Papers

Inspired by the latest progress in the hunt of acceptable cosmological model of the universe, present paper is devoted to explore a mathematical model of the universe having initial anisotropy and with accelerated evolution that attains isotropic character. We have considered a widely accepted anisotropic Bianchi type I space-time geometry of the universe to explore a physically viable model. To find an acceptable model, we adopted a hyperbolic form for the scale factor as $ a(t)= \left( sinh bt \right)^{\frac{1}{n}} $ and a relation $ \Lambda=\Lambda_0 \frac{\ddot{a}}{a}$. The model parameters are constrained by $ \chi^2$ minimization techniques. Using 35 CC measurements we obtain present day expansion rate $H_0= 65.715 Km/S/Mpc$ and the value of $ n= 1.3024$. Further to estimate the local uncertainties of the best fit parameter, we calculate covariance from the Jacobin of the normalized residuals. The corresponding 1 $\sigma$ uncertainties are $ H_0=65.715 \pm 2.392 Km/S/Mpc$ and $n= 1.3024 \pm 0.0842$. The physical and dynamical behaviour of present model has been discussed by a graphical representation of cosmological parameters. The observational constraints on the expansion history are found to be consistent with a present accelerated phase and a transition from deceleration to acceleration at an intermediate redshift. The corresponding evolution of the derived cosmological quantities is also investigated within the adopted model.

This article presents a wormhole solution constructed from a generalized ghost dark energy (GGDE) source in general relativity. At first, a brief review of GGDE is presented along with the necessary mathematical frameworks. We employed the Markov Chain Monte Carlo (MCMC) technique to constrain the free parameters of the model using the CC+BAO, $Pantheon^+$, and their combined datasets. Furthermore, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used to statistically assess the model's performance and determine its level of acceptance. Next, the basics of wormhole geometries along with the thin-shell formalism are explained in detail. After that, three different wormhole solutions associated with three different choices of the redshift function are presented ,and their various geometric properties as well as energy conditions are studied graphically. Finally, the stability of these thin–shell structures is examined analytically as well as graphically by studying the associated effective potential.

Dzyaloshinskii-Moriya (DM) interaction is a source of chiral magnetic physics, and it manifests microstructural symmetry which could link to exotic magnetic and electronic properties such as altermagnetism. For a new paradigm in magnetism and spintronics, it is crucial to have control on the DM interaction as well as ordering of the DM vectors which characterize the DM interaction. Here, we report on a strong interlayer DM interaction emerged in Co/Ru magnetic superlattices whose structural symmetry is unambiguously broken by alternating mechanical strains developed using the peculiar technique of our stress mechanism. Moreover, theoretical analyses reveal that our magnetic superlattices host a staggered order of the DM vectors. Our results indicate that the spatially varying strains with our breakthrough technique are quite effective in breaking the symmetry of the system and prove that it can control the DM interaction strength as well as the DM vector order.

A particle launched normally into a half space, scattering by the Henyey-Greenstein (HG) phase function, exits after a first-return time whose count of flights n approaches the distribution-free Sparre Andersen exponent, f(n) ~ n^{-3/2}, with an amplitude fixed by the launch rather than by the theorem. Conditioned on n, and past a correlation-set transient, the lateral exit position is Gaussian with variance proportional to n, a scaling we test directly. Subordinating that Gaussian displacement to the one-sided 1/2-stable first-return time gives, in the diffusion limit, an isotropic 1-stable scaling limit: the far tail of the point-spread function (PSF) for a normally incident pencil beam is the two-dimensional Cauchy tail p(r) ~ r^{-3}, the large-radius limit of the half-space Poisson kernel. The index alpha = 1 is the product of the central limit theorem and the distribution-free first-return exponent, so no phase function admitting the same diffusion limit - finite step variance, short-range angular correlation - can move it. Absorption weights each path by exp(-mu_a L) in its total length L, truncating the subordinator and producing the screened radial structure (mu_eff + 1/r) exp(-mu_eff r)/r^2 carried by each source term of the Farrell-Patterson-Wilson (1992) diffusion dipole; the finite-depth two-source dipole is not derived here. Keeping the launch-depth factor that the tail calculation discards continues the same kernel into a finite core of radius 1.7 l*, with no additional fitted parameter. A scale-dependent index alpha_eff from the empirical characteristic function tracks the similarity parameter gamma, separating phase functions matched in g_1 by 0.15-0.22 near one transport mean free path. Results are Monte Carlo at g = 0 to 0.9 with up to 10^6 walkers.

