Motion Of Clusters Research Articles

Bridging dynamic regimes of segmental relaxation and center-of-mass diffusion in associative protein hydrogels

Molecular transport in dense, crowded media is often affected by binding and collisions with obstacles, leading to complex spatial distributions and anomalous diffusion on multiple length scales. Here, neutron spin-echo (NSE) spectroscopy and forced Rayleigh scattering (FRS) were used in tandem to study the interplay between segmental and center-of-mass chain dynamics in the presence of strong binding interactions in a model protein hydrogel. The results provide evidence for several dynamic scaling regimes of gel relaxation behavior with varying length scale, including a caging regime bridging submolecular relaxation and center-of-mass diffusion due to transient binding. On mesoscopic length scales larger than the size of the cage, chains undergo two distinct regimes of apparent superdiffusive scaling, with different power-law exponents, before the onset of terminal Fickian diffusion. The combined NSE and FRS results are interpreted in the context of prior Brownian dynamics simulations of associating star polymers, revealing insight into structural length scales and binding kinetics governing the transition from segmental relaxation to self-diffusion in the protein hydrogel. Finally, single-sticker tracer diffusion measurements were performed to directly probe sticker association/dissociation dynamics within the gel, the results suggesting that cooperative cluster motion may play a role in network relaxation on larger length scales.Received 28 February 2020Accepted 30 October 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.043369Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasDiffusionPhysical SystemsPolymer gelsTechniquesLight scatteringNeutron scatteringNeutron spin echo spectroscopyPolymers & Soft Matter

Hindered surface diffusion of bonded molecular clusters mediated by surface defects

The design of low dimensional materials through surface assisted self-assembly requires a better understanding of the factors that limit and control surface diffusion. We reveal how substrate surface defects hinder the mobility of sub-monolayer organic adsorbates on a metal surface with the model CuPc/Cu(111) system. Post-deposition annealing bonds CuPc molecules into dendritelike clusters that are often mobile at room temperature. Surface defects on Cu(111) create energetic barriers that prevent CuPc cluster motion on the metal surface. This phenomenon was unveiled by the motion of small clusters that show rigid-body diffusion solely in the available space in between defects. When clusters are sufficiently surrounded by defects, they become completely pinned in place and become immobilized.

Probing thermalization in quenched integrable and nonintegrable Fermi-Hubbard models

Using numerically exact methods we examine the Fermi-Hubbard model on arbitrary cluster topology. We focus on the question which systems eventually equilibrate or even thermalize after an interaction quench when initially prepared in a state highly entangled between system and bath. We find that constants of motion in integrable clusters prevent equilibration to the thermal state. We discuss the size of fluctuations during equilibration and thermalization and the influence of integrability. The influence of real space topology and in particular of infinite-range graphs on equilibration and thermalization is studied.

Graph-based topic models for trajectory clustering in crowd videos

Probabilistic topic modelings, such as latent Dirichlet allocation (LDA) and correlated topic models (CTM), have recently emerged as powerful statistical tools for processing video content. They share an important property, i.e., using a common set of topics to model all data. However, such property can be too restrictive for modeling complex visual data such as crowd scenes where multiple fields of heterogeneous data jointly provide rich information about objects and events. This paper proposes graph-based extensions of LDA and CTM, referred to as GLDA and GCTM, to learn and analyze motion patterns by trajectory clustering in a highly cluttered and crowded environment. Unlike previous works that relied on a scene prior, we apply a spatio-temporal graph to uncover the spatial and temporal coherence between the trajectories of crowd motion during the learning process. The presented models advance the conventional approaches by integrating a manifold-based clustering as initialization and iterative statistical inference as optimization. The output of GLDA and GCTM are mid-level features that represent the motion patterns used later to generate trajectory clusters. Experiments on three different datasets show the effectiveness of the approaches in trajectory clustering and crowd motion modeling.

Symmetry Breaking and Emergence of Directional Flows in Minimal Actomyosin Cortices.

