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Marathons are one of the ultimate challenges of human endeavor. In this paper, we apply extreme value statistics to predict fastest marathon times achievable for ten marathons around the world and for both men and women. We concentrate on the theoretical minimum time for each gender at each venue, irrespective of who runs there. Thus, we measure the potential of each marathon, not the actual performance of the runners. • Predictions of fastest marathon times achievable for ten marathons given; • The first manuscript applying extreme value statistics for a range of marathons. • Predictions are given for both genders. [ABSTRACT FROM AUTHOR]

In this paper we investigate an SIRS-UAU disease-awareness spreading model on multiplex networks that incorporates the influence of mass media. Through the microscopic Markov chain approach, we derive the evolution equations for the probability of an individual being in each possible state and obtain the epidemic threshold, which is shown to be a continuous phase transition point between the disease-free state and the endemic state in the phase diagram. Our results show that increasing the immunity wanning rate will enlarge the epidemic prevalence and reduce the fraction of recovered individuals; however, the temporal immunity has no impact on the epidemic threshold. Moreover, implementing mass media broadcast helps raise the awareness incidence and decrease the fraction of recovered individuals. In addition, for the special case when awareness confers complete self-protection against infection, the mass media broadcast significantly diminishes the epidemic prevalence and increases the epidemic threshold. Nevertheless, when awareness provides only partial self-protective effectiveness, the mass media broadcast plays a limited role in changing the epidemic prevalence and the epidemic threshold. Furthermore, in the special case where the mass media broadcast is not included, we discover the metacritical point, a point above which the epidemic threshold starts to grow nonlinearly with the awareness spreading rate and below which the epidemic threshold is independent of the awareness spreading rate. Conversely, the metacritical point disappears as long as the mass media broadcast is incorporated into the model. Our analytical results are confirmed by extensive Monte Carlo simulations. • Studied a coupled model of awareness and disease spread with temporal immunity and media. • Temporal immunity affects prevalence, not threshold; media impact depends on protection. • Found a metacritical point for threshold on awareness; media presence removes it. • Provided phase diagram for epidemic prevalence and awareness, supported by simulations. [ABSTRACT FROM AUTHOR]

Driving behavior modeling is extremely crucial for designing safe, intelligent, and personalized autonomous driving systems. In this paper, a modeling framework based on Markov Decision Processes (MDPs) is introduced that emulates drivers' decision-making processes. The framework combines the Deep Maximum Entropy Inverse Reinforcement Learning (Deep MEIRL) and a reinforcement learning algorithm-proximal strategy optimization (PPO). A neural network structure is customized for Deep MEIRL, which uses the velocity of the ego vehicle, the pedestrian position, the velocity of surrounding vehicles, the lateral distance, the surrounding vehicles' type, and the distance to the crosswalk to recover the nonlinear reward function. The dataset of drone-based video footage is collected in Xi'an (China) to train and validate the framework. The outcomes demonstrate that Deep MEIRL-PPO outperforms traditional modeling frameworks (Maximum Entropy Inverse Reinforcement Learning (MEIRL) - PPO) in modeling and predicting human driving behavior. Specifically, in predicting human driving behavior, Deep MEIRL-PPO outperforms MEIRL-PPO by 50.71% and 43.90% on the basis of the MAE and HD, respectively. Furthermore, it is discovered that Deep MEIRL-PPO accurately learns the behavior of human drivers avoiding potential conflicts when lines of sight are occluded. This research can contribute to aiding self-driving vehicles in learning human driving behavior and avoiding unforeseen risks. • Proposing a novel framework is Deep MEIRL-PPO which integrates PPO and Deep MEIRL. • This framework considers the situation where the driver's line of sight is occluded. • Customized neural network fits the nonlinear reward function in Deep MEIRL. [ABSTRACT FROM AUTHOR]

The mixed traffic flow of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) will exist on highways for a long time, as the deployment of CAVs is gradual. To reduce the negative impact of HDVs on CAVs, the deployment of dedicated lanes has been considered an effective solution. Along with the dedicated lanes, three different lane management strategies will be formed, which are (C, H) strategy (CAVs dedicated lanes and HDVs dedicated lanes), (C, G) strategy (CAVs dedicated lanes and general lanes), and (G, H) strategy (general lanes and HDVs dedicated lanes). To evaluate the influence of dedicated lane settings on mixed traffic flow comprehensively, this paper proposes a framework for evaluating road segment efficiency and fuel consumption by considering lane management strategies. First, the possible traffic flow equilibrium states under three lane management strategies are discussed, and the characteristics of five car-following modes in mixed traffic flow are analyzed. Then, a mixed traffic flow capacity model considering platoon size is introduced to the traditional BPR function to establish a speed estimation model for mixed traffic flow and a fuel consumption estimation model for mixed traffic flow. Next, the traffic flow distribution model at the lane level in a steady state is derived for different lane management strategies. Based on the traffic flow distribution model, the speed estimation model and the fuel consumption estimation model for mixed traffic flow, which consider lane management strategies, are proposed. Finally, a numerical simulation is conducted to analyze the effects of different lane management strategies and configuration schemes on road segment efficiency and fuel consumption. The results of numerical experiments show that (1) at the same traffic demand, the operational speeds of vehicles under the (C, H) strategy and (G, H) strategy tend to increase and then decrease with the increase in the penetration rate of CAVs. While the speed of the vehicle under the (C, G) strategy increases with the increase in the penetration rate of CAVs. (2) Compared with the baseline strategy, all three management strategies can improve the operating efficiency of vehicles under certain traffic conditions. (3) At the same traffic demand, the average fuel consumption under the three strategies tends to decrease first and then increase slightly as the penetration rate increases. Increasing the number of dedicated lanes under specific traffic conditions can significantly increase the fuel consumption reduction rate under each strategy. At the same penetration rate, this advantage diminishes with the increase in traffic demand. (4) The increase in platoon size favors the efficiency of vehicle operations under different strategies. However, as platoon size increases, the marginal benefit of increasing platoon size becomes smaller and smaller. In addition, the average fuel consumption of vehicles has a low sensitivity to platoon size, and increasing platoon size may not always reduce fuel consumption. • Proposes a framework to estimate the speed and fuel consumption of road segments considering lane management strategies. • Analyses the effects of different lane management strategies and different lane configuration schemes on speed and fuel consumption of road segments under different traffic conditions. • Analyses the effects of platoon size on speed and fuel consumption of road segments. [ABSTRACT FROM AUTHOR]

As the demand for low-power electronics and IoT devices grows, ambient energy harvesting appears to be a promising alternative for powering such systems in the long run. However, optimizing power and efficiency concurrently in such systems is challenging, involving balancing a number of variables. This paper investigates the optimization of power and efficiency in ambient energy harvesting systems focusing on nonlinear oscillator electromechanical harvesters subjected to multiplicative time-correlated ambient noise. Through extensive numerical simulations, we reveal distinct relationships between power and efficiency, influenced by various parameters. We observe autonomous stochastic resonance phenomena, elucidating a linear power-efficiency trend for small noise correlation time under fixed noise variance but limiting simultaneous power and efficiency optimization beyond a threshold. Under fixed noise strength, there is a trade-off between power and efficiency. Additionally, damping strength, piezoelectric parameters, and capacitor charging time impact power and efficiency linearly. These insights enhance understanding of power efficiency dynamics in ambient energy harvesting, thereby offering practical recommendations for parameter selection to maximize both power output and efficiency in the next generation of electronics. • The power-efficiency dynamics of the ambient energy harvester is investigated. • Autonomous stochastic resonance phenomenon is studied in nonlinear energy harvester. • Power and efficiency follow linear as well as trade-off relationships. • Practical recommendations for optimizing both power and efficiency is offered. [ABSTRACT FROM AUTHOR]

The Sznajd model is one of sociophysics's well-known opinion dynamics models. Based on social validation, it has found application in diverse social systems and remains an intriguing subject of study, particularly in scenarios where interacting agents deviate from prevailing norms. This paper investigates the generalized Sznajd model, featuring independent agents on a complete graph and a two-dimensional square lattice. Agents in the network act independently with a probability p , signifying a change in their opinion or state without external influence. This model defines a paired agent size r , influencing a neighboring agent size n to adopt their opinion. This study incorporates analytical and numerical approaches, especially on the complete graph. Our results show that the macroscopic state of the system remains unaffected by the neighbor size n but is contingent solely on the number of paired agents r. Additionally, the time required to reach a stationary state is inversely proportional to the number of neighboring agents n. For the two-dimensional square lattice, two critical points p = p c emerge based on the configuration of agents. The results indicate that the universality class of the model on the complete graph aligns with the mean-field Ising universality class. Furthermore, the universality class of the model on the two-dimensional square lattice, featuring two distinct configurations, is identical and falls within the two-dimensional Ising universality class. [Display omitted] • The role of independence in the generalized Sznajd model is examined by introducing the agent size r and its independent neighboring size n. • The appearance of continuous and discontinuous phase transition phenomena. • The universality class of the model belongs to the mean-field and two-dimensional Ising model. [ABSTRACT FROM AUTHOR]

