Diffusion of a pointlike particle in a thin layer of varying thickness $h(x,y)$ bounded by reflecting boundaries is considered. Using the mapping technique derived for diffusion in a nonhomogeneous quasi-one-dimensional channel, we derive an effective two-dimensional Fick-Jacobs equation governing evolution of the marginal density $p(x,y,t)$ of the planar coordinates $x$ and $y$. The equation is modified by the space-dependent effective diffusion coefficient $\mathbb{D}(x,y)$, which is a 2×2 tensor introducing anisotropy of diffusion, depending on the shaping function $h(x,y)$. Biasing of the diffusional flux is demonstrated on a layer bounded by the sinusoidally corrugated upper wall.

Self-guided femtosecond laser pulses propagating in low-pressure gas can generate plasma filaments, establishing a framework for plasma wakefield acceleration (PWFA). This method exploits the intrinsic nonlinear light-matter interaction, reducing the energy required to generate plasma while enabling the realization of plasma stages suitable for PWFA. Here we report a proof-of-principle experimental demonstration of beam-driven wakefield acceleration of electron bunches with an accelerating field exceeding 250 MV/m in a laser-generated plasma filament. The witness bunch is accelerated while preserving both its charge and relative energy spread. The robustness and reproducibility of both the plasma source and the acceleration process are demonstrated with successful witness acceleration observed in 95% of the events and an rms energy jitter below 0.5%. The experimental observations are in agreement with particle-in-cell simulations, providing a consistent description of the underlying physical processes. These results demonstrate that laser-generated plasma filaments can serve as reliable plasma stages, delivering beam quality and operational reproducibility comparable to those achieved with established plasma-generation techniques. The concept bridges laser filamentation physics and compact accelerator technologies, offering a promising route toward sustainable, high-repetition-rate plasma-based facilities.

Diffusion with an incorporated resetting mechanism provides a reference framework for modeling a wide range of natural phenomena. Within this framework, the optimal resetting rate is a key quantity that arises from the optimization of the mean first-passage time. While substantial work has focused on the study of the optimal resetting rate in unbounded one-dimensional domains, little is still known about the optimization of the mean first-passage time in bounded systems, in particular when multiple resetting sites are available. In this work, we consider a particle diffusing along a ring and undergoing stochastic resetting, with an absorbing target site at a fixed location. Using the appropriate free propagator for this system, we compute the Laplace transform of the survival probability when resetting occurs to multiple sites drawn from an arbitrary probability density function. We also calculate the mean first-passage time at the target site and study the dependence of the optimal resetting rate in terms of the relevant parameters of the system in a two-resetting site configuration. Depending on the arc length between one of the resetting sites and the absorbing target site, and the weight of the remaining resetting site, the optimal resetting rate can exhibit abrupt (“first-order”) and continuous (“second-order”) transitions. Moreover, the behavior of the mean first-passage time is rich enough to allow both critical and tricritical points to exist in the parameter space. All the transitions have mirror symmetry around the selected target site and its corresponding diametrically opposite site.

When colloidal particles are vertically confined to a gap of between approximately 1.3 to 1.6 particle diameters, they pack into buckled crystals of particles in either “up” or “down” states. Neighboring particles tend to occupy opposite states, analogous to the behavior of antiferromagnetic spins. The particles sit on a nearly triangular lattice, and the spins of trios of adjacent particles are geometrically frustrated. Two levels of translational order exist in this system: that of the underlying triangular lattice in the horizontal plane, and that of the emergent frustrated spin lattice in the vertical dimension. We study the topological defects of both levels of translational order, and we find that both types of defects play a role in crystal grain-boundary structure and the formation of spin domains. We classify the spin defects and outline the basic rules for their motion, and we observe interactions between dislocations and spin defects. Finally, we map the phase space of spin coarsening in the buckled monolayer, characterizing which types of defects drive the dynamics. Understanding defect formation, motion, and interaction in the buckled monolayer is the first step in predicting the material properties and aging of this geometrically frustrated, self-assembled system.

