Diffusion in composite media with high contrasts between diffusion coefficients in fractal sets of inclusions and in their embedding matrices is modeled by lattice random walks (RWs) with probabilities p<1 of hops from fractal sites and 1 from matrix sites. Superdiffusion is predicted in time intervals that depend on p and with diffusion exponents that depend on the dimensions of matrix (E) and fractal (D_{F}) as ν=1/(2+D_{F}-E). This contrasts with the nonuniversal subdiffusion of RWs confined to fractal media. Simulations with four fractals show the anomaly at several time decades for p≲10^{-3} and the crossover to the asymptotic normal diffusion. These results show that superdiffusion can be observed in isotropic RWs with finite moments of hop length distributions and allow the estimation of the dimension of the inclusion set from the diffusion exponent. However, displacements within single trajectories have normal scaling, which shows transient ergodicity breaking.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1103/PhysRevE.110.L022102 | DOI Listing |
Chaos
January 2025
Departamento de Física, Universidad Nacional de Colombia, Bogotá, Colombia.
We consider a discrete-time Markovian random walk with resets on a connected undirected network. The resets, in which the walker is relocated to randomly chosen nodes, are governed by an independent discrete-time renewal process. Some nodes of the network are target nodes, and we focus on the statistics of first hitting of these nodes.
View Article and Find Full Text PDFChaos
January 2025
Instituto de Física, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico.
We study an exactly solvable random walk model with long-range memory on arbitrary networks. The walker performs unbiased random steps to nearest-neighbor nodes and intermittently resets to previously visited nodes in a preferential way such that the most visited nodes have proportionally a higher probability to be chosen for revisit. The occupation probability can be expressed as a sum over the eigenmodes of the standard random walk matrix of the network, where the amplitudes slowly decay as power-laws at large times, instead of exponentially.
View Article and Find Full Text PDFPLoS One
January 2025
School of Sports Science, Harbin Normal University, Harbin, China.
Objective: To explore the impact of aerobic and resistance training on walking and balance abilities (UPDRS-III, Gait Velocity, Mini-BESTest, and TUG) in individuals with Parkinson's disease (PD).
Method: All articles published between the year of inception and July 2024 were obtained from PubMed, Embase, and Web of Science. Meta-analysis was conducted with RevMan 5.
PLoS One
January 2025
Department of Orthopedics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Ratchathewi, Bangkok, Thailand.
Among control methods for robotic exoskeletons, biologically inspired control based on central pattern generators (CPGs) offer a promising approach to generate natural and robust walking patterns. Compared to other approaches, like model-based and machine learning-based control, the biologically inspired control provides robustness to perturbations, requires less computational power, and does not need system models or large learning datasets. While it has shown effectiveness, a comprehensive evaluation of its user experience is lacking.
View Article and Find Full Text PDFPLoS One
January 2025
Falls, Balance and Injury Research Centre, Neuroscience Research Australia, Sydney, Australia.
Background: Treadmill belt perturbations have high clinical feasibility for use in perturbation-based training in older people, but their kinematic validity is unclear. This study examined the kinematic validity of treadmill belt accelerations as a surrogate for overground walkway trips during gait in older people.
Methods: Thirty-eight community-dwelling older people were exposed to two unilateral belt accelerations (8 m s-2) whilst walking on a split-belt treadmill and two trips induced by a 14 cm trip-board whilst walking on a walkway with condition presentation randomised.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!