Physics-informed neural networks (PINNs) have emerged as a compelling pathway toward trustworthy artificial-intelligence-based computational fluid dynamics (CFD) by embedding governing equations directly into the learning process. Many existing AI flow models require large simulation or experimental datasets and often remain problem-specific, limiting their generalization and physical reliability. Data-free PINN frameworks offer an alternative by learning flow solutions from the Navier-Stokes equations and prescribed boundary conditions, potentially reducing dependence on expensive CFD datasets while retaining physical consistency. However, traditional PINN frameworks have struggled at high Reynolds numbers, limiting their application in CFD. In this work, we present NeuralFlowNet, a data-free, physics-informed proof-of-concept framework designed to solve steady Navier-Stokes problems across low- to high-Reynolds-number conditions. We describe the proposed methodological framework and demonstrate its applicability using benchmark problems with increasing physical and geometric complexity. The results demonstrate that NeuralFlowNet can accurately recover steady flow fields across a broad Reynolds-number range, including cases with strong pressure gradients, without training on external flow-field data and while maintaining good agreement with reference numerical solutions. These findings establish NeuralFlowNet as a reliable framework for future testing of unsteady and more complex simulations and could provide a basis for developing trustworthy and efficient AI solvers for high-Reynolds-number fluid dynamics.

Magnetically coupled damped rotational degrees of freedom can give rise to tribological loss when excited by a relative sliding motion, linking friction to the dynamics of magnetic moments. We analytically investigate such magnetic friction by studying a recently introduced simplified model for a rigid magnetic rotor array sliding over a commensurate magnetic substrate. Here, the array rotors can rotate about an axis perpendicular to the sliding direction and are damped by microscopic shaft friction, while the substrate magnets have a fixed in-plane orientation. The simplified model reduces the collective rotor dynamics of this setup to a set of coupled nonlinear differential equations, which can be studied by the corresponding energy landscape in the quasi-static regime. We find that the parameter plane spanned by layer separation and time can be divided into distinct chambers, in which the structure of the energy landscape remains invariant. Using these chambers, we identify the layer separation intervals corresponding to different dynamical states and recover the emergence of a regime where the moment alignment alternates globally while exhibiting peak dissipation. Additionally, we derive the leading nontrivial orders of the collective magnetic friction in the regimes of small and large gaps between the array and the substrate. For the alternating regime, we find that friction can be computed by tracking energy jumps from unstable to stable critical points at the chamber boundaries. These results provide an analytical foundation for the observed dynamics and dissipation in sliding rotor arrays. Because the simplified model is scale-free, our conclusions transfer across a broad range of microscopic and macroscopic length scales.

During many biological processes, cell aggregates spread on substrates in a process of active wetting. The existing models of tissue spreading, however, do not explicitly capture the state of partial wetting, in which the cell aggregate reaches a non-zero contact angle. Here, modeling cell aggregates as active polar liquid droplets, we predict a transition between partial and complete wetting. This transition emerges from the competition between tissue surface tension and active traction forces, which is captured in an active Young-Dupré equation. The active wetting transition is discontinuous, and it features a region of parameters in which both partial and complete wetting are possible. Unlike in passive wetting, the transition depends on dynamical parameters such as the tissue viscosity and substrate friction. Finally, adding contractile intercellular active stresses leads to higher contact angles and even full dewetting. Overall, our work predicts an active wetting transition for tissues and it captures the partial wetting states often seen in experiments.

