The development of wind power clusters has scaled in terms of both scale and coverage,and the impact of weather fluctuations on cluster output changes has become increasingly complex.Accurately identifying the forward...The development of wind power clusters has scaled in terms of both scale and coverage,and the impact of weather fluctuations on cluster output changes has become increasingly complex.Accurately identifying the forward-looking information of key wind farms in a cluster under different weather conditions is an effective method to improve the accuracy of ultrashort-term cluster power forecasting.To this end,this paper proposes a refined modeling method for ultrashort-term wind power cluster forecasting based on a convergent cross-mapping algorithm.From the perspective of causality,key meteorological forecasting factors under different cluster power fluctuation processes were screened,and refined training modeling was performed for different fluctuation processes.First,a wind process description index system and classification model at the wind power cluster level are established to realize the classification of typical fluctuation processes.A meteorological-cluster power causal relationship evaluation model based on the convergent cross-mapping algorithm is pro-posed to screen meteorological forecasting factors under multiple types of typical fluctuation processes.Finally,a refined modeling meth-od for a variety of different typical fluctuation processes is proposed,and the strong causal meteorological forecasting factors of each scenario are used as inputs to realize high-precision modeling and forecasting of ultra-short-term wind cluster power.An example anal-ysis shows that the short-term wind power cluster power forecasting accuracy of the proposed method can reach 88.55%,which is 1.57-7.32%higher than that of traditional methods.展开更多
Chaotic cryptography has been applied to image encryption;however,only the traditional low-dimensional chaotic systems has been widely analyzed or deciphered,which does not show satisfied security and efficiency.To so...Chaotic cryptography has been applied to image encryption;however,only the traditional low-dimensional chaotic systems has been widely analyzed or deciphered,which does not show satisfied security and efficiency.To solve this problem,a new algorithm based on cross-chaos map has been created in this article.The image pixels are scrambled under control of high-dimensional chaotic sequence,which is generated by cross chaotic map.The image pixels are substituted by ciphertext feedback algorithm.It can relate encryption required parameters with plaintext and can make a plaintext byte affect more ciphertext bytes.Proved by theoretical analysis and experimental results,the algorithm has higher complex degree and has passed SP800-22 pseudo-random number standard tests,and it has high encryption speed,high security,etc.It can be widely applied in the field of image encryption.展开更多
Climate change significantly affects vegetation dynamics.Thus,understanding interactions between vegetation and climatic factors is essential for ecological management.This study used kernel Normalized Difference Vege...Climate change significantly affects vegetation dynamics.Thus,understanding interactions between vegetation and climatic factors is essential for ecological management.This study used kernel Normalized Difference Vegetation Index(kNDVI)and climatic data(temperature,precipitation,humidity,and vapor pressure deficit(VPD))of China from 2000 to 2022,integrating Geographic Convergent Cross Mapping(GCCM)causal modeling,Extreme Gradient Boosting-Shapley Additive Explanations(XGBoost-SHAP)nonlinear threshold identification,and Geographical Simulation and Optimization Systems-Future Land Use Simulation(GeoSOS-FLUS)spatial prediction modeling to investigate vegetation spatiotemporal characteristics,driving mechanisms,nonlinear thresholds,and future spatial patterns.Results indicated that from 2000 to 2022,China's kNDVI showed an overall increasing trend(annual average ranging from 0.29 to 0.33 with distinct spatial differentiation:52.77%of areas locating in agricultural and ecological restoration regions in the central-eastern plain)experienced vegetation improvement,whereas 2.68%of areas locating in the southeastern coastal urbanized regions and the Yangtze River Delta experience vegetation degradation.The coefficient of variation(CV)of kNDVI at 0.30–0.40(accounting for 10.61%)was significantly higher than that of NDVI(accounting for 1.80%).Climate-driven mechanisms exhibited notable library length(L)dependence.At short-term scales(L<50),vegetation-driven transpiration regulated local microclimate,with a causal strength from kNDVI to temperature of 0.04–0.15;at long-term scales(L>100),cumulative temperature effects dominated vegetation dynamics,with a causal strength from temperature to kNDVI of 0.33.Humidity and kNDVI formed bidirectional positive feedback at long-term scales(L=210,causal strength>0.70),whereas the long-term suppressive effect of VPD was particularly pronounced(causal strength=0.21)in arid areas.The optimal threshold intervals identified were temperature at–12.18℃–0.67℃,precipitation at 24.00–159.74 mm,humidity of lower than 22.00%,and VPD of<0.07,0.17–0.24,and>0.30 kPa;notably,the lower precipitation threshold(24.00 mm)represented the minimum water requirements for vegetation recovery in arid areas.Future kNDVI spatial patterns are projected to continue the trend of"southeastern optimization and northwestern delay"from 2025 to 2040:the area proportion of high kNDVI value(>0.50)will rise from 40.43%to 41.85%,concentrated in the Sichuan Basin and the southern hills;meanwhile,the proportion of low-value areas of kNDVI(0.00–0.10)in the arid northwestern areas will decline by only 1.25%,constrained by sustained temperature and VPD stress.This study provides a scientific basis for vegetation dynamic regulation and sustainable development under climate change.展开更多
