Papers

Numerical modeling of aortic valve dynamics is essential for understanding the complex fluid-structure interaction (FSI) governing valve biomechanics in health and disease. Immersed methods provide a flexible computational framework for simulating the large deformations of valve leaflets and associated blood flow without requiring body-fitted meshes. Among these approaches, the Resistive Immersed Surface (RIS) and Immersed Boundary (IB) methods are widely used. However, systematic comparative analysis of these methods for realistic aortic valve simulations has not been performed. In this work, we compare a prescribed-kinematics RIS workflow implemented in SimVascular's svMultiPhysics solver with a fully coupled IB workflow using IBAMR for trileaflet and bicuspid aortic valve configurations. The RIS method represents the valve as a surface with prescribed kinematics embedded in the fluid domain and introduces a penalty force that drives the surrounding fluid velocity toward the prescribed leaflet velocity. This formulation reduces modeling complexity and provides useful hemodynamic predictions when representative leaflet kinematics are available. In contrast, the IB method models the leaflets as elastic structures fully immersed in the fluid domain and resolves leaflet deformation through fully coupled two-way FSI. The study focuses on the extent to which RIS reproduces bulk hemodynamic features and transvalvular pressure gradients. Results show that the RIS method captures the large-scale flow structures and predicts the mean transvalvular pressure gradient with a relative error within 15% of the fully coupled IB simulation, improving to within 5% when inlet boundary conditions are matched, while reducing computational cost by approximately 60%.

This article describes a dataset of total (spectrally-integrated rather than wavelength-dependent) emissivity values pertaining to gaseous media containing carbon dioxide (CO2) and/or water vapor (H2O) with the sum of their partial pressures being near the atmospheric level. These conditions may be particularly relevant to flue gases resulting from oxy-fuel combustion. The emissivities here are computed using the EM2C implementations of the statistical narrow band (SNB), and they are made conveniently available as 10 separate plain text files having a unified layout. Each data file in the dataset corresponds to a specific chemical composition (from pure CO2 to pure H2O), with eight intermediate H2O:CO2 molar ratios being 1:20, 1:8, 1:4, 1:2, 1:1, 2:1, 5:1, and 20:1. In addition, pure CO2 corresponds to the extreme lower-bound for the H2O:CO2 molar (as 0:1), and pure H2O corresponds to the extreme upper-bound for the H2O:CO2 molar ratio (1:0 or infinity). For each chemical composition, the total emissivity values are provided for different pressure-pathlengths and different absolute gas temperatures as two independent variables. The pressure-pathlength range spans about three orders of magnitude, from 0.01 atm.m to 50 atm.m, with 90 nonuniformly-distributed pressure-pathlength values. The absolute gas temperature spans a wide range from 300 K (room temperature) to 2900 K (high-temperature flames) with a uniform step of 25 K separating 105 temperature values. For each data file, there are 90 x 105 (9450) emissivity values; and the entire dataset contains 94500 emissivity values.

Joint full waveform inversion (FWI) of distributed acoustic sensing (DAS) and ocean-bottom node (OBN) data typically requires converting measured strain to particle velocity, introducing numerical noise and spectral distortion. To eliminate this, we present an elastic multi-parameter FWI framework using a velocity-stress-strain (VSS) formulation that directly models pressure, particle velocity, and gauge-length-averaged DAS strain from a single forward simulation. Data residuals are injected additively into a single backward simulation, making computational cost independent of the active sensor subsets. We benchmark individual and combined datasets on cross-talk and elastic Marmousi models. Our results show that joint inversion recovers elastic parameters more accurately than single deployments when the sensors offer complementary information. Specifically, pairing two-component geophones with a deviated borehole DAS cable yields the most accurate parameter recovery and mitigates inter-parameter cross-talk by providing a distinct physical observable and complementary depth aperture. We release our implementation as xFWI, an open-source, Devito-based Python package for scalable, multi-deployment inversions.

