Showing papers in "Physica A-statistical Mechanics and Its Applications in 2017"
TL;DR: Fernandez, Aurelio, et al. as mentioned in this paperernández et al., 2015. Universitat Rovira I Virgili, Espana, Spain.
Abstract: Fil: Fernandez, Aurelio. Universitat Rovira I Virgili; Espana. Consejo Nacional de Investigaciones Cientificas y Tecnicas; Argentina
366 citations
TL;DR: In this article, a new fractional operator of variable order with the use of the monotonic increasing function is proposed in sense of Caputo type, which is efficient in modeling a class of concentrations in the complex transport process.
Abstract: In this paper, a new fractional operator of variable order with the use of the monotonic increasing function is proposed in sense of Caputo type. The properties in term of the Laplace and Fourier transforms are analyzed and the results for the anomalous diffusion equations of variable order are discussed. The new formulation is efficient in modeling a class of concentrations in the complex transport process.
217 citations
TL;DR: A new mathematical model with adjustable sensitivity and smooth factor was proposed to describe the autonomous car’s moving behavior in which smooth factor is used to balance the front and back headway in a flow to support the stability criteria in traffic flow.
Abstract: We investigated the mixed traffic flow with human-driving and autonomous cars. A new mathematical model with adjustable sensitivity and smooth factor was proposed to describe the autonomous car’s moving behavior in which smooth factor is used to balance the front and back headway in a flow. A lemma and a theorem were proved to support the stability criteria in traffic flow. A series of simulations were carried out to analyze the mixed traffic flow. The fundamental diagrams were obtained from the numerical simulation results. The varying sensitivity and smooth factor of autonomous cars affect traffic flux, which exhibits opposite varying tendency with increasing parameters before and after the critical density. Moreover, the sensitivity of sensors and smooth factors play an important role in stabilizing the mixed traffic flow and suppressing the traffic jam.
199 citations
TL;DR: In this paper, a summary of techniques used in large deviation theory to study the fluctuations of time-additive quantities, called dynamical observables, defined in the context of Langevin-type equations, which model equilibrium and nonequilibrium processes driven by external forces and noise sources.
Abstract: These notes give a summary of techniques used in large deviation theory to study the fluctuations of time-additive quantities, called dynamical observables, defined in the context of Langevin-type equations, which model equilibrium and nonequilibrium processes driven by external forces and noise sources. These fluctuations are described by large deviation functions, obtained by solving a dominant eigenvalue problem similar to the problem of finding the ground state energy of quantum systems. This analogy is used to explain the differences that exist between the fluctuations of equilibrium and nonequilibrium processes. An example involving the Ornstein–Uhlenbeck process is worked out in detail to illustrate these methods. Exercises, at the end of the notes, also complement the theory.
157 citations
TL;DR: In this article, a macro traffic flow model was proposed to explore the effects of the driver's bounded rationality on the evolutions of traffic waves (which include shock and rarefaction waves) and small perturbation, and on the fuel consumption and emissions during the evolution process.
Abstract: In this paper, we propose a macro traffic flow model to explore the effects of the driver’s bounded rationality on the evolutions of traffic waves (which include shock and rarefaction waves) and small perturbation, and on the fuel consumption and emissions (that include CO, HC and NO X ) during the evolution process. The numerical results illustrate that considering the driver’s bounded rationality can prominently smooth the wavefront of the traffic waves and improve the stability of traffic flow, which shows that the driver’s bounded rationality has positive impacts on traffic flow; but considering the driver’s bounded rationality reduces the fuel consumption and emissions only at the upstream of the rarefaction wave while enhances the fuel consumption and emissions under other situations, which shows that the driver’s bounded rationality has positive impacts on the fuel consumption and emissions only at the upstream of the rarefaction wave, while negative effects on the fuel consumption and emissions under other situations. In addition, the numerical results show that the driver’s bounded rationality has little prominent impact on the total fuel consumption, and emissions during the whole evolution of small perturbation.
142 citations
TL;DR: Based on an improved neuron model, the effect of electromagnetic induction is described by using magnetic flux, and the modulation of magnetic flux on membrane potential is realized by using memristor coupling as mentioned in this paper.
