46,035 research outputs found
Multiple Uncertainties in Time-Variant Cosmological Particle Data
Though the mediums for visualization are limited, the potential dimensions of a dataset are not. In many areas of scientific study, understanding the correlations between those dimensions and their uncertainties is pivotal to mining useful information from a dataset. Obtaining this insight can necessitate visualizing the many relationships among temporal, spatial, and other dimensionalities of data and its uncertainties. We utilize multiple views for interactive dataset exploration and selection of important features, and we apply those techniques to the unique challenges of cosmological particle datasets. We show how interactivity and incorporation of multiple visualization techniques help overcome the problem of limited visualization dimensions and allow many types of uncertainty to be seen in correlation with other variables
Anomalous transport in the crowded world of biological cells
A ubiquitous observation in cell biology is that diffusion of macromolecules
and organelles is anomalous, and a description simply based on the conventional
diffusion equation with diffusion constants measured in dilute solution fails.
This is commonly attributed to macromolecular crowding in the interior of cells
and in cellular membranes, summarising their densely packed and heterogeneous
structures. The most familiar phenomenon is a power-law increase of the MSD,
but there are other manifestations like strongly reduced and time-dependent
diffusion coefficients, persistent correlations, non-gaussian distributions of
the displacements, heterogeneous diffusion, and immobile particles. After a
general introduction to the statistical description of slow, anomalous
transport, we summarise some widely used theoretical models: gaussian models
like FBM and Langevin equations for visco-elastic media, the CTRW model, and
the Lorentz model describing obstructed transport in a heterogeneous
environment. Emphasis is put on the spatio-temporal properties of the transport
in terms of 2-point correlation functions, dynamic scaling behaviour, and how
the models are distinguished by their propagators even for identical MSDs.
Then, we review the theory underlying common experimental techniques in the
presence of anomalous transport: single-particle tracking, FCS, and FRAP. We
report on the large body of recent experimental evidence for anomalous
transport in crowded biological media: in cyto- and nucleoplasm as well as in
cellular membranes, complemented by in vitro experiments where model systems
mimic physiological crowding conditions. Finally, computer simulations play an
important role in testing the theoretical models and corroborating the
experimental findings. The review is completed by a synthesis of the
theoretical and experimental progress identifying open questions for future
investigation.Comment: review article, to appear in Rep. Prog. Phy
Probing turbulent superstructures in Rayleigh-B\'{e}nard convection by Lagrangian trajectory clusters
We analyze large-scale patterns in three-dimensional turbulent convection in
a horizontally extended square convection cell by Lagrangian particle
trajectories calculated in direct numerical simulations. A simulation run at a
Prandtl number Pr , a Rayleigh number Ra , and an aspect ratio
is therefore considered. These large-scale structures, which are
denoted as turbulent superstructures of convection, are detected by the
spectrum of the graph Laplacian matrix. Our investigation, which follows
Hadjighasem {\it et al.}, Phys. Rev. E {\bf 93}, 063107 (2016), builds a
weighted and undirected graph from the trajectory points of Lagrangian
particles. Weights at the edges of the graph are determined by a mean dynamical
distance between different particle trajectories. It is demonstrated that the
resulting trajectory clusters, which are obtained by a subsequent -means
clustering, coincide with the superstructures in the Eulerian frame of
reference. Furthermore, the characteristic times and lengths
of the superstructures in the Lagrangian frame of reference agree
very well with their Eulerian counterparts, and ,
respectively. This trajectory-based clustering is found to work for times
. Longer time periods require a
change of the analysis method to a density-based trajectory clustering by means
of time-averaged Lagrangian pseudo-trajectories, which is applied in this
context for the first time. A small coherent subset of the pseudo-trajectories
is obtained in this way consisting of those Lagrangian particles that are
trapped for long times in the core of the superstructure circulation rolls and
are thus not subject to ongoing turbulent dispersion.Comment: 12 pages, 7 downsized figures, to appear in Phys. Rev. Fluid
Active Brownian Particles. From Individual to Collective Stochastic Dynamics
We review theoretical models of individual motility as well as collective
dynamics and pattern formation of active particles. We focus on simple models
of active dynamics with a particular emphasis on nonlinear and stochastic
dynamics of such self-propelled entities in the framework of statistical
mechanics. Examples of such active units in complex physico-chemical and
biological systems are chemically powered nano-rods, localized patterns in
reaction-diffusion system, motile cells or macroscopic animals. Based on the
description of individual motion of point-like active particles by stochastic
differential equations, we discuss different velocity-dependent friction
functions, the impact of various types of fluctuations and calculate
characteristic observables such as stationary velocity distributions or
diffusion coefficients. Finally, we consider not only the free and confined
individual active dynamics but also different types of interaction between
active particles. The resulting collective dynamical behavior of large
assemblies and aggregates of active units is discussed and an overview over
some recent results on spatiotemporal pattern formation in such systems is
given.Comment: 161 pages, Review, Eur Phys J Special-Topics, accepte
Active particles in heterogeneous media display new physics: existence of optimal noise and absence of bands and long-range order
We present a detailed study of the large-scale collective properties of
self-propelled particles (SPPs) moving in two-dimensional heterogeneous space.
The impact of spatial heterogeneities on the ordered, collectively moving phase
is investigated. We show that for strong enough spatial heterogeneity, the
well-documented high-density, high-ordered propagating bands that emerge in
homogeneous space disappear. Moreover, the ordered phase does not exhibit
long-range order, as occurs in homogeneous systems, but rather quasi-long range
order: i.e. the SPP system becomes disordered in the thermodynamical limit. For
finite size systems, we find that there is an optimal noise value that
maximizes order. Interestingly, the system becomes disordered in two limits,
for high noise values as well as for vanishing noise. This remarkable finding
strongly suggests the existence of two critical points, instead of only one,
associated to the collective motion transition. Density fluctuations are
consistent with these observations, being higher and anomalously strong at the
optimal noise, and decreasing and crossing over to normal for high and low
noise values. Collective properties are investigated in static as well as
dynamic heterogeneous environments, and by changing the symmetry of the
velocity alignment mechanism of the SPPs.Comment: 16 pages, 11 figures, 60 reference
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