Article
Mathematics, Applied
Xiaoyu Xue, WenYao Li, Yanyi Nie, Xun Lei, Tao Lin, Wei Wang
Summary: This study investigates the cooperative spreading of two epidemics in a simplicial complex, including structural and dynamical reinforcement effects. It reveals that increasing the reinforcement effect enhances the spreading dynamics, resulting in a larger outbreak size and smaller threshold.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2022)
Article
Computer Science, Interdisciplinary Applications
Jinlong Ma, Tingting Xiang, Yongbin Zhao
Summary: Recent studies have found that real-world systems can be described using multi-layer complex networks. This paper introduces a traffic-driven SIR epidemic spreading model on a logical-physical layered network. The study investigates the features of epidemic spreading on a layered network based on the density of infected and recovered nodes. The results show that traffic flow significantly affects the intensity and scope of epidemic spreading. Comparing the effects of different types of two-layer networks on epidemic spreading, it is found that homogenous logical or physical network structures promote epidemic spread more than heterogeneous networks. This work could contribute to the design of traffic-driven epidemic prevention and control strategies.
INTERNATIONAL JOURNAL OF MODERN PHYSICS C
(2023)
Article
Mathematics
Nickie Lefevr, Andreas Kanavos, Vassilis C. Gerogiannis, Lazaros Iliadis, Panagiotis Pintelas
Summary: Complex networks, derived from the observation and analysis of real-world networks, include biological networks focusing on connections and interfaces like epidemic models. Fuzzy logic, a powerful mathematical tool, deals with imprecision and aims to provide low-cost solutions to real-world problems. Fuzzy-based simulation scenarios for HIV spreading in a population of needle drug users demonstrate the importance of fuzziness in analyzing disease spread.
Article
Physics, Fluids & Plasmas
Shogo Mizutaka, Kizashi Mori, Takehisa Hasegawa
Summary: We investigate the effect of degree correlation on a susceptible-infected-susceptible (SIS) model with a nonlinear cooperative effect. Regardless of synergy, positive and negative degree correlation in the model reduces and raises the epidemic threshold, respectively. For networks with a strongly positive degree correlation, the model predicts the emergence of two discontinuous jumps in the steady-state infected density.
Article
Mathematics, Applied
Minyu Feng, Xiangxi Li, Yuhan Li, Qin Li
Summary: In this study, a two-layer network-based epidemic spreading model was proposed to investigate the influence of individuals with different properties in the awareness layer on disease transmission. It was found that individuals with high centrality in the awareness layer significantly inhibit the spread of infectious diseases, while individuals with low centrality in the awareness layer have an approximately linear effect on the number of infected individuals.
Article
Physics, Applied
Tianqiao Zhang, Ruijie Wang, Yang Zhang, Junliang Chen, Xuzhen Zhu
Summary: The study explores the impact of seeds on cooperative epidemic spreading on complex networks, showing continuous phase transition of node infection proportions on different networks with selection strategy not altering the transition types. Eigenvector centrality promotes cooperative spreading on artificial networks, while degree centrality promotes the spread of two cooperative diseases on real-world networks.
INTERNATIONAL JOURNAL OF MODERN PHYSICS B
(2021)
Article
Physics, Fluids & Plasmas
Bo Li, David Saad
Summary: The study focuses on the variant model of infectious diseases with presymptomatic transmission, using the method of dynamic message passing to provide a good estimate of the probabilistic evolution of spread. This facilitates the derivation of epidemic thresholds and impacts different containment strategies.
Article
Mathematics, Interdisciplinary Applications
Lilei Han, Zhaohua Lin, Qingqing Yin, Ming Tang, Shuguang Guan, Marian Boguna
Summary: This paper proposes a general formalism to study non-Markovian dynamics on non-Markovian temporal networks. The study finds that, under certain conditions, non-Markovian dynamics on temporal networks are equivalent to Markovian dynamics on static networks, independent of the underlying network topology.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Physics, Multidisciplinary
Michele Bellingeri, Daniele Bevacqua, Massimiliano Turchetto, Francesco Scotognella, Roberto Alfieri, Ngoc-Kim-Khanh Nguyen, Thi Trang Le, Quang Nguyen, Davide Cassi
Summary: Complex networks are the preferred framework for modeling spreading dynamics in real-world systems. Understanding the impact of network structure on epidemic outbreaks is crucial for assessing network vulnerability and disease control optimization. Research has found that network structure indexes based on node distance are the best predictors of disease spreading extent in real-world networks.
