Article
Mathematics, Applied
Diogo H. Silva, Celia Anteneodo, Silvio C. Ferreira
Summary: This study investigates how the perception of preventive behavior affects epidemic outbreaks. The results show that local awareness can raise the epidemic threshold, delay the peak of prevalence, and reduce the outbreak size. However, network heterogeneity reduces the efficacy of local awareness mechanisms.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2023)
Article
Mathematics, Applied
Dong Wang, Yi Zhao, Jianfeng Luo, Hui Leng
Summary: This work introduces a simplicial susceptible-infected-recovered-susceptible (SIRS) model that combines network higher-order structure with nonlinear incidence rates to study epidemic spreading. The proposed model demonstrates the ability to capture discontinuous transitions, bistability, and periodic outbreaks of epidemics in complex systems. Analyzing the stability of equilibrium points, deriving thresholds associated with bistable states and reinforcement factor critical values, this study expands the simplicial SIS model to SIRS model and offers insights into combining complex system higher-order structure with nonlinear incidence rates.
Article
Multidisciplinary Sciences
Ruslan Mukhamadiarov, Shengfeng Deng, Shannon R. Serrao, Priyanka, Riya Nandi, Louie Hong Yao, Uwe C. Tauber
Summary: The study suggests that delaying the release of restrictions and maintaining low levels of long-distance connections can effectively limit the intensity and spatial spread of an epidemic recurrence wave.
SCIENTIFIC REPORTS
(2021)
Article
Mathematics, Interdisciplinary Applications
Jing Ma, Xiangyi Meng, Lidia A. Braunstein
Summary: This article investigates the dynamic properties of a two-community system connected by bridge nodes and analyzes the peak fraction of infected individuals, I-max. By comparing the characteristic time scales of different parts of the system, the asymptotic behavior of I-max with respect to the bridge node fraction r as r -> 0 is analytically derived, revealing different power-law relations in each region of the phase diagram. Crossovers are also detected as I-max transitions from one power law to another, crossing different power-law regimes driven by r. The findings enable better prediction of the effectiveness of strategies targeting bridge nodes.
JOURNAL OF COMPLEX NETWORKS
(2022)
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
Mathematics, Interdisciplinary Applications
Hugo Mondragon-Nava, Didier Samayoa, Baltasar Mena, Alexander S. Balankin
Summary: This work focuses on modeling fracture networks, particularly the fractal features of fracture systems in geological formations and reservoirs. Two new fracture network models are introduced: one based on Bernoulli percolation in regular lattices and the other exploring site percolation in scale-free networks in two- and three-dimensional lattices. The key attributes of the model fracture networks are outlined. Surprisingly, the number of effective spatial degrees of freedom in the scale-free fracture network models is determined by the network embedding dimension, not the degree distribution. The effects of degree distribution on other fractal features of the model fracture networks are examined.
FRACTAL AND FRACTIONAL
(2023)
Article
Physics, Fluids & Plasmas
Michael Bestehorn, Thomas M. Michelitsch, Bernard A. Collet, Alejandro P. Riascos, Andrzej F. Nowakowski
Summary: This study introduces a compartment model with memory to analyze the dynamics of epidemic spreading in a constant population. The model incorporates a random duration of immunity, which introduces a memory effect that significantly impacts the epidemic dynamics. Computer simulations are used to investigate the influence of this memory effect on the space-time dynamics of the spreading, identifying relevant parameters for the spread or extinction of an epidemic.
Article
Physics, Multidisciplinary
Machiko Katori, Makoto Katori
Summary: This study focuses on the SIR models with contagious infection on supercritical percolation clusters formed on the Poisson point process (PPP) and the Ginibre point process (GPP), revealing differences in infection phenomena and disease transmission between the two.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2021)
Article
Physics, Multidisciplinary
Satoru Morita
Summary: In this study, targeted immunization on networks with heterogeneous degree distributions was analyzed. A method using the type reproduction number was proposed to quantify the impact of this approach on population immunity. A precise and simple formula was derived to calculate the immunization threshold, a novel result in the literature.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2022)
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
Peter Mann, V. Anne Smith, John B. O. Mitchell, Simon Dobson
Summary: This paper examines the emergent structures of random networks that have undergone a finite number of bond percolation processes. Two types of sequential branching processes, competitive and collaborative, are defined and their behavior and topological properties are investigated using generating functions. The model can be applied to explain seasonal disease spreading.
Article
Physics, Multidisciplinary
J. Menezes, B. Ferreira, E. Rangel, B. Moura
Summary: In this study, we investigate a cyclic game system in which organisms face an epidemic threat. Our results reveal the importance of adaptive altruistic behavior in survival strategies when disease mortality increases.
Article
Computer Science, Information Systems
Juan Fernando Balarezo, Song Wang, Karina Gomez Chavez, Akram Al-Hourani, Sithamparanathan Kandeepan
Summary: During the COVID-19 pandemic, there has been a shift towards increased reliance on remote connectivity, leading to a rise in networked devices and subsequent growth of botnets. Understanding how botnets are formed and propagate is crucial, and this paper introduces two analytic epidemic models to illustrate bot spread in different types of SDN networks.
Review
Mathematics, Interdisciplinary Applications
Xiaomei Wang, Qi An, Zilong He, Wei Fang
Summary: This study provides a comprehensive overview of the research frontiers in the relationship between social networks and epidemics, covering topics such as disease spread, factors influencing epidemic transmission, prevention and control strategies, and comparisons of different approaches. Several new research directions in this field are also discussed as areas of interest for researchers.
Article
Physics, Fluids & Plasmas
Mohadeseh Feshanjerdi, Abbas Ali Saberi
Summary: Inspired by recent viral epidemics, a model was developed to analyze the spread of infectious diseases. It was found that the key characteristics of the model are related to the system size and influenced by extreme states.
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)