Solvent environments surrounding a solute can significantly alter its chemical properties. These changes become more prominent in electrolyte solutions, where mobile ions largely influence the solute through electrostatic interactions. A theoretical description of these ionic effects will benefit the design of chemistry performed in electrolyte solutions. As a result, the non-linear Poisson-Boltzmann equation (NLPBE) has emerged as an efficient implicit solvent model for electronic structure calculations to address such effects. However, the limited availability of NLPBE solvers for molecular electronic structure calculations greatly hinders theoretical investigation into the ionic effects. To improve accessibility of the NLPBE, we present libNLPBE, an open-source Python library, that solves the NLPBE for molecular systems in combination with density functional calculations, specifically to obtain electrostatic correction terms arising from the electrolyte solution environment for the Fock matrix. The library employs the density fitting (DF) approximation to efficiently calculate solute electrostatic potentials. Furthermore, we develop the modified Damped Inexact Newton Multigrid developed by Holst (mDINMH) method for solving the NLPBE. The mDINMH features a symmetric preconditioner for solving the Newton equation, which enables the use of robust multigrid methods designed for symmetric linear operators. In addition, an algebraic multigrid method has been incorporated into the mDINMH to support a broad range of grid points. We also present a GPU-accelerated version of libNLPBE to leverage parallelization efficiency of GPUs.

Single electron spin qubits trapped in SiMOS quantum dots are a promising technology for scaling to thousand- or million-qubit systems due to their compatibility with mature CMOS manufacturing processes. A readout system that combines a single electron transistor with a custom cryogenic CMOS amplification and digitization chain offers key advantages by avoiding the use of bulky RF components or room-temperature interconnects. We present design techniques and simulation results for an optimized qubit-SET cryoCMOS interface, culminating in the design of the QNDR1 ASIC, the first cryogenic readout ASIC designed under the Quandarum project, which targets the development of a many-channel spin qubit based detector for use in high energy physics.

In polarization fields, Stokes skyrmions are continuous vectorial textures that encode integer-valued topological invariants across real space, enabling robust optical information encoding under complex perturbations. This topological resilience, however, fails when singular points occur where the Stokes vector has no unique limiting value, placing a fundamental constraint on skyrmion-based information manipulation. Here, we show, paradoxically, that the very defects that destroy conventional resilience can become the carriers of topological information. We introduce the resulting structures as Stokes defect skyrmions, in which singular Stokes responses constitute measurable topological degrees of freedom with theoretically minimal size. We design and realize one class of them using all-dielectric metasurfaces that combine arbitrarily controlled distinguished fast-axis singularities with customized retardance profiles. The resulting fields are then described by high-dimensional integer-valued topological tuples, providing theoretically unbounded information capacity at the nanoscale. As a proof-of-concept demonstration, selected tuple components are mapped to represent predefined alphabetic symbols, realizing controlled high-dimensional information representation within a single optical field. Our results establish Stokes defects as functional units for higher-dimensional topological encoding, expanding the role of defects from failure points to engineerable carriers of optical information.

The Pierre Auger Observatory, a $3000km^2$ detector array that sits 1400m above sea level in Argentina, has the ability to map the entire footprint of Terrestrial Gamma-ray Flashes (TGFs), which are short, intense bursts of gamma rays associated with lightning. This is in contrast to the vast majority of TGF detections that only occur in a single detector in space. In this paper we leverage this capability to perform two important analyses on an event from 2007, chosen for its large footprint and high signal quality. First, we triangulate the source of the TGF to $2.3\pm0.5$km height above ground level using the arrival time of the gamma rays themselves at the detector array, independently of any information regarding the lightning channel. With the source position established, we can analyze the spatial distribution of the TGF on the ground with respect to that source. Not only do we find that one side of the TGF footprint shows an order of magnitude higher flux than the other, but we also find an asymmetric azimuthal structure consisting of two clear peaks in intensity. These peaks are offset by about $120^o$ from each other. This azimuthal structure is not possible under the traditional uniform electric-field model of a TGF. Using Geant4 simulations of collimated beams of the relativistic runaway electron-avalanche spectrum, we attempt to reconstruct the source geometry of the TGF and find that it likely had significant components in the $\theta > 75^o$ range, where $\theta$ is the off-nadir angle.

