The widespread usage of rechargeable batteries in portable devices,electric vehicles,and energy storage systems has underscored the importance for accurately predicting their lifetimes.However,data scarcity often limi...The widespread usage of rechargeable batteries in portable devices,electric vehicles,and energy storage systems has underscored the importance for accurately predicting their lifetimes.However,data scarcity often limits the accuracy of prediction models,which is escalated by the incompletion of data induced by the issues such as sensor failures.To address these challenges,we propose a novel approach to accommodate data insufficiency through achieving external information from incomplete data samples,which are usually discarded in existing studies.In order to fully unleash the prediction power of incomplete data,we have investigated the Multiple Imputation by Chained Equations(MICE)method that diversifies the training data through exploring the potential data patterns.The experimental results demonstrate that the proposed method significantly outperforms the baselines in the most considered scenarios while reducing the prediction root mean square error(RMSE)by up to 18.9%.Furthermore,we have also observed that the penetration of incomplete data benefits the explainability of the prediction model through facilitating the feature selection.展开更多
Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently...Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.展开更多
Identifying vital nodes is one of the core issues of network science,and is crucial for epidemic prevention and control,network security maintenance,and biomedical research and development.In this paper,a new vital no...Identifying vital nodes is one of the core issues of network science,and is crucial for epidemic prevention and control,network security maintenance,and biomedical research and development.In this paper,a new vital nodes identification method,named degree and cycle ratio(DC),is proposed by integrating degree centrality(weightα)and cycle ratio(weight 1-α).The results show that the dynamic observations and weightαare nonlinear and non-monotonicity(i.e.,there exists an optimal valueα^(*)forα),and that DC performs better than a single index in most networks.According to the value ofα^(*),networks are classified into degree-dominant networks(α^(*)>0.5)and cycle-dominant networks(α^(*)<0.5).Specifically,in most degree-dominant networks(such as Chengdu-BUS,Chongqing-BUS and Beijing-BUS),degree is dominant in the identification of vital nodes,but the identification effect can be improved by adding cycle structure information to the nodes.In most cycle-dominant networks(such as Email,Wiki and Hamsterster),the cycle ratio is dominant in the identification of vital nodes,but the effect can be notably enhanced by additional node degree information.Finally,interestingly,in Lancichinetti-Fortunato-Radicchi(LFR)synthesis networks,the cycle-dominant network is observed.展开更多
Emergence refers to the existence or formation of collective behaviors in complex systems.Here,we develop a theoretical framework based on the eigen microstate theory to analyze the emerging phenomena and dynamic evol...Emergence refers to the existence or formation of collective behaviors in complex systems.Here,we develop a theoretical framework based on the eigen microstate theory to analyze the emerging phenomena and dynamic evolution of complex system.In this framework,the statistical ensemble composed of M microstates of a complex system with N agents is defined by the normalized N×M matrix A,whose columns represent microstates and order of row is consist with the time.The ensemble matrix A can be decomposed as■,where r=min(N,M),eigenvalueσIbehaves as the probability amplitude of the eigen microstate U_I so that■and U_I evolves following V_I.In a disorder complex system,there is no dominant eigenvalue and eigen microstate.When a probability amplitudeσIbecomes finite in the thermodynamic limit,there is a condensation of the eigen microstate UIin analogy to the Bose–Einstein condensation of Bose gases.This indicates the emergence of U_I and a phase transition in complex system.Our framework has been applied successfully to equilibrium threedimensional Ising model,climate system and stock markets.We anticipate that our eigen microstate method can be used to study non-equilibrium complex systems with unknown orderparameters,such as phase transitions of collective motion and tipping points in climate systems and ecosystems.展开更多
Currently,digital certificate systems based on blockchain have been extensively developed and adopted.However,most of them do not take into account the certificate quality.To evaluate the credibility of certificates i...Currently,digital certificate systems based on blockchain have been extensively developed and adopted.However,most of them do not take into account the certificate quality.To evaluate the credibility of certificates issued by educational institutions,we propose a novel blockchain-based system with credit self-adjustment(BC-CS).In BC-CS,employers can provide feedback according to the performances of their employees(i.e.,students)holding different certificates.Based on the feedback,BC-CS automatically adjusts the certificate credits by using our proposed credit self-adjustment algorithm.To verify the feasibility of our proposed system,a decentralized application prototype has been developed on an Ethereum network.Experimental results demonstrate that the proposed system can fully support multistep accreditation and automatic adjustment for certificate credit.展开更多
In recent years,mobile edge computing has attracted a considerable amount of attention from both academia and industry through its many advantages(such as low latency,computation efficiency and privacy)caused by its l...In recent years,mobile edge computing has attracted a considerable amount of attention from both academia and industry through its many advantages(such as low latency,computation efficiency and privacy)caused by its local model of providing storage and computation resources.展开更多
