Detecting overlapping communities in attributed networks remains a significant challenge due to the complexity of jointly modeling topological structure and node attributes,the unknown number of communities,and the ne...Detecting overlapping communities in attributed networks remains a significant challenge due to the complexity of jointly modeling topological structure and node attributes,the unknown number of communities,and the need to capture nodes with multiple memberships.To address these issues,we propose a novel framework named density peaks clustering with neutrosophic C-means.First,we construct a consensus embedding by aligning structure-based and attribute-based representations using spectral decomposition and canonical correlation analysis.Then,an improved density peaks algorithm automatically estimates the number of communities and selects initial cluster centers based on a newly designed cluster strength metric.Finally,a neutrosophic C-means algorithm refines the community assignments,modeling uncertainty and overlap explicitly.Experimental results on synthetic and real-world networks demonstrate that the proposed method achieves superior performance in terms of detection accuracy,stability,and its ability to identify overlapping structures.展开更多
Attributed graph clustering plays a vital role in uncovering hidden network structures,but it presents significant challenges.In recent years,various models have been proposed to identify meaningful clusters by integr...Attributed graph clustering plays a vital role in uncovering hidden network structures,but it presents significant challenges.In recent years,various models have been proposed to identify meaningful clusters by integrating both structural and attribute-based information.However,these models often emphasize node proximities without adequately balancing the efficiency of clustering based on both structural and attribute data.Furthermore,they tend to neglect the critical fuzzy information inherent in attributed graph clusters.To address these issues,we introduce a new framework,Markov lumpability optimization,for efficient clustering of large-scale attributed graphs.Specifically,we define a lumped Markov chain on an attribute-augmented graph and introduce a new metric,Markov lumpability,to quantify the differences between the original and lumped Markov transition probability matrices.To minimize this measure,we propose a conjugate gradient projectionbased approach that ensures the partitioning closely aligns with the intrinsic structure of fuzzy clusters through conditional optimization.Extensive experiments on both synthetic and real-world datasets demonstrate the superior performance of the proposed framework compared to existing clustering algorithms.This framework has many potential applications,including dynamic community analysis of social networks,user profiling in recommendation systems,functional module identification in biological molecular networks,and financial risk control,offering a new paradigm for mining complex patterns in high-dimensional attributed graph data.展开更多
Succession is one of the most extensively studied ecological phenomena,yet debates persist about the importance of dispersal and external factors in driving this process.We aimed to quantify the influence of these fac...Succession is one of the most extensively studied ecological phenomena,yet debates persist about the importance of dispersal and external factors in driving this process.We aimed to quantify the influence of these factors by investigating how wing-related traits evolve across succession of blowfly(Diptera:Calliphoridae)communities in South Brazil.Rat carrion was placed in both forest and grassland habitats,and the associated blowfly communities were documented throughout the decomposition process.Using morphometric analysis,we measured wing and thorax traits and assessed trait changes over succession through mixed models.Our findings revealed that carrion succession follows distinct trajectories in forest and grassland environments.Specifically,we observed that Calliphora lopesi predominantly visited carcasses during the final phase of decomposition,resulting in significant differences in species composition and wing size between habitats.In forests,wing size increased toward the later stages of succession,whereas an opposite trend was observed in grasslands.Notably,these trait patterns were only evident at the species level,indicating that intraspecific trait variation is irrelevant.Stronger dispersers tend to arrive during the later stages of succession,suggesting that dispersal has a negligible role in shaping successional dynamics.Instead,environmental differences between habitats drive trait patterns throughout succession.Our results suggest that community composition in ephemeral resources is governed by deterministic processes and that successional stages can be predicted based on blowfly wing traits.Specifically,the presence of the large-winged C.lopesi indicates late decay,while the small-winged Chrysomia albiceps and Lucilia eximia are indicative of early decay.展开更多
Attribute-Based Encryption(ABE)has emerged as a fundamental access control mechanism in data sharing,enabling data owners to define flexible access policies.A critical aspect of ABE is key revocation,which plays a piv...Attribute-Based Encryption(ABE)has emerged as a fundamental access control mechanism in data sharing,enabling data owners to define flexible access policies.A critical aspect of ABE is key revocation,which plays a pivotal role in maintaining security.However,existing key revocation mechanisms face two major challenges:(1)High overhead due to ciphertext and key updates,primarily stemming from the reliance on revocation lists during attribute revocation,which increases computation and communication costs.(2)Limited universality,as many attribute revocation mechanisms are tailored to specific ABE constructions,restricting their broader applicability.To address these challenges,we propose LUAR(Lightweight and Universal Attribute Revocation),a novel revocation mechanism that leverages Intel Software Guard Extensions(SGX)while minimizing its inherent limitations.Given SGX’s constrained memory(≈90 MB in a personal computer)and susceptibility to side-channel attacks,we carefully manage its usage to reduce reliance while mitigating potential collusion risks between cloud service providers and users.To evaluate LUAR’s lightweight and universality,we integrate it with the classic BSW07 scheme,which can be seamlessly replaced with other ABE constructions.Experimental results demonstrate that LUAR enables secure attribute revocation with low computation and communication overhead.The processing time within the SGX environment remains stable at approximately 55 ms,regardless of the complexity of access policies,ensuring no additional storage or computational burden on SGX.Compared to the Hardware-based Revocable Attribute-Based Encryption(HR-ABE)scheme(IEEE S&P 2024),LUAR incurs a slightly higher computational cost within SGX;however,the overall time from initiating a data request to obtaining plaintext is shorter.As access policies grow more complex,LUAR’s advantages become increasingly evident,showcasing its superior efficiency and broader applicability.展开更多