Language models have recently been applied to nanophotonic design, but it remains unclear whether they can reliably translate optical objectives into simulation-ready designs, execute electromagnetic analysis, and revise decisions from numerical feedback. We introduce HALO, a physics-aware framework that couples language-model planners with typed design specifications, electromagnetic simulation, diagnostic evaluation, and optional reuse of prior failure trajectories in an iterative design loop. We further introduce HALO-Bench, a 52-task benchmark spanning lab-derived, paper-derived, and open-ended nanophotonic design tasks under a shared evaluation protocol. We compare three planner configurations: a Fixed Structured Workflow, an Autonomous Structured Agent using the same simulation interface, and an Autonomous Coding Agent that directly writes and executes simulation code. The Fixed Structured Workflow is the most token-efficient and exhibits no observed code- or path-level failures, while autonomous coding can achieve higher task success with stronger models at the cost of additional operational failures. We also study reuse of prior failed trajectories. On targeted multi-round tasks, retrieved failure feedback reduces both iterations to first success and total token use. These results clarify the tradeoffs between explicit interfaces, autonomous execution, and reusable design experience in scientific agents.

In Nyström-collocation discretizations of the electric field integral equation (EFIE), the surface divergence acts on surface densities that may be discontinuous across patch boundaries, which degrades accuracy and convergence. We show that this not only affects the EFIE but every formulation in which the operator occurs, either in the equation itself or in the scattered field computation, and propose a high-order-accurate continuity-enforcing scheme for smooth surfaces as a remedy for the direct and indirect EFIEs, magnetic field integral equations (MFIEs), and regularized combined field integral equations (CFIEs) alike. The scheme comprises two ingredients: i) We show how to discretize the equations via a Chebyshev-based Nyström scheme, which admits closed quadrature rules. ii) Since unknowns and test vectors are in terms of patch-local curvilinear bases, continuity is enforced by a change of basis: we construct sparse mapping matrices assembled solely from the curvilinear geometry description. In doing so, we restore the accuracy of the EFIE such that it can be combined with the MFIEs with equal weights to form CFIEs. Numerical studies for the scattering from canonical and realistic geometries show that all considered formulations individually and combined benefit from the continuity enforcement in terms of better conditioning, reduced iterations of an iterative solver, and several more digits of accuracy in the scattered fields, despite reducing the total number of unknowns.

Tensor networks (TNs), originally developed for simulating many-body quantum systems, provide a systematic framework for approximating high-dimensional fields. This is achieved by factorizing the field into interconnected tensors with small bond dimensions, thereby restricting the correlations captured across field bipartitions. Belonging to the family of TNs, the matrix product state (MPS) ansatz is utilized here as a reduced-order modeling framework to construct truncated representations of isotropic turbulent flow data. Two direct numerical simulation (DNS) datasets are considered: the hydrodynamic field of an incompressible three-dimensional flow, and a conserved Fickian scalar in a similar flow. Each field is encoded as an MPS through a sequence of singular value decompositions (SVDs) in which small singular values are discarded. The truncated representation is contracted back to the full grid, and the resulting reconstructed field is compared against DNS. An interleaved ordering of the spatial tensor indices of the transport variables is applied prior to decomposition in order to localize the dominant inter-tensor correlations. Velocity reconstructions achieve $99.8\%$ fidelity using only $5\%$ of the original DNS memory, while the scalar field reaches the same fidelity at $15\%$ memory usage. A wide range of lower- and higher-order statistics, including velocity gradients, dissipation, and structure functions, are systematically examined. At these compression levels, the total kinetic energy and the scalar energy are both recovered within $0.2\%$ relative error, while the mean dissipation and mean scalar dissipation remain within approximately $10\%$ of the DNS generated values. These findings support the suitability of MPS for scalable reduced-order analysis of complex turbulent datasets and motivate further exploration of TN-based methods in computational turbulence.

