The spatial and temporal variation of green economic efficiency and its driving factors are of great significance for the construction of high-efficiency and low-consumption green development model and sustainable soc...The spatial and temporal variation of green economic efficiency and its driving factors are of great significance for the construction of high-efficiency and low-consumption green development model and sustainable socio-economic development.The research focused on the Yangtze River Economic Belt(YREB)and employed the miniumum distance to strong efficient frontier DEA(MinDs)model to measure the green economic efficiency of the municipalities in the region between 2008 and 2020.Then,the spatial autocorrelation model was used to analyze the evolution characteristics of its spatial pattern.Finally,Geodetector was applied to reveal the drivers and their interactions on green economic efficiency.It is found that:1)the overall green economic efficiency of the YREB from 2008 to 2020 shows a W-shaped fluctuating upward trend,green economic efficiency is greater in the downstream and smallest in the upstream;2)the spatial distribution of green economic efficiency shows clustering characteristics,with multi-core clustering based on‘city clusters-central cities'becoming more obvious over time;the High-High agglomeration type is mainly clustered in Jiangsu and Zheji-ang,while the Low-Low agglomeration type is clustered in the western Sichuan Plateau area and southwestern Yunnan;3)from input-output factors,whether it is the YREB as a whole or the upper,middle and lower reaches regions,the economic development level,labor input,and capital investment are the leading factors in the spatial-temporal evolution of green economic efficiency,with the com-prehensive influence of economic development level and pollution index being the most important interactive driving factor;4)from so-cio-economic factors,information technology drivers such as government intervention,transportation accessibility,information infra-structure,and Internet penetration are always high impact influencers and dominant interaction factors for green economic efficiency in the YREB and the three major regions in the upper,middle and lower reaches.Accordingly,the article puts forward relevant policy re-commendations in terms of formulating differentiated green transformation strategies,strengthening network leadership and informa-tion technology construction and coordinating multi-factor integrated development,which could provide useful reference for promoting synergistic green economic efficiency in the YREB.展开更多
The goal of this study was to determine the spatiotemporal characteristics of mangrove distribution and fragmentation patterns from 1988 through 2019 in Dongzhaigang.Land cover datasets were generated for Dongzhaigang...The goal of this study was to determine the spatiotemporal characteristics of mangrove distribution and fragmentation patterns from 1988 through 2019 in Dongzhaigang.Land cover datasets were generated for Dongzhaigang for multiple years via a decision tree method based on a classification and regression tree(CART)algorithm using Landsat time series images.Spatiotemporal transform and fragmentation patterns of mangrove distribution were separately assessed with a transfer matrix of land cover types and a landscape pattern index.The classification method combined with multi-band images showed good accuracy,with overall accuracy higher than 90%.Mangrove areas in 1988,1999,2009,and 2019 were 2050,1875,1818,and 1750 ha,respectively,with decreases mainly due to conversion to aquaculture ponds and farmland.A mangrove growth index(MGI)was proposed,reflecting the water-mangrove relationship,showing positive mangrove growth from 1988–2009 and negative growth from 2009–2019.Study results indicated anthropogenic factors play a leading role in the extent and scale of mangrove effects over the past 30 years.According to the analysis results,corresponding management and protection measures are proposed to provide reference for the sustainable development of Dongzhaigang Mangrove Wetland ecosystem.展开更多
For loops with UV divergences,assuming that the physical contributions of loops from UV regions are insignificant,a UV-free scheme method described by an equation is introduced to derive loop results without UV diverg...For loops with UV divergences,assuming that the physical contributions of loops from UV regions are insignificant,a UV-free scheme method described by an equation is introduced to derive loop results without UV divergences in the calculations,i.e.,a route of the analytic continuation T_(F)→T_(P)besides the traditional route∞-∞in the mathematical structure.This scheme provides a new perspective to an open question of the hierarchy problem of Higgs mass,i.e.,an alternative interpretation without fine-tuning within the standard model.展开更多
Hesitation analysis plays a crucial role in decision-making processes by capturing the intermediary position between supportive and opposing information.This study introduces a refined approach to addressing uncertain...Hesitation analysis plays a crucial role in decision-making processes by capturing the intermediary position between supportive and opposing information.This study introduces a refined approach to addressing uncertainty in decision-making,employing existing measures used in decision problems.Building on information theory,the Kullback–Leibler(KL)divergence is extended to incorporate additional insights,specifically by applying temporal data,as illustrated by time series data fromtwo datasets(e.g.,affirmative and dissent information).Cumulative hesitation provides quantifiable insights into the decision-making process.Accordingly,a modified KL divergence,which incorporates historical trends,is proposed,enabling dynamic updates using conditional probability.The efficacy of this enhanced KL divergence is validated through a case study predicting Korean election outcomes.Immediate and historical data are processed using direct hesitation calculations and accumulated temporal information.The computational example demonstrates that the proposed KL divergence yields favorable results compared to existing methods.展开更多
