We investigate the approach to catastrophic failure in a model porous granular material undergoing uniaxial compression. A discrete element computational model is used to simulate both the microstructure of the material and the complex dynamics and feedbacks involved in local fracturing and the production of crackling noise. Under strain-controlled loading, microcracks initially nucleate in an uncorrelated way all over the sample. As loading proceeds the damage localizes into a narrow damage band inclined at 30${}^{\circ }$–45${}^{\circ }$ to the load direction. Inside the damage band the material is crushed into a poorly sorted mixture of mainly fine powder hosting some larger fragments. The mass probability density distribution of particles in the damage zone is a power law of exponent 2.1, similar to a value of 1.87 inferred from observations of the length distribution of wear products (gouge) in natural and laboratory faults. Dynamic bursts of radiated energy, analogous to acoustic emissions observed in laboratory experiments on porous sedimentary rocks, are identified as correlated trails or cascades of local ruptures that emerge from the stress redistribution process. As the system approaches macroscopic failure consecutive bursts become progressively more correlated. Their size distribution is also a power law, with an equivalent Gutenberg-Richter $b$ value of 1.22 averaged over the whole test, ranging from 3 to 0.5 at the time of failure, all similar to those observed in laboratory tests on granular sandstone samples. The formation of the damage band itself is marked by a decrease in the average distance between consecutive bursts and an emergent power-law correlation integral of event locations with a correlation dimension of 2.55, also similar to those observed in the laboratory (between 2.75 and 2.25).

The efficacy of the common practice of tracking brain dynamics using a few key regions of interest is explained via the fact that these regions are sensitive to underlying extended modes of activity, not just local dynamics. This underlines the inseparable interplay between modes and regions and reflects the reality that brain functions range from highly localized to highly extended.

Motivated by recent experiments, we study a system of self-propelled colloids that experience short-range attractive interactions and are confined to a surface. Using simulations we find that the phase behavior for such a system is reentrant as a function of activity: phase-separated states exist in both the low- and high-activity regimes, with a homogeneous active fluid in between. To understand the physical origins of reentrance, we develop a kinetic model for the system's steady-state dynamics whose solution captures the main features of the phase behavior. We also describe the varied kinetics of phase separation, which range from the familiar nucleation and growth of clusters to the complex coarsening of active particle gels.

We study the stable phases of an attractor neural network model, with binary units, for hippocampal place cells encoding one-dimensional (1D) or 2D spatial maps or environments. Different maps correspond to random allocations (permutations) of the place fields. Based on replica calculations we show that, below critical levels for the noise in the neural response and for the number of environments, the network activity is spatially localized in one environment. For high noise and loads the network activity extends over space, either uniformly or with spatial heterogeneities due to the crosstalk between the maps, and memory of environments is lost. Remarkably the spatially localized regime is very robust against the neural noise until it reaches its critical level. Numerical simulations are in excellent quantitative agreement with our theoretical predictions.

In a multicellular organism different cell types express a gene in different amounts. Samples from which gene expression levels can be measured typically contain a mixture of different cell types; the resulting measurements thus give only averages over the different cell types present. Based on fluctuations in the mixture proportions from sample to sample it is in principle possible to reconstruct the underlying expression levels of each cell type: to deconvolute the sample. We use a statistical mechanics approach to the problem of deconvoluting such partial concentrations from mixed samples, explore this approach using Markov chain Monte Carlo simulations, and give analytical results for when and how well samples can be unmixed.

We describe an algorithm for simulating ultrasound propagation in random one-dimensional media, mimicking different microstructures by choosing physical properties such as domain sizes and mass densities from probability distributions. By combining a detrended fluctuation analysis (DFA) of the simulated ultrasound signals with tools from the pattern-recognition literature, we build a Gaussian classifier which is able to associate each ultrasound signal with its corresponding microstructure with a very high success rate. Furthermore, we also show that DFA data can be used to train a multilayer perceptron which estimates numerical values of physical properties associated with distinct microstructures.