We show that exchange symmetry in collisions of identical particles enables symmetry-protected coherent control of the total scattering cross section. For identical fermions, antisymmetrization enforces a common control phase among contributing channels within a given parity sector, yielding maximal control visibility. For identical bosons, phase-locking persists but with reduced visibility due to additional exchange (satellite) contributions. Collisions of distinguishable particles lack this symmetry-imposed phase-locking, leading to lower controllability and visibility. We elucidate these principles through coupled-channel quantum-scattering calculations for lithium-lithium collisions, comparing the $^{6}\mathrm{Li}{-}^{6}\mathrm{Li}$ (identical fermions), $^{7}\mathrm{Li}{-}^{7}\mathrm{Li}$ (identical bosons), and $^{6}\mathrm{Li}{-}^{7}\mathrm{Li}$ (distinguishable) systems. Furthermore, in the identical-particle cases, symmetry-enforced phase-locking enables full control over the parity of the final state even beyond the ultracold regime. This mechanism is broadly applicable to identical-particle collisions, including homonuclear molecules for which established approaches–DC electric fields or microwave shielding–are ineffective or unavailable.

Objective. Gold-standard depth-of-interaction (DOI) calibration using collimated gamma-ray irradiation is time-consuming and impractical for system-level calibration of detector arrays. This work investigates an efficient DOI and energy calibration method for detector panels using uncollimated irradiation, with gamma rays incident nearly parallel to the crystal depth direction. Approach. The 511-keV photopeak location in a dual-ended readout PET detector block was evaluated as a function of crystal depth using collimated and uncollimated $^{22}$Na irradiation. A $4\times4$ dual-ended readout PET detector panel was then assembled. Three detector blocks were calibrated using the gold-standard method, and two uncollimated-irradiation DOI calibration approaches–a physics-informed model and a multilayer perceptron (MLP)–were compared against it. Finally, the full panel was calibrated for DOI and energy using the MLP-based approach. Main Results. The median relative RMSE between second-order polynomial fits from collimated and uncollimated irradiation was 1%, showing that uncollimated irradiation can provide reliable estimates when accurate DOI calibration parameters are available. Compared with gold-standard DOI calibration, the physics-informed and MLP-based approaches achieved RMSEs of 0.38-0.58 mm and 0.36-0.61 mm, respectively. The MLP-based approach provided better DOI resolution estimates and was therefore used for full-panel calibration. After saturation correction, the panel achieved a mean energy resolution of 15.6% and a DOI resolution of 2.0 mm. Significance. The proposed MLP-based calibration requires only a single uniform 511-keV irradiation, making it simple to implement and suitable for in situ calibration of DOI-capable PET detector arrays.

High-fidelity neutronic analyses of advanced reactors require deterministic transport solvers capable of handling complex unstructured geometries while maintaining computational efficiency. This work presents the development and verification of three GPU-accelerated deterministic solvers implemented within a unified framework, Neutronics using Deterministic Finite Element Algorithm (NuDEAL): the planar Method of Characteristics coupled with the Hybrid Finite Element Method (MOC/HFEM), the Discontinuous Galerkin Method of Characteristics (DGMOC), and the Discontinuous Finite Element discrete ordinate method (DFEM-SN). These solvers provide complementary capabilities for consistently solving the multigroup transport equation and can be selectively employed to balance accuracy, computational cost, and memory requirements for a given problem. All methods emphasize efficient GPU execution by leveraging memory alignment, compressed-flux storage, and sequential azimuthal sweeps. The solvers are validated on the C5G7 benchmark and applied to advanced reactor problems, including the ABTR, Empire microreactor, and MSRE. DFEM-SN achieved the highest accuracy, with eigenvalue errors below 50 pcm, while MOC/HFEM and DGMOC provided superior efficiency, with single-GPU runtimes comparable to those of large CPU clusters. The results demonstrate that deterministic GPU solvers on unstructured meshes can deliver both accuracy and scalability, enabling practical whole-core simulations for heterogeneous advanced reactors. The unified NuDEAL framework establishes a foundation for future extensions toward transient and multiphysics analyses on large-scale GPU architectures.

Accurately resolving interfacial gradients is critical for simulating two-phase flows, particularly those involving phase transitions or active matter. The traditional diffuse-interface immersed boundary methods (IBMs) are highly efficient for such problems, but they typically suffer from a reduction to first-order accuracy near the phase-changing boundaries. We clarify that the main reason is the local derivative discontinuities. Here, we propose a smooth extension strategy to restore formal second-order spatial accuracy. By extrapolating the scalar field across the interface, the method structurally ensures derivative continuity. To preserve the divergence-free condition in incompressible fluid solvers, this smooth extension is applied exclusively to the scalar transport equations. The velocity field retains the standard diffuse-interface treatment. The proposed framework is systematically validated against classical phase-change benchmarks, specifically one-dimensional evaporation and boiling problems. Additionally, the method is applied to the spontaneous autophoretic motion of isotropic particles. The numerical results confirm the capability of our method in resolving the complex multi-physics boundary couplings.