Transit managers can use Intelligent Transportation System technologies to access large amounts of data to monitor network status.However,the presentation of the data lacks structural information.Existing single-netwo...Transit managers can use Intelligent Transportation System technologies to access large amounts of data to monitor network status.However,the presentation of the data lacks structural information.Existing single-network description technologies are ineffective in representing the temporal and spatial characteristics simultaneously.Therefore,there is a need for complementary methods to address these deficiencies.To address these limitations,this paper proposes an approach that combines Network Snapshots and Temporal Paths for the scheduled system.A dual information network is constructed to assess the degree of operational deviation considering the planning tasks.To validate the effectiveness,discussions are conducted through a modified cosine similarity calculation on theoretical analysis,delay level description,and the ability to identify abnormal dates.Compared to some state-of-the-art methods,the proposed method achieves an average Spearman delay correlation of 0.847 and a relative distance of 3.477.Furthermore,case analyses are invested in regions of China's Mainland,Europe,and the United States,investigating both the overall and sub-regional network fluctuations.To represent the impact of network fluctuations in sub-regions,a response loss value was developed.The times that are prone to fluctuations are also discussed through the classification of time series data.The research can offer a novel approach to system monitoring,providing a research direction that utilizes individual data combined to represent macroscopic states.Our code will be released at https://github.com/daozhong/STPN.git.展开更多
The enhanced mountain-to-plain convective storms in Beijing on 22 May 2021 were simulated using the highresolution Weather Research and Forecasting model,enabling detailed analyses of convective instability characteri...The enhanced mountain-to-plain convective storms in Beijing on 22 May 2021 were simulated using the highresolution Weather Research and Forecasting model,enabling detailed analyses of convective instability characteristics and underlying causes of stability variations.Generalized potential temperature outperformed traditional potential temperature and equivalent potential temperature in capturing instability variations associated with mid-level latent heating and near-surface evaporative cooling.Local instability variance was primarily governed by potential divergence and the advection of potential instability,with these factors exhibiting out-of-phase distributions.Prior to the onset of heavy precipitation,intense downdrafts transported unstable air from higher levels into more stable regions at lower levels,increasing local near-surface instability,which contributed to the formation of heavy precipitation.During the heavy precipitation stage,vertical divergence between slantwise updrafts and downdrafts in the lowmiddle stable layers led to destabilization,supporting sustained convective development within the precipitation area.At the leading edge of the heavy precipitation,instability enhancement was primarily driven by vertical advection,and less stable air in the lower levels was transported upward,enhancing instability at higher levels.展开更多
As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limite...As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limited research in recent years on the spatial-temporal distribution and emission of its atmospheric pollutants.To address this,this study conducted mobile observations of urban roads using the Mobile-DOAS instrument from June 2021 to May 2022.The monitoring results exhibit a favourable consistent with TROPOMI satellite data and ground monitoring station data.Temporally,there were pronounced seasonal variations in air pollutants.Spatially,high concentration of HCHO and NO_(2)were closely associated with traffic congestion on roadways,while heightened SO_(2)levels were attributed to winter heating and industrial emissions.The study also revealed that with the implementation of road policies,the average vehicle speed increased by 95.4%,while the NO concentration decreased by 54.4%.In the estimation of urban NO_(x)emission flux,it was observed that in temporal terms,compared with inventory data,the emissions calculated viamobile measurements exhibitedmore distinct seasonal patterns,with the highest emission rate of 349 g/sec in winter and the lowest of 142 g/sec in summer.In spatial terms,the significant difference in emissions between the inner and outer ring roads also suggests the presence of the city’s primary NO_(x)emission sources in the area between these two rings.This study offers data support for formulating the next phase of air pollution control measures in urban areas.展开更多
While interval-valued picture fuzzy sets(IvPFSs)provide a powerful tool for modeling uncertainty and ambiguity in various fields,existing divergence measures for IvPFSs remain limited and often produce counterintuitiv...While interval-valued picture fuzzy sets(IvPFSs)provide a powerful tool for modeling uncertainty and ambiguity in various fields,existing divergence measures for IvPFSs remain limited and often produce counterintuitive results.To address these shortcomings,this paper introduces two novel divergencemeasures for IvPFSs,inspired by the Jensen-Shannon divergence.The fundamental properties of the proposed measures-non-degeneracy,symmetry,triangular inequality,and boundedness-are rigorously proven.Comparative analyses with existing measures are conducted through specific cases and numerical examples,clearly demonstrating the advantages of our approach.Furthermore,we apply the new divergence measures to develop an enhanced interval-valued picture fuzzy TOPSIS method for risk assessment in construction projects,showing the practical applicability and effectiveness of our contributions.展开更多
In the current situation of decelerating economic expansion,examining the digital economy(DE)as a novel economic model is beneficial for the local economy’s sustainable and high-quality development(HQD).We analyzed p...In the current situation of decelerating economic expansion,examining the digital economy(DE)as a novel economic model is beneficial for the local economy’s sustainable and high-quality development(HQD).We analyzed panel data from the Yellow River(YR)region from 2013 to 2021 and discovered notable spatial variances in the composite index and coupling coordination of the two systems.Specifically,the downstream region exhibited the highest coupling coordination,while the upstream region had the lowest.We identified that favorable factors such as economic development,innovation,industrial upgrading,and government intervention can bolster the coupling.Our findings provide a valuable framework for promoting DE and HQD in the YR region.展开更多
