The spatially resolved diffusive dynamic cross correlations of a pair of colloids in dense quasi-two-dimensional monolayers of identical particles are studied experimentally and theoretically at early times where motion is Fickian. In very dense systems where strong oscillatory equilibrium packing correlations are present, we find an exponential decay of the dynamic cross correlations on small and intermediate length scales. At large separations where structure becomes random, an apparent power law decay with an exponent of approximately −2.2 is observed. For a moderately dense suspension where local structural correlations are essentially absent, this same apparent power law decay is observed over all probed interparticle separations. A microscopic nonhydrodynamic theory is constructed for the dynamic cross correlations which is based on interparticle frictional effects and effective structural forces. Hydrodynamics enters only via setting the very short-time single-particle self-diffusion constant. No-adjustable-parameter quantitative predictions of the theory for the dynamic cross correlations are in good agreement with experiment over all length scales. The origin of the long-range apparent power law is the influence of the constraint of fixed interparticle separation on the amplitude of the mean square force exerted on the two tagged particles by the surrounding fluid. The theory is extended to study high-packing-fraction 3D hard sphere fluids. The same pattern of an oscillatory exponential form of the dynamic cross correlation function is predicted in the structural regime, but the long-range tail decays faster than in monolayers with an exponent of −3.

In the present work we perform temperature jumps in a surfactant solution by means of Monte Carlo simulations, investigating the dynamics of micelle formation. We use a lattice model that allows orientational freedom and hydrogen bonding for solvent molecules, which can make a connection between the different time scales of hydrogen bond formation and amphiphilic aggregation. When we perform a large jump between a high-temperature nonmicellized state and a micellized state, there is strong hysteresis between the heating and cooling processes, the latter showing the formation of premicelles that act as nucleation centers for the assembly of larger aggregates and the former is a drive for dissociation of the existing aggregates. Hysteresis is not seen when we perform a small jump between two states that can be both micellized or nonmicellized. Looking for a more detailed analysis of the hydrophobic effect that drives aggregation, we compare the time evolution of the solvent hydrogen bonds in our system close and far from micelles and how that is affected by the formation of large clusters at low temperatures. We find a strong connection between them, with the total number of hydrogen bonds in the system always increasing when micelles are formed. To gain insights into the mechanism of premicellar formation and growth, we measure the lifetime of micellized amphiphiles as a function of the aggregate size and the stage of the aggregation process. Our results indicate that the premicelles are always unstable, quickly exchanging amphiphiles with the solution due to their low probabilty in equilibrium. Furthermore, we find that the stability of individual surfactants in micelles increases with the aggregate size, with the lifetime of amphiphiles in large micelles being as much as 35 times longer than in the case of the unstable premicellar region.

We construct a generic coarse-grained dynamics of a thin inflexible planar layer of polar-ordered suspension of active particles that is frictionally coupled to an embedding isotropic passive fluid medium with a friction coefficient $\mathrm{\Gamma }$. Being controlled by $\mathrm{\Gamma }$, our model provides a unified framework to describe the long-wavelength behavior of a variety of thin polar-ordered systems, ranging from wet to dry active matter and free-standing active films. Investigations of the linear instabilities around a chosen orientationally ordered uniform reference state reveal generic moving and static instabilities in the system that can depend sensitively on $\mathrm{\Gamma }$. Based on our results, we discuss estimation of bounds on $\mathrm{\Gamma }$ in experimentally accessible systems.

We present a minimal model to describe the onset of collective motion seen when a population of locusts are placed in an annular arena. At low densities motion is disordered, while at high densities locusts march in a common direction, which may reverse during the experiment. The data are well captured by an individual-based model, in which demographic noise leads to the observed density-dependent effects. By fitting the model parameters to equation-free coefficients, we give a quantitative comparison, showing time series, stationary distributions, and the mean switching times between states.