The mechanisms governing triboelectric charging between insulating particles of the same material remain an open question in nonequilibrium physics, with several competing models proposed to explain observed charging behavior. Here, we investigate charge evolution in a minimal system consisting of two acoustically levitated particles undergoing repeated binary collisions. To quantify particle charge transfer, purpose-built Faraday cage picoammeters were developed and calibrated. The MultiLev acoustic levitation system was used alongside Ultraino simulations to generate transducer control signals, enabling controlled collision and separation of polystyrene (PS) particles. Although individual collision events exhibit stochastic charge transfer, described by skew-normal distributions, we demonstrate that the cumulative charge evolution of an individual acoustically levitated particle pair follows the saturation behavior predicted by the condenser model of triboelectric charging, with fits achieving $R^2>0.99$. Under identical conditions, conductive graphite-coated PS particles also undergo charge transfer, but accumulate substantially less charge, consistent with the distinct charging behavior expected for conductive materials.

On the path to gelation, attractive colloids develop network connectivity, mechanical rigidity, and slow dynamics. Herein, we employ molecular dynamics simulations to quantitatively characterize these processes using a coarse-grained model of patchy colloidal particles that form equilibrium networks through linker-mediated, reversible binding interactions. We find that linker-mediated gelation proceeds through a cascade of coupled but distinct structural, mechanical, and dynamical changes. Ordinary connectivity percolation first produces a system-spanning network, while rigidity percolation is associated with the formation of a percolating backbone characterized by an average colloid coordination number of approximately 2.4 and a percolating subset of colloids with a coordination number of 3 or more. The rheologically defined gel point occurs at larger bond strengths and higher global coordination, where linker-mediated colloid-colloid connections persist over the deformation timescale. This separation between rigidity percolation and rheological gelation is accompanied by a pronounced increase in the bond persistence time as gelation is approached, indicating that persistent effective network connections are required for a macroscopic gel-like response. Together, these results show that connectivity is central to equilibrium, linker-mediated colloidal gelation, but that different connectivity measures govern ordinary percolation, rigidity percolation, and rheological gelation. These observations reinforce the idea that gelation in homogeneous networks of linked colloids can be understood as a connectivity-controlled, dynamical-mechanical transition, distinct from heterogeneous, nonequilibrium gels, where rigidity often arises from irreversible aggregation and kinetic arrest.

The origin of the recently observed spontaneous chiral symmetry breaking in polar fluids composed of achiral molecules is an unsolved problem, raising fundamental questions about how heliconical structures emerge in such systems. Here, we investigate the pretransitional fluctuations leading to the formation of the spontaneously chiral twist-bend ferroelectric nematic phase using dielectric spectroscopy, light scattering, and small-angle x-ray scattering. We observe simultaneous softening of the bend elastic constant and the emergence of a collective dielectric mode on approaching the transition. By developing a theoretical model, we show that these phenomena are signatures of a flexoelectricity-driven transition arising from the coupling between electric polarization and bend deformation.

We present a simple analytical description of the first wave of the COVID-19 pandemic based on a phenomenologically motivated ansatz. The proposed expression describes the time evolution of the number of currently infected individuals (active cases), $N\left(t\right)$, with an effective exponent $\delta$ determined empirically from the epidemic data. For $\delta =1$, the expression reduces to a Gaussian time dependence. The model is validated against epidemiological data from Israel, Austria, New Zealand, and Germany and shows good agreement with the observations. The analytical framework enables estimates of the epidemic peak, its duration, and the temporal evolution of the number of infected individuals. Furthermore, the ratio $N_{\mathrm{r},\mathrm{d}}/N$ of the cumulative number of recovered and deceased patients to the number of active infected individuals is shown to exhibit an approximately S-shaped time dependence when plotted on a log-log scale, also in good agreement with the data.