Compact ultrasound imaging systems are increasingly constrained by receiver-side sampling, conversion, memory, and data-transfer requirements. This work presents a compressed-sensing pipelined successive-approximation-register analog-to-digital converter (CS-SAR ADC) for acquisition-side temporal compression of pre-beamformed medical ultrasound radio-frequency (RF) data. Pseudo-random polarity modulation and charge-domain accumulation are embedded in the SAR sampling network so that multiple consecutive RF samples are encoded into one measurement before quantization, supporting temporal compression ratios of $N_{cT}=1$, 2, and 4. The compressed outputs are recovered off chip using a probe-specific pulse-dictionary RF model and then processed with conventional ultrasound beamforming. A 65-nm CMOS prototype was measured with a 1.2-V supply and 50-MHz master clock. In the non-compressed mode, it operates at 10 MS/s, consumes 964.49~$\mu$W, and achieves 44.12-dB SNDR and 56.40-dB SFDR for a 7.7-kHz input. The ADC output rates decrease to 5 MS/s and 2.5 MS/s for $N_{cT}=2$ and 4. Across all evaluated RF traces, median NCC values were 0.981 and 0.932, with median NRMSE values of 0.36 and 0.56, respectively. Wire-phantom localization error remained below 0.04 mm with no appreciable FWHM degradation. In the speckle-rich cyst phantom, SSIM remained 0.94 and 0.87, while CNR decreased from 3.534 in the reference to 2.047 and 1.379. These results demonstrate a hardware-realistic tradeoff in which temporal compression substantially reduces ADC conversion count and output data rate while preserving point-target geometry, whereas low-contrast cyst conspicuity is more compression-sensitive.

Many interactions perturb (or shift) the hyperfine coupling in group-1 atoms between their nuclear spin and single valence electron. Previous work provided an approach to estimate this shift for non-reactive collisions with other atoms or molecules at long range, and extended it to estimate the shift from static electric fields. We further extend this approach to estimate the shift due to a nearby conductive surface, and improve all these estimates by numerically computing updated values for the characteristic energies they use. We investigate how to estimate these energies by scaling arguments, and suggest how to adapt the approach to other applications. The results are relevant to pressure shifts in vapor cells, Stark shifts, and wall shifts in nanoscale devices.

Graphene nanogaps represent an emerging platform for nanoscale electrochemical and sensing devices, with potential applications in next-generation biomolecular sequencing. However, their interfacial behavior in aqueous environments remains poorly characterized, particularly with respect to frequency-dependent impedance and charge transport mechanisms at the graphene edge. We fabricate graphene nanogaps by controlled electrical breakdown in an inert atmosphere and study their electrochemical response. Upon exposure to ambient conditions, a surface contamination layer supports electrochemical activity within an adsorbed ultrathin conductive film between the graphene edges. Electrochemical impedance spectroscopy reveals distinct frequency-dependent responses consistent with a Warburg element associated with diffusion in this confined interfacial film. The impedance evolves systematically with liquid $\mathrm{p}H$, reflecting changes in electrochemical reaction-diffusion processes at the graphene edges. A quantitative equivalent-circuit model captures these effects and enables extraction of an effective nanogap length scale from impedance spectra, providing information complementary to that obtained from tunneling measurements.

Gas-phase rotational Raman lineshape metrology of hydrogen (H2) is challenging due to its weak Raman scattering cross-sections and intrinsically narrow linewidths. We report the first measurement of the complete Dicke-narrowing evolution of the H2 rotational Raman transitions S0(1) and S0(0), enabled by intracavity dual-resonance stimulated Raman spectroscopy with cavity ringdown readout and kHz-level spectral resolution. Our results establish a benchmark for gas-phase Raman lineshape measurements and provide stringent constraints for regime-based pressure-dependent linewidth models.