The variation laws of runoff and sediment load under different climate,vegetation,and human activity scenarios are significantly different.Exploring the impacts of climate change and human activities on runoff and sed...The variation laws of runoff and sediment load under different climate,vegetation,and human activity scenarios are significantly different.Exploring the impacts of climate change and human activities on runoff and sediment load dynamics can provide a profound understanding of the mechanism of runoff and sediment load variability in basins,which is crucial for the sustainable development of regional ecosystems.This study investigates the Tao River Basin(TRB)on the Tibetan Plateau,as well as the Zuli River Basin(ZRB)and Jing River Basin(JRB)on the Loess Plateau,to differentiate the impacts of climate change and human activities on runoff and sediment load dynamics.The runoff and sediment load of the three watersheds have shown a decreasing trend over the past 40 years,and combined with the DMC(Dual mass curve)method,it was found that the slope of the runoff sediment gradually tends to flatten out.After the time period was divided,the CA(Cumulative anomaly)method was used for verification,which revealed good correspondence between the two before 2000 and then gradual deviations.The power function best represents the relationship between runoff and sediment load.During the initial period,climate had a significant impact on runoff variation in the TRB and JRB,with contribution rates of-54.93%and-63.02%,respectively.In the later period,human activities became the dominant influence,contributing more than-60%of the runoff variation.In the ZRB,human activities consistently dominated runoff variation,with contribution rates of-72.72%and-55.66%during both periods.In the early stages of research,the impact of climate change on sediment load was more severe in the TRB and JRB,and human activities played a significant role in the later stages.However,in the ZRB,human activities have always been the main contributor.Based on the actual local situation,runoff and sediment load in the TRB are influenced primarily by engineering measures,and vegetation and check dams exert greater impacts on the ZRB and JRB.This study explores the attribution of water and sediment load changes in different ecological geographic regions from a comparative perspective,providing a valuable theoretical basis and reference for understanding global runoff and sediment transport changes in similar areas.展开更多
Mountain communities in Nepal are increasingly exposed to climate-induced shifts in water availability,driven by glacial retreat,altered precipitation/snowmelt regimes,and declining groundwater sources.This study pres...Mountain communities in Nepal are increasingly exposed to climate-induced shifts in water availability,driven by glacial retreat,altered precipitation/snowmelt regimes,and declining groundwater sources.This study presents an integrated framework combining hydrological source analysis with socio-demographic survey data to evaluate seasonal water contributions and communitylevel water use patterns in the Upper Marsyangdi catchment,Manang District,Nepal.Isotopic(δ^(18)O)and geochemical(silica)tracers were used in a Bayesian mixing model to quantify the seasonal contributions of glacial melt,snow,rain,and groundwater to river flow.Findings indicate that groundwater dominates pre-monsoon flow(60%-70%)while post-monsoon discharge reflects more balanced inputs from all sources.In parallel,120 household surveys were analysed using Latent Class Analysis to characterise water use across domestic,agricultural,energy,and tourism sectors.Results reveal spatial and demographic gradients in water source dependency,including gender and occupation as important predictors of water use.Respondents reported perceived increases in spring flow,alongside reductions in the availability of snow for household and tourism use and deteriorating river water quality and quantity,particularly affecting hydropower operations.Adaptation strategies include increased reliance on water storage infrastructure and source switching.The study highlights the value of applying probabilistic methods to hydrological and sociocultural data to identify vulnerable populations and inform targeted,context-sensitive adaptation strategies.The proposed framework is transferable to other high-altitude regions,offering a robust approach for assessing climate resilience through the synthesis of scientific and local knowledge systems.展开更多
The global monsoon system,encompassing the Asian-Australian,African,and American monsoons,sustains two-thirds of the world’s population by regulating water resources and agriculture.Monsoon anomalies pose severe risk...The global monsoon system,encompassing the Asian-Australian,African,and American monsoons,sustains two-thirds of the world’s population by regulating water resources and agriculture.Monsoon anomalies pose severe risks,including floods and droughts.Recent research associated with the implementation of the Global Monsoons Model Intercomparison Project under the umbrella of CMIP6 has advanced our understanding of its historical variability and driving mechanisms.Observational data reveal a 20th-century shift:increased rainfall pre-1950s,followed by aridification and partial recovery post-1980s,driven by both internal variability(e.g.,Atlantic Multidecadal Oscillation)and external forcings(greenhouse gases,aerosols),while ENSO drives interannual variability through ocean-atmosphere interactions.Future projections under greenhouse forcing suggest long-term monsoon intensification,though regional disparities and model uncertainties persist.Models indicate robust trends but struggle to quantify extremes,where thermodynamic effects(warming-induced moisture rise)uniformly boost heavy rainfall,while dynamical shifts(circulation changes)create spatial heterogeneity.Volcanic eruptions and proposed solar radiation modification(SRM)further complicate predictions:tropical eruptions suppress monsoons,whereas high-latitude events alter cross-equatorial flows,highlighting unresolved feedbacks.The emergent constraint approach is booming in terms of correcting future projections and reducing uncertainty with respect to the global monsoons.Critical challenges remain.Model biases and sparse 20th-century observational data hinder accurate attribution.The interplay between natural variability and anthropogenic forcings,along with nonlinear extreme precipitation risks under warming,demands deeper mechanistic insights.Additionally,SRM’s regional impacts and hemispheric monsoon interactions require systematic evaluation.Addressing these gaps necessitates enhanced observational networks,refined climate models,and interdisciplinary efforts to disentangle multiscale drivers,ultimately improving resilience strategies for monsoon-dependent regions.展开更多
Background:Chronic kidney disease(CKD)is common and ranks among the leading causes of mortality and morbidity.This analysis aimed to present global CKD estimates using the Global Burden of Diseases,Injuries,and Risk F...Background:Chronic kidney disease(CKD)is common and ranks among the leading causes of mortality and morbidity.This analysis aimed to present global CKD estimates using the Global Burden of Diseases,Injuries,and Risk Factors Study(GBD)2023 to inform evidence-based policies for CKD identification and treatment.Methods:This analysis focused on adults aged 20 years and older over the period 1990 to 2023,from 204 countries and territories.展开更多
Primary human hepatocytes(PHH)are used as the FDA-recognized"gold standard"for liver-related studies in vitro.The world’s first PHH group standard(T/CSCB 0008–2021,CSCB standard)was released by Chinese Soc...Primary human hepatocytes(PHH)are used as the FDA-recognized"gold standard"for liver-related studies in vitro.The world’s first PHH group standard(T/CSCB 0008–2021,CSCB standard)was released by Chinese Society for Cell Biology in 2021.In order to justify this standard,six key quality attributes of ten different batches from commercial PHHs,including cell viability,cell morphology,cell markers,albumin secretion,drug metabolism function and bile secretion,were characterized using the designated test methods in the standard.The PHHs from various batches all exhibited typical hepatocytic morphology,high cell viability,and sufficient albumin secretion;whereas,tremendous variations in cell markers,drug metabolism functions,and bile secretion were unexpectedly detected across the board.Flow cytometric assessment of hepatocyte markers revealed the percentages of ALB^(+)or HNF4A^(+)cells in six batches of PHHs,ranging from 49.4%to 98.9%and from 37.7%to 91.4%,respectively.Single cell transcriptomic analysis also revealed significant cell heterogeneity across the different batches,with the proportions of hepatocytes ranging from 69.2%to 98.9%.Considerable heterogeneity in drug metabolism functions across the batches were also found in substrate clearance rate(SCR)and metabolite formation rate(MFR)for six representative CYP450 enzymes,while the results didn’t influence current SCR attribute of CYP3A4.Metabolic capacity and purity are two independent attributes for PHH.The varied biliary excretion indexes around criteria(30%)indicated heterogeneity of PHH biliary excretion capacity.These results confirmed the robustness of most quality attributes in current CSCB standard,while highlighting the need to refine remaining parameters to enhance its practical applicability.展开更多