Quantum algorithms for quantum chemistry and other many-body fermionic systems work by expressing the Hamiltonian in a basis of qubits and fragmenting the Hamiltonian into a sum of products of Pauli operators whose exponentials are easily encoded on a quantum device. Applying the product of exponentials, known as Trotterization, leads to an error associated with the non-commutativity of operators. This error can lead to breaking the symmetries of the Hamiltonian because the fragments are not symmetry conserving in general. Nonetheless, many algorithms for time evolution rely on Trotterization, including time evolution and quantum phase estimation. We show that we can express the Hamiltonian in terms of Hermitian excitation operators which map to sums of commuting Pauli strings for any encoding and conserve symmetries corresponding to Abelian groups of symmetry operators. Symmetries corresponding to non-Abelian groups, on the other hand, are not fully conserved by Trotterized Hermitian excitation operators, so we developed “operator kirigami” to cut the sum of non-commuting operators by orthogonal projection and to fold terms together using unitary rotations. We tested pools of operators for small molecules and basis sets, and found that electron number and spin symmetry conserving pools led to greater errors that decreased for larger molecules and were negated with second-order Trotterization. Our work shows the potential for testing quantum computing algorithms on classical computers by adapting tools used in electronic structure theory with conserved symmetries.

Physics-informed neural networks (PINNs) provide a mesh-free framework for solving partial differential equations, yet their performance in nonlinear problems is often limited by slow convergence, gradient imbalance, and insufficient resolution to capture localized intermittent structures such as shock waves[1]. These issues arise primarily from the use of fixed weights of loss and uniform collocation point distributions, which cannot adapt to the evolving complexity of the solution field during training. To address these challenges, Gradient-Guided Gaussian Adaptive Sampling Physics-Informed Neural Networks (3GAS-PINNs) is proposed in this paper, which combines uniform probability distribution and Gaussian-smoothed probability distribution derived from the spatial gradients of solution, to maintain global constraint satisfaction as well as concentrating collocation points in regions of high gradient. Thus, intermittency structures like shock wave and solitons can be accurately captured. The method is evaluated on three benchmark nonlinear problems, including one-dimensional forced Burgers equation, Korteweg-de Vries (KdV) equation and nonlinear Schrodinger equation, all of which exhibit steep gradients or strong nonlinearity. In comparison with baseline PINNs, 3GAS-PINNs can effectively promote the physical consistency in intermittent regions. The accuracy of the numerical simulation can be improved by a factor of up to 14.

In association football, collective player motion is organized around the ball. We ask whether this many-agent motion can be described by an effective field analogous to gravitational attraction, emphasizing that the measured quantity is a radial drift velocity, as in overdamped dynamics, rather than a Newtonian acceleration. Using public tracking data from ten matches, we characterize this radial drift velocity through the distance-dependent mean D(r) and the ball-position-dependent field K(x,y). After excluding restarts and other constrained phases of play, both goalkeepers, the player nearest to the ball, and small player-ball separations, a persistent inward drift velocity remains, with a global mean of about 0.9 m/s. The distance dependence is not described by an inverse power of r: D(r) decreases at short range and then forms a broad plateau that persists to the largest measured separations. The mean K(x,y) is positive over most of the pitch, with a central depression about 15 percent below the non-goalmouth average and stronger suppression near the goalmouths, where the mean drift velocity becomes slightly negative, corresponding to weak effective repulsion. The role-resolved D(r) shows a pronounced defender minimum near 12 m and a pronounced attacker maximum near 60 m, whereas only defenders produce effective repulsion near the goals in K(x,y). These results show that football tracking data can reveal simple effective laws of active many-body motion while retaining clear signatures of player role and pitch geometry.