Cortical actomyosin flows, among other mechanisms, scale up spontaneous symmetry breaking and thus play pivotal roles in cell differentiation, division, and motility. According to many model systems, myosin motor-induced local contractions of initially isotropic actomyosin cortices are nucleation points for generating cortical flows. However, the positive feedback mechanisms by which spontaneous contractions can be amplified towards large-scale directed flows remain mostly speculative. To investigate such a process on spherical surfaces, we reconstituted and confined initially isotropic minimal actomyosin cortices to the interfaces of emulsion droplets. The presence of ATP leads to myosin-induced local contractions that self-organize and amplify into directed large-scale actomyosin flows. By combining our experiments with theory, we found that the feedback mechanism leading to a coordinated directional motion of actomyosin clusters can be described as asymmetric cluster vibrations, caused by intrinsic non-isotropic ATP consumption with spatial confinement. We identified fingerprints of vibrational states as the basis of directed motions by tracking individual actomyosin clusters. These vibrations may represent a generic key driver of directed actomyosin flows under spatial confinement in vitro and in living systems.

From Molecular Aggregation to a One-Dimensional Quantum Crystal of Deuterium Inside a Carbon Nanotube of 1 nm Diameter.

The quantum motion of clusters of up to four deuterium molecules under confinement in a single-wall (1 nm diameter) carbon nanotube is investigated by applying a highly accurate full quantum treatment of the most relevant nuclear degrees of freedom and an ab initio-derived potential model of the underlying dispersion-dominated intermolecular interactions. The wave functions and energies are calculated using an ad hoc-developed discrete variable representation (DVR) numerical approach in internal coordinates, with the space grid approaching a few billion grid points. We unambiguously demonstrate the formation of a solid-like pyramidal one-dimensional chain structure of molecules under the cylindrical nanotube confinement. The onset of solid-like packing is explained by analyzing the potential minima landscape. The stabilization of collective rotational motion through "rigid rotations" of four deuterium molecules provides conclusive evidence for the onset of a quantum solid-like behavior resembling that of quantum rings featuring persistent current (charged particles) or persistent flow (neutral particles).

Assembly of Materials Building Blocks into Integrated Complex Functional Systems

Assembly of Materials Building Blocks into Integrated Complex Functional Systems

Cluster Motion in a Two-Contour System with Priority Rule for Conflict Resolution

A deterministic dynamical system representing the contour network is considered. The number of contours is two. At each contour there is a segment moving with a constant velocity which is called the cluster, because in the discrete variant of the system it corresponds to the cluster of particles, that is, to the group of particles occupying the adjacent cells and moving simultaneously. The lengths of contours and the lengths of clusters are prescribed. There is a common point named a node. Clusters cannot pass a node simultaneously. A cluster stops and waits for the node to empty if this cluster comes to the node at the instance when another cluster passes through the node. If clusters come to a node simultaneously, then precedence is given to the cluster considered the priority cluster (the priority rule of conflict resolution). The theorems on the average speed of cluster motion are proved taking delays in different types of the system’s behavior into account. It is established that the average speed of motion of each cluster in the system is independent of the cluster position at the initial time instance in contrast to the analogous system with another rule of conflict resolution considered previously, where such dependence appears in the general case. The possible practical interpretation of the studied system is given. The presented system is referred to as the class of dynamical networks introduced and investigated by A.P. Buslaev. The results may be applied to solve questions on the automatization of motion of a continuous mass, simulate the motion of transport, and other areas.

Turbulent pressure support and hydrostatic mass bias in the intracluster medium

ABSTRACT The degree of turbulent pressure support by residual gas motions in galaxy clusters is not well known. Mass modelling of combined X-ray and Sunyaev–Zel’dovich observations provides an estimate of turbulent pressure support in the outer regions of several galaxy clusters. Here, we test two different filtering techniques to disentangle bulk from turbulent motions in non-radiative high-resolution cosmological simulations of galaxy clusters using the cosmological hydrocode enzo. We find that the radial behaviour of the ratio of non-thermal pressure to total gas pressure as a function of cluster-centric distance can be described by a simple polynomial function. The typical non-thermal pressure support in the centre of clusters is ∼5 per cent, increasing to ∼15 per cent in the outskirts, in line with the pressure excess found in recent X-ray observations. While the complex dynamics of the intracluster medium makes it impossible to reconstruct a simple correlation between turbulent motions and hydrostatic bias, we find that a relation between them can be established using the median properties of a sample of objects. Moreover, we estimate the contribution of radial accelerations to the non-thermal pressure support and conclude that it decreases moving outwards from 40 per cent (in the core) to 15 per cent (in the cluster’s outskirts). Adding this contribution to one provided by turbulence, we show that it might account for the entire observed hydrostatic bias in the innermost regions of the clusters, and for less than 80 per cent of it at r > 0.8 r200,m.