Urban rail transit passenger flow modeling is the foundation of urban rail transit planning, design, and operation. The motivation of this paper is to accurately and efficiently simulate passenger flow dynamics in urban rail transit systems. To this end, we propose a State-dependent Multi-agent Discrete Event Simulation (SdMaDES). The state-dependence (or congestion-dependence) means that the service abilities (or travel times) of bottleneck facilities (e.g. platform screen doors and transfer corridors) are not constant and they interact with the congestion dynamics. Specifically, we first establish a modular Multi-agent Discrete Event Simulation (MaDES), which includes four types of modules (passenger, train, station, and network modules) and three types of agents (passenger, train, and station agents). The logical connections and event-triggering modes between the modules are analyzed and defined, and thirty types of events are designed. The state-dependence is then captured by an Improved Social Force Model (ISFM), which adds an autonomous obstacle avoidance mechanism. The ISFM reproduces passenger movement behavior within bottleneck facilities of urban rail transit systems at the microscopic level. These state-dependent functions or general rules are explicitly formulated by fitting the results of ISFM and are subsequently applied to the proposed MaDES model, resulting in the SdMaDES. This integration aims to enhance the accuracy of the simulation. We conducted a real case from the Chengdu Metro network. Some interesting results are found. (a) The maximum number of boarding passengers in a train carriage is a complex nonlinear function that is dependent on the state (density inside the train carriage). This challenges the linear function commonly utilized in most studies. (b) Compared to the actual data, the proposed SdMaDES model shows a cumulative error of 9.85% after data smoothing, while the conventional MaDES model exhibits a much higher cumulative error of 21.9% after data smoothing. (c) As the overall traffic demand level increases, the gap between the two simulation models' results is getting wider and wider due to the amplified nonlinear impact of congestion. • We propose a State-dependent Multi-agent Discrete Event Simulation for modeling passenger dynamics in rail systems. • The proposed method integrates state-dependent functions captured by the Improved Social Force Model. • The proposed method demonstrates 18.33% improved accuracy compared to the traditional model, while maintaining computational efficiency. • The real case from the Chengdu Metro network illustrates the effectiveness of the method. [ABSTRACT FROM AUTHOR]

With the advancement of technology, connected and autonomous vehicles (CAVs) can be applied to complex tunnel networks in long tunnel construction to enhance vehicle operation safety and efficiency. This paper proposes an optimization method for CAVs' operation in long tunnel constructions. Firstly, a spatiotemporal coordinated optimization model with decentralized time and hierarchical networks is proposed to minimize the total working time for completing transportation services. The model integrates macro task allocation and micro node control and optimizes the vehicle-space-time relationships of CAVs to prevent conflicts and collisions. Secondly, a heuristic algorithm named Search-Adjustment Genetic Algorithm (SAGA) is developed to solve the problem considering the model's complexity and engineering characteristics. Thirdly, numerical experiments are designed to validate the feasibility and efficiency of the proposed model and algorithm. The results indicate that (1) the proposed model can effectively deconflict CAVs in the road network to ensure safety and obtain a low total working time to fulfill the transportation demand. (2) Compared to the commercial solver Gurobi, the proposed algorithm demonstrates significantly superior solution accuracy and efficiency within an acceptable time limit. (3) The solution ensures the safety and efficiency of CAVs and increases their utilization compared with engineering-oriented methods, resulting in a 50 % reduction in CAV acquisition costs, a 29 % and 85 % reduction in running time and delay respectively, and a reduction in fuel consumption. (4) As the number of transportation services and the complexity of the road network increases, the efficiency gains become more prominent and better adapted to the needs of the actual long tunnel construction project. To sum up, the proposed model and algorithm can ensure the safety and efficiency of providing transportation services in future long tunnel construction. Moreover, it can be adapted for controlling CAVs in road networks such as other construction scenarios and urban road networks. [ABSTRACT FROM AUTHOR]

Many networks in the real world are directed networks, e.g., email networks, citation networks, etc. Community structure also exists in directed networks, and community detection can help us analyze the structure and function of the network. Currently, the number of community detection algorithms proposed for directed networks is small. The common method for detecting communities in directed networks is transforming the directed network into the undirected network. However, this method ignores the information carried by directed edges, which makes the quality of detected communities poor. In this paper, we propose a community detection algorithm for directed networks, which is based on Coulomb's law in physics, named CLBA. Moreover, we propose a new method to measure the node importance in directed networks that considers the situation of different networks in which the out-degree and in-degree of a node have different effects to the node importance. We abstract the nodes in the directed network as sets of charged particles and measure the charge of a node by its importance. Based on Coulomb's law, we can obtain the attraction between nodes and assign the attraction as the weight to the edges, which serves as the basis for the label propagation in directed networks. We also prove that our model is consistent with the point proposed by Kim et al. in 2010 for directed networks. The CLBA algorithm not only has low time complexity, but also, experimental results on benchmark and real networks show that our algorithm can detect communities effectively and efficiently. [ABSTRACT FROM AUTHOR]

In this paper, we simulate the hybrid pedestrian movement considering both operational and tactical levels. The video data of our pedestrian route choice experiments are used for validations. Based on the CA model proposed by Nowak and Schadschneider, we build a new modeling framework and introduce six new parameters. Their values are determined by sensitivity analysis. To consider the influence of five statistical results in the simulations, including the velocities and densities of two routes, and the proportion of choosing the longer route, we propose a new metric named OPE (Overall Percentage Error). In order to reduce OPE, four models with different configurations are discussed and compared. We find that the use of shorter time step, modified SFF and the two-stage decision can help to improve the model performance. The simulation results in some other experimental run also shows the validity and universality of the model. In addition, we also compare the outputs of some typical pedestrian simulation software (e.g., AnyLogic), and we find our model has better results. • A hybrid pedestrian model which combines operational and tactical level is discussed. • A floor field cellular automaton model with two-level framework is proposed. • The data from pedestrian route choice experiments are used for validations. • A new indicator named OPE is proposed for evaluating the model performance. • The comparison shows that the model is better than current commercial software. [ABSTRACT FROM AUTHOR]

In the current urbanisation process occurring worldwide, the management of pedestrian flow in large public spaces to ensure public area safety has become a highly scrutinised issue. This paper introduces a convolutional neural network-based model for simulating pedestrian movement, aimed at improving crowd management and public safety. The model predicts pedestrian trajectories by analysing historical data and comprises four key components: trajectory embedding network, encoder, decoder, and trajectory output network. In addition, the model employs highly parallelisable fully connected layers and convolutional layers to efficiently handle temporal dependencies. The results demonstrate the excellent performance of the model in both unidirectional and bidirectional pedestrian flow scenarios. For example, the model not only successfully reproduced pedestrians' self-organising behaviour (lane formation) but also rapidly (< 5 ms) and accurately simulated their fundamental features, such as density, velocity, and flow rate. To quantitatively evaluate the precision of the simulation, the average displacement error (ADE) and final displacement error (FDE) were applied and were calculated to be 0.104 m and 0.188 m, respectively, in unidirectional flow scenarios, and 0.126 m and 0.226 m, respectively, in bidirectional scenarios. Furthermore, the fluctuation of ADE across various scenarios remained within 0.05 m, and trajectories with ADE exceeding 0.3 m accounted for less than 5 % of the total, demonstrating the model's strong generalisability and robustness. The results indicate that the model is reasonable and capable of rapidly providing situational awareness for security personnel and enhancing crowd management. • A convolutional neural networks-based model is proposed to simulate pedestrian flow. • Highly parallelisable layers efficiently handle temporal dependencies. • The model exhibits robustness and generalisability across various scenarios. • The model is capable of rapidly providing situational awareness. [ABSTRACT FROM AUTHOR]