This work investigates extreme ultraviolet (EUV) emission at 13.5 nm from laser produced tin plasmas generated using a mass limited laser blow off (LBO) plume.

Magnetic levitation is one of the most captivating phenomena in physics, combining a striking visual appeal with the rich underlying physics of forces and interactions in equilibrium. Bringing it into undergraduate laboratories in a quantitative way is nevertheless often hindered by the intrinsic instability of static magnetic systems and by the high cost of active control systems. Here we present a simple, safe, and very low-cost experiment that uses ring-shaped ferrite magnets together with a mechanical stabilization system fabricated by 3D printing in a male–female configuration. The apparatus lets undergraduate students explore directly and quantitatively the relationship between the magnitude of the magnetic force and the separation distance. We assess the robustness of the device through experimental tests, whose results show excellent quantitative agreement with the equivalent current-loop model. Furthermore, we validate this model through a complementary, independent first-principles-based methodology based on axial magnetic field profiling. To support reproducibility, the 3D design files are made available in a public, open-access repository.

Rapid prediction of pedestal linear stability is important for exploring tokamak operating space, uncertainty quantification, and future model-informed control, but mode-resolved magnetohydrodynamic stability calculations using BOUT++ are computationally expensive. We present a focused implementation of ELMO–the Edge Learning and Modeling Orchestrator–as an uncertainty-aware simulation-to-surrogate workflow integrating equilibrium generation, field-aligned mesh construction, large-scale BOUT++ calculations, automated campaign execution and data reduction, and Gaussian Process Regression (GPR). For a single DIII-D plasma shape, 3,869 of 7,992 requested configurations completed equilibrium reconstruction, mesh generation, stability calculation, and quality control. Each retained equilibrium was evaluated at sixteen toroidal mode numbers, $n=5$–80 with $\Delta n=5$, using ideal-MHD and ideal-plus-diamagnetic models, producing 123,808 mode-resolved calculations. Using eight pedestal features, the GPR surrogate predicts two sixteen-mode growth-rate spectra with latent posterior uncertainty estimates. Across five independent test realizations, the maximum-growth-rate prediction achieved $R^2=0.978\pm0.013$ for ideal MHD and $R^2=0.966\pm0.009$ for ideal-plus-diamagnetic physics. The surrogate reproduces the spectral shape and dominant unstable mode. Calibration diagnostics indicate that posterior uncertainties are useful for relative acquisition but are underdispersed and should not be interpreted as calibrated prediction intervals. Prediction of all 32 outputs requires about 20 ms on one CPU core, compared with about 21 min using 128 CPU cores for the corresponding BOUT++ scan, giving a $6.3\times10^4$-fold wall-clock speedup and an $8.1\times10^6$-fold reduction in computational cost.

A collapsing cavity throws up a ligament, which fragments into a train of droplets whose sizes are commonly given a lower bound at a fixed multiple of the viscocapillary length $\ell_\mu=\mu^2/\rho\sigma$. We show that no such bound exists, for a reason more general than the fate of one parameter: an impulsive jet possesses no length of its own. The similarity solutions that govern each pinch contain none, the cascade of stretching and iterated satellites introduces none, and $\ell_\mu$ belongs to the singularity rather than to the droplets. A size distribution built on such a process can inherit a scale from one place only, the cavity that launched the jet. Computations of bubble bursting, confined and unconfined, bear this out. The floor they display is the one the mesh imposes, not a property of the fluid, and wherever the mesh looks below $\ell_\mu$ it finds droplets. The census of unique emitted droplets is exponential, with a single scale set by the cavity radius, indifferent both to how vigorous the event is and to how much liquid surrounds it. An exponential is the least structured census compatible with a prescribed mean, so the fragmentation keeps the size of the cavity and no other memory of its parent. Sustained stretching does keep such a memory, imprinting the corrugation of the parent thread and yielding the peaked, gamma-like distributions reported for ligament-mediated fragmentation. The manner of loading, and not the fluid alone, selects the shape of a spray.

