Let G be a finite group of order n. The strong power graph of G is the undirected graph whose vertex set is G and two distinct vertices x and y are adjacent if x^n1 = y^n2 for some positive integers n1,n2 < n. In t...Let G be a finite group of order n. The strong power graph of G is the undirected graph whose vertex set is G and two distinct vertices x and y are adjacent if x^n1 = y^n2 for some positive integers n1,n2 < n. In this paper, we give the characteristic polynomials of the distance and adjacency matrix of the strong power graph of G, and compute its distance and adjacency spectrum.展开更多
.The intersection power graph of a finite group G is a simple graph whose vertex set is G,in which two distinct vertices and y are adjacent if and only if either one of a and y is the identity element,or(a)n(y)is non-....The intersection power graph of a finite group G is a simple graph whose vertex set is G,in which two distinct vertices and y are adjacent if and only if either one of a and y is the identity element,or(a)n(y)is non-trivial.A number of important graph classes,including cographs,chordal graphs,split graphs,and threshold graphs,can be defined either structurally or in terms of forbidden induced subgraphs.In this paper,we characterize the finite groups whose intersection power graphs are cographs,split graphs,and threshold graphs.We also classify the finite nilpotent groups whose intersection power graphs are chordal.展开更多
The bidirectional reflectance distribution function(BRDF)of the land surface contains information relating to its physical structure and composition.Accurate BRDF modeling for heterogeneous pixels is important for glo...The bidirectional reflectance distribution function(BRDF)of the land surface contains information relating to its physical structure and composition.Accurate BRDF modeling for heterogeneous pixels is important for global ecosystem monitoring and radiation balance studies.However,the original kerneldriven models,which many operational BRDF/Albedo algorithms have adopted,do not explicitly consider the heterogeneity within heterogeneous pixels,which may result in large fitting residuals.In this paper,we attempted to improve the fitting ability of the kernel-driven models over heterogeneous pixels by changing the inversion approach and proposed a dynamic weighted least squares(DWLS)inversion approach.The performance of DWLS and the traditional ordinary least squares(OLS)inversion approach were compared using simulated data.We also evaluated its ability to reconstruct multiangle satellite observations and provide accurate BRDF using unmanned aerial vehicle observations.The results show that the developed DWLS approach improves the accuracy of modeled BRDF of heterogeneous pixels.The DWLS approach applied to satellite observations shows better performance than the OLS method in study regions and exhibits smaller mean fitting residuals both in the red and near-infrared bands.The DWLS approach also shows higher BRDF modeling accuracy than the OLS approach with unmanned aerial vehicle observations.These results indicate that the DWLS inversion approach can be a better choice when kernel-driven models are used for heterogeneous pixels.展开更多
Drylands play a critical role in the global carbon cycle.Recent studies have documented widespread dryland greening largely attributed to CO_(2) fertilization,yet the role of human activities remained unclear.Here,we ...Drylands play a critical role in the global carbon cycle.Recent studies have documented widespread dryland greening largely attributed to CO_(2) fertilization,yet the role of human activities remained unclear.Here,we used satellite gross primary productivity(GPP)and state-of-the-art dynamic global vegetation models to quantify the global dryland productivity trend from 2001 to 2024 and determine the contributions of climatic and anthropogenic factors.Our results revealed that 29.20%of global drylands experienced significant greening;(P value<0.1)only 4.91%showed a significant browning trend.The robustness of the satellite GPP trend was corroborated by multiple lines of evidence from leaf area index,enhanced vegetation index,and machine-learning-based upscaling of in situ flux tower GPP(FLUXCOM-X).From 2001 to 2024,the global dryland GPP increased by 1,899 Tg C,with Asia contributing the largest share(983 Tg C).Partial least squares regression analysis revealed that human activities(effect size=0.68)played a more important role than the CO_(2) fertilization effect(0.32),with climate factors having only a minor role(0.03).Dynamic global vegetation models from the latest TRENDY v12 project substantially underestimated global dryland greening(multimodel ensemble mean GPP slope=0.18±2.45 g C m^(-2) y^(−1) versus mean satellite GPP slope=7.66±4.92 g C m^(-2) y^(−1))and overemphasized the role of CO_(2) fertilization effect and climate.Our results therefore not only advanced our mechanic understanding of global dryland greening but also highlighted the urgent need for refined representation of human land use activities in Earth system models to accurately predict dryland ecosystem dynamics amid global warming and escalating freshwater demand.展开更多
