The unique geometry of the two-dimensional tripartite Kagome lattice is responsible for shaping diverse families of spatially localized and time-periodic nonlinear modes, known as discrete breathers. We state conditions for the existence of breathers and compute their spatiotemporal profiles near the edges of the linear phonon spectrum. Our findings include the existence of strongly nonlinear and dynamically stable breathers inside the band gap on the infinite lattice, asymptotic expressions for breather energy thresholds in the weakly nonlinear regime, and explicit breather solutions that remain compactly supported on the lattice and undergo stability transitions.

Many ecological populations are known to display a cyclic behavior with period 2. Previous work has shown that when a metapopulation (a group of coupled populations) with such dynamics is allowed to interact via nearest-neighbor dispersal in two dimensions, it undergoes a phase transition from disordered (spatially asynchronous) to ordered (spatially synchronous) that falls under the two-dimensional Ising universality class. While nearest-neighbor dispersal may satisfactorily describe how most individuals migrate between habitats, we should expect a small fraction of individuals to venture on a journey to further locations. We model this behavior by considering ecological oscillators on dynamically rewired small-world networks, in which at each time step a fraction $p$ of the nearest-neighbor interactions is replaced by a new interaction with a random node on the network. We measure how this connectivity change affects the critical point for synchronizing ecological oscillators. Our results indicate that increasing the amount of long-range interaction (increasing $p$) favors the ordered regime, but the presence of memory in ecological oscillators leads to quantitative differences in how much long-range dispersal is needed to order the network, relative to an analogous network of Ising spins. We also show that, even for very small values of $p$, the phase transition falls into the mean-field universality class, and we argue that ecosystems where dispersal can occasionally happen across the system's length scale will display a phase transition in the mean-field universality class.

The remarkable flight capabilities of insects stem from the subtle modulation of their wing kinematics. Dipteran insects can beat their wings hundreds of times per second, with each wing reversing its pitch direction twice per wingbeat. However, the neural signals hardly match up to such high frequencies, making it interesting to consider how insects regulate wing pitch in a timely manner. In this study, we investigate this problem through observations and simulations involving droneflies. Wing kinematics modulation is induced by imposing unilateral wing damage and recorded by high-speed videography. In tethered flight, the wing pitches passively without mechanical feedback, creating an imbalanced roll moment, which can be overcome by active actuation in free hover. Simulations, based on the torsional spring passive pitching model, suggest that insects modulate wing pitch through the coupling of aerodynamic, inertial, and wing hinge elastic forces. The model predicts the trend of wing pitch subjected to wing area loss as the observation and achieves a similar control effect of balancing roll moment with a slight adjustment to spring parameters. Based on these results, we show that insects combine passive and active mechanisms for wing pitch modulation.

We study the decay of tangential velocity profiles with distance from a local disturbance in hard-sphere colloidal suspensions as the colloidal glass transition is approached. The disturbance, generated by a dimer of superparamagnetic particles rotated by an external magnetic field, enables a precise characterization of the system's response through confocal microscopy and tracking of individual particle dynamics. The tangential velocity profiles exhibit nearly exponential decay with distance. As particle density increases toward the colloidal glass transition, the characteristic length scale derived from exponential fits grows. We also observe that the colloidal particles slip against the rotating dimer, with less slip in samples that are closer to the glass transition.

The relaxation oscillation in sliding systems is termed stick-slip (SS) since the two surfaces in contact seem to repeat the stick and slip states. However, several precise measurements have revealed that extremely slow slip occurs in even apparently stick states before every stick-to-slip transition, which is here termed the static friction paradox. This study theoretically investigates instabilities induced by solid viscoelasticity using a minimal toy model without friction laws: a two-degrees-of-freedom sliding system consisting of a rigid probe and a Kelvin-Voigt viscoelastic foundation (VEF). Taking advantage of its simplicity, we discuss the mechanisms generating various system dynamics based on stability analysis and numerical simulation. As a result, surprisingly, even though the sliding system does not employ static friction, it exhibits intermittent oscillation resembling the typical SS instability. It comprises two slip states for the contact between the probe and the VEF, i.e., slow-slip and fast-slip states, providing a purely mechanical explanation for the static friction paradox.