Dear Editor,Severe fever with thrombocytopenia syndrome(SFTS)is an emerging tick-borne infectious disease caused by a novel bunyavirus called SFTS virus(SFTSV).It was initially identified in China in 2009(Yu et al.,20...Dear Editor,Severe fever with thrombocytopenia syndrome(SFTS)is an emerging tick-borne infectious disease caused by a novel bunyavirus called SFTS virus(SFTSV).It was initially identified in China in 2009(Yu et al.,2011).Since then,the number of reported SFTS cases has rapidly increased in China,South Korea,and Japan(Li,et al.,2018;Takahashi et al.,2014;Kim et al.,2018).Sporadic SFTS cases have also been identified in several other Asian countries,such as Vietnam,Pakistan,Myanmar,and Thailand(Takahashi et al.,2014;Tran et al.,2019;Li et al.,2021).This disease is recognized as a highly lethal viral hemorrhagic fever with a mortality rate ranging from 12%to 50%(Yu et al.,2011;Li et al.,2018;Takahashi et al.,2014;Kim et al.,2013).SFTS primarily spreads to humans through bites from ticks infected with SFTSV,with the Haemaphysalis longicornis tick acting as the predominant vector for SFTSV(Zhuang et al.,2018).展开更多
Purpose:Based on real-world academic data,this study aims to use network embedding technology to mining academic relationships,and investigate the effectiveness of the proposed embedding model on academic collaborator...Purpose:Based on real-world academic data,this study aims to use network embedding technology to mining academic relationships,and investigate the effectiveness of the proposed embedding model on academic collaborator recommendation tasks.Design/methodology/approach:We propose an academic collaborator recommendation model based on attributed network embedding(ACR-ANE),which can get enhanced scholar embedding and take full advantage of the topological structure of the network and multi-type scholar attributes.The non-local neighbors for scholars are defined to capture strong relationships among scholars.A deep auto-encoder is adopted to encode the academic collaboration network structure and scholar attributes into a low-dimensional representation space.Findings:1.The proposed non-local neighbors can better describe the relationships among scholars in the real world than the first-order neighbors.2.It is important to consider the structure of the academic collaboration network and scholar attributes when recommending collaborators for scholars simultaneously.Research limitations:The designed method works for static networks,without taking account of the network dynamics.Practical implications:The designed model is embedded in academic collaboration network structure and scholarly attributes,which can be used to help scholars recommend potential collaborators.Originality/value:Experiments on two real-world scholarly datasets,Aminer and APS,show that our proposed method performs better than other baselines.展开更多
The homogeneity analysis of multi-airport system can provide important decision-making support for the route layout and cooperative operation.Existing research seldom analyzes the homogeneity of multi-airport system f...The homogeneity analysis of multi-airport system can provide important decision-making support for the route layout and cooperative operation.Existing research seldom analyzes the homogeneity of multi-airport system from the perspective of route network analysis,and the attribute information of airport nodes in the airport route network is not appropriately integrated into the airport network.In order to solve this problem,a multi-airport system homogeneity analysis method based on airport attribute network representation learning is proposed.Firstly,the route network of a multi-airport system with attribute information is constructed.If there are flights between airports,an edge is added between airports,and regional attribute information is added for each airport node.Secondly,the airport attributes and the airport network vector are represented respectively.The airport attributes and the airport network vector are embedded into the unified airport representation vector space by the network representation learning method,and then the airport vector integrating the airport attributes and the airport network characteristics is obtained.By calculating the similarity of the airport vectors,it is convenient to calculate the degree of homogeneity between airports and the homogeneity of the multi-airport system.The experimental results on the Beijing-Tianjin-Hebei multi-airport system show that,compared with other existing algorithms,the homogeneity analysis method based on attributed network representation learning can get more consistent results with the current situation of Beijing-Tianjin-Hebei multi-airport system.展开更多
Objective To estimate the lung cancer burden that may be attributable to ambient fine particulate matter （PM2.5） pollution in Guangzhou city in China from 2005 to 2013. Methods The data regarding PM2.5 exposure were...Objective To estimate the lung cancer burden that may be attributable to ambient fine particulate matter （PM2.5） pollution in Guangzhou city in China from 2005 to 2013. Methods The data regarding PM2.5 exposure were obtained from the ＆#39;Ambient air pollution exposure estimation for the Global Burden of Disease 2013＇ dataset at 0.1° ×0.1° spatial resolution. Disability-adjusted life years （DALYs） were estimated based on the information of mortality and incidence of lung cancer. Comparative risk analysis and integrated exposure-response function were used to estimate attributed disease burden. Results The population-weighted average concentration of PM2.5 was increased by 34.6% between 1990 and 2013, from 38.37 μg/m3 to 51.31 μg/m^3. The lung cancer DALYs in both men and women were increased by 36.2% from 2005 to 2013. The PM2.5 attributed lung cancer DALYs increased from 12105.0 （8181.0 for males and 3924.0 for females） in 2005 to 16489.3 （11291.7 for males and 5197.6 for females） in 2013. An average of 23.1% lung cancer burden was attributable to PM2.5 pollution in 2013. Conclusion PM2.5 has caused serious but under-appreciated public health burden in Guangzhou and the trend deteriorates. Effective strategies are needed to tackle this major public health problem.展开更多
Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many scholars.Inspired by the self-nonself disc...Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many scholars.Inspired by the self-nonself discrimination paradigmin the biological immune system,the negative representation of information indicates features such as simplicity and efficiency,which is very suitable for preserving social network privacy.Therefore,we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks,called AttNetNRI.Specifically,a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept private.Moreover,a negative database-based method is proposed to hide node attributes,so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes,which is crucial to the analysis of social networks.To evaluate the performance of the AttNetNRI,empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social networks.The experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding parts.The experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.展开更多
