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An ideal regression method should have several characteristics including precision, accuracy and generalization. In many studies in the field of nanofluid, the precision of models is more highlighted. Nevertheless, a lack of generalization may lead to over fitted models. In this paper, two nonlinear regression methods, namely the ANN and SVR are employed to predict the thermal conductivity of MWCNT-CuO/water hybrid nanofluid with temperature and volume fraction. It is seen that precision of SVR & ANN approaches are able to be compared. However, SVR generalization is more convenient, compared to ANN because SVR method utilizes less parameters. Hence SVR can show better persistence to overfitting in little-size datasets compared to ANN. Therefore, SVR is more authentic approach for the regression with little-size datasets.

Present work aims to introduce a new novel method of Support Vector Regression as a substitute for Artificial Neural Network to predict nanofluid properties, for the first time. Then its performance is evaluated according to the empirical results of SWCNT/EG versus temperature and concentration. Hence two LS-SVM and ANN models are trained to estimate the dynamic viscosity of nanofluid made of single-wall carbon nanotubes in ethylene glycol in terms of the temperature (T = 30 to 60 °C) and solid concentration ( ϕ = 0 . 01 to 0.1%). The results indicate that the precision of the LS-SVM and ANN models are comparable; nevertheless, the LS-SVM generalization is much better than the ANN. This is due to the fact that the LS-LSM models have a less number of parameters in comparison with the ANN. Therefore, the LS-LSM is more resistant to overfitting than the ANN, especially in handling small-size datasets. Hence, the LS-SVM may be a more reliable method for function estimation problems with small-size datasets.

The mixture of Al2O3/deionized water nanofluid thermal conductivity is experimentally examined at various temperatures and mass fractions. Then, a new prediction approach of fuzzy lookup table method (FLTM) is developed to estimate the mixture thermal conductivity. The thermal conductivity of Al2O3/deionized water nanofluid is measured by several experiments; and then the statistical/numerical approach of fuzzy lookup table method is presented. It is seen that more temperature and nanoparticles concentration correspond to more nanofluid thermal conductivity. It is also observed that the proposed model can be utilized to predict the output at the training data-set in order to verify its precision. Moreover, the model outputs error percentages respect to the measured thermal conductivity for dissimilar temperatures and nanoparticle concentrations are small which means the resultant model can be interpreted as an acceptable approach due to small values of errors and less computation costs.

We present an analysis of the empirical data and the agent-based modeling of the emotional behavior of users on the Web portals where the user interaction is mediated by posted comments, like Blogs and Diggs. We consider the dataset of discussion-driven popular Diggs, in which all comments are screened by machine-learning emotion detection in the text, to determine positive and negative valence (attractiveness and aversiveness) of each comment. By mapping the data onto a suitable bipartite network, we perform an analysis of the network topology and the related time-series of the emotional comments. The agent-based model is then introduced to simulate the dynamics and to capture the emergence of the emotional behaviors and communities. The agents are linked to posts on a bipartite network, whose structure evolves through their actions on the posts. The emotional states (arousal and valence) of each agent fluctuate in time, subject to the current contents of the posts to which the agent is exposed. By an agent’s action on a post its current emotions are transferred to the post. The model rules and the key parameters are inferred from the considered empirical data to ensure their realistic values and mutual consistency. The model assumes that the emotional arousal over posts drives the agent’s action. The simulations are preformed for the case of constant flux of agents and the results are analyzed in full analogy with the empirical data. The main conclusions are that the emotion-driven dynamics leads to long-range temporal correlations and emergent networks with community structure, that are comparable with the ones in the empirical system of popular posts. In view of pure emotion-driven agents actions, this type of comparisons provide a quantitative measure for the role of emotions in the dynamics on real blogs. Furthermore, the model reveals the underlying mechanisms which relate the post popularity with the emotion dynamics and the prevalence of negative emotions (critique). We also demonstrate how the community structure is tuned by varying a relevant parameter in the model. All data used in these works are fully anonymized.

