Papers

Cavity Quantum Electrodynamics (Cavity-QED) offers a powerful framework for controlling light-matter interactions, yet first-principles approaches commonly assume ideal, lossless cavity fields. Here, we introduce a minimal extension to the Pauli-Fierz Hamiltonian that accounts for cavity dissipation through the experimentally accessible cavity quality factor Q. The electromagnetic field is described as a damped harmonic oscillator within the very weak damping approximation. In this framework, cavity losses only enter the light-matter interaction and dipole self-energy terms, without introducing additional photonic degrees of freedom. A time-dependent unitary transformation yields a real-valued Hermitian Hamiltonian that can be incorporated into existing ab-initio QED methods without additional computational cost or changes in scaling. We combined the approach to the QED-CCSD method and investigated the effects of cavity dissipation on the optical spectrum of a p-nitroaniline molecule under strong light-matter coupling. We found that, for realistic quality factors, cavity dissipation can reduce the Rabi splitting by more than 20% compared to the ideal-cavity limit, even though the system remains in the strong-coupling regime. Convergence towards the ideal-cavity limit then requires substantially higher quality factors than those needed to establish strong coupling. These results demonstrate that cavity dissipation can significantly affect polaritonic energetics and highlight the importance of incorporating experimentally realistic cavity losses into quantitative ab-initio Cavity-QED simulations to accurately guide experimental protocols.

We propose a distribution-free metric to rate individual skill in “player-versus-environment” settings, where participants face heterogeneous tasks without direct opponents. Such settings are common in digital platforms, games, education, finance, and the benchmark evaluation of AI agents. They combine high randomness, tasks of widely varying difficulty, and unknown heterogeneity across individuals. Our metric maps each task outcome to a bounded performance score with zero population mean and variance bounded by $1/3$, regardless of the outcome distribution, so that scores are directly comparable across tasks. Aggregating these scores over tasks yields an interpretable skill score for each individual (the “Solo” rating), and a reshuffling null model tests whether that score exceeds what chance alone would produce. We validate the approach on the progression of $2\times10^5$ players in the mobile games Candy Crush Saga and Bubble Witch 3 Saga. The metric identifies high- and low-skill players with high statistical confidence, their classification persists over hundreds of subsequent levels, and a windowed version of the score tracks changes in performance along progression.

We study the charge resolved structure of the pairwise mode entanglement produced by the Schwinger effect. Using the $U(1)$ electric charge of quantum electrodynamics, we decompose the entanglement of the reduced density matrices of momentum modes into individual charge sectors, applying the symmetry resolved entanglement entropy to the pure bipartition reductions and the charge imbalance resolved negativity to the mixed particle-antiparticle pair. For a constant electric field we obtain closed form results for every mode pairing, with all field dependence controlled by the single pair production probability $|\beta|^2=e^{-\pi\mu}$. For the particle, particle and antiparticle antiparticle pairs the entanglement entropy within each charge sector is field independent, so that the field acts only by redistributing statistical weight among the sectors; for the mixed particle antiparticle pair the populated sector additionally carries a field dependent entropy. The vacuum created pair is the richest case: its resolved negativity is distributed over three imbalance sectors and is transported from one sector to the next as the field increases, the sectors contributing equally only at the critical field. The entanglement across a fixed momentum bipartition, by contrast, is frozen at its initial value because pair creation conserves the sector charge. Symmetry resolution thus refines the total entanglement measures into a sector resolved picture, exposing where in charge space the entanglement of the Schwinger mechanism resides and how the electric field moves it, information beyond that contained in the total measures or in particle number distributions.

Vickers hardness characterizes resistance to indentation but does not distinguish the magnitude of local densification from the volume of glass affected. Here, low-coherence holotomography reconstructs three-dimensional refractive-index (RI) images of Vickers-indented fused silica, borosilicate, and soda-lime glass across applied loads and nominal dwell times. Analysis of RI changes using the Lorentz-Lorenz relation quantifies densification, while segmentation measures the spatial extent of the densified region. Cracks are observed within the same reconstructed RI images. Densified volume increased with load even when peak densification plateaued, and glasses with similar hardness showed different densification responses. Longer nominal dwell times were associated with larger volumes above fixed RI thresholds and comparatively small changes in peak densification. Joint analysis of densification and crack morphology thus characterizes subsurface indentation damage beyond hardness alone. Densification is quantified as an optical estimate under the Lorentz-Lorenz assumptions; residual stress, polarizability, and reconstruction can also affect RI.

