In vitro reconstituted active systems, such as the adenosine triphosphate (ATP)-driven microtubule bundle suspension developed by the Dogic group [T. Sanchez, D. T. Chen, S. J. DeCamp, M. Heymann, and Z. Dogic, Nature (London) 491, 431 (2012)], provide a fertile testing ground for elucidating the phenomenology of active liquid crystalline states. Controlling such novel phases of matter crucially depends on our knowledge of their material and physical properties. In this Rapid Communication, we show that the shear viscosity of an active nematic film can be probed by varying its hydrodynamic coupling to a bounding oil layer. Using the motion of disclinations as intrinsic tracers of the flow field and a hydrodynamic model, we obtain an estimate for the shear viscosity of the nematic film. Knowing this now provides us with an additional handle for robust and precision tunable control of the emergent dynamics of active fluids.

We set up a mesoscopic theory for interacting Brownian particles embedded in a nonequilibrium environment, starting from the microscopic interacting many-body theory. Using nonequilibrium linear-response theory, we characterize the effective dynamical interactions on the mesoscopic scale and the statistics of the nonequilibrium environmental noise, arising upon integrating out the fast degrees of freedom. As hallmarks of nonequilibrium, the breakdown of the fluctuation-dissipation and action-reaction relations for Brownian degrees of freedom is exemplified with two prototypical models for the environment, namely active Brownian particles and stirred colloids.

A master equation approach is applied to a reversible and conservative cellular automaton model (Q2R). The Q2R model is a dynamical variation of the Ising model for ferromagnetism that possesses quite a rich and complex dynamics. The configuration space is composed of a huge number of cycles with exponentially long periods. Following Nicolis and Nicolis [G. Nicolis and C. Nicolis, Phys. Rev. A 38, 427 (1988)], a coarse-graining approach is applied to the time series of the total magnetization, leading to a master equation that governs the macroscopic irreversible dynamics of the Q2R automata. The methodology is replicated for various lattice sizes. In the case of small systems, we show that the master equation leads to a tractable probability transfer matrix of moderate size, which provides a master equation for a coarse-grained probability distribution. The method is validated and some explicit examples are discussed.

We study diffusion of hard-core particles on a one-dimensional periodic lattice subjected to a constraint that the separation between any two consecutive particles does not increase beyond a fixed value $n+1$; an initial separation larger than $n+1$ can however decrease. These models undergo an absorbing state phase transition when the conserved particle density of the system falls below a critical threshold ${\rho }_c=1/(n+1)$. We find that the ${\varphi }_k$, the density of 0-clusters (0 representing vacancies) of size $0\le k<n$, vanish at the transition point along with activity density ${\rho }_a$. The steady state of these models can be written in matrix product form to obtain analytically the static exponents ${\beta }_k=n-k$ and $\nu =1=\eta$ corresponding to each ${\varphi }_k$. We also show from numerical simulations that, starting from a natural condition, ${\varphi }_k\left(t\right)\mathrm{s}$ decay as $t^{-{\alpha }_k}$ with ${\alpha }_k=(n-k)/2$ even though other dynamic exponents ${\nu }_t=2=z$ are independent of $k$; this ensures the validity of scaling laws $\beta =\alpha {\nu }_t$ and ${\nu }_t=z\nu$.

Relaxational processes in ordered phases of one-dimensional Ising models with long-range interactions are investigated by Monte Carlo simulations. Three types of spin model, the pure ferromagnetic, the diluted ferromagnetic, and the spin glass models, are examined. The effective dimension of the one-dimensional systems are controlled by a parameter $\sigma$, which tunes the rate of interaction decay. Systematical investigations of droplet dynamics, from the lower to the upper critical dimension, are conducted by changing the value of $\sigma$. Comparing numerical data with the droplet theory, it is found that the surface dimension of droplets is distributed around the effective dimension. The distribution in the surface dimension makes the droplet dynamics complex and extremely enhances dynamical crossover.

Quantum master equations form an important tool in the description of transport problems in open quantum systems. However, they suffer from the difficulty that the shape of the Lindblad dissipator depends sensibly on the system Hamiltonian. Consequently, most of the work done in this field has focused on phenomenological dissipators which act locally on different parts of the system. In this paper we show how to construct Lindblad dissipators to model a one-dimensional bosonic tight-binding chain connected to two baths at the first and last site, kept at different temperatures and chemical potentials. We show that even though the bath coupling is local, the effective Lindblad dissipator stemming from this interaction is inherently nonlocal, affecting all normal modes of the system. We then use this formalism to study the current of particles and energy through the system and find that they have the structure of Landauer's formula, with the bath spectral density playing the role of the transfer integral. Finally, we consider infinitesimal temperature and chemical potential gradients and show that the currents satisfy Onsager's reciprocal relations, which is a consequence of the fact that the microscopic quantum dynamics obeys detailed balance.