We compare the probability distribution of returns for the three major stock-market indexes (Nasdaq, S&P500, and Dow-Jones) with an analytical formula recently derived by Dragulescu and Yakovenko for the Heston model with stochastic variance. For the period of 1982-1999, we find a very good agreement between the theory and the data for a wide range of time lags from 1 to 250 days. On the other hand, deviations start to appear when the data for 2000-2002 are included. We interpret this as a statistical evidence of the major change in the market from a positive growth rate in 1980s and 1990s to a negative rate in 2000s., Elsevier style (enclosed), 7.5 pages, 7 figures with 14 eps files. Submitted to Physica A, Proceedings of International Econophysics Conference in Bali, 28-31 August 2002

An artificial neural network (ANN) approach is used to determine the thermal conductivity of Al2O3 – Cu / EG with an equal volume (50:50). For this purpose, a mixture of Al2O3 and Cu (50:50) nanoparticles are added in to EG at various concentrations of 0.125 to 2.0 at T=25 to T=50 °C. The method of two-step approach is applied to add nanoparticles through the base fluid. Moreover, the feedforward multilayer perceptron of NN is examined to simulate the thermal conduction coefficient of Al2O3 – Cu nanofluid. So that, more than thirty six measured points are achieved through the experiments; while twenty five ones are chosen for ANN and eleven remained ones are applied to validate the network. It is seen that the ANN proposed approach can present the thermal conduction coefficient of hybrid nanofluids with suitable accuracy and good agreement with those of available empirical data.

A new multi-objective optimization model composed of the artificial neural network (ANN) and the genetic algorithm (GA) methods based on the empirical thermo-physical characteristics of CuO/liquid paraffin nanofluid flow in a pipe is presented for the first time. It means a new optimization /statistical approach is achieved based on ANN together with GA; so that at first ANN is employed to predict the nanofluid thermo-physical properties and then the heat transfer coefficient and the pressure drop ratios of the nanofluid to the basefluid, are optimized as well as to minimize the pressure drop ratio and maximize the heat transfer coefficient ratio by using the multi-objective optimization approach of GA. The results of the multi-objective optimization via the GA show that the Pareto optimal front quantifies the trade-offs in satisfying the two fitness function of heat transfer coefficient and the pressure drop ratios.

The exponential degree distribution has been found in many real world complex networks, based on which, the random growing process has been introduced to analyze the formation principle of such kinds of networks. Inspired from the non-equilibrium network theory, we construct the network according to two mechanisms: growing and adjacent random attachment. By using the Kolmogorov–Smirnov Test (KST), for the same number of nodes and edges, we find the simulation results are remarkably consistent with the predictions of the non-equilibrium network theory, and also surprisingly match the empirical databases, such as the Worldwide Marine Transportation Network (WMTN), the Email Network of University at Rovira i Virgili (ENURV) in Spain and the North American Power Grid Network (NAPGN). Our work may shed light on interpreting the exponential degree distribution and the evolution mechanism of the complex networks.

Understanding, quantifying and predicting market fluctuation has become increasingly important in recent decades. Volatility and volume are the two commonly used quantities to study the market dynamics and the relationship between these two has been modeled and debated for years with several hypothesis been put forward. Using empirical data, we investigate the causality and correlation between volume and volatility and find new ways in which they interact, particularly when the levels of both are high. We find that the volume-conditional volatility distribution scales with volume as a power-law function with an exponential cutoff. We exploit the characteristics of a volume-volatility scatterplot and find a strong correlation between logarithmic volume and a quantity we define as local maximum volatility (LMV), the highest volatility observed in a given range of volume. This supports our empirical analysis, showing that volume is an effective parameter for prediction of the maximum value of volatility for both same-day and near-future time periods. The joint conditional probability of volume and volatility also indicates if we invoke both quantities, the prediction of the largest next-day volatility will be better than invoking either one alone. This approach is thus a greatly improved method of risk assessment.

In this work, we employ a minimal agent-based model to explore the mechanisms that regulate competition between memes that spread online. We investigate the case in which each piece of information has a quality, and the higher is the quality the higher are the chances of being transmitted. The model allows us to study the impact of influential nodes on the spreading behavior. We show that meme’s quality does not guarantee virility, but there is a strong correlation between the meme’s success and the influence of the agent who introduced it. When trust is incorporated into the model and the agents can decided whether or not to accept a meme, we show that both lifetime and popularity distributions have broad power-law tails indicating that only a few memes spread virally through the population reproducing perfectly the broad distributions obtained from empirical data.