Reverse engineering the Milky Way

ABSTRACT The ages, metallicities, alpha-elements, and integrals of motion of globular clusters (GCs) accreted by the Milky Way from disrupted satellites remain largely unchanged over time. Here we have used these conserved properties in combination to assign 76 GCs to five progenitor satellite galaxies – one of which we dub the Koala dwarf galaxy. We fit a leaky-box chemical enrichment model to the age–metallicity distribution of GCs, deriving the effective yield and the formation epoch of each satellite. Based on scaling relations of GC counts we estimate the original halo mass, stellar mass, and mean metallicity of each satellite. The total stellar mass of the five accreted satellites contributed around 109 M⊙ in stars to the growth of the Milky Way but over 50 per cent of the Milky Way’s GC system. The five satellites formed at very early times and were likely accreted 8–11 Gyr ago, indicating rapid growth for the Milky Way in its early evolution. We suggest that at least three satellites were originally nucleated, with the remnant nucleus now a GC of the Milky Way. 11 GCs are also identified as having formed ex situ but could not be assigned to a single progenitor satellite.

Effect of channel temperature and mass window in the fission decay of Re*181

The dynamical cluster-decay model (DCM) with mass window restriction and channel temperature is applied to study the decay of hot and rotating compound nucleus $^{181}\mathrm{Re}^{*}$ formed through the reaction $^{12}\mathrm{C}+^{169}\mathrm{Tm}$ at three different experimental incident energies, 77.18, 83.22, and 89.25 MeV, for spherical and deformed fragments. So far in DCM, the evaluation of various fission observables such as mass and charge distribution, decay cross section, kinetic energy, excitation energy, etc., were studied considering the entire mass range covering the limiting values of the mass asymmetry from 0 to 1, accounting for emission of light particles, complex intermediate mass fragments, as well as fission fragments. These emissions are considered as the dynamical collective mass motion of preformed clusters through the barrier. In this work, restriction of the mass window is considered in DCM as that of the experimental data for which cross section of the fission fragments is measured. The mass window restriction enhances the formation probability of the fragments. Further, instead of using a fixed temperature for all mass asymmetries (or channels), temperature tuning is attempted to see its effect on the outcome of the model. The obtained results of cross section values are found to compare reasonably well with the experimental data.

Dynamics of Bubble Cluster Rising in the Presence of a Surfactant

Abstract—Experimental results of studying the process of compact monodisperse bubble cluster rise in a fluid with a surfactant at Reynolds numbers Re = 0.001–1 are presented. For analysis of the effect of the surfactant on the bubble cluster motion, an experimental set-up was designed. The influence of the bubble size, the bubble concentration in the cluster, and the concentration of the surfactant in the fluid on the dynamics of bubble cluster rise is investigated. Empirical correlations for the drag coefficient of the bubble cluster rising in the presence and absence of the surfactant are obtained.

A Catalog of Newly Identified Star Clusters in Gaia DR2

Abstract We present the Star cluster Hunting Pipeline (SHiP) that can identify star clusters in Gaia second data release (DR2) data and establish a star cluster catalog for the Galactic disk. A friend-of-friend-based cluster finder method is used to identify star clusters using five-dimensional stellar parameters, , and μ δ . Our new catalog contains 2443 star cluster candidates identified from disk stars located within and with G < 18 mag. An automatic isochrone fitting scheme is applied to all cluster candidates. With a combination of parameters obtained from isochrone fitting, we classify cluster candidates into three classes (Class 1, 2, and 3). Class 1 clusters are the most probable star cluster candidates with the most stringent criteria. Most of these clusters are nearby (within 4 kpc). Our catalog is crossmatched with three Galactic star cluster catalogs, Kharchenko et al., Gaudin et al., and Bica et al. The proper motion and parallax of matched star clusters are in good agreement with these earlier catalogs. We discover 76 new star cluster candidates that are not listed in these three catalogs. The majority of these are clusters older than log(age/yr) = 8.0 and are located in the inner disk with . The recent discovery of new star clusters suggests that current Galactic star cluster catalogs are still incomplete. Among the Class 1 cluster candidates, we find 56 candidates for star cluster groups. The pipeline, the catalog, and the member list containing all candidates star clusters and star cluster groups have been made publicly available.