Critical systems have always intrigued physicists and precipitated the development of new techniques. Recently, there has been renewed interest in the information contained in the configurations of classical critical systems, whose computation do not require full knowledge of the wave function. Inspired by holographic duality, we investigated the entanglement properties of the classical configurations (snapshots) of the Potts model by introducing an Ansatz ensemble of random fractal images. By virtue of the central limit theorem, our Ansatz accurately reproduces the entanglement spectra of actual Potts snapshots without any fine tuning of parameters or artificial restrictions on ensemble choice. It provides a microscopic interpretation of the results of previous studies, which established a relation between the scaling behavior of snapshot entropy and the critical exponent. More importantly, it elucidates the role of ensemble disorder in restoring conformal invariance, an aspect previously ignored. Away from criticality, the breakdown of scale invariance leads to a renormalization of the parameter $\mathrm{\Sigma }$ in the random fractal Ansatz, whose variation can be used as an alternative determination of the critical exponent. We conclude by providing a recipe for the explicit construction of fractal unit cells consistent with a given scaling exponent.

One of the most important problems in quantum thermodynamics is how to distinguish work and heat in autonomous quantum systems. In this paper, work and heat are defined through the following criterion, i.e., work is the energy that cannot change the entropy of the energy resource, and satisfies the Jarzynski equality, while heat does not. Two kinds of definitions satisfying the two corresponding requirements are proposed and demonstrated, and the consistency condition of the two kinds is given. Through the first definition, the problem of entropy production is investigated. A model study is also presented to verify the proposal.

For generic Hamiltonian systems we derive predictions for dynamical tunneling from regular to chaotic phase-space regions. In contrast to previous approaches, we account for the resonance-assisted enhancement of regular-to-chaotic tunneling in a nonperturbative way. This provides the foundation for future semiclassical complex-path evaluations of resonance-assisted regular-to-chaotic tunneling. Our approach is based on a new class of integrable approximations which mimic the regular phase-space region and its dominant nonlinear resonance chain in a mixed regular-chaotic system. We illustrate the method for the standard map.

We investigate the death and revival of chaos under the impact of a monotonous time-dependent forcing that changes its strength with a non-negligible rate. Starting on a chaotic attractor it is found that the complexity of the dynamics remains very pronounced even when the driving amplitude has decayed to rather small values. When after the death of chaos the strength of the forcing is increased again with the same rate of change, chaos is found to revive but with a different history. This leads to the appearance of a hysteresis in the complexity of the dynamics. To characterize these dynamics, the concept of snapshot attractors is used, and the corresponding ensemble approach proves to be superior to a single trajectory description, that turns out to be nonrepresentative. The death (revival) of chaos is manifested in a drop (jump) of the standard deviation of one of the phase-space coordinates of the ensemble; the details of this chaos-nonchaos transition depend on the ratio of the characteristic times of the amplitude change and of the internal dynamics. It is demonstrated that chaos cannot die out as long as underlying transient chaos is present in the parameter space. As a condition for a “quasistatically slow” switch-off, we derive an inequality which cannot be fulfilled in practice over extended parameter ranges where transient chaos is present. These observations need to be taken into account when discussing the implications of “climate change scenarios” in any nonlinear dynamical system.

Complex diseases can be modeled as damage to intracellular networks that results in abnormal cell behaviors. Network-based dynamic models such as Boolean models have been employed to model a variety of biological systems including those corresponding to disease. Previous work designed compensatory interactions to stabilize an attractor of a Boolean network after single node damage. We generalize this method to a multinode damage scenario and to the simultaneous stabilization of multiple steady state attractors. We classify the emergent situations, with a special focus on combinatorial effects, and characterize each class through simulation. We explore how the structural and functional properties of the network affect its resilience and its possible repair scenarios. We demonstrate the method's applicability to two intracellular network models relevant to cancer. This work has implications in designing prevention strategies for complex disease.

