We aim to identify the spatial distribution of vegetation and its growth dynamics with the purpose of obtaining a qualitative assessment of vegetation characteristics tied to its condition, productivity and health, and to land degradation. To do so, we compare a statistical model of vegetation growth and land surface imagery derived vegetation indices. Specifically, we analyze a stochastic cellular automata model and data obtained from satellite images, namely using the normalized difference vegetation index and the leaf area index. In the experimental data, we look for areas where vegetation is broken into small patches and qualitatively compare it to the percolating, fragmented, and degraded states that appear in the cellular automata model. We model the periodic effect of seasons, finding numerical evidence of a periodic fragmentation and recovery phenomenology if the model parameters are sufficiently close to the model's percolation transition. We qualitatively recognize these effects in real-world vegetation images and consider them a signal of increased environmental stress and vulnerability. Finally, we show an estimation of the environmental stress in land images by considering both the vegetation density and its clusterization.

A hypergraph is a generalization of a graph that arises naturally when attribute-sharing among entities is considered. Compared to graphs, hypergraphs have the distinct advantage that they contain explicit communities and are more convenient to manipulate. An open problem in hypergraph research is how to accurately and efficiently calculate node distances on hypergraphs. Estimating node distances enables us to find a node's nearest neighbors, which has important applications in such areas as recommender system, targeted advertising, etc. In this paper, we propose using expected hitting times of random walks to compute hypergraph node distances. We note that simple random walks cannot accurately compute node distances on highly complex real-world hypergraphs, which motivates us to introduce frustrated random walks (FRW) for this task. We further benchmark our method against DeepWalk, and show that while the latter can achieve comparable results, FRW has a distinct computational advantage in cases where the number of targets is fairly small. For such cases, we show that FRW runs in significantly shorter time than DeepWalk. Finally, we analyze the time complexity of our method, and show that for large and sparse hypergraphs, the complexity is approximately linear, rendering it superior to the DeepWalk alternative.

Living systems are naturally complex and adaptive and offer unique insights into the strategies for achieving and sustaining stochastic homeostasis in different conditions. Here we focus on homeostasis in the context of stochastic growth and division of individual bacterial cells. We take advantage of high-precision long-term dynamical data that have recently been used to extract emergent simplicities and to articulate empirical intra- and intergenerational scaling laws governing these stochastic dynamics. From these data, we identify the core motif in the mechanistic coupling between division and growth, which naturally yields these precise rules, thus also bridging the intra- and intergenerational phenomenologies. By developing and utilizing techniques for solving a broad class of first-passage processes, we derive the exact analytic necessary and sufficient condition for sustaining stochastic intergenerational cell-size homeostasis within this framework. Furthermore, we provide predictions for the precision kinematics of cell-size homeostasis and the shape of the interdivision time distribution, which are compellingly borne out by the high-precision data. Taken together, these results provide insights into the functional architecture of control systems that yield robust yet flexible stochastic homeostasis.

The dynamics of the growth and relaxation of the magnetization in ferrofluids are determined using theory based on the Fokker-Planck-Brown equation, and Brownian-dynamics simulations. Magnetization growth starting from an equilibrium nonmagnetized state in zero field, and following an instantaneous application of a uniform field of arbitrary strength, is studied with and without interparticle interactions. Similarly, magnetization relaxation is studied starting from an equilibrium magnetized state in a field of arbitrary strength, and following instantaneous removal of the field. In all cases, the dynamics are studied in terms of the time-dependent magnetization $m\left(t\right)$. The field strength is described by the Langevin parameter $\alpha$, the strength of the interparticle interactions is described by the Langevin susceptibility ${\chi }_L$, and the individual particles undergo Brownian rotation with time ${\tau }_B$. For noninteracting particles, the average growth time decreases with increasing $\alpha$ due to the torque exerted by the field, while the average relaxation time stays constant at ${\tau }_B$; with vanishingly weak fields, the timescales coincide. The same basic picture emerges for interacting particles, but the weak-field timescales are larger due to collective particle motions, and the average relaxation time exhibits a weak, nonmonotonic field dependence. A comparison between theoretical and simulation results is excellent for noninteracting particles. For interacting particles with ${\chi }_L=1$ and 2, theory and simulations are in qualitative agreement, but there are quantitative deviations, particularly in the weak-field regime, for reasons that are connected with the description of interactions using effective fields.