FRONTIERS IN PHYSICS
(2022)
Article
Mathematics, Interdisciplinary Applications
Ping Huang, Xiao-Long Chen, Ming Tang, Shi-Min Cai
Summary: The study proposes a coupled resource-epidemic dynamic model to simulate the relationship between resource diffusion and epidemic spreading in dynamic social networks. Results indicate trivially asymmetric interactions between resource diffusion and epidemic spreading, with individual characteristics in the resource and epidemic layers impacting the dynamics.
Article
Mathematics, Interdisciplinary Applications
Lasko Basnarkov
Summary: The study investigates the SEAIR epidemic spreading model of COVID-19, analyzing its infectious characteristics and the relationships between epidemic thresholds. The results show that the eigenvector centrality of a node approximately determines its risk to become infected.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Mathematics
Bo Song, Huiming Wu, Yurong Song, Xu Wang, Guoping Jiang
Summary: In this paper, a novel weighted co-evolving multiplex network model is proposed to describe the interaction between information diffusion in online social networks and epidemic spreading in adaptive physical contact networks. The simulation results show that the maximum infection scale decreases as the information acceptance probability grows, and the final infection decreases as the rewiring behaviors increase. Interestingly, an infection peak appears in our model due to the interaction between information diffusion and epidemic spread.
Article
Physics, Multidisciplinary
Mengqi Jia, Xin Li, Li Ding
Summary: This paper introduces a susceptible-alert-infected-susceptible epidemic spreading model based on coupled activity-driven networks and analyzes the critical conditions for epidemic outbreaks. Explicit expressions of the critical conditions are obtained, which are determined by the multi-layer activity-driven network structure associated with propagation parameters. The influence of network structure and propagation parameters on the epidemic outbreak critical condition is also discussed, providing insights for future epidemic control strategies.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2021)
Article
Computer Science, Interdisciplinary Applications
Linhe Zhu, Wenshan Liu, Zhengdi Zhang
Summary: A novel coupled two-layered networking framework is proposed for modeling epidemic spreading and information spreading. By establishing mathematical equations and conducting numerical simulations, the model can help prevent and control epidemic outbreaks effectively.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2021)
Article
Mathematics, Interdisciplinary Applications
Paulo Cesar Ventura, Alberto Aleta, Francisco A. Rodrigues, Yamir Moreno
Summary: This study presents a model for epidemic spreading in temporal networks of mobile agents that incorporates local behavioral responses. It shows that the mechanism of behavioral responses can effectively reduce the spread of disease when the spatial density of agents is low, but it can cause an abrupt phase transition and the emergence of a new bistable phase at higher densities. The study also characterizes the temporal networks formed in the fast mobility regime and examines how the behavioral mechanism affects degree distributions and other metrics.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Computer Science, Interdisciplinary Applications
Laurita dos Santos, Debora C. Correa, David M. Walker, Moacir F. de Godoy, Elbert E. N. Macau, Michael Small
Summary: This paper proposes a new data-driven methodology using nonlinear time series analysis to classify the autonomic nervous system (ANS) conditions in newborns. Mapping time series to ordinal partition networks and using complexity quantifiers, the study differentiates the dynamical processes in premature and full-term newborns. The results show that complexity quantifiers can differentiate the two groups by detecting time asymmetry in their data.
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
(2022)
Article
Geochemistry & Geophysics
Ayham Zaitouny, Erick Ramanaidou, June Hill, David M. Walker, Michael Small
Summary: Modelling of 3D domain boundaries using information from drill holes is a common procedure in mineral exploration. Manual interpretation of drill hole data can be subjective and time-consuming. This paper presents a new objective methodology that utilizes hyperspectral analysis to identify lithological boundaries in high dimensional data. The results demonstrate its effectiveness in identifying transitions in the downhole data and improving boundary detection.
