Predicting the currency exchange rate is crucial for financial agents,risk managers,and policymakers.Traditional approaches use publicly announced news on macroeconomic and financial variables as predictors of currenc...Predicting the currency exchange rate is crucial for financial agents,risk managers,and policymakers.Traditional approaches use publicly announced news on macroeconomic and financial variables as predictors of currency exchange.However,the rise of social media may have changed the source of information.For instance,tweets can help investors make informed decisions about the foreign exchange(FX)market by reflecting market sentiment and opinion.From another aspect,changes in currency exchange may incite agents to post tweets.Are tweets good predictors of currency exchange?Is the relationship between tweets and currency exchange bidirectional?We investigate the comovement/causality between the number of#dolar(“enflasyon”resp.)tweets and USDTRY currency exchange using wavelet coherence and transfer entropy(TE)to answer these questions.Wavelet coherence allows us to determine the relationship between the number of tweets and the USDTRY rate by considering the time–frequency domain.TE enables us to quantify the net information flow between the number of tweets and USDTRY.Data from October 2020 to March 2022 were used.The obtained results remain robust regardless of the frequency of retained data(daily or hourly)and the methods used(wavelet or TE).Based on our results,USDTRY is correlated with the number of#dolar tweets(#inflation)mainly in the short run and a few times in the medium run.These relationships change through time and frequency(wavelet analysis results).However,the results from TE indicate a bidirectional relationship between the#dolar(#inflation)tweets number and the USDTRY exchange rate.The influence of the exchange rate on the number of tweets is highly pronounced.Financial agents,risk managers,policymakers,and investors should then pay moderate attention to the number of#dolar(#inflation)tweets in trading/forecasting the USD–TRY exchange rate.展开更多
This paper investigates China's coal price volatility spreaders(CPVSs)from the supply side to locate the volatility source since coal price volatility may destabilize many downstream products'prices or even br...This paper investigates China's coal price volatility spreaders(CPVSs)from the supply side to locate the volatility source since coal price volatility may destabilize many downstream products'prices or even bring uncertainties to macroeconomic output.Especially in the carbon neutrality context,China's coal market is being reconstructed and responding to imbalances between supply and demand;identifying the CPVSs helps alleviate rising market instability and prevent energy-induced system risk.To achieve this objective,we explore causalities among 938 weekly coal prices reported by different coal-producing areas of China from 2006.9.4 to 2021.7.12 using the transfer entropy method.Then,coal price volatility influence is quantified to identify the CPVSs by conjointly using complex network theory and a rank aggregation method.The validity test demonstrates that the proposed hybrid method efficiently identifies the CPVSs as it correlates to many price determinants,e.g.,electricity and coal consumption and generation.The empirical results show that causalities among coal prices changed dramatically in 2016,2018,and 2020,affected by coal decapacity and carbon neutrality policies.Before 2018,coal-producing provinces with strong demand for coal and electricity,e.g.,Jiangxi,Chongqing,and Sichuan,were CPVSs;after 2019,those with comparative advantages in coal supply,e.g.,Gansu and Ningxia,were CPVSs.Overall,the coal market is unstable and sensitive to energy policy and external shocks.Policymakers and market participants are recommended to monitor and manage the CPVSs to improve energy security,avoid policy-induced instability and prevent risks caused by coal price fluctuations.展开更多
In this paper, we use symbolic transfer entropy to study the coupling strength between premature signals. Numerical experiments show that three types of signal couplings are in the same direction. Among them, normal s...In this paper, we use symbolic transfer entropy to study the coupling strength between premature signals. Numerical experiments show that three types of signal couplings are in the same direction. Among them, normal signal coupling is the strongest, followed by that of premature ventricular contractions, and that of atrial premature beats is the weakest. The T test shows that the entropies of the three signals are distinct. Symbolic transfer entropy requires less data, can distinguish the three types of signals and has very good computational efficiency.展开更多
Correlations between two time series,including the linear Pearson correlation and the nonlinear transfer entropy,have attracted significant attention.In this work,we studied the correlations between multiple stock dat...Correlations between two time series,including the linear Pearson correlation and the nonlinear transfer entropy,have attracted significant attention.In this work,we studied the correlations between multiple stock data with the introduction of a time delay and a rolling window.In most cases,the Pearson correlation and transfer entropy share the same tendency,where a higher correlation provides more information for predicting future trends from one stock to another,but a lower correlation provides less.Considering the computational complexity of the transfer entropy and the simplicity of the Pearson correlation,using the linear correlation with time delays and a rolling window is a robust and simple method to quantify the mutual information between stocks.Predictions made by the long short-term memory method with mutual information outperform those made only with selfinformation when there are high correlations between two stocks.展开更多