Convective turbulence in planets and stars is often driven by internal heating. This forcing mechanism has also been proposed as a means of accessing the diffusion-free scalings of the geostrophic turbulence (GT) regime at modest forcing. We test this with the asymptotically reduced quasi-geostrophic model, which is formally valid in the limit of vanishing Ekman number, $Ek \rightarrow 0$, and retains the Prandtl number as an independent parameter. We find no such shortcut: the Nusselt and Reynolds numbers are no closer to their diffusion-free predictions than in the boundary-heated case, and at $Pr = 7$ they are further from them; the predicted $Pr^{-1/2}$ collapse fails; and the prefactor depends on the form of the heating. Our no-slip/stress-free cases track the radiatively driven simulations of Hadjerci et al. (2024) case by case, yet a $25\%$ variation in $Nu$ persists between sweeps that share the same reduced Rayleigh and Prandtl numbers but differ in Ekman number. Those data span only the narrow range of forcing over which the compensated Nusselt number is stationary; across the wider range accessible to the reduced model it rises to a maximum and then falls. We argue that bulk transport scalings are incomplete diagnostics of GT, whereas the saturation of the interior mean temperature gradient and of the vertical velocity kurtosis remain reliable indicators.

Urban daily mobility determines how far we travel and whom we encounter through the city. As local-living and polycentric planning gain popularity, a key question is whether more localised mobility can preserve the socioeconomic mixing that cities enable. Yet systematic evidence on how different scales of movement contribute to experienced social exposure remains limited. Here, we use anonymised GPS traces from ~20 million devices across 226 functional urban areas in France, Germany and the UK to decompose everyday mobility into three scales - proximal, medial and distal - and link these to the socioeconomic composition of destinations. Proximal trips remain similar in length across neighbourhoods, whereas medial and distal trips lengthen toward the periphery. Distal trips account for 33% of travel but facilitate 42% of inter-group encounters; reallocating them to local movement reduces experienced mixing by up to one third. These findings reveal a trade-off between shorter travel and integrated urban living.

We investigate the fluctuating nonlinear dynamics of multiple modes of a nanomechanical doubly clamped beam resonator. Each mode is driven by Gaussian force noise centered around the resonance; the mode response is monitored while the force magnitude is increased, inducing a transition from harmonic to the nonlinear Duffing regime. To characterize the dynamics, we introduce an effective temperature based on the mode fluctuation amplitude. As the nonlinearity becomes prominent at large amplitudes, we observe a reduction in the effective thermal energy and a crossover in response statistics from Gaussian to platykurtic, consistent with the dynamics expected in a Duffing potential.

We derive necessary conditions for instantaneous dynamo action for rotating convection. A magnetohydrodynamic model is considered in two settings: the rapidly rotating plane layer where inertia and viscosity are absent, and at an arbitrary rotation rate where viscosity is finite. In contrast to kinematic dynamo bounds, the evolution of the magnetic field is coupled via an inertialess force balance. The buoyancy-driven part of the flow $\mathbf{u}^{\mathrm{A}}$ in the event of dynamo action must in fact satisfy, for $3\leq p \leq \infty$ $$ Rm\, A_p\| \mathbf{u}^{\mathrm{A}}\|_{L^p} \geq 1 $$ where $A_p$ is an explicit constant, and $Rm$ is the magnetic Reynolds number. In the inviscid model, $\mathbf{u}^{\mathrm{A}}$ depends only on the horizontal gradients of the vertical primitive of temperature. A refinement via the poloidal-toroidal decomposition allows us to replace $L^p$ in our constraint with an anisotropic norm for $L^{\infty}_z \dot{H}^1_{x,y}$. For the viscous model, we also derive necessary conditions for the growth of magnetic enstrophy and a combined thermo-magnetic energy. One branch of our constraints implies that the scaling $Ra_\nu \gtrsim Ek^{-3/2}$ is necessary for dynamo action, where $Ra_\nu$ is the classical Rayleigh number and $Ek$ is the Ekman number.