Software architectures shift the focus of developers from lines-of-code to coarser-grained architectural elements and their overall interconnection structure. There are, however, many features of the distributed softw...Software architectures shift the focus of developers from lines-of-code to coarser-grained architectural elements and their overall interconnection structure. There are, however, many features of the distributed software that make the developing methods of distributed software quite different from the traditional ways. Furthermore, the traditional centralized ways with fixed interfaces cannot adapt to the flexible requirements of distributed software. In this paper, the attributed grammar (AG) is extended to refine the characters of distributed software, and a distributed software architecture description language (DSADL) based on attributed grammar is introduced, and then a model of integrated environment for software architecture design is proposed. It can be demonstrated by the practice that DSADL can help the programmers to analyze and design distributed software effectively, so the efficiency of the development can be improved greatly.展开更多
Parameter estimation of the attributed scattering center(ASC) model is significant for automatic target recognition(ATR). Sparse representation based parameter estimation methods have developed rapidly. Construction o...Parameter estimation of the attributed scattering center(ASC) model is significant for automatic target recognition(ATR). Sparse representation based parameter estimation methods have developed rapidly. Construction of the separable dictionary is a key issue for sparse representation technology. A compressive time-domain dictionary(TD) for ASC model is presented. Two-dimensional frequency domain responses of the ASC are produced and transformed into the time domain. Then these time domain responses are cutoff and stacked into vectors. These vectored time-domain responses are amalgamated to form the TD. Compared with the traditional frequency-domain dictionary(FD), the TD is a matrix that is quite spare and can markedly reduce the data size of the dictionary. Based on the basic TD construction method, we present four extended TD construction methods, which are available for different applications. In the experiments, the performance of the TD, including the basic model and the extended models, has been firstly analyzed in comparison with the FD. Secondly, an example of parameter estimation from SAR synthetic aperture radar(SAR) measurements of a target collected in an anechoic room is exhibited. Finally, a sparse image reconstruction example is from two apart apertures. Experimental results demonstrate the effectiveness and efficiency of the proposed TD.展开更多
The sparse recovery algorithms formulate synthetic aperture radar （SAR） imaging problem in terms of sparse representation （SR） of a small number of strong scatters＇ positions among a much large number of potentia...The sparse recovery algorithms formulate synthetic aperture radar （SAR） imaging problem in terms of sparse representation （SR） of a small number of strong scatters＇ positions among a much large number of potential scatters＇ positions, and provide an effective approach to improve the SAR image resolution. Based on the attributed scatter center model, several experiments were performed with different practical considerations to evaluate the performance of five representative SR techniques, namely, sparse Bayesian learning （SBL）, fast Bayesian matching pursuit （FBMP）, smoothed 10 norm method （SL0）, sparse reconstruction by separable approximation （SpaRSA）, fast iterative shrinkage-thresholding algorithm （FISTA）, and the parameter settings in five SR algorithms were discussed. In different situations, the performances of these algorithms were also discussed. Through the comparison of MSE and failure rate in each algorithm simulation, FBMP and SpaRSA are found suitable for dealing with problems in the SAR imaging based on attributed scattering center model. Although the SBL is time-consuming, it always get better performance when related to failure rate and high SNR.展开更多
Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently.However,there are still two challenges.First,most of t...Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently.However,there are still two challenges.First,most of the real‐word system are multiple relations,where entities are linked by different types of relations,and each relation is a view of the graph network.Second,the rich multi‐scale information(structure‐level and feature‐level)of the graph network can be seen as self‐supervised signals,which are not fully exploited.A novel contrastive self‐supervised representation learning framework on attributed multiplex graph networks with multi‐scale(named CoLM^(2)S)information is presented in this study.It mainly contains two components:intra‐relation contrast learning and interrelation contrastive learning.Specifically,the contrastive self‐supervised representation learning framework on attributed single‐layer graph networks with multi‐scale information(CoLMS)framework with the graph convolutional network as encoder to capture the intra‐relation information with multi‐scale structure‐level and feature‐level selfsupervised signals is introduced first.The structure‐level information includes the edge structure and sub‐graph structure,and the feature‐level information represents the output of different graph convolutional layer.Second,according to the consensus assumption among inter‐relations,the CoLM^(2)S framework is proposed to jointly learn various graph relations in attributed multiplex graph network to achieve global consensus node embedding.The proposed method can fully distil the graph information.Extensive experiments on unsupervised node clustering and graph visualisation tasks demonstrate the effectiveness of our methods,and it outperforms existing competitive baselines.展开更多
Synthetic aperture radar(SAR)is able to acquire high-resolution method using the active microwave imaging method.SAR images are widely used in target recognition,classification,and surface analysis,with extracted feat...Synthetic aperture radar(SAR)is able to acquire high-resolution method using the active microwave imaging method.SAR images are widely used in target recognition,classification,and surface analysis,with extracted features.Attribute scattering center(ASC)is able to describe the image features for these tasks.However,sidelobe effects reduce the accuracy and reliability of the estimated ASC model parameters.This paper incorporates the SAR super-resolution into the ASC extraction to improve its performance.Both filter bank and subspace methods are demonstrated for preprocessing to supress the sidelobe.Based on the preprocessed data,a reinforcement based ASC method is used to get the parameters.The experimental results show that the super-resolution method can reduce noise and suppress sidelobe effect,which improve accuracy of the estimated ASC model parameters.展开更多
基金supported by the Natural Science Foundation of China(Grant No.72571150)。
文摘Detecting overlapping communities in attributed networks remains a significant challenge due to the complexity of jointly modeling topological structure and node attributes,the unknown number of communities,and the need to capture nodes with multiple memberships.To address these issues,we propose a novel framework named density peaks clustering with neutrosophic C-means.First,we construct a consensus embedding by aligning structure-based and attribute-based representations using spectral decomposition and canonical correlation analysis.Then,an improved density peaks algorithm automatically estimates the number of communities and selects initial cluster centers based on a newly designed cluster strength metric.Finally,a neutrosophic C-means algorithm refines the community assignments,modeling uncertainty and overlap explicitly.Experimental results on synthetic and real-world networks demonstrate that the proposed method achieves superior performance in terms of detection accuracy,stability,and its ability to identify overlapping structures.