Article
Mathematics, Applied
Zahra Shahriari, Shannon D. Algar, David M. Walker, Michael Small
Summary: We propose a robust algorithm for constructing first return maps of dynamical systems from time series without embedding. Our method is based on ordinal partitions of the time series, and the first return map is constructed from successive intersections with specific ordinal sequences. We define entropy-based measures to guide our selection of the ordinal sequence for a good first return map and show that this method can robustly be applied to time series from classical chaotic systems.
Review
Mathematics, Applied
Eugene Tan, Shannon Algar, Debora Correa, Michael Small, Thomas Stemler, David Walker
Summary: Delay embedding methods are important tools in time series analysis and prediction. The selection of embedding parameters can greatly impact the analysis, leading researchers to develop various methods for optimization. This paper provides a comprehensive overview of embedding theory, outlining existing methods for selecting embedding lag in both uniform and non-uniform cases. The proposed method, SToPS, combines dynamical and topological arguments to select embedding lags, and performs similarly to existing methods for non-uniform embedding. It also outperforms other methods when predicting fast-slow time series.
Article
Computer Science, Information Systems
Tongfeng Weng, Xiaolu Chen, Zhuoming Ren, Huijie Yang, Jie Zhang, Michael Small
Summary: We adopt reservoir computing, a machine learning technique, to study synchronization phenomena in complex networks. By constructing a coupled configuration, we demonstrate that coupled reservoir oscillators exhibit synchrony with the learned dynamical system. Through this synchronization scheme, we recover the observed system's bifurcation behavior solely based on its chaotic dynamics. Our work provides an alternative framework for studying synchronization phenomena in nature when only observed data are available.
INFORMATION SCIENCES
(2023)
Article
Physics, Multidisciplinary
Siyang Jiang, Jin Zhou, Michael Small, Jun-an Lu, Yanqi Zhang
Summary: Searching for key nodes and edges in a network has been a longstanding problem. Recently, there has been increased attention on the cycle structure in networks. This study proposes a ranking algorithm for cycle importance by identifying key cycles that contribute significantly to the network's dynamics. The researchers provide a concrete definition of importance using the Fiedler value and present a neat index for ranking cycles based on the sensitivity of the Fiedler value to different cycles. Numerical examples demonstrate the effectiveness of this method.
PHYSICAL REVIEW LETTERS
(2023)
Article
Mathematics, Applied
Lixiang Liu, Shanshan Chen, Michael Small, Jack Murdoch Moore, Keke Shang
Summary: This paper presents a novel SIRS model on scale-free networks that considers behavioral memory and time delay to depict an adaptive behavioral feedback mechanism in the spread of epidemics. The study includes a rigorous analysis of the dynamics of the model, determines the basic reproduction number R0, uniform persistence, and global asymptotic stability of equilibria. The model exhibits a sharp threshold property, and optimal control strategies for effective vaccination and treatment are demonstrated. Stochastic network simulations validate the findings and indicate that time delay does not affect R0, but behavioral memory does.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2023)
Editorial Material
Biology
Shannon D. Algar, Jennifer Rodger, Michael Small
PHYSICS OF LIFE REVIEWS
(2023)
Article
Engineering, Industrial
Yucheng Hao, Limin Jia, Enrico Zio, Yanhui Wang, Michael Small, Man Li
Summary: The researchers studied the optimization repair strategy for high-speed trains by establishing an interdependent network and introducing a resilience metric based on network theory. They developed an interdependent machine-electricity-communication network and related cascading failure models and proposed comprehensive robustness metrics for the network and nodes. They solved the resilience optimization model of the network using a tabu search algorithm. They analyzed the optimal repair strategy for different numbers of failed nodes and analyzed the characteristics of the preferentially repaired node. The optimal repair strategy is not necessarily determined by topological metrics.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Physics, Fluids & Plasmas
Jack Murdoch Moore, Haiying Wang, Michael Small, Gang Yan, Huijie Yang, Changgui Gu
Summary: The network correlation dimension controls the distribution of network distance in terms of a power-law model and has significant impacts on both structural properties and dynamical processes. We have developed new maximum likelihood methods that can robustly and objectively identify network correlation dimension as well as a bounded interval of distances where the model accurately represents the structure. We have also compared the traditional practice of estimating correlation dimension with a proposed alternative method using the fraction of nodes at a distance modeled as a power law.