We study a model of continuous opinion dynamics with both positive and negative mutual interactions. The model shows a continuous phase transition between a phase with consensus (order) and a phase having no consensus (disorder). The mean field version of the model was already studied. Using extensive numerical simulations, we study the same model in two and three dimensions. The critical points of the phase transitions for various cases and the associated critical exponents have been estimated. The universality class of the phase transitions in the model is found to be same as Ising model in the respective dimensions.

We propose and solve analytically a stochastic model for the dynamics of a binary biological switch, defined as a DNA unit with two mutually exclusive configurations, each one triggering the expression of a different gene. Such a device has the potential to be used as a memory unit for biological computing systems designed to operate in noisy environments. We discuss a recent implementation of this switch in living cells, the recombinase addressable data (RAD) module. In order to understand the behavior of a RAD module we compute the exact time-dependent joint distribution of the two expressed genes starting in one state and evolving to another asymptotic state. We consider two operating regimes of the RAD module, a fast and a slow stochastic switching regime. The fast regime is aggregative and produces unimodal distributions, whereas the slow regime is separative and produces bimodal distributions. Both regimes can serve to prepare pure memory states when all cells are expressing the same gene. The slow regime can also separate mixed states by producing two subpopulations, each one expressing a different gene. Compared to the genetic toggle switch based on positive feedback, the RAD module ensures more rapid memory operations for the same quality of the separation between binary states. Our model provides a simplified phenomenological framework for studying RAD memory devices and our analytic solution can be further used to clarify theoretical concepts in biocomputation and for optimal design in synthetic biology.

The single-particle dynamics of the glass-forming $\mathrm{C}{\mathrm{u}}_{50}\mathrm{Z}{\mathrm{r}}_{50}$ alloy, from the supercooled liquid well above the glass-transition temperature, $T_{\mathrm{g}}$ to the glassy state, is studied by using the molecular dynamics simulations. When the liquid is cooled below $1.2T_{\mathrm{g}}$, the dynamics heterogeneity characterized by the cage-jump motion becomes increasingly pronounced. The analyses based on the continuous time random walk method indicate that the liquid falls out of equilibrium in the present simulation time scale when it is cooled into the regime below $1.02T_{\mathrm{g}}$. However, we find that the jump length and the jump rate do not display the non-equilibrium behaviors even in the glassy state below $T_{\mathrm{g}}$, which allows us to study the intrinsic dynamic characteristics through $T_{\mathrm{g}}$. The mean waiting time between two successive jumps has a rapid growth following the Vogel-Fulcher-Tammann law as the non-equilibrium regime is approached, in analogy with the temperature behaviors of transport properties for fragile supercooled liquids. In contrast, the jump rate maintains the Arrhenius decay and the jump length has even a weaker temperature dependence when the liquid is cooled into glassy state. We find that a pronounced enhancement of the spatial correlation of jumps occurs accompanied by the glass transition: the string-like cooperative jumps dominate the fast motion instead of the uncorrelated and individual jumps. Our work offers an insight into the equilibrium effect of the single-particle dynamics in glass transition.

This paper aims to look into how system rotation may modify the role played by boundary slip in controlling flow through a rotating channel bounded by stick-slip walls. A semianalytical model is developed for pressure-driven flow in a slit channel that rotates about an axis perpendicular to its walls, which are superhydrophobic surfaces patterned with periodic alternating no-shear and no-slip stripes. The cases where the flow is driven by a pressure gradient parallel or normal to the stripes are considered. The effects of the no-shear area fraction on the velocities and effective slip lengths for the primary and secondary flows are investigated as functions of the rotation rate and the channel height. It is mathematically proved that the secondary flow rate is exactly the same in the two cases, irrespective of whether the primary flow is parallel or normal to the wall stripes. For any rotation speed, there is an optimal value of the no-shear area fraction at which the primary flow rate is maximum. This is a consequence of two competing effects: the no-shear part of the wall may serve to reduce the wall resistance, thereby enhancing the flow especially at low rotation, but it also weakens the formation of the near-wall Ekman layer, which is responsible for pumping the flow especially at high rotation. Wall slip in a rotating environment is to affect flow in the Ekman layer, but not flow in the geostrophic core.