A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing values.This method achieves...A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing values.This method achieves precise adjustment of the network structure by constructing a preliminary random network model and introducing small-world network characteristics and combines L1 norm minimization regularization techniques to control model complexity and optimize the inference process of variable dependencies.In the experiment of game network reconstruction,when the success rate of the L1 norm minimization model’s existence connection reconstruction reaches 100%,the minimum data required is about 40%,while the minimum data required for a sparse Bayesian learning network is about 45%.In terms of operational efficiency,the running time for minimizing the L1 normis basically maintained at 1.0 s,while the success rate of connection reconstruction increases significantly with an increase in data volume,reaching a maximum of 13.2 s.Meanwhile,in the case of a signal-to-noise ratio of 10 dB,the L1 model achieves a 100% success rate in the reconstruction of existing connections,while the sparse Bayesian network had the highest success rate of 90% in the reconstruction of non-existent connections.In the analysis of actual cases,the maximum lift and drop track of the research method is 0.08 m.The mean square error is 5.74 cm^(2).The results indicate that this norm minimization-based method has good performance in data efficiency and model stability,effectively reducing the impact of outliers on the reconstruction results to more accurately reflect the actual situation.展开更多
Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address ...Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.展开更多
Objective:With Persicaria capitata as test materials,we compared and analyzed the chloroplast(cp)genome characteristics as well as their phylogenetic relationships and evolutionary history with related species of Pers...Objective:With Persicaria capitata as test materials,we compared and analyzed the chloroplast(cp)genome characteristics as well as their phylogenetic relationships and evolutionary history with related species of Persicaria nepalensis,Persicaria japonica,Persicaria chinensis,Persicaria filiformis,Persicaria perfoliata,Persicaria pubescens,Persicaria hnydropiper.Methods:The Illumina HiSeq high-throughput sequencing platform was used for the first time for P.capitata cp genome sequencing.NOVOPlasty and CpGAVAS2 were used for assembly and annotation,and Codon W,DnaSP,and MISA were used to conduct a series of comparative genomic analyses between the plant and seven species of the same genus.A phylogenetic tree was constructed using the maximum likelihood(ML)and neighbor-joining(NJ)methods,and divergence time was estimated using BEAST.Results:The total length of P.capitata cp genome was 158,821 bp,with a guanine and cytosine(GC)content of 38.0%,exhibiting a typical circular tetrad structure.The genome contains 127 annotated genes,including 82 protein-coding and 45 tRNA-encoding genes.The cp genome harbors simple sequence repeat(SSR)loci primarily composed of A/T.The conserved species structure of this genus is reinforced by the expansion and contraction of the inverted repeat(IR)region.The non-coding regions of the cp genomes exhibited significant differences among the genera.Six different mutation hotspots(psbK-psbI,atpI-rps2,petN-psbD,atpB-rbcL,cemA-petA,ndhI-ndhA-ycf1)were screened from the non-coding regions of genes with high nucleotide variability(pI).These hotspots were expected to define the phylogenetic species of Persicaria.Furthermore,phylogenetic analysis of Polygonaceae plants showed that P.capitata was more closely related to P.chinensis than P.nepalensis.Analysis of divergence time indicated that Polygonaceae originated in the Late Cretaceous(~180 Ma)and began to differentiate during the Middle Miocene.Persicaria differentiated~66.44 million years ago,during the Miocene.Conclusions:Our findings will serve as a scientific basis for further research on species identification and evolution,population genetics,and phylogenetic analysis of P.capitata.Further,we provide valuable information for understanding the origin and evolution of Persicaria in Polygonaceae and estimating the differentiation time of Persicaria and its population.展开更多
Uncertainty and ambiguity are pervasive in real-world intelligent systems,necessitating advanced mathematical frameworks for effective modeling and analysis.Fermatean fuzzy sets(FFSs),as a recent extension of classica...Uncertainty and ambiguity are pervasive in real-world intelligent systems,necessitating advanced mathematical frameworks for effective modeling and analysis.Fermatean fuzzy sets(FFSs),as a recent extension of classical fuzzy theory,provide enhanced flexibility for representing complex uncertainty.In this paper,we propose a unified parametric divergence operator for FFSs,which comprehensively captures the interplay among membership,nonmembership,and hesitation degrees.The proposed operator is rigorously analyzed with respect to key mathematical properties,including non-negativity,non-degeneracy,and symmetry.Notably,several well-known divergence operators,such as Jensen-Shannon divergence,Hellinger distance,andχ2-divergence,are shown to be special cases within our unified framework.Extensive experiments on pattern classification,hierarchical clustering,and multiattribute decision-making tasks demonstrate the competitive performance and stability of the proposed operator.These results confirm both the theoretical significance and practical value of our method for advanced fuzzy information processing in machine learning and intelligent decision-making.展开更多
As a practicing anatomic pathologist specialized in urologic pathology,a vast difference may be observed between what pathologists designate as neuroendocrine(or small cell)carcinoma of the prostate,and what clinician...As a practicing anatomic pathologist specialized in urologic pathology,a vast difference may be observed between what pathologists designate as neuroendocrine(or small cell)carcinoma of the prostate,and what clinicians or basic scientists define as such.展开更多
This study introduces a novel distance measure(DM)for(p,q,r)-spherical fuzzy sets((p,q,to improve decision-making in complex and uncertain environments.Many existing distance measures eitherr)-SFSs)fail to satisfy ess...This study introduces a novel distance measure(DM)for(p,q,r)-spherical fuzzy sets((p,q,to improve decision-making in complex and uncertain environments.Many existing distance measures eitherr)-SFSs)fail to satisfy essential axiomatic properties or produce unintuitive outcomes.To address these limitations,we propose a new three-dimensional divergence-based DM that ensures mathematical consistency,enhances the discrimination of information,and adheres to the axiomatic framework of distance theory.Building on this foundation,we construct a multi-criteria decision-making(MCDM)model that utilizes the proposed DM to evaluate and rank alternatives effectively.The applicability and robustness of the model are validated through a practical case study,demonstrating that it leads to more rational,consistent,and reliable decision outcomes compared to existing approaches.展开更多