基金supported by the National Natural Science Foundation of China(Grant No.72571150)Beijing Natural Science Foundation(Grant No.9182015)。
文摘Attributed graph clustering plays a vital role in uncovering hidden network structures,but it presents significant challenges.In recent years,various models have been proposed to identify meaningful clusters by integrating both structural and attribute-based information.However,these models often emphasize node proximities without adequately balancing the efficiency of clustering based on both structural and attribute data.Furthermore,they tend to neglect the critical fuzzy information inherent in attributed graph clusters.To address these issues,we introduce a new framework,Markov lumpability optimization,for efficient clustering of large-scale attributed graphs.Specifically,we define a lumped Markov chain on an attribute-augmented graph and introduce a new metric,Markov lumpability,to quantify the differences between the original and lumped Markov transition probability matrices.To minimize this measure,we propose a conjugate gradient projectionbased approach that ensures the partitioning closely aligns with the intrinsic structure of fuzzy clusters through conditional optimization.Extensive experiments on both synthetic and real-world datasets demonstrate the superior performance of the proposed framework compared to existing clustering algorithms.This framework has many potential applications,including dynamic community analysis of social networks,user profiling in recommendation systems,functional module identification in biological molecular networks,and financial risk control,offering a new paradigm for mining complex patterns in high-dimensional attributed graph data.
文摘Succession is one of the most extensively studied ecological phenomena,yet debates persist about the importance of dispersal and external factors in driving this process.We aimed to quantify the influence of these factors by investigating how wing-related traits evolve across succession of blowfly(Diptera:Calliphoridae)communities in South Brazil.Rat carrion was placed in both forest and grassland habitats,and the associated blowfly communities were documented throughout the decomposition process.Using morphometric analysis,we measured wing and thorax traits and assessed trait changes over succession through mixed models.Our findings revealed that carrion succession follows distinct trajectories in forest and grassland environments.Specifically,we observed that Calliphora lopesi predominantly visited carcasses during the final phase of decomposition,resulting in significant differences in species composition and wing size between habitats.In forests,wing size increased toward the later stages of succession,whereas an opposite trend was observed in grasslands.Notably,these trait patterns were only evident at the species level,indicating that intraspecific trait variation is irrelevant.Stronger dispersers tend to arrive during the later stages of succession,suggesting that dispersal has a negligible role in shaping successional dynamics.Instead,environmental differences between habitats drive trait patterns throughout succession.Our results suggest that community composition in ephemeral resources is governed by deterministic processes and that successional stages can be predicted based on blowfly wing traits.Specifically,the presence of the large-winged C.lopesi indicates late decay,while the small-winged Chrysomia albiceps and Lucilia eximia are indicative of early decay.
基金support from the National Key Research and Development Program of China(Grant No.2021YFF0704102)the Chongqing Education Commission Key Project of Science and Technology Research(Grant No.KJZD-K202400610)the Chongqing Natural Science Foundation General Project(Grant No.CSTB2025NSCQ-GPX1263).
文摘Attribute-Based Encryption(ABE)has emerged as a fundamental access control mechanism in data sharing,enabling data owners to define flexible access policies.A critical aspect of ABE is key revocation,which plays a pivotal role in maintaining security.However,existing key revocation mechanisms face two major challenges:(1)High overhead due to ciphertext and key updates,primarily stemming from the reliance on revocation lists during attribute revocation,which increases computation and communication costs.(2)Limited universality,as many attribute revocation mechanisms are tailored to specific ABE constructions,restricting their broader applicability.To address these challenges,we propose LUAR(Lightweight and Universal Attribute Revocation),a novel revocation mechanism that leverages Intel Software Guard Extensions(SGX)while minimizing its inherent limitations.Given SGX’s constrained memory(≈90 MB in a personal computer)and susceptibility to side-channel attacks,we carefully manage its usage to reduce reliance while mitigating potential collusion risks between cloud service providers and users.To evaluate LUAR’s lightweight and universality,we integrate it with the classic BSW07 scheme,which can be seamlessly replaced with other ABE constructions.Experimental results demonstrate that LUAR enables secure attribute revocation with low computation and communication overhead.The processing time within the SGX environment remains stable at approximately 55 ms,regardless of the complexity of access policies,ensuring no additional storage or computational burden on SGX.Compared to the Hardware-based Revocable Attribute-Based Encryption(HR-ABE)scheme(IEEE S&P 2024),LUAR incurs a slightly higher computational cost within SGX;however,the overall time from initiating a data request to obtaining plaintext is shorter.As access policies grow more complex,LUAR’s advantages become increasingly evident,showcasing its superior efficiency and broader applicability.