Physics-informed neural networks (PINNs) provide a mesh-free framework for solving partial differential equations, but their training is often affected by loss imbalance, optimization stiffness, and difficulty in capturing localized or multi-mode solution structures. Hard-soft PINNs (HSPINN) alleviate part of this difficulty by embedding Dirichlet or periodic constraints directly into the trial space, but the resulting fixed admissible representation can still be poorly conditioned for sharp or heterogeneous residual fields. This paper proposes a reliability-aware hard-soft PINN (RA-HSPINN) that preserves exact embedded constraints while introducing a bounded learnable reliability field to modulate the interior representation. The method combines this reliability-aware ansatz with inverse-EMA global loss balancing and lightweight regularization, while retaining the standard mean-square residual form. The reliability field is a numerical modulation variable, not a physical parameter or calibrated probability. RA-HSPINN is evaluated on nonlinear Burgers equations, periodic convection, a mixed-boundary Poisson problem, and a mixed first-order Poisson system. Compared with HSPINN, it reduces the relative error by $98.65%$ for sharp-gradient Burgers, $72.42%$ for Burgers data with noisy and incompatible initial conditions, $61.18%$ for smooth periodic convection, $60.02%$ for localized periodic convection, $29.36%$ for mixed-boundary Poisson, and $82.17%$ for a multi-mode mixed first-order Poisson system. The results show that reliability-aware modulation is most beneficial when hard-soft trial spaces are admissible but difficult to optimize, especially in localized, unreliable-data, and multi-mode PDE regimes.

Motivated by dry dambreak flows, a perturbative framework for solving the 1D shallow water equations (SWE) over parabolic bathymetry, together with the inviscid Burgers' equation, is developed. Expressed in Riemann variables, the solutions are expanded as analytic power series in the bathymetric curvature $\omega^2$ and summed in closed form in terms of trigonometric functions. The solutions, termed generalised rarefaction waves (GRWs), exhibit finite-time singularities and are shown to describe the asymptotic behaviour of the fluid near dry (vacuum) points. The GRWs bear direct relevance to wave run-up at a shore. A repulsive parabolic hill bathymetry is also considered, yielding solutions in terms of hyperbolic functions. The connection to the nonlinear Schrödinger/Gross-Pitaevskii equation that models Bose-Einstein condensates confined in a harmonic potential is discussed. The analytical results agree with direct numerical simulations of the Burgers' equation and of the SWE in different scenarios.

In this study, we reconstruct the dark energy (DE) as a Dirac-Born-Infeld (DBI) scalar field from the Hubble dataset (32 CC + 26 BAO) and the DESI dataset using the Gaussian process (GP). As the GP is a non-parametric and model-independent way to reconstruct a function and its derivative using the data, our reconstruction of the DE equation of state, the DE density parameter, and the potential does not assume any particular model of cosmology. Using Monte Carlo realizations of the GP-reconstructed expansion history, we derive a posterior estimate of the Hubble constant, obtaining $H_0 = 69.53 \pm 2.68$ km s$^{-1}$ Mpc$^{-1}$. This method offers a fully model-independent estimate of $H_0$, relying only on data and GP priors, and provides an unbiased intermediate value useful for reassessing the Planck-SH0ES tension. Using the reconstructed profiles of the scalar potential as a function of the field $\phi$, along with their associated uncertainties, we perform a chi-square curve fitting procedure to assess the viability of four different scalar field potentials, such as Exponential, Power-law, Free Field (quadratic), and Higgs-like potential. This allows us to identify which potential best fits the reconstructed data. We also employ MCMC analysis to place quantitative constraints on the model parameters associated with each potential. Furthermore, we do a $\chi^2$ analysis for all four potentials and comment on the goodness of the fit for each of them. Finally, we discuss possible generalizations of our model-independent framework and outline the phenomenological implications of our findings.

In homogeneous magnetohydrodynamic (MHD) turbulence without a background magnetic field driven by mechanical forces, an exact decomposition of the energy fluxes (D. Capocci et al., Journal of Plasma Physics, 91(1), E11 (2025)) has shown that current-sheet thinning is the dominant physical mechanism responsible for transferring energy from large to small scales. In contrast, mechanisms that are characteristic of hydrodynamic turbulence, such as vortex stretching and strain self-amplification, are strongly suppressed. Here, we extend this analysis to MHD turbulence in the presence of weak and strong imposed magnetic field, as previously driven by mechanical forces, and confirm that current-sheet thinning remains the leading process driving the energy cascade toward smaller scales in these more realistic configurations, and find enhanced scale invariance in the subfluxes. In addition to that, a decomposition of the contributions from the fluctuating and the background magnetic field to the conversion between kinetic and magnetic energies shows that the background-field-dependent contribution results in a nonlinear dynamo, that is an effective kinetic-to-magnetic conversion at large and intermediate scales. However, at small scales, it has the opposite effect, resulting in a net conversion of magnetic to kinetic energy.