Spatial-temporal traffic prediction technology is crucial for network planning,resource allocation optimizing,and user experience improving.With the development of virtual network operators,multi-operator collaboratio...Spatial-temporal traffic prediction technology is crucial for network planning,resource allocation optimizing,and user experience improving.With the development of virtual network operators,multi-operator collaborations,and edge computing,spatial-temporal traffic data has taken on a distributed nature.Consequently,noncentralized spatial-temporal traffic prediction solutions have emerged as a recent research focus.Currently,the majority of research typically adopts federated learning methods to train traffic prediction models distributed on each base station.This method reduces additional burden on communication systems.However,this method has a drawback:it cannot handle irregular traffic data.Due to unstable wireless network environments,device failures,insufficient storage resources,etc.,data missing inevitably occurs during the process of collecting traffic data.This results in the irregular nature of distributed traffic data.Yet,commonly used traffic prediction models such as Recurrent Neural Networks(RNN)and Long Short-Term Memory(LSTM)typically assume that the data is complete and regular.To address the challenge of handling irregular traffic data,this paper transforms irregular traffic prediction into problems of estimating latent variables and generating future traffic.To solve the aforementioned problems,this paper introduces split learning to design a structured distributed learning framework.The framework comprises a Global-level Spatial structure mining Model(GSM)and several Nodelevel Generative Models(NGMs).NGM and GSM represent Seq2Seq models deployed on the base station and graph neural network models deployed on the cloud or central controller.Firstly,the time embedding layer in NGM establishes the mapping relationship between irregular traffic data and regular latent temporal feature variables.Secondly,GSM collects statistical feature parameters of latent temporal feature variables from various nodes and executes graph embedding for spatial-temporal traffic data.Finally,NGM generates future traffic based on latent temporal and spatial feature variables.The introduction of the time attention mechanism enhances the framework’s capability to handle irregular traffic data.Graph attention network introduces spatially correlated base station traffic feature information into local traffic prediction,which compensates for missing information in local irregular traffic data.The proposed framework effectively addresses the distributed prediction issues of irregular traffic data.By testing on real world datasets,the proposed framework improves traffic prediction accuracy by 35%compared to other commonly used distributed traffic prediction methods.展开更多
Thornthwaite Memorial model and other statistic methods were used to calculate the climate-productivity of plants with the meteorological data from 1961 to 2007 at 9 stations distributed on Inner Mongolia desert stepp...Thornthwaite Memorial model and other statistic methods were used to calculate the climate-productivity of plants with the meteorological data from 1961 to 2007 at 9 stations distributed on Inner Mongolia desert steppe.The spatial and temporal variation characteristics of climate-productivity were analyzed by using the methods of the tendency rate of the climate trend,accumulative anomaly,and spatial difference and so on.The results showed that the climate-productivity kept linear increased trend over Inner Mongolia desert steppe in recent 47 years,but not significant.In spatial distribution,the climate-productivity reduced with the increased latitude.The climate-productivity in southwest part of Inner Mongolia desert steppe was growing while that in the southeast was reducing.The variation rate of the climate-productivity increased from the northwest part to the southeast part of Inner Mongolia desert steppe.In recent 47 years,the climate-productivity in southeast Jurh underwent the greatest decreasing extent,and the region was the sensitive area of the climate-productivity variation.展开更多
[Objective] The aim was to discuss the relationship between forest fire and meterological elements （precipitation and temprature） in each region of China.[Method] Firstly,the average precipitation and temperature in...[Objective] The aim was to discuss the relationship between forest fire and meterological elements （precipitation and temprature） in each region of China.[Method] Firstly,the average precipitation and temperature in forest area of each province in fire season were obtained based on meterological data,forest distribution data,seasonal and monthly data of forest fire in China.Secondly,the relationship among forest fire area,precipitation and temperature was discussed through temporal and correlation analysis.[Result] The changes of precipitation and temperature with time could reflect the annual variation of fire area well.Forest fire area went up with the decrease of precipitation and increase of temprature,and visa versa.Meanwhile,there existed diffirences in the relationship in various regions over time.Correlation analyses revealed that there was positive correlation between forest fire area and temperature,especailly Northwest China （R=0.367,P〈0.01）,Southwest China （R=0.327,P〈0.05）,South China （R=0.33,P〈0.05）,East China （R=0.516,P〈0.01） and Xinjiang （R=0.447,P〈0.05） with obviously positive correlation.At the same time,the correlation between forest fire area and precipitation was significantly positive in Northwest China （R=0.482,P〈0.01）,while it was significantly negaive in South China （R=-0.323,P=0.03）,but there was no significant correlation in other regions.[Conclusion] Relationships between forest fire and meteorological elements （precipitation and temprature） revealed in the study would be useful for fire provention and early warning in China.展开更多
[Objective] This paper aimed to understand the area change and distribu- tion of medium-low yield farmland, and offered basis to the improvement of mediumlow farmland and its increase of grain production in Tianjin. [...[Objective] This paper aimed to understand the area change and distribu- tion of medium-low yield farmland, and offered basis to the improvement of mediumlow farmland and its increase of grain production in Tianjin. [Method] Based on the statistical date of Tianjin and its relevant counties and districts, the yield standard was set up to classify high-yield, medium-yield and low-yield farmland in Tianjin. The author analyzed area change of medium-low yield farmland in six agricultural counties and districts （including Jixian County, Wuqing District, Baodi District, Ninghe County, Jinghai County and Dagang district of Binghai New Area） from 1980 to 2010. [Result] The results showed that the average yield of grain rose from 2 445 kg/hm^2 in 1980 to 5 130 kg/hm^2 in 2010, increasing 109.82%. The area of mediumlow yield farmland was reduced from 291 250.13 hm^2 in 1985 to 76 489.87 hm^2 in 2010, coming down 74%. In Tianjin, the area of medium-low yield farmland of 2010 accounted for 19% of the total farmland, of which the ratios of medium-low yield farmland of Jinghai County, Jixian County, Dagang district of Binghai New Area, Wuqing District, Baodi District and Ninghe County were 43.12%, 18.59%, 17.23%, 14.01%, 7.05% and 0, respectively. Low soil nutrient content, drought and water shortage, as well as soil salinization were the main yield limiting factors to mediumlow yield farmland in Tianjin in 2010. [Conclusion] The countermeasures to improve the medium-low yield farmland were proposed, involving enhancing the investment of the government, strengthening the construction of water conservancy infrastructure, further improving the soil fertility, as well as saline and alkaline land, optimizing the farming system and planting drought and salt tolerance crops, etc.展开更多