Mesozooplankton are critical components of marine ecosystems,acting as key intermediaries between primary producers and higher trophic levels by grazing on phytoplankton and influencing fish populations.They play pivo...Mesozooplankton are critical components of marine ecosystems,acting as key intermediaries between primary producers and higher trophic levels by grazing on phytoplankton and influencing fish populations.They play pivotal roles in the pelagic food web and export production,affecting the biogeochemical cycling of carbon and nutrients.Therefore,accurately modeling and visualizing mesozooplankton community dynamics is essential for understanding marine ecosystem patterns and informing effective management strategies.However,modeling these dynamics remains challenging due to the complex interplay among physical,chemical,and biological factors,and the detailed parameterization and feedback mechanisms are not fully understood in theory-driven models.Graph neural network(GNN)models offer a promising approach to forecast multivariate features and define correlations among input variables.The high interpretive power of GNNs provides deep insights into the structural relationships among variables,serving as a connection matrix in deep learning algorithms.However,there is insufficient understanding of how interactions between input variables affect model outputs during training.Here we investigate how the graph structure of ecosystem dynamics used to train GNN models affects their forecasting accuracy for mesozooplankton species.We find that forecasting accuracy is closely related to interactions within ecosystem dynamics.Notably,increasing the number of nodes does not always enhance model performance;closely connected species tend to produce similar forecasting outputs in terms of trend and peak timing.Therefore,we demonstrate that incorporating the graph structure of ecosystem dynamics can improve the accuracy of mesozooplankton modeling by providing influential information about species of interest.These findings will provide insights into the influential factors affecting mesozooplankton species and emphasize the importance of constructing appropriate graphs for forecasting these species.展开更多
Multivariate Time Series(MTS)forecasting is an essential problem in many fields.Accurate forecasting results can effectively help in making decisions.To date,many MTS forecasting methods have been proposed and widely ...Multivariate Time Series(MTS)forecasting is an essential problem in many fields.Accurate forecasting results can effectively help in making decisions.To date,many MTS forecasting methods have been proposed and widely applied.However,these methods assume that the predicted value of a single variable is affected by all other variables,ignoring the causal relationship among variables.To address the above issue,we propose a novel end-to-end deep learning model,termed graph neural network with neural Granger causality,namely CauGNN,in this paper.To characterize the causal information among variables,we introduce the neural Granger causality graph in our model.Each variable is regarded as a graph node,and each edge represents the casual relationship between variables.In addition,convolutional neural network filters with different perception scales are used for time series feature extraction,to generate the feature of each node.Finally,the graph neural network is adopted to tackle the forecasting problem of the graph structure generated by the MTS.Three benchmark datasets from the real world are used to evaluate the proposed CauGNN,and comprehensive experiments show that the proposed method achieves state-of-the-art results in the MTS forecasting task.展开更多
Heat transfer and entropy generation of developing laminar forced convection flow of water-Al_2O_3 nanofluid in a concentric annulus with constant heat flux on the walls is investigated numerically. In order to determ...Heat transfer and entropy generation of developing laminar forced convection flow of water-Al_2O_3 nanofluid in a concentric annulus with constant heat flux on the walls is investigated numerically. In order to determine entropy generation of fully developed flow, two approaches are employed and it is shown that only one of these methods can provide appropriate results for flow inside annuli. The effects of concentration of nanoparticles, Reynolds number and thermal boundaries on heat transfer enhancement and entropy generation of developing laminar flow inside annuli with different radius ratios and same cross sectional areas are studied. The results show that radius ratio is a very important decision parameter of an annular heat exchanger such that in each Re, there is an optimum radius ratio to maximize Nu and minimize entropy generation. Moreover, the effect of nanoparticles concentration on heat transfer enhancement and minimizing entropy generation is stronger at higher Reynolds.展开更多
The synchronicity effect between the financial market and online response for time-series forecasting is an important task with wide applications.This study combines data from the Baidu index(BDI),Google trends(GT),an...The synchronicity effect between the financial market and online response for time-series forecasting is an important task with wide applications.This study combines data from the Baidu index(BDI),Google trends(GT),and transfer entropy(TE)to forecast a wide range of futures prices with a focus on China.A forecasting model based on a hybrid gray wolf optimizer(GWO),convolutional neural network(CNN),and long short-term memory(LSTM)is developed.First,Baidu and Google dual-platform search data were selected and constructed as Internetbased consumer price index(ICPI)using principal component analysis.Second,TE is used to quantify the information between online behavior and futures markets.Finally,the effective Internet-based consumer price index(ICPI)and TE are introduced into the GWO-CNN-LSTM model to forecast the daily prices of corn,soybean,polyvinyl chloride(PVC),egg,and rebar futures.The results show that the GWO-CNN-LSTM model has a significant improvement in predicting future prices.Internet-based CPI built on Baidu and Google platforms has a high degree of real-time performance and reduces the platform and language bias of the search data.Our proposed framework can provide predictive decision support for government leaders,market investors,and production activities.展开更多
The study of heat transfer is of significant importance in many biological and biomedical industry problems.This investigation comprises of the study of entropy generation analysis of the blood flow in the arteries wi...The study of heat transfer is of significant importance in many biological and biomedical industry problems.This investigation comprises of the study of entropy generation analysis of the blood flow in the arteries with permeable walls. The convection through the flow is studied with compliments to the entropy generation. Governing problem is formulized and solved for low Reynold's number and long wavelength approximations. Exact analytical solutions have been obtained and are analyzed graphically. It is seen that temperature for pure water is lower as compared to the copper water. It gains magnitude with an increase in the slip parameter.展开更多