At first glance, combining Newton's laws of motion with his law of gravitation seems straightforward. Students learn to distinguish inertial from gravitational mass and that their empirical equality is a remarkable fact about nature that will later serve as the conceptual gateway to general relativity. However, a closer look reveals a further and often neglected distinction within Newtonian gravity: that between active and passive gravitational mass. A common textbook argument maintains that these must be equal, for otherwise Newton's third law would be violated. We review and critically re-examine this familiar reasoning and show that the supposed theoretical proof is not compelling. Our analysis highlights subtle structural assumptions within Newtonian mechanics and offers physics teachers and researchers a fresh opportunity to explore foundational questions with potentially interesting applications in observational astronomy. In addition, an extended appendix, which is not part of the published AJP paper, offers some mathematical background material on the Galilean group and its action as group of dynamical symmetries for the type of dynamical equations considered here.

We investigate fluid mixing induced by microswimmers using mutual information as a global, information-theoretic measure of mixing efficiency. For a two-dimensional squirmer model in a confined domain, we compute numerically the swimmer-generated flows and solve the advection-diffusion equation for the transport of tracer particles in the fluid. We show that the spatial distribution of swimmers strongly affects mixing, which is suppressed by swimmer aggregation and enhanced by positional and orientational disorder. At fixed energy dissipation, mixing efficiency depends non-monotonically on the squirmer parameter, with an optimal finite value arising from the balance between swimmer translation and dipolar flow generation. When hydrodynamic interactions are included, pushers outperform pullers. The mutual information as a function of time decays in three stages: an initial diffusion-dominated stage, an intermediate advection enhanced regime, and a final relaxation stage controlled by system size. Our results demonstrate that mutual information, previously validated as a measure of mixing efficiency only in simplified model systems, can equally be used in complex flows. Its application reveals that mixing by microswimmers is subject to a trade-off between the generation of strong shear flows and achieving optimal dispersion across the fluid domain.

The inverse scattering transform (IST) provides the standard theoretical framework for deriving soliton dynamics. Traditionally, such derivations have been of an analytical, rather than data-driven, nature. In this paper, we combine the conceptual framework of the IST with weak-form system identification methods to discover effective soliton dynamics directly from observed scattering data, without assuming prior knowledge of the scattering equations. Our method avoids parameterizing solitary waves via ad hoc curve-fitting by working in the scattering domain, yielding interpretable low-dimensional models that remain valid in perturbed and near-integrable regimes. We demonstrate the performance of the proposed approach on synthetic and experimental data governed by shallow-water equations of Korteweg–de Vries-type and recover models that are consistent with canonical IST theory.

We study hydrodynamic spin coupling in a two-rotor corral using DNS of 2D incompressible viscous fluid flow. An active rotor is driven at angular velocity W, and a nearby torque-free passive rotor selects an angular velocity w through hydrodynamic torque balance. The signed gear ratio Gamma=w/W distinguishes corotation from counterrotation, with Reynolds number Re=|\Omega|r^2/\nu. Motivated by a recent quasi-two-dimensional experiment, we use a DLM/FD method to compute planar phase diagrams of $\Gamma(G,Re)$ at corral sizes C=3, 4.5, and 6. The planar model recovers the benchmark gap route at Re=20: an intermediate counterrotation band, a wide-gap transition to corotation, gear-ratio magnitudes of order 10^{-2}, and the observed sequence of vortex attachment, detachment, and merger. It also produces a reentrant-like gap structure with a small-gap corotation region whose relation to the experimental close-range geometric state remains unresolved. The main discrepancy is the high-Re boundary. At the experimental mid-gap transect G about 0.3, the planar gear ratio approaches zero from the counterrotating side but does not cross through Re=400; at the narrower gap G=0.22, by contrast, the planar terminal spin reverses near Re=44. Wall-traction diagnostics show that this crossing is not the experimental shear-competition mechanism: the gap-facing counterrotating arc narrows but does not collapse or deflect as in the experiment, and the reversal at G=0.22 occurs by redistribution of the integrated planar torque. The strictly planar model therefore captures the broad gap-route architecture and the existence of a Reynolds-driven spin boundary, but displaces that boundary in gap and alters its surface-stress mechanism. The remaining mismatch points to finite-depth secondary motion, end-wall stresses, and apparatus geometry as plausible contributors to the experimental shear balance.