A weakly nonlinear stability analysis is performed to investigate the influence of thermal buoyancy on phototactic bioconvection in a suspension of swimming microorganisms confined between horizontal boundaries and subjected to bottom heating or cooling. The objective is to examine how thermal forcing modifies the post-critical evolution of bioconvective instability through its influence on bifurcation behaviour and finite-amplitude convection. Linear stability theory is first employed to determine the critical conditions for the onset of convection. The corresponding direct and adjoint eigenvalue problems, together with the second-order resolvent system, are then solved numerically to derive the Stuart-Landau amplitude equation governing the nonlinear evolution of disturbances. A systematic analysis is carried out for varying thermal Rayleigh numbers. The results show that, as the thermal Rayleigh number approaches the classical Rayleigh-Benard critical value, the critical bioconvection Rayleigh number decreases to zero, indicating a gradual transition from phototaxis-dominated to thermally driven convection. The computed positive Landau coefficient predicts supercritical pitchfork and Hopf bifurcations for stationary and oscillatory instabilities, respectively. In parameter regimes with oscillatory onset, increasing the thermal Rayleigh number drives a transition from a supercritical Hopf bifurcation to a supercritical pitchfork bifurcation. The nonlinear dynamics are further characterized through amplitude evolution, phase portraits, potential functions, and finite-amplitude flow structures.

In physics instruction, equations are often treated primarily as tools for obtaining numerical results. Yet physicists also use them to describe dependencies, explain why those dependencies hold, predict what happens in new situations, test expressions against physical knowledge, and explore consequences that are not immediately apparent. Although such uses appear throughout the physics education literature, they have not been brought together in a systematic account, and the terminology used to describe them remains inconsistent. Drawing on research in mathematics education on symbol sense and the function concept, we distinguish six epistemic functions of physics equations: calculating, describing, explaining, predicting, evaluating, and exploring. We then characterize six strategies through which these functions can be realized: dimensional analysis, order of magnitude analysis, covariational analysis, special case analysis, limiting case analysis, and formal analogies, illustrating each with examples from introductory physics. To examine how these strategies are viewed within the discipline, we surveyed 31 physicists at one university faculty about five of the strategies. Respondents regarded the strategies as indispensable to physics, judged limiting case analysis to be more difficult for students than dimensional analysis, and reported that their own courses could devote more time to them. By distinguishing what physicists use equations for from the strategies by which they do so, the paper provides a vocabulary for sensemaking of physics equations and specifies what explicit instruction in these strategies involves.

Optical manipulation enables the control of microscopic particles using light and has become an active area of research in recent years. One particularly interesting aspect is the possibility of generating optical pulling forces with hyperbolic metamaterials (HMMs), which leads to unusual light-matter interactions. In this work, the pulling response of plasmonic, chiral, and dielectric nanorods is studied on an HMM substrate. The system is illuminated by a time-harmonic, plane-polarized laser beam propagating along the +Z to -Z direction. The analysis is carried out under two different configurations. In the first case, plasmonic, chiral, and dielectric nanorods are arranged in a heterogeneous cluster over the HMMs and analyzed for the optical force response under three environmental conditions: fully immersed in water, partially immersed (half air-half water), and fully in air. Full-wave simulations reveal that in all three environments, all the objects (plasmonic, chiral, dielectric) nanorods consistently experience an optical pulling force at the 1E(-20)N scales for clustered configuration. In the second case, each nanorod type was independently placed on the same HMMs substrate within a full-air medium and investigated their force behaviors, while the plasmonic experience 100 times more than others for the single setup, which is 1E(-19)N ranges. Like the case of the cluster, three nanorods alone demonstrate a stable optical pulling force, also getting the pulling force with the variation of length, radius, and distance between them, which makes this optical setup more practical. Plasmonic behavior changes a lot with the arrangement of the nanostructures. On the other hand, dielectric nanorods are affected more by their length than their position. In our results, optical pulling appears for all the nanorods even with a simple HMM structure...