Since the development of urban public mass transport in cities, transport infrastructures are a milestone for the economies of the cities and those of their respective regions. The construction of underground systems has seen impressive growth in many transport networks in the past few years, becoming the hinge of mobility in many of the principal cities of the world. Due to the latter and because of the possibility of disruptive events (natural disasters, random failures, and intentional attacks), many scholars and governments have worried about how robust the networks are. For this reason, in this paper we reviewed the robustness of networks by using graph theory tools to analyze and measure them through a new centrality index, called Sum of Weighted Distances Index (SWDI). The index measures the percentage of changes in traveled distances in the event that the node and all edges containing it are disrupted. To evaluate the robustness of a network, we compare the outcomes of SWDI at a standard working configuration and at a degraded one, modeled as a reduction of performance at a certain edge, edge by edge. SWDI is able to show at what edges and nodes of the network the degradation will have the highest impact. Summing up the normalized SWDI outcomes for all the nodes and edges, the final evaluation of the robustness of the network is obtained. Applications to the two case studies of Boston and Barcelona are developed, compared, and discussed, limited to their topology. [ABSTRACT FROM AUTHOR]

There is a rich literature on microscopic models for opinion dynamics; most of them fall into one of two categories – agent-based models or differential equation models – with a general understanding that the two are connected in certain scaling limits. In this paper we show rigorously this is indeed the case. In particular we show that both ordinary and stochastic differential equations can be obtained as a limit of agent-based models by simultaneously rescaling time and the extent to which an agent updates their opinion after an interaction. This approach provides a pathway to analyse much more diverse modelling paradigms, for example: the motivation behind several possible multiplicative noise terms in stochastic differential equation models; the connection between selection noise and the mollification of the discontinuous bounded confidence interaction function; and how the method for selecting interacting pairs can determine the normalisation in the corresponding differential equation. Our computational experiments confirm our findings, showing excellent agreement of solutions to the two classes of models in a variety of settings. • Proves the connection between agent-based and differential equation models of opinion formation. • Explains the relationship between selection noise and mollifying interaction functions. • Motivates multiplicative diffusion in stochastic differential equations using additional noise terms in an agent-based model. • Demonstrates the convergence of each model through numerical simulations. [ABSTRACT FROM AUTHOR]

This paper investigates the challenges of quantifying stochasticity and dynamics in traffic states, crucial to efficient transportation systems, particularly during oversaturated conditions at signalized intersections. We build upon Newell's polynomial fluid queuing-theoretic ordinary differential equation (ODE) model as an example of model order reduction, with a focus on time-dependent stochastic arrival rates and departure rates. These assumptions facilitate dynamic arrival rate approximation using polynomial functions, which, when integrated with queue lengths, yield total delay per episode. Validation of Newell's polynomial fluid-queue based dynamic patterns is sought by analyzing cumulative cycles during peak periods, segmented based on residual queue values. A comprehensive real-world experiment employs emerging high resolution video detector cameras to capture various queue lengths, revealing recurring congestion patterns. Real-time arrival and departure count during peak periods further allow for the creation of cumulative curves, enhancing our understanding of congestion dynamics at signalized intersections under partially oversaturated conditions. This foundational understanding is essential for applying deep reinforcement learning in such environments, where the goal is to observe environment states to maximize expected long-term reward. [ABSTRACT FROM AUTHOR]

The Grains and Oilseeds Index (GOI) and its sub-indices of wheat, maize, soyabeans, rice, and barley are daily price indexes reflect the price changes of the global spot markets of staple agro-food crops. In this paper, we carry out a visibility graph (VG) analysis of the GOI and its five sub-indices. Our findings reveal that the degree distributions of the VGs, except for rice, exhibit exponentially truncated power-law tails, while the rice VG conforms to a power-law tail. The average clustering coefficients of the six VGs are quite large (> 0. 5) and exhibit a nice power-law relation with respect to the average degrees of the VGs. For each VG, the clustering coefficients of nodes are inversely proportional to their degrees for large degrees and are correlated to their degrees as a power law for small degrees. All six VGs exhibit small-world characteristics. The degree-degree correlation coefficients show that the VGs for maize and soyabeans indices exhibit weak assortative mixing patterns, while the other four VGs are weakly disassortative. The average nearest neighbor degree functions have similar patterns, and each function shows a more complex mixing pattern that decreases for small degrees, increases for mediate degrees, and decreases again for large degrees. • We perform visibility graph analysis on staple agro-food crop price indices. • Most visibility graphs are small-world networks. • All the visibility graphs exhibit similar mixing patterns. [ABSTRACT FROM AUTHOR]

Accurately identifying the passing and waiting behavior of pedestrians and non-motorized vehicles at intersection is essential to planning management measures for non-motorized transport. Much previous study in this field has focused on methods based on continuous individual trajectory detection, which are almost ineffective under the poor detection condition in dense traffic scenarios. To address the task, this paper establish a workable framework for inferring the probabilities of passing or waiting behavior of pedestrians and non-motorized vehicles in arbitrary space using disordered trajectory point data. First, a two-channel model is proposed to perform a formalized grid-level representation of an intersection, in which the functional attributes and occupancy characteristics of grids are comprehensively defined and quantified. Then, the quantitative characteristics of the grids are used to detect the real-world occurrence space of passing and waiting behaviors, by the clustering and expanding operations on grids. Finally, through feature transfer along path, characteristics is decomposed into passing and waiting occurrence characteristics for behavior probability computation. Results indicate that the method achieves over 91% accuracy of behavior recognition, which is better than compared methods in various Multiple Object Tracking Accuracy (MOTA). Although the method is sensitive to spatial detection conditions, it obtains steady accuracy under various target detection settings. • Behavior recognition based on poor trajectory in dense traffic scenarios. • Deviation between actual occurrence space of behaviors and the designed space. • Grid-based methods work well at low to medium tracking rates. • Grid-based methods are more robust and faster compared to other methods. [ABSTRACT FROM AUTHOR]

Both liquidity risk and jump risk are factors that cannot be ignored in the financial markets. In this paper, we investigate the pricing issue of American options within the framework of underlying asset prices encompassing the aforementioned two risks. First, based the empirical statistical analysis, we propose a novel model to capture the stochastic liquidity risk and jump risk of the underlying asset, and then convert the model into a model under a risk-neutral measure, and derive the characteristic function by solving the governing PDEs. Second, the analytical approximation formulas for the prices of the American options are obtained by using the COS method and Richardson extrapolation scheme. Finally, we conduct detailed numerical experiments. The numerical results compared with the Monte Carlo simulation demonstrate the accuracy and efficiency of our pricing formulas. We perform sensitivity analysis to elucidate the influence of liquidity risk and jump risk on option prices, examining variations in American option prices across various parameters of the model. Additionally, we further substantiated the rationale for incorporating liquidity risk and jump risk into the model by examining the price behavior of option prices under different models. • Empirical evidence shows the significance of stochastic liquidity and jump risks. • A stochastic model capturing both stochastic liquidity and jump risks is established. • The pricing formulas of American options under the new model are investigated. • Numerical experiments validate the rationality of pricing method and our model. [ABSTRACT FROM AUTHOR]

Multi-party private set intersection (MP-PSI), an essential branch of secure multiparty computation (SMC), enables multiple users to collaboratively determine the intersection of their private sets without revealing any private information except for the set intersection. Although MP-PSI has many practical applications, none of the existing MP-PSI protocols are quantum-proof security. Compared to classical and post-quantum cryptography, quantum cryptography, which relies on quantum mechanics, can provide a superior solution to resist various quantum attacks and achieve quantum-proof security. In this paper, we propose a multi-party quantum private set intersection (MP-QPSI) protocol, which can compute the set intersection with one protocol execution. The proposed protocol can not only keep the private sets undisclosed to the participants and third party (TP), but also prevent external attacks from outsider eavesdroppers. It utilizes easy-to-implement technologies such as single photons, single-particle measurements, and rotation operations. To validate the feasibility of the proposed protocol, a quantum circuit for the simulation experiment is designed and executed on IBM Quantum Experience. Compared to previous studies, our protocol demonstrates improved performance in terms of security and feasibility. • A quantum multiparty private set intersection protocol is proposed. • The protocol can achieve quantum-secure and long-term security. • Circuit simulation of the proposed protocol using IBM Quantum Platform. • The private sets apart from the set intersection will remain undisclosed. [ABSTRACT FROM AUTHOR]