Critical dynamics of cortical neurons have been intensively studied over the past decade. Neuronal avalanches provide the main experimental as well as theoretical tools to consider criticality in such systems. Experimental studies show that critical neuronal avalanches show mean-field behavior. There are structural as well as recently proposed [Phys. Rev. E 89, 052139 (2014)] dynamical mechanisms that can lead to mean-field behavior. In this work we consider a simple model of neuronal dynamics based on threshold self-organized critical models with synaptic noise. We investigate the role of high-average connectivity, random long-range connections, as well as synaptic noise in achieving mean-field behavior. We employ finite-size scaling in order to extract critical exponents with good accuracy. We conclude that relevant structural mechanisms responsible for mean-field behavior cannot be justified in realistic models of the cortex. However, strong dynamical noise, which can have realistic justifications, always leads to mean-field behavior regardless of the underlying structure. Our work provides a different (dynamical) origin than the conventionally accepted (structural) mechanisms for mean-field behavior in neuronal avalanches.

We show rigorously that the solutions of a class of chaotic oscillators are characterized by regularly timed events in which the derivative of the solution is instantaneously zero. The perfect regularity of these events is in stark contrast with the well-known unpredictability of chaos. We explore some consequences of these regularly timed events through experiments using chaotic electronic circuits. First, we show that a feedback loop can be implemented to phase lock the regularly timed events to a periodic external signal. In this arrangement the external signal regulates the timing of the chaotic signal but does not strictly lock its phase. That is, phase slips of the chaotic oscillation persist without disturbing timing of the regular events. Second, we couple the regularly timed events of one chaotic oscillator to those of another. A state of synchronization is observed where the oscillators exhibit synchronized regular events while their chaotic amplitudes and phases evolve independently. Finally, we add additional coupling to synchronize the amplitudes, as well, however in the opposite direction illustrating the independence of the amplitudes from the regularly timed events.

Critical transitions in multistable systems have been discussed as models for a variety of phenomena ranging from the extinctions of species to socioeconomic changes and climate transitions between ice ages and warm ages. From bifurcation theory we can expect certain critical transitions to be preceded by a decreased recovery from external perturbations. The consequences of this critical slowing down have been observed as an increase in variance and autocorrelation prior to the transition. However, especially in the presence of noise, it is not clear whether these changes in observation variables are statistically relevant such that they could be used as indicators for critical transitions. In this contribution we investigate the predictability of critical transitions in conceptual models. We study the quadratic integrate-and-fire model and the van der Pol model under the influence of external noise. We focus especially on the statistical analysis of the success of predictions and the overall predictability of the system. The performance of different indicator variables turns out to be dependent on the specific model under study and the conditions of accessing it. Furthermore, we study the influence of the magnitude of transitions on the predictive performance.

We present a detailed study on the characterization of the degeneration process in combustion instability based on dynamical systems theory. We deal with combustion instability in a lean premixed-type gas-turbine model combustor, one of the fundamentally and practically important combustion systems. The dynamic behavior of combustion instability in close proximity to lean blowout is dominated by a stochastic process and transits to periodic oscillations created by thermoacoustic combustion oscillations via chaos with increasing equivalence ratio [Chaos 21, 013124 (2011); Chaos 22, 043128 (2012)]. Thermoacoustic combustion oscillations degenerate with a further increase in the equivalence ratio, and the dynamic behavior leads to chaotic fluctuations via quasiperiodic oscillations. The concept of dynamical systems theory presented here allows us to clarify the nonlinear characteristics hidden in complex combustion dynamics.

We present a series of numerical simulations of turbulent thermal convection of air in an intermediate range or Rayleigh numbers $({10}^6\le \mathrm{Ra}\le {10}^9)$ with different configurations of a thermally active lower surface. The geometry of the lower surface is designed in such a way that it represents a simplified version of a mountain slope with different inclinations (i.e., “$\mathrm{\Lambda }$”- and “$\mathsf{V}$”-shaped geometry). We find that different wall inclinations significantly affect the local heat transfer by imposing local clustering of instantaneous thermal plumes along the inclination peaks. The present results reveal that significant enhancement of the integral heat transfer can be obtained (up to 32%) when compared to a standard Rayleigh-Bénard configuration with flat horizontal walls. This is achieved through combined effects of the enlargement of the heated surface and reorganization of the large-scale flow structures.