We adopt a stochastic approach to study the charge transport in transistors. In this approach, the hole and electron densities are ruled by diffusion-reaction stochastic partial differential equations satisfying a local detailed balance condition. The electric field is supposed to be concentrated in very narrow regions around the two junctions and is also approximated to be static. In this way, not only are the laws of electricity, thermodynamics, and microreversibility consistent within this approach, but also the transistor can be easily modeled as a two-dimensional system. We perform the full counting statistics of the two coupled currents and the fluctuation theorem is shown to hold. Moreover, we show that the geometric shape of the transistor exerts great influence on the transport behavior. By modeling the transistor in two dimensions, the signal-amplification factor up to about 164 can be achieved, which is comparable to the typical value of realistic transistors in the industry.

We investigate the orientational ordering and close-packing behavior of a quasi-one-dimensional (q1D) system of hard superellipses, where the centers of the particles are confined to a line, but they can rotate freely within a two-dimensional plane. The particle shape is tuned between an ellipse and a rectangle by varying the deformation parameter ($n$). The elongation of the particle is changed using the aspect ratio ($k$). The pressure ratio between freely rotating and parallel hard superellipses, which displays a single peak, serves as an effective marker for the continuous structural change from quasi-isotropic to nematic ordering. Our findings reveal a competition between the parameters $k$ and $n$, with $k$ promoting nematic alignment and $n$ favoring tetratic ordering. Notably, in the close-packing regime, the packing properties become independent of $k$, as the relevant exponents depend solely on $n$. Furthermore, certain combinations of these exponents exhibit universality, remaining invariant with respect to particle shape.

Randomness plays a key role in the order transition of active matter but has not yet been explicitly considered in pairwise interaction connection. In this paper, we introduce the perception rate $P$ into the Vicsek model as the probability of the interaction connections and model the connections as superposition states. We show that with increasing $P$, the polar order number undergoes an order transition and then saturation. The order transition is a first-order phase transition with band formation, and the effect of $P$ is different from density. The change of the order number is linked with the interaction structure. The order transition, order saturation, and phase separation correspond to different critical changes in the local interaction number. The global interaction structure is further analyzed as a network. The decrease of $P$ is comparable to random edge removal, under which the network experiences modal transitions near the critical points of the order number, and the network exhibits surprising robustness. Our results suggest that random interaction can be a new important factor in active matter models, with potential applications in robotic swarms and social activities.

The birhythmic Hindmarsh-Rose model, characterized by limit cycles with different spikes, is investigated for its bifurcations and resonances, and stochastic sensitivity analysis is performed. The study indicates that the stationary probability density changes by selecting the noise level and the system parameter as bifurcation parameters, signifying the emergence of stochastic phenomenological bifurcation. By increasing the noise, changes in the sign of the largest Lyapunov exponent reveal that the system experiences stochastic dynamical bifurcation. The stochastic sensitivity analysis is utilized as well to explore the dispersion of stochastic solutions around the limit cycle. This paper identifies both anticoherence resonance and coherence resonance induced by noise, as revealed by the coefficient of variation. Furthermore, considering the response to the harmonic signal, noise-induced suppression as well as stochastic resonance phenomena are revealed by analyzing the signal-to-noise ratio. Studying the stochastic dynamical behavior of Hindmarsh-Rose neuron model can provide better insights into how neurons respond to noise, aiding in the simulation and analysis of neuronal activity in neurological disease states.