The key-value store can provide flexibility of data types because it does not need to specify the data types to be stored in advance and can store any types of data as the value of the key-value pair.Various types of ...The key-value store can provide flexibility of data types because it does not need to specify the data types to be stored in advance and can store any types of data as the value of the key-value pair.Various types of studies have been conducted to improve the performance of the key-value store while maintaining its flexibility.However,the research efforts storing the large-scale values such as multimedia data files(e.g.,images or videos)in the key-value store were limited.In this study,we propose a new key-value store,WR-Store++aiming to store the large-scale values stably.Specifically,it provides a new design of separating data and index by working with the built-in data structure of the Windows operating system and the file system.The utilization of the built-in data structure of the Windows operating system achieves the efficiency of the key-value store and that of the file system extends the limited space of the storage significantly.We also present chunk-based memory management and parallel processing of WR-Store++to further improve its performance in the GET operation.Through the experiments,we show that WR-Store++can store at least 32.74 times larger datasets than the existing baseline key-value store,WR-Store,which has the limitation in storing large-scale data sets.Furthermore,in terms of processing efficiency,we show that WR-Store++outperforms not only WR-Store but also the other state-ofthe-art key-value stores,LevelDB,RocksDB,and BerkeleyDB,for individual key-value operations and mixed workloads.展开更多
The purpose of this study is to present the numerical performancesand interpretations of the SEIR nonlinear system based on the Zika virusspreading by using the stochastic neural networks based intelligent computingso...The purpose of this study is to present the numerical performancesand interpretations of the SEIR nonlinear system based on the Zika virusspreading by using the stochastic neural networks based intelligent computingsolver. The epidemic form of the nonlinear system represents the four dynamicsof the patients, susceptible patients S(y), exposed patients hospitalized inhospital E(y), infected patients I(y), and recovered patients R(y), i.e., SEIRmodel. The computing numerical outcomes and performances of the systemare examined by using the artificial neural networks (ANNs) and the scaledconjugate gradient (SCG) for the training of the networks, i.e., ANNs-SCG.The correctness of the ANNs-SCG scheme is observed by comparing theproposed and reference solutions for three cases of the SEIR model to solvethe nonlinear system based on the Zika virus spreading dynamics throughthe knacks of ANNs-SCG procedure based on exhaustive experimentations.The outcomes of the ANNs-SCG algorithm are found consistently in goodagreement with standard numerical solutions with negligible errors. Moreover,the procedure’s constancy, dependability, and exactness are perceived by usingthe values of state transitions, error histogram measures, correlation, andregression analysis.展开更多
In trying to explain why Hong Kong of China ranks highest in life expectancy in the world,we review what various experts are hypothesizing,and how data science methods may be used to provide more evidence-based conclu...In trying to explain why Hong Kong of China ranks highest in life expectancy in the world,we review what various experts are hypothesizing,and how data science methods may be used to provide more evidence-based conclusions.While more data become available,we find some data analysis studies were too simplistic,while others too overwhelming in answering this challenging question.We find the approach that analyzes life expectancy related data(mortality causes and rate for different cohorts)inspiring,and use this approach to study a carefully selected set of targets for comparison.In discussing the factors that matter,we argue that it is more reasonable to try to identify a set of factors that together explain the phenomenon.展开更多
The Earth's climate operates as a complex,dynamically interconnected system,driven by both anthropogenic and natural forcings and modulated by nonlinear interactions and feedback loops.This study employs a theoret...The Earth's climate operates as a complex,dynamically interconnected system,driven by both anthropogenic and natural forcings and modulated by nonlinear interactions and feedback loops.This study employs a theoretical framework and the Eigen Microstate(EM)approach of statistical physics to examine global surface temperature variations since 1948,as revealed by a global reanalysis.We identified EMs significantly correlated with key climate phenomena such as the global monsoon system,tropical climates,and El Niño.Our analysis reveals that these EMs have increasingly influenced global surface temperature variations over recent decades,highlighting the critical roles of hemispheric differences,land-sea contrasts,and tropical climate fluctuations in a warming world.Additionally,we used model simulations from more than 10 Coupled Model Intercomparison Project Phase 6(CMIP6)under three future climate scenarios to perform a comparative analysis of the changes in each EM contribution.The results indicate that under future warming scenarios,tropical climate fluctuations will become increasingly dominant,while traditional hemispheric and monsoonal patterns may decline.This shift underscores the importance of understanding tropical dynamics and their impact on global climate from a physics-based perspective.Our study provides a new perspective on understanding and addressing global climate change,enhancing the theoretical foundation of this critical field,and yielding findings with significant practical implications for improving climate models and developing effective mitigation and adaptation strategies.展开更多
In this study,we utilize a potentially versatile Bayesian parameter approach to compute the value of the pion charge radius and quantify its uncertainty from several experimental e^(+)e^(-) datasets for the pion vecto...In this study,we utilize a potentially versatile Bayesian parameter approach to compute the value of the pion charge radius and quantify its uncertainty from several experimental e^(+)e^(-) datasets for the pion vector form factor.We employ dispersion relations to model the pion vector form factor to extract the radius.Nested model selection is used to determine the order of polynomial appearing in the form factor formulation that can be supported by the data,adapting the computation of Bayes evidence and Bayesian effective complexity based on Occam's razor.Our findings indicate that five out of six used datasets favor the nine-parameter model for radius extraction,and accordingly,we average the radii from the datasets.Despite some inconsistencies with the most updated radius values,our approach may serve as a more intuitive method of addressing parameter estimations in dispersion theory.展开更多