Viscosity of nanofluids containing anisotropic particles: A critical review and a comprehensive model.

Compared to nanofluids with spherical particles, nanofluids with anisotropic particles possess higher thermal conductivity and present a better enhancement option in heat transfer applications. The viscosity variation of such nanofluids becomes of great importance in evaluating their pumping power in thermal systems. This paper presents a comprehensive review of the experimental and theoretical studies on the viscosity of nanofluids with anisotropic particles. The internal mechanisms of viscosity evolution are investigated considering three aspects: particle clustering, particle interactions, and Brownian motion. In experimental studies, important factors including classification and synthetic methods for particle preparation, base fluid, particle loading, particle shape and size, temperature, p H, shear stress and electric field are investigated in detail. Classical theoretical models and empirical relations of the effective viscosity of suspensions are discussed. Some crucial factors such as maximum particle packing fraction, fractal index and intrinsic viscosity models, are examined. A comparison of predictions and experimental results shows that the classical models underestimate suspension viscosity. A comprehensive combination of the modified Krieger-Dougherty (K-D) model with intrinsic viscosity relations for different aspect ratios is suggested for low particle loadings, and the modified Maron-Pierce model (M-D) is recommended for high particle loadings. Possible directions for future works are discussed.

Landing-limited aggregation and prime number detection

An aggregation model is introduced based on the principles that N freely diffusing monomers stick together to form a cluster when they touch, the motion of clusters is directed, and limited by the requirement that a cluster be displaced over its entire size in one go if space permits. A successful displacement thus implies that the cluster lands on an unoccupied space adjacent to, and of the same size and shape as, the space which it occupied before the move. This “landing-limited aggregation” (LLA) can be motivated, for example, in the context of statistical mechanical modelling of the migration of animals herds, either self-organized in the wild or under human surveillance. This LLA is studied at fixed monomer density 1/2 on a periodic 1d lattice, or variations of it (staircase or double chain lattice), and on the 2d square lattice. In general the dynamics leads to three possible final states for the aggregate: a single cluster or “N-mer” containing the entire population, an arrested state in which multiple clusters are stuck in place, or multiple clusters that continue to move on the lattice without ever aggregating. On the 1d and the staircase lattices prime numbers play an interesting role in excluding certain final states. For example, for an initial condition on the periodic 1d lattice consisting of N monomers mutually separated by a vacant site, the final state never consists of just two clusters if and only if N is prime.

Nonlocalized clustering and evolution of cluster structure in nuclei

We explain various facets of the THSR (Tohsaki-Horiuchi-Schuck-R\"opke) wave function. We first discuss the THSR wave function as a wave function of cluster-gas state, since the THSR wave function was originally introduced to elucidate the 3$\alpha$-condensate-like character of the Hoyle state ($0_2^+$ state) of $^{12}$C. We briefly review the cluster-model studies of the Hoyle state in 1970's in order to explain how there emerged the idea to assign the $\alpha$ condensate character to the Hoyle state. We then explain that the THSR wave function can describe very well also non-gaslike ordinary cluster states with spatial localization of clusters. This fact means that the dynamical motion of clusters is of nonlocalized nature just as in gas-like states of clusters and the localization of clusters is due to the inter-cluster Pauli principle which is against the close approach of two clusters. The nonlocalized cluster dynamics is formulated by the container model of cluster dynamics. The container model describes gas-like state and non-gaslike states as the solutions of the Hill-Wheeler equation with respect to the size parameter of THSR wave function which is just the size parameter of the container. When we notice that fact that the THSR wave function with the smallest value of size parameter is equivalent to the shell-model wave function, we see that the container model describes the evolution of cluster structure from the ground state with shell-model structure up to the gas-like cluster state via ordinary non-gaslike cluster states. For the description of various cluster structure, more generation of THSR wave function have been introduced and we review some typical examples with their actual applications.