In order to explore the complexity of multivariate time series, we propose a novel method: multiscale multivariate weighted fractional entropy (MMWFE). The research results show that MMWFE is able to measure the complexity of multivariate data correctly and reflect more information contained in the time series. In this paper, the reliability of the proposed method is supported by simulations on generated and empirical data. We analyze simulated pink noise and white noise to test the validity of this method, and the result is consistent with the fact that pink noise is more complex than white noise. Meanwhile, MMWFE shows a better robustness. MMWFE is then employed to bivariate stock return and volume to explore the complexity of stock markets. It successfully distinguishes Asia, Europe and Americas markets. Finally, dynamic MMWFE is applied to explore the evolution of complexity for mining more information containing in nonlinear time series.

The origin of intelligence has fascinated scientists for a long time. Over the past 100 years, many scholars have observed the connection between entropy and intelligence. In the present study, we investigated a potential origin of the swarm intelligence in humans. The present study shows that a competitive economy consisting of a large number of self-interested agents can be mapped to a Boltzmann-like system, where entropy and energy play roles of swarm intelligence and income, respectively. However, different from the physical entropy in the Boltzmann system, the entropy (or swarm intelligence) in the economic system is a self-referential variable, which may be a key characteristic for distinguishing between biological and physical systems. Furthermore, we employ the household income data from 66 countries and Hong Kong SAR to test the validity of the Boltzmann-like distribution. Remarkably, the empirical data are perfectly consistent with the theoretical results. This finding implies that the competitive behaviors among a colony of self-interested agents will spontaneously prompt the colony to evolve to a state of higher technological level, although each agent has no willingness to evolve.

Children evacuation is a critical but challenging issue. Unfortunately, existing researches fail to effectively describe children evacuation, which is likely due to the lack of experimental and empirical data. In this paper, a questionnaire-based experiment was conducted with children aged 8–12 years to study children route choice behavior during evacuation from in a classroom with two exits. 173 effective questionnaires were collected and the corresponding data were analyzed. From the statistical results, we obtained the following findings: (1) position, congestion, group behavior, and backtracking behavior have significant effects on children route choice during evacuation; (2) age only affects children backtracking behavior, and (3) no prominent effects based on gender and guidance were observed. The above findings may help engineers design some effective evacuation strategies for children.

Peak Model states that each collective action has a life circle, which contains four periods of “prepare”, “outbreak”, “peak”, and “vanish”; and the peak determines the max energy and the whole process. The peak model’s re-simulation indicates that there seems to be a stable ratio between the peak’s timing (TP) and the total span ( T ) or duration of collective actions, which needs further validations through empirical data of collective actions. Therefore, the daily big data of online collective actions is applied to validate the model; and the key is to check the ratio between peak’s timing and the total span. The big data is obtained from online data recording & mining of websites. It is verified by the empirical big data that there is a stable ratio between TP and T ; furthermore, it seems to be normally distributed. This rule holds for both the general cases and the sub-types of collective actions. Given the distribution of the ratio, estimated probability density function can be obtained, and therefore the span can be predicted via the peak’s timing. Under the scenario of big data, the instant span (how long the collective action lasts or when it ends) will be monitored and predicted in real-time. With denser data (Big Data), the estimation of the ratio’s distribution gets more robust, and the prediction of collective actions’ spans or durations will be more accurate.

