Collisionless shocks are multiscale objects. Energetic ion distributions are gyrotropic at sufficiently large distances upstream and downstream of the shock transition while at the transition itself the ion dynamics is significantly gyrophase dependent. Magnetic-moment conservation of an ion is widely used as a viable approximation during the shock crossing. It is shown that this approximation is inconsistent with the required entropy increase due to the loss of the gyrophase information.

Bacteria communicate by secreting and detecting diffusible small molecule signals or pheromones. Using the local concentrations of these signals to regulate gene expression, individual cells can synchronize changes in phenotype population-wide, a behavior known as quorum sensing (QS). In unstirred media, the interplay between diffusion of signals, bacterial growth, and regulatory feedback can generate complex spatial and temporal patterns of expression of QS-controlled genes. Here we identify the parameters that allow a local signal to trigger a self-sustaining, traveling activation of QS behavior. Using the natural bioluminescence of wild-type Vibrio fischeri as a readout of its lux QS system, we measure the induction of a spreading QS response by a localized triggering stimulus in unstirred media. Our data show that a QS response propagates outward, sustained by positive feedback in synthesis of the diffusible signal, and that this response occurs only if the triggering stimulus exceeds a critical threshold. We also test how the autonomous or untriggered activation of the V. fischeri QS pathway changes at very low initial population densities. At the lowest population densities, clusters of cells do not transition to a self-sensing behavior, but rather remain in communication via signal diffusion until they reach sufficiently large size that their own growth slows. Our data, which are reproduced by simple growth and diffusion simulations, indicate that in part owing to bacterial growth behavior, natural QS systems can be characterized by long distance communication through signal diffusion even in very heterogeneous and spatially dispersed populations.

Granular packings of nonconvex or elongated particles can form freestanding structures like walls or arches. For some particle shapes, such as staples, the rigidity arises from interlocking of pairs of particles, but the origins of rigidity for noninterlocking particles remains unclear. We report on experiments and numerical simulations of sheared columns of “hexapods,” particles consisting of three mutually orthogonal sphero-cylinders whose centers coincide. We vary the length-to-diameter aspect ratio, $\alpha$, of the sphero-cylinders and subject the packings to quasistatic direct shear. For small $\alpha$, we observe a finite yield stress. For large $\alpha$, however, the column becomes rigid when sheared, supporting stresses that increase sharply with increasing strain. Analysis of x-ray microcomputed tomography (micro-CT) data collected during the shear reveals that the stiffening is associated with a tilted, oblate cluster of hexapods near the nominal shear plane in which particle deformation and average contact number both increase. Simulation results show that the particles are collectively under tension along one direction, even though they do not interlock pairwise. These tensions comes from contact forces carrying large torques, and they are perpendicular to the compressive stresses in the packing. They counteract the tendency to dilate, thus stabilizing the particle cluster.

We present an approach to derive the connectivity properties of pairwise interacting $n$-body systems in thermal equilibrium. We formulate an integral equation that relates the pair connectedness to the distribution of nearest neighbors. For one-dimensional systems with nearest-neighbor interactions, the nearest-neighbor distribution is in turn related to the pair-correlation function $g$ through a simple integral equation. As a consequence, for those systems, we arrive at an integral equation relating $g$ to the pair connectedness, which is readily solved even analytically if $g$ is specified analytically. We demonstrate the procedure for a variety of pair potentials including fully penetrable spheres as well as impenetrable spheres, the only two systems for which analytical results for the pair connectedness exist. However, the approach is not limited to nearest-neighbor interactions in one dimension. Hence, we also outline the treatment of external fields and long-range interactions and we illustrate how the formalism can applied to higher-dimensional systems using the three-dimensional ideal gas as an example.

In this work, we use the finite differences in time domain (FDTD) numerical method to compute and assess the validity of Hopf solutions, or hopfions, for the electromagnetic field equations. In these solutions, field lines form closed loops characterized by different knot topologies which are preserved during their time evolution. Hopfions have been studied extensively in the past from an analytical perspective but never, to the best of our knowledge, from a numerical approach. The implementation and validation of this technique eases the study of more complex cases of this phenomena; e.g., how these fields could interact with materials (e.g., anisotropic or nonlinear), their coupling with other physical systems (e.g., plasmas), and also opens the path on their artificial generation by different means (e.g., antenna arrays or lasers).

