Article
Physics, Multidisciplinary
Izaak Neri
Summary: This study focuses on estimating the mean entropy production rate in a nonequilibrium process by measuring the first-passage quantities associated with a single current. The research shows that first-passage ratios can accurately estimate the rate of dissipation, even in the presence of nonMarkovian statistics.
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
(2022)
Article
Physics, Fluids & Plasmas
Tapas Singha
Summary: We investigate the mean first-passage time (MFPT) of a one-dimensional active fluctuating membrane that is stochastically returned to the same flat initial condition at a finite rate. By using the Fokker-Planck equation and method of characteristics, we obtain the joint distribution of the membrane height and active noise, as well as a relation between MFPT and a propagator that includes stochastic resetting. We find that the MFPT increases with a larger resetting rate and decreases with a smaller rate, and there exists an optimal resetting rate. We compare the results of the MFPT with active and thermal noises for different membrane properties.
Article
Mathematics, Applied
Elvira Di Nardo, Giuseppe D'Onofrio, Tommaso Martini
Summary: This paper presents a method to approximate the first passage time probability density function using Laguerre-Gamma polynomial approximation. An iterative algorithm is proposed to find the best degree of the polynomial that preserves a normalization condition. The algorithm relies on recursion formulas involving first passage time moments, which can be computed recursively from cumulants. The method is tested on the first passage time problem of a geometric Brownian motion to demonstrate its feasibility in fitting the density and estimating the parameters.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2023)
Article
Physics, Multidisciplinary
Hanshuang Chen, Guofeng Li, Feng Huang
Summary: This paper investigates the effect of stochastic resetting on the first passage properties of discrete-time absorbing Markov chains. The authors derive the mean first passage time and splitting probabilities using a renewal approach. They also present a sufficient condition for optimizing the mean first passage time and apply their results to two specific examples.
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
(2022)
Article
Mathematics, Applied
Sean D. Lawley
Summary: The paper investigates the distribution of extreme first passage times of piecewise deterministic Markov processes (PDMPs) by using classical extreme value theory to prove general theorems for the distribution and moments of extreme FPTs in the limit of many searchers. The results are then applied to canonical PDMPs, including run and tumble searchers in different dimensions, and discussed in the context of biological systems. The approach also addresses an unphysical property of diffusion that can be problematic for extreme statistics.
Article
Operations Research & Management Science
Mario Lefebvre
Summary: This study examines the relationship between a one-dimensional controlled diffusion process and a deterministic process, aiming to adjust the X(t)/Y(t) ratio to a specific value while another optimizer attempts to prevent the ratio from reaching that value. Explicit solutions are derived for both cases when control variables are assumed to be constants and in the general case.
Article
Physics, Fluids & Plasmas
Daniel J. Sharpe, David J. Wales
Summary: In this study, state-reduction algorithms are described for analyzing first-passage processes in finite Markov chains, enabling the computation of exact mean first-passage times, stationary probabilities, and committor probabilities for nonabsorbing nodes. The algorithms can be generalized to calculate the committor probabilities for any number of alternative target states. These methods provide valuable tools for analyzing Markov chains in practical applications, particularly in models of dynamical processes with rare events and metastability.
Article
Biology
Kuheli Biswas, Mohit Kumar Jolly, Anandamohan Ghosh
Summary: This study explores the important role of microRNAs in gene regulation by repressing protein synthesis to regulate the timing efficiency of the network. A stochastic model of the translation-initiation mechanism is proposed, and the steady state distribution of protein number in the linear regime is solved. The research shows that modulating slow rates of translation process can lead to efficient and robust timing mechanism.
JOURNAL OF THEORETICAL BIOLOGY
(2021)
Article
Chemistry, Physical
Riley J. Preston, Maxim F. Gelin, Daniel S. Kosov
Summary: Theoretical frameworks combining various methods were utilized in this study to develop a reaction-rate theory for current-activated chemical reactions, which was later applied to different transport scenarios. The natural emergence of Landauer's blowtorch effect was demonstrated as a result of the interplay between configuration-dependent viscosity and diffusion coefficients, with localized heating and bond deformations due to current-induced forces being determining factors in chemical reaction rates within the system.
JOURNAL OF CHEMICAL PHYSICS
(2021)
Article
Mechanics
Gabriel Mercado-Vasquez, Denis Boyer, Satya N. Majumdar
Summary: Resetting the searcher's position to the starting point during a random search can decrease the mean completion time of the process. In this study, we theoretically investigate a protocol that can be implemented experimentally and exhibits unusual optimization properties. By controlling the switch-on and switch-off rates of the confining potential, various behaviors can be observed. These behaviors are not present in ideal resetting models.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2022)
Article
Mathematics, Interdisciplinary Applications
Giuseppe D'Onofrio, Alessandro Lanteri
Summary: We investigate the problem of first passage time for a jump diffusion process with a nonlocal Jacobi operator as its infinitesimal generator. Due to the lack of analytical solutions, we propose a discretization scheme to simulate the trajectories of jump diffusion processes with state-dependent jumps in both frequency and amplitude. We obtain numerical approximations for the first passage time probability density functions and study the qualitative behavior of other statistics associated with this random variable. Additionally, we provide two examples illustrating the application of this method with different jump generation mechanisms.
FRACTAL AND FRACTIONAL
(2023)
Article
Physics, Multidisciplinary
Debraj Das, Luca Giuggioli
Summary: This paper investigates lattice random walks with resetting dynamics. By constructing a discrete renewal equation and deriving closed-form expressions, the authors provide a formalism for analyzing various quantities in resetting dynamics based on the reset-free propagator or Green's function. The formalism is applied to biased random walks in one-dimensional unbounded space, and the continuous limits yield results consistent with diffusion with resetting. The paper also explores the resetting dynamics of biased random walkers with periodic and reflecting boundaries, and observes non-monotonic behavior in the first-passage probability in periodic domains as the resetting probability varies. Additionally, the authors study the transmission dynamics of two lattice walkers with resetting in a one-dimensional domain bounded by periodic and reflecting boundaries, and find non-monotonic behavior in the probability of definite transmission as the resetting probabilities change.
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
(2022)
Article
Biochemical Research Methods
Catalina Rivera, David Hofmann, Ilya Nemenman
Summary: The study proposes a mathematical framework for building phenomenological models of biochemical networks based on experimental data, focusing on accurately approximating the time it takes to complete the process. The method is applied to study statistical properties of spiking in Purkinje cells, making nontrivial predictions about this system.
PLOS COMPUTATIONAL BIOLOGY
(2021)
Article
Computer Science, Information Systems
Maria A. Larchenko, Pavel Osinenko, Grigory Yaremenko, Vladimir V. Palyulin
Summary: The study reveals bias effects in agents trained using different methods, leading to convergence to suboptimal solutions in practical learning scenarios. High learning rates may inhibit exploration of certain regions, while low rates can increase agent presence in those regions, potentially impacting the application of reinforcement learning methods in real-world scenarios.
Article
Physics, Fluids & Plasmas
Yoshihiko Hasegawa
Summary: We derive a thermodynamic uncertainty relation for first passage processes in quantum Markov chains, and obtain bounds for the observables using the Loschmidt echo. We show that the lower bound corresponds to the quantum Fisher information and reduce the bound to a thermodynamic uncertainty relation for classical first passage processes in classical dynamics.
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)