This paper constitutes Part III of a series investigating the excitation of non-modal perturbations in hypersonic blunt-nose boundary layers. Based on the numerical shock-fitting harmonic linearised Navier-Stokes (SF-HLNS) approach presented in Part I (Zhao & Dong, J. Fluid Mech. 2025, vol. 1013: A44) and the asymptotic analysis developed in Part II (Dong et al., J. Fluid Mech., under review), the present work focuses specifically on the influence of nose bluntness (Reynolds number R) and wall temperature T_w on receptivity efficiency through seven detailed case studies. Analysed via the three-step asymptotic model, the downstream perturbation amplitude is governed by two distinct factors: the stagnation vorticity perturbation, determined through shock-perturbation interaction and the slow-down convection mechanism, and the parabolic evolution of non-modal perturbations driven by the lift-up mechanism. Increasing R strongly amplifies the stagnation vorticity response, with only mild enhancement of the lift-up effect. In contrast, decreasing T_w strengthens the stagnation vorticity perturbation, but slightly weakens the lift-up effect; the net result is an overall intensification of the downstream perturbation amplitude with decreasing T_w. Furthermore, explicit scaling laws for receptivity efficiency with respect to R and T_w are established. This study completes the theoretical foundation for non-modal receptivity in hypersonic blunt boundary layers, and serves as a key step toward a physics-based transition prediction framework.

A bubble bursting inside a drop resting on a substrate can eject jet droplets where a bubble at a bath would not. We compute how much liquid it ejects and how the ejected droplets are distributed in size. The volume ejected by the jet grows by more than an order of magnitude as the Bond number decreases from $0.5$ to $0.1$ and then saturates, and the larger the Bond number, the smaller the viscosity that suppresses ejection. At zero Bond number the substrate alone extends the ejection boundary to larger Ohnesorge numbers, contrary to expectation, and yet reduces the volume ejected at the same distance from it. Gravity sets how much liquid the jet carries, not how the jet fragments: scaled with its mean, the droplet-size distribution is independent of the Bond number and has an exponential tail, as for free hollow droplets. The simulations, built on a closed family of sessile equilibria, reproduce the measured velocity of the first droplet within 7\,%, while the volumes emitted in experiments are eight to ten times those computed for the same geometry.

Abridged. We aim to extend Paper I to the eccentric secondary orbit case and now analyse the elliptic restricted three-body problem (ERTBP). Our secondary masses include Ceres, Mercury, Earth, and Neptune so that we can help infer the presence of both large asteroids and planets through our analysis. We study dust particles on both planar (2D-ERTBP) and non-coplanar (3D-ERTBP) orbits. We computed families of 1/1 resonant symmetric and asymmetric periodic orbits in the 2D-ERTBP and 3D-ERTBP. Doing so produces novel results in their own right and diagnostic tools for precise guidance for our next steps. Based on them, we generated detailed phase portraits. Finally, we run suitably seeded $N$-body simulations to compare with the periodic orbits, and to provide observational links with debris close to WDs. New families of asymmetric periodic orbits, called $E^A_{41}$, $E^A_{42}$, and $E^A_{43}$ in the high-eccentricity 2D-ERTBP regime were discovered. They are generated by the new bifurcation point, $B_T^4$, along the new $A_2$ family, showcased in Paper I. Moreover, new isolated families of asymmetric periodic orbits, namely $E^A_{44}$, $E^A_{45}$, and $E^A_{46}$ were discovered. Families of short- and long- period asymmetric orbits starting from $L_4$, called $E^A_S$ and $E^A_L$, were also computed. In both the 2D- and 3D-ERTBP regimes, highly eccentric motion is exhibited for the dust particles in the majority of the families of either symmetric or asymmetric periodic orbits. The $N$-body simulations reveal that over ten years, the orbital period deviations of the dust are higher than in the circular restricted three-body problem (Paper I); in some architectures, Mercury-mass planets can regularly generate deviations exceeding $\sim$100 s, which potentially aids observational inferences of hidden asteroids or planets orbiting WDs.

In this letter, we demonstrate that employing the Lens Aperture as a Knife-Edge (LAKE) with two identical LEDs has the distinct advantage of obtaining schlieren images along two cutoff orientations. It is possible to control LED brightness to obtain a desirable weighted sum of both horizontal and vertical schlieren cutoff images on each camera frames. Powering the LED sources by an affordable and programmable Arduino board enables a rapid switch between the horizontal and vertical schlieren cutoff that is exactly synced to the camera frames, resulting in crosstalk-free schlieren image sequence. Furthermore, with a little postprocessing, this monochrome image series can be easily transformed into a colorful sequence of bidirectional schlieren images.