Abstract: Complex electrical activities in neuron can induce time-varying electromagnetic field and the effect of various electromagnetic inductions should be considered in dealing with electrical activities of neuron. Based on an improved neuron model, the effect of electromagnetic induction is described by using magnetic flux, and the modulation of magnetic flux on membrane potential is realized by using memristor coupling. Furthermore, additive phase noise is imposed on the neuron to detect the dynamical response of neuron and phase transition in modes. The dynamical properties of electrical activities are detected and discussed, and double coherence resonance behavior is observed, respectively. Furthermore, multiple modes of electrical activities can be observed in the sampled time series for membrane potential of the neuron model.
135 citations
TL;DR: A novel rumor propagation S E I R model on heterogeneous network is presented and formula of the rumor spreading threshold for the model is given and the globally dynamical behaviors of the rumors free equilibrium set are analyzed.
Abstract: Considering that the exposed nodes may become the removed nodes at a rate, we present a novel rumor propagation S E I R model on heterogeneous network. We give formula of the rumor spreading threshold for the model and analyze the globally dynamical behaviors of the rumor free equilibrium set. Meanwhile, we discuss two immunization strategies for the rumor spreading. Numerical simulations are given to demonstrate the main results.
135 citations
TL;DR: It is revealed that synchronization(anti-synchronization) is faster as the order increases, and the relationship between the order and synchronization (anti- synchronized) is demonstrated numerically.
Abstract: This paper is concerned with the issues of synchronization and anti-synchronization for fractional chaotic financial system with market confidence by taking advantage of active control approach. Some sufficient conditions are derived to guarantee the synchronization and anti-synchronization for the proposed fractional system. Moreover, the relationship between the order and synchronization(anti-synchronization) is demonstrated numerically. It reveals that synchronization(anti-synchronization) is faster as the order increases. Finally, two illustrative examples are exploited to verify the efficiency of the obtained theoretical results.
124 citations
TL;DR: A speed guidance model is proposed to explore the influences of the drivers’ bounded rationality on the vehicle’s fuel consumption and emissions during the whole process of the vehicle passing through the signalized intersection.
Abstract: In this paper, we propose a speed guidance model to explore the influences of the drivers’ bounded rationality on the vehicle’s fuel consumption and emissions during the whole process of the vehicle passing through the signalized intersection. In the proposed speed guidance model, we apply three parameters (i.e., the response time, acceptance threshold value and execution level) to quantify the driver’s bounded rationality. In the numerical tests, we use the signalized intersection (consisting of the Xinan Road and the Wuyi Road in Dalian, China) as the simulation scenario, the Vissim to describe the vehicle’s movement and the MOVES (motor vehicle emission simulator) to explore the vehicle’s fuel consumption and emissions. The numerical results show that the driver’s bounded rationality has prominent effects on the vehicle’s fuel consumption and emissions, but the impacts are directly dependent on each parameter of the driver’s bounded rationality.
120 citations
TL;DR: In this article, a new approach of fractional derivative with a new local kernel is suggested in which the kernel introduced in this work is the well-known normal distribution that is a very common continuous probability distribution.
Abstract: New approach of fractional derivative with a new local kernel is suggested in this paper. The kernel introduced in this work is the well-known normal distribution that is a very common continuous probability distribution. This distribution is very important in statistics and also highly used in natural science and social sciences to portray real-valued random variables whose distributions are not known. Two definitions are suggested namely Atangana–Gomez Averaging in Liouville–Caputo and Riemann–Liouville sense. We presented some relationship with existing integrals transform operators. Numerical approximations for first and second order approximation are derived in detail. Some Applications of the new mathematical tools to describe some real world problems are presented in detail. This is a new door opened the field of statistics, natural and socials sciences.
115 citations
TL;DR: In this paper, the authors investigated the impact of social groups on evacuations and found that explicitly cooperative behavior among group members leads to a stronger aggregation of group members and an intermittent way of evacuation.
Abstract: Pedestrian crowds often include social groups, i.e. pedestrians that walk together because of social relationships. They show characteristic configurations and influence the dynamics of the entire crowd. In order to investigate the impact of social groups on evacuations we performed an empirical study with pupils. Several evacuation runs with groups of different sizes and different interactions were performed. New group parameters are introduced which allow to describe the dynamics of the groups and the configuration of the group members quantitatively. The analysis shows a possible decrease of evacuation times for large groups due to self-ordering effects. Social groups can be approximated as ellipses that orientate along their direction of motion. Furthermore, explicitly cooperative behaviour among group members leads to a stronger aggregation of group members and an intermittent way of evacuation.