A micron-sized negatively charged particle in a complex plasma is typically surrounded by an ion stream. The latter is focused by the former and shapes the region of elevated ion concentration (ion wake). As a result, the interaction between dust particles in a complex plasma involves both a force governed by the screened interaction of the charges of the particles and additional parametric interactions. One is associated with attraction to the ion wake of a nearby particle and the other with a change of charge of a particle in the ion wake of another particle. This parametric interaction can be seen as an imaginary violation of Newton's third law and can lead to various types of instabilities in the ensemble of dust particles. In this work, individual particles with a diameter of $7.17\mu \text{m}$ move above a monolayer of $10.41\mu \text{m}$ particles in plasma sheath. The light particle temporarily becomes localized above one of the larger particles in the course of its movement. The resulting pairs perform finite, persistent motion. Three-dimensional analysis of this motion allows us to estimate the charges of the dust particles, measure the relative change in charge of the lower particle located in the ion wake under the upper particle, and quantitatively assess the attraction force exerted by the ion wake on the lower particle.

We investigate the balanced $M=4, p=4$ spin-glass model for a one-dimensional long-range proxy for the finite-dimensional short-range $p$-spin glass model to examine the nature of the glass transition beyond mean-field theory. We perform large-scale Monte Carlo equilibrated simulations for both fully connected and power-law diluted versions of the model. The critical temperatures extracted from the finite-size scaling (FSS) analysis of spin-glass susceptibility are in good agreement with theoretical predictions for $\sigma =0,0.25$ and 0.55. For these values of the long-range exponent $\sigma$ (which is the power of the decrease of the interactions between the spins with their separation), one might have expected that mean-field theory would provide a good description of the system. However, the spin-overlap distribution and the value of the $\lambda$-parameter do not provide numerical evidence for a one-step replica symmetry breaking (1RSB) phase transition. Instead, our results indicate a direct transition from the paramagnetic state to a full replica symmetry broken phase, with a renormalized value of $\lambda \equiv {\omega }_2/{\omega }_1<1$ suggesting a continuous FRSB transition, despite this ratio being equal to 2 at mean-field level. A value of $\lambda >1$ is required for the discontinuous 1RSB transition. We argue that strong finite-size effects and closely spaced transition temperatures remove the expected 1RSB transition for the system sizes which we can study. For values of the exponent $\sigma =0.85$ which roughly correspond to a three-dimensional system, we find that the renormalized value of $\lambda$ is again less than 1, with no signs of either the 1RSB transition or the continuous FRSB transition, suggesting that the Kauzmann temperature $T_K$ in three dimensions might be zero, and the complete absence of phase transitions in structural glasses.

We present a method for analyzing general time series by employing the history state formalism of quantum mechanics. This formalism allows us to describe a complete evolution based on a single quantum state, the history state, which simultaneously includes—also as a quantum system—the reference clock. It naturally leads to the concept of system-time entanglement, with the ensuing entanglement entropy constituting a measure of the effective number of distinguishable states visited in the history. Through a quantum coherent state embedding of the time series data, it is then possible to associate a quantum history state to the series. The Gaussian overlap between these coherent states provides thus a smooth measure of distinguishability between the series data. The eigenvalues of the corresponding overlap matrix determine, in fact, the entanglement spectrum and entropy of the history state, which provide a rigorous characterization of the evolution. As illustration, the formalism is applied to typical financial time-series data. Through the entanglement entropy and spectrum, different evolution regimes can be identified. Entanglement-based volatility indicators are also derived, and compared with standard volatility measures.

A hallmark of aging is loss of information in gene regulatory networks: gene expression becomes less correlated with biological age. These networks are tightly connected, raising the question of whether information could be restored by perturbing single genes. We develop a simple theoretical framework for information transmission in gene regulatory networks that describes the information gained or lost when a gene is “knocked in” (exogenously expressed). Applying the framework to gene expression data from muscle cells in young and old mice, we find that single knock-ins can restore network information by up to 10%. Our work advances the study of information flow in networks and identifies potential gene targets for rejuvenation.