Conservation of global biodiversity requires scalable tools to monitor species richness patterns,and satellite remote sensing offers a promising avenue.However,a great challenge lies in identifying how best to transla...Conservation of global biodiversity requires scalable tools to monitor species richness patterns,and satellite remote sensing offers a promising avenue.However,a great challenge lies in identifying how best to translate satellite data into ecologically meaningful biodiversity metrics.This study examines the effectiveness of dynamic habitat indices(DHIs)derived from satellite vegetation products,including gross primary productivity(GPP),fraction of absorbed photosynthetically active radiation,leaf area index,normalized difference vegetation index,enhanced vegetation index,and solar-induced chlorophyll fluorescence,in capturing global species richness across amphibians,birds,mammals,and reptiles.The DHIs consist of 3 subindices,with each representing an important productivity-species richness hypothesis,namely,annual cumulative productivity(DHI Cum,available energy hypothesis),annual minimum productivity(DHI Min,environmental stress hypothesis),and coefficient of variation of productivity(DHI CV,environmental stability hypothesis).Results showed that DHIs derived from satellite GPP data explain a large proportion of the variance in species richness globally(R^(2)=0.70 for amphibians,R^(2)=0.78 for birds,R^(2)=0.77 for mammals,R^(2)=0.77 for reptiles,and R^(2)=0.82 when all taxa combined),outperforming other satellite vegetation products.Validation with in situ DHIs calculated from tower-measured GPP at 124 globally FLUXNET sites demonstrated strong agreement with satellite DHIs,supporting the reliability of the satellite GPP-based DHIs.Furthermore,the relatively higher uncertainty of satellite DHIs at low-productivity sites also urges further development of satellite GPP algorithms.Globally,protected areas showed significantly higher DHI Cum and Min and lower DHI CV(P<0.0001),underscoring their superior habitat quality for biodiversity conservation.These findings highlight the potential of DHIs as a powerful and scalable tool for linking satellite observations to global biodiversity patterns,thus bridging the gap between remote sensing and biodiversity conservation community.展开更多
基金Supported by the National Natural Science Foundation of China(Grant No.11801441)the Scientific Research Program Funded by Shaanxi Provincial Education Department(Program No.18JK0623)the Natural Science Foundation of Shaanxi Province(Grant No.2019JQ-056)
文摘Let G be a finite group of order n. The strong power graph of G is the undirected graph whose vertex set is G and two distinct vertices x and y are adjacent if x^n1 = y^n2 for some positive integers n1,n2 < n. In this paper, we give the characteristic polynomials of the distance and adjacency matrix of the strong power graph of G, and compute its distance and adjacency spectrum.
基金supported by the National Natural Science Foundation of China(Grant Nos.11801441,61976244)the Natural Science Basic Research Program of Shaanxi(Program No.2020JQ-761)the Shaanxi Fundamental Science Research Project for Mathematics and Physics(Grant No.22JSQ024).
文摘.The intersection power graph of a finite group G is a simple graph whose vertex set is G,in which two distinct vertices and y are adjacent if and only if either one of a and y is the identity element,or(a)n(y)is non-trivial.A number of important graph classes,including cographs,chordal graphs,split graphs,and threshold graphs,can be defined either structurally or in terms of forbidden induced subgraphs.In this paper,we characterize the finite groups whose intersection power graphs are cographs,split graphs,and threshold graphs.We also classify the finite nilpotent groups whose intersection power graphs are chordal.
基金supported by the National Natural Science Foundation of China(Nos.42090013,42192580,and 42271356).
文摘The bidirectional reflectance distribution function(BRDF)of the land surface contains information relating to its physical structure and composition.Accurate BRDF modeling for heterogeneous pixels is important for global ecosystem monitoring and radiation balance studies.However,the original kerneldriven models,which many operational BRDF/Albedo algorithms have adopted,do not explicitly consider the heterogeneity within heterogeneous pixels,which may result in large fitting residuals.In this paper,we attempted to improve the fitting ability of the kernel-driven models over heterogeneous pixels by changing the inversion approach and proposed a dynamic weighted least squares(DWLS)inversion approach.The performance of DWLS and the traditional ordinary least squares(OLS)inversion approach were compared using simulated data.We also evaluated its ability to reconstruct multiangle satellite observations and provide accurate BRDF using unmanned aerial vehicle observations.The results show that the developed DWLS approach improves the accuracy of modeled BRDF of heterogeneous pixels.The DWLS approach applied to satellite observations shows better performance than the OLS method in study regions and exhibits smaller mean fitting residuals both in the red and near-infrared bands.The DWLS approach also shows higher BRDF modeling accuracy than the OLS approach with unmanned aerial vehicle observations.These results indicate that the DWLS inversion approach can be a better choice when kernel-driven models are used for heterogeneous pixels.
