The quantum anomalous Hall effect shows great promise for the realization of the ohm without the need for an external magnetic field. The most mature material platform is magnetically doped topological insulators. In these materials, precise quantization is limited to low temperatures, with the activation energy for dissipative transport typically in the range of 1 K. One potential source of dissipative transport is nonchiral edge states. These states are expected to be present in sufficiently thick samples. In this work, we perform extensive Hall and nonlocal resistance measurements in a Hall bar geometry at 2 K. We perform 15 distinct measurements on a single device, each independent with respect to device symmetry and Onsager reciprocity. By comparing the results to different transport models, we find that the system behavior is well described by a simple continuum Ohm's law model. The addition of nonchiral edge states to the model does not significantly improve the fitting, and we conclude that there is not strong evidence for these states. We discuss the implications of our results for the prospect of a high-temperature quantized anomalous Hall effect in these materials.

Three-dimensional (3D) asymmetries and their associated residual kinetic energy (RKE) are a major performance-degradation mechanism in inertial-confinement fusion (ICF) implosions at the National Ignition Facility (NIF). These asymmetries can be diagnosed with the three neutron imaging systems fielded with orthogonal views to the implosion, as well as a suite of real-time neutron activation diagnostics (RTNADs) and neutron spectrometers. Conventional tomographic reconstructions are typically used to reconstruct the 3D morphology of the hot spot and surrounding dense fuel in an implosion using neutron images, but the reconstruction problem is ill-posed with only three imaging lines of sight. In this work a 3D reconstruction technique has been developed and used to overcome these limitations by combining information from the neutron images, RTNAD data, and neutron spectra to infer RKE. Markov chain Monte Carlo is used along with a group of neural networks to fit a physics model to the observed data to robustly reconstruct the 3D morphology of the hot spot and dense fuel in a NIF implosion. Bayesian statistics provides quantification of the uncertainty in the inference. Initial inferences find that the normalized RKE on several NIF experiments is between 2% and 5% of the total implosion energy, and is inversely related to the yield of an implosion, as expected. This technique is being developed to help guide designs toward minimizing asymmetries in ICF implosions and achieving higher performance.

Synchronization control in networked dynamical systems requires regulating not only whether coherence is achieved but also when and to what extent it emerges. We propose a physics-informed neural network framework for continuous-time synchronization regulation, in which system trajectories and control inputs are jointly parameterized and constrained by the governing dynamics. Macroscopic synchronization objectives are imposed directly at the trajectory level by enforcing persistence conditions on the order parameter after a prescribed target time. This formulation enables simultaneous control of synchronization time and coherence level without assuming any explicit feedback law or solving a strict optimal control problem. Numerical studies on networked Kuramoto oscillators demonstrate smooth synchronization with reduced transient control effort and competitive cumulative cost relative to analytical baselines. The framework remains effective in nongradient and frustrated dynamics, highlighting physics-informed neural control as a flexible trajectory-level approach to synchronization regulation. This perspective enables controlled access to nontrivial collective states, including chimera and chaotic dynamics.

Couette geometry refers to two concentric rings in two dimensions (or cylinders) in three dimensions with a medium in between. Typically, the inner and outer rings (or cylinders) rotate at different rates and the response of the medium is studied. Here we study a medium which is a two-dimensional amorphous solid, and we rotate the inner ring quasistatically. As stress accumulates, plastic avalanches can result in shear localization, characterized by adjacent parts of the system rotating in opposite directions, with the maximum shear localized between them. We derive an analytic theory that describes and explains the shear localization, providing a priori predictions for the angle-averaged displacement field associated with the plastic drops and the shear localization.

Chains of 1000 granules in mutual contact, without precompression, and with random radii that follow a normal distribution are studied numerically. The standard deviation σ controls the disorder level. The chains are subject to (i) a striker impact and (ii) an actuator that imposes band-limited white-noise motion at one boundary. For striker excitation, a critical σ_{c}≈0.1 separates weak and strong disorder, and the propagation of the median peak momentum exhibits two regimes: (i) for short distances, a σ-dependent decay, and (ii), at long distances, a universal power law ∝k^{-0.83}, where k denotes the granule position. For actuator excitation, the transfer function reveals a transition from a low-pass response, at σ<0.3, to broadband mechanical noise suppression for higher disorder levels. The narrow passbands at a few hundred hertz that we observe are consistent with the preferred transmission bands in soils reported experimentally in the literature. Thus, simple disordered granular chains provide a minimal model to investigate selective transmission or attenuation in random granular media. The findings are relevant to near-surface site characterization, shallow buried-object detection, and related geophysical applications.