Ordered chain structures from topological defects of opposite charges (“necklaces” of defects) were prepared and their dynamics and cooperative rearrangement were investigated. We studied topological defects in nematic films with change of the Euler characteristic induced by temperature. Topological defects emerged due to competing surface anchoring on the nematic-isotropic and nematic-solid interfaces. Transformation of the structure with a circular chain from topological defects to the structure with a single defect and then to a structure without defects takes place as the nematic geometry changes. The temporal evolution of the number of topological defects at their annihilation in the chains differs from coarsening in two-dimensional (2D) and 3D geometry.

The transport and distribution of organisms such as larvae, seeds, or litter in the ocean as well as particles in industrial flows is often approximated by a transport of tracer particles. We present a theoretical investigation to check the accuracy of this approximation by studying the transport of inertial particles between different islands embedded in an open hydrodynamic flow aiming at the construction of a Lagrangian flow network reflecting the connectivity between the islands. To this end, we formulate a two-dimensional kinematic flow field which allows the placement of an arbitrary number of islands at arbitrary locations in a flow of prescribed direction. To account for the mixing in the flow, we include a von Kármán vortex street in the wake of each island. We demonstrate that the transport probabilities of inertial particles making up the links of the Lagrangian flow network essentially depend on the properties of the particles, i.e., their Stokes number, the properties of the flow, and the geometry of the setup of the islands. We find a strong segregation between aerosols and bubbles. Upon comparing the mobility of inertial particles to that of tracers or neutrally buoyant particles, it becomes apparent that the tracer approximation may not always accurately predict the probability of movement. This can lead to inconsistent forecasts regarding the fate of marine organisms, seeds, litter, or particles in industrial flows.

The Wentzel, Kramers, and Brillouin (WKB) approximation of geometrical optics is widely used in plasma physics, quantum mechanics, and reduced wave modeling, in general. However, it is well-known that the approximation breaks down at focal and turning points. In this paper, we present an unsupervised numerical implementation of the recently developed metaplectic geometrical optics framework, which extends the applicability of geometrical optics beyond the limitations of WKB, such that the wave field remains finite at caustics. The implementation is in 1D and uses a combination of Gauss-Freud quadrature and barycentric rational function inter- and extrapolation to perform an inverse metaplectic transform numerically. The capabilities of the numerical implementation are demonstrated on Airy's and Weber's equations, which both have exact solutions to compare with. Finally, the implementation is applied to the plasma physics problem of linear conversion of X mode to electron Bernstein waves at the upper hybrid layer and a comparison is made with results from fully kinetic particle-in-cell simulations. In all three applications, we find good agreement between the exact results and a reduced wave modeling paradigm of metaplectic geometrical optics.

Ferroelectric nematic liquid crystals are currently being subject to a plethora of investigations since they are of great fundamental interest and could potentially foster promising applications. However, many basic aspects are still poorly understood, among which the problem of the dielectric constant stands out. Ferroelectric nematics have previously been reported to exhibit giant dielectric constants whose value depends on the thickness of the measurement cell, claims that have recently been shown to be the result of an incorrect interpretation of the experimental data. The intrinsic permittivity of these materials has not been determined yet. In this work, we tackle this problem by modeling the dielectric response of the liquid crystal filled cell through an equivalent circuit proposed by Clark et al. [Phys. Rev. Res. 6, 013195 (2024)] accounting for polarization reorientation and the effect of insulating interfacial layers. We arrive at the conclusion that the perpendicular component of the permittivity ${\varepsilon }_{\perp }$ is of the order of 10 in the prototypical ferroelectric nematogen DIO, while the parallel component ${\varepsilon }_{\parallel }$ is of the order of 150. In this way, the dielectric tensor of DIO has been fully determined.

Through controlled numerical simulations in a one-dimensional fiber bundle model with local stress concentration, we established an inverse correlation between the strength of the material and the cracks which grow inside it—both the maximum crack and the one that sets in instability within the system, defined to be the critical crack. Through the Pearson correlation function as well as probabilistic study of individual configurations, we found that the maximum and the critical crack often differ from each other unless the disorder strength is extremely low. A phase diagram on the plane of disorder vs system size demarcates between the regions where the largest crack is the most vulnerable one and where they differ from each other but still show moderate correlation.