基金funding from the Natural Science Foundation of Shanxi Province(202403021222245,20240302121217)。
文摘The variation laws of runoff and sediment load under different climate,vegetation,and human activity scenarios are significantly different.Exploring the impacts of climate change and human activities on runoff and sediment load dynamics can provide a profound understanding of the mechanism of runoff and sediment load variability in basins,which is crucial for the sustainable development of regional ecosystems.This study investigates the Tao River Basin(TRB)on the Tibetan Plateau,as well as the Zuli River Basin(ZRB)and Jing River Basin(JRB)on the Loess Plateau,to differentiate the impacts of climate change and human activities on runoff and sediment load dynamics.The runoff and sediment load of the three watersheds have shown a decreasing trend over the past 40 years,and combined with the DMC(Dual mass curve)method,it was found that the slope of the runoff sediment gradually tends to flatten out.After the time period was divided,the CA(Cumulative anomaly)method was used for verification,which revealed good correspondence between the two before 2000 and then gradual deviations.The power function best represents the relationship between runoff and sediment load.During the initial period,climate had a significant impact on runoff variation in the TRB and JRB,with contribution rates of-54.93%and-63.02%,respectively.In the later period,human activities became the dominant influence,contributing more than-60%of the runoff variation.In the ZRB,human activities consistently dominated runoff variation,with contribution rates of-72.72%and-55.66%during both periods.In the early stages of research,the impact of climate change on sediment load was more severe in the TRB and JRB,and human activities played a significant role in the later stages.However,in the ZRB,human activities have always been the main contributor.Based on the actual local situation,runoff and sediment load in the TRB are influenced primarily by engineering measures,and vegetation and check dams exert greater impacts on the ZRB and JRB.This study explores the attribution of water and sediment load changes in different ecological geographic regions from a comparative perspective,providing a valuable theoretical basis and reference for understanding global runoff and sediment transport changes in similar areas.
基金funded by the Natural Environment Research Council’s Global Challenges Research Fund(NE/P016146/1)。
文摘Mountain communities in Nepal are increasingly exposed to climate-induced shifts in water availability,driven by glacial retreat,altered precipitation/snowmelt regimes,and declining groundwater sources.This study presents an integrated framework combining hydrological source analysis with socio-demographic survey data to evaluate seasonal water contributions and communitylevel water use patterns in the Upper Marsyangdi catchment,Manang District,Nepal.Isotopic(δ^(18)O)and geochemical(silica)tracers were used in a Bayesian mixing model to quantify the seasonal contributions of glacial melt,snow,rain,and groundwater to river flow.Findings indicate that groundwater dominates pre-monsoon flow(60%-70%)while post-monsoon discharge reflects more balanced inputs from all sources.In parallel,120 household surveys were analysed using Latent Class Analysis to characterise water use across domestic,agricultural,energy,and tourism sectors.Results reveal spatial and demographic gradients in water source dependency,including gender and occupation as important predictors of water use.Respondents reported perceived increases in spring flow,alongside reductions in the availability of snow for household and tourism use and deteriorating river water quality and quantity,particularly affecting hydropower operations.Adaptation strategies include increased reliance on water storage infrastructure and source switching.The study highlights the value of applying probabilistic methods to hydrological and sociocultural data to identify vulnerable populations and inform targeted,context-sensitive adaptation strategies.The proposed framework is transferable to other high-altitude regions,offering a robust approach for assessing climate resilience through the synthesis of scientific and local knowledge systems.
基金supported by the National Key Research and Development Program of China(Grant No.2020YFA0608904)the International Partnership Program of the Chinese Academy of Sciences(Grant Nos.060GJHZ2023079GC and 134111KYSB20160031)+1 种基金supported by the Office of Science,U.S.Department of Energy(DOE)Biological and Environmental Research as part of the Regional and Global Model Analysis program area through the Water Cycle and Climate Extremes Modeling(WACCEM)scientific focus areaoperated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830。
文摘The global monsoon system,encompassing the Asian-Australian,African,and American monsoons,sustains two-thirds of the world’s population by regulating water resources and agriculture.Monsoon anomalies pose severe risks,including floods and droughts.Recent research associated with the implementation of the Global Monsoons Model Intercomparison Project under the umbrella of CMIP6 has advanced our understanding of its historical variability and driving mechanisms.Observational data reveal a 20th-century shift:increased rainfall pre-1950s,followed by aridification and partial recovery post-1980s,driven by both internal variability(e.g.,Atlantic Multidecadal Oscillation)and external forcings(greenhouse gases,aerosols),while ENSO drives interannual variability through ocean-atmosphere interactions.Future projections under greenhouse forcing suggest long-term monsoon intensification,though regional disparities and model uncertainties persist.Models indicate robust trends but struggle to quantify extremes,where thermodynamic effects(warming-induced moisture rise)uniformly boost heavy rainfall,while dynamical shifts(circulation changes)create spatial heterogeneity.Volcanic eruptions and proposed solar radiation modification(SRM)further complicate predictions:tropical eruptions suppress monsoons,whereas high-latitude events alter cross-equatorial flows,highlighting unresolved feedbacks.The emergent constraint approach is booming in terms of correcting future projections and reducing uncertainty with respect to the global monsoons.Critical challenges remain.Model biases and sparse 20th-century observational data hinder accurate attribution.The interplay between natural variability and anthropogenic forcings,along with nonlinear extreme precipitation risks under warming,demands deeper mechanistic insights.Additionally,SRM’s regional impacts and hemispheric monsoon interactions require systematic evaluation.Addressing these gaps necessitates enhanced observational networks,refined climate models,and interdisciplinary efforts to disentangle multiscale drivers,ultimately improving resilience strategies for monsoon-dependent regions.
文摘Background:Chronic kidney disease(CKD)is common and ranks among the leading causes of mortality and morbidity.This analysis aimed to present global CKD estimates using the Global Burden of Diseases,Injuries,and Risk Factors Study(GBD)2023 to inform evidence-based policies for CKD identification and treatment.Methods:This analysis focused on adults aged 20 years and older over the period 1990 to 2023,from 204 countries and territories.
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences(XDB1150102)National Science Foundation of China(82173878 and 82172001)Shanghai Municipal Science and Technology Major Project.