基金Under the auspices of the National Natural Science Foundation of China(No.71974070)‘CUG Scholar'Scientific Research Funds at China University of Geosciences(Wuhan)(No.2022005)。
文摘The spatial and temporal variation of green economic efficiency and its driving factors are of great significance for the construction of high-efficiency and low-consumption green development model and sustainable socio-economic development.The research focused on the Yangtze River Economic Belt(YREB)and employed the miniumum distance to strong efficient frontier DEA(MinDs)model to measure the green economic efficiency of the municipalities in the region between 2008 and 2020.Then,the spatial autocorrelation model was used to analyze the evolution characteristics of its spatial pattern.Finally,Geodetector was applied to reveal the drivers and their interactions on green economic efficiency.It is found that:1)the overall green economic efficiency of the YREB from 2008 to 2020 shows a W-shaped fluctuating upward trend,green economic efficiency is greater in the downstream and smallest in the upstream;2)the spatial distribution of green economic efficiency shows clustering characteristics,with multi-core clustering based on‘city clusters-central cities'becoming more obvious over time;the High-High agglomeration type is mainly clustered in Jiangsu and Zheji-ang,while the Low-Low agglomeration type is clustered in the western Sichuan Plateau area and southwestern Yunnan;3)from input-output factors,whether it is the YREB as a whole or the upper,middle and lower reaches regions,the economic development level,labor input,and capital investment are the leading factors in the spatial-temporal evolution of green economic efficiency,with the com-prehensive influence of economic development level and pollution index being the most important interactive driving factor;4)from so-cio-economic factors,information technology drivers such as government intervention,transportation accessibility,information infra-structure,and Internet penetration are always high impact influencers and dominant interaction factors for green economic efficiency in the YREB and the three major regions in the upper,middle and lower reaches.Accordingly,the article puts forward relevant policy re-commendations in terms of formulating differentiated green transformation strategies,strengthening network leadership and informa-tion technology construction and coordinating multi-factor integrated development,which could provide useful reference for promoting synergistic green economic efficiency in the YREB.
基金financially supported by the National Natural Science Foundation of China(Nos.U2244225 and 42020104005)the Ministry of Education of China(111 Project)the Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan)and China Geological Survey(No.DD20211391)。
文摘The goal of this study was to determine the spatiotemporal characteristics of mangrove distribution and fragmentation patterns from 1988 through 2019 in Dongzhaigang.Land cover datasets were generated for Dongzhaigang for multiple years via a decision tree method based on a classification and regression tree(CART)algorithm using Landsat time series images.Spatiotemporal transform and fragmentation patterns of mangrove distribution were separately assessed with a transfer matrix of land cover types and a landscape pattern index.The classification method combined with multi-band images showed good accuracy,with overall accuracy higher than 90%.Mangrove areas in 1988,1999,2009,and 2019 were 2050,1875,1818,and 1750 ha,respectively,with decreases mainly due to conversion to aquaculture ponds and farmland.A mangrove growth index(MGI)was proposed,reflecting the water-mangrove relationship,showing positive mangrove growth from 1988–2009 and negative growth from 2009–2019.Study results indicated anthropogenic factors play a leading role in the extent and scale of mangrove effects over the past 30 years.According to the analysis results,corresponding management and protection measures are proposed to provide reference for the sustainable development of Dongzhaigang Mangrove Wetland ecosystem.
基金supported by the open project of the theoretical physics academic exchange platform of Chongqing University。
文摘For loops with UV divergences,assuming that the physical contributions of loops from UV regions are insignificant,a UV-free scheme method described by an equation is introduced to derive loop results without UV divergences in the calculations,i.e.,a route of the analytic continuation T_(F)→T_(P)besides the traditional route∞-∞in the mathematical structure.This scheme provides a new perspective to an open question of the hierarchy problem of Higgs mass,i.e.,an alternative interpretation without fine-tuning within the standard model.
基金Uzbekistan to China International Science and Technology Innovation Cooperation:IL-8724053120-R11National Research Foundation of Korea:NRF-2025S1A5A2A01011466.
文摘Hesitation analysis plays a crucial role in decision-making processes by capturing the intermediary position between supportive and opposing information.This study introduces a refined approach to addressing uncertainty in decision-making,employing existing measures used in decision problems.Building on information theory,the Kullback–Leibler(KL)divergence is extended to incorporate additional insights,specifically by applying temporal data,as illustrated by time series data fromtwo datasets(e.g.,affirmative and dissent information).Cumulative hesitation provides quantifiable insights into the decision-making process.Accordingly,a modified KL divergence,which incorporates historical trends,is proposed,enabling dynamic updates using conditional probability.The efficacy of this enhanced KL divergence is validated through a case study predicting Korean election outcomes.Immediate and historical data are processed using direct hesitation calculations and accumulated temporal information.The computational example demonstrates that the proposed KL divergence yields favorable results compared to existing methods.
