In the analysis of signal regularity from a physiological system such as the human heart, Approximate entropy $\left(H_A\right)$ and Sample entropy $\left(H_S\right)$ have been the most popular statistical tools used so far. While studying heart rate dynamics, it nevertheless becomes more important to extract information about complexities associated with the heart, rather than the regularity of signal patterns produced by it. A complex physiological system does not necessarily produce irregular signals and vice versa. In order to equip a regularity statistic to see through the respective system's level of complexity, the idea of multiscaling was introduced in $H_S$ estimation. Multiscaling ideally requires an input signal to be (a) long and (b) stationary. However, the longer the data is the less stationary it is. The requirement multiscaling places on its data length largely limits its accuracy. We propose a novel method of entropy profiling that makes multiscaling require very short signal segments, granting better prospects of signal stationarity and estimation accuracy. With entropy profiling, an efficient multiscale $H_S$ based analysis requires only 500-beat signals of atrial fibrillated data, as opposed to the earlier case that required at least 20 000 beats.

We examine the motion of a probe particle driven through a chiral fluid composed of circularly swimming disks. We find that the probe particle travels in both the longitudinal direction, parallel to the driving force, and in the transverse direction, perpendicular to the driving force, giving rise to a Hall angle. Under constant driving force, we show that the probe particle velocity in both the longitudinal and transverse directions exhibits nonmonotonic behavior as a function of the activity of the circle swimmers. The Hall angle is maximized when a resonance occurs between the frequency of the chiral disks and the motion of the probe particle. As the density of the chiral fluid increases, the Hall angle gradually decreases before reaching zero when the system enters a jammed state. We show that the onset of jamming depends on the chiral particle swimming frequency, with a fluid state appearing at low frequencies and a jammed solid occurring at high frequencies.

In this study, we formulate a density functional theory (DFT) for systems of labeled particles, considering a two-dimensional bead-spring lattice with a magnetic dipole on every bead as a model for ferrogels. On the one hand, DFT has been widely studied to investigate fluidlike states of materials, in which constituent particles are not labeled as they can exchange their positions without energy cost. On the other hand, in ferrogels consisting of magnetic particles embedded in elastic polymer matrices, the particles are labeled by their positions as their neighbors do not change over time. We resolve such an issue of particle labeling, introducing a mapping of the elastic interaction mediated by springs onto a pairwise additive interaction (pseudosprings) between unlabeled particles. We further investigate magnetostriction and changes in the elastic constants under altered magnetic interactions employing the pseudospring potential. It is revealed that there are two different response scenarios in the mechanical properties of the dipole-spring systems: While systems at low packing fractions are hardened as the magnetic moments increase in magnitude, at high packing fractions softening due to diminishing effects from the steric force, associated with increases in the volume, is observed. The validity of the theory is also verified by Monte Carlo simulations with both real springs and pseudosprings. We expect that our DFT approach may promote our understanding of materials with particle inclusions.

Jammed particulate systems composed of various shapes of particles undergo the jamming transition as they are compressed or decompressed. To date, sphere packings have been extensively studied in many previous works, where isostaticity at the transition and scaling laws with the pressure of various quantities, including the contact number and the vibrational density of states, have been established. Additionally, much attention has been paid to nonspherical packings, and particularly recent work has made progress in understanding ellipsoidal packings. In this work, we study the dimer packings in two dimensions, which have been much less understood than systems of spheres and ellipsoids. We first study the contact number of dimers near the jamming transition. It turns out that packings of dimers have “rotational rattlers,” each of which still has a free rotational motion. After correcting this effect, we show that dimers become isostatic at the jamming, and the excess contact number obeys the same critical law and finite-size scaling law as those of spheres. We next study the vibrational properties of dimers near the transition. We find that the vibrational density of states of dimers exhibits two characteristic plateaus that are separated by a peak. The high-frequency plateau is dominated by the translational degree of freedom, while the low-frequency plateau is dominated by the rotational degree of freedom. We establish the critical scaling laws of the characteristic frequencies of the plateaus and the peak near the transition. In addition, we present detailed characterizations of the real space displacement fields of vibrational modes in the translational and rotational plateaus.

We show that sensorial delay alters the collective motion of self-propelling agents with aligning interactions: In a two-dimensional Vicsek model, short delays enhance the emergence of clusters and swarms, while long or negative delays prevent their formation. In order to quantify this phenomenon, we introduce a global clustering parameter based on the Voronoi tessellation, which permits us to efficiently measure the formation of clusters. Thanks to its simplicity, sensorial delay might already play a role in the organization of living organisms and can provide a powerful tool to engineer and dynamically tune the behavior of large ensembles of autonomous robots.