Understanding how group interactions influence opinion dynamics is fundamental to the study of collective behavior. In this work, we propose and study a model of opinion dynamics on d-uniform hypergraphs, where individuals interact through group-based (higher-order) structures rather than simple pairwise connections. Each one of the two opinions A and B is characterized by a quality, Q_{A} and Q_{B}, and agents update their opinions according to a general mechanism that takes into account the weighted fraction of agents supporting either opinion and the pooling error, α, a proxy for the information lost during the interaction. Through bifurcation analysis of the mean-field model, we identify two critical thresholds, α_{crit}^{(1)} and α_{crit}^{(2)}, which delimit stability regimes for the consensus states. These analytical predictions are validated through extensive agent-based simulations on both random and scale-free hypergraphs. Moreover, the analytical framework demonstrates that the bifurcation structure and critical thresholds are independent of the underlying topology of the higher-order network, depending solely on the parameters d, i.e., the size of the interaction groups, and the quality ratio. Finally, we bring to the fore a nontrivial effect: The large sizes of the interaction groups could drive the system toward the adoption of the worst option.

Control of transcription presides over a vast array of biological processes, including those mediated by gene regulatory circuits that exhibit multistability. Within these circuits, two- and three-gene network motifs are particularly critical to the repertoire of metabolic and developmental pathways. Theoretical models of these circuits, however, often vary parameters such as dissociation constants, transcription rates, and degradation rates without specifying precisely how these parameters are controlled biologically. In this study, we examine the role of effector molecules, which can alter the concentrations of the active transcription factors that control regulation, and are ubiquitous in regulatory processes across many biological settings. We specifically consider allosteric regulation in the context of extending the standard bistable switch to three-gene networks, and explore the rich multistable dynamics exhibited in these architectures as a function of effector concentrations. We then analyze how the dynamics evolve under various interpretations of regulatory circuit mechanics, underlying inducer activity, and perturbations thereof. Notably, the biological mechanism by which we model effector control over dual-function proteins transforms not only the phenotypic trend of dynamic tuning but also the set of available dynamic regimes. In this way, we determine key parameters and regulatory features that drive phenotypic decisions, and offer an experimentally tunable structure for encoding inducible multistable behavior arising from both single- and dual-function allosteric transcription factors.

We investigate the interplay between nonreciprocal interactions and chemical-potential imbalance in a two-species nonreciprocal Blume-Capel model. Combining a systematic mean-field bifurcation analysis with large-scale Monte Carlo simulations in two and three dimensions, we map the model's dynamical regimes and transitions. Mean-field theory predicts a rich phase structure-disorder, a time-dependent "swap" (limit-cycle) phase, and static ordered states-separated by Hopf, saddle-node on invariant circle, saddle-node of limit cycles, pitchfork, and saddle-node bifurcations. In two dimensions, Monte Carlo simulations reveal that spiral defects destabilize global swapping and, unless vacancies are strongly favored, destroy long-range order. Crucially, a finite single-ion anisotropy Δ_{α}=-Δ_{β} promotes vacancy occupation in the α species and suppresses nonreciprocal dynamics, thereby restoring a robust static ordered phase. Finite-size scaling of susceptibility and Binder cumulants places the disorder → static transition firmly in the two-dimensional Ising universality class. Moreover, within the static ordered phase, we observe a crossover that sharpens into a line of first-order phase transitions; these two regimes are separated by a critical point, which is analogous to the liquid-gas coexistence curve terminating at a critical point. In three dimensions, simulations largely mirror mean-field expectations, though swap → static ordering occurs indirectly via a disordered regime. Our results demonstrate that vacancy energetics provide a simple, experimentally relevant control knob that stabilizes equilibrium-like order in nonreciprocal systems and that defects can generate novel critical behavior.

The reoriented potential mixer (RPM) can enhance mixing and reaction during in situ remediation of contaminated groundwater, in which a chemical or biological amendment is introduced into a contaminant plume to react with and degrade the contaminant. Each step of the RPM consists of dipole flow between a pair of wells-one for injection and one for extraction-that is active for a dimensionless time τ. Subsequently, a different pair of wells is activated at a reorientation angle Θ. Certain {τ,Θ} pairs lead to chaotic advection, with elliptic islands embedded in a chaotic sea. Importantly, a central elliptic island encompasses a "trapped" region containing fluid that does not reach an extraction well during typical groundwater remediation timescales. Indeed for groundwater remediation, the RPM must be designed so that the contaminant remains within such a trapped region, eliminating the possibility of degrading previously uncontaminated regions of the aquifer. In our model, this trapped region consists of two parts: an inner region surrounding an elliptic fixed point within which the fluid is minimally mixed by weak shear, and an outer region with stronger shear that can produce substantial stretching of the interface between the contaminant and amendment plumes. An RPM designed to contain the contaminant and amendment plumes within the trapped region has the potential to enhance reaction during in situ remediation of contaminated groundwater, while preventing the spread of contaminated groundwater into previously uncontaminated regions of the aquifer.

We show that the bias of the approximate maximum-likelihood estimation (MLE) objective of a persistent chain energy-based model (EBM) is precisely equal to the thermodynamic excess work of an overdamped Langevin dynamical system. We then answer the question of whether such a model can be trained with minimal excess work, that is, energy dissipation, in a finite amount of time. We find that a Gaussian energy function with constant variance can be trained with minimal excess work by controlling only the learning rate. This proves that it is possible to train a persistent chain EBM in a finite amount of time with minimal dissipation and also provides a lower bound on the energy required for the computation. We refer to such a learning process that minimizes the excess work as minimal-dissipation learning. We then provide a generalization of the optimal learning rate schedule to general potentials and find that it induces a natural gradient flow on the MLE objective, a well-known second-order optimization method.