Membrane extrusion experiments offer a powerful framework for investigating cell surface mechanics. However, providing a robust physical interpretation of the observed force-extension curves (FECs) remains a significant challenge. In this work, we introduce a minimal statistical-mechanical model for cellular membrane extrusion that accounts for discrete lipids, adhesion molecules, and the finite-size elasticity of the loading device. Focusing on a peeling geometry to isolate the essential physics of extrusion, we derive exact analytical expressions for the force-extension relations under two loading conditions: isometric (constant extension) and isotensional (constant force). Our results elucidate how lipid-lipid interactions, adhesion energies, and device stiffness govern the emergence of a mechanically induced conformational transition, providing a formal interpretation of the characteristic mechanical signatures reported in membrane-extrusion experiments, including force peaks, plateau regimes, and relaxation patterns. A central outcome of our analysis is that the isometric and isotensional conditions provide qualitatively different FECs of the same membrane system. This finding carries profound physical implications, suggesting that distinct experimental protocols applied to the same membrane may yield different mechanical responses.

Brains adapt to the statistical structure of their input. In the visual system, local light intensities change rapidly, the variance of the intensity changes more slowly, and the dynamic range of contrast itself changes more slowly still. We use a motion-sensitive neuron in the fly visual system to probe this hierarchy of adaptation phenomena, delivering naturalistic stimuli that have been simplified to have a clear separation of timescales. We show that the neural response to visual motion depends on contrast and that this dependence itself varies with context. Using the spike-triggered average velocity trajectory as a response measure, we find that this context dependence is confined to a low-dimensional space, dominated by a single principal dimension. To quantify the functional consequences of this structure, we measure the mutual information between stimulus and single-spike responses under models with and without adaptive structure. Across a wide range of conditions, incorporating adaptation consistently increases mutual information, indicating that context-dependent changes in the encoding model improve information transmission.

The mechanical behavior of granular media is strongly influenced by particle geometry, including both size and shape. While critical-state shear strength is widely reported to be insensitive to particle size distribution (PSD) in assemblies of grains sharing a common shape, grain shape itself has a strong impact on strength, with angular particles generally producing higher resistance than rounded ones. Many natural and industrial granular materials, however, combine broad size distributions with significant variability in particle shape, often correlated with particle size. Whether the classical PSD-insensitivity of shear strength still holds when particle shape varies across size classes remains largely unexplored. Here, we present a systematic experimental investigation of sheared granular assemblies composed of injection-molded particles with controlled sizes and shapes. Mono, bi, and polyshaped mixtures built from six characteristic grain geometries are tested under triaxial compression. We show that the classical PSD-insensitivity of critical-state shear strength breaks down when particle shape becomes size dependent. For a given PSD, assemblies with angular small grains and rounded large grains exhibit significantly higher shear strength than those with the opposite size–shape arrangement. This behavior persists over a wide range of PSDs and can be explained by enhanced geometrical interlocking promoted by angular small particles filling the voids between larger grains.

Growing bacterial colonies behave as active matter, with growth providing the activity. These colonies can take on granular properties such as microdomains. Based on this similarity, we perform a statistical analysis of pressure in simulated growing bacterial colonies under confinement, using methods commonly used to describe avalanches in driven granular materials and other intermittent systems. We find that as cells in a confined colony continue to grow, intermittent pressure drops occur alongside cell rearrangements. The statistics of these events have scaling exponents similar to mean-field predictions for avalanches in granular materials. This similarity suggests a new category of avalanche behavior, in which avalanches can be driven by single-particle growth rather than system-wide shearing or compression. These intermittent pressure-relieving cell rearrangements help explain how growing cell colonies can navigate complex environments with full confinement or growth bottlenecks.

The theoretical understanding of pattern formation in active systems remains a central problem of interest. Heterogeneous flocks made up of multiple species can exhibit a remarkable diversity of collective states that cannot be obtained from single-species models. In this paper, we derive a kinetic theory for multispecies systems of self-propelled particles with (anti)alignment interactions. We summarize the numerical results for the binary system before employing linear stability analysis on the coarse-grained system. We find good agreement between theoretical predictions and particle simulations, and our kinetic theory is able to capture the correct lengthscale in the emergent coexistence phases through a Turing-Hopf instability. Extending the kinetic framework to multispecies systems with cyclic alignment interactions, we recover precisely the same emergent ordering as corresponding simulations of the microscopic model. More generally, our kinetic theory provides an extensible framework for analyzing pattern formation and collective order in multispecies active matter systems.