Crowd egress at narrow exit is a popular research topic, due to its intrinsic importance in architectural designs and building codes. However, relatively few studies have been conducted to verify the performance of pedestrian models for crowd escape at exits, especially relating to different exit designs. This paper aims to verify the applicability of a microscopic pedestrian simulation model, Social Force Model (SFM), embedded in Viswalk software to reproduce the effect of exit design on egress flow under normal and emergency conditions. Empirical data from controlled experiments considering the effects of obstacles size and location of exits under normal and emergency conditions were tested and compared with the simulation from the SFM. Results indicated that after parameter optimization, Viswalk simulation model can provide reasonable estimates for crowd escape under normal situations with a mean RMSE value 1.97s for total evacuation time. However, the simulation model was less capable in reproducing the emergency condition. As compared to the empirical data, clogging events were less spotted under emergency in the simulation. Faster-is-slower effects were not found in both empirical and simulation scenarios. In addition, the exit location effects from simulation data agreed with empirical data, corner exits were more efficient than middle exits under both situations. Meanwhile, the obstacle effects, as observed in empirical data, were less reproduced in the simulation, especially under emergency conditions. The results suggest that the application of the Viswalk model in simulating emergency situations needs scrutiny and further investigations in the future with empirical data.

The phase transition that network turns from free-flow state to congestion state is greatly influenced by the traffic. Empirical data analyses proved that actual traffic shows self-similarity (or long-range dependence) due to heavy-tailed user behaviors. Related literature works have pointed that there is a stable critical point of packets generation rate (PGR in short) at which the phase transition occurs, however, these works have ignored the heavy-tailed user behaviors and are only applicable to the short-range dependent traffic. In this paper, we make new contributions by analyzing the phase transition considering heavy-tailed user behaviors modeled by Pareto ON/OFF sources. We theoretically analyzed the critical point of PGR and proved that: (1) different from the previous works the critical point of PGR is varying with the heavy-tailed user behavior, which shows that it is unstable; (2) however, the average of critical point of PGR is derived to be same to the stable critical point of PGR with short-range dependent traffic; (3) particularly in the lattice networks with i.i.d heavy-tailed user behavior model, the average critical point of PGR is mainly determined by the average users number and an estimation of the critical point of average users number is provided. Numerical simulations have illustrated the effectiveness and validity of the theoretical results. Moreover, we also find the heavy-tailed behavior could make the network more congested and reduce the network transport efficiency by the simulations.

Understanding individuals’ travel patterns has great impact on practical issues such as traffic control and urban planning. Here, we analyze a 4G dataset of 1000 randomly selected individuals in Shijiazhuang city, China during half month which contains the position information of these cell phone users in each second and GPS logs of 182 volunteers in a period of over five years. We find that the dynamics of locations’ visitations is characterized by bursts, namely the distributions of the relative variation of visitation traffic per day, week display a long tail. On that basis we propose the Exploration and Rank-shift Return model combined the classic Exploration and Preferential Return model with a rank-shift mechanism where every location may move up to a higher ranking position with probability. The model qualitatively recovers the statistical properties observed in the empirical data and reproduces the existence of bursty visitation of locations.

This paper is a contribution to interweaving two lines of research that have progressed in separate ways: network analyses of international trade and the literature on African trade and development. Gathering empirical data on African countries has important limitations and so does the space occupied by African countries in the analyses of trade networks. Here, these limitations are dealt with by the definition of two independent bipartite networks: a destination share network and\ a\ commodity share network. These networks - together with their corresponding minimal spanning trees - allow to uncover some ordering emerging from African exports in the broader context of international trade. The emerging patterns help to understand important characteristics of African exports and its binding relations to other economic, geographic and organizational concerns as the recent literature on African trade, development and growth has shown., 31 pages, 8 figures

A previous model for a novel system is reinterpreted for the traditional systems of funding allocation. Empirical data can be well described. Both research funding and education funding are analyzed. The effect of merit-based cumulative advantage is more significant in research funding, where a slight difference is noticed between basic sciences and applied sciences. In contrast, the counter effect of cumulative advantage can be observed in education funding. Simple parameters are useful to distinguish between different distributions. The theoretical model presents three distinct regimes: equal sharing, cumulative advantage effect, and counter effect. The regime of equal sharing presents as a valley. Both cumulative advantage effect and counter effect result in the concentration of funding, which present as two plateaus of different heights.