Fluid-based locomotion at low Reynolds number is subject to the constraints of Purcell's scallop theorem: reciprocal shape kinematics identical under a time-reversal symmetry cannot cause locomotion. In particular, a single degree-of-freedom scallop undergoing opening and closing motions cannot swim. Most strategies for symmetry breaking and locomotion rely on direct control of the swimmer's shape kinematics. Less is known about indirect control via actuation of the fluid medium. To address how such indirect actuation strategies can lead to locomotion, we analyze a $\mathrm{\Lambda }$-shaped model system analogous to Purcell's scallop but able to deform passively in oscillatory flows. Neutrally buoyant scallops undergo no net locomotion. We show that dense, elastic scallops can exhibit passive locomotion in zero-mean oscillatory flows. We examine the efficiency of swimming parallel to the background flow and analyze the stability of these motions. We observe transitions from stable to unstable swimming, including ordered transitions from fluttering to chaoticlike motions and tumbling. Our results demonstrate that flow oscillations can be used to passively actuate and control the motion of microswimmers, which may be relevant to applications such as surgical robots and cell sorting and manipulation in microfluidic devices.

In this work, we experimentally investigate the impact of water droplets onto soft viscoelastic surfaces with a wide range of impact velocities. Several impact phenomena, which depend on the dynamic interaction between the droplets and viscoelastic surfaces, have been identified and analyzed. At low $\mathrm{We}$, complete rebound is observed when the impact velocity is between a lower and an upper threshold, beyond which droplets are deposited on the surface after impact. At intermediate $\mathrm{We}$, entrapment of an air bubble inside the impinging droplets is found on soft surfaces, while a bubble entrapment on the surface is observed on rigid surfaces. At high $\mathrm{We}$, partial rebound is only identified on the most rigid surface at $\mathrm{We}\gtrsim 92$. Rebounding droplets behave similarly to elastic drops rebounding on superhydrophobic surfaces and the impact process is independent of surface viscoelasticity. Further, surface viscoelasticity does not influence drop spreading after impact—as the surfaces behave like rigid surfaces—but it does affect drop recoiling. Also, the postimpact drop oscillation on soft viscoelastic surfaces is influenced by dynamic wettability of these surfaces. Comparing sessile drop oscillation with a damped harmonic oscillator allows us to conclude that surface viscoelasticity affects the damping coefficient and liquid surface tension sets the spring constant of the system.

We study the relation between flow structure and fluid deformation in steady flows through two-dimensional heterogeneous media, which are characterized by a broad spectrum of stretching behaviors, ranging from sub- to superlinear. We analyze these behaviors from first principles, which uncovers intermittent shear events to be at the origin of subexponential stretching. We derive explicit expressions for Lagrangian deformation and demonstrate that stretching obeys a coupled continuous-time random walk, which for broad distributions of flow velocities becomes a Lévy walk. The derived model provides a direct link between the flow and deformation statistics, and a natural way to quantify the impact of intermittent shear events on the stretching behavior.

We show that the Bayesian inference of recently measured x-ray diffraction spectra from laser-shocked aluminum [L. B. Fletcher et al., Nat. Photon. 9, 274 (2015)] with the one-component-plasma (OCP) model performs remarkably well at estimating the ionic density and temperature. This statistical approach requires many evaluations of the OCP static structure factor, which were done using a recently derived analytic fit. The atomic form factor is approximated by an exponential function in the diffraction window of the first peak. The electronic temperature is then estimated from a comparison of this approximated form factor with the electronic structure of an average atom model. Out-of-equilibrium states, with electrons hotter than ions, are diagnosed for the spectra obtained early after the pump, whereas at a late time delay the plasma is at thermal equilibrium. Apart from the present findings, this OCP-based modeling of warm dense matter has an important role to play in the interpretation of x-ray Thomson scattering measurements currently performed at large laser facilities.

In the problem of matrix compressed sensing, we aim to recover a low-rank matrix from a few noisy linear measurements. In this contribution, we analyze the asymptotic performance of a Bayes-optimal inference procedure for a model where the matrix to be recovered is a product of random matrices. The results that we obtain using the replica method describe the state evolution of the Parametric Bilinear Generalized Approximate Message Passing (P-BiG-AMP) algorithm, recently introduced in J. T. Parker and P. Schniter [IEEE J. Select. Top. Signal Process. 10, 795 (2016)]. We show the existence of two different types of phase transition and their implications for the solvability of the problem, and we compare the results of our theoretical analysis to the numerical performance reached by P-BiG-AMP. Remarkably, the asymptotic replica equations for matrix compressed sensing are the same as those for a related but formally different problem of matrix factorization.