The evidential reasoning(ER)rule framework has been widely applied in multi-attribute decision analysis and system assessment to manage uncertainty.However,traditional ER implementations rely on two critical limitatio...The evidential reasoning(ER)rule framework has been widely applied in multi-attribute decision analysis and system assessment to manage uncertainty.However,traditional ER implementations rely on two critical limitations:1)unrealistic assumptions of complete evidence independence,and 2)a lack of mechanisms to differentiate causal relationships from spurious correlations.Existing similarity-based approaches often misinterpret interdependent evidence,leading to unreliable decision outcomes.To address these gaps,this study proposes a causality-enhanced ER rule(CER-e)framework with three key methodological innovations:1)a multidimensional causal representation of evidence to capture dependency structures;2)probabilistic quantification of causal strength using transfer entropy,a model-free information-theoretic measure;3)systematic integration of causal parameters into the ER inference process while maintaining evidential objectivity.The PC algorithm is employed during causal discovery to eliminate spurious correlations,ensuring robust causal inference.Case studies in two types of domains—telecommunications network security assessment and structural risk evaluation—validate CER-e’s effectiveness in real-world scenarios.Under simulated incomplete information conditions,the framework demonstrates superior algorithmic robustness compared to traditional ER.Comparative analyses show that CER-e significantly improves both the interpretability of causal relationships and the reliability of assessment results,establishing a novel paradigm for integrating causal inference with evidential reasoning in complex system evaluation.展开更多
Obstructive sleep apnea-hypopnea syndrome(OSAHS)significantly impairs children's growth and cognition.This study aims to elucidate the pathophysiological mechanisms underlying OSAHS in children,with a particular f...Obstructive sleep apnea-hypopnea syndrome(OSAHS)significantly impairs children's growth and cognition.This study aims to elucidate the pathophysiological mechanisms underlying OSAHS in children,with a particular focus on the alterations in cortical information interaction during respiratory events.We analyzed sleep electroencephalography before,during,and after events,utilizing Symbolic Transfer Entropy(STE)for brain network construction and information flow assessment.The results showed a significant increase in STE after events in specific frequency bands during N2 and rapid eye movement(REM)stages,along with increased STE during N3 stage events.Moreover,a noteworthy rise in the information flow imbalance within and between hemispheres was found after events,displaying unique patterns in central sleep apnea and hypopnea.Importantly,some of these alterations were correlated with symptom severity.These findings highlight significant changes in brain region coordination and communication during respiratory events,offering novel insights into OSAHS pathophysiology in children.展开更多
Stock markets in the world are linked by complicated and dynamical relationships into a temporal network.Extensive works have provided us with rich findings from the topological properties and their evolutionary traje...Stock markets in the world are linked by complicated and dynamical relationships into a temporal network.Extensive works have provided us with rich findings from the topological properties and their evolutionary trajectories,but the underlying dynamical mechanism is still not in order.In the present work,we proposed a technical scheme to reveal the dynamical law from the temporal network.The index records for the global stock markets form a multivariate time series.One separates the series into segments and calculates the information flows between the markets,resulting in a temporal market network representing the state and its evolution.Then the technique of the Koopman decomposition operator is adopted to find the law stored in the information flows.The results show that the stock market system has a high flexibility,i.e.,it jumps easily between different states.The information flows mainly from high to low volatility stock markets.And the dynamical process of information flow is composed of many dynamic modes distribute homogenously in a wide range of periods from one month to several ten years,but there exist only nine modes dominating the macroscopic patterns.展开更多
We propose a novel measure to assess causality through the comparison of symbolic mutual information between the future of one random quantity and the past of the other.This provides a new perspective that is differen...We propose a novel measure to assess causality through the comparison of symbolic mutual information between the future of one random quantity and the past of the other.This provides a new perspective that is different from the conventional conceptions.Based on this point of view,a new causality index is derived that uses the definition of directional symbolic mutual information.This measure presents properties that are different from the time delayed mutual information since the symbolization captures the dynamic features of the analyzed time series.In addition to characterizing the direction and the amplitude of the information flow,it can also detect coupling delays.This method has the property of robustness,conceptual simplicity,and fast computational speed.展开更多
The uneven spatial distribution of stations providing precipitable water vapor(PWV)observations in China hinders the effective use of these data in assimilation,nowcasting,and prediction.In this study,we proposed a co...The uneven spatial distribution of stations providing precipitable water vapor(PWV)observations in China hinders the effective use of these data in assimilation,nowcasting,and prediction.In this study,we proposed a complex network framework for exploring the topological structure and the collective behavior of PWV in the mainland of China.We used the Pearson correlation coefficient and transfer entropy to measure the linear and nonlinear relationships of PWV amongst different stations and to set up the undirected and directed complex networks,respectively.Our findings revealed the statistical and geographical distribution of the variables influencing PWV networks and identified the vapor information source and sink stations.Specifically,the findings showed that the statistical and spatial distributions of the undirected and directed complex vapor networks in terms of degree and distance were similar to each other(the common interaction mode for vapor stations and their locations).The betweenness results displayed different features.The largest betweenness ratio for directed networks tended to be larger than that of the undirected networks,implying that the transfer of directed PWV networks was more efficient than that of the undirected networks.The findings of this study are heuristic and will be useful for constructing the best strategy for the PWV data in applications such as vapor observational networks design and precipitation prediction.展开更多