Linking meteorology and air pollutants is a key challenge.The study investigated meteorological effects on PM_(2.5)concentration using the advanced convergent cross mapping method,utilizing hourly PM_(2.5)concentratio...Linking meteorology and air pollutants is a key challenge.The study investigated meteorological effects on PM_(2.5)concentration using the advanced convergent cross mapping method,utilizing hourly PM_(2.5)concentration and six meteorological factors across eight provinces and cities in Vietnam.Results demonstrated that temperature(ρ=0.30)and radiation(ρ=0.30)produced the highest effects,followed by humidity(ρ=0.28)and wind speed(ρ=0.24),while pressure(ρ=0.22)and wind direction(ρ=0.17)produced the weakest effects on PM_(2.5)concentration.Comparing theρvalues showed that temperature,wind speed,and wind direction had greater impacts on PM_(2.5)concentration during the dry season whereas radiation had a more influence during the wet season;Southern stations experienced larger meteorological effects.Temperature,humidity,pressure,and wind direction had both positive and negative influences on PM_(2.5)concentration,while radiation and wind speed mostly had negative influences.During PM_(2.5)pollution episodes,there wasmore contribution ofmeteorological effects on PM_(2.5)concentration indicated byρvalues.At contaminated levels,humidity(ρ=0.45)was the most dominant factor affecting PM_(2.5)concentration,followed by temperature(ρ=0.41)and radiation(ρ=0.40).Pollution episodes were pointed out to be more prevalent under higher humidity,higher pressure,lower temperature,lower radiation,and lower wind speed.Theρcalculation also revealed that lower temperature,lower radiation,and higher humidity greatly accelerated each other under pollution episodes,further enhancing PM_(2.5)concentration.The findings contributed to the literature on meteorology and air pollution interaction.展开更多
Reflected signals from global navigation satellite systems（GNSSs） have been widely acknowledged as an important remote sensing tool for retrieving sea surface wind speeds.The power of GNSS reflectometry（GNSS-R）sig...Reflected signals from global navigation satellite systems（GNSSs） have been widely acknowledged as an important remote sensing tool for retrieving sea surface wind speeds.The power of GNSS reflectometry（GNSS-R）signals can be mapped in delay chips and Doppler frequency space to generate delay Doppler power maps（DDMs）,whose characteristics are related to sea surface roughness and can be used to retrieve wind speeds.However,the bistatic radar cross section（BRCS）,which is strongly related to the sea surface roughness,is extensively used in radar.Therefore,a bistatic radar cross section（BRCS） map with a modified BRCS equation in a GNSS-R application is introduced.On the BRCS map,three observables are proposed to represent the sea surface roughness to establish a relationship with the sea surface wind speed.Airborne Hurricane Dennis（2005） GNSS-R data are then used.More than 16 000 BRCS maps are generated to establish GMFs of the three observables.Finally,the proposed model and classic one-dimensional delay waveform（DW） matching methods are compared,and the proposed model demonstrates a better performance for the high wind speed retrievals.展开更多
The intricate interplay among plant productivity and soil factors is a pivotal driver for sustaining the carbon sequestration capacity of coastal wetlands.Yet,it remains uncertain whether climate warming will reshape ...The intricate interplay among plant productivity and soil factors is a pivotal driver for sustaining the carbon sequestration capacity of coastal wetlands.Yet,it remains uncertain whether climate warming will reshape the cause-and-effect interactions between coastal plant productivity and soil factors.In this study,we combined a manipulative warming experiment with a convergent cross-mapping technique to quantify the causal relationships,which can be either unidirectional or bidirectional,between plants(gross primary productivity,GPP)and soil environment(e.g.soil temperature,moisture and salinity).Our findings revealed that warming amplified the interaction between GPP and soil salinity in the coastal wetland ecosystem.While soil temperature primarily drove this causal relationship in control plots,a more complex interaction emerged in warming plots:soil salinity not only directly influenced GPP but also indirectly affected it by altering soil temperature and moisture.Overall,warming increased the number of causal pathways linking GPP with soil environmental factors,such as the effect of soil salinity on GPP and the impacts of GPP on soil moisture.These findings provide experimental evidence of intensified plant-soil causality in coastal wetlands under climate warming.展开更多
