We study trapping of particles diffusing in a two-dimensional rectangular chamber by a binding site located at the end of a rectangular sleeve. To reach the site a particle first has to enter the sleeve. After that it has two options: to come back to the chamber or to diffuse to the site where it is trapped. The main result of the present work is a simple expression for the mean particle lifetime as a function of its starting position and geometric parameters of the system. This expression is obtained by an approximate reduction of the initial two-dimensional problem to the effective one-dimensional one which can be solved with relative ease. Our analytical predictions are tested against the results obtained from Brownian dynamics simulations. The test shows excellent agreement between the two for a wide range of the geometric parameters of the system.

The Lévy walk is a popular and more ‘physical’ model to describe the phenomena of superdiffusion, because of its finite velocity. The movements of particles are under the influence of external potentials at almost any time and anywhere. In this paper, we establish a Langevin system coupled with a subordinator to describe the Lévy walk in a time-dependent periodic force field. The effects of external force are detected and carefully analyzed, including the nonzero first moment (even though the force is periodic), adding an additional dispersion on the particle position, a consistent influence on the ensemble- and time-averaged mean-squared displacement, etc. Besides, the generalized Klein-Kramers equation is obtained, not only for the time-dependent force but also for the space-dependent one.

We derive a general lower bound on distributions of entropy production in interacting active matter systems. The bound is tight in the limit that interparticle correlations are small and short-ranged, which we explore in four canonical active matter models. In all models studied, the bound is weak where collective fluctuations result in long-ranged correlations, which subsequently links the locations of phase transitions to enhanced entropy production fluctuations. We develop a theory for the onset of enhanced fluctuations and relate it to specific phase transitions in active Brownian particles. We also derive optimal control forces that realize the dynamics necessary to tune dissipation and manipulate the system between phases. In so doing, we uncover a general relationship between entropy production and pattern formation in active matter, as well as ways of controlling it.

Time-resolved tunable laser absorption spectroscopy is used to characterize the physical properties of ultrafast laser-produced plasmas. The plasmas were produced from an Inconel target, with $\le 0.4\mathrm{wt}\%$ Al, using $\sim 35\mathrm{fs}$, $\sim 800\mathrm{nm}$, $\sim 5\mathrm{mJ}$ laser pulses at varying Ar background pressures from 1 to 100 Torr. The absorption spectrum of atomic Al is measured with high spectral and temporal resolution when the probe laser is stepped across the selected Al transition at 394.4 nm. Spectral fitting is used to infer linewidths, kinetic temperature, Al column density, and pressure broadening coefficient. The late time physical properties of plasmas are compared for various pressure levels. Our studies highlight that a significant lower state population exists even at early times of ultrafast laser-produced plasma evolution, and lower state population persistence decreases with increasing ambient pressure. We also show that the fundamental optical properties, such as pressure broadening, can be measured using ultrafast laser-produced plasmas combined with laser absorption spectroscopy.

Fluctuations strongly affect the dynamics and functionality of nanoscale thermal machines. Recent developments in stochastic thermodynamics have shown that fluctuations in many far-from-equilibrium systems are constrained by the rate of entropy production via so-called thermodynamic uncertainty relations. These relations imply that increasing the reliability or precision of an engine's power output comes at a greater thermodynamic cost. Here we study the thermodynamics of precision for small thermal machines in the quantum regime. In particular, we derive exact relations between the power, power fluctuations, and entropy production rate for several models of few-qubit engines (both autonomous and cyclic) that perform work on a quantized load. Depending on the context, we find that quantum coherence can either help or hinder where power fluctuations are concerned. We discuss design principles for reducing such fluctuations in quantum nanomachines and propose an autonomous three-qubit engine whose power output for a given entropy production is more reliable than would be allowed by any classical Markovian model.

We study heat rectification in a minimalistic model composed of two unequal atoms subjected to linear forces and in contact with effective Langevin baths induced by Doppler lasers. Analytic expressions of the heat currents in the steady state are spelled out. Asymmetric heat transport is found in this linear system if both the bath temperatures and the temperature-dependent bath-system couplings are exchanged. The model can be realized with two ions in either common or individual traps. This physical setting allows for a natural temperature dependence of the coupling to the baths. We also explore the parameter space of the model to optimize asymmetric heat current and find conditions for maximal rectification. High rectification corresponds to a good match of the power spectra of the ions for forward temperature bias and mismatch for reverse bias, which may be understood by the behavior of dissipative normal modes.