In this paper, we propose an SAIS epidemic model with awareness programs by media on higher-order networks to study the impact of media and higher-order networks on disease transmission, in which the simplicial complex is utilized to construct a social network that describes connections between nodes, where a single link can connect more than two individuals. We use the cumulative density of awareness programs to quantify the impact of media and use the Microscopic Markov Chains method to get the probability evolution equation of each node. In order to solve the computational difficulties caused by the excessively high dimension of the equation, we use the mean field method to reduce the dimension of the equation to get the equilibrium of the system. Through dynamical analysis, we obtain the threshold of disease outbreak and conditions for the existence and stability of the disease-free equilibrium and the endemic equilibrium. The numerical simulations of SAIS model prove that the mean field method has good predictive performance. We also obtain the influence of parameters on threshold and system dynamical behavior through sensitivity analysis. The results show that: on one hand, the introduce of 2-simplex increases the probability of disease transmission in healthy populations, and the system appears discontinuous transition and bistable coexistence of health state and disease epidemic state, on the other hand, awareness programs by media can increase the threshold of disease outbreak, decrease the final density of infected individuals and inhibit the explosive growth of disease, thus effectively control the spread of diseases. • A simplicial epidemic model with media and higher-order interactions is proposed. • The threshold is related to media and the transmission rate of 2-simplex. • Media and 2-simplex have a significant impact on the spread of diseases. [ABSTRACT FROM AUTHOR]

There is a deep connection between thermodynamics, information and work extraction. Ever since the birth of thermodynamics, various types of Maxwell demons have been introduced in order to deepen our understanding of the second law. Thanks to them, it has been shown that there is a deep connection between thermodynamics and information, and between information and work in a thermal system. In this paper we study the problem of energy extraction from a thermodynamic system satisfying detailed balance by an agent with perfect information, e.g. one that has an optimal strategy, given by the solution of the Bellman equation, in the context of Ising models. We call these agents kobolds, in contrast to Maxwell's demons which do not necessarily need to satisfy detailed balance. This is in stark contrast with typical Monte Carlo algorithms, which choose an action at random at each time step. It is thus natural to compare the behavior of these kobolds to a Metropolis algorithm. For various Ising models, we study numerically and analytically the properties of the optimal strategies, showing that there is a transition in the behavior of the kobold as a function of the parameter characterizing its strategy. [ABSTRACT FROM AUTHOR]

In the context of disease spreading, two factors merit particular attention. One factor involves individuals instinctively seeking advantages while avoiding the risk of infection. The second factor pertains to the influence of the environment in which individuals reside. This paper introduces an adaptive mechanism and the surroundings reinforcement in a two-layer network. Meanwhile, a novel model, named aUAU-sSIR, is proposed to explore the impact of the adaptive mechanism and the surroundings reinforcement on disease spreading in the two-layer network. Then, based on micro Markov chain approach (MMCA), we derive the corresponding MMCA equation for the newly proposed model and articulate the mathematical expression of disease transmission threshold. Finally, comprehensive simulations demonstrate the feasibility and effectiveness of the theoretical analysis, elucidating the impact of both the adaptive mechanism and the surroundings reinforcement. The simulations reveal that adaptive behavior within the awareness-layer impedes the awareness diffusion and promotes the disease spreading. Additionally, our findings indicate that increasing the surrounding reinforcement factor can effectively accelerate disease spread. [ABSTRACT FROM AUTHOR]

The integrated use of dockless bike-sharing (DBS) and metro has greatly promoted the development of multimodal transportations. It is challenging to understand the travel mobilities of DBS and metro due to their complicated flow structures. In this paper, we propose an interconnected double-layer network to analyze the mobilities, in which each travel mode constructs a layer network. The studied area is divided into grids with same size. The grids are considered as nodes and trips between nodes are considered as edges. The edges between different layers are transfer trips that are found by space-time constraints. The results show that the metro trip network is connected more tightly with high efficiency than DBS trip network. Moreover, the Gini coefficient of aggregate network is more than 0.9, which is larger than metro trip network and DBS trip network. It is observed that the transfer ratios between layers are high in morning peak, but low in evening peak. The multiplex participation coefficient is adopted to measure the disparity of degree distributions of a node in different layers. It is found that passengers are distributed more evenly in the central area than suburbs between two layers. Our findings could be helpful in planning and managing multimodal transportations. • Building a multilayer network to analyze the integrated use of dockless bike-sharing and metro. • The metro trip network is connected mote tightly with high efficiency than DBS trip network. • The metro trip network, DBS trip network and aggregate network show 'small world' characteristics. • The Gini coefficient of aggregate network is more than 0.9, which is larger than metro trip network and DBS trip network. [ABSTRACT FROM AUTHOR]

Previous studies indicate that congestion is more likely to occur in the exit restriction area. Besides the background music in public places may have a potential impact on pedestrian movement. However, the influence mechanisms of low-boundary exit and background music on pedestrian evacuation are still unclear. Therefore, in this paper, the evacuation characteristics of pedestrians at a low-boundary exit were investigated with a series of controlled experiments. Different commands (listening to music while walking and following the tempo of the music) and different types of music (music and metronome) were designed in the experiment to analyze the influence of sound signal on the evacuation process. The result indicates that a larger width of low-boundary exit leads to a shorter evacuation time. The flow of low-boundary exit is higher than that of high-boundary exit. In addition, it is found that sound signal has a negative effect on evacuation efficiency, and the influence level is varied with different commands and music types. Specifically, the flow of following the beat of music is lower than that of listening to music, while the flow of walking with the beat of metronome is higher than that of walking with the beat of music. This study can contribute to understanding the evacuation dynamics of pedestrians when passing through a low-boundary exit. Furthermore, the performance of sound signal can be useful to develop management strategies for pedestrians under the emergency evacuation. [ABSTRACT FROM AUTHOR]

In recent years, cryptocurrency has attracted widespread research attention. Against the backdrop of financial globalization, the high volatility of cryptocurrency prices may pose a threat to the stability of the entire financial system. To explore the potential association and transmission of financial risks between cryptocurrency and traditional financial assets, this paper constructs a complex network using the Granger causality method and the DY spillover method. Based on data from 24 categories of assets or index returns from different markets, networks of association and risk spillover between cryptocurrency market and traditional financial markets are established under different time periods, the study covers the period from January 2015 to November 2023. Analyzing the mutual influence between cryptocurrency and traditional financial markets from the perspective of complex networks, and evaluating the market positioning, risk characteristics, and investment potential of cryptocurrency. Research findings indicate that cryptocurrency possesses not only monetary characteristics but also functions as an investment tool, significantly impacting the stability of traditional financial markets. Furthermore, different types of cryptocurrencies vary in market functions, risk characteristics, and investment potentials. Future research could further explore the nonlinear and dynamic associations between cryptocurrency and traditional financial markets, as well as the micro mechanisms and behavioral factors within cryptocurrency markets. • Construct networks of association and risk spillover through time series analysis. • Cryptocurrency easily spreads and contagion risks with traditional financial markets. • Cryptocurrency combines currency and capital functions, creating new value and risks. • Cryptocurrency's market functions and risk vary with its types and features. [ABSTRACT FROM AUTHOR]

MaxCut is an NP-hard combinatorial optimization problem in graph theory. The quantum approximate optimization algorithms (QAOAs) offer new methods for solving such problems, which are potentially better than classical schemes. However, the requirement of N qubits for solving graphs with N -vertex in QAOA, coupled with the large- N trainability issue due to barren plateaus, poses a substantial challenge for noisy intermediate-scale quantum (NISQ) devices. Recently, a hybrid quantum–classical algorithm has been proposed for solving semidefinite programs (SDPs), named NISQ SDP Solver (NSS). In this paper, we study the performance of the NSS for solving MaxCut problem via the introduction of a near-term quantum algorithm and execution of experimental simulations. Since the MaxCut problem admits a relaxation into an SDP formulation, the SDP can be solved using NSS, with a subsequent classical post-processing step converting the hybrid density matrix into a MaxCut solution. After performing these steps, the near-term quantum algorithm will also inherit the advantages of NSS. We analyze the algorithm as compared to QAOAs and find that the depth of the quantum circuit is independent of the number of edges on the graph. Our algorithm requires log N qubits and has O (1) circuit depth. This implies that it uses exponentially fewer qubits compared to QAOAs while also requiring a significantly reduced circuit depth to solve the MaxCut problem. To demonstrate the effectiveness of the NSS, we focused on 3-regular graphs, 9-regular graphs, and ER graphs. Numerical results indicate that the quantum algorithm achieves a comparable approximation ratio with classical methods by solving the original high dimensional problem within a lower dimensional ansatz space. Analysis under various initial states shows that the algorithm exhibits a remarkably stable performance. [ABSTRACT FROM AUTHOR]