We theoretically investigate quantum parameter estimation in quantum chaotic systems. Our analysis is based on an effective description of quantum ergodic systems in terms of a random matrix Hamiltonian. Based on this approach, we derive an analytical expression for the time evolution of the quantum Fisher information (QFI), which we find to have three distinct timescales. Initially, the QFI increase is quadratic in time, characterizing the timescale over which initial information is extractable from local measurements only. This quickly passes into linear increase with slope determined by the decay rate of the measured spin observable. When the information is fully spread among all degrees of freedom, a second quadratic timescale determines the long-time behavior of the QFI. We test our random matrix theory prediction with the exact diagonalization of a nonintegrable spin system, focusing on the estimation of a local magnetic field by measurements of the many-body state. Our numerical calculations agree with the effective random matrix theory approach and show that the information on the local Hamiltonian parameter is distributed throughout the quantum system during the quantum thermalization process.

We have found long-living periodic solutions of the complex cubic-quintic Ginzburg-Landau equation (CCQGLE) perturbed with intrapulse Raman scattering. To achieve this we have applied a model system of ordinary differential equations (SODE). A set of the fixed points of the system has been described. A complete phase portrait as well as phase portraits near the fixed points have been built for a proper choice of parameters. The behavior of the model system near the fixed points has been determined. We have presented a detailed description of the subcritical Poincaré-Andronov-Hopf bifurcation due to the intrapulse Raman scattering that appears at one of the fixed points. We have established that there appears an unstable limit cycle in the SODE. To check the validity of the obtained results from the model system we have compared them with the results of the numerical solution of the CCQGLE perturbed with intrapulse Raman scattering. There has been found a remarkable correspondence between the obtained numerical results for the amplitude and frequency of the soliton pulses and the results for these parameters of the bifurcation theory. We have observed that the numerical characteristics of the propagating solitonlike pulses—amplitude, frequency, width, and position—periodically change if we change the distance with a period determined by the bifurcation analysis.

The established thermodynamic formalism of chaotic dynamics, valid at statistical equilibrium, is here generalized to systems out of equilibrium that have yet to relax to a steady state. A relation between information, escape rate, and the phase-space average of an integrated observable (e.g., Lyapunov exponent, diffusion coefficient) is obtained for finite time. Most notably, the thermodynamic treatment may predict the phase-space profile of any integrated observable for finite time, from the leading and subleading eigenfunctions of the Perron-Frobenius or Koopman transfer operator. Examples of that equivalence are shown, and the theory is tested analytically on the Bernoulli map while numerically on the perturbed cat map, the Hénon map, and the Ikeda map, all paradigms of chaos.

Nonlinear two-dimensional (IGWs) in the atmospheres of the Earth and the Sun are studied. The resulting two-dimensional nonlinear equation has the form of a generalized nonlinear Schrödinger equation with nonlocal nonlinearity, that is, when the nonlinear response depends on the wave intensity at some spatial domain. The modulation instability of IGWs is predicted, and specific cases for the Earth's atmosphere are considered. In a number of particular cases, the instability thresholds and instability growth rates are analytically found. Despite the nonlocal nonlinearity, we demonstrate the possibility of critical collapse of IGWs due to the scale homogeneity of the nonlinear term in spatial variables.

The cognitive process of opinion formation is often characterized by stubbornness or resistance of agents to changes of opinion. To capture this feature we introduce a constant latency time in the standard voter model of opinion dynamics: after switching opinion, an agent must keep it for a while. This seemingly simple modification drastically changes the stochastic diffusive behavior of the original model, leading to deterministic dynamical oscillations in the average opinion of the agents. We explain the origin of the oscillations and develop a mathematical formulation of the dynamics that is confirmed by extensive numerical simulations. We further characterize the rich phase space of the model and its asymptotic behavior. Our work offers insights into understanding and modeling the phenomenon of opinion swings, often observed in diverse social contexts.

Enzyme-substrate kinetics form the basis of many biomolecular processes. The interplay between substrate binding and substrate geometry can give rise to long-range interactions between enzyme binding events. Here we study a general model of enzyme-substrate kinetics with restricted long-range interactions described by an exponent $-\gamma$. We employ a coherent-state path integral and renormalization group approach to calculate the first moment and two-point correlation function of the enzyme-binding profile. We show that starting from an empty substrate the average occupancy follows a power law with an exponent $1/(1-\gamma )$ over time. The correlation function decays algebraically with two distinct spatial regimes characterized by exponents $-\gamma$ on short distances and $-(2/3)(2-\gamma )$ on long distances. The crossover between both regimes scales inversely with the average substrate occupancy. Our work allows associating experimental measurements of bound enzyme locations with their binding kinetics and the spatial conformation of the substrate.