In this paper, aggregation question based on group decision making and a single decision making is studied. The theory of entropy is applied to the sets pair analysis. The system of relation entropy and the transferab...In this paper, aggregation question based on group decision making and a single decision making is studied. The theory of entropy is applied to the sets pair analysis. The system of relation entropy and the transferable entropy notion are put. The character is studied. An potential by the relation entropy and transferable entropy are defined. It is the consistency measure on the group between a single decision making. We gained a new aggregation effective definition on the group misjudge.展开更多
In this study, the entropy generation and the heat transfer of pulsating air flow in a horizontal channel with an open cavity heated from below with uniform temperature distribution are numerically investigated. A num...In this study, the entropy generation and the heat transfer of pulsating air flow in a horizontal channel with an open cavity heated from below with uniform temperature distribution are numerically investigated. A numerical method based on finite volume method is used to discretize the governing equations. At the inlet of the channel, pulsating velocity is imposed for a range of Strouhal numbers Stpfrom 0 to 1 and amplitude Apfrom 0 to 0.5. The effects of the governing parameters, such as frequency and amplitude of the pulsation, Richardson number, Ri, and aspect ratio of the cavity, L/H, on the flow field, temperature distribution, average Nusselt number and average entropy generation, are numerically analyzed. The results indicate that the heat transfer and entropy generation are strongly affected by the frequency and amplitude of the pulsation and this depends on the Richardson number and aspect ratio of the cavity. The pulsation is more effective with the aspect ratio of the cavity L/H= 1.5 in terms of heat transfer enhancement and entropy generation minimization.展开更多
As one of the new generation flexible AC transmission systems(FACTS)devices,the interline power flow controller(IPFC)has the significant advantage of simultaneously regulating the power flow of multiple lines.Neverthe...As one of the new generation flexible AC transmission systems(FACTS)devices,the interline power flow controller(IPFC)has the significant advantage of simultaneously regulating the power flow of multiple lines.Nevertheless,how to choose the appropriate location for the IPFC converters has not been discussed thoroughly.To solve this problem,this paper proposes a novel location method for IPFC using entropy theory.To clarify IPFC’s impact on system power flow,its operation mechanism and control strategies of different types of serial converters are discussed.Subsequently,to clarify the system power flow characteristic suitable for device location analysis,the entropy concept is introduced.In this process,the power flow distribution entropy index is used as an optimization index.Using this index as a foundation,the power flow transfer entropy index is also generated and proposed for the IPFC location determination study.Finally,electromechanical electromagnetic hybrid simulations based on ADPSS are implemented for validation.These are tested in a practical power grid with over 800 nodes.A modular multilevel converter(MMC)-based IPFC electromagnetic model is also established for precise verification.The results show that the proposed method can quickly and efficiently complete optimized IPFC location and support IPFC to determine an optimal adjustment in the N-1 fault cases.展开更多
Grouping is a common phenomenon that occurs everywhere.The leader-follower relationship inside groups has often been qualitatively characterized in previous models using simple heuristics.However,a general method is l...Grouping is a common phenomenon that occurs everywhere.The leader-follower relationship inside groups has often been qualitatively characterized in previous models using simple heuristics.However,a general method is lacking to quantitatively explain leadership in an evacuating group.To understand the evolution of single-group dynamics throughout an evacuation,we developed an extended social force model integrated with a group force.A series of single-group evacuations from a room were simulated.An information-theoretic method,transfer entropy(TE),was applied to detect predefined and undeclared leadership among evacuees.The results showed that the predefined leader was correctly detected by TE,suggesting its capability in measuring leadership based on time series of evacuees’movement information(e.g.,velocity and acceleration).When evacuees were grouped together,TE was higher than when they were alone.Leaders presented a monotonically increasing cumulative influence curve over the investigated period,whereas followers showed a diminishing tendency.We found that leadership emergence correlated with evacuees’spatial positions.The individual located in the foremost part of the group was most likely to become a leader of those in the rear,which concurred with the experimental observations.We observed how a large group split into smaller ones with undeclared leadership during evacuation.These observations were quantitatively verified by TE results.This study provides novel insights into quantifying leadership and understanding single-group dynamics during evacuations.展开更多