Discovering causality from observed time series data is of great importance in various disciplines but also a challenging task.In recent years,cross-mapping methods have been developed to solve the non-separability or...Discovering causality from observed time series data is of great importance in various disciplines but also a challenging task.In recent years,cross-mapping methods have been developed to solve the non-separability or false-negative problem that traditional methods,e.g.,Granger causality or transfer entropy,cannot handle.However,these cross-mapping methods suffer still from nonlinearity and robustness problems on the noisy data.Here,we propose cross-mapping cardinality(CMC),which detects direct causality in a robust and nonlinear manner by quantifying the intersectional cardinality(IC)from the neighbors of the cause variable to the cross-mapping neighbors of the effect variable in the delay embedding space.We theoretically and computationally show the new causal concept“IC concavity”,i.e.concave IC curve against the neighbor size implies causality in the sense of dynamical causality,in contrast to the non-causality of linear IC curve.Thus,the causal strength is measured reliably by the IC curve,which exploits both IC continuity and information transfer of the cross-mapping function from effect to cause variables.Through verification on various simulated and real-world datasets,the accuracy and robustness of CMC are demonstrated significantly better than existing methods.In particular,we validated CMC with the pulse data from motor cortex neurons by training a rhesus monkey to conduct a flexible manual interception experiment.CMC effectively identified the causal relations between neurons while the traditional methods failed.In summary,our approach with the new concept of IC concavity provides a powerful data-driven tool for detecting dynamical causality in complex systems.展开更多
基金funded by the State Grid Science and Technology Project“Research on Key Technologies for Prediction and Early Warning of Large-Scale Offshore Wind Power Ramp Events Based on Meteorological Data Enhancement”(4000-202318098A-1-1-ZN).
文摘The development of wind power clusters has scaled in terms of both scale and coverage,and the impact of weather fluctuations on cluster output changes has become increasingly complex.Accurately identifying the forward-looking information of key wind farms in a cluster under different weather conditions is an effective method to improve the accuracy of ultrashort-term cluster power forecasting.To this end,this paper proposes a refined modeling method for ultrashort-term wind power cluster forecasting based on a convergent cross-mapping algorithm.From the perspective of causality,key meteorological forecasting factors under different cluster power fluctuation processes were screened,and refined training modeling was performed for different fluctuation processes.First,a wind process description index system and classification model at the wind power cluster level are established to realize the classification of typical fluctuation processes.A meteorological-cluster power causal relationship evaluation model based on the convergent cross-mapping algorithm is pro-posed to screen meteorological forecasting factors under multiple types of typical fluctuation processes.Finally,a refined modeling meth-od for a variety of different typical fluctuation processes is proposed,and the strong causal meteorological forecasting factors of each scenario are used as inputs to realize high-precision modeling and forecasting of ultra-short-term wind cluster power.An example anal-ysis shows that the short-term wind power cluster power forecasting accuracy of the proposed method can reach 88.55%,which is 1.57-7.32%higher than that of traditional methods.
基金Supported by the National Natural Science Foundation of China (60973162)the Natural Science Foundation of Shandong Province of China (ZR2009GM037)+2 种基金the Science and Technology Project of Shandong Province,China (2010GGX10132,2012GGX10110)the Key Natural Science Foundation of Shan-dong Province of China (Z2006G01)the Soft Science Project of Shangdong Province of China (2012RKA10009)
文摘Chaotic cryptography has been applied to image encryption;however,only the traditional low-dimensional chaotic systems has been widely analyzed or deciphered,which does not show satisfied security and efficiency.To solve this problem,a new algorithm based on cross-chaos map has been created in this article.The image pixels are scrambled under control of high-dimensional chaotic sequence,which is generated by cross chaotic map.The image pixels are substituted by ciphertext feedback algorithm.It can relate encryption required parameters with plaintext and can make a plaintext byte affect more ciphertext bytes.Proved by theoretical analysis and experimental results,the algorithm has higher complex degree and has passed SP800-22 pseudo-random number standard tests,and it has high encryption speed,high security,etc.It can be widely applied in the field of image encryption.