文摘Primary human hepatocytes(PHH)are used as the FDA-recognized"gold standard"for liver-related studies in vitro.The world’s first PHH group standard(T/CSCB 0008–2021,CSCB standard)was released by Chinese Society for Cell Biology in 2021.In order to justify this standard,six key quality attributes of ten different batches from commercial PHHs,including cell viability,cell morphology,cell markers,albumin secretion,drug metabolism function and bile secretion,were characterized using the designated test methods in the standard.The PHHs from various batches all exhibited typical hepatocytic morphology,high cell viability,and sufficient albumin secretion;whereas,tremendous variations in cell markers,drug metabolism functions,and bile secretion were unexpectedly detected across the board.Flow cytometric assessment of hepatocyte markers revealed the percentages of ALB^(+)or HNF4A^(+)cells in six batches of PHHs,ranging from 49.4%to 98.9%and from 37.7%to 91.4%,respectively.Single cell transcriptomic analysis also revealed significant cell heterogeneity across the different batches,with the proportions of hepatocytes ranging from 69.2%to 98.9%.Considerable heterogeneity in drug metabolism functions across the batches were also found in substrate clearance rate(SCR)and metabolite formation rate(MFR)for six representative CYP450 enzymes,while the results didn’t influence current SCR attribute of CYP3A4.Metabolic capacity and purity are two independent attributes for PHH.The varied biliary excretion indexes around criteria(30%)indicated heterogeneity of PHH biliary excretion capacity.These results confirmed the robustness of most quality attributes in current CSCB standard,while highlighting the need to refine remaining parameters to enhance its practical applicability.
基金funded by the National Nature Science Foundation of China(81,825,019 and 82,330,103)performed with approval from the Ethical Committee of Beijing Institute of Microbiology and Epidemiology(AF/SC-08/02.128).
文摘Dear Editor,Severe fever with thrombocytopenia syndrome(SFTS)is an emerging tick-borne infectious disease caused by a novel bunyavirus called SFTS virus(SFTSV).It was initially identified in China in 2009(Yu et al.,2011).Since then,the number of reported SFTS cases has rapidly increased in China,South Korea,and Japan(Li,et al.,2018;Takahashi et al.,2014;Kim et al.,2018).Sporadic SFTS cases have also been identified in several other Asian countries,such as Vietnam,Pakistan,Myanmar,and Thailand(Takahashi et al.,2014;Tran et al.,2019;Li et al.,2021).This disease is recognized as a highly lethal viral hemorrhagic fever with a mortality rate ranging from 12%to 50%(Yu et al.,2011;Li et al.,2018;Takahashi et al.,2014;Kim et al.,2013).SFTS primarily spreads to humans through bites from ticks infected with SFTSV,with the Haemaphysalis longicornis tick acting as the predominant vector for SFTSV(Zhuang et al.,2018).
基金supported by National Natural Science Foundation of China(No.61603310)the Fundamental Research Funds for the Central Universities(No.XDJK2018B019).
文摘Purpose:Based on real-world academic data,this study aims to use network embedding technology to mining academic relationships,and investigate the effectiveness of the proposed embedding model on academic collaborator recommendation tasks.Design/methodology/approach:We propose an academic collaborator recommendation model based on attributed network embedding(ACR-ANE),which can get enhanced scholar embedding and take full advantage of the topological structure of the network and multi-type scholar attributes.The non-local neighbors for scholars are defined to capture strong relationships among scholars.A deep auto-encoder is adopted to encode the academic collaboration network structure and scholar attributes into a low-dimensional representation space.Findings:1.The proposed non-local neighbors can better describe the relationships among scholars in the real world than the first-order neighbors.2.It is important to consider the structure of the academic collaboration network and scholar attributes when recommending collaborators for scholars simultaneously.Research limitations:The designed method works for static networks,without taking account of the network dynamics.Practical implications:The designed model is embedded in academic collaboration network structure and scholarly attributes,which can be used to help scholars recommend potential collaborators.Originality/value:Experiments on two real-world scholarly datasets,Aminer and APS,show that our proposed method performs better than other baselines.
基金supported by the Natural Science Foundation of Tianjin(No.20JCQNJC00720)the Fundamental Research Fund for the Central Universities(No.3122021052)。
文摘The homogeneity analysis of multi-airport system can provide important decision-making support for the route layout and cooperative operation.Existing research seldom analyzes the homogeneity of multi-airport system from the perspective of route network analysis,and the attribute information of airport nodes in the airport route network is not appropriately integrated into the airport network.In order to solve this problem,a multi-airport system homogeneity analysis method based on airport attribute network representation learning is proposed.Firstly,the route network of a multi-airport system with attribute information is constructed.If there are flights between airports,an edge is added between airports,and regional attribute information is added for each airport node.Secondly,the airport attributes and the airport network vector are represented respectively.The airport attributes and the airport network vector are embedded into the unified airport representation vector space by the network representation learning method,and then the airport vector integrating the airport attributes and the airport network characteristics is obtained.By calculating the similarity of the airport vectors,it is convenient to calculate the degree of homogeneity between airports and the homogeneity of the multi-airport system.The experimental results on the Beijing-Tianjin-Hebei multi-airport system show that,compared with other existing algorithms,the homogeneity analysis method based on attributed network representation learning can get more consistent results with the current situation of Beijing-Tianjin-Hebei multi-airport system.
基金supported by the Centre for Health Statistics Information,National Health and Family Planning Commission of the People’s Republic of China
文摘Objective To estimate the lung cancer burden that may be attributable to ambient fine particulate matter （PM2.5） pollution in Guangzhou city in China from 2005 to 2013. Methods The data regarding PM2.5 exposure were obtained from the ＆#39;Ambient air pollution exposure estimation for the Global Burden of Disease 2013＇ dataset at 0.1° ×0.1° spatial resolution. Disability-adjusted life years （DALYs） were estimated based on the information of mortality and incidence of lung cancer. Comparative risk analysis and integrated exposure-response function were used to estimate attributed disease burden. Results The population-weighted average concentration of PM2.5 was increased by 34.6% between 1990 and 2013, from 38.37 μg/m3 to 51.31 μg/m^3. The lung cancer DALYs in both men and women were increased by 36.2% from 2005 to 2013. The PM2.5 attributed lung cancer DALYs increased from 12105.0 （8181.0 for males and 3924.0 for females） in 2005 to 16489.3 （11291.7 for males and 5197.6 for females） in 2013. An average of 23.1% lung cancer burden was attributable to PM2.5 pollution in 2013. Conclusion PM2.5 has caused serious but under-appreciated public health burden in Guangzhou and the trend deteriorates. Effective strategies are needed to tackle this major public health problem.