文摘Transit managers can use Intelligent Transportation System technologies to access large amounts of data to monitor network status.However,the presentation of the data lacks structural information.Existing single-network description technologies are ineffective in representing the temporal and spatial characteristics simultaneously.Therefore,there is a need for complementary methods to address these deficiencies.To address these limitations,this paper proposes an approach that combines Network Snapshots and Temporal Paths for the scheduled system.A dual information network is constructed to assess the degree of operational deviation considering the planning tasks.To validate the effectiveness,discussions are conducted through a modified cosine similarity calculation on theoretical analysis,delay level description,and the ability to identify abnormal dates.Compared to some state-of-the-art methods,the proposed method achieves an average Spearman delay correlation of 0.847 and a relative distance of 3.477.Furthermore,case analyses are invested in regions of China's Mainland,Europe,and the United States,investigating both the overall and sub-regional network fluctuations.To represent the impact of network fluctuations in sub-regions,a response loss value was developed.The times that are prone to fluctuations are also discussed through the classification of time series data.The research can offer a novel approach to system monitoring,providing a research direction that utilizes individual data combined to represent macroscopic states.Our code will be released at https://github.com/daozhong/STPN.git.
基金funded by the Beijing Municipal Science and Technology Commission [grant number Z221100005222012]the Department of Science and Technology of Hebei Province [grant number 22375404D]+2 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences [grant number XDB0760303]the National Natural Science Foundation of China [grant numbers U2233218 and 42275010]the Open Foundation of the Key Open Laboratory of Urban Meteorology,China Meteorological Administration [grant number LUM-2023-06]。
文摘The enhanced mountain-to-plain convective storms in Beijing on 22 May 2021 were simulated using the highresolution Weather Research and Forecasting model,enabling detailed analyses of convective instability characteristics and underlying causes of stability variations.Generalized potential temperature outperformed traditional potential temperature and equivalent potential temperature in capturing instability variations associated with mid-level latent heating and near-surface evaporative cooling.Local instability variance was primarily governed by potential divergence and the advection of potential instability,with these factors exhibiting out-of-phase distributions.Prior to the onset of heavy precipitation,intense downdrafts transported unstable air from higher levels into more stable regions at lower levels,increasing local near-surface instability,which contributed to the formation of heavy precipitation.During the heavy precipitation stage,vertical divergence between slantwise updrafts and downdrafts in the lowmiddle stable layers led to destabilization,supporting sustained convective development within the precipitation area.At the leading edge of the heavy precipitation,instability enhancement was primarily driven by vertical advection,and less stable air in the lower levels was transported upward,enhancing instability at higher levels.
基金supported by the National Natural Science Foundation of China(Nos.U19A2044,42105132,42030609,41975037,and 42105133)the National Key Research and Development Program of China(No.2022YFC3703502)+1 种基金the Plan for Anhui Major Provincial Science&Technology Project(No.202203a07020003)Hefei Ecological Environment Bureau Project(No.2020BFFFD01804).
文摘As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limited research in recent years on the spatial-temporal distribution and emission of its atmospheric pollutants.To address this,this study conducted mobile observations of urban roads using the Mobile-DOAS instrument from June 2021 to May 2022.The monitoring results exhibit a favourable consistent with TROPOMI satellite data and ground monitoring station data.Temporally,there were pronounced seasonal variations in air pollutants.Spatially,high concentration of HCHO and NO_(2)were closely associated with traffic congestion on roadways,while heightened SO_(2)levels were attributed to winter heating and industrial emissions.The study also revealed that with the implementation of road policies,the average vehicle speed increased by 95.4%,while the NO concentration decreased by 54.4%.In the estimation of urban NO_(x)emission flux,it was observed that in temporal terms,compared with inventory data,the emissions calculated viamobile measurements exhibitedmore distinct seasonal patterns,with the highest emission rate of 349 g/sec in winter and the lowest of 142 g/sec in summer.In spatial terms,the significant difference in emissions between the inner and outer ring roads also suggests the presence of the city’s primary NO_(x)emission sources in the area between these two rings.This study offers data support for formulating the next phase of air pollution control measures in urban areas.
基金the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Small Research Project under grant number RGP1/141/46.
文摘While interval-valued picture fuzzy sets(IvPFSs)provide a powerful tool for modeling uncertainty and ambiguity in various fields,existing divergence measures for IvPFSs remain limited and often produce counterintuitive results.To address these shortcomings,this paper introduces two novel divergencemeasures for IvPFSs,inspired by the Jensen-Shannon divergence.The fundamental properties of the proposed measures-non-degeneracy,symmetry,triangular inequality,and boundedness-are rigorously proven.Comparative analyses with existing measures are conducted through specific cases and numerical examples,clearly demonstrating the advantages of our approach.Furthermore,we apply the new divergence measures to develop an enhanced interval-valued picture fuzzy TOPSIS method for risk assessment in construction projects,showing the practical applicability and effectiveness of our contributions.
基金supported by the National Office for Philosophy and Social Sciences(grant reference 22&ZD067).
文摘In the current situation of decelerating economic expansion,examining the digital economy(DE)as a novel economic model is beneficial for the local economy’s sustainable and high-quality development(HQD).We analyzed panel data from the Yellow River(YR)region from 2013 to 2021 and discovered notable spatial variances in the composite index and coupling coordination of the two systems.Specifically,the downstream region exhibited the highest coupling coordination,while the upstream region had the lowest.We identified that favorable factors such as economic development,innovation,industrial upgrading,and government intervention can bolster the coupling.Our findings provide a valuable framework for promoting DE and HQD in the YR region.
基金supported by the Scientific and Technological Developing Scheme of Jilin Province,China(No.20240101371JC)the National Natural Science Foundation of China(No.62107008).