Calibrating microscopic traffic models for digital twins is an expensive black-box optimization problem: tuning car-following and lane-changing parameters requires a full simulation run, affording only a tight budget per recalibration window. Matching raw trajectories yields a rugged objective that sparse surrogates cannot learn, reducing sequential acquisition to near-random probing. We present FLAT, which couples what to optimize with where to sample next. An eight-dimensional behavioral fingerprint smooths the parameter-error landscape, making the objective learnable from a few dozen samples; annealed lower-confidence-bound (LCB) acquisition then spends each remaining run where it most reduces error. The surrogate, interchangeable among a Gaussian process (GP), random forest (RF), or multi-layer-perceptron (MLP) ensemble, plugs into the same LCB loop. Across six heterogeneous real-world scenes, FLAT-GP achieves the lowest scene-averaged behavioral error, winning 6/6 scenes against SPSA, GA, and CMA-ES and 5/6 against TPE under the matched budget. Some baselines need up to 4.4 times more simulations to match. Ablations show objective choice shifts final behavioral error by 81% on average, removing sequential LCB raises the six-scene mean by 20%, and surrogate choice shifts it by at most 4.2%, confirming gains trace to objective geometry and sequential allocation rather than surrogate capacity.

Recent advances in generative world models have increased interest in digital models that reproduce both the appearance of real objects and their response to physical interaction. Three-dimensional reconstruction techniques, including 3D Gaussian Splatting, capture detailed surface geometry and appearance from images and videos. However, extending these representations beyond plausible animation to mechanically interpretable models for constitutive behavior, boundary conditions, and inverse parameter identification remains less explored. In this work, a differentiable Lagrangian-coupled 3DGS-smoothed particle hydrodynamics (SPH) model is proposed for forward simulation and inverse analysis of deformable solids. The observed object is first reconstructed from multi-view calibrated visual dataset as a 3DGS rendering model. An envelope-based procedure then generates an independent SPH support for the solid-mechanics model, avoiding the direct use of rendering primitives as mechanical particles. A reference-configuration Lagrangian transfer maps SPH deformation to Gaussian positions and covariances, thereby coupling the physical model and the image observation model while preserving a differentiable computational path. The SPH formulation supports linear elastic, hyperelastic, and Kelvin–Voigt viscoelastic responses, together with fixed, free, and Robin-type boundary conditions. Numerical studies validate the SPH response against finite-element results, assess accuracy and efficiency against a conventional model using Gaussian centers as surface SPH particles, and demonstrate forward simulations on beam, bridge, and liver-shaped examples. Inverse analyses further estimate constitutive and boundary parameters from rendered deformation observations, including noisy cases, demonstrating the feasibility of the proposed model for mechanics-based parameter identification from image data.

Seismic hazard and structural analyses require broadband ground-motion time histories, yet the high-frequency band (above 1~Hz) is the hardest to obtain: physics-based simulations are too costly at these frequencies, and stochastic methods lose the deterministic phase, directivity, and long-period coherence that control structural demand. We present HighFEM-2, a latent denoising-diffusion waveform model trained with rectified flow matching on a multi-modal diffusion transformer. It synthesizes three-component acceleration up to 50~Hz, conditioned on a low-frequency waveform below the source corner frequency and on magnitude, hypocenter and station location, and $V_{S30}$. The low-frequency waveform supplies the deterministic source, path, and directivity, so the network learns only the regional high-frequency scattering. Conditioning each station on its own waveform lets HighFEM-2 generate records for all stations of an earthquake that share its source and low-frequency path terms. Trained on 195{,}138 Italian INSTANCE records ($M_w$ 1.4–6.5, within 200~km), HighFEM-2 produces realistic waveforms: it reproduces observed peak amplitudes with small bias ($0.042 \pm 0.088$ log units for PGA, $0.057 \pm 0.090$ for PGV) and matches observed envelopes, Fourier spectra, and integrated velocity and displacement. The conditioning waveform can come from a simulation or a filtered record, and ensembles of realizations sample the high-frequency variability given that input. HighFEM-2 thus provides fast, regionally calibrated broadband time histories for hazard and risk assessment, including near-fault scenarios where recordings are scarce.

Efficiently transferring kinetic energy from a slow, high-force driver to a light payload, with a near-uniform output force, requires a transmission whose mechanical advantage rises along a steeply convex profile. Continuous-contour mechanisms (variable-radius drums, cams) become prohibitive at scale through diameter span, inertia and rim burst. The RopeComb bypasses these limits by synthesising profiles ($G = 0 \to 100+$) from discrete constant-radius sheaves deflecting tension members into staggered spans. Each fold's ratio vanishes at first contact, enabling snatch-free engagement at 10 m/s without clutches or dampers. Because the array ratio is closed-form, bounded least squares matches uniform-force targets to within 0.6-1.5% across mass ratios of 100:1 to 10,000:1 using 8-13 members. To carry large carriage reactions without the overturning moment a single array imposes, two independent arrays flank a central guide and drive a partitioned fixed-ratio stage ($n_1 + n_2 = k$). Sizing array ratios and member counts in the fall-count ratio $n_2 : n_1$ cancels 97.7% of the single-array reaction and 95% of its overturning moment in simulated 1,000:1 launches at $k=7$, reducing compliant peak-to-mean payload force from 1.40 to 1.13. All results are numerical.

