statistical model can accurately capture the different multimodal structures presented in all the bivariate samples under mixed atmospheric conditions,giving acceptable predictions of the joint probability distributions.Proper consideration of wind shear coefficient variation is crucial in estimating height-dependent wind resource characteristics.Importantly,unlike traditional methods that are limited to specific hub heights,the model developed here can estimate wind energy potential across different hub heights,enhancing the economic viability assessment of wind power projects.