TL;DR: A method to identify the influence of the node based on Analytic Hierarchy Process (AHP) is proposed and several different centrality measures are considered as the multi-attribute of complex network in AHP application.
Abstract: In the field of complex networks, how to identify influential nodes in the network is still an important research topic. In this paper, a method to identify the influence of the node based on Analytic Hierarchy Process (AHP) is proposed. AHP, as a multiple attribute decision making (MADM) technique has become an important branch of decision making since then. Every centrality measure has its own disadvantages and limitations, thus we consider several different centrality measures as the multi-attribute of complex network in AHP application. AHP is used to aggregate the multi-attribute to obtain the evaluation of the influence of each node. The experiments on four real networks and an informative network show the efficiency and practicability of the proposed method.
TL;DR: This study proposes a multiple sources and multiple measures based traffic flow prediction algorithm using the chaos theory and support vector regression method, which has better performance for the short-term traffic flow Prediction in terms of the accuracy and timeliness.
Abstract: This study proposes a multiple sources and multiple measures based traffic flow prediction algorithm using the chaos theory and support vector regression method. In particular, first, the chaotic characteristics of traffic flow associated with the speed, occupancy, and flow are identified using the maximum Lyapunov exponent. Then, the phase space of multiple measures chaotic time series are reconstructed based on the phase space reconstruction theory and fused into a same multi-dimensional phase space using the Bayesian estimation theory. In addition, the support vector regression (SVR) model is designed to predict the traffic flow. Numerical experiments are performed using the data from multiple sources. The results show that, compared with the single measure, the proposed method has better performance for the short-term traffic flow prediction in terms of the accuracy and timeliness.
TL;DR: In this article, the authors investigated the evolution of mean and volatility spillovers between oil and stock markets in the time and frequency dimensions, and employed WTI crude oil prices, the S&P 500 (USA) index and the MICEX index (Russia) for the period Jan. 2003-Dec. 2014 as sample data.
Abstract: Aiming to investigate the evolution of mean and volatility spillovers between oil and stock markets in the time and frequency dimensions, we employed WTI crude oil prices, the S&P 500 (USA) index and the MICEX index (Russia) for the period Jan. 2003–Dec. 2014 as sample data. We first applied a wavelet-based GARCH–BEKK method to examine the spillover features in frequency dimension. To consider the evolution of spillover effects in time dimension at multiple-scales, we then divided the full sample period into three sub-periods, pre-crisis period, crisis period, and post-crisis period. The results indicate that spillover effects vary across wavelet scales in terms of strength and direction. By analysis the time-varying linkage, we found the different evolution features of spillover effects between the Oil-US stock market and Oil-Russia stock market. The spillover relationship between oil and US stock market is shifting to short-term while the spillover relationship between oil and Russia stock market is changing to all time scales. That result implies that the linkage between oil and US stock market is weakening in the long-term, and the linkage between oil and Russia stock market is getting close in all time scales. This may explain the phenomenon that the US stock index and the Russia stock index showed the opposite trend with the falling of oil price in the post-crisis period.
TL;DR: It is proven that placing an obstacle in panic situations does not reduce or absorb the pressure in the region of exit, on the contrary, promotes the pressure to a much higher level, hence the physical mechanism behind the evacuation efficiency enhancement is a significant reduction of high density region by effective separation in space which finally causes the increasing of escape speed and evacuation outflow.
Abstract: To improve the pedestrian outflow in panic situations by suitably placing an obstacle in front of the exit, it is vital to understand the physical mechanism behind the evacuation efficiency enhancement. In this paper, a robust differential evolution is firstly employed to optimize the geometrical parameters of different shaped obstacles in order to achieve an optimal evacuation efficiency. Moreover, it is found that all the geometrical parameters of obstacles could markedly influence the evacuation efficiency of pedestrians, and the best way for achieving an optimal pedestrian outflow is to slightly shift the obstacle from the center of the exit which is consistent with findings of extant literature. Most importantly, by analyzing the profiles of density, velocity and specific flow, as well as the spatial distribution of crowd pressure, we have proven that placing an obstacle in panic situations does not reduce or absorb the pressure in the region of exit, on the contrary, promotes the pressure to a much higher level, hence the physical mechanism behind the evacuation efficiency enhancement is not a pressure decrease in the region of exit, but a significant reduction of high density region by effective separation in space which finally causes the increasing of escape speed and evacuation outflow. Finally, it is clearly demonstrated that the panel-like obstacle is considerably more robust and stable than the pillar-like obstacle to guarantee the enhancement of evacuation efficiency under different initial pedestrian distributions, different initial crowd densities as well as different desired velocities.