Systemic risks of default contagion in the Russian interbank market are investigated. The analysis is based on considering the bow-tie structure of the weighted oriented graph describing the structure of the interbank loans. A probabilistic model of interbank contagion explicitly taking into account the empirical bow-tie structure reflecting functionality of the corresponding nodes (borrowers, lenders, borrowers and lenders simultaneously), degree distributions and disassortativity of the interbank network under consideration based on empirical data is developed. The characteristics of contagion-related systemic risk calculated with this model are shown to be in agreement with those of explicit stress tests., Final version, to appear in Physica A

Fluctuations in particle density functions in 28 Si+Ag(Br) collision at 14.5A GeV and 32 S+Ag(Br) collision at 200A GeV are investigated using the multifractal detrended moving average (MFDMA) method. Multifractal parameters obtained from the data analysis are systematically compared with the ultra-relativistic quantum molecular dynamics (UrQMD) model simulation. It is found that the single particle density functions in both the experiments are multifractal in nature. Further, the degree of multifractality in the simulated event samples is almost equal to the corresponding empirical data. The results of this analysis differ significantly from those obtained from other conventional techniques of multifractal analysis previously used for the same sets of data.

Community/cluster is one of the most important features in social networks. Many cluster detection methods were proposed to identify such an important pattern, but few were able to identify the statistical significance of the clusters by considering the likelihood of network structure and its attributes. Based on the definition of clustering, we propose a scanning method, originated from analyzing spatial data, for identifying clusters in social networks. Since the properties of network data are more complicated than those of spatial data, we verify our method’s feasibility via simulation studies. The results show that the detection powers are affected by cluster sizes and connection probabilities. According to our simulation results, the detection accuracy of structure clusters and both structure and attribute clusters detected by our proposed method is better than that of other methods in most of our simulation cases. In addition, we apply our proposed method to some empirical data to identify statistically significant clusters.

Pedestrian movement modeling is a popular out-of-equilibrium problem in statistical and computational physics. As a kind of typical pedestrian movement, multidirectional flow is quite common in real-life, and examples include the bidirectional flow in corridors and cross flow at intersections. In the discrepancies of the multidirectional flows, the behaviors of pedestrians should play a crucial role. Therefore, in this paper, the following behavior and the right-side-preference are investigated in three different types of multidirectional flow. By the floor field cellular automaton (FFCA), the dynamic floor field is redefined, and the right-preferred floor field and the order parameter for lanes formation are formulated. Then, the fundamental diagram, lanes formation, density distribution and passing time are analyzed in the multidirectional flows considering the influence of following behavior and right-side-preference. Finally, the simulation is compared with the empirical data, indicating that the proposed FFCA models the multidirectional flow well.

Individual pedestrian velocities vary over time and space depending on the crowd size, location of individuals’ within the crowd, and formation of self-organized lanes. We use empirical data to explore the spatial fluctuations of pedestrian velocities in bidirectional streams. We find that, unlike ordinary fluids, the velocity profile in bidirectional pedestrian streams does not necessarily follow a hyperbolic form. Rather, the shape of the velocity profile is highly dependent on the formation of self-organized lanes. We also show that the spatial fluctuations of pedestrian velocities along and transverse to the flow direction are widely distributed and can be modeled by a sum of Gaussian distributions. Results suggest that the effect of self-organization phenomenon is strong enough that for the same crowd size, the velocity distribution does not significantly change when pedestrians are highly mixed compared to when separate lanes are formed.

Urban public traffic networks are typical complex systems. Understanding their evolution mechanism attracts much attention in recent years. Here, we propose that the evolution of urban public traffic network can be considered as a game process between two network manipulators, i.e., passengers and company, and the equilibrium solution to the game determines the steady-state behavior of the network. Both analytical solution and numerical simulations to such game model can well describe the empirical data collected from the urban public traffic systems in four Chinese cities (Beijing, Shanghai, Nanjing, and Hangzhou) and the Boston subway. Our results suggest that the manipulator game model grasps the fundamental characteristics of the evolution mechanism of the urban public traffic systems. Similar idea may be extended to other complex systems which have small number of manipulators.