Recent experiments have reported a novel splay nematic phase, which has alternating domains of positive and negative splay. To model this phase, previous studies have considered a one-dimensional (1D) splay modulation of the director field, accompanied by a 1D modulation of polar order. When the flexoelectric coupling between splay and polar order becomes sufficiently strong, the uniform nematic state becomes unstable to the formation of a modulated phase. Here we reexamine this theory in terms of a recent approach to liquid crystal elasticity, which shows that pure splay deformation is double splay rather than planar single splay. Following that reasoning, we propose a structure with a two-dimensional (2D) splay modulation of the director field, accompanied by a 2D modulation of polar order, and show that the 2D structure generally has a lower free energy than the 1D structure.

We numerically explore the behavior of repelling and aligning self-propelled polar particles (boids) in two dimensions enclosed by a damped flexible and elastic loop-shaped boundary. We observe disordered, polar ordered, jammed, and circulating states. The latter produce a rich variety of boundary shapes, including circles, ovals, irregulars, ruffles, or sprockets, depending upon the bending moment of the boundary and the boundary to particle mass ratio. With the exception of the circulating states with nonround boundaries, states resemble those exhibited by attracting self-propelled particles, but here the confining boundary acts in place of a cohesive force. We attribute the formation of ruffles to instability mediated by pressure on the boundary when the speed of waves on the boundary approximately matches the self-propelled particle's swim speed.

We investigate experimentally and numerically the response of hinged shallow arches subjected to a transverse midpoint displacement. We find that this simple system supports a rich set of responses, which, to date, have received relatively little attention. We observe not only the snapping of the arches to their inverted equilibrium configuration, but also an earlier dynamic transition from a symmetric to an asymmetric shape that results in a sudden strength loss. Moreover, we find that the response of plastically deformed arches is nonreciprocal with respect to the loading direction. Finally, we discover that, while elastically deformed arches always snap to the inverted stable configuration, for plastically deformed ones there is a critical rise below which the structures are monostable.

At equilibrium, the structure and response of ordered phases are typically determined by the spontaneous breaking of spatial symmetries. Out of equilibrium, spatial order itself can become a dynamically emergent concept. In this article, we show that spatially anisotropic viscous coefficients and stresses can be designed in a far-from-equilibrium fluid by applying to its constituents a time-modulated drive. If the drive induces a rotation whose rate is slowed down when the constituents point along specific directions, then anisotropic structures and mechanical responses arise at long timescales. We demonstrate that the viscous response of such two-dimensional anisotropic driven fluids can acquire a tensorial, dissipationless component called anisotropic odd (or Hall) viscosity. Classical fluids with internal torques can display additional components of the odd viscosity neglected in previous studies of quantum Hall fluids that assumed angular momentum conservation. We show that, unlike their isotropic counterparts, these anisotropic and angular momentum-violating odd-viscosity coefficients can change even the bulk flow of an incompressible fluid by acting as a source of vorticity. In addition, shear distortions in the shape of an inclusion result in torques. We derive how the odd-viscous coefficients depend on the nonlinear, dissipative response of a fluid of rotating rods, i.e., odd viscosity is not simply given by angular momentum density.

Slender elastic objects such as a column tend to buckle under loads. While static buckling is well understood as a bifurcation problem, the evolution of shapes during dynamic buckling is much harder to study. Elastic rings under normal pressure have emerged as a theoretical and experimental paradigm for the study of dynamic buckling with controlled loads. Experimentally, an elastic ring is placed within a soap film. When the film outside the ring is removed, surface tension pulls the ring inward, mimicking an external pressurization. Here we present a theoretical analysis of this process by performing a postbifurcation analysis of an elastic ring under pressure. This analysis allows us to understand how inertia, material properties, and loading affect the observed shape. In particular, we combine direct numerical solutions with a postbifurcation asymptotic analysis to show that inertia drives the system towards higher modes that cannot be selected in static buckling. Our theoretical results explain experimental observations that cannot be captured by a standard linear stability analysis.

We study a one-dimensional disordered Bose fluid using bosonization, the replica method, and a nonperturbative functional renormalization-group approach. We find that the Bose-glass phase is described by a fully attractive strong-disorder fixed point characterized by a singular disorder correlator whose functional dependence assumes a cuspy form that is related to the existence of metastable states. At nonzero momentum scale $k$, quantum tunneling between the ground state and low-lying metastable states leads to a rounding of the cusp singularity into a quantum boundary layer (QBL). The width of the QBL depends on an effective Luttinger parameter $K_k\sim k^{\theta }$ that vanishes with an exponent $\theta =z-1$ related to the dynamical critical exponent $z$. The QBL encodes the existence of rare “superfluid” regions, controls the low-energy dynamics, and yields a (dissipative) conductivity vanishing as ${\omega }^2$ in the low-frequency limit. These results reveal the glassy properties (pinning, “shocks,” or static avalanches) of the Bose-glass phase and can be understood within the “droplet” picture put forward for the description of glassy (classical) systems.