Lumpy skin disease(LSD)is a transboundary disease affecting cattle and has a detrimental effect on the cattle industries in numerous countries in Africa,Europe and Asia.In 2021,LSD outbreaks have been reported in almo...Lumpy skin disease(LSD)is a transboundary disease affecting cattle and has a detrimental effect on the cattle industries in numerous countries in Africa,Europe and Asia.In 2021,LSD outbreaks have been reported in almost all of Thailand's provinces.Indeed,fitting LSD occurrences using mathematical models provide important knowledge in the realm of animal disease modeling.Thus,the objective of this study is to fit the pattern of daily new LSD cases and daily cumulative LSD cases in Thailand using mathematical models.The first-and second-order models in the forms of Lorentzian,Gaussian and Pearson-type VII models are used to fit daily new LSD cases whereas Richard's growth,Boltzmann sigmoidal and Power-law growth models are utilized to fit the curve of cumulative LSD cases.Based on the root-mean-squared error(RMSE)and Akaike information criterion(AIC),results showed that both first and second orders of Pearson-type VII models and Richard's growth model(RGM)were fit to the data better than other models used in the present study.The obtained models and their parameters can be utilized to describe the LSD outbreak in Thailand.For disease preparedness purposes,we can use the first order of the Pearson-type VII model to estimate the time of maximum infected cases occurring when the growth rate of infected cases starts to slow down.Furthermore,the period when the growth rate changes at a slower rate,known as the inflection time,obtained from RGM allows us to anticipate when the pandemic has peaked and the situation has stabilized.This is the first study that utilizes mathematical methods to fit the LSD epidemics in Thailand.This study offers decision-makers and authorities with valuable information for establishing an effective disease control strategy.展开更多
Battery lifetime prediction at early cycles is crucial for researchers and manufacturers to examine product quality and promote technology development.Machine learning has been widely utilized to construct data-driven...Battery lifetime prediction at early cycles is crucial for researchers and manufacturers to examine product quality and promote technology development.Machine learning has been widely utilized to construct data-driven solutions for high-accuracy predictions.However,the internal mechanisms of batteries are sensitive to many factors,such as charging/discharging protocols,manufacturing/storage conditions,and usage patterns.These factors will induce state transitions,thereby decreasing the prediction accuracy of data-driven approaches.Transfer learning is a promising technique that overcomes this difficulty and achieves accurate predictions by jointly utilizing information from various sources.Hence,we develop two transfer learning methods,Bayesian Model Fusion and Weighted Orthogonal Matching Pursuit,to strategically combine prior knowledge with limited information from the target dataset to achieve superior prediction performance.From our results,our transfer learning methods reduce root-mean-squared error by 41%through adapting to the target domain.Furthermore,the transfer learning strategies identify the variations of impactful features across different sets of batteries and therefore disentangle the battery degradation mechanisms and the root cause of state transitions from the perspective of data mining.These findings suggest that the transfer learning strategies proposed in our work are capable of acquiring knowledge across multiple data sources for solving specialized issues.展开更多
The performance of photovoltaic(PV)systems is in-fluenced by various factors,including atmospheric conditions,geographical locations,and spatial and temporal characteristics.Consequently,the optimization of PV systems...The performance of photovoltaic(PV)systems is in-fluenced by various factors,including atmospheric conditions,geographical locations,and spatial and temporal characteristics.Consequently,the optimization of PV systems relies heavily on the global maximum power point tracking(GMPPT)methods.In this paper,we adopt virtual reality(VR)technology to visual-ize PV entities and simulate their performances.The integra-tion of VR technology introduces a novel spatial and temporal dimension to the shading pattern recognition(SPR)of PV sys-tems,thereby enhancing their descriptive capabilities.Further-more,we introduce an interactive GMPPT(IGMPPT)method based on VR technology.This method leverages interactive search techniques to narrow down search regions,thereby en-hancing the search efficiency.Experimental results demonstrate the effectiveness of the proposed IGMPPT in representing the spatial and temporal characteristics of PV systems and improv-ing the efficiency of GMPPT.展开更多
Brazilian farming influences directly the worldwide economy.Thus,fast and reliable information on areas sown with the main crops is essential for planning logistics and public or private commodity market policies.Rece...Brazilian farming influences directly the worldwide economy.Thus,fast and reliable information on areas sown with the main crops is essential for planning logistics and public or private commodity market policies.Recent farming practices have embraced remote sensing to provide fast and reliable information on commodity dynamics.Medium-to-low resolution free orbital images,such as those from Landsat 8 and Sentinel 2,have been used for crop mapping;however,satellite image processing requires high computing power,especially when monitoring vast areas.Therefore,cloud data processing has been the only feasible option to deal with a large amount of orbital data and its processing and analysis.Thus,our goal was to develop a method to map the two main crops(soybeans and corn)in Paraná,one of the major Brazilian state producers.Landsat-8,Sentinel-2,SRTM+,and field data from 2016 to 2018 were used with the Simple Non-Iterative Clustering segmentation method and the Continuous Naive Bayes classifier,to identify cropped areas.A minimum global accuracy of 90%was found for both crops.Comparison with field data showed correlations of 0.96 and agreement coefficients no lower than 0.86.This ensures mapping quality when using Sentinel and/or Landsat imagery on the GEE platform.展开更多
