The fluctuation of the quantum Otto engine has recently received a lot of attention, while applying the many body with a long-range interaction to a quantum heat engine may enhance our ability of controlling it. Using the two-point measurement and its generalization, we explore the fluctuation theorem of work and heat in a single stroke as well as in a cycle. We discover that the fluctuations of work in a cycle as well as fluctuations of heat in a single stroke or cycle can be connected to the fluctuation of work in a single stroke. Then we numerically investigate the effect of a long-range interaction on these fluctuation theorems, and our result shows that the fluctuation can be improved by manipulating the long-range interaction.

Using numerical simulations, we investigate the impact of the demagnetization field and finite temperature on the hysteresis phenomena in disordered ferromagnetics systems. We model the behavior of thin systems employing the thermal nonequilibrium random field Ising model driven by a finite-driving rate protocol to study the shape of the hysteresis loop and demagnetization line and the magnetization fluctuations for varied parameters. Our results reveal a significant interplay of the disorder, the demagnetizing fields, and thermal fluctuations. In particular, at a fixed disorder and temperature, the increasing demagnetization coefficient gradually prologues the magnetization reversal process, altering the multifractal nature of the magnetization fluctuations. The process accompanies the appearance of extended linear segments in the hysteresis loop and changes in the demagnetization line while practically preserving the value of the coercive field and slightly changing the remanent magnetization. On the other hand, increasing temperature expands the system's response fluctuations and narrows the loop, affecting the coercive field and remanent magnetization. The interplay of thermal fluctuations and demagnetizing fields fully manifests in limiting the large-scale magnetization fluctuations, revealed by the multifractal spectra and the scaling functions of the avalanches. Our research offers new modeling perspectives with a more realistic scenario and provides insight into new hysteresis loop phenomena relevant to theoretical analysis and applications.

For a circuit made of thermodynamic devices in stationary nonequilibrium, we determine the mean currents (of energy, matter, charge, etc.) exchanged with external reservoirs driving the circuit out of equilibrium. Starting from the conductance matrix describing the nonlinear current-force characteristics of each device, we obtain the conductance matrix of the composite device. This generalizes the rule of resistance addition (serial association) or conductance addition (parallel association) in stationary out-of-equilibrium thermodynamics and for multiple coupled potentials and currents of different natures. Our work emphasizes the pivotal role of conservation laws when creating circuits of complex devices. Finally, two examples illustrate among others the determination of the conservation laws for the serial and parallel associations of thermodynamic devices.

We propose an extension to the inertial spin model (ISM) of flocking and swarming. The model has been introduced to explain certain dynamic features of swarming (second sound, a lower than expected dynamic critical exponent) while preserving the mechanism for onset of order provided by the Vicsek model. The inertial spin model (ISM) has only been formulated with an imitation (“ferromagnetic”) interaction between velocities. Here we show how to add position-dependent forces in the model, which allows to consider effects such as cohesion, excluded volume, confinement, and perturbation with external position-dependent field, and thus study this model without periodic boundary conditions. We study numerically a single particle with an harmonic confining field and compare it to a Brownian harmonic oscillator and to a harmonically confined active Browinian particle, finding qualitatively different behavior in the three cases.

We investigate experimentally the impact process of sand particles onto a cohesive granular packing made of similar particles. We use a sand-oil mixture with varying liquid content to tune the cohesive strength of the packing. The outcome of the impact is analyzed in terms of the production of ejected particles from the packing. We quantify this production as a function of the impact velocity of the particles for increasing cohesion strength. We identified three different regimes depending on the cohesion number Co, defined as the ratio of the interparticle cohesive force to the particle weight. For small cohesion (i.e., $\text{Co}⪅1$), the ejection process is not modified by the cohesion. For intermediate cohesion (i.e., $1⪅\text{Co}⪅20$), the ejection process becomes less efficient: the number of ejected particles per impact for a given impact velocity is decreased but the critical impact velocity to trigger the ejection process remains unchanged. Finally, for strong cohesion (i.e., $\text{Co}⪆20$), we observed a progressive increase of the critical impact velocity. These experimental results confirm spectacularly the outcomes of recent numerical simulations on the collision process of a particle onto a cohesive packing and open avenues to model the aeolian transport of moist sand.