基金supported by the Key Program of the Natural Science Foundation of Gansu Province,China(Grant No.25JRRA646CI:X.Ma)+1 种基金the Fengyun Application Pioneering Project(FY-APP-2024.0302CI:X.Ma)。
文摘Drylands play a critical role in the global carbon cycle.Recent studies have documented widespread dryland greening largely attributed to CO_(2) fertilization,yet the role of human activities remained unclear.Here,we used satellite gross primary productivity(GPP)and state-of-the-art dynamic global vegetation models to quantify the global dryland productivity trend from 2001 to 2024 and determine the contributions of climatic and anthropogenic factors.Our results revealed that 29.20%of global drylands experienced significant greening;(P value<0.1)only 4.91%showed a significant browning trend.The robustness of the satellite GPP trend was corroborated by multiple lines of evidence from leaf area index,enhanced vegetation index,and machine-learning-based upscaling of in situ flux tower GPP(FLUXCOM-X).From 2001 to 2024,the global dryland GPP increased by 1,899 Tg C,with Asia contributing the largest share(983 Tg C).Partial least squares regression analysis revealed that human activities(effect size=0.68)played a more important role than the CO_(2) fertilization effect(0.32),with climate factors having only a minor role(0.03).Dynamic global vegetation models from the latest TRENDY v12 project substantially underestimated global dryland greening(multimodel ensemble mean GPP slope=0.18±2.45 g C m^(-2) y^(−1) versus mean satellite GPP slope=7.66±4.92 g C m^(-2) y^(−1))and overemphasized the role of CO_(2) fertilization effect and climate.Our results therefore not only advanced our mechanic understanding of global dryland greening but also highlighted the urgent need for refined representation of human land use activities in Earth system models to accurately predict dryland ecosystem dynamics amid global warming and escalating freshwater demand.
基金supported by the Director Fund of the International Research Center of Big Data for Sustainable Development Goals(grant number CBAS2022DF006)the Open Fund of the Key Laboratory of Land Satellite Remote Sensing Applications,Ministry of Natural Resources of the People’s Republic of China(grant number KLSMNR-202308)+2 种基金the Key Program of the Natural Science Foundation of Gansu Province,China(grant number 25JRRA646)the Fengyun Application Pioneering Project(grant number FY-APP-2024.0302)the National Natural Science Foundation of China(grant numbers 42171305 and 42311540014).
文摘Conservation of global biodiversity requires scalable tools to monitor species richness patterns,and satellite remote sensing offers a promising avenue.However,a great challenge lies in identifying how best to translate satellite data into ecologically meaningful biodiversity metrics.This study examines the effectiveness of dynamic habitat indices(DHIs)derived from satellite vegetation products,including gross primary productivity(GPP),fraction of absorbed photosynthetically active radiation,leaf area index,normalized difference vegetation index,enhanced vegetation index,and solar-induced chlorophyll fluorescence,in capturing global species richness across amphibians,birds,mammals,and reptiles.The DHIs consist of 3 subindices,with each representing an important productivity-species richness hypothesis,namely,annual cumulative productivity(DHI Cum,available energy hypothesis),annual minimum productivity(DHI Min,environmental stress hypothesis),and coefficient of variation of productivity(DHI CV,environmental stability hypothesis).Results showed that DHIs derived from satellite GPP data explain a large proportion of the variance in species richness globally(R^(2)=0.70 for amphibians,R^(2)=0.78 for birds,R^(2)=0.77 for mammals,R^(2)=0.77 for reptiles,and R^(2)=0.82 when all taxa combined),outperforming other satellite vegetation products.Validation with in situ DHIs calculated from tower-measured GPP at 124 globally FLUXNET sites demonstrated strong agreement with satellite DHIs,supporting the reliability of the satellite GPP-based DHIs.Furthermore,the relatively higher uncertainty of satellite DHIs at low-productivity sites also urges further development of satellite GPP algorithms.Globally,protected areas showed significantly higher DHI Cum and Min and lower DHI CV(P<0.0001),underscoring their superior habitat quality for biodiversity conservation.These findings highlight the potential of DHIs as a powerful and scalable tool for linking satellite observations to global biodiversity patterns,thus bridging the gap between remote sensing and biodiversity conservation community.