In this study, computer simulations are performed on three-dimensional granular systems under shear conditions. The system comprises granular particles that are confined between two rigid plates. The top plate is subj...In this study, computer simulations are performed on three-dimensional granular systems under shear conditions. The system comprises granular particles that are confined between two rigid plates. The top plate is subjected to a normal force and driven by a shearing velocity. A positive shear-rate dependence of granular friction, known as velocity-strengthening, exists between the granular and shearing plate. To understand the origin of the dependence of frictional sliding, we treat the granular system as a complex network, where granular particles are nodes and normal contact forces are weighted edges used to obtain insight into the interiors of granular matter. Community structures within granular property networks are detected under different shearing velocities in the steady state. Community parameters, such as the size of the largest cluster and average size of clusters, show significant monotonous trends in shearing velocity associated with the shear-rate dependence of granular friction. Then, we apply an instantaneous change in shearing velocity. A dramatic increase in friction is observed with a change in shearing velocity in the non-steady state. The community structures in the non-steady state are different from those in the steady state. Results indicate that the largest cluster is a key factor affecting the friction between the granular and shearing plate.展开更多
We propose the finite-size scaling of correlation functions in finite systems near their critical points.At a distance r in a ddimensional finite system of size L,the correlation function can be written as the product...We propose the finite-size scaling of correlation functions in finite systems near their critical points.At a distance r in a ddimensional finite system of size L,the correlation function can be written as the product of|r|^(-(d-2+η))and a finite-size scaling function of the variables r/L and tL^(1/ν),where t=(T-T_c)/T_c,ηis the critical exponent of correlation function,andνis the critical exponent of correlation length.The correlation function only has a sigificant directional dependence when|r|is compariable to L.We then confirm this finite-size scaling by calculating the correlation functions of the two-dimensional Ising model and the bond percolation in two-dimensional lattices using Monte Carlo simulations.We can use the finite-size scaling of the correlation function to determine the critical point and the critical exponentη.展开更多
Herein,percolation phase transitions on a two-dimensional lattice were studied using machine learning techniques.Results reveal that different phase transitions belonging to the same universality class can be identifi...Herein,percolation phase transitions on a two-dimensional lattice were studied using machine learning techniques.Results reveal that different phase transitions belonging to the same universality class can be identified using the same neural networks(NNs),whereas phase transitions of different universality classes require different NNs.Based on this finding,we proposed the universality class of machine learning for critical phenomena.Furthermore,we investigated and discussed the NNs of different universality classes.Our research contributes to machine learning by relating the NNs with the universality class.展开更多
The search for novel materials with new functionalities and applications potential is continuing to intensify.Quantum anomalous Hall(QAH)effect was recently realized in magnetic topological insulators(TIs)but only at ...The search for novel materials with new functionalities and applications potential is continuing to intensify.Quantum anomalous Hall(QAH)effect was recently realized in magnetic topological insulators(TIs)but only at extremely low temperatures.Here,based on our first-principles electronic structure calculations,we predict that chemically functionalized Ⅲ-Bi honeycombs can support large-gap QAH insulating phases.Specifically,we show that functionalized AlBi and TlBi films harbor QAH insulator phases.GaBi and InBi are identified as semimetals with non-zero Chern number.Remarkably,TlBi exhibits a robust QAH phase with a band gap as large as 466 meV in a buckled honeycomb structure functionalized on one side.Furthermore,the electronic spectrum of a functionalized TlBi nanoribbon with zigzag edge is shown to possess only one chiral edge band crossing the Fermi level within the band gap.Our results suggest that Ⅲ-Bi honeycombs would provide a new platform for developing potential spintronics applications based on the QAH effect.展开更多
文摘The widespread usage of rechargeable batteries in portable devices,electric vehicles,and energy storage systems has underscored the importance for accurately predicting their lifetimes.However,data scarcity often limits the accuracy of prediction models,which is escalated by the incompletion of data induced by the issues such as sensor failures.To address these challenges,we propose a novel approach to accommodate data insufficiency through achieving external information from incomplete data samples,which are usually discarded in existing studies.In order to fully unleash the prediction power of incomplete data,we have investigated the Multiple Imputation by Chained Equations(MICE)method that diversifies the training data through exploring the potential data patterns.The experimental results demonstrate that the proposed method significantly outperforms the baselines in the most considered scenarios while reducing the prediction root mean square error(RMSE)by up to 18.9%.Furthermore,we have also observed that the penetration of incomplete data benefits the explainability of the prediction model through facilitating the feature selection.