The recently proposed nonadditive stochastic model (NSM) offers a coherent physical interpretation for diffusive phenomena in glass-forming systems. This model presents nonexponential relationships between viscosity, activation energy, and temperature, characterizing the non-Arrhenius behavior observed in supercooled liquids. In this work, we fit the NSM viscosity equation to experimental temperature-dependent viscosity data corresponding to 25 glass-forming liquids and compare the fit parameters with those obtained using the Vogel-Fulcher-Tammann (VFT), Avramov-Milchev (AM), and Mauro-Yue-Ellison-Gupta-Allan (MYEGA) models. The results demonstrate that the NSM provides an effective fitting equation for modeling viscosity experimental data in comparison with other established models (VFT, AM, and MYEGA), characterizing the activation energy in fragile liquids, presenting a reliable indicator of the degree of fragility of the glass-forming liquids.

We study the temporal evolution of avalanches in the fiber bundle model of disordered solids, when the model is gradually driven towards the critical breakdown point. We use two types of loading protocols: (i) quasistatic loading and (ii) loading by a discrete amount. In the quasistatic loading, where the load is increased by the minimum amount needed to initiate an avalanche, the temporal shapes of avalanches are asymmetric away from the critical point and become symmetric as the critical point is approached. A measure of asymmetry $\left(A\right)$ follows a universal form $A\sim {(\sigma -{\sigma }_c)}^{\theta }$, with $\theta \approx 0.25$, where $\sigma$ is the load per fiber and ${\sigma }_c$ is the critical load per fiber. This behavior is independent of the disorder present in the system in terms of the individual failure threshold values. Thus it is possible to use this asymmetry measure as a precursor to imminent failure. For the case of discrete loading, the load is always increased by a fixed amount. The dynamics of the model in this case can be solved in the mean field limit. It shows that the avalanche shapes always remain asymmetric. We also present a variable range load sharing version of this case, where the results remain qualitatively similar.

We present a mean-field solution of the dynamics of a Greenberg-Hastings neural network with both excitatory and inhibitory units. We analyze the dynamical phase transitions that appear in the stationary state as the model parameters are varied. Analytical solutions are compared with numerical simulations of the microscopic model defined on a fully connected network. We found that the stationary state of this system exhibits a first-order dynamical phase transition (with the associated hysteresis) when the fraction of inhibitory units $f$ is smaller than some critical value $f_t\lesssim 1/2$, even for a finite system. Moreover, any solution for $f<1/2$ can be mapped to a solution for purely excitatory systems ($f=0$). In finite systems, when the system is dominated by inhibition ($f>f_t$), the first-order transition is replaced by a pseudocritical one, namely a continuous crossover between regions of low and high activity that resembles the finite size behavior of a continuous phase transition order parameter. However, in the thermodynamic limit (i.e., infinite-system-size limit), we found that $f_t\to 1/2$ and the activity for the inhibition dominated case ($f\ge f_t$) becomes negligible for any value of the parameters, while the first-order transition between low- and high-activity phases for $f<f_t$ remains.

We define the $Q$ factor in the percolation problem as the quotient of the size of the largest cluster and the average size of all clusters. As the occupation probability $p$ is increased, the $Q$ factor for the system size $L$ grows systematically to its maximum value $Q_{\text{max}}\left(L\right)$ at a specific value $p_{\text{max}}\left(L\right)$ and then gradually decays. Our numerical study of site percolation problems on the square, triangular, and simple cubic lattices exhibits that the asymptotic values of $p_{\text{max}}$, though close, are distinct from the corresponding percolation thresholds of these lattices. We also show, using scaling analysis, that at $p_{\text{max}}$ the value of $Q_{\text{max}}\left(L\right)$ diverges as $L^d$ ($d$ denoting the dimension of the lattice) as the system size approaches its asymptotic limit. We further extend this idea to nonequilibrium systems such as the sandpile model of self-organized criticality. Here the $Q(\rho ,L)$ factor is the quotient of the size of the largest avalanche and the cumulative average of the sizes of all the avalanches, with $\rho$ the drop density of the driving mechanism. This study was prompted by some observations in sociophysics.