文摘A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing values.This method achieves precise adjustment of the network structure by constructing a preliminary random network model and introducing small-world network characteristics and combines L1 norm minimization regularization techniques to control model complexity and optimize the inference process of variable dependencies.In the experiment of game network reconstruction,when the success rate of the L1 norm minimization model’s existence connection reconstruction reaches 100%,the minimum data required is about 40%,while the minimum data required for a sparse Bayesian learning network is about 45%.In terms of operational efficiency,the running time for minimizing the L1 normis basically maintained at 1.0 s,while the success rate of connection reconstruction increases significantly with an increase in data volume,reaching a maximum of 13.2 s.Meanwhile,in the case of a signal-to-noise ratio of 10 dB,the L1 model achieves a 100% success rate in the reconstruction of existing connections,while the sparse Bayesian network had the highest success rate of 90% in the reconstruction of non-existent connections.In the analysis of actual cases,the maximum lift and drop track of the research method is 0.08 m.The mean square error is 5.74 cm^(2).The results indicate that this norm minimization-based method has good performance in data efficiency and model stability,effectively reducing the impact of outliers on the reconstruction results to more accurately reflect the actual situation.
基金supported by the National Natural Science Foundation of China(Grant Nos.62472149,62376089,62202147)Hubei Provincial Science and Technology Plan Project(2023BCB04100).
文摘Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.
基金supported by the National Natural Science Foundation of China(82060913).
文摘Objective:With Persicaria capitata as test materials,we compared and analyzed the chloroplast(cp)genome characteristics as well as their phylogenetic relationships and evolutionary history with related species of Persicaria nepalensis,Persicaria japonica,Persicaria chinensis,Persicaria filiformis,Persicaria perfoliata,Persicaria pubescens,Persicaria hnydropiper.Methods:The Illumina HiSeq high-throughput sequencing platform was used for the first time for P.capitata cp genome sequencing.NOVOPlasty and CpGAVAS2 were used for assembly and annotation,and Codon W,DnaSP,and MISA were used to conduct a series of comparative genomic analyses between the plant and seven species of the same genus.A phylogenetic tree was constructed using the maximum likelihood(ML)and neighbor-joining(NJ)methods,and divergence time was estimated using BEAST.Results:The total length of P.capitata cp genome was 158,821 bp,with a guanine and cytosine(GC)content of 38.0%,exhibiting a typical circular tetrad structure.The genome contains 127 annotated genes,including 82 protein-coding and 45 tRNA-encoding genes.The cp genome harbors simple sequence repeat(SSR)loci primarily composed of A/T.The conserved species structure of this genus is reinforced by the expansion and contraction of the inverted repeat(IR)region.The non-coding regions of the cp genomes exhibited significant differences among the genera.Six different mutation hotspots(psbK-psbI,atpI-rps2,petN-psbD,atpB-rbcL,cemA-petA,ndhI-ndhA-ycf1)were screened from the non-coding regions of genes with high nucleotide variability(pI).These hotspots were expected to define the phylogenetic species of Persicaria.Furthermore,phylogenetic analysis of Polygonaceae plants showed that P.capitata was more closely related to P.chinensis than P.nepalensis.Analysis of divergence time indicated that Polygonaceae originated in the Late Cretaceous(~180 Ma)and began to differentiate during the Middle Miocene.Persicaria differentiated~66.44 million years ago,during the Miocene.Conclusions:Our findings will serve as a scientific basis for further research on species identification and evolution,population genetics,and phylogenetic analysis of P.capitata.Further,we provide valuable information for understanding the origin and evolution of Persicaria in Polygonaceae and estimating the differentiation time of Persicaria and its population.
文摘Uncertainty and ambiguity are pervasive in real-world intelligent systems,necessitating advanced mathematical frameworks for effective modeling and analysis.Fermatean fuzzy sets(FFSs),as a recent extension of classical fuzzy theory,provide enhanced flexibility for representing complex uncertainty.In this paper,we propose a unified parametric divergence operator for FFSs,which comprehensively captures the interplay among membership,nonmembership,and hesitation degrees.The proposed operator is rigorously analyzed with respect to key mathematical properties,including non-negativity,non-degeneracy,and symmetry.Notably,several well-known divergence operators,such as Jensen-Shannon divergence,Hellinger distance,andχ2-divergence,are shown to be special cases within our unified framework.Extensive experiments on pattern classification,hierarchical clustering,and multiattribute decision-making tasks demonstrate the competitive performance and stability of the proposed operator.These results confirm both the theoretical significance and practical value of our method for advanced fuzzy information processing in machine learning and intelligent decision-making.
文摘As a practicing anatomic pathologist specialized in urologic pathology,a vast difference may be observed between what pathologists designate as neuroendocrine(or small cell)carcinoma of the prostate,and what clinicians or basic scientists define as such.
文摘This study introduces a novel distance measure(DM)for(p,q,r)-spherical fuzzy sets((p,q,to improve decision-making in complex and uncertain environments.Many existing distance measures eitherr)-SFSs)fail to satisfy essential axiomatic properties or produce unintuitive outcomes.To address these limitations,we propose a new three-dimensional divergence-based DM that ensures mathematical consistency,enhances the discrimination of information,and adheres to the axiomatic framework of distance theory.Building on this foundation,we construct a multi-criteria decision-making(MCDM)model that utilizes the proposed DM to evaluate and rank alternatives effectively.The applicability and robustness of the model are validated through a practical case study,demonstrating that it leads to more rational,consistent,and reliable decision outcomes compared to existing approaches.
基金supported by the Beijing Natural Science Foundation(Certificate Number:L234025).