基金National Natural Science Foundation of China(11971211,12171388).
文摘Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.
基金Project supported by Yunnan Fundamental Research Projects(Grant No.202401AT070359)。
文摘Identifying vital nodes is one of the core issues of network science,and is crucial for epidemic prevention and control,network security maintenance,and biomedical research and development.In this paper,a new vital nodes identification method,named degree and cycle ratio(DC),is proposed by integrating degree centrality(weightα)and cycle ratio(weight 1-α).The results show that the dynamic observations and weightαare nonlinear and non-monotonicity(i.e.,there exists an optimal valueα^(*)forα),and that DC performs better than a single index in most networks.According to the value ofα^(*),networks are classified into degree-dominant networks(α^(*)>0.5)and cycle-dominant networks(α^(*)<0.5).Specifically,in most degree-dominant networks(such as Chengdu-BUS,Chongqing-BUS and Beijing-BUS),degree is dominant in the identification of vital nodes,but the identification effect can be improved by adding cycle structure information to the nodes.In most cycle-dominant networks(such as Email,Wiki and Hamsterster),the cycle ratio is dominant in the identification of vital nodes,but the effect can be notably enhanced by additional node degree information.Finally,interestingly,in Lancichinetti-Fortunato-Radicchi(LFR)synthesis networks,the cycle-dominant network is observed.
基金supported by the Key Research Program of Frontier Sciences,Chinese Academy of Sciences(Grant No.QYZD-SSW-SYS019)。
文摘Emergence refers to the existence or formation of collective behaviors in complex systems.Here,we develop a theoretical framework based on the eigen microstate theory to analyze the emerging phenomena and dynamic evolution of complex system.In this framework,the statistical ensemble composed of M microstates of a complex system with N agents is defined by the normalized N×M matrix A,whose columns represent microstates and order of row is consist with the time.The ensemble matrix A can be decomposed as■,where r=min(N,M),eigenvalueσIbehaves as the probability amplitude of the eigen microstate U_I so that■and U_I evolves following V_I.In a disorder complex system,there is no dominant eigenvalue and eigen microstate.When a probability amplitudeσIbecomes finite in the thermodynamic limit,there is a condensation of the eigen microstate UIin analogy to the Bose–Einstein condensation of Bose gases.This indicates the emergence of U_I and a phase transition in complex system.Our framework has been applied successfully to equilibrium threedimensional Ising model,climate system and stock markets.We anticipate that our eigen microstate method can be used to study non-equilibrium complex systems with unknown orderparameters,such as phase transitions of collective motion and tipping points in climate systems and ecosystems.
文摘Currently,digital certificate systems based on blockchain have been extensively developed and adopted.However,most of them do not take into account the certificate quality.To evaluate the credibility of certificates issued by educational institutions,we propose a novel blockchain-based system with credit self-adjustment(BC-CS).In BC-CS,employers can provide feedback according to the performances of their employees(i.e.,students)holding different certificates.Based on the feedback,BC-CS automatically adjusts the certificate credits by using our proposed credit self-adjustment algorithm.To verify the feasibility of our proposed system,a decentralized application prototype has been developed on an Ethereum network.Experimental results demonstrate that the proposed system can fully support multistep accreditation and automatic adjustment for certificate credit.
文摘In recent years,mobile edge computing has attracted a considerable amount of attention from both academia and industry through its many advantages(such as low latency,computation efficiency and privacy)caused by its local model of providing storage and computation resources.
文摘The key-value store can provide flexibility of data types because it does not need to specify the data types to be stored in advance and can store any types of data as the value of the key-value pair.Various types of studies have been conducted to improve the performance of the key-value store while maintaining its flexibility.However,the research efforts storing the large-scale values such as multimedia data files(e.g.,images or videos)in the key-value store were limited.In this study,we propose a new key-value store,WR-Store++aiming to store the large-scale values stably.Specifically,it provides a new design of separating data and index by working with the built-in data structure of the Windows operating system and the file system.The utilization of the built-in data structure of the Windows operating system achieves the efficiency of the key-value store and that of the file system extends the limited space of the storage significantly.We also present chunk-based memory management and parallel processing of WR-Store++to further improve its performance in the GET operation.Through the experiments,we show that WR-Store++can store at least 32.74 times larger datasets than the existing baseline key-value store,WR-Store,which has the limitation in storing large-scale data sets.Furthermore,in terms of processing efficiency,we show that WR-Store++outperforms not only WR-Store but also the other state-ofthe-art key-value stores,LevelDB,RocksDB,and BerkeleyDB,for individual key-value operations and mixed workloads.