With the rapid development of the city, urban metro network is growing rapidly, and the corresponding topology and the importance of stations are changing dynamically. It is of great significance to investigate the node importance and dynamic evolution of urban metro networks for scientific advice on urban metro construction, planning, and operation. In this paper, a new node importance algorithm RNEL (Node importance algorithm based on ReLU function considering Neighborhood force, Entropy and Local link similarity (LLS)) is proposed based on node force, entropy, and topology, and simulation experiments are carried out on the relevant datasets. In addition, the topology of Wuhan metro network from 2004 to 2022 is constructed using Space L, and the network topology parameters of 8 periods are analyzed, and the importance of stations is compared horizontally and vertically. The experimental results show that the proposed RNEL algorithm is superior to the nine node importance algorithms on four evaluation metrics for nine publicly available datasets and four artificial network models. With the spatiotemporal evolution of the Wuhan metro network, nodes become more and more dense, the network tends to be matched, the average distance between stations becomes longer and longer, and the importance of the different types of stations is changing over time. In addition, the importance of the Wuhan metro stations gradually spread from L-1 to L-3 northwest and L-5 southeast. The importance of a stations changes dynamically over time, driven by changes in the type of station, the stations connected to it, and the network environment. This study provides scientific suggestions and aids decision-making for the construction location, station setting and line extension direction of new lines in Wuhan Metro network. • A new node importance algorithm is proposed. • The validity of the algorithm is verified in Wuhan metro network and 13 data sets. • Dynamic analysis of Wuhan metro network in 8 periods. • The importance of the station is compared horizontally and vertically. [ABSTRACT FROM AUTHOR]

The bathtub model is an applicable and effective tool for simulating the characteristics of traffic dynamics in downtown areas during morning rush hour, especially the characteristics of hypercongested traffic. In order to reduce the complexity of the research, previous studies have adopted a linear velocity–density relationship. This has limitations in analyzing the cost and duration of hypercongestion in traffic dynamics. In this paper, we develop a bathtub model with one kind of nonlinear velocity–density Relation, i.e. , Pipes–Munjal Relation, based on continuous scheduling preferences and analyze its solutions in both no-toll user equilibrium and social optimum states. Due to the intractability in solving this bathtub model, there exists local analytical solutions, but for a complete solution of the model, only numerical solutions are available. The results indicate that as the value of n increases, the model can simulate traffic dynamics with higher cost and longer duration of hypercongestion under equilibrium, and can simulate traffic states with lower system cost under social optimum. We also explore the results of implementing optimal time-varying toll to realize social optimum. This study expands the relevant theories of bathtub models and provides a new perspective for traffic congestion. • Derive UE and SO for the bathtub model with nonlinear velocity–density relation. • Compare the numerical results under different values of parameter n. • Analyze the optimal time-varying toll to realize social optimum. [ABSTRACT FROM AUTHOR]

Public opinion is subject to peer interaction via social networks and external pressure from the media, advertising, and other actors. In this paper, we study the interaction between external and peer influence on the stochastic opinion dynamics of a majority vote model. We introduce a model where agents update their opinions based on the combined influence of their local neighbourhood (peers) and an external actor in the transition rates. In the first model, the external influence is only felt by agents non-aligned with the external actor ("push strategy"). In the second model, agents are affected by external influence, independently of their opinions ("nudging strategy"). In both cases, the external influence increases the possible macroscopic outcomes. These outcomes are determined by the chosen influence strategy. We also find that the social network structure affects the opinion dynamics, with social clustering positively reinforcing the external influence whereas degree heterogeneity weakens the external forces. These findings are relevant to businesses and policy making, helping to understand how groups of individuals collectively react to external actors. • The interaction between peer pressure and an external field leads to more complex behaviour than reinforcing the external opinion. • The intensity and weight of an external field push the population into a greater variation of macroscopic states. • Macroscopic outcomes depend on the alignment of agents to the external field. • Social clustering reinforces the influence of an external field. • Degree heterogeneity and hubs hinder the effect of an external field. [ABSTRACT FROM AUTHOR]

The flight delay propagation and recovery processes in airline networks differ due to differences in airline network structures, capacity distributions, and base airport locations. When each airline network is regarded as a network layer, flight delay propagation and interaction in a multiplex network system (MNS) composed of multiple airlines will become more complex and variable. In this paper, flight delays from different airlines are considered propagation sources, and an MNS-based flight delay propagation model is established under the framework of the susceptible-infected-susceptible model. By comparing the independent propagation process of delays in a single-layer airline network and the interactive propagation process of delays in an MNS, we investigate the impact of network structure on flight delays to propose corresponding delay control strategies for different airlines. The results show that unlike in single-layer networks, in an MNS, the behavior of airlines at an airport facilitates or inhibits delay propagation in other layers by influencing the network characteristics and propagation rate. The same airport may even play different roles in different layers, depending on the function it has. Therefore, not all flights are infected or have aggravated delays when accessing the MNS; instead, they are impacted by combined factors such as on-time performance, network structure, and flight interaction. • The new characteristics of multiplex network system (MNS) is identified to investigate their impact on flight delay propagation. • An MNS-based flight delay propagation model is constructed to reveal the interactive process of flight delay between layers. • We suggest a development trend for the MNS and propose delay control strategiesfor different airlines. [ABSTRACT FROM AUTHOR]

During epidemics, individual responses to prevention and control information vary significantly, affecting the execution of preventive measures. This paper introduces a novel model to analyze the co-evolution of the information-behavior-epidemic dynamic, accounting for the heterogeneity in individual behavioral adoption thresholds and network structures. The model consists of three layers: the upper layer, utilizing scale-free social networks to represent the diffusion of information; the middle and lower layers, employing community networks, to illustrate individuals' decision-making and epidemic transmission processes, respectively. We applied the Heaviside step function to depict the effects of information and local environmental factors on individuals' decisions. Through the microscopic Markov chain approach (MMCA), we determined the epidemic threshold. Our methodology also included extensive Monte Carlo (MC) simulations to validate our theoretical predictions. Our results reveal several key insights: (i) reducing community mobility and the threshold for adopting preventive behaviors can significantly restrain the scale of the epidemic and delay outbreaks; (ii) the level of individual activity in the physical contact network has a significant influence on epidemic transmission; (iii) integrating individuals' behavior adoption thresholds induces a two-stage phase change, deviating from the continuous phase transition typically observed in epidemic transmission. • Integrated model predicts epidemic dynamics accurately. • Lowering thresholds reduces epidemic risk significantly. • Community structures exert significant influence on transmission dynamics. • Individual activity levels play a crucial role in epidemic transmission. • Effective vaccination campaigns bolster outbreak control efforts. [ABSTRACT FROM AUTHOR]

Critical links have the characteristics of carrying large traffic flow and quickly affecting the operational performance of the road network. Existing regional traffic congestion coordination control methods do not consider the impact of critical links. To solve this issue, this paper proposes a regional traffic congestion coordination control method based on critical links. First, the median, gravitational index, and traffic sharing ratio are selected as the identification indexes of critical links in urban networks. Second, the road network vulnerability is assessed, and road link criticality is calculated based on joint entropy theory and multi-scale factor synthesis of multi-metric identification results. Then, the regional signal control range is determined, and a multi-stage signal control optimization model is developed. Finally, the optimal traffic signals for intersections are obtained by minimizing the pressure at the maximum phase of the road link and maximizing the capacity of critical links. To demonstrate the effectiveness of the proposed methods, two benchmarks (i.e., the MAXBAND method and existing traffic signals) are compared. The results show that (1) in terms of boundary control, the average vehicle delay at the boundary intersection of the road network is 34.62 s, an increase of 7.49 s and 5.03 s compared to the benchmark methods, respectively. (2) In terms of internal control, the average delay of the road network based on the proposed method is 87.54 s, which is reduced by 16.38 % and 28.73 % compared with the benchmark methods, and the queue lengths of the critical links are reduced by 21.47 % and 34.15 %, respectively. (3) The control process of the proposed method enables the number of vehicles within the road network to be maintained near the optimal carrying capacity, which gives the road network an efficient operating efficiency. [ABSTRACT FROM AUTHOR]