An elastic membrane with embedded nematic molecules is considered as a model of anisotropic fluid membrane with internal ordering. By considering the geometric coupling between director field and membrane curvature, the nematic texture is shown to induce anisotropic stresses additional to Canham-Helfrich elasticity. Building upon differential geometry, analytical expressions are found for the membrane stress and torque induced by splaying, twisting, and bending of the nematic director as described by the Frank energy of liquid crystals. The forces induced by prototypical nematic textures are visualized on the sphere and on cylindrical surfaces.

Using bi-dimensional discrete element simulations, the shear strength and microstructure of granular mixtures composed of particles of different shapes are systematically analyzed as a function of the proportion of grains of a given number of sides and the combination of different shapes (species) in one sample. We varied the angularity of the particles by varying the number of sides of the polygons from 3 (triangles) up to 20 (icosagons) and disks. The samples analyzed were built keeping in mind the following cases: (1) increase of angularity and species starting from disks; (2) decrease of angularity and increase of species starting from triangles; (3) random angularity and increase of species starting from disks and from polygons. The results show that the shear strength vary monotonically with increasing numbers of species (it may increase or decrease), even in the random mixtures (case 3). At the micro-scale, the variation in shear strength as a function of the number of species is due to different mechanisms depending on the cases analyzed. It may result from the increase of both the geometrical and force anisotropies, from only a decrease of frictional anisotropy, or from compensation mechanisms involving geometrical and force anisotropies.

Using random walk analyses we explore diffusive transport on networks obtained from contacts between isotropically compressed, monodisperse, frictionless sphere packings generated over a range of pressures in the vicinity of the jamming transition $p\to 0$. For conductive particles in an insulating medium, conduction is determined by the particle contact network with nodes representing particle centers and edges contacts between particles. The transition rate is not homogeneous, but is distributed inhomogeneously due to the randomness of packing and concomitant disorder of the contact network, e.g., the distribution of the coordination number. A narrow escape time scale is used to write a Markov process for random walks on the particle contact network. This stochastic process is analyzed in terms of spectral density of the random, sparse, Euclidean and real, symmetric, positive, semidefinite transition rate matrix. Results show network structures derived from jammed particles have properties similar to ordered, euclidean lattices but also some unique properties that distinguish them from other structures that are in some sense more homogeneous. In particular, the distribution of eigenvalues of the transition rate matrix follow a power law with spectral dimension 3. However, quantitative details of the statistics of the eigenvectors show subtle differences with homogeneous lattices and allow us to distinguish between topological and geometric sources of disorder in the network.

The transient dynamics of a growing droplet in a yarn is explored following the spatiotemporal evolution of the three-phase contact line as well as the liquid-air interface with the help of videographic techniques and subsequent image analyses. The spontaneous capillary flow of liquids in a porous network is used to generate a droplet on the freely hanging end of a yarn whose other end is dipped continuously in a liquid reservoir. The growing droplet initially moves upward due to surface tension until the attainment of a critical volume, beyond which gravity is able to pull it downward until detachment. Based upon the spatiotemporal trajectory of the three-phase contact line of the droplet, the entirety of the associated growth dynamics can be divided in three distinct regimes, namely, “radial growth,” “axial growth,” and “motion” stages. The transition from one to the other is governed by the subtle interplay between the capillary and the gravity forces. Several experimental fluids are considered to elucidate the effect of the fluid properties on the transient contact line and interfacial dynamics of drops. The kinetics of the three-phase contact line and the radius of the droplet is found to follow two distinct exponential scaling laws, developed through the combination of the relevant forces. A mathematical model has also been proposed to predict the critical volume of the growing droplet in relation to its final volume, beyond which gravity controls the transient dynamics.

The structure of Langmuir plasma waves carrying a finite orbital angular momentum is revised in the paraxial approximation. It is shown that the kinetic effects related to higher-order momenta of the electron distribution function lead to coupling of Laguerre-Gaussian modes and result in a modification of the wave dispersion and damping. The theoretical analysis is compared to the three-dimensional particle-in-cell numerical simulations for a mode with orbital momentum $l=2$. It is demonstrated that propagation of such a plasma wave is accompanied with generation of quasistatic axial and azimuthal magnetic fields which result from the orbital and longitudinal momenta transported with the wave, respectively.

We study wave reciprocity in one-dimensional asymmetric systems constructed by multiple nonlinear $\delta$-function scatters embedded within linear scatters. A general reciprocal condition is proposed, in terms of the rotation symmetry between forward and backward transfer matrices. We then derive various resonance conditions, under which all scatterers behave as merging into either a single nonlinear $\delta$ scatter, or a symmetric nonlinear barrier configuration. As such, the reciprocity appears periodically by changing widths of linear constant potentials between neighboring nonlinear $\delta$ scatters. Moreover, the wave reciprocity will not be violated if one replaces the linear constant potential between two $\delta$-nonlinear scatters with any other kind of transparent scatterers.

