Self-avoiding random walks on graphs can be seen as walkers interacting with their own past history. Here, we consider a complementary class of dynamics, mutual future-avoiding random walks (MFARWs), where stochastically driven walkers are avoiding each other's planned future trajectories. Such systems arise naturally in conceptual models of shared mobility. We show that periodic behavior emerges spontaneously in two different MFARWs, and that periodic and unstructured behavior coexist, providing an example of chimera like behavior of nonoscillatory paths on networks. Further, we analytically describe and predict the onset of structure. We find that the transition from unstructured to periodic behavior is driven by a mechanism of self-amplifying coupling to the periodic components of the stochastic drivers of the system. In the context of shared mobility applications, these states imply a regime of naturally stable coexistence between flexible and line-based public transport.

Plasma wakefield acceleration of charged particle beams relies on specialized devices for plasma generation and confinement. In both particle-driven wakefield acceleration and beam-focusing experiments, these devices are designed to precisely control plasma properties to optimize beam-plasma interactions. Recent studies employing centimeter-scale, gas-filled discharge plasma sources have demonstrated high accelerating and focusing gradients while preserving electron beam quality, motivating the extension of these techniques to larger structures to achieve higher beam energies. At the SPARC_LAB test facility, multistage accelerating modules based on plasma generation by electrical discharges have been developed to produce tailored longitudinal plasma profiles for simultaneous beam acceleration and focusing. The results presented here show that the longitudinal plasma density within each stage can be independently tuned by modifying the plasma generation conditions, including the applied voltage, current pulse parameters, discharge ignition timing, and plasma recombination dynamics.

Understanding bidirectional pedestrian movement in angled corridors is important for pedestrian facility design and crowd management. This study presents controlled laboratory experiments with 48 participants. The experiments cover six corridor angles from $0^{\circ }$ to ${90}^{\circ }$ and four flow-ratio conditions (6:0, 5:1, 4:2, and 3:3). Individual trajectories are analyzed to characterize lane formation, phase proportions, lane number, lane entropy, and movement performance. Self-organized lanes emerge across the tested bidirectional conditions. The balanced 3:3 flow generally exhibits clearly separated counterflow lanes. Its Phase 3 (full bidirectional flow) proportion exceeds that of the highly unbalanced 5:1 flow at four of the six angles. Their proportions are comparable at $0^{\circ }$, and the ordering reverses at ${60}^{\circ }$. The mean lane-entropy difference between these flows is larger at ${75}^{\circ }$ and ${90}^{\circ }$ than at $0^{\circ }-{60}^{\circ }$. Thus, their lateral occupancy dispersion differs more at the two largest angles. Longitudinal velocity components generally decrease with corridor angle, while lateral components increase. Crossing time and traffic efficiency show no uniform monotonic trend. These findings suggest that the relationship between flow ratio and lateral lane organization depends on corridor geometry.

When two initially separated reactants A and B are put in contact, a chemical front can develop if a bimolecular $\mathrm{A}+\mathrm{B}\to \mathrm{C}$ reaction interplays with diffusion. In the case of a precipitation reaction, a phase transition may occur in the wake of this reaction front provided that the concentration of C is larger than a critical threshold. We analyze the scalings of resulting phase transition patterns in both one-dimensional (1D) and two-dimensional (2D) rectilinear and radial geometries by numerical simulation of the Cahn-Hilliard equation with a reactive source term, solution of reaction-diffusion-advection equations for the concentrations of A and B. We find that the temporal and spatial scalings of the precipitate patterns depend on the properties of the $\mathrm{A}+\mathrm{B}\to \mathrm{C}$ fronts and can be controlled by the injection flow rate $Q$. In particular, we show that the Matalon-Packter law classically used to characterize Liesegang patterns in one-dimensional rectilinear reaction-diffusion systems can be generalized to 1D radial geometry with injection. We discuss the further generalization of pattern selection between stripes and spotted patterns in 2D systems.