Whether topology directly shapes chemical dynamics remains an open question in theoretical chemistry. The issue arises because degeneracies of adiabatic electronic states can generate nontrivial topological structure, and such degeneracies are common in polyatomic molecules. Existing work has largely emphasized static characterizations and dynamical studies of low-energy, highly symmetric models. Here we develop a symmetry-based analysis of nonadiabatic dynamics in two-state conical-intersection models that is predictive without invoking topological invariants. We show that the nodal-line structures associated with dynamics near a conical intersection are robust in highly symmetric settings, but should not in general be expected to persist once the relevant symmetry is broken.

In the early-mid 1990s, scientists emigrating from the former Soviet Union to the United States – especially physicists, engineers, chemists, and biologists – frequently secured prestigious and visible positions, including professorships, named chairs, and laboratory leadership; comparable scientists arriving after about 2000 built more modest, less visible, and often non-academic careers. Against the common view that this reflects the people – the elite having left first – this article sets aside the thin apex of Nobel- and Fields-level émigrés and examines the larger cohort of capable but non-stellar scientists, showing that similar scientists fared differently by year of arrival. The explanation therefore lies in the structure of the receiving market, not primarily in individual ability. Reading premium appointments backward from later Nobel-level recognition risks survivorship bias: celebrated successes obscure the broader demand for Soviet scientific capital. I weigh four conditions that favoured the 1990s cohort and had largely closed by the mid-2000s: technology transfer and the export of a finite, distinctive stock of Soviet expertise that commanded a career premium; the favourable immigration regime created by the Soviet Scientists Immigration Act of 1992; the surge of U.S.-trained Chinese and Indian competitors; and the securitizing aftermath of 11~September 2001. All four mattered, but technology transfer and knowledge export were primary: their premium opened the window, and their depletion – as exported knowledge was published and absorbed into global science – removed the demand on which the other factors depended. A further cross-cutting mechanism, the cultural “ghettoization” of émigrés into co-national laboratory enclaves, capped their visibility and independent advancement. The imbalance between émigré generations was structural, not personal.

Next-generation photonic architectures for AI, sensing, and quantum computing require thousands to millions of reprogrammable photonic devices on a chip[1]. The monolithic integration of Electronically-backed Photonic Integrated Circuits (EPICs) allows for very high density electrical interconnection and electronic drivers that can scale with photonics. Piezo-optomechanical photonic integrated circuits (POMPICs) offer low power consumption, high speed modulation, cryogenic compatibility and broadband optical transparency from ultraviolet to infrared wavelengths[2,3], but have not been demonstrated with monolithically integrated CMOS electronics. Here, we show a fully monolithic, all-CMOS fabricated platform for POMPICs co-fabricated with commercial control electronics. 200 millimeter photonic wafers are constructed directly on completed CMOS driver wafers by back-end-of-line processing, connecting integrated piezoelectric actuators under broadband silicon nitride waveguides to a high-density digital backplane comprising >2 million electrical connections per die with 6.4x6.4 micron electrode pitch. We introduce segmented POMPIC components as Photonic Digital-to-Analog converters (PDACs) that convert low-voltage digital electronic signals to multi-bit analog optical phase and amplitude modulation, and we demonstrate parallel control of optical phase shifters, Mach-Zehnder interferometers, optical routing trees, and tunable ring resonators using a standard HDMI interface to program CMOS electronics. We test multiple reticles and perform electronic and photonic characterization across the entire wafer to establish uniformity and yield, demonstrating wafer-scale integration of POMPICs on an electronic backplane and enabling dense, scalable electronic control of piezo-optomechanical circuits.