TL;DR: Two statistical properties of networks: normalized network structure entropy and cumulative probability of degree, are utilized to explore hourly variation in traffic flow and demonstrate these two statistical quantities express similar pattern to traffic flow parameters with morning and evening peak hours.
Abstract: Discovering dynamic characteristics in traffic flow is the significant step to design effective traffic managing and controlling strategy for relieving traffic congestion in urban cities. A new method based on complex network theory is proposed to study multivariate traffic flow time series. The data were collected from loop detectors on freeway during a year. In order to construct complex network from original traffic flow, a weighted Froenius norm is adopt to estimate similarity between multivariate time series, and Principal Component Analysis is implemented to determine the weights. We discuss how to select optimal critical threshold for networks at different hour in term of cumulative probability distribution of degree. Furthermore, two statistical properties of networks: normalized network structure entropy and cumulative probability of degree, are utilized to explore hourly variation in traffic flow. The results demonstrate these two statistical quantities express similar pattern to traffic flow parameters with morning and evening peak hours. Accordingly, we detect three traffic states: trough, peak and transitional hours, according to the correlation between two aforementioned properties. The classifying results of states can actually represent hourly fluctuation in traffic flow by analyzing annual average hourly values of traffic volume, occupancy and speed in corresponding hours.
TL;DR: The results indicate that the proposed EEMD–MKNN model has a higher forecast precision than EMD–KNN, KNN method and ARIMA, and has high predictive precision for short-term forecasting.
Abstract: In this paper, we propose a new two-stage methodology that combines the ensemble empirical mode decomposition (EEMD) with multidimensional k -nearest neighbor model (MKNN) in order to forecast the closing price and high price of the stocks simultaneously. The modified algorithm of k -nearest neighbors (KNN) has an increasingly wide application in the prediction of all fields. Empirical mode decomposition (EMD) decomposes a nonlinear and non-stationary signal into a series of intrinsic mode functions (IMFs), however, it cannot reveal characteristic information of the signal with much accuracy as a result of mode mixing. So ensemble empirical mode decomposition (EEMD), an improved method of EMD, is presented to resolve the weaknesses of EMD by adding white noise to the original data. With EEMD, the components with true physical meaning can be extracted from the time series. Utilizing the advantage of EEMD and MKNN, the new proposed ensemble empirical mode decomposition combined with multidimensional k -nearest neighbor model (EEMD–MKNN) has high predictive precision for short-term forecasting. Moreover, we extend this methodology to the case of two-dimensions to forecast the closing price and high price of the four stocks (NAS, S&P500, DJI and STI stock indices) at the same time. The results indicate that the proposed EEMD–MKNN model has a higher forecast precision than EMD–KNN, KNN method and ARIMA.
TL;DR: Simulation results show that pedestrians can obtain the correct moving direction through information transmission mechanism and that the modified model can simulate actual pedestrian behavior during an emergency evacuation, contributing in optimizing a number of efficient emergency evacuation schemes for large public places.
Abstract: In this paper, the information transmission mechanism is introduced into the social force model to simulate pedestrian behavior in an emergency, especially when most pedestrians are unfamiliar with the evacuation environment. This modified model includes a collision avoidance strategy and an information transmission model that considers information loss. The former is used to avoid collision among pedestrians in a simulation, whereas the latter mainly describes how pedestrians obtain and choose directions appropriate to them. Simulation results show that pedestrians can obtain the correct moving direction through information transmission mechanism and that the modified model can simulate actual pedestrian behavior during an emergency evacuation. Moreover, we have drawn four conclusions to improve evacuation based on the simulation results; and these conclusions greatly contribute in optimizing a number of efficient emergency evacuation schemes for large public places.
