Symbolic transfer entropy-based premature signal analysis

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.

Pearson correlation and transfer entropy in the Chinese stock market with time delay

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.

Multivariate Time Series Forecasting with Transfer Entropy Graph

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.

Optimal selection of annulus radius ratio to enhance heat transfer with minimum entropy generation in developing laminar forced convection of water-Al2O3 nanofluid flow

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.

China futures price forecasting based on online search and information transfer

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.

Measuring causality by taking the directional symbolic mutual information approach

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.

Characteristics of vapor based on complex networks in China

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.

Information flow between stock markets:A Koopman decomposition approach

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.

A Novel Location Method for Interline Power Flow Controllers Based on Entropy Theory

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.

Relation Entropy and Transferable Entropy Think of Aggregation on Group Decision Making

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.

Numerical investigation of entropy generation and heat transfer of pulsating flow in a horizontal channel with an open cavity

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.

Entropy Generation Analysis in Convective Ferromagnetic Nano Blood Flow Through a Composite Stenosed Arteries with Permeable Wall

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.

Altered Cortical Information Interaction During Respiratory Events in Children with Obstructive Sleep Apnea-Hypopnea Syndrome

tObstructive 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.

Study on single-group dynamics in evacuation considering pre-defined and undeclared leadership

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.