The study of fiber networks is of great importance due to their presence in a wide range of natural and industrial processes. These networks are particularly complex because their properties depend not only on the characteristics of individual fibers but also on their collective arrangements within the network. In this study, we investigate the case of nylon fiber aggregates generated in a turbulent von Kármán flow. The key properties of these aggregates are characterized, including their three-dimensional structures, analyzed through x-ray tomography, and their mechanical responses. The results are compared with those observed for aegagropilae, a natural fiber aggregate commonly found on Mediterranean seashores

We employ molecular dynamics simulation to study the phase separation and rheological properties of a three-dimensional binary liquid mixture with hydrodynamics undergoing simple shear deformation. The impact of shear intensity on domain growth is investigated, with a focus on how shear primarily distorts the domains, leading to the formation of anisotropic structures. The structural anisotropy is quantified by evaluating domain sizes along the flow and shear direction. The rheological properties of the system are studied in terms of shear stress and excess viscosity. At low shear rates, the system behaves like a Newtonian fluid. However, the strong-shear case is marked by a transition characterized by non-Newtonian behavior.

We investigate the diffusive dynamics of Debye Brownian oscillators in tilted periodic potentials. These oscillators are driven by Debye-type noise characterized by a spectral density with a hard cutoff at high finite frequencies. For force-free Debye Brownian oscillators, we derive an explicit expression for the self-oscillating frequency of the velocity autocorrelation function. When subjected to weakly tilted forces in a periodic potential, the oscillators exhibit a resonant diffusion phenomenon. Specifically, the effective diffusion coefficient reaches its maximum when the self-oscillating frequency matches the frequency of local wells in the tilted periodic potential. We attribute this resonance diffusion to the competition between fluctuation and friction. At resonance, the effective temperature exhibits a more pronounced increase compared with the effective friction. As the tilted force increases, the resonant diffusion phenomenon gradually disappears. This is accompanied by a monotonic decrease in the effective diffusion coefficient, caused by stronger friction with increasing cutoff frequency. This study provides insights into diffusive dynamics in realistic systems with limited resources, which may have implications for understanding various physical processes involving Brownian motion in complex environments.

Understanding the thermodynamic properties of quantum systems is essential for developing energy-efficient quantum technologies. In this regard, this work explores the application of quantum computational methods to study the quantum properties and work extraction processes in a dinuclear quantum battery model. Our results demonstrate that variational quantum algorithms can reproduce key trends in experimental data, making it possible to analyze the effectiveness of the presented protocol in noisy environments and providing insights into the feasibility of quantum batteries in near-term devices. We have shown that the presence of a noisy environment hinders the accuracy of the evaluation of the amount of energy stored in the system. Additionally, we analyze the work extraction precision, revealing that although the system can store energy at room temperature, the protocol is highly precise only at low temperatures, and its accuracy at ambient conditions remains limited, compromising its usability.

We analyze spatial patterns emerging in self-organized systems of swarmalators, focusing on constellations where all entities arrange in a sorted manner along one or more concentric circles. Key properties of these patterns are explored by computing their radii and examining stability criteria based on the number of entities and a coupling parameter. The findings can be applied to swarm robotics and other collective motion systems.

Higher-order interactions are prevalent in real-world complex systems and exert unique influences on system evolution that cannot be captured by pairwise interactions. We incorporate game transitions into the higher-order prisoner's dilemma game model, where these transitions consistently promote cooperation. Moreover, in systems with game transitions, the proportion of higher-order interactions has a dual impact, either enhancing the emergence and persistence of cooperation or facilitating invasions that promote defection within an otherwise cooperative system. Correspondingly, bistable states, consisting of mutual defection and either mutual cooperation or coexistence, are consistently identified in both theoretical analyses and simulation results.

Available experimental data for a single component lipid bilayer reveal opposite dependencies on the lipid structure of two important parameters: mean area per lipid and main phase transition temperature. The mean area per lipid increases with lipid head size or tails unsaturation, and decreases with the number of hydrocarbon groups per tail. The main phase transition temperature changes reversely: it decreases with lipid head size, or tails unsaturation, and increases with the number of hydrocarbon groups per tail. In this paper we derive these effects theoretically from the flexible strings model of lipid bilayer previously introduced by the authors. The model describes a single component lipid bilayer by considering a single lipid in an entropic repulsive mean field created by its neighbors. Dipalmitoylphosphatidylcholine (DPPC) is used as a reference lipid: particular changes are computed by considering deviations of parameters from their DPPC values.