We explore the energy transfer dynamics in an array of two chains of identical rigid interacting dipoles. Varying the distance $b$ between the two chains of the array, a crossover between two different ground-state (GS) equilibrium configurations is observed. Linearizing around the GS configurations, we verify that interactions up to third nearest neighbors should be accounted to accurately describe the resulting dynamics. Starting with one of the GS, we excite the system by supplying it with an excess energy $\mathrm{\Delta }K$ located initially on one of the dipoles. We study the time evolution of the array for different values of the system parameters $b$ and $\mathrm{\Delta }K$. Our focus is hereby on two features of the energy propagation: the redistribution of the excess energy $\mathrm{\Delta }K$ among the two chains and the energy localization along each chain. For typical parameter values, the array of dipoles reaches both the equipartition between the chains and the thermal equilibrium from the early stages of the time evolution. Nevertheless, there is a region in parameter space $(b,\mathrm{\Delta }K)$ where even up to the long computation time of this study, the array does neither reach energy equipartition nor thermalization between chains. This fact is due to the existence of persistent chaotic breathers.

Characterizing the emergence of chaotic dynamics of complex networks is an essential task in nonlinear science with potential important applications in many fields such as neural control engineering, microgrid technologies, and ecological networks. Here, we solve a critical outstanding problem in this multidisciplinary research field: the emergence and persistence of spatiotemporal chaos in complex networks of damped-driven nonlinear oscillators in the significant weak-coupling regime, while they exhibit regular behavior when uncoupled. By developing a comprehensive theory with the aid of standard analytical methods, a hierarchy of lower-dimensional effective models, and extensive numerical simulations, we uncover and characterize the basic physical mechanisms concerning both heterogeneity-induced and impulse-induced emergence, enhancement, and suppression of chaos in starlike and scale-free networks of periodically driven, dissipative nonlinear oscillators.

We consider a nematic liquid crystal film confined to a flat cell with homeotropic and planar patterned hybrid anchoring and show, using Monte Carlo simulations, the possibility of the system to stabilize line and point defects. The planar anchoring surface is patterned with a chessboardlike grid of squares with alternating random or parallel homogeneous planar anchoring. The simulations show only line defects when the individual domains are small enough, but also point defects when the domain size is significantly larger than the sample thickness. In the latter case, defect lines are not observed in domains with random surface anchoring, although lines and points are connected by a thick line which separates two regions with different director tilts. Increasing the anchoring strength, the defect lines appear a few layers above the surface, with the two ends just above the randomly oriented domains.

The particle contact model is important for powder simulations. Although several contact models have been proposed, their validity has not yet been well established. Therefore, we perform molecular dynamics (MD) simulations to clarify the particle interaction. We simulate head-on collisions of two particles with impact velocities less than a few percent of the sound velocity to investigate the dependence of the interparticle force and the coefficient of restitution on the impact velocity and particle radius. In this study, we treat particles with a radius of 10–100 nm and perform simulations with up to 0.2 billion atoms. We find that the interparticle force exhibits hysteresis between the loading and unloading phases. Larger impact velocities result in strong hysteresis and plastic deformation. For all impact velocities and particle radii, the coefficient of restitution is smaller than that given by the Johnson-Kendall-Robert theory, which is a contact model that gives the force between elastic spherical particles. A contact model of inelastic particles cannot reproduce our MD simulations. In particular, the coefficient of restitution is significantly reduced when the impact velocity exceeds a certain value. This significant energy dissipation cannot be explained even by the contact models including plastic deformation. We also find that the coefficient of restitution increases with increasing particle radius. We also find that the previous contact models including plastic deformation cannot explain the strong energy dissipation obtained in our MD simulations, although they agree with the MD results for very low impact velocities. Accordingly, we have constructed a new dissipative contact model in which the dissipative force increases with the stress generated by collisions. The new stress-dependent model successfully reproduces our MD results over a wider range of impact velocities than the conventional models do. In addition, we proposed another, simpler, dissipative contact model that can also reproduce the MD results.

Random matrix theory is a useful tool in the study of the physics of multiple scattering systems, often striking a balance between computation speed and physical rigour. Propagation of waves through thick disordered media, as arises, for example, in optical scattering or electron transport, typically necessitates cascading of multiple random matrices drawn from an underlying ensemble for thin media, greatly increasing the computational burden. Here we propose a dual pool based bootstrapping approach to speed up statistical studies of scattering in thick random media. We examine how potential matrix reuse in a pool based approach can impact statistical estimates of population averages. Specifically, we discuss how both bias and additional variance in the sample mean estimator are introduced through bootstrapping. In the diffusive scattering regime, the extra estimator variance is shown to originate from samples in which cascaded transfer matrices are permuted matrix products. Through analysis of the combinatorics and cycle structure of permutations we quantify the resulting correlations. Proofs of several analytic formulas enumerating the frequency with which correlations of different strengths occur are derived. Extension to the ballistic regime is briefly considered.