We investigate the low-Reynolds-number propulsion of a slender elastic filament with a dipolar magnetic head actuated by an oscillating field in a viscous fluid by studying its strokes and net forward motion. To capture these dynamics, we employ an elasto-hydrodynamic (EH) framework that couples Euler-Bernoulli beam mechanics with resistive force theory. Unlike prescribed-kinematics models, filament shapes here emerge self-consistently from the actuation and the force and torque boundary conditions (BCs). We demonstrate that viscous boundary contributions are crucial for quantitative agreement and show that the swimming dynamics are governed by the EH length and a magneto-viscous-elastic stroke amplitude introduced here. The swimming speed is non-monotonic with increasing ratio of the swimmer length to the EH length, and is shown to reach a maximum when the swimmer length is on the order of the EH length. We further discuss the analytical limit in which the tail BCs can be described as free, and the limitations that arise when viscous contributions to the BCs are ignored.

High Energy Physics experiments at flagship colliders produce and process some of the biggest datasets on Earth, with the current generation of flagship experiments at the Large Hadron Collider producing more than a tenth of the world's total internet traffic every second. Moreover the quantities of data produced have increased exponentially over the past decades and this trend shows no sign of slowing down. In parallel, the use of picosecond timing is becoming more common in HEP detectors, enabling qualitatively new approaches to real-time processing and selections. I review the planned introduction of precision timing information into the upcoming upgrades of the CMS, ATLAS, and LHCb experiments. I discuss the ways in which the combination of timing and networking technology may enable future detectors to be designed as triggereless from the ground up, and reflect on the physics benefits of such a paradigm shift for the field.

A specially shielded CdTe detector based hard X-ray (HXR) monitoring system equipped with a lead collimator has been developed and installed on the Aditya-U tokamak to investigate the dynamics of fast electrons (~20-200 keV) generated during sawtooth activity. The pre-existing HXR monitor in Aditya-U is exposed to the entire HXR bremsstrahlung emission from the plasma volume, peripheral limiters, and other structural components, which limits its ability to separately study the dynamics of lost and confined runaway electrons (REs). In contrast, the newly developed diagnostic has successfully measured the chord-averaged thin-target HXR bremsstrahlung emission encompassing the core plasma region, particularly within and around the sawtooth inversion radius. The measured HXR spectra are validated through forward modelling code that incorporates plasma parameters, confined RE characteristics, and the geometric configuration of the diagnostic system. The results confirm the capability of the developed HXR monitor to probe the fast-electron dynamics during internal plasma instabilities.

Photonic integrated circuits on the silicon-on-insulator (SOI) platform typically interface with free space via grating couplers, but scaling these to collimated beams with diameters beyond 100 $\mu$m requires a fundamentally different regime of extremely weak, spatially distributed coupling. While such large-area couplers have been demonstrated, their beam quality has remained largely uncharacterized, even though applications such as coupling into high-finesse resonators or trapping of cold atoms require both a large aperture and a near-Gaussian profile. This article presents an SOI meta-grating coupler that emits collimated, near-Gaussian beams of approximately 300 $\mu$m waist diameter. The design synthesizes the required emission profile from a spatially tailored coupling strength, realized by locally varying a sub-wavelength unit cell while independently setting the local emission angle. This approach achieves the very low coupling strengths required for large beams and yields a measured beam quality of $M^2 \leq 1.10$. The scheme extends directly to other target profiles, such as flat-top or higher-order modes, rendering meta-grating couplers a practical chip-to-free-space interface for mode-matching-sensitive applications.

We derive an extended wave action model for equatorial chorus waves, identifying a wave activity index (a version of the Benjamin-Feir index, BFI) which indicates non-Gaussian frequency spectra emerge when BFI$>$0.5. Global maps of this index indicate the night and dawn sectors ($0<{\rm MLT}<9)$ of the magnetosphere as the primary region for non-Gaussian wave statistics to emerge. Comparisons with events measured by the Van Allen probe A demonstrate good qualitative agreement whilst identifying key aspects for model refinement. A key strength of our model that our work highlights is its ability to account for the asymmetric frequency spectra characteristic of non-Gaussian chorus. This work ultimately establishes the first wave activity index that distinguishes Gaussian and non-Gaussian wave scenarios from first principles, providing the groundwork for a threshold-based quantification for use in space weather modelling.