Comprehending the principles underlying the interaction between DNA molecules in electrolyte solution is of crucial importance for understanding the phenomena of DNA condensation, recognition of homologous genes, and properties of DNA liquid crystals. Although DNA was considered in all its helical complexity over the last two decades, only a primitive, local, electrostatic model of the aqueous solution was considered. The forces acting between DNA molecules however must be influenced by the structure of the liquid separating them. In this paper we make a step towards unravelling earlier neglected effects of water structure on DNA–DNA interactions. We develop a model of DNA interaction accounting for the nonlocal dielectric response of water and of the electrolyte plasma dissolved in it. We focus particularly on the study of the dipolar overscreening effect, which leads to decaying oscillations of the electrostatic potential about DNA, and how it will affect electrostatic energy surfaces as a function of interaxial separation and mutual azimuthal orientation of the parallelly aligned interacting DNAs. We found that going beyond the classical electrostatic description of water (the so-called primitive model) reveals a complex oscillatory interaction surface in space, with many possible metastable configurations. These however vanish rapidly with smearing of the DNA charge distribution, due to dephasing of the oscillatory components of the interaction. This dephasing results in the suppression of oscillation amplitudes and leads to the dominance of non-oscillatory components in spatial dipolar correlations and the Debye screening. These dominant correlation patterns are shown to lead to a substantial enhancement of the difference between the interaction of homologous and nonhomologous DNA sequences, deepening and sharpening the homology recognition well. [ABSTRACT FROM AUTHOR]

For digital assets (cryptocurrencies, DeFi tokens and NFTs) and traditional financial assets such as stock indices and commodities, this paper established a dynamic downside risk network to explore the network aggregating structure, important nodes and the tail risk spillover between asset groups. This study reveals that there exist tail risk spillover between digital financial assets and traditional financial assets, where the risk spillover between the digital assets and the commodities is bidirectional and the risk spillovers from the stocks to the digital assets as well as the commodities to stocks are unidirectional. For important assets, the NFT Decentraland and Brazil's BVSP stock index play central roles in the network, while the FTSE stock index and gold exhibit strong hedging potential against global downside risk spillovers. Furthermore, we consider the impact of uncertainty in US economic policy and US stock volatility on tail risk contagion, which is the network aggregation level. Regarded as exogenous shocks, the US stock circuit breaker in March 2020 and the US Federal Reserve rate rising in March 2022 were found to significantly increase the tail risk contagion, especially the risk spillover from the stocks market to the digital financial market. Moreover, by using the TVP-SV-VAR model, we find that increases in the US EPU index and the US VIX index can both exacerbate the tail-risk contagion between digital and traditional financial assets. This expands the influencing factors of risk contagion between financial assets. The empirical findings carry important implications not only for portfolio managers and investors in portfolio allocation and investment strategies between digital and traditional financial assets, but also for government regulators in risk management and economic policy formulation. • The tail risk network contains the cryptocurrency, DeFi, NFT and traditional assets. • Decentraland and BVSP exhibit outstanding centrality and influence in the network. • US stock circuit and Federal Reserve rate rising increase network aggregation level. • Intensities of the risk spillover between asset groups increase during the crises. • VIX and US EPU drive network aggregation level significantly. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of social dilemmas that arise when passengers are faced with choosing between personal vehicles and buses in a mixed traffic flow. We conducted the computer simulation using a cellular automata framework to analyze the case. The Revised S-NFS model describes the dynamics of vehicles in a single lane, while Kukida's model defines the rules for lane changing. The presence of social dilemma is determined by the social efficiency deficit. We selected four scenarios that represent different states on the fundamental diagram of personal vehicle flow: free flow, critical point, congested flow, and heavy congested flow. The simulations were performed on an infinite two-lane road. The results show that a social dilemma of the prisoner type exists in congested and heavy congested flow, but not in free flow. Additionally, we have conducted a simulation for the scenario with a dedicated bus lane. This simulation has shown the presence of the chicken-type social dilemma. Simulation results can enhance our comprehension of choosing transportation modes. [ABSTRACT FROM AUTHOR]

The emergence of connected and autonomous vehicles (CAVs) holds promise for fine-grained traffic control. However, due to the longevity of future mixed traffic scenarios, there is a need for an in-depth exploration of integrating the microscopic speed control of CAVs with the macroscopic variable speed limit (VSL) of human-driven vehicles (HDVs). This paper proposes a Cooperative Traffic Optimization with Multi-agent Reinforcement Learning and Evolutionary VSL (CTO-ME) framework, which combines microscopic CAV control with macroscopic VSL control. The framework incorporates a Graph Attention Mechanism (GATs) into the multi-agent reinforcement learning framework for intelligent decision-making by microscopic-level vehicles. Additionally, an evolutionary strategy is developed to design the VSL network architecture, enabling macroscopic level real-time speed limit adjustments based on infrastructure. A multi-objective reward function is proposed to optimize both micro and macro efficiency and safety, accounting for both vehicle behavior and traffic flow. Experiments on the designed Bottleneck traffic scenarios show that the proposed approach, CTO-ME, is able to achieve superior performance and outperforms other baselines in terms of traffic throughput, average speed, and safety. Specifically, CTO-ME enhances average velocity by 37%, increases overall throughput by 309%, and raises arrival ratio by 70% than traditional Intelligent Driver Model (IDM). • Macro–Micro Control Integration: We tackled the challenge of harmonizing macroscopic (Variable Speed Limit - VSL) and microscopic (Connected and Automated Vehicles - CAVs) controls to enhance both granular and overarching traffic efficiency. Our proposed method, the Cooperative Traffic Optimization with Multi-agent Reinforcement Learning and Evolutionary VSL (CTO-ME), cooperates with these control methods, paving the way for synchronized and efficient traffic management. • We integrated the Graph Attention Mechanism (GATs) into the multi-agent reinforcement learning framework to amplify the decision-making capabilities of vehicles and developed an advanced evolutionary strategy for VSL control to facilitate real-time speed limit adjustments. • We proposed a multi-objective reward function, considering both micro and macro efficiency and safety considerations, to promote optimized traffic flow and enhance overall control performance. [ABSTRACT FROM AUTHOR]

Due to globally uneven distribution of forest resources and timely difference of supply and demand, forest products are difficult to achieve market equilibrium among neighboring countries. The global forest product trade not only alleviates the contradiction of supply and demand, but also ensures the sustainable development of the economic construction for each country. Previous literature analyzed the structure and evolution mechanism of forest product trade from different perspectives, but ignored the competitive relationships among forest product importers. Based on complex network theory, this paper selected global wood forest product trade data from 2004 to 2016. Then we constructed global unweighted and weighted wood-forest-product competitive network from import perspective, and three indicators (density, average path length, and clustering coefficient), core–periphery model and two indicators of competitive intensity were introduced to study the spatial competitive evolution of global wood forest product trade among countries. The results showed that there was wide competition in global wood forest product trade among continents, and the tightness of competitive relationships gradually decreased with time. Secondly, core countries and periphery countries were clearly identified in the competitive network, and China, Japan, the U.S., were the main core countries influencing the evolution of core–periphery structure. Finally, the U.S. played an important role in the global competition system. But with the increment of demand for Asian market, China and other Asian countries gradually became the center of wood-forest-product competition. The research tried to provide some references for importers to follow the laws of global wood forest product trade, and formulate the corresponding policy under constructing sustainable society and facing climate change. • The paper considered the competitive relationship of global wood forest product trade. • TThe paper constructed global unweighted and weighted wood-forest-product competitive network, and studied spatial competitive evolution of global wood forest product trade. • The U.S. played an important role in the global competition system, but China and other Asian countries gradually became the center of global wood-forest-product competition. [ABSTRACT FROM AUTHOR]

Evolutionary potential games provide an analogy for equilibrium statistical physics under suitable conditions that is broken if cyclic components are added. Using Monte Carlo simulations we study the effect of weak cyclic perturbations on the equilibrium in a system of players located on a square lattice while the dynamics is controlled by a logit rule. The pair interaction is composed of a symmetric three-strategy coordination game of unit strength, a weak self-dependent term, and a cyclic (rock–paper–scissors) component. The self-dependent component favors the first (rock) strategy which dominates the system behavior at low noises if the strength of the cyclic component is below a threshold value. In the opposite case the predator of the first strategy (paper) rules the behavior at low noises while the first strategy can occasionally form growing domains (bursts) diminished after the appearance of the third one. The variation of noise modifies the invasion velocities as well as the rate of nucleation processes and yields drastic changes in the strategy frequencies, fluctuations, average payoffs, and spatio-temporal patterns. • Evolutionary potential games are perturbed by a cyclic component for logit rule. • The cyclic component causes paradoxical effects and bursts in the spatial model. • The bursts are related to two consecutive nucleation processes. • The noise dependence of frequencies, payoffs and fluctuations are determined. [ABSTRACT FROM AUTHOR]