Using population data of high spatial resolution for a region in the south of Europe, we define cities by aggregating individuals to form connected clusters. The resulting cluster-population distributions show a smooth decreasing behavior covering six orders of magnitude. We perform a detailed study of the distributions, using state-of-the-art statistical tools. By means of scaling analysis we rule out the existence of a power-law regime in the low-population range. The logarithmic-coefficient-of-variation test allows us to establish that the power-law tail for high population, characteristic of Zipf's law, has a rather limited range of applicability. Instead, lognormal fits describe the population distributions in a range covering from a few dozen individuals to more than $1\times {10}^6$ (which corresponds to the population of the largest cluster).

Entropy production characterizes the thermodynamic irreversibility and reflects the amount of heat dissipated into the environment and free energy lost in nonequilibrium systems. According to the thermodynamic uncertainty relation, we propose a deterministic method to estimate the entropy production from a single trajectory of system states. We explicitly and approximately compute an optimal current that yields the tightest lower bound using predetermined basis currents. Notably, the obtained tightest lower bound is intimately related to the multidimensional thermodynamic uncertainty relation. By proving the saturation of the thermodynamic uncertainty relation in the short-time limit, the exact estimate of the entropy production can be obtained for overdamped Langevin systems, irrespective of the underlying dynamics. For Markov jump processes, because the attainability of the thermodynamic uncertainty relation is not theoretically ensured, the proposed method provides the tightest lower bound for the entropy production. When entropy production is the optimal current, a more accurate estimate can be further obtained using the integral fluctuation theorem. We illustrate the proposed method using three systems: a four-state Markov chain, a periodically driven particle, and a multiple bead-spring model. The estimated results in all examples empirically verify the effectiveness and efficiency of the proposed method.

We show that a collection of independent Ising spins evolving stochastically can display surprisingly large fluctuations toward ordered behavior, as quantified by certain types of time-integrated plaquette observables, despite the underlying dynamics being noninteracting. In the large-deviation (LD) regime of long times and large system size, this can give rise to a phase transition in trajectory space. As a noninteracting system we consider a collection of spins undergoing single spin-flip dynamics at infinite temperature. For the dynamical observables we study, the associated tilted generators have an exact and explicit spin-plaquette duality. Such setup suggests the existence of a transition (in the large size limit) at the self-dual point of the tilted generator. The nature of the LD transition depends on the observable. We consider explicitly two situations: (i) for a pairwise bond observable the LD transition is continuous and equivalent to that of the transverse field Ising model and (ii) for a higher-order plaquette observable, in contrast, the LD transition is first order. Case (i) is easy to prove analytically, while we confirm case (ii) numerically via an efficient trajectory sampling scheme that exploits the noninteracting nature of the original dynamics.

Multibody interactions can reveal higher-order dynamical effects that are not captured by traditional two-body network models. In this work, we derive and analyze models for consensus dynamics on hypergraphs, where nodes interact in groups rather than in pairs. Our work reveals that multibody dynamical effects that go beyond rescaled pairwise interactions can appear only if the interaction function is nonlinear, regardless of the underlying multibody structure. As a practical application, we introduce a specific nonlinear function to model three-body consensus, which incorporates reinforcing group effects such as peer pressure. Unlike consensus processes on networks, we find that the resulting dynamics can cause shifts away from the average system state. The nature of these shifts depends on a complex interplay between the distribution of the initial states, the underlying structure, and the form of the interaction function. By considering modular hypergraphs, we discover state-dependent, asymmetric dynamics between polarized clusters where multibody interactions make one cluster dominate the other.

The molecular mechanism of specific interactions between double stranded DNA molecules has been investigated for many years. Problems remain in how confinement, ions, and condensing agents change the interactions. We consider how the orientational alignment of DNAs contributes to the interactions via free energy simulations. Here we report on the effective interactions between two parallel DNA double helices in 150-mM NaCl solution using all atom models. We calculate the potential of mean force (PMF) of DNA-DNA interactions as a function of two coordinates, interhelical separation of parallel double helices and relative rotation of a DNA molecule with respect to the other about the helical axis. We generate the two-dimensional PMF to better understand the effective interactions when a DNA molecule is in juxtaposition with another. The analysis of the ion and solvent distributions around the DNA and particularly in the interface region shows that certain alignments of the DNA pair enhance the interactions. At local free energy minima in distance and alignment, water molecules and $\mathrm{N}{\mathrm{a}}^{+}$ ions form a hydrogen bonded network with the phosphates from each DNA. This network contributes an attractive energy component to the DNA-DNA interactions. Our results provide a molecular mechanism whereby local DNA-DNA interactions, depending on the helical orientation, give a potential mechanism for stabilizing pairing of much larger lengths of homologous DNA that have been seen experimentally. The study suggests an atomically detailed local picture of relevance to certain aspects of DNA condensation or aggregation.