There are numerous examples of studied real-world systems that can be described as dynamical systems characterized by individual phases and coupled in a networklike structure. Within the framework of oscillatory models, much attention has been devoted to the Kuramoto model, which considers a collection of oscillators interacting through a sinus function of the phase differences. In this paper, we draw on an extension of the Kuramoto model, called the Kuramoto-Sakaguchi model, which adds a phase lag parameter to each node. We construct a general formalism that allows us to compute the set of lag parameters that may lead to any phase configuration within a linear approximation. In particular, we devote special attention to the cases of full synchronization and symmetric configurations. We show that the set of natural frequencies, phase lag parameters, and phases at the steady state is coupled by an equation and a continuous spectra of solutions is feasible. In order to quantify the system's strain to achieve that particular configuration, we define a cost function and compute the optimal set of parameters that minimizes it. Despite considering a linear approximation of the model, we show that the obtained tuned parameters for the case of full synchronization enhance frequency synchronization in the nonlinear model as well.

When the interactions of agents on a network are assumed to follow the Deffuant opinion dynamics model, the outcomes are known to depend on the structure of the underlying network. This behavior cannot be captured by existing mean-field approximations for the Deffuant model. In this paper, a generalized mean-field approximation is derived that accounts for the effects of network topology on Deffuant dynamics through the degree distribution or community structure of the network. The accuracy of the approximation is examined by comparison with large-scale Monte Carlo simulations on both synthetic and real-world networks.

In the present paper, we study the self-diffusion of aggregating magnetic particles in bidisperse ferrofluids. We employ density functional theory (DFT) and coarse-grained molecular dynamics (MD) simulations to investigate the impact of granulometric composition of the system on the cluster self-diffusion. We find that the presence of small particles leads to the overall increase of the self-diffusion rate of clusters due the change in cluster size and composition.

We report a generic theoretical framework for accurate simulation of the temporal and spatial evolution of fused fiber-optic components, fabricated by the “heat and pull” technique. The methodology is based on the solution of quasi-3D incompressible Navier-Stokes equations formulated for immiscible two-phase flow. The two-phase interface is resolved by employing an interface tracking approach combined with the immersed boundary method. The model facilitates accurate spatiotemporal prediction of the evolution of both the external shape of the optical component and the internal dopant concentration during fabrication. Validation of the model was obtained by extensive comparison to experimental results. The model was found to be a convenient theoretical tool that may reliably facilitate the design and fabrication process of a wide spectrum of optic components.

Recent experiments have indicated that many biological systems self-organize near their critical point, which hints at a common design principle. While it has been suggested that information transmission is optimized near the critical point, it remains unclear how information transmission depends on the dynamics of the input signal, the distance over which the information needs to be transmitted, and the distance to the critical point. Here we employ stochastic simulations of a driven two-dimensional Ising system and study the instantaneous mutual information and the information transmission rate between a driven input spin and an output spin. The instantaneous mutual information varies nonmonotonically with the temperature but increases monotonically with the correlation time of the input signal. In contrast, there exists not only an optimal temperature but also an optimal finite input correlation time that maximizes the information transmission rate. This global optimum arises from a fundamental trade-off between the need to maximize the frequency of independent input messages, the necessity to respond fast to changes in the input, and the need to respond reliably to these changes. The optimal temperature lies above the critical point but moves toward it as the distance between the input and output spin is increased.

We investigate the dynamics of a conservative version of Conway's Game of Life, in which a pair consisting of a dead and a living cell can switch their states following Conway's rules but only by swapping their positions, irrespective of their mutual distance. Our study is based on square-lattice simulations as well as a mean-field calculation. As the density of dead cells is increased, we identify a discontinuous phase transition between an inactive phase, in which the dynamics freezes after a finite time, and an active phase, in which the dynamics persists indefinitely in the thermodynamic limit. Further increasing the density of dead cells leads the system back to an inactive phase via a second transition, which is continuous on the square lattice but discontinuous in the mean-field limit.

Artificial compressibility methods aim to reduce the stiffness of the compressible Navier-Stokes equations by artificially decreasing the velocity of acoustic waves in the fluid. This approach has originally been developed as an alternative to the incompressible Navier-Stokes equations as this avoids the resolution of a Poisson equation. This paper extends the method to anisothermal low Mach number flows, allowing the simulations of subsonic flows submitted to large temperature variations, including dilatational effects. The procedure is shown to be stable and accurate using a finite-difference method in a staggered grid system for the simulation of strongly anisothermal turbulent channel flow. The highly scalable nature of the approach is well suited to complex high-fidelity simulations and GPU processing.

A ground-state phase diagram for a three-layered Heisenberg ferromagnet with magnetic anisotropy and dipole-dipole interactions is inferred from a linear analysis of the fastest growing mode for the time-dependent Ginzburg-Landau equation. It is also investigated numerically by solving the Landau-Lifshitz equation for the model. The phase diagrams obtained from the two methods resemble each other. There is an in-plane ferromagnetic state, and states with out-of-plane stripe orders. Although the phase diagram is qualitatively similar to that for the corresponding two-dimensional model, the region of the in-plane ferromagnetic state shrinks considerably compared to that for the two-dimensional model.