Stroke is a common clinical cardiovascular and cerebrovascular disease,which can cause severe motor dysfunction.Therefore,it is an important topic to investigate the abnormality mechanism of the cerebral cortex and th...Stroke is a common clinical cardiovascular and cerebrovascular disease,which can cause severe motor dysfunction.Therefore,it is an important topic to investigate the abnormality mechanism of the cerebral cortex and the corresponding muscles after stroke.In this study,we investigated the functional corticomuscular coupling(FCMC)at specific frequencies by analyzing differences between stroke patients and healthy controls in hand movements.The transfer spectral entropy(TSE)method was used to analyze simultaneous electroencephalography(EEG)and electromyography(EMG)in the right-hand steady state force task.The results illustrated that healthy subjects had the highest TSE values at the beta band in the EMG→EEG and EEG→EMG directions,and the TSE value in the EEG→EMG direction was higher than that in the EMG→EEG direction.In contrast,for stroke patients,beta band coupling was weakened,and there was a notably higher enhancement of alpha and gamma bands in the EMG→EEG direction relative to the EEG→EMG direction.Further analysis found significant correlations between TSE area values at beta2 and gamma2 bands and clinical rating scales.This study demonstrates the frequency specificity properties of FCMC estimated by TSE can assess the rehabilitation status of stroke patients and contribute to our comprehension of the potential mechanism of motor control systems.展开更多
文摘Predicting the currency exchange rate is crucial for financial agents,risk managers,and policymakers.Traditional approaches use publicly announced news on macroeconomic and financial variables as predictors of currency exchange.However,the rise of social media may have changed the source of information.For instance,tweets can help investors make informed decisions about the foreign exchange(FX)market by reflecting market sentiment and opinion.From another aspect,changes in currency exchange may incite agents to post tweets.Are tweets good predictors of currency exchange?Is the relationship between tweets and currency exchange bidirectional?We investigate the comovement/causality between the number of#dolar(“enflasyon”resp.)tweets and USDTRY currency exchange using wavelet coherence and transfer entropy(TE)to answer these questions.Wavelet coherence allows us to determine the relationship between the number of tweets and the USDTRY rate by considering the time–frequency domain.TE enables us to quantify the net information flow between the number of tweets and USDTRY.Data from October 2020 to March 2022 were used.The obtained results remain robust regardless of the frequency of retained data(daily or hourly)and the methods used(wavelet or TE).Based on our results,USDTRY is correlated with the number of#dolar tweets(#inflation)mainly in the short run and a few times in the medium run.These relationships change through time and frequency(wavelet analysis results).However,the results from TE indicate a bidirectional relationship between the#dolar(#inflation)tweets number and the USDTRY exchange rate.The influence of the exchange rate on the number of tweets is highly pronounced.Financial agents,risk managers,policymakers,and investors should then pay moderate attention to the number of#dolar(#inflation)tweets in trading/forecasting the USD–TRY exchange rate.
基金supported by the National Natural Science Foundation of China(Grant No.72401207 and 42101300)Beijing Municipal Education Commission,China(Grant No.SM202110038001).
文摘This paper investigates China's coal price volatility spreaders(CPVSs)from the supply side to locate the volatility source since coal price volatility may destabilize many downstream products'prices or even bring uncertainties to macroeconomic output.Especially in the carbon neutrality context,China's coal market is being reconstructed and responding to imbalances between supply and demand;identifying the CPVSs helps alleviate rising market instability and prevent energy-induced system risk.To achieve this objective,we explore causalities among 938 weekly coal prices reported by different coal-producing areas of China from 2006.9.4 to 2021.7.12 using the transfer entropy method.Then,coal price volatility influence is quantified to identify the CPVSs by conjointly using complex network theory and a rank aggregation method.The validity test demonstrates that the proposed hybrid method efficiently identifies the CPVSs as it correlates to many price determinants,e.g.,electricity and coal consumption and generation.The empirical results show that causalities among coal prices changed dramatically in 2016,2018,and 2020,affected by coal decapacity and carbon neutrality policies.Before 2018,coal-producing provinces with strong demand for coal and electricity,e.g.,Jiangxi,Chongqing,and Sichuan,were CPVSs;after 2019,those with comparative advantages in coal supply,e.g.,Gansu and Ningxia,were CPVSs.Overall,the coal market is unstable and sensitive to energy policy and external shocks.Policymakers and market participants are recommended to monitor and manage the CPVSs to improve energy security,avoid policy-induced instability and prevent risks caused by coal price fluctuations.
基金Project supported by the Jiangsu Province Science Foundation,China(Grant No.BK2011759)
文摘In this paper, we use symbolic transfer entropy to study the coupling strength between premature signals. Numerical experiments show that three types of signal couplings are in the same direction. Among them, normal signal coupling is the strongest, followed by that of premature ventricular contractions, and that of atrial premature beats is the weakest. The T test shows that the entropies of the three signals are distinct. Symbolic transfer entropy requires less data, can distinguish the three types of signals and has very good computational efficiency.
文摘Correlations between two time series,including the linear Pearson correlation and the nonlinear transfer entropy,have attracted significant attention.In this work,we studied the correlations between multiple stock data with the introduction of a time delay and a rolling window.In most cases,the Pearson correlation and transfer entropy share the same tendency,where a higher correlation provides more information for predicting future trends from one stock to another,but a lower correlation provides less.Considering the computational complexity of the transfer entropy and the simplicity of the Pearson correlation,using the linear correlation with time delays and a rolling window is a robust and simple method to quantify the mutual information between stocks.Predictions made by the long short-term memory method with mutual information outperform those made only with selfinformation when there are high correlations between two stocks.
基金supported by the Ministry of Science and ICT(MSIT)through a Sejong Science Fellowship funded by the National Research Foundation of Korea(NRF)(No.2022R1C1C2003649).