The slow-to-start models are a classical cellular automata model in simulating vehicle traffic. However, to our knowledge, the slow-to-start effect has not considered in modeling pedestrian dynamic. We verify the similar behavior between pedestrian and vehicle, and propose an new lattice gas (LG) model called the slow reaction (SR) model to describe the pedestrian's delayed reaction in single-file movement. We simulate and reproduce the Seyfried's field experiments at the research centre Julich, and use its empirical data to validate our SR model. We compare the SR model with the standard LG model. We test different probability of slow reaction ps in SR model and found the simulation data of ps=0.3 fit the empirical data best. The RMS error of mean velocity of SR model is smaller than that of standard LG model. In the range of ps=0.1~0.3, our fundamental diagram between velocity and density by simulation coincides with field experiments. The distribution of individual velocity in fundamental diagram in SR model agrees with the empirical data better than that of standard LG model. In addition, we observe the stop-and-go waves and phase separation in pedestrian flow by simulation. We reproduced the phenomena of uneven distribution of interspaces by SR model while the standard LG model did not implement. The SR model can reproduce the evolution of spatio-temporal structures of pedestrian flow with higher fidelity to Seyfried's experiments than the standard LG model., 12 pages, 7 figures

Understanding the psychological impacts of defect sites on drivers, and the resulting driving behaviors are crucial to the accident management and traffic safety improvement. This paper presents a new traffic flow model based on the two-lane cellular automaton model. In a model where a finite number of particles (e.g. vehicles) or sites (e.g. traffic incident sites) have different properties from the rest these are usually called defects. The defective site's impact is introduced, bringing the changes of acceleration, deceleration, random deceleration and headway. At the defect site, the vehicles decelerate spontaneously. The greater the impact is, the larger deceleration probability will be. Simulations of the proposed model and the classic NaSch model are given. The results suggest the remaining capacity of the proposed model is approximately 54.6% of that of NaSch model. Compared to empirical data, the model can describe the traffic flow at defect site better than NaSch model. 2014 Elsevier B.V. All rights reserved. Language: en

Robust analysis of coauthorship networks is based on high quality data. However, ground-truth data are usually unavailable. Empirical data suffer several types of errors, a typical one of which is called merging error, identifying different persons as one entity. Specific features of authors have been used to reduce merging errors. We proposed a Bayesian model on the merging errors of coauthorship data. When knowing the ground truth of specific empirical datasets obtained by a given method, the model contributes to finding informative features to reduce the merging errors of the datasets obtained by the same method. When being given the useful features of reducing merging errors, the model can be utilized to calculate the rate of merging errors for the name entities of authors. Therefore, the model can help to detect compromised name entities; thus has potential contribution to improving the quality of empirical coauthorship data.

We perform an extensive empirical analysis of scaling properties of equity returns, suggesting that financial data show time varying multifractal properties. This is obtained by comparing empirical observations of the weighted generalised Hurst exponent (wGHE) with time series simulated via Multifractal Random Walk (MRW) by Bacry \textit{et al.} [\textit{E.Bacry, J.Delour and J.Muzy, Phys.Rev.E \,{\bf 64} 026103, 2001}]. While dynamical wGHE computed on synthetic MRW series is consistent with a scenario where multifractality is constant over time, fluctuations in the dynamical wGHE observed in empirical data are not in agreement with a MRW with constant intermittency parameter. We test these hypotheses of constant multifractality considering different specifications of MRW model with fatter tails: in all cases considered, although the thickness of the tails accounts for most of anomalous fluctuations of multifractality, still cannot fully explain the observed fluctuations., 27 pages, 10 figures

In a well-known work (Phys. Rev. Lett. 84 (2000) 5224) it was shown that behaviour of returns for foreign exchange markets in different time scales can be described in terms of Fokker–Planck equation, with Kramers–Moyal coefficients being estimated from the empirical data. In the current paper the authors provide analytical solution for stationary Fokker–Planck equation, which allows explanation of non Gaussian tails of the distribution function. It is also shown that while approximating empirical data one needs to observe some limitations for correct results obtaining.