We experimentally examine dissolution-generated, density-driven convection with an inclined boundary in both a Hele-Shaw cell and in a porous medium. The convection, manifested by descending, dense fingers, is generated by a diffusive mixing of two liquids at the interface. We investigate the dynamics, widths, and wavelengths of the fingers and characterize the global convective transport for a wide range of permeabilities and tilt angles of the boundaries. Our results have implications for CO${}_2$ storage in a saline aquifer when brine saturated with CO${}_2$ produces a heavier mixture, which may result in an enhanced mass transfer by convection. Our measurements reveal a further enhancement of convection with inclined boundaries, which suggests that sloping formations provide improved sites for CO${}_2$ storage.

When submitted to the repeated passages of vehicles unpaved roads made of sand or gravel can develop a ripply pattern known as washboard or corrugated road. We propose a stability analysis based on experimental measurements of the force acting on a blade (or plow) dragged on a circular sand track and show that a linear model is sufficient to describe the instability near onset. The relation between the trajectory of the plow and the profile of the sand bed left after its passage is studied experimentally. The various terms in the expression of the lift force created by the flow of granular material on the plow are determined up to first order by imposing a sinusoidal trajectory to the blade on an initially flat sand bed, as well as by imposing a horizontal trajectory on an initially rippled sand bed. Our model recovers all the previously observed features of washboard road and accurately predicts the most unstable wavelength near onset as well as the critical velocity for the instability.

We discuss generalizations of a previously published coarse-grained description [Mergell et al., Phys. Rev. E 68, 021911 (2003)] of double stranded DNA (dsDNA). The model is defined at the base-pair level and includes the electrostatic repulsion between neighbor helices. We show that the model reproduces mechanical and elastic properties of several DNA nanostructures (DNA origamis). We also show that electrostatic interactions are necessary to reproduce atomic force microscopy measurements on planar DNA origamis.

The energy cost of measurement is an important fundamental question, and may have profound implications for quantum technologies. In the context of Maxwell's demon, it is often stated that measurement has no minimum energy cost, while information has a work value. However, as we elucidate, the first of these statements does not refer to the cost paid by the measuring device. Here we show that it is only when a measuring device has access to a zero-temperature reservoir—that is, never—that measurement requires no energy. To obtain a given amount of information, all measuring devices must pay a cost equal to that which a heat engine would pay to obtain the equivalent work value of that information.

Ocean waves are complex and often turbulent. While most ocean-wave interactions are essentially linear, sometimes two or more waves interact in a nonlinear way. For example, two or more waves can interact and yield waves that are much taller than the sum of the original wave heights. Most of these shallow-water nonlinear interactions look like an X or a Y or two connected Ys; at other times, several lines appear on each side of the interaction region. It was thought that such nonlinear interactions are rare events: they are not. Here we report that such nonlinear interactions occur every day, close to low tide, on two flat beaches that are about 2000 km apart. These interactions are closely related to the analytic, soliton solutions of a widely studied multidimensional nonlinear wave equation. On a much larger scale, tsunami waves can merge in similar ways.

The ability to predict the collapse of ecological communities is of significant concern in light of global patterns of rapid species extinctions. Here, we use a recently developed dynamic Boolean network-based model of mutualistic plant-pollinator community formation to investigate the stability of simulated ecological communities in the face of sequential species extinctions. We assess communities in terms of the relative change in biodiversity after species loss, and find that communities that experience a significant loss of biodiversity differ from more robust communities according to a number of topological characteristics. Notably, we show that high nestedness, a property commonly believed to promote community stability, may in extreme circumstances promote a critical over-reliance on individual species. Furthermore, the species important to the survival of the rest of the ecosystem occupy different positions in the network than less important species. Our results suggest that network measures may be applied to real ecosystems to yield insight into both their stability and the identity of potentially critical species.

Motor proteins actively contract the actin cytoskeleton of cells and thereby give rise to nonequilibrium fluctuations as well as changes in the architecture of the cytoskeleton. Here, we show, by video microrheology of a reconstituted cytoskeleton, that motors generate time-dependent nonequilibrium fluctuations, which evolve as the network is remodeled. At earlier times, the fluctuation spectrum is dominated by strong non-Gaussian fluctuations, which arise from large displacements. At later times, directed displacements are infrequent and finally disappear. We show that these effects are due to contractile coarsening of the network into large actin-myosin foci.