文摘Mesozooplankton are critical components of marine ecosystems,acting as key intermediaries between primary producers and higher trophic levels by grazing on phytoplankton and influencing fish populations.They play pivotal roles in the pelagic food web and export production,affecting the biogeochemical cycling of carbon and nutrients.Therefore,accurately modeling and visualizing mesozooplankton community dynamics is essential for understanding marine ecosystem patterns and informing effective management strategies.However,modeling these dynamics remains challenging due to the complex interplay among physical,chemical,and biological factors,and the detailed parameterization and feedback mechanisms are not fully understood in theory-driven models.Graph neural network(GNN)models offer a promising approach to forecast multivariate features and define correlations among input variables.The high interpretive power of GNNs provides deep insights into the structural relationships among variables,serving as a connection matrix in deep learning algorithms.However,there is insufficient understanding of how interactions between input variables affect model outputs during training.Here we investigate how the graph structure of ecosystem dynamics used to train GNN models affects their forecasting accuracy for mesozooplankton species.We find that forecasting accuracy is closely related to interactions within ecosystem dynamics.Notably,increasing the number of nodes does not always enhance model performance;closely connected species tend to produce similar forecasting outputs in terms of trend and peak timing.Therefore,we demonstrate that incorporating the graph structure of ecosystem dynamics can improve the accuracy of mesozooplankton modeling by providing influential information about species of interest.These findings will provide insights into the influential factors affecting mesozooplankton species and emphasize the importance of constructing appropriate graphs for forecasting these species.
基金supported in part by the National Natural Science Foundation of China (No.62002035)the Natural Science Foundation of Chongqing (No.cstc2020jcyj-bshX0034).
文摘Multivariate Time Series(MTS)forecasting is an essential problem in many fields.Accurate forecasting results can effectively help in making decisions.To date,many MTS forecasting methods have been proposed and widely applied.However,these methods assume that the predicted value of a single variable is affected by all other variables,ignoring the causal relationship among variables.To address the above issue,we propose a novel end-to-end deep learning model,termed graph neural network with neural Granger causality,namely CauGNN,in this paper.To characterize the causal information among variables,we introduce the neural Granger causality graph in our model.Each variable is regarded as a graph node,and each edge represents the casual relationship between variables.In addition,convolutional neural network filters with different perception scales are used for time series feature extraction,to generate the feature of each node.Finally,the graph neural network is adopted to tackle the forecasting problem of the graph structure generated by the MTS.Three benchmark datasets from the real world are used to evaluate the proposed CauGNN,and comprehensive experiments show that the proposed method achieves state-of-the-art results in the MTS forecasting task.
文摘Heat transfer and entropy generation of developing laminar forced convection flow of water-Al_2O_3 nanofluid in a concentric annulus with constant heat flux on the walls is investigated numerically. In order to determine entropy generation of fully developed flow, two approaches are employed and it is shown that only one of these methods can provide appropriate results for flow inside annuli. The effects of concentration of nanoparticles, Reynolds number and thermal boundaries on heat transfer enhancement and entropy generation of developing laminar flow inside annuli with different radius ratios and same cross sectional areas are studied. The results show that radius ratio is a very important decision parameter of an annular heat exchanger such that in each Re, there is an optimum radius ratio to maximize Nu and minimize entropy generation. Moreover, the effect of nanoparticles concentration on heat transfer enhancement and minimizing entropy generation is stronger at higher Reynolds.
文摘The synchronicity effect between the financial market and online response for time-series forecasting is an important task with wide applications.This study combines data from the Baidu index(BDI),Google trends(GT),and transfer entropy(TE)to forecast a wide range of futures prices with a focus on China.A forecasting model based on a hybrid gray wolf optimizer(GWO),convolutional neural network(CNN),and long short-term memory(LSTM)is developed.First,Baidu and Google dual-platform search data were selected and constructed as Internetbased consumer price index(ICPI)using principal component analysis.Second,TE is used to quantify the information between online behavior and futures markets.Finally,the effective Internet-based consumer price index(ICPI)and TE are introduced into the GWO-CNN-LSTM model to forecast the daily prices of corn,soybean,polyvinyl chloride(PVC),egg,and rebar futures.The results show that the GWO-CNN-LSTM model has a significant improvement in predicting future prices.Internet-based CPI built on Baidu and Google platforms has a high degree of real-time performance and reduces the platform and language bias of the search data.Our proposed framework can provide predictive decision support for government leaders,market investors,and production activities.
文摘The study of heat transfer is of significant importance in many biological and biomedical industry problems.This investigation comprises of the study of entropy generation analysis of the blood flow in the arteries with permeable walls. The convection through the flow is studied with compliments to the entropy generation. Governing problem is formulized and solved for low Reynold's number and long wavelength approximations. Exact analytical solutions have been obtained and are analyzed graphically. It is seen that temperature for pure water is lower as compared to the copper water. It gains magnitude with an increase in the slip parameter.
基金supported by the Natural Science Foundation of China(Nos.U22A2099,62273113,62203461,62203365)the Innovation Project of Guangxi Graduate Education under Grant YCBZ2023130by the Guangxi Higher Education Undergraduate Teaching Reform Project Key Project,grant number 2022JGZ130.