The large digital archives of the American Physical Society (APS) offer an opportunity to quantitatively analyze the structure and evolution of scientific communication. In this paper, we perform a comparative analysis of the language used in eight APS journals (Phys. Rev. A, B, C, D, E, Lett., X, Rev. Mod. Phys.) using methods from statistical linguistics. We study word rank distributions (from monograms to hexagrams), finding that they are consistent with Zipf's law. We also analyze rank diversity over time, which follows a characteristic sigmoid shape. To quantify the linguistic similarity between journals, we use the Rank-Biased Overlap (RBO) distance, comparing the journals not only to each other, but also to corpora from Google Books and Twitter (now X). This analysis reveals that the most significant differences emerge when focusing on content words rather than the full vocabulary. By identifying the unique and common content words for each specialized journal, we develop an article classifier that predicts a paper's journal of origin based on its unique word distribution. This classifier uses a proposed “importance factor” to weigh the significance of each word. Finally, we analyze the frequency of mention of prominent physicists and compare it to their cultural recognitions ranked in the Pantheon dataset, finding a low correlation that highlights the context-dependent nature of scientific fame. These results demonstrate that scientific language itself can serve as a quantitative window into the organization and evolution of science.

We present an interacting model of neural network dynamics that incorporates key biological features, including multiple forms of inhibitory interactions. We develop a hierarchy of analytical mean-field approximations to characterize nonequilibrium phase transitions between ordered, disordered, and chaotic regimes, complemented by a detailed stability analysis. We show that inhibition generically enhances the stability of network dynamics. The model is consistent with the quasicriticality hypothesis, exhibiting regions of maximal dynamical susceptibility and mutual information, modulated by the strength of external stimuli. We further demonstrate that, at the mean-field level, the critical transition belongs to the mean-field directed percolation universality class, in agreement with prior experimental and theoretical studies. More broadly, our framework may offer insights into neurological disorders, with the unstable regime exhibiting chaotic dynamics that may be associated with epileptic seizures.

What decides whether people do things together or separately? Many activities cannot be carried out alone, and an individual must rank them against the private tasks competing for the same time. We address this within the priority-queue description of human activity by letting each agent choose the priority of a shared task rather than drawing it from a fixed distribution: the value of the joint activity, discounted by the estimated risk that the partner will not take part. Participation becomes strategic, and the model acquires a phase transition. A coupled phase, in which joint activity is sustained, is separated from an absorbing solitary phase by a saddle-node bifurcation that we obtain in closed form. The transition is discontinuous and the solitary phase is absorbing, so collapse is irreversible unless an agent persists unilaterally for of order one memory time, a cost we also compute. The heavy-tailed interevent statistics that motivate queueing models of human dynamics survive only in a narrow window at the transition, and there the exponent is fixed by the fraction of time spent coupled rather than by queue length: the universality classes of the nonstrategic model do not survive the introduction of choice. On a network, attention divides as $1/(k+a)$ and fixes a critical degree beyond which no coupled state exists, so the solitary phase percolates according to the Molloy-Reed criterion with the second moment truncated at that degree—formally an attack on hubs, with no attacker. For group activities the critical degree falls as the $(m-1)\mathrm{th}$ root, implying a maximum group size. The pair results are analytic and reproduced by simulation; the network and group thresholds are mean-field predictions, tested against simulations that locate the transition and characterize the fluctuations that displace it, but that do not resolve its critical behavior. The transition organizes two quantities already measured in communication records: a finite capacity for keeping ties active, and the decay of ties whose rhythm is interrupted.

The resting brain must preserve stable representations while flexibly switching among activity patterns. Here we show that noise can tune this balance in a connectome-informed modular spiking network storing multiple spatiotemporal memories through spike-timing-dependent plasticity. With global inhibition fixed, varying excitatory coupling and noise reveals a noise-controlled sequence of retrieval transitions: no effective retrieval, a first-order-like hysteretic transition, a fluctuation-dominated continuous-like regime with critical-like signatures, a reentrant first-order-like transition, and noise-dominated disorder. At intermediate noise, multiple stored patterns are transiently reactivated, producing recurrent spontaneous switching among memory states. Time-dependent noise modulation further drives the same learned network between persistent replay, disordered firing, and multipattern switching. The qualitative transition sequence persists after degree-preserving randomization of the structural connectome, indicating robustness to the detailed empirical topology. A reduced phenomenological model based on memory-overlap dynamics interprets the switching regime as arising from a balance between noise-assisted activation of memory attractors and noise-induced escape from them. These results identify noise as a control parameter for retrieval transitions, attractor stability, and flexible memory-state exploration in modular brain networks.