Exponentially decaying long-range hoppings are ubiquitous in realistic tight-binding models and are often truncated to obtain a finite-range description. We show that this approximation can fail dramatically in non-Hermitian systems under open boundary conditions: an infinitesimal long-range hopping can nonperturbatively reconstruct the spectrum and eigenstates of a short-range non-Hermitian system. The mechanism is controlled by a competition between the decay length of infinitesimal long-range hoppings and the localization length of non-Hermitian skin modes, leading to a sharp transition as the decay rate is tuned. In one dimension, we show that a squeezed generalized Brillouin zone (GBZ) replaces the original GBZ of the short-ranged Hamiltonian, yielding the reconstructed open-boundary spectrum. In two or higher dimensions, we formulate a squeezed amoeba formulation describing the reconstructed spectral density. We further show that long-range hoppings can qualitatively reshape Green's function, which can be readily detected in experiments.

Experiments on superconducting cavities have found that under large RF fields the quality factor can improve with increasing field amplitude, a so-called “anti-Q slope.” We numerically solve the Bogoliubov-de Gennes equations at a superconducting surface in a parallel magnetic field, finding at large fields there are surface quasiparticle states with energies below the bulk superconducting gap that emerge and disappear as the field cycles. Modifying the standard two-fluid model, we introduce a “three”-fluid model where we partition the normal fluid to consider continuum and surface quasiparticle states separately. We compute dissipation in a semi-classical theory of conductivity, where we provide physical estimates of elastic scattering times of Bogoliubov quasiparticles with point-like impurities having potential strengths informed from complementary ab initio calculations of impurities in bulk niobium. We show, in this simple yet effective framework, how the relative scattering rates of surface and continuum quasiparticle states can play a role in producing an anti-Q slope while demonstrating how this model naturally includes a mechanism for turning the anti-Q slope on and off.

A protein's function follows from the structure it adopts, and in some cases which structure that is depends on the pathway taken. In programmable matter the target is fixed before assembly, and whatever else forms is treated as error. Here we show that pathways themselves form a design space. Using reinforcement learning, we fold model DNA-coated droplet chains into rigid two-dimensional geometries, uncovering two classes of pathways: downhill, in which bonds are only added, and detour, in which bonds are broken and remade before the target is reached: for some the only route that exists. Coarse-graining pathways by interactions gives experimentally realizable protocols. Some produce one geometry, others several: structures sharing a detour route can be cycled between, while those that coexist assemble into superstructures inaccessible to a uniform product. Function emerges from the pathways rather than being designed. Designing the process instead of the components could give colloidal materials that reconfigure on demand.

Perfusion of soft materials such as biological tissue or hydrogels is essential for the functioning of organ and laboratory systems such as chromatographic columns and bioreactors. Inspired by these applications, we model fluid-driven compaction in a long, thin cylindrical porous medium bounded by an impermeable elastic membrane and study how flow regimes relate to elastic parameters. Using a Lagrangian formulation of Darcy flow coupled to small-strain linear elasticity with porosity dependent permeability and elastic moduli, we perform an asymptotic reduction in the small aspect ratio limit and obtain a leading-order nonlinear diffusion equation for the porosity, which we solve numerically. Whereas rigid boundaries produce a compaction plateau, compliant walls exhibit, at most, an intermediate plateau beyond which the flow increases once the imposed pressure becomes comparable to the product of membrane stiffness and initial porosity. When the membrane is less stiff than the porous medium, flow rate can exceed that expected for a rigid medium. A parameter space map distinguishes regimes where plateau and breakthrough occur, where the steady flow rate is below (sub-Darcy) or above (super-Darcy) the undeformable-medium prediction, and delineates the small-strain domain in which the theory applies. An asymptotic solution for negligible gravity captures the departure from the plateau and yields compact expressions for effective permeability and flow rate.