TL;DR: This research presents a methodology which uses the results of medical tests as input, extracts a reduced dimensional feature subset and provides diagnosis of heart disease through accuracy, specificity and sensitivity over three datasets of UCI.
Abstract: Automatic diagnosis of human diseases are mostly achieved through decision support systems. The performance of these systems is mainly dependent on the selection of the most relevant features. This becomes harder when the dataset contains missing values for the different features. Probabilistic Principal Component Analysis (PPCA) has reputation to deal with the problem of missing values of attributes. This research presents a methodology which uses the results of medical tests as input, extracts a reduced dimensional feature subset and provides diagnosis of heart disease. The proposed methodology extracts high impact features in new projection by using Probabilistic Principal Component Analysis (PPCA). PPCA extracts projection vectors which contribute in highest covariance and these projection vectors are used to reduce feature dimension. The selection of projection vectors is done through Parallel Analysis (PA). The feature subset with the reduced dimension is provided to radial basis function (RBF) kernel based Support Vector Machines (SVM). The RBF based SVM serves the purpose of classification into two categories i.e., Heart Patient (HP) and Normal Subject (NS). The proposed methodology is evaluated through accuracy, specificity and sensitivity over the three datasets of UCI i.e., Cleveland, Switzerland and Hungarian. The statistical results achieved through the proposed technique are presented in comparison to the existing research showing its impact. The proposed technique achieved an accuracy of 82.18%, 85.82% and 91.30% for Cleveland, Hungarian and Switzerland dataset respectively.
TL;DR: The dynamic analysis on relationship between investor sentiment and stock market is proposed based on Thermal Optimal Path (TOP) method and the results show that the sentiment data was not always leading over stock market price, and it can be used to predict the stock price only when the stock has high investor attention.
Abstract: With the development of the social network, the interaction between investors in stock market became more fast and convenient. Thus, investor sentiment which can influence their investment decisions may be quickly spread and magnified through the network, and to a certain extent the stock market can be affected. This paper collected the user comments data from a popular professional social networking site of China stock market called Xueqiu, then the investor sentiment data can be obtained through semantic analysis. The dynamic analysis on relationship between investor sentiment and stock market is proposed based on Thermal Optimal Path (TOP) method. The results show that the sentiment data was not always leading over stock market price, and it can be used to predict the stock price only when the stock has high investor attention.
TL;DR: In this article, a new continuum model based on full velocity difference car following model with the consideration of driver's anticipation effect is developed with a new model's linear stability, and the key improvement of this new model is that the anticipation effect can improve the stability of traffic flow.
Abstract: In this paper, a new continuum model based on full velocity difference car following model is developed with the consideration of driver’s anticipation effect. By applying the linear stability theory, the new model’s linear stability is obtained. Through nonlinear analysis, the KdV–Burgers equation is derived to describe the propagating behavior of traffic density wave near the neutral stability line. Numerical simulation shows that the new model possesses the local cluster, and it is capable of explaining some particular traffic phenomena Numerical results show that when considering the effects of anticipation, the traffic jams can be suppressed efficiently. The key improvement of this new model is that the anticipation effect can improve the stability of traffic flow.
TL;DR: In this paper, a detrended cross-correlation approach (DCCA) was applied to investigate the co-movements of the oil price and exchange rate in 12 Asian countries.
Abstract: Most empirical literature investigates the relation between oil prices and exchange rate through different models. These models measure this relationship on two time scales (long and short terms), and often fail to observe the co-movement of these variables at different time scales. We apply a detrended cross-correlation approach (DCCA) to investigate the co-movements of the oil price and exchange rate in 12 Asian countries. This model determines the co-movements of oil price and exchange rate at different time scale. The exchange rate and oil price time series indicate unit root problem. Their correlation and cross-correlation are very difficult to measure. The result becomes spurious when periodic trend or unit root problem occurs in these time series. This approach measures the possible cross-correlation at different time scale and controlling the unit root problem. Our empirical results support the co-movements of oil prices and exchange rate. Our results support a weak negative cross-correlation between oil price and exchange rate for most Asian countries included in our sample. The results have important monetary, fiscal, inflationary, and trade policy implications for these countries.
TL;DR: Universal bounds on this rate function follow which prove and generalize the thermodynamic uncertainty relation that quantifies the inevitable trade-off between cost and precision of any biomolecular process.