Article
Physics, Multidisciplinary
Eugene Tan, Shannon D. Algar, Debora Correa, Thomas Stemler, Michael Small
Summary: A method of constructing a discretised network representation of a system's attractor is proposed and its applicability in identifying dynamical change points in different systems is demonstrated.
COMMUNICATIONS PHYSICS
(2023)
Article
Mathematics, Interdisciplinary Applications
Tongfeng Weng, Xiaolu Chen, Zhuoming Ren, Huijie Yang, Jie Zhang, Michael Small
Summary: This study investigates the collective behavior of multiply moving reservoir computing oscillators. These oscillators gradually exhibit coherent rhythmic behavior when their number is large enough, showing excellent agreement with their learned dynamical system. Furthermore, the oscillators can exhibit significantly distinct collective behaviors resembling bifurcation phenomenon when changing a critical reservoir parameter. Intermittent synchronization emerges among the oscillators when studying a continuous chaotic system.
CHAOS SOLITONS & FRACTALS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Eugene Tan, Debora C. Correa, Thomas Stemler, Michael Small
Summary: Dynamical networks are commonly used to model large networks of interacting time-varying components. This paper proposes a backpropagation regression method to infer local node dynamics and connectivity structure from measured node signals. The method detects network attacks by comparing prediction error statistics and shows promising results on a simulated network.
AI 2022: ADVANCES IN ARTIFICIAL INTELLIGENCE
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Braden Thorne, Thomas Jungling, Michael Small, Debora Correa, Ayham Zaitouny
Summary: Understanding the complexity of signals is important when working with time series data. Permutation entropy is a commonly used measure for time series complexity, but we propose an alternative method based on reservoir computing that captures similar information and shows similar behavior in experiments to other measures.
AI 2022: ADVANCES IN ARTIFICIAL INTELLIGENCE
(2022)
Article
Physics, Fluids & Plasmas
Ke-ke Shang, Michael Small
Summary: Link prediction is the problem of predicting the relationship between nodes based on observed structural information of a network. Existing algorithms have limitations in predicting certain network structures, but the proposed algorithm in this paper performs well on different network structures.
Article
Physics, Multidisciplinary
Xiaoyu Shi, Jian Zhang, Xia Jiang, Juan Chen, Wei Hao, Bo Wang
Summary: This study presents a novel framework using offline reinforcement learning to improve energy consumption in road transportation. By leveraging real-world human driving trajectories, the proposed method achieves significant improvements in energy consumption. The offline learning approach demonstrates generalizability across different scenarios.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Junhyuk Woo, Soon Ho Kim, Hyeongmo Kim, Kyungreem Han
Summary: Reservoir computing (RC) is a new machine-learning framework that uses an abstract neural network model to process information from complex dynamical systems. This study investigates the neuronal and network dynamics of liquid state machines (LSMs) using numerical simulations and classification tasks. The findings suggest that the computational performance of LSMs is closely related to the dynamic range, with a larger dynamic range resulting in higher performance.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Yuwei Yang, Zhuoxuan Li, Jun Chen, Zhiyuan Liu, Jinde Cao
Summary: This paper proposes an extreme learning machine (ELM) algorithm based on residual correction and Tent chaos sequence (TRELM-DROP) for accurate prediction of traffic flow. The algorithm reduces the impact of randomness in traffic flow through the Tent chaos strategy and residual correction method, and avoids weight optimization using the iterative method. A DROP strategy is introduced to improve the algorithm's ability to predict traffic flow under varying conditions.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Chengwei Dong, Min Yang, Lian Jia, Zirun Li
Summary: This work presents a novel three-dimensional system with multiple types of coexisting attractors, and investigates its dynamics using various methods. The mechanism of chaos emergence is explored, and the periodic orbits in the system are studied using the variational method. A symbolic coding method is successfully established to classify the short cycles. The flexibility and validity of the system are demonstrated through analogous circuit implementation. Various chaos-based applications are also presented to show the system's feasibility.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Viorel Badescu
Summary: This article discusses the maximum work extraction from confined particles energy, considering both reversible and irreversible processes. The results vary for different types of particles and conditions. The concept of exergy cannot be defined for particles that undergo spontaneous creation and annihilation. It is also noted that the Carnot efficiency is not applicable to the conversion of confined thermal radiation into work.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