We investigate the extent to which the eigenstate thermalization hypothesis (ETH) is valid or violated in the nonintegrable and the integrable spin-$1/2 XXZ$ chains. We perform the energy-resolved analysis of statistical properties of matrix elements of observables in the energy eigenstate basis. The Hilbert space is divided into energy shells of constant width, and a block submatrix is constructed whose columns and rows correspond to the eigenstates in the respective energy shells. In each submatrix, we measure the second moment of off-diagonal elements in a column. The columnar second moments are distributed with a finite variance for finite-sized systems. We show that the relative variance of the columnar second moments decreases as the system size increases in the non-integrable system. The self-averaging behavior indicates that the energy eigenstates are statistically equivalent to each other, which is consistent with the ETH. In contrast, the relative variance does not decrease with the system size in the integrable system. The persisting eigenstate-to-eigenstate fluctuation implies that the matrix elements cannot be characterized with the energy parameters only. Our result explains the origin for the breakdown of the fluctuation dissipation theorem in the integrable system. The eigenstate-to-eigenstate fluctuations sheds a new light on the meaning of the ETH.

We investigate, in detail, a triple quantum dot system that exploits Coulomb coupling to achieve nonlocal refrigeration. The system under investigation is a derivative of the nonlocal thermodynamic engine, originally proposed by Sánchez and Büttiker [Phys. Rev. B 83, 085428 (2011)], that employs quadruple quantum dots to attain efficient nonlocal heat harvesting. Investigating the cooling performance and operating regime using the quantum master equation approach, we point out some crucial aspects of the refrigeration engine. In particular, we demonstrate that the maximum cooling power for the setup is limited to about $70\%$ of the optimal design. Proceeding further, we point out that to achieve a target reservoir temperature lower than the average temperature of the current path, the applied voltage must be greater than a given threshold voltage $V_{\mathrm{TH}}$ that increases with the decrease in the target reservoir temperature. In addition, we demonstrate that the maximum cooling power, as well as the coefficient of performance, deteriorates as one approaches a lower target reservoir temperature. The triple quantum dot system, investigated in this paper, combines fabrication simplicity along with descent cooling power and may pave the way towards the practical realization of efficient nonlocal cryogenic refrigeration systems.

Turing's theory of pattern formation has been used to describe the formation of self-organized periodic patterns in many biological, chemical, and physical systems. However, the use of such models is hindered by our inability to predict, in general, which pattern is obtained from a given set of model parameters. While much is known near the onset of the spatial instability, the mechanisms underlying pattern selection and dynamics away from onset are much less understood. Here, we provide physical insight into the dynamics of these systems. We find that peaks in a Turing pattern behave as point sinks, the dynamics of which is determined by the diffusive fluxes into them. As a result, peaks move toward a periodic steady-state configuration that minimizes the mass of the diffusive species. We also show that the preferred number of peaks at the final steady state is such that this mass is minimized. Our work presents mass minimization as a potential general principle for understanding pattern formation in reaction diffusion systems far from onset.

The noisy voter model is a stylized representation of opinion dynamics. Individuals copy opinions from other individuals, and are subject to spontaneous state changes. In the case of two opinion states this model is known to have a noise-driven transition between a unimodal phase, in which both opinions are present, and a bimodal phase, in which one of the opinions dominates. The presence of zealots can remove the unimodal and bimodal phases in the model with two opinion states. Here we study the effects of zealots in noisy voter models with $M>2$ opinion states on complete interaction graphs. We find that the phase behavior diversifies, with up to six possible qualitatively different types of stationary states. The presence of zealots removes some of these phases, but not all. We analyze situations in which zealots affect the entire population, or only a fraction of agents, and show that this situation corresponds to a single-community model with a fractional number of zealots, further enriching the phase diagram. Our study is conducted analytically based on effective birth-death dynamics for the number of individuals holding a given opinion. Results are confirmed in numerical simulations.

Due to previous technical challenges with the collection of data on riding behaviors, there have only been a few studies focusing on patterns and regularities of biking traffic, which are crucial to understand to help achieve a greener and more sustainable future urban development. Recently, with the booming of the sharing economy, and the development of the Internet of Things (IoT) and mobile payment technology, dockless bike-sharing systems that record information for every trip provide us with a unique opportunity to study the patterns of biking traffic within cities. We first reveal a spatial scaling relation between the cumulative volume of riding activities and the corresponding distance to the city center, and a power law distribution on the volume of biking flows between fine-grained locations in both Beijing and Shanghai. We validate the effectiveness of the general gravity model on predicting biking traffic at fine spatial resolutions, where population-related parameters are less than unity, indicating that smaller populations are relatively more important per capita in generating biking traffic. We then further study the impacts of spatial scale on the gravity model and reveal that the distance-related parameter grows in a similar way as population-related parameters when the spatial scale of the locations increases. In addition, the flow patterns of some special locations (sources and sinks) that cannot be fully explained by the gravity model are studied.