High-amplitude, chaotic or turbulent electromagnetic fluctuations are ubiquitous in high-energy-density laboratory and astrophysical plasmas, where they can be excited by various kinetic-streaming and/or anisotropy-driven instabilities, such as the Weibel instability. These fields typically exist on “sub-Larmor scales”—scales smaller than the electron Larmor radius. Electrons moving through such magnetic fields undergo small-angle stochastic deflections of their pitch angles, thus establishing diffusive transport on long time scales. We show that this behavior, under certain conditions, is equivalent to Coulomb collisions in collisional plasmas. The magnetic pitch-angle diffusion coefficient, which acts as an effective “collision” frequency, may be substantial in these, otherwise, collisionless environments. We show that this effect, colloquially referred to as the plasma “quasicollisionality,” may radically alter the expected radiative transport properties of candidate plasmas. We argue that the modified magneto-optic effects in these plasmas provide an attractive, radiative diagnostic tool for the exploration and characterization of small-scale magnetic turbulence, as well as affect inertial confinement fusion and other laser-plasma experiments.

We study the convergence of statistical estimators used in the estimation of large-deviation functions describing the fluctuations of equilibrium, nonequilibrium, and manmade stochastic systems. We give conditions for the convergence of these estimators with sample size, based on the boundedness or unboundedness of the quantity sampled, and discuss how statistical errors should be defined in different parts of the convergence region. Our results shed light on previous reports of “phase transitions” in the statistics of free energy estimators and establish a general framework for reliably estimating large-deviation functions from simulation and experimental data and identifying parameter regions where this estimation converges.

We modify the kinetic Ising model with Metropolis dynamics, allowing each spin to interact only with $q$ spins randomly chosen from the whole system, which corresponds to the topology of a complete graph. We show that the model with $q\ge 3$ exhibits a phase transition between ferromagnetic and paramagnetic phases at temperature $T^{*}$, which linearly increases with $q$. Moreover, we show that for $q=3$ the phase transition is continuous and that it is discontinuous for larger values of $q$. For $q>3$, the hysteresis exhibits oscillatory behavior—expanding for even values of $q$ and shrinking for odd values of $q$. Due to the mean-field-like nature of the model, we are able to derive the analytical form of transition probabilities and, therefore, calculate not only the probability density function of the order parameter but also precisely determine the hysteresis and the effective potential showing stable, unstable, and metastable steady states. Our results show that a seemingly small modification of the kinetic Ising model leads not only to the switch from a continuous to a discontinuous phase transition, but also to an unexpected oscillating behavior of the hysteresis and a puzzling phenomenon for $q=5$, which might be taken as evidence for the so-called mixed-order phase transition.

A possible approach to the statistical description of granular assemblies starts from Edwards's assumption that all blocked states occupying the same volume are equally probable [Edwards and Oakeshott, Physica A 157, 1080 (1989)]. We performed computer simulations using two-dimensional polygonal particles excited periodically according to two different protocols: excitation by pulses of “negative gravity” and excitation by “rotating gravity.” The first protocol exhibits a nonmonotonous dependency of the mean volume fraction on the pulse strength. The overlapping histogram method is used in order to test whether the volume distribution is described by a Boltzmann-like distribution and to calculate the inverse compactivity as well as the logarithm of the partition sum. We find that the mean volume is a unique function of the measured granular temperature, independently of the protocol and of the branch in $\varphi \left(g\right)$, and that all determined quantities are in agreement with Edwards's theory.

Using a millifluidics and macroscale setup, we study quantitatively the impact of gas exchange on bacterial growth. In millifluidic environments, the permeability of the incubator materials allows an unlimited oxygen supply by diffusion. Moreover, the efficiency of diffusion at small scales makes the supply instantaneous in comparison with the cell division time. In hermetic closed vials, the amount of available oxygen is low. The growth curve has the same trend but is quantitatively different from the millifluidic situation. The analysis of all the data allows us to write a quantitative modeling enabling us to capture the entire growth process.

We present a numerical method for the simultaneous localization and parameter estimation of a fluorescent particle undergoing a discrete-time continuous-state Markov process. In particular, implementation of the method proposed in this work yields an approximation to the posterior density of the particle positions over time in addition to maximum likelihood estimates of fixed, unknown parameters. The method employs sequential Monte Carlo methods and can take into account complex, potentially nonlinear noise models, including shot noise and camera-specific readout noise, as well as a wide variety of motion models and observation models, including those representing recent engineered point spread functions. We demonstrate the technique by applying it to four scenarios, including a particle undergoing free, confined, and tethered diffusions.