Spiky oscillations are characterized by slow-fast dynamics and are observed in excitable media such as neuronal membranes and cardiac cells. In a turbulent reactive flow system, we observe that the heat release rate exhibits self-sustained periodic, spiky oscillations in synchrony with the sinusoidal periodic acoustic pressure oscillations. These self-sustained oscillations are a consequence of thermoacoustic instability, which arises due to a positive feedback between the acoustic and the heat release rate fields. One of the primary mechanisms for fluctuations in the heat release rate is the modulation in the topology of the flame, a thin interface separating the reactants and products. In this work, we explore the dynamics of the space-filling nature of the flame, quantified by its fractal dimension in relation to the spiky heat release rate oscillations in a turbulent reactive flow system. We discover that the periodic oscillatory dynamics in the space-filling nature of the flame lead to periodic, spiky heat release rate oscillations. Based on this result, we show that the spiky oscillations in the heat release rate can be approximated as $e^{\sin \left(\omega t\right)}$ during the dynamical state of generalized synchronization between the heat release rate and the acoustic pressure oscillations in a turbulent reactive flow system. In synchronization theory, generalized synchronization is characterized by an emergent functional relationship $\mathrm{\Phi }$ between the interacting subsystems. We unravel this emergent functional relation between the heat release rate and the acoustic pressure oscillations in a turbulent reactive flow system. Rather than the specific mathematical expression, the present study unveils that the periodic modulation of the fractal dimension leads to periodic spiky oscillations.

The space-time correlation of a passive scalar advected by a Gaussian colored-noise velocity with wavenumber-dependent correlation times and power-law spatial spectra is investigated in the present paper. Within the inertial-convective subrange, we derive an analytical solution for the space-time correlation. This solution validates the elliptic approximation (EA) model proposed by He and Zhang [Phys. Rev. E 73, 055303(R) (2006)], demonstrating that the iso-correlation contours are self-similar in the co-moving space-time frame $(r-U\tau ,V\tau )$, with a constant spatial-to-temporal intercept ratio of 1.55. Unlike the classic Kraichnan white-noise model, our formulation simultaneously recovers the Obukhov-Corrsin scaling for spatial correlations (when the velocity obeys Kolmogorov scaling) and reproduces the random-sweeping mechanism, yielding Gaussian (rather than exponential) temporal decorrelation of scalar Fourier modes. Our results clarify the underlying decorrelation mechanism of passive scalars: mean-flow advection and large-scale sweeping dominate temporal decorrelation, and small-scale distortion dominates spatial decorrelation.

The in-plane dielectric constant of nanoconfined water has attracted growing interest in recent years. Nevertheless, assigning a bulklike dielectric constant to a molecularly thin film is convention dependent because it requires choosing an effective water width. We propose the in-plane 2D polarizability, ${\alpha }_{\parallel }$, as a macroscopic characterization of the in-plane dielectric response that avoids this choice. Using classical molecular dynamics simulations, we compute ${\alpha }_{\parallel }$ via two independent and consistent methods: based on fluctuation-dissipation theory, and from the induced dipole moment when water is placed in a capacitor. Our results provide the framework to quantify the in-plane dielectric response of polar liquids across simulations and experiments.

Generalized Langevin equations (GLEs) provide a powerful framework for describing slow dynamics in soft-matter systems, but deriving an exact GLE for a reaction coordinate from an underlying many-body system remains generally difficult. Here, we analytically derive an exact coupled GLE for the overdamped dynamics of two tagged beads in arbitrary harmonic elastic networks. We further derive an exact GLE for their relative coordinate. The memory kernel and effective restoring force are expressed explicitly in terms of the network matrices, thereby providing a systematic reduction of the high-dimensional network dynamics to a pair coordinate. Within the small-displacement approximation, we further derive a GLE for the interbead distance, a central observable in distance-sensitive single-molecule experiments. These results therefore have broad potential applications in modeling proteins and other soft-matter systems.

Particles suspended in turbulent flows tend to cluster within specific regions of the flow field. Previous research consistently reported that clustering is reduced at both very small and very large Stokes numbers for moderate Reynolds numbers. However, understanding how clustering evolves with varying Stokes numbers remains elusive. This work utilizes kinematic simulations to explore clustering of particles with varying inertia at high Reynolds numbers. Our results show that when the Stokes number varies over time, noticeable clusters form, even for low-Stokes-number particles that typically exhibit negligible clustering. This suggests that transient effects associated with evolving particle inertia can significantly enhance clustering behavior beyond what would be predicted based on a constant Stokes number alone. Our findings may suggest a different outlook on the enhanced clustering observed in many applications, including natural and atmospheric science, and cloud microphysics.