Much effort has been put into developing theories for dense fluids; as a result of these efforts many theories work for a certain type of particle or in a certain concentration regime. Rosenfeld proposed a dependence of the self-diffusion coefficient on the excess entropy. Our proposal is similar to Rosenfeld's in that it also attempts to describe diffusion in terms of a thermodynamic function but, instead of the excess entropy, we use the thermodynamic factor or the excess chemical potential. Simulations were taken for hard spheres and our model was fitted with two free parameters. Simulations were then carried out for a Lennard-Jones gas and our model correctly described the new data with the value of the free parameters that we had obtained for hard spheres. This is a feature of our model that we wish to emphasize, since the usual situation is that parameters have to be readjusted for different interaction potentials. An experimental xenon self-diffusion data set was used as an example of where the model can be applied, especially in the high-density regime.

We analyze the relaxation dynamics of Feynman-Kac path integral kernel functions, in terms of branching diffusion processes with killing. This amounts to the killing versus branching approach to path integration, which seems to be a novelty in the pathwise description of conditioned Brownian motions and diffusion processes with absorbing boundaries. There, Feynman-Kac kernels appear as building blocks of inferred (Fokker-Planck) transition probability density functions. A consistent probabilistic meaning is hereby provided (killing versus branching time rate) to bounded from below Feynman-Kac potential functions, which instead of being positive-definite (a standard killing paradigm), may take negative values on bounded spatial subdomains (that inflicts trajectory branching).

March's celebrated agent-based simulation model for organizational learning [J. G. March, Org. Sci. 2, 71 (1991)] has been extensively studied in recent decades. Yet the model has not been fully understood due to the lack of analytical solutions of the model. We simplify the March model to take an analytical approach using master equations. We then derive exact solutions for some of the simplest yet nontrivial cases, and perform numerical estimation of master equations for more complicated cases. Both analytical and numerical results are in good agreement with agent-based simulations. These results are also compared to those of the original March model. Our approach enables us to rigorously understand the results of the simplified model as well as the original model, to a large extent.

The interior of cellular nuclei, the nucleoplasm, is a crowded fluid that is pervaded by protein-decorated DNA polymers, the chromatin. Due to the complex architecture of chromatin and a multitude of associated nonequilibrium processes, e.g., DNA repair, the nucleoplasm can be expected to feature nontrivial material properties and hence anomalous transport phenomena. Here, we have used single-particle tracking on nuclear actin rods to probe such transport phenomena. Our analysis reveals that short actin rods in the nucleus show an intermittent, antipersistent subdiffusion with clear signatures of fractional Brownian motion. Moreover, the diffusive motion is heterogeneous with clear signatures of an intermittent switching of trajectories between at least two different mobilities, most likely due to transient associations with chromatin. In line with this interpretation, hyperosmotic stress is seen to stall the motion of nuclear actin rods, whereas hypo-osmotic conditions yield a reptationlike motion. Our data highlights the heterogeneity of transport in the nucleoplasm that needs to be taken into account for an understanding of nucleoplasmic organization and the mechanobiology of nuclei.

Flocking behavior is observed in biological systems from the cellular to superorganismal length scales, and the mechanisms and purposes of this behavior are objects of intense interest. In this paper, we study the collective dynamics of bovine sperm cells in a viscoelastic fluid. These cells appear not to spontaneously flock, but transition into a long-lived flocking phase after being exposed to a transient ordering pulse of fluid flow. Surprisingly, this induced flocking phase has many qualitative similarities with the spontaneous polar flocking phases predicted by Toner-Tu theory, such as anisotropic giant number fluctuations and nontrivial transverse density correlations, despite the induced nature of the phase and the clearly important role of momentum conservation between the swimmers and the surrounding fluid in these experiments. We also find a self-organized global vortex state of the sperm cells, and map out an experimental phase diagram of states of collective motion as a function of cell density and motility statistics. We compare our experiments with a parameter-matched computational model of persistently turning active particles and find that the experimental order-disorder phase boundary as a function of cell density and persistence time can be approximately predicted from measures of single-cell properties. Our results may have implications for the evaluation of sample fertility by studying the collective phase behavior of dense groups of swimming sperm.