基金funded by the Key Science and Technology Research Projects of Henan Province(252102320172).
文摘Climate change significantly affects vegetation dynamics.Thus,understanding interactions between vegetation and climatic factors is essential for ecological management.This study used kernel Normalized Difference Vegetation Index(kNDVI)and climatic data(temperature,precipitation,humidity,and vapor pressure deficit(VPD))of China from 2000 to 2022,integrating Geographic Convergent Cross Mapping(GCCM)causal modeling,Extreme Gradient Boosting-Shapley Additive Explanations(XGBoost-SHAP)nonlinear threshold identification,and Geographical Simulation and Optimization Systems-Future Land Use Simulation(GeoSOS-FLUS)spatial prediction modeling to investigate vegetation spatiotemporal characteristics,driving mechanisms,nonlinear thresholds,and future spatial patterns.Results indicated that from 2000 to 2022,China's kNDVI showed an overall increasing trend(annual average ranging from 0.29 to 0.33 with distinct spatial differentiation:52.77%of areas locating in agricultural and ecological restoration regions in the central-eastern plain)experienced vegetation improvement,whereas 2.68%of areas locating in the southeastern coastal urbanized regions and the Yangtze River Delta experience vegetation degradation.The coefficient of variation(CV)of kNDVI at 0.30–0.40(accounting for 10.61%)was significantly higher than that of NDVI(accounting for 1.80%).Climate-driven mechanisms exhibited notable library length(L)dependence.At short-term scales(L<50),vegetation-driven transpiration regulated local microclimate,with a causal strength from kNDVI to temperature of 0.04–0.15;at long-term scales(L>100),cumulative temperature effects dominated vegetation dynamics,with a causal strength from temperature to kNDVI of 0.33.Humidity and kNDVI formed bidirectional positive feedback at long-term scales(L=210,causal strength>0.70),whereas the long-term suppressive effect of VPD was particularly pronounced(causal strength=0.21)in arid areas.The optimal threshold intervals identified were temperature at–12.18℃–0.67℃,precipitation at 24.00–159.74 mm,humidity of lower than 22.00%,and VPD of<0.07,0.17–0.24,and>0.30 kPa;notably,the lower precipitation threshold(24.00 mm)represented the minimum water requirements for vegetation recovery in arid areas.Future kNDVI spatial patterns are projected to continue the trend of"southeastern optimization and northwestern delay"from 2025 to 2040:the area proportion of high kNDVI value(>0.50)will rise from 40.43%to 41.85%,concentrated in the Sichuan Basin and the southern hills;meanwhile,the proportion of low-value areas of kNDVI(0.00–0.10)in the arid northwestern areas will decline by only 1.25%,constrained by sustained temperature and VPD stress.This study provides a scientific basis for vegetation dynamic regulation and sustainable development under climate change.
文摘Linking meteorology and air pollutants is a key challenge.The study investigated meteorological effects on PM_(2.5)concentration using the advanced convergent cross mapping method,utilizing hourly PM_(2.5)concentration and six meteorological factors across eight provinces and cities in Vietnam.Results demonstrated that temperature(ρ=0.30)and radiation(ρ=0.30)produced the highest effects,followed by humidity(ρ=0.28)and wind speed(ρ=0.24),while pressure(ρ=0.22)and wind direction(ρ=0.17)produced the weakest effects on PM_(2.5)concentration.Comparing theρvalues showed that temperature,wind speed,and wind direction had greater impacts on PM_(2.5)concentration during the dry season whereas radiation had a more influence during the wet season;Southern stations experienced larger meteorological effects.Temperature,humidity,pressure,and wind direction had both positive and negative influences on PM_(2.5)concentration,while radiation and wind speed mostly had negative influences.During PM_(2.5)pollution episodes,there wasmore contribution ofmeteorological effects on PM_(2.5)concentration indicated byρvalues.At contaminated levels,humidity(ρ=0.45)was the most dominant factor affecting PM_(2.5)concentration,followed by temperature(ρ=0.41)and radiation(ρ=0.40).Pollution episodes were pointed out to be more prevalent under higher humidity,higher pressure,lower temperature,lower radiation,and lower wind speed.Theρcalculation also revealed that lower temperature,lower radiation,and higher humidity greatly accelerated each other under pollution episodes,further enhancing PM_(2.5)concentration.The findings contributed to the literature on meteorology and air pollution interaction.