The Impact Factor has become a well-known measure of the average citation number of articles published in a scientific journal. A journal with a high Impact Factor is assumed to have a low percentage of uncited articles. We show that the scaling relation between the Impact Factor and the uncited percentage can be understood by a simple mechanism. The empirical data can be reproduced by a random mechanism with the cumulative advantage. To further explore the robustness of such a mechanism, we investigate the relation between the average citation number and the uncited percentage from different perspectives. We apply the idea of Impact Factor to the publications of an institute in addition to its general application to the publications of a journal. We find that the same scaling relation can be obtained. We also show that a static relation can be applied to describe the time evolution of a dynamical process. These results provide further justification for the same citation mechanism behind different research fields.

In a recent work, we introduced two models for the dynamics of customers trying to find the business that best corresponds to their expectation for the price of a commodity. In agreement with the empirical data, a power-law distribution for the business sizes was obtained, taking the number of customers of a business as a proxy for its size. Here, we extend one of our previous models in two different ways. First, we introduce a business aggregation rate that is fitness dependent, which allows us to reproduce a spread in empirical data from one country to another. Second, we allow the bankruptcy rate to take a different functional form, to be able to obtain a log-normal distribution with power-law tails for the size of the businesses.

Based on real traffic data measured on American, UK and German freeways, we study common features of traffic congestion. We have found that traffic features [J] and [S] defining traffic phases “wide moving jam” (J) and “synchronized flow” (S) in Kerner’s three-phase theory are indeed common spatiotemporal traffic features observed in the UK, the USA and Germany. For the testing of Kerner’s “line J”, representing the propagation of the wide moving jam’s downstream front, four different methods for a study of moving jam propagation in empirical data are studied and compared for each congested traffic situation occurring in the three countries. A statistical study of velocities of wide moving jam fronts is presented, which has been performed through the analysis of database containing more than 280.000 min of observed wide moving jams measured on about 1200 km long freeway network in Hessen (Germany) during more than two years.

A model of epidemic spreading that is applicable to email worms, for instance, is studied analytically and numerically. It is built on mean-field percolation, and incorporates two time scales originating in spreading dynamics and immunization. A comparison to empirical data is provided. The long-time limit of the dynamics is governed by an exponential decay. We derive an analytic expression for the characteristic time of the decay, and find a good agreement with numerics. There is a similar decay also in empirical observations.

The correlation of foreign exchange rates in currency markets is investigated based on the empirical data of DKK/USD, NOK/USD, CAD/USD, JPY/USD, KRW/USD, SGD/USD, THB/USD and TWD/USD for a period from 1995 to 2002. Cross-SampEn (cross-sample entropy) method is used to compare the returns of every two exchange rate time series to assess their degree of asynchrony. The calculation method of confidence interval of SampEn is extended and applied to cross-SampEn. The cross-SampEn and its confidence interval for every two of the exchange rate time series in periods 1995–1998 (before the Asian currency crisis) and 1999–2002 (after the Asian currency crisis) are calculated. The results show that the cross-SampEn of every two of these exchange rates becomes higher after the Asian currency crisis, indicating a higher asynchrony between the exchange rates. Especially for Singapore, Thailand and Taiwan, the cross-SampEn values after the Asian currency crisis are significantly higher than those before the Asian currency crisis. Comparison with the correlation coefficient shows that cross-SampEn is superior to describe the correlation between time series.

We study the cumulative distribution for the magnitude of earthquakes in the context of nonextensive statistical mechanics. A new modification of the Gutenberg–Richter (GR) law is introduced. We use seismic data sets which were recorded in two different regions, Iran and California, to compute the cumulative distribution of the magnitudes. The empirical data are fitted extremely well by our suggested expression for the modified GR law over the whole range of magnitudes.

We study the dynamics of the adoption of new products by agents with continuous opinions and discrete actions (CODA). The model is such that the refusal in adopting a new idea or product is increasingly weighted by neighbor agents as evidence against the product. Under these rules, we study the distribution of adoption times and the final proportion of adopters in the population. We compare the cases where initial adopters are clustered to the case where they are randomly scattered around the social network and investigate small world effects on the final proportion of adopters. The model predicts a fat tailed distribution for late adopters which is verified by empirical data., Comment: 14 pages, 8 figures, revised text, a new section with empirical evidence has been added