Random, uncorrelated displacements of particles on a lattice preserve the hyperuniformity of the original lattice, that is, normalized density fluctuations vanish in the limit of infinite wavelengths. In addition to a diffuse contribution, the scattering intensity from the the resulting point pattern typically inherits the Bragg peaks (long-range order) of the original lattice. Here we demonstrate how these Bragg peaks can be hidden in the effective diffraction pattern of independent and identically distributed perturbations. All Bragg peaks vanish if and only if the sum of all probability densities of the positions of the shifted lattice points is a constant at all positions. The underlying long-range order is then “cloaked” in the sense that it cannot be reconstructed from the pair correlation function alone. On the one hand, density fluctuations increase monotonically with the strength of perturbations $a$, as measured by the hyperuniformity order metric $\overline{\mathrm{\Lambda }}$. On the other hand, the disappearance and reemergence of long-range order, depending on whether the system is cloaked as the perturbation strength increases, is manifestly captured by the $\tau$ order metric. Therefore, while the perturbation strength $a$ may seem to be a natural choice for an order metric of perturbed lattices, the $\tau$ order metric is a superior choice. It is noteworthy that cloaked perturbed lattices allow one to easily simulate very large samples (with at least ${10}^6$ particles) of disordered hyperuniform point patterns without Bragg peaks.

Trajectories of human breast cancer cells moving on one-dimensional circular tracks are modeled by the non-Markovian version of the Langevin equation that includes an arbitrary memory function. When averaged over cells, the velocity distribution exhibits spurious non-Gaussian behavior, while single cells are characterized by Gaussian velocity distributions. Accordingly, the data are described by a linear memory model which includes different random walk models that were previously used to account for various aspects of cell motility such as migratory persistence, non-Markovian effects, colored noise, and anomalous diffusion. The memory function is extracted from the trajectory data without restrictions or assumptions, thus making our approach truly data driven, and is used for unbiased single-cell comparison. The cell memory displays time-delayed single-exponential negative friction, which clearly distinguishes cell motion from the simple persistent random walk model and suggests a regulatory feedback mechanism that controls cell migration. Based on the extracted memory function we formulate a generalized exactly solvable cell migration model which indicates that negative friction generates cell persistence over long timescales. The nonequilibrium character of cell motion is investigated by mapping the non-Markovian Langevin equation with memory onto a Markovian model that involves a hidden degree of freedom and is equivalent to the underdamped active Ornstein-Uhlenbeck process.

We propose a strategy based on the site-bond percolation to minimize the propagation of Phytophthora zoospores on plantations, consisting in introducing physical barriers between neighboring plants. Two clustering processes are distinguished: (i) one of cells with the presence of the pathogen, detected on soil analysis, and (ii) that of diseased plants, revealed from a visual inspection of the plantation. The former is well described by the standard site-bond percolation. In the latter, the percolation threshold is fitted by a Tsallis distribution when no barriers are introduced. We provide, for both cases, the formulas for the minimal barrier density to prevent the emergence of the spanning cluster. Though this work is focused on a specific pathogen, the model presented here can also be applied to prevent the spreading of other pathogens that disseminate, by other means, from one plant to the neighboring ones. Finally, the application of this strategy to three types of commercially important Mexican chili plants is also shown.

In experiments on membrane protein polyhedral nanoparticles (MPPNs) [Basta et al., Proc. Natl. Acad. Sci. USA 111, 670 (2014)], it has been observed that membrane proteins and lipids can self-assemble into closed lipid bilayer vesicles with a polyhedral arrangement of membrane proteins. In particular, MPPNs formed from the mechanosensitive channel of small conductance (MscS) were found to have the symmetry of the snub cube—a chiral, Archimedean solid—with one MscS protein located at each one of the 24 vertices of the snub cube. It is currently unknown whether MPPNs with heterogeneous protein composition maintain a high degree of symmetry. Inspired by previous work on viral capsid symmetry, we employ here computational modeling to study the symmetry of MPPNs with heterogeneous protein size. We focus on MPPNs formed from MscS proteins, which can exist in closed or open conformational states with distinct sizes. We find that, as an increasing number of closed-state MscS proteins transitions to the open conformational state of MscS, the minimum-energy MscS arrangement in MPPNs follows a strikingly regular pattern, with the dominant MPPN symmetry always being provided by the snub cube. Our results suggest that MPPNs with heterogeneous protein size can be highly symmetric, with a well-defined polyhedral ordering of membrane proteins of different sizes.