In Hermitian topological systems, topological modes (TMs) are bound to interfaces or defects of a lattice. Recent discoveries show that non-Hermitian effects can reshape the wavefunctions of the TMs and even turn them into extended modes occupying the entire bulk lattice. In this letter, we experimentally demonstrate such an extended TM (ETM) in a one-dimensional (1D) non-Hermitian acoustic topological crystal. The acoustic crystal is formed by a serie of coupled acoustic resonant cavities, and the non-Hermiticity is introduced as the non-reciprocal coupling coefficient using active electroacoustic controllers (AECs). Our work highlights the potential universality of ETMs in different physical systems and resolves the technical challenges in the further study of ETMs in acoustic waves.

Given recent changes in federal climate policy, the United States is unlikely to meet its original 2030 Paris Agreement emission target of a 50-52% reduction from 2005 levels. However, rapid near-term abatement remains achievable through targeted multi-sector energy transitions. Extending the open-source energy system model, PyPSA-USA, to perform multi-sector analysis, we evaluate the primary drivers of USA energy costs and emissions though applying global sensitivity analysis. Our results suggest that fossil fuel price volatility is the dominant driver of marginal electricity and energy costs across most of the nation, however, uncoordinated state-level renewable mandates can induce localized cost spikes due to regional bottlenecks. We find that system climate impact (CO2e) is overwhelming sensitive to fugitive methane leakage rates and global warming potential assumptions. Addressing upstream methane leaks will play a crucial role in abating climate-related damages. Finally, demand-side electrification, specifically light-duty electric vehicles and service sector heating, can act as immediate levers for carbon abatement. The results of this work suggest that many of the Inflation Reduction Act's clean energy initiatives, that have since been repealed, are effective near-term solutions to reduce exposure to fossil fuel price and mitigate future financial penalties associated with the rising social cost of carbon.

Classical models of opinion dynamics represent individual opinions as scalar or vector values governed by the classical probability theory, either as deterministic quantities or random variables. This framework does not account for empirically observed phenomena such as cognitive ambivalence (where an individual simultaneously holds conflicting views) and order effects (where survey responses depend on the order in which questions are asked). We propose a quantum model of opinion dynamics in which each agent's cognitive state is represented by a density matrix that encodes both the expressed opinion and cognitive ambivalence. Survey questions become non-commuting self-adjoint operators, which provides a principled explanation for order effects. Our model also identifies quantities without classical counterparts, including quantum coherence and pairwise opinion covariances. Under a product state approximation, the quantum model reduces to the classical Friedkin–Johnsen opinion model. We test the framework on synthetic and real-world networks and observe that pairwise correlations follow network-dependent transient dynamics but converge to the same steady state regardless of the network, and that quantum coherence decays exponentially at a rate independent of the network.

Cyclic pore-pressure or normal stress variations arise both in relation to natural earthquakes and in engineered subsurface systems, yet their effect on fault stability remains poorly constrained at the grain scale. Here we numerically model, using a coupled Discrete Element–fluid dynamics model, the response of a sheared, fluid-saturated or dry, gouge-filled fault to effective normal stress oscillations over a wide frequency range (0.5-10000 Hz). The effective normal stress is oscillated either by cycling the pore-pressure or by directly cycling the normal stress, while keeping the stress state below the Mohr-Coulomb threshold measured in continuous loading. Despite this sub-critical loading, we observe failure across most frequencies, with a non-monotonic frequency dependence. A distinct non-failure window emerges at intermediate frequencies (30-200 Hz), bounded by failure at both lower and higher frequencies; the system exhibits four regimes from cyclic failure-and-arrest to continuous sliding. Pore-pressure and normal stress oscillations produce the same regime structure, confirming that they act as equivalent forcings via Terzaghi's principle, with fluid coupling adding only a delay due to dilatant hardening. Sub-critical failure arises from dilation-induced strength deterioration via two mechanisms: (i) low-frequency cycles allow sufficient time for shear-driven ratcheting dilation, while (ii) high-frequency cycles induce dynamic dilation (acoustic fluidization) via amplified seepage forces, stress gradients and inertial forces. The intermediate non-failure window represents the gap between these mechanisms. These results identify frequency as a controlling parameter for failure in granular materials, with implications for dynamic earthquake triggering and cyclic injection protocols.