We study a two-species Vicsek model with intraspecies alignment and asymmetric interspecies couplings, where one species aligns with the other while the latter antialigns. Motivated by recent results showing that globally coherent chiral motion is not a generic large-scale state of finite-range nonreciprocal flocking, we ask whether a chiral state can nevertheless be stabilized in the discrete-time, metric, nonreciprocal two-species Vicsek model, and if so, under what conditions. For equal populations and motilities, we find that the global chiral state is a long-lived finite-time state rather than an asymptotically stable phase at nonzero motility. Its breaking time increases rapidly as the self-propulsion speed is reduced or the strength of the nonreciprocal coupling is enhanced, explaining why systems with very low motility can appear stably chiral over conventional simulation timescales. This finite-time chiral regime is further limited to high-density systems and to system sizes that are small relative to the interaction range. Within this window, we also find that chirality appears primarily when aligning interactions dominate over antialignment, whereas stronger antialignment leads to species segregation and suppresses chirality. Conversely, introducing species asymmetry through population imbalance drives transitions from the finite-time chiral regime to porous parallel-flocking or antiparallel-flocking liquids; motility imbalance induces asynchronous oscillations and, in extreme cases, leads to segregation into moving clusters of the faster species within a more dispersed background of slower particles. Overall, these results indicate that chirality in the nonreciprocal two-species Vicsek model arises within a restricted regime set by density, motility, interspecies coupling, and system size, rather than being a generic outcome of nonreciprocal interactions.

The aggregation or clustering of proteins plays an important role in the formation of postsynaptic domains (PSDs) at excitatory and inhibitory synapses in neurons. PSDs are rich in scaffolding proteins that can transiently trap transmembrane neurotransmitter receptors, allowing them to regulate the strength of synaptic connections during learning and memory. Recently, a two-dimensional diffusion-mediated aggregation model of PSD formation was developed in which the spatial locations of the clusters are determined by a set of fixed anchoring sites. The system is kept out of equilibrium by the recycling of particles between the cell membrane and interior. This results in a nontrivial stationary state consisting of multiple stable protein clusters. In this paper, we use matched asymptotic methods to reduce the underlying reaction-diffusion model with moving interior boundaries to a corresponding finite-dimensional nonlinear system. The latter couples the cluster radii to the spatially averaged protein concentration in the bulk domain. We assume that the diffusivity $D$ is $O(1/\nu )$, where $\nu =-1/ln\epsilon$ and $\epsilon$ is a small parameter that characterizes the size of the clusters relative to the size of the bulk domain. The reduced dynamical system allows us to explore both the existence and stability of the multicluster stationary state while maintaining the effects of diffusion-mediated interactions between clusters.

Many morphogenetic processes in living systems are driven by active stresses within epithelial sheets that undergo simultaneous changes in thickness, curvature, and internal order. Existing theoretical descriptions of active nematics have largely focused on effectively two-dimensional layers of fixed thickness or prescribed geometry, limiting their ability to capture the coupled thickness-shape dynamics that are central to epithelial morphogenesis. Here, starting from a three-dimensional active nematohydrodynamic description, we derive a hierarchy of low-dimensional continuum models for thin active layers with finite thickness that can deform, flow, and reorient. Using an asymptotic long-wavelength theory, we show how activity couples nematic order to thickness variations and surface geometry in both flat and curved films. In flat layers, allowing thickness to evolve qualitatively modifies classical bend and splay instabilities, producing activity-driven modes that are not directionally constrained. In curved geometries, such as cylindrical films relevant to tubular epithelia and cnidarian body columns, thickness and shape instabilities become intrinsically coupled. Notably, we find that in isotropic phases both extensile and contractile activity can induce nematic order when thickness and shape are dynamic—contrasting sharply with active nematics on fixed substrates. Our results suggest experimentally testable mechanisms by which active stresses in epithelia can drive invagination, thickening, and curvature generation during development. More broadly, this framework provides a minimal physical theory linking active nematic order, thickness regulation, and morphogenesis in living tissues.