The self-removal of sessile droplets under low-pressure conditions is critical for extreme-environment surface engineering. Combining high-speed photography with quantitative analysis, we reveal the unique dynamics of ethylene glycol-water solution droplets on irregular micro-nanostructured superhydrophobic surfaces. The experimental results demonstrate asymmetric depinning driven by evaporation-induced local overpressure gradients, generating a recoil momentum toward the pinned side that propels the droplet to move along the surface at a steady acceleration. This acceleration follows a quadratic scaling law with the evaporation flux. When the local overpressure exceeds the triple-phase line pinning forces, the droplets transition to the levitation phase. However, the unevenness of the surface micro-nanostructures leads to the instability of levitation, causing the droplet to rapidly recontact the surface and enter the secondary steady acceleration phase. Subsequently, the droplet maintains a dual-phase periodic transition mode of acceleration-levitation until it disappears from the field of view. In this study, we elucidate the synergy between recoil momentum and dynamic overpressure, advancing the fundamental understanding of low-pressure complex droplet dynamics and enabling surface morphology design strategies for droplet control in extreme environments.

We investigated the local dynamics of polybutadiene over a wide temperature range, from the glassy state to the liquid (rubbery) state, using quasielastic neutron scattering (QENS) combined in a recently developed modified mode distribution analysis. This approach enabled high-precision separation of QENS spectra into vibrational components and multiple relaxation processes without relying on specific model functions. Among these processes, the subpicosecond fast process was analyzed using a local diffusion model, which characterizes the dynamics via two physically meaningful parameters: the local diffusion coefficient and the harmonic potential stiffness. Temperature-dependent analysis revealed a distinct dynamic transition near the glass transition, which is interpreted as the disappearance of cooperative diffusion involving multiple segments. Furthermore, an additional relaxation mode, termed the extra-fast process, was identified and interpreted as a higher-order mode within the local diffusion model. These findings suggest a unified physical framework for the evolution of local motions across the glass transition and establish a robust methodology for quantifying polymer dynamics at subnanometer and subpicosecond scales.

Nanoparticle-protein dispersions constitute complex soft materials in which competing attractive and repulsive interactions can strongly influence their phase behavior, including gelation. In this work, we demonstrate a strategy to utilize interaction between anionic silica nanoparticles and anionic protein bovine serum albumin to achieve heat-induced gels with tunable physical properties. Upon heating, the protein molecules in solution undergo unfolding followed by hydrophobic aggregation, leading to the formation of a three-dimensional gel network. The introduction of negatively charged nanoparticles generates additional electrostatic repulsion that competes with the attractive hydrophobic interactions between partially unfolded proteins. This competition modifies both the structure and mechanical properties of the resulting gels. In particular, nanoparticle-protein gels exhibit markedly enhanced optical transparency (∼90%) compared with gels formed from pure protein solutions (<1%). Rheological measurements further show shear-thinning behavior, with the gel strength decreasing systematically with increasing nanoparticle concentration, leading to progressively softer gels. At sufficiently high nanoparticle content, gelation is completely suppressed, thereby stabilizing the protein dispersion against thermal aggregation. The underlying mechanism is elucidated in terms of interaction potentials obtained by modeling small-angle neutron scattering data measured in situ during gel formation. Finally, we demonstrate that nanoparticle concentration and ionic strength serve as effective parameters to control gel opacity and mechanical rigidity, enabling the formation of both soft and rigid gels. These results demonstrate how nanoparticle-mediated interactions can regulate aggregation and gelation in protein-based soft matter systems.

Shock-augmented ignition (SAI) [R. H. H. Scott et al., Phys. Rev. Lett. 129, 195001 (2022)0031-900710.1103/PhysRevLett.129.195001] is an alternative inertial confinement fusion concept that is designed to achieve high energy gain by combining improved resilience to instabilities with enhanced fuel compression. In SAI, lower implosion velocities can improve hydrodynamic stability and limited laser intensities reduce the excitation of detrimental laser-plasma instabilities, enabling greater areal density accumulation. Here we report an experimental investigation of SAI using a series of warm D_{2} implosions. By timing the augmenting shock to arrive at the implosion center immediately prior to peak compression, the areal density and hot-spot pressure increase by 50% and 112%, respectively, relative to unoptimized designs. Ignition-scale simulations reproduce the observed timing dependence and demonstrate the potential of SAI for inertial fusion energy.

We present an analytical model for the frequency response of a gas microbubble oscillating near a spherical inclusion of arbitrary size and mechanical nature (rigid, fluid, or viscoelastic) immersed in a viscous compressible fluid. The model considers both radial and nonspherical oscillations in the linear regime and predicts how their resonance frequencies and oscillation amplitudes are altered by the bubble size, material properties, and distance to the nearby sphere. As a key application, we demonstrate that scanning the frequency response of a bubble near a viscoelastic object, such as an erythrocyte-like particle mimicking a biological cell, offers a way to recover its mechanical properties through inverse modeling, opening new possibilities for high-resolution elastography at the microscale.