Modelling molecular absorption spectra from scalable ground-state representations is challenging because conventional approaches often require explicit excited states or time propagation. Here, we introduce a method to obtain Absorption Using Recursive Operator Response with vibronic Augmentation (Aurora), which combines neural quantum states (NQSs) with continued-fraction memory-kernel coupling theory (CF-MKCT). Aurora maps the zero-temperature Liouvillian response hierarchy onto repeated wavefunction-space applications of a shifted Hamiltonian to a dipole-prepared state, enabling direct spectral reconstruction from variational ground states. The implementation combines variational Monte Carlo, implicit-restarted-Lanczos-assisted NQS optimization, matrix-free Hamiltonian application, and adaptive recursion control. The resulting electronic spectra closely agree with full-configuration-interaction and conventional MKCT references for tractable molecules, with the restarted-Lanczos strategy improving difficult NQS cases. For electron-phonon Hamiltonians, Aurora combines Huang-Rhys mode selection with local singular-value-decomposition (SVD) phonon encoders. The H2 benchmark indicates that compression retains the dominant vibronic envelope and improves upon bare local-basis truncation, while paired calculations for N2, H2O, H2S, and NH3 yield similar principal features with and without encoding. We further obtain vibronic spectra for C6H6 and a 10-electron, 10-orbital C10H8 active space. Overall, Aurora provides a unified route from variational molecular ground states to electronic and vibronic spectra without constructing excited-state manifolds.

Branched flow of light arises when spatially correlated disorder folds families of rays into caustics. Here, we demonstrate a temporal analog in the retarded-time coordinate of an optical pulse in a waveguide, where branched flow becomes hyper-dispersive. Retardation during pulse propagation induces spatiotemporal correlations that break propagation reciprocity and imprint a trajectory memory. This nonreciprocity enables unidirectional temporal branched flow with no spatial counterpart. A memory model is developed combining ray-density and phase fluctuations with single-ray dynamics in a memory cell. The model reveals competition between fluctuation-induced focusing and destruction, which yields nontrivial diagrams of propagation regimes with distinct dispersion scalings. For a 532-nm pulse in a $\mathrm{LiNbO_3}$ fiber, we predict that refractive-index disorder induced by active modulations or quantum vacuum fluctuations can produce first caustics on meter-to-kilometer scales, offering routes to experimental observation and to sensing ultra-weak fluctuations.

Reservoir computing (RC) offers an efficient data-driven approach for forecasting extreme events (EEs), which correspond to rare and large-amplitude dynamical occurrences. We propose a dual-stage cascade framework that jointly predicts both the timing and peak intensity of upcoming EEs. A traditional RC branch integrates long-term precursor dynamics to support stable detection and long-horizon prediction, while an NGRC branch captures local nonlinear waveform geometry to improve fine-grained time-to-peak localization and complement peak-intensity estimation. The fused features then feed a ridge classifier that issues a binary alarm upon detecting precursors. Only then do two ridge regressors, trained on true-positive snapshots, estimate time-to-peak and peak intensity. This classify-then-regress design addresses severe class imbalance without data resampling. Evaluated on simulated pump-modulated VCSEL data, the hybrid model achieves a SEDI value >0.8, with MAEs around 0.2 ns and 0.2 a. u., maintaining performance up to a 20 ns warning horizon. The framework advances extreme-event forecasting from binary warnings to fully quantitative dual-objective prediction.