In online social network, it is of great significance in the popularity perception of social topics. Aiming to deeply analyze the nonlinear dynamics mechanism in the propagation process of social topics, the chaotic characteristics of information dissemination are thoroughly explored in this paper, and Bayesian estimation theory is employed to integrate the cross-platform factors affecting the topic popularity. Firstly, the time series of topic popularity from different platforms are redefined. And Principal Component Analysis (PCA) method is introduced to quantize the redefined time series and receive the main components affecting popularity. Secondly, this paper focuses on specific topics and performs small-data method to calculate the maximum Lyapunov exponent, which can be found that there are chaotic characteristics in time series of topic popularity. Then, the main components affecting popularity are reconstructed based on the phase space reconstruction theory, and the chaotic attractor is recovered in the high-dimensional reconstructed phase space. Smoothly, the evolution regularities and properties of complex system are restored to perceive the propagating situation of topic popularity. At the same time, the novel and fused phase space is obtained by using the Bayesian estimation theory to optimally fuse multiple variables in the same high-dimensional space. Finally, RBF (Radial Basis Function) neural network is employed to predict the fused popularity due to its strong ability to approximate nonlinear functions. Experiments show that the prediction approach can not only merge topic popularity from multiple social platforms, but also can improve the prediction accuracy to some degree. In the meanwhile, the developing trend of topics can be perceived from a more fine-grained perspective. • The chaotic characteristics of topic propagation is found. • The features of popularity time series on different social platforms are explored. • PCA method is introduced to quantize the cross-platform popularity. • A multivariate fusion model for topic popularity prediction is proposed. [ABSTRACT FROM AUTHOR]

The process of reading a specific text is considered complex and little known in neuroscience, since it involves the vision, memory, motor control, learning, among others. In this sense, an excellent possibility to study the brain activity in the reading task can be achieved by the analysis of the multi-channel Electroencephalogram (EEG) and also with new statistical methods, like the detrended fluctuation analysis method (DFA). In this paper it will be proposed a model to analyze the brain activity in the reading task, performed by two subjects using a 22-channels EEG (NEUROMAP® model E Q S A 260). In order to test our model, two adults subjects (graduates) were tested here. These subjects were arranged in a chair facing a panel with the specific text, excluding involuntary movements that activated regions of the brain that were not being stimulated by reading. For the first subject, chosen at random, the text was presented before the task for understanding and some memorization. For the other subject the text was presented at the time of task. For the signal processing we chose 11 bio-electrodes located at the frontal, parietal, temporal and occipital regions of the brain. Therefore, to treat these non-stationary bio-signals we must apply robust and modern statistical techniques. With this objective, DFA method was applied in order to analyze the F D F A (n) fluctuation function in multi-channel EEG bio-sensors, more specifically the difference of its logarithm, i.e., Δ l o g F D F A. The results show that the use of this new function can be useful for brain activities. This paper, as we shall see here, is an initial contribution for EEG data analyze, that would be of medical interest, mainly in neuroscience area. • A model to analyze the brain activity (EEG signal) in the reading task is proposed. • In order to test our model, two adults subjects (graduate) were tested. • DFA method was applied in order to analyze the F D F A (n) fluctuation function. • For brain activities studies, new possibilities in the area of neuroscience is now open. [ABSTRACT FROM AUTHOR]

The link prediction problem can be used for predicting the link changes that are difficult to understand and costly in network science. So far, many different models have been proposed to predict links in the network. Most of the proposed models in the link prediction have focused on the unimodal networks. In this paper, we present a link prediction model that attempts to predict links in the large-scale bipartite networks. The proposed method consists of two stages: The first stage is the extraction of the potential link set. A limitation of the previous works that addressed the link prediction problem in bipartite networks is that only the structural properties of the projected networks that were transformed from the bipartite networks are used to extract the potential links. In this paper, in addition to the structural properties of the projected networks, we take into account the structural properties of the bipartite network. Also, we employ the strengthened projection network model instead of the classical projection network models. The second stage is the calculation of prediction score of each potential link. The classical proximity measures have several disadvantages in dynamic complex networks. When network evolution that considers valuable information between node pairs are ignored, the potential information source is not efficiently used in the link prediction. Therefore, in this stage, we propose a time-aware proximity measure based on social network evolution, instead of classical proximity measures using the current state of the network. To the best of our knowledge, this is the first effort that considers the evolution in bipartite networks. To test the performance of the proposed method, we made some experiments on two real networks that have different frame size. Experimental results show that the success of our method is promising. • In this study , we first propose a novel strengthened projection model. • Then, we present a new link prediction approach to find links in the future. • The proposed proximity measure considers the evolution of the bipartite networks. • The experimental results show that the success of the proposed method is promising. [ABSTRACT FROM AUTHOR]

This paper examines the dynamical impact of market wide liquidity on the volatility by using the Markov switching-regime approach on the Heterogeneous Autoregressive model of the realized range-based volatility (MS-HAR-RRV-LIQ). First, based on the simple partition of the liquidity measures by their historical means, we find the impact of the market liquidity on the realized range-based volatility (RRV) is non-linear with significantly stronger when the market is fluctuating. Second, by utilizing the method of Markov regimes switching, we find that in sample results, the impact of market liquidity on RRV is dynamics with much stronger when the market is fluctuating. In addition, the impact of liquidity on RRV are persistent for both the fluctuating state and the stable state, which indicate that the impact of liquidity on volatility would remain in its original state unless there are major economic events. Third, in order to eliminate the hidden information of volatility in liquidity measures, we further decompose the liquidity measures to obtain the volatility-adjusted liquidity, and find that the impact of liquidity driven by the component of liquidity uncorrelated with volatility on market volatility is also dynamics with much stronger when the market is fluctuating. Finally, out-of-sample results suggest that the models of HAR-RRV-LIQ and their corresponding volatility-adjusted liquidity models with regimes switching can increase the forecasting ability significantly than those without regime switching. • This paper examines the dynamical impact of market wide liquidity on volatility. • The impact of the market wide liquidity on RRV is non-linear. • The impact of market wide liquidity on RRV is dynamics. • Our proposed models can increase the forecasting ability significantly. [ABSTRACT FROM AUTHOR]

This paper discusses various consequences of the fact that the physical nature of process of generation, propagation and measurement of biopotentials is inherently ionic. Source of macroscopic biopotential is the temporary deficit of charge captured by ionic channels, which polarizes the organ border. Electric properties of the living tissue seem to be dominated by the electrolyte which polarizes easily and fast. Current theory of biopotential which treats the body as a volume conductor of certain conductivity is an idealization, which does not consider polarization of tissue. Polarization in quasistatic approximation may be reliably described using the well-known Debye–Hückel theory for electrolytes, derived directly from Boltzmann equation and Poisson equation. The polarization theory for biopotential generation, propagation and measurement is conceptually simple and enables to model wider range of experimental conditions, such as dependence of the potential on ionic strength. Modification of the basic numerical assumption for biopotential modeling: the electroneutrality condition is proposed. The accuracy of the theory is verified by comparison with existing experimental results, which shows very good accuracy. This paper shows the importance of a proper physical description to interpretation of biopotentials. The topic is of special importance to electrophysiology and neuroscience, but affects all biopotential measurements, theories and numerical studies. • Biopotentials come from ion diffusion polarizing the organ border. • Language of excess charge and polarization nicely complements the theory of membrane potential. • Living tissue resembles an electrically isolated conductor, filled with electrolyte. • Debye–Hückel theory for electrolytes well describes propagation of biopotentials. • Polarization theory may help to improve simulation in neuroscience. • Many macroscopic experimental cases may be described using single theory. [ABSTRACT FROM AUTHOR]

In this paper, a modified continuum model considering the vehicle's taillight effect is presented. The high order fluid dynamics model of taillight effect is derived by means of the transformation from micro to macro. Combined with the continuous conservation equation of traffic flow, the linear and nonlinear analysis of the new macroscopic hydrodynamic model is carried out. The linear analysis proves that the model has the characteristics of heterogeneity and the stability condition of traffic flow is obtained. KdV–Burgers equations describing the qualitative characteristics of density waves are obtained by nonlinear analysis, and the corresponding density wave solutions are obtained. Numerical simulation results show that the unstable state region of the macro model with taillight effect is shrinking. In addition, the energy consumption of the model is simulated numerically, and the results show that the energy consumption of the vehicle is reduced by considering the taillight effect. Therefore, we believe that the taillight effect is beneficial to traffic flow stability and reduce energy consumption. • An extended macro model with the consideration of vehicle's taillight effect is proposed in this paper. • The new model's stability condition and KdV–Burgers equation are deduced through linear stability theory and nonlinear analysis, respectively. • The influence of vehicle's taillight on new model's stability is carried out. • The energy consumption of the new model is explored. [ABSTRACT FROM AUTHOR]