文摘The evidential reasoning(ER)rule framework has been widely applied in multi-attribute decision analysis and system assessment to manage uncertainty.However,traditional ER implementations rely on two critical limitations:1)unrealistic assumptions of complete evidence independence,and 2)a lack of mechanisms to differentiate causal relationships from spurious correlations.Existing similarity-based approaches often misinterpret interdependent evidence,leading to unreliable decision outcomes.To address these gaps,this study proposes a causality-enhanced ER rule(CER-e)framework with three key methodological innovations:1)a multidimensional causal representation of evidence to capture dependency structures;2)probabilistic quantification of causal strength using transfer entropy,a model-free information-theoretic measure;3)systematic integration of causal parameters into the ER inference process while maintaining evidential objectivity.The PC algorithm is employed during causal discovery to eliminate spurious correlations,ensuring robust causal inference.Case studies in two types of domains—telecommunications network security assessment and structural risk evaluation—validate CER-e’s effectiveness in real-world scenarios.Under simulated incomplete information conditions,the framework demonstrates superior algorithmic robustness compared to traditional ER.Comparative analyses show that CER-e significantly improves both the interpretability of causal relationships and the reliability of assessment results,establishing a novel paradigm for integrating causal inference with evidential reasoning in complex system evaluation.
基金supported by the National Natural Science Foundation of China (82001919)the Guangdong Basic and Applied Basic Research Foundation (2022A1515010050)+2 种基金the China Postdoctoral Science Foundation (2022M711219)the Key Realm R&D Program of Guangdong Province (2019B03035001)the Foundation of Guangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instruments (2020B1212060077).
文摘Obstructive sleep apnea-hypopnea syndrome(OSAHS)significantly impairs children's growth and cognition.This study aims to elucidate the pathophysiological mechanisms underlying OSAHS in children,with a particular focus on the alterations in cortical information interaction during respiratory events.We analyzed sleep electroencephalography before,during,and after events,utilizing Symbolic Transfer Entropy(STE)for brain network construction and information flow assessment.The results showed a significant increase in STE after events in specific frequency bands during N2 and rapid eye movement(REM)stages,along with increased STE during N3 stage events.Moreover,a noteworthy rise in the information flow imbalance within and between hemispheres was found after events,displaying unique patterns in central sleep apnea and hypopnea.Importantly,some of these alterations were correlated with symptom severity.These findings highlight significant changes in brain region coordination and communication during respiratory events,offering novel insights into OSAHS pathophysiology in children.
基金the National Nature Science Foundation of China(Grant Nos.11875042 and 11505114)the Orientational Scholar Program Sponsored by the Shanghai Education Commission,China(Grant Nos.D-USST02 and QD2015016)the Shanghai Project for Construction of Top Disciplines,China(Grant No.USST-SYS-01).
文摘Stock markets in the world are linked by complicated and dynamical relationships into a temporal network.Extensive works have provided us with rich findings from the topological properties and their evolutionary trajectories,but the underlying dynamical mechanism is still not in order.In the present work,we proposed a technical scheme to reveal the dynamical law from the temporal network.The index records for the global stock markets form a multivariate time series.One separates the series into segments and calculates the information flows between the markets,resulting in a temporal market network representing the state and its evolution.Then the technique of the Koopman decomposition operator is adopted to find the law stored in the information flows.The results show that the stock market system has a high flexibility,i.e.,it jumps easily between different states.The information flows mainly from high to low volatility stock markets.And the dynamical process of information flow is composed of many dynamic modes distribute homogenously in a wide range of periods from one month to several ten years,but there exist only nine modes dominating the macroscopic patterns.
基金Project supported by the National Natural Science Foundation of China (Grant No. 60904039)
文摘We propose a novel measure to assess causality through the comparison of symbolic mutual information between the future of one random quantity and the past of the other.This provides a new perspective that is different from the conventional conceptions.Based on this point of view,a new causality index is derived that uses the definition of directional symbolic mutual information.This measure presents properties that are different from the time delayed mutual information since the symbolization captures the dynamic features of the analyzed time series.In addition to characterizing the direction and the amplitude of the information flow,it can also detect coupling delays.This method has the property of robustness,conceptual simplicity,and fast computational speed.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.41775081,41975100,41901016,and 41875100)the Innovation Project of the China Meteorological Administration(Grant No.CXFZ2021Z034)the National Key Research and Development Program of China(Grant No.2018YFC1507702)。
文摘The uneven spatial distribution of stations providing precipitable water vapor(PWV)observations in China hinders the effective use of these data in assimilation,nowcasting,and prediction.In this study,we proposed a complex network framework for exploring the topological structure and the collective behavior of PWV in the mainland of China.We used the Pearson correlation coefficient and transfer entropy to measure the linear and nonlinear relationships of PWV amongst different stations and to set up the undirected and directed complex networks,respectively.Our findings revealed the statistical and geographical distribution of the variables influencing PWV networks and identified the vapor information source and sink stations.Specifically,the findings showed that the statistical and spatial distributions of the undirected and directed complex vapor networks in terms of degree and distance were similar to each other(the common interaction mode for vapor stations and their locations).The betweenness results displayed different features.The largest betweenness ratio for directed networks tended to be larger than that of the undirected networks,implying that the transfer of directed PWV networks was more efficient than that of the undirected networks.The findings of this study are heuristic and will be useful for constructing the best strategy for the PWV data in applications such as vapor observational networks design and precipitation prediction.
基金This projectis supported by National Natural Science Foundation of China( No.79930 90 0 )
文摘In this paper, aggregation question based on group decision making and a single decision making is studied. The theory of entropy is applied to the sets pair analysis. The system of relation entropy and the transferable entropy notion are put. The character is studied. An potential by the relation entropy and transferable entropy are defined. It is the consistency measure on the group between a single decision making. We gained a new aggregation effective definition on the group misjudge.