Dielectric screening of electric fields is modified by interfaces and confinement, and any accurate description of interfacial chemistry necessitates properly modeling the nonuniform dielectric tensor. However, models with short range interactions, such as local machine learned interatomic potentials (MLIPs), are increasingly used in simulations of molecular interfaces without quantitative understanding of their nonuniform dielectric response. To build this understanding, we use the framework provided by local molecular field (LMF) theory to quantify the roles of short and long range electrostatics in confined water. Short and long range models predict the same transverse dielectric profile but short range models predict qualitatively different longitudinal dielectric profiles than their long range counterparts. To understand these differences, we develop a Landau theory that shows that long range interactions stiffen polarization fluctuations by a factor of the bulk dielectric constant and reduce the interfacial polarization correlation length by an order of magnitude. LMF theory, which captures long range interactions with an averaged field, can correct the structure and dielectric response, but the standard fluctuation relation no longer holds. We derive a fluctuation relation and integral equation for LMF theory and demonstrate their accuracy for predicting longitudinal dielectric response. MLIPs that incorporate interfacial configurations in their training essentially learn a static mean field correction at the interface, such that the integral equation can be used to predict their nonuniform dielectric profile from polarization fluctuations. We also show that short range MLIPs can be combined with symmetry preserving mean field (SPMF) theory to provide a linear-scaling alternative for long range interactions and predicting nonuniform dielectric functions.

Life support equipment coupled to the human lungs forms a complex, nonlinear dynamic system. Current respiratory mask test standards rely on sinusoidal breathing machines and surrogate trachea geometries that substantially reduce physiologically relevant airway inertance. This paper shows that the ISO 16900-5 trachea does not generate physiologically meaningful exhalation momentum, limiting the ability of standard test configurations to reveal valve dynamics relevant to human use. A physiologically motivated “lip-nozzle” surrogate is introduced as a diagnostic tool to restore realistic momentum transfer, enabling observation of exhalation behavior that is not observable under existing test conditions. Nonlinear dynamic simulations incorporating this surrogate reveal a force-balanced operating regime in which exhalation-jet momentum can offset flow-induced valve resistance, in one representative case reversing exhalation mean effective pressure from resistive to assistive. This effect depends on the relationship between the valve's restoring force and lift, representing a design consideration relevant to future valve development. Implications for mask design, valve placement, and future test methods are discussed. This work does not propose new performance limits or acceptance criteria; rather, it demonstrates how current test fixtures suppress inertance- and momentum-driven effects that can materially influence exhalation valve dynamics.

Significance: RF-induced heating of endovascular devices limits MRI-guided interventions, including targeted drug delivery to brain tumors. Purpose: To present a tool that rapidly generates randomized guidewire trajectories for RF heating risk assessment. Methods: A MATLAB tool models the guidewire as a spline with randomized control nodes and a fixed nominal length. As a use case, tip sSAR was estimated for 1000 trajectories using straight-wire transfer functions and 64 MHz local and body coil fields (matched B1) in the Duke phantom. Results: Length accuracy was 99.84%, and 3000 trajectories were generated in 11.5 s. Body-to-local sSAR ratios at the 50th, 95th, and 99th percentiles were 1.5, 1.6, and 2.27, but some trajectories heated more under the local coil. Conclusion: The tool enables large-sample heating assessment, and coil type alone did not reliably predict individual trajectory risk.

Narrow linewidth (high-Q) quantum discriminators are essential for realizing solid-state $^{229}$Th nuclear clocks, yet the observed transition linewidths remain at the tens-to-hundreds-of-kilohertz level, far exceeding the sub-Hertz linewidth set by the radiative lifetime of the nuclear isomer. Here, we investigate the origin of this broadening in $^{229}$Th:CaF$_2$ through systematic measurements of the linewidth as functions of temperature, thorium concentration, and quadrupole state, accompanied by theoretical analysis and first-principle simulations. We find that the linewidth is independent of temperature, increases linearly with thorium concentration, and depends strongly on the quadrupole state. Data analysis identifies variations of the local electric-field gradient (EFG) as the dominant source of inhomogeneous broadening and reveals that the EFG disorder increases linearly with thorium concentration. The observed Lorentzian line shapes and linewidth linear concentration dependence are explained naturally from the $1/r^3$ scaling of a random distribution of long-range dopant-induced perturbations. Complementary atomistic and ab initio density-functional-theory calculations reproduce these characteristic features and provide insights into the microscopic defect configurations responsible for the broadening. These results establish the dominant broadening mechanism in solid-state $^{229}$Th and provide guidance for engineering nuclear-clock materials with reduced inhomogeneous broadening.