This paper proposes a new compound model to investigate the dynamic linkage mechanism between the natural gas and crude oil markets from the multi-scale perspective. In the proposed model, two main steps are involved: multi-scale analysis and network research. Based on the bivariate empirical mode decomposition (BEMD) and Fine-to-Coarse algorithm, the multi-scale analysis ensures the relationship between price fluctuations can be studied under three different time-scales, which corresponding to market disequilibrium, significant events, and long term trend. By integrating the grey correlation degree and Coarse-Gaining algorithm, the network research reveals the dynamic spillover effects of the two markets at different time-scales. To capture the different linked characteristic in different periods, the sample data of Henry Hub and WTI spot prices from 1997 to 2017 is divided into three periods. We transfer the daily correlations between the price fluctuations with specific time-scale into the correlation patterns and establish the network based on their transmission relations. The crucial correlation patterns and the significant transmission relations are revealed by some network indicators. And the proposed approach in this paper can be applied to other field of research. • A new compound model is proposed based on multi-scale analysis and complex network. • The spillover effects of the two markets under three inner factors are studied. • The linkage mechanisms of the two markets at different time-scales are varying. • The important correlation patterns and transmission relations are revealed. [ABSTRACT FROM AUTHOR]

This paper presents a financial heuristic named as Pleonexia rule which is applied to Pakistan Stock Exchange. Pleonexia is a Greek word which means 'Greed for wealth or material possession or Avaricious'. Applying Pleonexia rule to stock exchange, if investors experience upward movements in the market and they want to get higher returns then they must keep investing in the same stock portfolios with the same pace. There is no need to get impatient and overly avaricious and, in turn, alter the original investment strategy. This rule is developed by using performance measure, that is, Sharpe ratio. The rule is developed in two steps. In first step, probabilities are checked by forecasting. In second step, these probabilities are used for the development and testing of Pleonexia rule. The data for the empirical analysis is taken from Pakistan stock exchange on monthly basis from 2000–2018. The results show that there is no difference between estimated and actual values for all classes of stock portfolios as shown in the first step of methodology. Furthermore, this rule is applicable for all three classes of stock portfolios which are growth, risk and value. The findings show that investors have on average 86% chances of getting gains up to the benchmark but in future they may be able to beat the market, thereby also achieving the level of healthy appetite. These chances are quite optimistic for developing countries and investor need to vigilant and remember 'Don't let the Greed catch you'. • This paper presents a financial heuristic named as Pleonexia rule. • This rule is developed by using performance measures i.e. Sharpe ratio. • This rule is empirically applied to an emerging stock market of Pakistan. • This rule is found to be valid and is applicable to all three classes of stock portfolios i.e. growth, risk and value. [ABSTRACT FROM AUTHOR]

To properly manage market risk industrial companies use tools based on value-at-risk, which requires proper modeling of future risk factors dynamics. One of the major challenges faced by this technique applied to modeling currency exchange rates is the choice of an appropriate model for simulation of the future paths. In order to fit the data, it should reflect its properties including heavier than Gaussian tail distributions, stabilizing volatility and mean reversion in long term horizon. In this paper, we propose to apply the Chan–Karolyi–Longstaff–Sanders (CKLS) model for the description of currency exchange rates data. This model was introduced for describing the evolution of the short interest rate and could be considered as the natural extension of the classical Ornstein–Uhlenbeck process. The standard version of CKLS process was based on the Brownian motion (BM). However, it can be easily extended to any class of distributions. Since the financial data of interest exhibit non-Gaussian behavior, we modified the CKLS model to use skewed generalized Student's t-distribution (SGT). In this paper, we apply the generalized method of moments to estimate the parameters of the CKLS/SGT process. The accuracy and behavior of the estimators are analyzed using Monte Carlo simulations. Further, we propose a new validation and selection technique. We apply the CKLS/SGT and GBM models to two currency exchange rates and compare their performance. • The CKLS model with general class of SGT distribution is considered. • The innovative estimation procedure for CKLS model parameters is proposed. • The Monte Carlo approach is used in order to check the effectiveness of the proposed estimation procedure. • The new model validation factor is proposed. • The proposed model is applied to two real data sets describing the currency exchange rates, the main risk factors in the KGHM company. [ABSTRACT FROM AUTHOR]

This paper deals with the analysis of the relationship between cryptocurrencies and gold prices. In particular, we use fractional integration and cointegration techniques to examine the degree of persistence of the series and the possibility of short and long run equilibrium relationships between them. Our results indicate that there is evidence of mean reversion in gold prices and also in some of the cryptocurrencies; however, cointegration is only found in a few cases with a very small degree of cointegration in the long run relationship. Testing the hypothesis of convergence throughout the ratios, again we only found evidence of mean reversion in the cases of Bytecoin, Dash, Ether, Monero and Ether. • The paper analyses the relationship between cryptocurrencies and gold prices. • Fractional integration and cointegration is used to examine the degree of persistence. • Results indicate that there is evidence of mean reversion in gold prices. • Also in some of the cryptocurrencies. • Cointegration is found in a few cases with a very small degree of cointegration. [ABSTRACT FROM AUTHOR]

This paper proposes a novel model to construct quasi scale-free geographically-embedded networks, in which the network's nodes have been generated by means of a Diffusion Limited Aggregation (DLA) process, and the links connecting a new node i to others are generated using a link radius R l i based on the Euclidean distance. The network is constructed by means of an evolutionary-based algorithm, aiming at minimizing the error between the actual degree distribution of the network under construction and that of the scale free networks, k − γ. Several algorithms for generating scale-free geographically-embedded networks over regular Euclidean lattices can be found in the literature but, to the best of our knowledge, the general case over complex or fractal substrates had not been tackled up until now. Although well-known in very large complex networks (with a huge number of nodes and links), the scale-free property has received much less attention for small geographically-embedded networks, in which the study of networks' properties is much more difficult. The idea of this work is to evolve the link radii for all the nodes in the network, aiming at finally fulfilling the scale-free property, if possible. Our experimental work shows that the proposed model is actually able to generate quasi scale-free geographically embedded networks in an efficient way. Discussions on the algorithm's performance to generate scale-free networks, a special encoding to improve the search, and the algorithm's computational evolution are given in the paper. Alternative possibilities of distribution objective (such as Poissonian, which leads to random geographically-embedded networks) are also tested and discussed in this work. • We propose a meta-heuristic to construct geographically-embedded scale-free networks. • We consider fractal distribution of nodes from a DLA Scheme. • We show that it is possible to generate scale-free networks in this context. • We propose a Coral Reefs Optimization algorithm to construct the networks. [ABSTRACT FROM AUTHOR]

The paper explores the frequency and size distributions of firm-size in a novel dataset for the mid-Victorian era from a recent extraction of the England and Wales population censuses of 1851, 1861, 1871, and 1881. The paper contrasts the hypothesis of the Power Laws against the Lognormal model for the tails of the distributions using maximum likelihood estimation, log likelihood ratio, clipped sample coefficient of variation UMPU-Wilks test, Kolmogorov–Smirnov statistic, among other state-of-the-art statistical methods. Our results show that the Power Law hypothesis is accepted for the size distribution for the years 1851 and 1861, while 1871 is marginally non-significant, but for 1881 the test is inconclusive. The paper discusses the process that generates these distributions citing recent literature that shows how after adding an i.i.d. noise to the Gibrat's multiplicative model one can recreate a Power Law behaviour. Overall, the paper provides, describes and statistically tests for the very first time a unique historical dataset confirming that the tails of the distributions at least for 1851 and 1861 follow a Pareto model and that the Lognormal model is firmly rejected. • We test the tail behaviour of mid-Victorian firm-size distributions. • Pareto model fits the tail of the size distribution of firm size for 1851 and 1861. • The tests are inconclusive for the frequency distributions. • Modern data show a fattening of the tails. [ABSTRACT FROM AUTHOR]

The method to estimate the predictability of human mobility was proposed in Song et al. (2010), which is extensively followed in exploring the predictability of disparate time series. However, the ambiguous description in the original paper leads to some misunderstandings, including the inconsistent logarithm bases in the entropy estimator and the entropy-predictability-conversion equation, as well as the details in the calculation of the Lempel–Ziv estimator, which further results in remarkably overestimated predictability. This paper demonstrates the degree of overestimation by four different types of theoretically generated time series and an empirical data set, and shows the intrinsic deviation of the Lempel–Ziv estimator for highly random time series. This work provides a clear picture on this issue and thus helps researchers in correctly estimating the predictability of time series. • Time series prediction is a long-standing challenge with significant applications in disparate domains. • Predictability indeed provides insights to the system itself. • This work clarifies the method for predictability estimation. [ABSTRACT FROM AUTHOR]