P. M. Centres, D. J. Perez-Morelo, R. Guzman, L. Reinaudi, M. C. Gimenez
Summary: In this study, a phenomenological investigation of epidemic spread was conducted using a model of agent diffusion over a square region based on the SIR model. Two possible contagion mechanisms were considered, and it was observed that the number of secondary infections produced by an individual during its infectious period depended on various factors.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Zuan Jin, Minghui Ma, Shidong Liang, Hongguang Yao
Summary: This study proposes a differential variable speed limit (DVSL) control strategy considering lane assignment, which sets dynamic speed limits for each lane to attract vehicle lane-changing behaviors before the bottleneck and reduce the impact of traffic capacity drop. Experimental results show that the proposed DVSL control strategy can alleviate traffic congestion and improve efficiency.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Matthew Dicks, Andrew Paskaramoorthy, Tim Gebbie
Summary: In this study, we investigate the learning dynamics of a single reinforcement learning optimal execution trading agent when it interacts with an event-driven agent-based financial market model. The results show that the agents with smaller state spaces converge faster and are able to intuitively learn to trade using spread and volume states. The introduction of the learning agent has a robust impact on the moments of the model, except for the Hurst exponent, which decreases, and it can increase the micro-price volatility as trading volumes increase.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Zhouzhou Yao, Xianyu Wu, Yang Yang, Ning Li
Summary: This paper developed a cooperative lane-changing decision system based on digital technology and indirect reciprocity. By introducing image scoring and a Q-learning based reinforcement learning algorithm, drivers can continuously evaluate gains and adjust their strategies. The study shows that this decision system can improve driver cooperation and traffic efficiency, achieving over 50% cooperation probability under any connected vehicles penetration and traffic density, and reaching 100% cooperation probability under high penetration and medium to high traffic density.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Josephine Nanyondo, Henry Kasumba
Summary: This paper presents a multi-class Aw-Rascle (AR) model with area occupancy expressed in terms of vehicle class proportions. The qualitative properties of the proposed equilibrium velocity and the stability conditions of the model are established. The numerical results show the effect of proportional densities on the flow of vehicle classes, indicating the realism of the proposed model.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Oliver Smirnov
Summary: This study proposes a new method for simultaneously estimating the parameters of the 2D Ising model. The method solves a constrained optimization problem, where the objective function is a pseudo-log-likelihood and the constraint is the Hamiltonian of the external field. Monte Carlo simulations were conducted using models of different shapes and sizes to evaluate the performance of the method with and without the Hamiltonian constraint. The results demonstrate that the proposed estimation method yields lower variance across all model shapes and sizes compared to a simple pseudo-maximum likelihood.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Przemyslaw Chelminiak
Summary: The study investigates the first-passage properties of a non-linear diffusion equation with diffusivity dependent on the concentration/probability density through a power-law relationship. The survival probability and first-passage time distribution are determined based on the power-law exponent, and both exact and approximate expressions are derived, along with their asymptotic representations. The results pertain to diffusing particles that are either freely or harmonically trapped. The mean first-passage time is finite for the harmonically trapped particle, while it is divergent for the freely diffusing particle.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Hidemaro Suwa
Summary: The choice of transition kernel is crucial for the performance of the Markov chain Monte Carlo method. A one-parameter rejection control transition kernel is proposed, and it is shown that the rejection process plays a significant role in determining the sampling efficiency.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Xudong Wang, Yao Chen
Summary: This article investigates the joint influence of expanding medium and constant force on particle diffusion. By starting from the Langevin picture and introducing the effect of external force in two different ways, two models with different force terms are obtained. Detailed analysis and derivation yield the Fokker-Planck equations and moments for the two models. The sustained force behaves as a decoupled force, while the intermittent force changes the diffusion behavior with specific effects depending on the expanding rate of the medium.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