Rydberg atom-based electrometry is a pivotal technology that enables radio-frequency (rf) field measurements traceable to the International System of Units. This is typically accomplished by converting the direct rf power measurement of classical probes to a frequency measurement referenced to two well-defined spectral features, a commercial wavemeter, or narrow cavity lines produced via clock disciplined electro-optic modulators, although there exist cases where none of these are available. In this article, we demonstrate that a known rf frequency scale can be used to infer a laser's frequency scan using Autler-Townes (AT) splitting. In other words, we show that frequency-axis calibration can be achieved using the avoided-crossing behavior of near-resonant rf radiation. The relative separation between AT-split features is measured as a function of an applied rf field's power and detuning from atomic resonance. These peak separations are then fit to a simple, two-level model using a weighted nonlinear least-squares regression analysis that utilizes three fitting parameters, one of which is the desired calibration factor. While we do not benchmark this method against an absolute frequency reference, we do show that this method is (1) consistent with calibration via known fine-structure separation to within approximately 1.11% or better, and (2) robust against substantial experimental deviation from the system's known rf power dependence.

Global streamflow forecasting supports flood preparedness, yet data-driven models trained on reanalysis lose skill when driven by operational forecasts, increasingly so with lead time. AIFL addresses this shift with a two-stage transfer-learning recipe and a deterministic LSTM, but it feeds one forecast lead time into the whole input window and gives no uncertainty. We ask how far this open recipe can be extended without changing its recurrent core. KNOU-Flood keeps a single-layer LSTM and the two-stage training and changes the information around it: a longer input window (424 days), lead-aware forecast inputs, GraphCast as a second forecast source next to ECMWF HRES, and a latent-noise ensemble trained with the fair CRPS. We compare against a re-implementation of the AIFL recipe (its code is not public) under identical data, stations, splits and metrics. Our contributions are threefold. First, over three seeds and 2,099 test basins (2021-2023), KNOU-Flood reaches a median KGE' of 0.65 at LT1 and 0.48 at LT10 against 0.62 and 0.34, with significant gains at all ten lead times under two scoring protocols, also against a 1024-unit baseline and on held-out basins (one seed each). Second, identical weights give an LT10 KGE' of 0.18 or 0.34 depending on how forecasts are fed, so much of the lead-time decay reported for AIFL is an input-protocol effect; the probabilistic head contributes most to the gain, followed by the raw GraphCast channels, the longer window and mixed fine-tuning. Third, the ensembles reach lower CRPS than probabilistic versions of the baseline, are nearly calibrated with inference-time dropout, and detect 1.5- and 2-year floods better than all baselines; the benefit for rarer floods depends on the seed, and an exploratory tail-weighted variant improves 10-year and larger events. KNOU-Flood is a controlled extension of an open recipe, not a new architecture.

We investigate slow-roll inflation in a modified teleparallel framework with a non-minimal coupling between torsion and the matter sector. Focusing on Starobinsky-type plateau inflation, we derive the modified background dynamics and the corresponding slow-roll observables, and examine how the gravitational couplings affect the scalar spectral index, tensor-to-scalar ratio, and running of the spectral index. The model continuously recovers the standard Starobinsky scenario in the appropriate weak-coupling limit, while the matter–geometry coupling produces a non-monotonic modification of the inflationary predictions. We find that weak couplings leave the standard predictions essentially unchanged, whereas stronger couplings can significantly shift the scalar spectral index and eventually become incompatible with current cosmic microwave background constraints. The quadratic torsion correction has a comparatively mild impact within the controlled low-energy regime, primarily affecting the tensor amplitude. We confront the resulting predictions with the Planck and BICEP/Keck constraints and find that the model remains viable over a broad range of the inflationary parameter space. Our results demonstrate that the matter–geometry coupling can leave a potentially testable imprint on primordial observables while retaining the successful plateau-inflation phenomenology of the Starobinsky scenario.

PyCC is a Python-based quantum chemistry package that emphasizes coupled cluster and related methods. Developed over the last five years, PyCC's purpose is to provide a clear reference implementation that will serve as a validation suite for production code, a platform for rapid prototyping and testing of new methods, and an educational resource for newcomers to the field. The package covers both closed- and open-shell single-determinant references in spatial- and spin-orbital formulations for the coupled cluster hierarchy through triples-including methods such as CC3, as well as for MP2 and CI methods. PyCC provides a broad range of molecular properties such as analytic first and second energy derivatives including gradients, Hessians, polarizabilities and atomic polar tensors, as well as non-energy-derivative properties, such as velocity-gauge atomic polar tensors and diagonal Born-Oppenheimer corrections, plus dynamic linear response functions, and excitation energies. The package includes a number of unique capabilities, such as real-time CC propagation (up through CC3), atomic axial tensors for simulating vibrational circular dichroism spectra at the MP2 and CI levels, and local correlation methods including perturbation-aware correlation domains. The code is designed around a shared Wavefunction base and a method-agnostic derivative layer that enables a new method to inherit the full property set once it supplies its reduced densities and their first-order responses. PyCC is fully open source and available on GitHub under the BSD 3-clause license.