基金The National Natural Science Foundation of China under contract No.41371355the Director Fund Project of Institute of Remote Sensing and Digital Earth of CAS under contract No.Y6SJ0600CX
文摘Reflected signals from global navigation satellite systems（GNSSs） have been widely acknowledged as an important remote sensing tool for retrieving sea surface wind speeds.The power of GNSS reflectometry（GNSS-R）signals can be mapped in delay chips and Doppler frequency space to generate delay Doppler power maps（DDMs）,whose characteristics are related to sea surface roughness and can be used to retrieve wind speeds.However,the bistatic radar cross section（BRCS）,which is strongly related to the sea surface roughness,is extensively used in radar.Therefore,a bistatic radar cross section（BRCS） map with a modified BRCS equation in a GNSS-R application is introduced.On the BRCS map,three observables are proposed to represent the sea surface roughness to establish a relationship with the sea surface wind speed.Airborne Hurricane Dennis（2005） GNSS-R data are then used.More than 16 000 BRCS maps are generated to establish GMFs of the three observables.Finally,the proposed model and classic one-dimensional delay waveform（DW） matching methods are compared,and the proposed model demonstrates a better performance for the high wind speed retrievals.
基金supported by the National Key R§D Program of China(2022YFF0802103)the China Postdoctoral Program for Innovative Talents(BX20220081)+1 种基金the Natural Science Foundation of China(42071126)the Shanghai Pilot Program for Basic Research(TQ20220102).
文摘The intricate interplay among plant productivity and soil factors is a pivotal driver for sustaining the carbon sequestration capacity of coastal wetlands.Yet,it remains uncertain whether climate warming will reshape the cause-and-effect interactions between coastal plant productivity and soil factors.In this study,we combined a manipulative warming experiment with a convergent cross-mapping technique to quantify the causal relationships,which can be either unidirectional or bidirectional,between plants(gross primary productivity,GPP)and soil environment(e.g.soil temperature,moisture and salinity).Our findings revealed that warming amplified the interaction between GPP and soil salinity in the coastal wetland ecosystem.While soil temperature primarily drove this causal relationship in control plots,a more complex interaction emerged in warming plots:soil salinity not only directly influenced GPP but also indirectly affected it by altering soil temperature and moisture.Overall,warming increased the number of causal pathways linking GPP with soil environmental factors,such as the effect of soil salinity on GPP and the impacts of GPP on soil moisture.These findings provide experimental evidence of intensified plant-soil causality in coastal wetlands under climate warming.
基金supported by National Key R&D Program(2022YFA1004800,2022YFC2704600,2022YFC2704604)National Science Foundation of China(12131020,31930022,12026608)+5 种基金CAS Strategic Priority Research Program(XDB38040400,XDB32040103)Lingang Lab(LG202105-01-02)Shanghai Municipal Science and Technology Major Project(2021SHZDZX)Special Fund for Science and Technology Innovation Strategy of Guangdong Province(2021B0909050004,2021B0909060002)Major Key Project of Peng Cheng Laboratory(PCL2021A12)JST Moonshot R&D(JPMJMS2021).
文摘Discovering causality from observed time series data is of great importance in various disciplines but also a challenging task.In recent years,cross-mapping methods have been developed to solve the non-separability or false-negative problem that traditional methods,e.g.,Granger causality or transfer entropy,cannot handle.However,these cross-mapping methods suffer still from nonlinearity and robustness problems on the noisy data.Here,we propose cross-mapping cardinality(CMC),which detects direct causality in a robust and nonlinear manner by quantifying the intersectional cardinality(IC)from the neighbors of the cause variable to the cross-mapping neighbors of the effect variable in the delay embedding space.We theoretically and computationally show the new causal concept“IC concavity”,i.e.concave IC curve against the neighbor size implies causality in the sense of dynamical causality,in contrast to the non-causality of linear IC curve.Thus,the causal strength is measured reliably by the IC curve,which exploits both IC continuity and information transfer of the cross-mapping function from effect to cause variables.Through verification on various simulated and real-world datasets,the accuracy and robustness of CMC are demonstrated significantly better than existing methods.In particular,we validated CMC with the pulse data from motor cortex neurons by training a rhesus monkey to conduct a flexible manual interception experiment.CMC effectively identified the causal relations between neurons while the traditional methods failed.In summary,our approach with the new concept of IC concavity provides a powerful data-driven tool for detecting dynamical causality in complex systems.