Abstract: In these lecture notes, the basic principles of stochastic thermodynamics are developed starting with a closed system in contact with a heat bath. A trajectory undergoes Markovian transitions between observable meso-states that correspond to a coarse-grained description of, e.g., a biomolecule or a biochemical network. By separating the closed system into a core system and into reservoirs for ligands and reactants that bind to, and react with the core system, a description as an open system controlled by chemical potentials and possibly an external force is achieved. Entropy production and further thermodynamic quantities defined along a trajectory obey various fluctuation theorems. For describing fluctuations in a non-equilibrium steady state in the long-time limit, the concept of a rate function for large deviations from the mean behavior is derived from the weight of a trajectory. Universal bounds on this rate function follow which prove and generalize the thermodynamic uncertainty relation that quantifies the inevitable trade-off between cost and precision of any biomolecular process. Specific illustrations are given for molecular motors, Brownian clocks and enzymatic networks that show how these tools can be used for thermodynamic inference of hidden properties of a system.
TL;DR: The tracking of diffusion links in the real spreading dynamics of information verifies the effectiveness of the proposed method for identifying influential spreaders in OSNs as compared with degree centrality, PageRank, and original K-core.
Abstract: Online social networks (OSNs) have become a vital part of everyday living. OSNs provide researchers and scientists with unique prospects to comprehend individuals on a scale and to analyze human behavioral patterns. Influential spreaders identification is an important subject in understanding the dynamics of information diffusion in OSNs. Targeting these influential spreaders is significant in planning the techniques for accelerating the propagation of information that is useful for various applications, such as viral marketing applications or blocking the diffusion of annoying information (spreading of viruses, rumors, online negative behaviors, and cyberbullying). Existing K-core decomposition methods consider links equally when calculating the influential spreaders for unweighted networks. Alternatively, the proposed link weights are based only on the degree of nodes. Thus, if a node is linked to high-degree nodes, then this node will receive high weight and is treated as an important node. Conversely, the degree of nodes in OSN context does not always provide accurate influence of users. In the present study, we improve the K-core method for OSNs by proposing a novel link-weighting method based on the interaction among users. The proposed method is based on the observation that the interaction of users is a significant factor in quantifying the spreading capability of user in OSNs. The tracking of diffusion links in the real spreading dynamics of information verifies the effectiveness of our proposed method for identifying influential spreaders in OSNs as compared with degree centrality, PageRank, and original K-core.
TL;DR: By using network science and graph theory, this article investigates ten theoretical and four numerical robustness metrics and their performance in quantifying the robustness of 33 metro networks under random failures or targeted attacks and finds that Tokyo and Rome are the most robust networks.
Abstract: Metros (heavy rail transit systems) are integral parts of urban transportation systems. Failures in their operations can have serious impacts on urban mobility, and measuring their robustness is therefore critical. Moreover, as physical networks, metros can be viewed as topological entities, and as such they possess measurable network properties. In this article, by using network science and graph theory, we investigate ten theoretical and four numerical robustness metrics and their performance in quantifying the robustness of 33 metro networks under random failures or targeted attacks. We find that the ten theoretical metrics capture two distinct aspects of robustness of metro networks. First, several metrics place an emphasis on alternative paths. Second, other metrics place an emphasis on the length of the paths. To account for all aspects, we standardize all ten indicators and plot them on radar diagrams to assess the overall robustness for metro networks. Overall, we find that Tokyo and Rome are the most robust networks. Rome benefits from short transferring and Tokyo has a significant number of transfer stations, both in the city center and in the peripheral area of the city, promoting both a higher number of alternative paths and overall relatively short path-lengths.
TL;DR: VMD–ICA–ARIMA, a hybrid method which combines variational mode decomposition (VMD), independent component analysis (ICA) and autoregressive integrated moving average ( ARIMA) can forecast the crude oil price more accurately.