Time-reversal of propagating waves has been intensely studied during the last years and successfully implemented in different experimental contexts. It has been argued that ergodic or chaotic ray dynamics improve the refocusing resolution. In this work we consider this fundamental aspect by studying the reversion of sound waves in two-dimensional reflecting cavities numerically. The boundary of the enclosure is deformed from a rectangle with regular ray dynamics to a completely chaotic hyperbolic billiard. We observed that both the regular and chaotic cases display a prominent refocusing peak, and also that in the first scenario many secondary maxima appear. We developed measures of the spatial and temporal contrasts of the reconstructed signal in order to gain insight on these phenomena and to distinguish between cases. The results obtained point to the necessity for a reconsideration of what is usually understood by successful time-reversal processes.

This paper reports an interesting net fluid flow in the induced-charge electro-osmosis (ICEO) of poly(sodium 4-styrenesulfonate) (NaPSS) solutions measured through microparticle image velocimetry ($\mu \mathrm{PIV}$). The net fluid flow is attributed to the significantly unequal cations and poly-anions of NaPSS. Owing to the phase delay effect of ions, different flow patterns appear with the alternating electric field. The inflow velocity and outflow velocity are found to be unequal and their relative magnitude shows a dependence on the electric field strength. The ICEO velocity is positively correlated with the NaPSS concentration. As NaPSS introduces the non-Newtonian effect, the well-known quadratic relationship between ICEO velocity and electric field strength in Newtonian fluids breaks. The ICEO velocity varies differently with the electric field strength as the NaPSS concentration changes. These new findings can contribute to the understanding of ICEO of complex fluids, e.g., biofluids.

Melting in the presence of electrohydrodynamic (EHD) flow driven by the Coulomb force in dielectric phase change material is numerically studied. A model is developed for the EHD flow in the solid-liquid phase change process. The fully coupled equations including mechanical equations, electrical equations, energy equations, and the continuity equations in the solid-liquid interface are solved using a unified lattice Boltzmann model (LBM). Firstly, the numerical model is validated by several cases in the hydrostatic state, and all LBM results are found to be highly consistent with analytical solutions. Besides, our LBM code is able to reproduce the step changes in the distribution of charge density and electric field due to the discontinuous distribution of physical properties at the interface. Then, a systematical investigation is conducted on various nondimensional parameters, including electric Rayleigh number $T$, Prandtl number Pr, and Stefan number St. Results are presented for the transient evolutions of temperature, fluid flow, charge density fields, and liquid fraction. Four flow stages in the melting process together with three kinds of flow instabilities are observed. It is found that the electric field has significant influence on the melting, especially at high $T$ and Pr and low St. Over the tested cases, a maximum melting time saving of around 50% is found.

We study active particles performing independent run-and-tumble motion on an infinite line with velocities $v_0\sigma \left(t\right)$, where $\sigma \left(t\right)=\pm 1$ is a dichotomous telegraphic noise with constant flipping rate $\gamma$. We first consider a single particle in the presence of an absorbing wall at $x=0$ and calculate the probability that it has survived up to time $t$ and is at position $x$ at time $t$. We then consider two particles with independent telegraphic noises and compute exactly the probability that they do not cross up to time $t$. In contrast with the case of passive (Brownian) particles this problem of two run-and-tumble particles (RTPs) cannot be reduced to a single RTP with an absorbing wall. Nevertheless, we are able to compute exactly the probability of no crossing of two independent RTPs up to time $t$ and find that it decays at large time as $t^{-1/2}$ with an amplitude that depends on the initial condition. We show that this amplitude vanishes if one extrapolates the starting distance between the two particles to a negative value—similarly to the Milne extrapolation length in neutron scattering.

Fluctuation-induced forces occur generically when long-range correlations (e.g., in fluids) are confined by external bodies. In classical systems, such correlations require specific conditions, e.g., a medium close to a critical point. On the other hand, long-range correlations appear more commonly in certain nonequilibrium systems with conservation laws. Consequently, a variety of nonequilibrium fluctuation phenomena, including fluctuation-induced forces, have been discovered and explored recently. Here we address a long-standing problem of nonequilibrium critical Casimir forces emerging after a quench to the critical point in a confined fluid with order-parameter-conserving dynamics and non-symmetry-breaking boundary conditions. The interplay of inherent (critical) fluctuations and dynamical nonlocal effects (due to density conservation) gives rise to striking features, including correlation functions and forces exhibiting oscillatory time dependences. Complex transient regimes arise, depending on initial conditions and the geometry of the confinement. Our findings pave the way for exploring a wealth of nonequilibrium processes in critical fluids (e.g., fluctuation-mediated self-assembly or aggregation). In certain regimes, our results are applicable to active matter.