文摘In this study, the entropy generation and the heat transfer of pulsating air flow in a horizontal channel with an open cavity heated from below with uniform temperature distribution are numerically investigated. A numerical method based on finite volume method is used to discretize the governing equations. At the inlet of the channel, pulsating velocity is imposed for a range of Strouhal numbers Stpfrom 0 to 1 and amplitude Apfrom 0 to 0.5. The effects of the governing parameters, such as frequency and amplitude of the pulsation, Richardson number, Ri, and aspect ratio of the cavity, L/H, on the flow field, temperature distribution, average Nusselt number and average entropy generation, are numerically analyzed. The results indicate that the heat transfer and entropy generation are strongly affected by the frequency and amplitude of the pulsation and this depends on the Richardson number and aspect ratio of the cavity. The pulsation is more effective with the aspect ratio of the cavity L/H= 1.5 in terms of heat transfer enhancement and entropy generation minimization.
基金supported by the Natural Science Foundation of Sichuan Province of China(No.2022NSFSC0262)the Fundamental Research Funds for the Central Universities(No.2022SCU12005).
文摘As one of the new generation flexible AC transmission systems(FACTS)devices,the interline power flow controller(IPFC)has the significant advantage of simultaneously regulating the power flow of multiple lines.Nevertheless,how to choose the appropriate location for the IPFC converters has not been discussed thoroughly.To solve this problem,this paper proposes a novel location method for IPFC using entropy theory.To clarify IPFC’s impact on system power flow,its operation mechanism and control strategies of different types of serial converters are discussed.Subsequently,to clarify the system power flow characteristic suitable for device location analysis,the entropy concept is introduced.In this process,the power flow distribution entropy index is used as an optimization index.Using this index as a foundation,the power flow transfer entropy index is also generated and proposed for the IPFC location determination study.Finally,electromechanical electromagnetic hybrid simulations based on ADPSS are implemented for validation.These are tested in a practical power grid with over 800 nodes.A modular multilevel converter(MMC)-based IPFC electromagnetic model is also established for precise verification.The results show that the proposed method can quickly and efficiently complete optimized IPFC location and support IPFC to determine an optimal adjustment in the N-1 fault cases.
基金The Research Grants Council of the Hong Kong Special Administrative Region,China(Project No.CityU 11208119)a grant from CityU(Project No.SRG-Fd 7005769)supported this study.
文摘Grouping is a common phenomenon that occurs everywhere.The leader-follower relationship inside groups has often been qualitatively characterized in previous models using simple heuristics.However,a general method is lacking to quantitatively explain leadership in an evacuating group.To understand the evolution of single-group dynamics throughout an evacuation,we developed an extended social force model integrated with a group force.A series of single-group evacuations from a room were simulated.An information-theoretic method,transfer entropy(TE),was applied to detect predefined and undeclared leadership among evacuees.The results showed that the predefined leader was correctly detected by TE,suggesting its capability in measuring leadership based on time series of evacuees’movement information(e.g.,velocity and acceleration).When evacuees were grouped together,TE was higher than when they were alone.Leaders presented a monotonically increasing cumulative influence curve over the investigated period,whereas followers showed a diminishing tendency.We found that leadership emergence correlated with evacuees’spatial positions.The individual located in the foremost part of the group was most likely to become a leader of those in the rear,which concurred with the experimental observations.We observed how a large group split into smaller ones with undeclared leadership during evacuation.These observations were quantitatively verified by TE results.This study provides novel insights into quantifying leadership and understanding single-group dynamics during evacuations.
基金This work was funded by the National Natural Science Foundation of China under grant(No.U20A20192and No.62076216)Natural Science Foundation of Hebei Province of China grant(No.F2022203079and No.F2022203002)+1 种基金the Key Research and Development Program of Hebei Province of China(No.21372005D)the Funding Program for Innovative Ability Training of graduate students of Hebei Provincial Department of Education grant(No.CXZZSS2022123).
文摘Stroke is a common clinical cardiovascular and cerebrovascular disease,which can cause severe motor dysfunction.Therefore,it is an important topic to investigate the abnormality mechanism of the cerebral cortex and the corresponding muscles after stroke.In this study,we investigated the functional corticomuscular coupling(FCMC)at specific frequencies by analyzing differences between stroke patients and healthy controls in hand movements.The transfer spectral entropy(TSE)method was used to analyze simultaneous electroencephalography(EEG)and electromyography(EMG)in the right-hand steady state force task.The results illustrated that healthy subjects had the highest TSE values at the beta band in the EMG→EEG and EEG→EMG directions,and the TSE value in the EEG→EMG direction was higher than that in the EMG→EEG direction.In contrast,for stroke patients,beta band coupling was weakened,and there was a notably higher enhancement of alpha and gamma bands in the EMG→EEG direction relative to the EEG→EMG direction.Further analysis found significant correlations between TSE area values at beta2 and gamma2 bands and clinical rating scales.This study demonstrates the frequency specificity properties of FCMC estimated by TSE can assess the rehabilitation status of stroke patients and contribute to our comprehension of the potential mechanism of motor control systems.