基金support from the NSRF via the program anagement Unit for Human Resources&Institutional Development,Research and Innovation[Grant number B05F640183]Chiang Mai University.Watcharaporn Cholamjiak would like to thank National Research Council of Thailand (N42A650334)Thailand Science Research and Innovation,the University of Phayao (Grant No.FF66-UoE).
文摘The purpose of this study is to present the numerical performancesand interpretations of the SEIR nonlinear system based on the Zika virusspreading by using the stochastic neural networks based intelligent computingsolver. The epidemic form of the nonlinear system represents the four dynamicsof the patients, susceptible patients S(y), exposed patients hospitalized inhospital E(y), infected patients I(y), and recovered patients R(y), i.e., SEIRmodel. The computing numerical outcomes and performances of the systemare examined by using the artificial neural networks (ANNs) and the scaledconjugate gradient (SCG) for the training of the networks, i.e., ANNs-SCG.The correctness of the ANNs-SCG scheme is observed by comparing theproposed and reference solutions for three cases of the SEIR model to solvethe nonlinear system based on the Zika virus spreading dynamics throughthe knacks of ANNs-SCG procedure based on exhaustive experimentations.The outcomes of the ANNs-SCG algorithm are found consistently in goodagreement with standard numerical solutions with negligible errors. Moreover,the procedure’s constancy, dependability, and exactness are perceived by usingthe values of state transitions, error histogram measures, correlation, andregression analysis.
基金support of funding(No.UGC/IDS(R)11/21)from the Hong Kong SAR Government.
文摘In trying to explain why Hong Kong of China ranks highest in life expectancy in the world,we review what various experts are hypothesizing,and how data science methods may be used to provide more evidence-based conclusions.While more data become available,we find some data analysis studies were too simplistic,while others too overwhelming in answering this challenging question.We find the approach that analyzes life expectancy related data(mortality causes and rate for different cohorts)inspiring,and use this approach to study a carefully selected set of targets for comparison.In discussing the factors that matter,we argue that it is more reasonable to try to identify a set of factors that together explain the phenomenon.
基金supported by the National Natural Science Foundation of China(Grant Nos.42450183,12275020,12135003,12205025,and42461144209)the National Key Research and Development Program of China(Grant No.2023YFE0109000)supported by the Fundamental Research Funds for the Central Universities。
文摘The Earth's climate operates as a complex,dynamically interconnected system,driven by both anthropogenic and natural forcings and modulated by nonlinear interactions and feedback loops.This study employs a theoretical framework and the Eigen Microstate(EM)approach of statistical physics to examine global surface temperature variations since 1948,as revealed by a global reanalysis.We identified EMs significantly correlated with key climate phenomena such as the global monsoon system,tropical climates,and El Niño.Our analysis reveals that these EMs have increasingly influenced global surface temperature variations over recent decades,highlighting the critical roles of hemispheric differences,land-sea contrasts,and tropical climate fluctuations in a warming world.Additionally,we used model simulations from more than 10 Coupled Model Intercomparison Project Phase 6(CMIP6)under three future climate scenarios to perform a comparative analysis of the changes in each EM contribution.The results indicate that under future warming scenarios,tropical climate fluctuations will become increasingly dominant,while traditional hemispheric and monsoonal patterns may decline.This shift underscores the importance of understanding tropical dynamics and their impact on global climate from a physics-based perspective.Our study provides a new perspective on understanding and addressing global climate change,enhancing the theoretical foundation of this critical field,and yielding findings with significant practical implications for improving climate models and developing effective mitigation and adaptation strategies.
文摘In this study,we utilize a potentially versatile Bayesian parameter approach to compute the value of the pion charge radius and quantify its uncertainty from several experimental e^(+)e^(-) datasets for the pion vector form factor.We employ dispersion relations to model the pion vector form factor to extract the radius.Nested model selection is used to determine the order of polynomial appearing in the form factor formulation that can be supported by the data,adapting the computation of Bayes evidence and Bayesian effective complexity based on Occam's razor.Our findings indicate that five out of six used datasets favor the nine-parameter model for radius extraction,and accordingly,we average the radii from the datasets.Despite some inconsistencies with the most updated radius values,our approach may serve as a more intuitive method of addressing parameter estimations in dispersion theory.