基金supported by the National Natural Science Foundation of China(Nos.62006001,62372001)the Natural Science Foundation of Chongqing City(Grant No.CSTC2021JCYJ-MSXMX0002).
文摘Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many scholars.Inspired by the self-nonself discrimination paradigmin the biological immune system,the negative representation of information indicates features such as simplicity and efficiency,which is very suitable for preserving social network privacy.Therefore,we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks,called AttNetNRI.Specifically,a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept private.Moreover,a negative database-based method is proposed to hide node attributes,so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes,which is crucial to the analysis of social networks.To evaluate the performance of the AttNetNRI,empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social networks.The experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding parts.The experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.
基金Project (No. 2000K08-G12) supported by Shaanxi Provincial Science and Technology Development Plan, China
文摘Software architectures shift the focus of developers from lines-of-code to coarser-grained architectural elements and their overall interconnection structure. There are, however, many features of the distributed software that make the developing methods of distributed software quite different from the traditional ways. Furthermore, the traditional centralized ways with fixed interfaces cannot adapt to the flexible requirements of distributed software. In this paper, the attributed grammar (AG) is extended to refine the characters of distributed software, and a distributed software architecture description language (DSADL) based on attributed grammar is introduced, and then a model of integrated environment for software architecture design is proposed. It can be demonstrated by the practice that DSADL can help the programmers to analyze and design distributed software effectively, so the efficiency of the development can be improved greatly.
基金Project(NCET-11-0866)supported by Education Ministry's new Century Excellent Talents Supporting Plan,China
文摘Parameter estimation of the attributed scattering center(ASC) model is significant for automatic target recognition(ATR). Sparse representation based parameter estimation methods have developed rapidly. Construction of the separable dictionary is a key issue for sparse representation technology. A compressive time-domain dictionary(TD) for ASC model is presented. Two-dimensional frequency domain responses of the ASC are produced and transformed into the time domain. Then these time domain responses are cutoff and stacked into vectors. These vectored time-domain responses are amalgamated to form the TD. Compared with the traditional frequency-domain dictionary(FD), the TD is a matrix that is quite spare and can markedly reduce the data size of the dictionary. Based on the basic TD construction method, we present four extended TD construction methods, which are available for different applications. In the experiments, the performance of the TD, including the basic model and the extended models, has been firstly analyzed in comparison with the FD. Secondly, an example of parameter estimation from SAR synthetic aperture radar(SAR) measurements of a target collected in an anechoic room is exhibited. Finally, a sparse image reconstruction example is from two apart apertures. Experimental results demonstrate the effectiveness and efficiency of the proposed TD.
基金Project(61171133)supported by the National Natural Science Foundation of ChinaProject(11JJ1010)supported by the Natural Science Fund for Distinguished Young Scholars of Hunan Province,ChinaProject(61101182)supported by National Natural Science Foundation for Young Scientists of China
文摘The sparse recovery algorithms formulate synthetic aperture radar （SAR） imaging problem in terms of sparse representation （SR） of a small number of strong scatters＇ positions among a much large number of potential scatters＇ positions, and provide an effective approach to improve the SAR image resolution. Based on the attributed scatter center model, several experiments were performed with different practical considerations to evaluate the performance of five representative SR techniques, namely, sparse Bayesian learning （SBL）, fast Bayesian matching pursuit （FBMP）, smoothed 10 norm method （SL0）, sparse reconstruction by separable approximation （SpaRSA）, fast iterative shrinkage-thresholding algorithm （FISTA）, and the parameter settings in five SR algorithms were discussed. In different situations, the performances of these algorithms were also discussed. Through the comparison of MSE and failure rate in each algorithm simulation, FBMP and SpaRSA are found suitable for dealing with problems in the SAR imaging based on attributed scattering center model. Although the SBL is time-consuming, it always get better performance when related to failure rate and high SNR.
基金support by the National Natural Science Foundation of China(NSFC)under grant number 61873274.
文摘Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently.However,there are still two challenges.First,most of the real‐word system are multiple relations,where entities are linked by different types of relations,and each relation is a view of the graph network.Second,the rich multi‐scale information(structure‐level and feature‐level)of the graph network can be seen as self‐supervised signals,which are not fully exploited.A novel contrastive self‐supervised representation learning framework on attributed multiplex graph networks with multi‐scale(named CoLM^(2)S)information is presented in this study.It mainly contains two components:intra‐relation contrast learning and interrelation contrastive learning.Specifically,the contrastive self‐supervised representation learning framework on attributed single‐layer graph networks with multi‐scale information(CoLMS)framework with the graph convolutional network as encoder to capture the intra‐relation information with multi‐scale structure‐level and feature‐level selfsupervised signals is introduced first.The structure‐level information includes the edge structure and sub‐graph structure,and the feature‐level information represents the output of different graph convolutional layer.Second,according to the consensus assumption among inter‐relations,the CoLM^(2)S framework is proposed to jointly learn various graph relations in attributed multiplex graph network to achieve global consensus node embedding.The proposed method can fully distil the graph information.Extensive experiments on unsupervised node clustering and graph visualisation tasks demonstrate the effectiveness of our methods,and it outperforms existing competitive baselines.
基金supported by the National Natural Foundation of China(No.62201158).
文摘Synthetic aperture radar(SAR)is able to acquire high-resolution method using the active microwave imaging method.SAR images are widely used in target recognition,classification,and surface analysis,with extracted features.Attribute scattering center(ASC)is able to describe the image features for these tasks.However,sidelobe effects reduce the accuracy and reliability of the estimated ASC model parameters.This paper incorporates the SAR super-resolution into the ASC extraction to improve its performance.Both filter bank and subspace methods are demonstrated for preprocessing to supress the sidelobe.Based on the preprocessed data,a reinforcement based ASC method is used to get the parameters.The experimental results show that the super-resolution method can reduce noise and suppress sidelobe effect,which improve accuracy of the estimated ASC model parameters.