Deep learning has been immensely successful at a variety of tasks, ranging from classification to artificial intelligence. Learning corresponds to fitting training data, which is implemented by descending a very high-dimensional loss function. Understanding under which conditions neural networks do not get stuck in poor minima of the loss, and how the landscape of that loss evolves as depth is increased, remains a challenge. Here we predict, and test empirically, an analogy between this landscape and the energy landscape of repulsive ellipses. We argue that in fully connected deep networks a phase transition delimits the over- and underparametrized regimes where fitting can or cannot be achieved. In the vicinity of this transition, properties of the curvature of the minima of the loss (the spectrum of the Hessian) are critical. This transition shares direct similarities with the jamming transition by which particles form a disordered solid as the density is increased, which also occurs in certain classes of computational optimization and learning problems such as the perceptron. Our analysis gives a simple explanation as to why poor minima of the loss cannot be encountered in the overparametrized regime. Interestingly, we observe that the ability of fully connected networks to fit random data is independent of their depth, an independence that appears to also hold for real data. We also study a quantity $\mathrm{\Delta }$ which characterizes how well ($\mathrm{\Delta }<0$) or badly ($\mathrm{\Delta }>0$) a datum is learned. At the critical point it is power-law distributed on several decades, $P_{+}\left(\mathrm{\Delta }\right)\sim {\mathrm{\Delta }}^{\theta }$ for $\mathrm{\Delta }>0$ and $P_{-}\left(\mathrm{\Delta }\right)\sim {(-\mathrm{\Delta })}^{-\gamma }$ for $\mathrm{\Delta }<0$, with exponents that depend on the choice of activation function. This observation suggests that near the transition the loss landscape has a hierarchical structure and that the learning dynamics is prone to avalanche-like dynamics, with abrupt changes in the set of patterns that are learned.

In this paper, we consider the problem of parameter sensitivity in models of complex dynamical systems through the lens of information geometry. We calculate the sensitivity of model behavior to variations in parameters. In most cases, models are sloppy, that is, exhibit an exponential hierarchy of parameter sensitivities. We propose a parameter classification scheme based on how the sensitivities scale at long observation times. We show that for oscillatory models, either with a limit cycle or a strange attractor, sensitivities can become arbitrarily large, which implies a high effective dimensionality on the model manifold. Sloppy models with a single fixed point have model manifolds with low effective dimensionality, previously described as a “hyper-ribbon.” In contrast, models with high effective dimensionality translate into multimodal fitting problems. We define a measure of curvature on the model manifold which we call the winding frequency that estimates the density of local minima in the model's parameter space. We then show how alternative choices of fitting metrics can “unwind” the model manifold and give low winding frequencies. This prescription translates the model manifold from one of high effective dimensionality into the hyper-ribbon structures observed elsewhere. This translation opens the door for applications of sloppy model analysis and model reduction methods developed for models with low effective dimensionality.

We demonstrate that with appropriate quantum correlation function, a real-space network model can be constructed to study the phase transitions in quantum systems. For a three-dimensional bosonic system, a single-particle density matrix is adopted to construct an adjacency matrix. We show that a Bose-Einstein condensate transition can be interpreted as a transition into a small-world network, which is accurately captured by a small-world coefficient. For a one-dimensional disordered system, using the electron diffusion operator to build the adjacency matrix, we find that Anderson localized states create many weakly linked subgraphs, which significantly reduces the clustering coefficient and lengthens the shortest path. We show that the crossover from delocalized to localized regimes as a function of the disorder strength can be identified as a loss of global connection, which is revealed by the small-world coefficient as well as other independent measures such as robustness, efficiency, and algebraic connectivity. Our results suggest that quantum phase transitions can be visualized in real space and characterized by network analysis with suitable choices of quantum correlation functions.

Diffusion on a free-energy landscape is a fundamental framework for describing molecular motors. In the landscape framework, energy conversion between different forms of energy, e.g., chemical and mechanical, is explicitly described using multidimensional nonseparable potential landscapes. We present a $k$-space method for reconstructing multidimensional free-energy landscapes from stochastic single-molecule trajectories. For a variety of two-dimensional model potential landscapes, we demonstrate the robustness of the method by reconstructing the landscapes using full dynamic information, i.e., simulated two-dimensional stochastic trajectories. We then consider the case where the stochastic trajectory is known only along one dimension. With this partial dynamic information, the reconstruction of the full two-dimensional landscape is severely limited in the majority of cases. However, we reconstruct effective one-dimensional landscapes for the two-dimensional model potentials. We discuss the interpretation of the one-dimensional landscapes and identify signatures of energy conversion. Finally, we consider the implications of these results for biological molecular motors experiments.