文摘Lumpy skin disease(LSD)is a transboundary disease affecting cattle and has a detrimental effect on the cattle industries in numerous countries in Africa,Europe and Asia.In 2021,LSD outbreaks have been reported in almost all of Thailand's provinces.Indeed,fitting LSD occurrences using mathematical models provide important knowledge in the realm of animal disease modeling.Thus,the objective of this study is to fit the pattern of daily new LSD cases and daily cumulative LSD cases in Thailand using mathematical models.The first-and second-order models in the forms of Lorentzian,Gaussian and Pearson-type VII models are used to fit daily new LSD cases whereas Richard's growth,Boltzmann sigmoidal and Power-law growth models are utilized to fit the curve of cumulative LSD cases.Based on the root-mean-squared error(RMSE)and Akaike information criterion(AIC),results showed that both first and second orders of Pearson-type VII models and Richard's growth model(RGM)were fit to the data better than other models used in the present study.The obtained models and their parameters can be utilized to describe the LSD outbreak in Thailand.For disease preparedness purposes,we can use the first order of the Pearson-type VII model to estimate the time of maximum infected cases occurring when the growth rate of infected cases starts to slow down.Furthermore,the period when the growth rate changes at a slower rate,known as the inflection time,obtained from RGM allows us to anticipate when the pandemic has peaked and the situation has stabilized.This is the first study that utilizes mathematical methods to fit the LSD epidemics in Thailand.This study offers decision-makers and authorities with valuable information for establishing an effective disease control strategy.
基金This work is supported by the startup fund of Shanghai Jiao Tong UniversitySouthern University of Science and TechnologyS.J.H is supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory under U.S.Department of Energy contract no.DE-AC02-05CH11231.
文摘Battery lifetime prediction at early cycles is crucial for researchers and manufacturers to examine product quality and promote technology development.Machine learning has been widely utilized to construct data-driven solutions for high-accuracy predictions.However,the internal mechanisms of batteries are sensitive to many factors,such as charging/discharging protocols,manufacturing/storage conditions,and usage patterns.These factors will induce state transitions,thereby decreasing the prediction accuracy of data-driven approaches.Transfer learning is a promising technique that overcomes this difficulty and achieves accurate predictions by jointly utilizing information from various sources.Hence,we develop two transfer learning methods,Bayesian Model Fusion and Weighted Orthogonal Matching Pursuit,to strategically combine prior knowledge with limited information from the target dataset to achieve superior prediction performance.From our results,our transfer learning methods reduce root-mean-squared error by 41%through adapting to the target domain.Furthermore,the transfer learning strategies identify the variations of impactful features across different sets of batteries and therefore disentangle the battery degradation mechanisms and the root cause of state transitions from the perspective of data mining.These findings suggest that the transfer learning strategies proposed in our work are capable of acquiring knowledge across multiple data sources for solving specialized issues.
基金This research was supported by the Suzhou Science and Technology Project-Key Industrial Technology Innovation(No.SYG202122)the XJTLU Postgraduate Research Scholarship(No.PGRS1906004)+1 种基金the XJTLU AI University Research CentreJiangsu(Provincial)Data Science and Cognitive Computational Engineering Research Centre.
文摘The performance of photovoltaic(PV)systems is in-fluenced by various factors,including atmospheric conditions,geographical locations,and spatial and temporal characteristics.Consequently,the optimization of PV systems relies heavily on the global maximum power point tracking(GMPPT)methods.In this paper,we adopt virtual reality(VR)technology to visual-ize PV entities and simulate their performances.The integra-tion of VR technology introduces a novel spatial and temporal dimension to the shading pattern recognition(SPR)of PV sys-tems,thereby enhancing their descriptive capabilities.Further-more,we introduce an interactive GMPPT(IGMPPT)method based on VR technology.This method leverages interactive search techniques to narrow down search regions,thereby en-hancing the search efficiency.Experimental results demonstrate the effectiveness of the proposed IGMPPT in representing the spatial and temporal characteristics of PV systems and improv-ing the efficiency of GMPPT.
基金carried out with the support of the Higher Education Personnel Improvement Coordination–Brazil[CAPES]–Financing Code[number 001].Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior.
文摘Brazilian farming influences directly the worldwide economy.Thus,fast and reliable information on areas sown with the main crops is essential for planning logistics and public or private commodity market policies.Recent farming practices have embraced remote sensing to provide fast and reliable information on commodity dynamics.Medium-to-low resolution free orbital images,such as those from Landsat 8 and Sentinel 2,have been used for crop mapping;however,satellite image processing requires high computing power,especially when monitoring vast areas.Therefore,cloud data processing has been the only feasible option to deal with a large amount of orbital data and its processing and analysis.Thus,our goal was to develop a method to map the two main crops(soybeans and corn)in Paraná,one of the major Brazilian state producers.Landsat-8,Sentinel-2,SRTM+,and field data from 2016 to 2018 were used with the Simple Non-Iterative Clustering segmentation method and the Continuous Naive Bayes classifier,to identify cropped areas.A minimum global accuracy of 90%was found for both crops.Comparison with field data showed correlations of 0.96 and agreement coefficients no lower than 0.86.This ensures mapping quality when using Sentinel and/or Landsat imagery on the GEE platform.