The separation of chiral matter has garnered significant attention due to its wide-ranging applications in biological and chemical processes. In prior researches, particle interactions were predominantly repulsive, but the indiscriminate attraction among particles under attractive interactions makes the separation of mixtures more difficult. The question of whether chiral mixed particles, characterized by attractive effects, can undergo spontaneous separation, remains unresolved. We study a binary mixture of chiral (counterclockwise or clockwise) active particles with attractive interactions. It is demonstrated that attractive chiral particles can undergo spontaneous separation without the aid of any specific strategies. The key factor driving the separation is the attractive interactions, enabling the formation of stable clusters of particles with same chirality. There exist optimal parameters (self-propelled velocity, angular velocity, and packing fraction) at which the separation is optimal. Our results may contribute to a deeper understanding of the mechanisms behind chiral matter separation and potentially catalyze further experimental investigations in this field.

We execute a numerical simulation of active nematics with particles interacting by an excluded-volume effect. Systems with rhomboidal particles and with elliptical particles are considered in order to investigate the effect of the direct contact of particles. In our simulation, the void regions, where the local number density is almost zero, appear in both systems when the aspect ratio of the particles is high. We focus on the relationship between the void regions and the particle orientation of the bulk. The particle number density, particle orientation, topological defects, and void regions are analyzed for different aspect ratios in both systems. The systems with rhomboidal particles have characteristic void sizes, which increase with an increase in the aspect ratio. In contrast, the distribution of the void-region size in the systems with elliptical particles is broad. The present results suggest that the void size in the systems with rhomboidal particles is determined by the correlation length of the particle orientational field around the void regions, while the void size might be determined by the system size in the systems with elliptical particles.

The theory of gauge transformations in linearized gravitation is investigated. After a brief discussion of the fundamentals of the kinetic theory in curved spacetime, the Einstein-Vlasov-Maxwell (EVM) system of equations in terms of gauge-invariant quantities is established without neglecting the equations of motion associated with the dynamics of the nonradiative components of the metric tensor. The established theory is applied to a noncollisional electron-positron plasma, leading to a dispersion relation for gravitational waves in this model system. The problem of Landau damping is addressed and some attention is given to the issue of the energy exchanges between the plasma and the gravitational wave. In a future paper, a more complete set of approximate dispersion relations for waves and oscillations in plasmas will be presented, including the dynamics of nonradiative components of the metric tensor, with special attention to the problems of the Landau damping and of the energy exchanges between matter, the electromagnetic field and the gravitational field.

Accurate measurement at low temperatures is essential for both gaining a fundamental understanding of physical processes and developing technological applications. In this paper, we propose a theoretical framework for quantum temperature sensing in a composite environment with non-Markovian dynamics. Our suggested system uses a single qubit as a temperature sensor to test its sensitivity in calculating the temperature of a composite environment. We show that the temperature sensor's sensitivity can saturate the quantum Cramér-Rao bound by measuring the ${\widehat{\sigma }}_z$ observable of the probe qubit. Temperature sensing performance is measured using the quantum signal-to-noise ratio. We underline how non-Markovianity can enhance the performance of our thermometers. Furthermore, we emphasize that nonequilibrium conditions do not always result in the best sensitivities in temperature estimation.

A hidden state in which a spin does not interact with any other spin contributes to the entropy of an interacting spin system. We explore the $q$-state Potts model with extra $r$ hidden states using the Ginzburg-Landau formalism in the mean-field limit. We analytically demonstrate that when $1<q\le 2$, the model exhibits a rich phase diagram comprising a variety of phase transitions such as continuous, discontinuous, two types of successive, and hybrid transitions; moreover, several characteristics such as critical point, critical endpoint, and tricritical point are identified. The critical line and critical endline merge in a singular form at the tricritical point. That supercritical behavior emerging at the tricritical point is, to the best our knowledge, novel. We microscopically investigate the origin of the discontinuous transition; it is induced by the competition between the interaction and entropy of the system in the Ising limit, whereas by the bistability of the hidden spin states in the percolation limit. Finally, we discuss the similarity and difference of the phase diagram generated in two types of Ising spins model.