Magnetic multicore nanoparticles offer a versatile route to tuning magnetic response via particle geometry and internal structure. In this work, we investigate the dynamic magnetic behavior of ellipsoidal multicore particles, focusing on the interplay between elongation, intercore dipolar interactions, and magnetic anisotropy. Using Brownian dynamics simulations with explicit Néel and Brownian relaxation, combined with a spectral reconstruction method based on non-negative least-squares fitting of autocorrelation functions, we resolve the underlying relaxation modes across a wide frequency range. To interpret the simulation results, we develop a theoretical model for the static susceptibility of multicore ellipsoids that incorporates demagnetization effects within a modified mean-field framework. We show that while elongation merely shifts the Brownian relaxation peak in weakly interacting systems, it strongly enhances dipolar correlations at higher interaction strengths, leading to spectral broadening, multiple relaxation modes, and a redistribution of dissipation towards lower frequencies. As a consequence, elongated multicores display a specific loss power at low frequencies that is improved by over an order of magnitude, while maintaining their performance at high frequencies without any decrease. This behavior is particularly relevant for magnetic hyperthermia, where operational constraints limit the accessible frequency range. Our findings provide a microscopic basis for tailoring magnetic response through particle shape and internal architecture.

Analytical theories of polymer dynamics typically focus on the linear response around some steady state obtained via perturbative calculations. The present study derives a dynamical, nonlinear mean-field theory that yields unified access to the evolution of conformations, contact probabilities, and fluctuations of active polymers within a single analytical framework. As in previous mean-field descriptions of passive polymers, the object of interest is the two-point correlator, which is the lowest-order relevant observable in the general Langevin equation of a polymer. In contrast, the present focus is on active polymers driven by heterogeneous monomer-level kicks and spatially correlated active flows, while allowing nonreciprocal couplings, in the presence of hydrodynamic and contact interactions. Using a Gaussian closure to truncate higher-order correlations leads to a self-consistent diffusion equation for the chain correlations. In some cases, this can be further simplified to the dynamics of pairwise squared separations in closed form. In the continuum limit, the theory reveals useful analogies to similar mean-field approximations for reaction-diffusion systems, as well as fractional diffusion in different scale-free regimes. Deriving asymptotic solutions in different parameter regimes of the continuum limit recovers the scalings of coiled, globular, and self-avoiding polymers, thus validating the theory. Applied to hydrodynamically coupled active polymers, such as chromatin, the framework predicts a sequence of crossover scalings between coiled, swollen, and hypercompacted fractal states.

Transition waves are central to phase-boundary propagation in solid phase transformations and sequential state switching in multistable metamaterials. However, existing studies are often limited to specific structural prototypes or simplified constitutive models, failing to reveal how wave kinetics and stability can be independently modulated by bistable element parameters. Here, we investigate transition waves in viscoelastic bistable lattices using a trilinear constitutive model with independently tunable parameters. Exact traveling-wave solutions are obtained through the nonlinear map approach, while the quasicontinuum approximation provides analytical insight and efficient initial guesses for numerical continuation. Results indicate that the energy release rate, boundary strain, transition zone width, and wave profile are strongly regulated by damping and the stiffness characteristics of the bistable spring. Floquet analysis supports the proposed macroscopic criterion that instability arises when the energy release rate decreases with increasing wave velocity, and further reveals that the stable velocity region is mainly governed by the stiffnesses of both the untransformed and transformed phases. Time-domain simulations show that low-velocity waves are typically unstable, manifesting in two distinct postinstability behaviors: they either split into two separated linear waves and a pinned transition interface, or self-adjust toward a more stable propagation state. Finally, a Riemann-type initial value problem is considered to determine the initial strain conditions required to initiate transition waves at prescribed velocities. These results establish a direct link between bistable constitutive parameters, kinetic relations, stability boundaries, postinstability evolution, and initiation conditions, providing a theoretical basis for designing transition-wave-mediated multistable metamaterials.