基金supported by the National Natural Science Foundation of China(Grant Nos.61573173,and 11504384)the Key Research Program of Frontier Sciences,Chinese Academy Sciences(Grant No.QYZDSSW-SYS019)the postdoctoral fellowship program funded by the Kunming University of Science and Technology
文摘In this study, computer simulations are performed on three-dimensional granular systems under shear conditions. The system comprises granular particles that are confined between two rigid plates. The top plate is subjected to a normal force and driven by a shearing velocity. A positive shear-rate dependence of granular friction, known as velocity-strengthening, exists between the granular and shearing plate. To understand the origin of the dependence of frictional sliding, we treat the granular system as a complex network, where granular particles are nodes and normal contact forces are weighted edges used to obtain insight into the interiors of granular matter. Community structures within granular property networks are detected under different shearing velocities in the steady state. Community parameters, such as the size of the largest cluster and average size of clusters, show significant monotonous trends in shearing velocity associated with the shear-rate dependence of granular friction. Then, we apply an instantaneous change in shearing velocity. A dramatic increase in friction is observed with a change in shearing velocity in the non-steady state. The community structures in the non-steady state are different from those in the steady state. Results indicate that the largest cluster is a key factor affecting the friction between the granular and shearing plate.
基金supported by the Key Research Program of Frontier Sciences,Chinese Academy of Sciences(Grant No.QYZD-SSW-SYS019)received a postdoctoral fellowship funded by the KunmingUniversity of Science and Technology
文摘We propose the finite-size scaling of correlation functions in finite systems near their critical points.At a distance r in a ddimensional finite system of size L,the correlation function can be written as the product of|r|^(-(d-2+η))and a finite-size scaling function of the variables r/L and tL^(1/ν),where t=(T-T_c)/T_c,ηis the critical exponent of correlation function,andνis the critical exponent of correlation length.The correlation function only has a sigificant directional dependence when|r|is compariable to L.We then confirm this finite-size scaling by calculating the correlation functions of the two-dimensional Ising model and the bond percolation in two-dimensional lattices using Monte Carlo simulations.We can use the finite-size scaling of the correlation function to determine the critical point and the critical exponentη.
基金supported by the National Natural Science Foundation of China(Grant Nos.12135003,and 12275020)。
文摘Herein,percolation phase transitions on a two-dimensional lattice were studied using machine learning techniques.Results reveal that different phase transitions belonging to the same universality class can be identified using the same neural networks(NNs),whereas phase transitions of different universality classes require different NNs.Based on this finding,we proposed the universality class of machine learning for critical phenomena.Furthermore,we investigated and discussed the NNs of different universality classes.Our research contributes to machine learning by relating the NNs with the universality class.
基金support from the National Center for Theoretical Sciences and the Ministry of Science and Technology of Taiwan under Grants Nos.MOST-104-2112-M-110-002-MY3 and MOST-103-2112-M-110-008-MY3the support under NSYSU-NKMU JOINT RESEARCH PROJECT#105-P005 and#106-P005+3 种基金supported by the US Department of Energy(DOE),Office of Science,Basic Energy Sciences grant number DE-FG02-07ER46352(core research)benefited from Northeastern University’s Advanced Scientific Computation Center(ASCC),the NERSC supercomputing center through DOE grant number DE-AC02-05CH11231support(applications to layered materials)from the DOE EFRC:Center for the Computational Design of Functional Layered Materials(CCDM)under DE-SC0012575the Singapore National Research Foundation for support under NRF Award No.NRFNRFF2013-03.
文摘The search for novel materials with new functionalities and applications potential is continuing to intensify.Quantum anomalous Hall(QAH)effect was recently realized in magnetic topological insulators(TIs)but only at extremely low temperatures.Here,based on our first-principles electronic structure calculations,we predict that chemically functionalized Ⅲ-Bi honeycombs can support large-gap QAH insulating phases.Specifically,we show that functionalized AlBi and TlBi films harbor QAH insulator phases.GaBi and InBi are identified as semimetals with non-zero Chern number.Remarkably,TlBi exhibits a robust QAH phase with a band gap as large as 466 meV in a buckled honeycomb structure functionalized on one side.Furthermore,the electronic spectrum of a functionalized TlBi nanoribbon with zigzag edge is shown to possess only one chiral edge band crossing the Fermi level within the band gap.Our results suggest that Ⅲ-Bi honeycombs would provide a new platform for developing potential spintronics applications based on the QAH effect.