The effect of magnetic field on the hydration of cation in solution revealed by THz spectroscopy and MDs

The influence of an external magnetic field on the picosecond network dynamics of the salt (KCl) solution is investigated by terahertz time-domain spectroscopy. Because hydration changes the motions of water molecules clusters, we test hydration by measuring the change of dielectric loss caused by the slow relaxation of water molecules clusters. We find experimentally that the magnetic field enhances the number of water molecules involved in hydration and the radius of dynamic hydration layer in salt solutions. However, with the increasing concentration, there is a downward in the hydration enhancement per equivalent. Meanwhile, the simulated results show the number of water molecules in hydration layers increases which is consistent with our experimental results. Besides, the simulation reveals that external magnetic field mainly affects the second hydration layer and the stability of water molecules network in hydration layer is enhanced with external magnetic field.

From Mass to Motion: Conceptualizing and Measuring the Temporal Dynamics of Industry Clusters

This article outlines a temporal dynamics approach to the study of industry clusters. Despite extensive work on cluster size (or “mass”), little attention has been paid to their temporal dynamics (or “motion”). We propose that understanding cluster dynamics is important, because clusters are seldom stable, and cluster dynamics may have strategic implications not accounted for by existing approaches. We introduce a novel measure of cluster motion. Applying this measure to data on establishments in the U.S. computer and semiconductor industries, we document wide variation in cluster mass over time, both within and across regions. Furthermore, utilizing patent data, we find that cluster motion correlates with localized knowledge spillovers in ways different from cluster mass, suggesting that our approach may offer novel insights to strategy researchers.

Experimental analysis of the vibration dissipation induced by granular materials included into a 1 degree of freedom oscillator

The use of granular material is particularly attractive to damp vibrations in hollow structures, due to grains relative motion that induce shocks and friction, interaction grains/structure and possible deformation of the grains. Therefore, granular dampers are of great interest for many application types.However, predictive modelling of the damping performance of a granular material remains particularly difficult. The objective of this experimental work is to evaluate the vibration damping performance of various granular material samples, when inserted in a vertically shaken rigid cavity. This is done by Lissajous’ representation of displacement and force time signals, which, applied to this type of system, is a methodological originality allowing a detailed analysis of the overall motion of the grain cluster. The main insights are the identification of links between certain control parameters of the granular material and the vibratory energy dissipated. From Lissajous’ representation post-processing, it is shown that dissipation increases linearly with grain mass; at given mass the dissipation increases with grain size; the use of a viscoelastic material notably increases the performance, particularly at low acceleration levels; that the roughness of surfaces in contact with the grains plays a secondary role. Finally, the set of parametric variations give a better physical understanding of the phenomena involved, which gives orientations for future modelling works.

Computer vision-guided intelligent traffic signaling for isolated intersections

Computer vision-guided traffic management is an emerging area of research. Intelligent traffic signal control using computer vision is a less explored area of research. In this paper, we propose a new approach of traffic flow-based intelligent signal timing by temporally clustering optical flow features of moving vehicles using Temporal Unknown Incremental Clustering (TUIC) model. First, we propose a new inference scheme that works approximately 5-times faster as compared to the one originally proposed in TUIC in a dense traffic intersection. The new inference scheme can trace clusters representing moving objects that may be occluded while being tracked. Cluster counts of approach roads have been used for signal timing for traffic intersections. It is done by detecting cluster motion inside the regions-of-interest (ROI) marked at the entry and exit locations of intersection approaches. Departure rates are learned using Gaussian regression to parameterize traffic variations. Using the learned parameters as a function of cluster count, an adaptive signal timing algorithm, namely Throughput and Average Waiting Time Optimization (TAWTO) has been proposed. Experimental results reveal that the proposed method can achieve better average waiting time and throughput as compared to the state-of-the-art signal timing algorithms. We intend to publish two datasets as part of this work for enabling the research community to explore computer vision aided solutions for typical problems such as intelligent traffic controlling, violation detection in chaotic road intersections, etc.