The Hamiltonian mean field model of coupled inertial Hamiltonian rotors is a prototype for conservative dynamics in systems with long-range interactions. We consider the case where the interactions between the rotors are governed by a network described by a weighted adjacency matrix. By studying the linear stability of the incoherent state, we find that the transition to synchrony begins when the coupling constant $K$ is inversely proportional to the largest eigenvalue of the adjacency matrix. We derive a closed system of equations for a set of local order parameters to study the effect of network heterogeneity on the synchronization of the rotors. When $K$ is just beyond the transition to synchronization, we find that the degree of synchronization is highly dependent on the network's heterogeneity, but that for large $K$ the degree of synchronization is robust to changes in the degree distribution. Our results are illustrated with numerical simulations on Erdös-Renyi networks and networks with power-law degree distributions.

A general model for random walks (RWs) on networks is proposed. It incorporates damping and time-dependent links, and it includes standard (undamped, noninteracting) RWs (SRWs), coalescing RWs, and coalescing-branching RWs as special cases. The exact, time-dependent solutions for the average numbers of visits $\left(w\right)$ to nodes and their fluctuations $\left({\sigma }^2\right)$ are given, and the long-term $\sigma -w$ relation is studied. Although $\sigma \propto w^{1/2}$ for SRWs, this power law can be fragile when coalescing-branching interaction is present. Damping, however, often strengthens it but with an exponent generally different from $1/2$.

We study the impact of the Peterlin approximation on the statistics of the end-to-end separation of polymers in a turbulent flow. The finitely extensible nonlinear elastic (FENE) model and the FENE model with the Peterlin approximation (FENE-P) are numerically integrated along a large number of Lagrangian trajectories resulting from a direct numerical simulation of three-dimensional homogeneous isotropic turbulence. Although the FENE-P model yields results in qualitative agreement with those of the FENE model, quantitative differences emerge. The steady-state probability of large extensions is overestimated by the FENE-P model. The alignment of polymers with the eigenvectors of the rate-of-strain tensor and with the direction of vorticity is weaker when the Peterlin approximation is used. At large Weissenberg numbers, the correlation times of both the extension and of the orientation of polymers are underestimated by the FENE-P model.

The impact of a single liquid drop on a flowing liquid film is experimentally and theoretically studied. The drop impact produces a crownlike rising liquid sheet, which radially expands. Small droplets can be formed from the crown sheet, resulting in splash. The present study results in three major contributions. (1) A theoretical model is developed to predict the expansion of the crown base. The model with an introduced energy loss factor is shown to be in satisfactory agreement with our experimental observations of drop impact on both stationary and flowing films. The energy loss factor is correlated to the properties of the film and drop. (2) Analysis is conducted to derive an equation for evaluating the stretching rate of the rising crown sheet, which is the local gradient of the rising velocity at the top edge of the crown sheet. It shows that the highest stretching rate appears where the drop spreading flow is right opposite to the film flow, which helps explain why the same location is most probable for splash to take place. (3) A parameter as a function of modified Weber and Reynolds numbers is defined to predict splash and nonsplash of drop impact on flowing films. The two nondimensional numbers evaluate the competition of the two flows (drop and film) against viscosity and surface tension effects. A threshold value of the parameter is found for the occurrence of splash impact on flowing films.

Many eukaryotic cells undergo frequent shape changes (described as amoeboid motion) that enable them to move forward. We investigate the effect of confinement on a minimal model of amoeboid swimmer. A complex picture emerges: (i) The swimmer's nature (i.e., either pusher or puller) can be modified by confinement, thus suggesting that this is not an intrinsic property of the swimmer. This swimming nature transition stems from intricate internal degrees of freedom of membrane deformation. (ii) The swimming speed might increase with increasing confinement before decreasing again for stronger confinements. (iii) A straight amoeoboid swimmer's trajectory in the channel can become unstable, and ample lateral excursions of the swimmer prevail. This happens for both pusher- and puller-type swimmers. For weak confinement, these excursions are symmetric, while they become asymmetric at stronger confinement, whereby the swimmer is located closer to one of the two walls. In this study, we combine numerical and theoretical analyses.