Nanofluidic systems subject to viscosity gradients are ubiquitous to technology and nature, including desalination and energy harvesting systems that utilize fresh water and seawater, thermoionics that leverage large temperature gradients of an electrolyte, and even ion channels that are sandwiched in between intercellular and extracellular fluids. However, to date, we lack a fundamental understanding of how a gradient in the viscosity changes the electrical response of these systems. In this work, using the well-known Poisson-Nernst-Planck equations, we derive a simple, self-consistent, analytical expression for the current-voltage, $i\text{−}V$, response of a nanofluidic system subject to a viscosity gradient at isothermal conditions. From this $i\text{−}V$, all the major characteristics are calculated. In particular, we provide a novel expression for the Ohmic conductance, $g_{\text{Ohmic}}=i$/V, as a function of the viscosity field. We also address the open question as to whether or not a viscosity gradient can induce an electrical current and shift it away from the origin of the $i\text{−}V$. We show and explain why the shift does not occur. Our convection-less model predicts current rectification using a different mechanism from the commonly invoked mechanism of electroosmotic flows that are used to rationalize experiments. Our model provides a new, simpler, and more robust mechanism for current rectification. This work provides the first robust and reliable framework for analyzing the experiments and numerical simulations of nanofluidic systems, subject to a viscosity gradient, that are used for energy harvesting purposes.

Drop coalescence plays a crucial role in nature and industry. In continuum theory, after the two drops are taken to touch at a point at the onset of coalescence, two scaling regimes for the temporal growth of the bridge connecting the drops have been identified. Coalescence, however, is initiated at length and timescales at which the discrete nature of matter is of importance. Therefore, a continuum description provides no information about the mechanistic details of the actual initiation of bridge formation. To provide molecular-level insights into bridge formation and growth, we study the initial stages of coalescence using a hybrid Monte Carlo-molecular dynamics (MC-MD) simulation method. We reduce the required computational effort by only simulating those parts of the drops directly facing each other. Particle reservoirs, along with grand-canonical MC steps (GCMC), are used to maintain the bulk densities of the drops at specified values. These GCMC steps also enable the drops to be both thermally and chemically equilibrated prior to coalescence, thereby eliminating any potential evaporative phenomena that may confound the analysis. We identify three distinct regimes of droplet coalescence. In the first regime, the bridge expands linearly with time, $t$. The new simulations show that this regime is initiated by “molecular jumps” into the bridge, in agreement with the results of prior MD simulations of others, and where the density in the bridge is seen to quickly increase until it reaches nearly the bulk value. Afterwards, the bridge dynamics eventually transition to a second, viscous regime in which the bridge radius appears to scale with time as $-tlnt$, as also predicted in continuum analyses but which heretofore had not been observed in molecular simulations. Finally, a third, inertial regime is observed at longer times, during which the bridge radius follows a scaling law akin to the $t^{1/2}$ scaling also predicted in continuum analyses.

A computationally efficient method to calculate the capillary pressure-saturation relations of immiscible multiphase flow on two-dimensional pore morphologies is presented. The method is an extension of the porous morphology method that includes the wetting angle and trapped mechanism of the displaced fluid, and calculation of material properties using density functional theory. After the method was validated with microchip fluid injection experiments, it was used to relate pore morphology to the capillary pressure-saturation relation using square-lattice pore morphologies. Because the method uses only morphological binary operations, it is more efficient than well-established high-resolution voxel dynamics methods such as lattice-Boltzmann methods and level-set computational fluid dynamics. Apart from pore morphology, only the material parameters related to the contact angle (wettability) and interfacial tension are required to connect the pore-saturation relation and pore throat distribution. We investigate the effects of interfacial tension, wettability, sample size, and pore throat distribution on entry pressure and residual saturation.