文摘Spatial-temporal traffic prediction technology is crucial for network planning,resource allocation optimizing,and user experience improving.With the development of virtual network operators,multi-operator collaborations,and edge computing,spatial-temporal traffic data has taken on a distributed nature.Consequently,noncentralized spatial-temporal traffic prediction solutions have emerged as a recent research focus.Currently,the majority of research typically adopts federated learning methods to train traffic prediction models distributed on each base station.This method reduces additional burden on communication systems.However,this method has a drawback:it cannot handle irregular traffic data.Due to unstable wireless network environments,device failures,insufficient storage resources,etc.,data missing inevitably occurs during the process of collecting traffic data.This results in the irregular nature of distributed traffic data.Yet,commonly used traffic prediction models such as Recurrent Neural Networks(RNN)and Long Short-Term Memory(LSTM)typically assume that the data is complete and regular.To address the challenge of handling irregular traffic data,this paper transforms irregular traffic prediction into problems of estimating latent variables and generating future traffic.To solve the aforementioned problems,this paper introduces split learning to design a structured distributed learning framework.The framework comprises a Global-level Spatial structure mining Model(GSM)and several Nodelevel Generative Models(NGMs).NGM and GSM represent Seq2Seq models deployed on the base station and graph neural network models deployed on the cloud or central controller.Firstly,the time embedding layer in NGM establishes the mapping relationship between irregular traffic data and regular latent temporal feature variables.Secondly,GSM collects statistical feature parameters of latent temporal feature variables from various nodes and executes graph embedding for spatial-temporal traffic data.Finally,NGM generates future traffic based on latent temporal and spatial feature variables.The introduction of the time attention mechanism enhances the framework’s capability to handle irregular traffic data.Graph attention network introduces spatially correlated base station traffic feature information into local traffic prediction,which compensates for missing information in local irregular traffic data.The proposed framework effectively addresses the distributed prediction issues of irregular traffic data.By testing on real world datasets,the proposed framework improves traffic prediction accuracy by 35%compared to other commonly used distributed traffic prediction methods.
基金Supported by The Inner Mongolia Natural Science Foundation (2009ms0603)Inner Mongolia Scientific Innovation Program (nmqxkjcx200706)Special Fund for Scientific Research in Central Public Welfare Institution Fundamental(Grassland Research Institute of Chinese Academy of Agricultural Science)
文摘Thornthwaite Memorial model and other statistic methods were used to calculate the climate-productivity of plants with the meteorological data from 1961 to 2007 at 9 stations distributed on Inner Mongolia desert steppe.The spatial and temporal variation characteristics of climate-productivity were analyzed by using the methods of the tendency rate of the climate trend,accumulative anomaly,and spatial difference and so on.The results showed that the climate-productivity kept linear increased trend over Inner Mongolia desert steppe in recent 47 years,but not significant.In spatial distribution,the climate-productivity reduced with the increased latitude.The climate-productivity in southwest part of Inner Mongolia desert steppe was growing while that in the southeast was reducing.The variation rate of the climate-productivity increased from the northwest part to the southeast part of Inner Mongolia desert steppe.In recent 47 years,the climate-productivity in southeast Jurh underwent the greatest decreasing extent,and the region was the sensitive area of the climate-productivity variation.
基金Supported by National Natural Science Foundation of China(40801216/D011002)~~
文摘[Objective] The aim was to discuss the relationship between forest fire and meterological elements （precipitation and temprature） in each region of China.[Method] Firstly,the average precipitation and temperature in forest area of each province in fire season were obtained based on meterological data,forest distribution data,seasonal and monthly data of forest fire in China.Secondly,the relationship among forest fire area,precipitation and temperature was discussed through temporal and correlation analysis.[Result] The changes of precipitation and temperature with time could reflect the annual variation of fire area well.Forest fire area went up with the decrease of precipitation and increase of temprature,and visa versa.Meanwhile,there existed diffirences in the relationship in various regions over time.Correlation analyses revealed that there was positive correlation between forest fire area and temperature,especailly Northwest China （R=0.367,P〈0.01）,Southwest China （R=0.327,P〈0.05）,South China （R=0.33,P〈0.05）,East China （R=0.516,P〈0.01） and Xinjiang （R=0.447,P〈0.05） with obviously positive correlation.At the same time,the correlation between forest fire area and precipitation was significantly positive in Northwest China （R=0.482,P〈0.01）,while it was significantly negaive in South China （R=-0.323,P=0.03）,but there was no significant correlation in other regions.[Conclusion] Relationships between forest fire and meteorological elements （precipitation and temprature） revealed in the study would be useful for fire provention and early warning in China.
文摘[Objective] This paper aimed to understand the area change and distribu- tion of medium-low yield farmland, and offered basis to the improvement of mediumlow farmland and its increase of grain production in Tianjin. [Method] Based on the statistical date of Tianjin and its relevant counties and districts, the yield standard was set up to classify high-yield, medium-yield and low-yield farmland in Tianjin. The author analyzed area change of medium-low yield farmland in six agricultural counties and districts （including Jixian County, Wuqing District, Baodi District, Ninghe County, Jinghai County and Dagang district of Binghai New Area） from 1980 to 2010. [Result] The results showed that the average yield of grain rose from 2 445 kg/hm^2 in 1980 to 5 130 kg/hm^2 in 2010, increasing 109.82%. The area of mediumlow yield farmland was reduced from 291 250.13 hm^2 in 1985 to 76 489.87 hm^2 in 2010, coming down 74%. In Tianjin, the area of medium-low yield farmland of 2010 accounted for 19% of the total farmland, of which the ratios of medium-low yield farmland of Jinghai County, Jixian County, Dagang district of Binghai New Area, Wuqing District, Baodi District and Ninghe County were 43.12%, 18.59%, 17.23%, 14.01%, 7.05% and 0, respectively. Low soil nutrient content, drought and water shortage, as well as soil salinization were the main yield limiting factors to mediumlow yield farmland in Tianjin in 2010. [Conclusion] The countermeasures to improve the medium-low yield farmland were proposed, involving enhancing the investment of the government, strengthening the construction of water conservancy infrastructure, further improving the soil fertility, as well as saline and alkaline land, optimizing the farming system and planting drought and salt tolerance crops, etc.