Abstract: As one of the most vital energy resources in the world, crude oil plays a significant role in international economic market. The fluctuation of crude oil price has attracted academic and commercial attention. There exist many methods in forecasting the trend of crude oil price. However, traditional models failed in predicting accurately. Based on this, a hybrid method will be proposed in this paper, which combines variational mode decomposition (VMD), independent component analysis (ICA) and autoregressive integrated moving average (ARIMA), called VMD–ICA–ARIMA. The purpose of this study is to analyze the influence factors of crude oil price and predict the future crude oil price. Major steps can be concluded as follows: Firstly, applying the VMD model on the original signal (crude oil price), the modes function can be decomposed adaptively. Secondly, independent components are separated by the ICA, and how the independent components affect the crude oil price is analyzed. Finally, forecasting the price of crude oil price by the ARIMA model, the forecasting trend demonstrates that crude oil price declines periodically. Comparing with benchmark ARIMA and EEMD–ICA–ARIMA, VMD–ICA–ARIMA can forecast the crude oil price more accurately.
TL;DR: It is proposed that the topology of power grids should be designed so the loads are homogeneously distributed, or functional hubs and their neighbors have high tolerance capacity to enhance resilience.
Abstract: In this work, we present topological and resilience analyses of the South Korean power grid (KPG) with a broad voltage level. While topological analysis of KPG only with high-voltage infrastructure shows an exponential degree distribution, providing another empirical evidence of power grid topology, the inclusion of low voltage components generates a distribution with a larger variance and a smaller average degree. This result suggests that the topology of a power grid may converge to a highly skewed degree distribution if more low-voltage data is considered. Moreover, when compared to ER random and BA scale-free networks, the KPG has a lower efficiency and a higher clustering coefficient, implying that highly clustered structure does not necessarily guarantee a functional efficiency of a network. Error and attack tolerance analysis, evaluated with efficiency, indicate that the KPG is more vulnerable to random or degree-based attacks than betweenness-based intentional attack. Cascading failure analysis with recovery mechanism demonstrates that resilience of the network depends on both tolerance capacity and recovery initiation time. Also, when the two factors are fixed, the KPG is most vulnerable among the three networks. Based on our analysis, we propose that the topology of power grids should be designed so the loads are homogeneously distributed, or functional hubs and their neighbors have high tolerance capacity to enhance resilience.
TL;DR: This article investigated the reliability of a single frontier model based on the application of data envelopment analysis and stochastic frontier approach to a sample of Chinese local banks and found that these models provide steady information on the efficiency of the banking system as a whole, but they become divergent at the individual level.
Abstract: This study investigates to which extent results produced by a single frontier model are reliable, based on the application of data envelopment analysis and stochastic frontier approach to a sample of Chinese local banks. Our findings show they produce a consistent trend on global efficiency scores over the years. However, rank correlations indicate they diverge with respect to individual performance diagnoses. Therefore, these models provide steady information on the efficiency of the banking system as a whole, but they become divergent at the individual level.
TL;DR: In this paper, the space-time fractional diffusion equation with Mittag-Leffler kernel and Riemann-Liouville sense was examined separately; with fractional spatial derivative and fractional temporal derivative.
Abstract: In this paper, using the fractional operators with Mittag-Leffler kernel in Caputo and Riemann–Liouville sense the space–time fractional diffusion equation is modified, the fractional equation will be examined separately; with fractional spatial derivative and fractional temporal derivative. For the study cases, the order considered is 0 β , γ ≤ 1 respectively. In this alternative representation we introduce the appropriate fractional dimensional parameters which characterize consistently the existence of the fractional space–time derivatives into the fractional diffusion equation, these parameters related to equation results in a fractal space–time geometry provide a new family of solutions for the diffusive processes. The proposed mathematical representation can be useful to understand electrochemical phenomena, propagation of energy in dissipative systems, viscoelastic materials, material heterogeneities and media with different scales.
TL;DR: In this paper, the authors present a pedagogically review of recent advances in the study of the non-equilibrium dynamics of isolated quantum systems and emphasise the role played by the reduced density matrix and by the entanglement entropy in the understanding of the stationary properties after a quantum quench.
Abstract: In these lectures, I pedagogically review some recent advances in the study of the non-equilibrium dynamics of isolated quantum systems. In particular I emphasise the role played by the reduced density matrix and by the entanglement entropy in the understanding of the stationary properties after a quantum quench. The idea that the stationary thermodynamic entropy is the entanglement accumulated during the non-equilibrium dynamics is introduced and used to provide quantitative predictions for the time evolution of the entanglement itself. The harmonic chain is studied as an elementary model in which the quench dynamics can be easily and exactly worked out. This example provides a useful playground where general concepts can be simply understood and later applied to more complex and realistic systems.