Porous media experiments commonly rely on geometry-controlled and optically transparent experimental flow cells. Examples include studies of flow structure, solute mixing, multiphase flow, bacterial dynamics, and reactive transport. Here, we provide a reproducible and open-source protocol to fabricate transparent monolithic porous flow cells by stereolithography 3D printing. The protocol covers digital design, resin printing, internal flushing, and resin coating. The flushing and coating steps are central to the workflow, enabling enclosed single-material cells to be cleared of uncured resin and made optically transparent for direct imaging. The protocol produces optically transparent, millimetre-scale, and quasi-two-dimensional flow cells suitable for simultaneous pressure and image-based measurements. As a proof of concept, we study flow-reversal asymmetry in steady oil-air two-phase flow in quasi-two-dimensional Hele-Shaw cells containing either triangular or cylindrical obstacles. Directional differences are quantified from pressure measurements and from image-based active-width measurements. The triangular geometry produces a clear directional response, whereas the cylindrical geometry remains symmetric within experimental uncertainty. These results demonstrate that our fabrication protocol can link digitally designed pore geometries to macroscopic pressure response and steady-state two-phase flow organisation. By making the design files, scripts, and manufacturing workflow openly available, we aim to make 3D-printed flow cells readily adaptable to other experimental systems and research fields.

The Eigenray method solves two-point boundary-value ray-tracing problems in heterogeneous anisotropic elastic media using a variational formulation based on the eikonal equation. Here, we extend the method to finite frequencies using the trajectory-mechanics formulation in which finite-frequency effects are represented by a frequency-dependent potential that couples phase and amplitude. We show that finite-frequency propagation can be described within the same variational framework through an equivalent effective medium, preserving the structure of the original method while introducing corrections to trajectories, phase, amplitudes, geometric spreading, wavefront curvature, and caustic behavior. The resulting Eigenwave formulation requires only local modifications of the ray velocity and its derivatives and reduces to classical ray theory in the high-frequency limit. Numerical examples involving multipathing and cusp caustics demonstrate accurate modeling of finite-frequency propagation effects. While ray-based amplitudes become unreliable near caustics, a phase-space (Maslov) treatment successfully reproduces finite-difference wavefield solutions. These results establish Eigenwave theory as a unified framework connecting ray theory and finite-frequency wave propagation in heterogeneous anisotropic media.

Additive friction stir deposition (AFSD) involves coupled thermal and material-flow fields governed by frictional heating, plastic deformation, and tool-imposed boundary conditions. High-fidelity finite-volume methods (FVMs) can resolve these fields but are computationally expensive, while constant-property approximations can distort the predicted thermo-mechanical response. This work develops a steady-state multi-task coupled physics-informed neural network (MCoPINN) to predict three-dimensional temperature and material-flow fields while reconstructing temperature-dependent thermal conductivity, density, and specific heat from sparse property measurements. A theoretical analysis decomposes prediction error into property reconstruction and neural field-solver contributions and characterizes the discrepancy associated with constant-property models. A controlled one-dimensional nonlinear-conduction study evaluates property reconstruction under sparse measurements. The framework is then evaluated for AFSD against a temperature-dependent FVM benchmark and experimental thermocouple measurements. MCoPINN reproduces the reference thermal and material-flow fields and reduces average temperature root-mean-square error from 50.13 K for the constant-property CoPINN to 30.28 K across four traverse speeds. MCoPINN requires approximately 8.5 hours of training per condition, compared with 52 hours for the FVM benchmark. These results demonstrate computationally efficient, physics-coupled prediction with temperature-dependent material properties.

We present ab initio calculations of atomic ionization by bare ions and antiprotons within the continuum distorted-wave eikonal initial-state (CDW-EIS) model. The dataset covers atomic targets from H to Kr, impacted by bare ions over a broad range of projectile charges and by antiprotons. Particular attention is given to elements in periods 3 and 4 of the periodic table, which are relevant to pigment analysis and technologically important materials. The resulting subshell-resolved total ionization cross sections are made openly available through an online repository covering a broad range of projectile–target systems, occupied initial subshells, and projectile velocities. We analyze systematic trends in the cross sections, including their dependence on projectile charge and velocity, binding-energy scaling, and charge-sign effects in proton and antiproton impact. To extend the practical coverage of the database, we also develop a neural-network surrogate, CDWEIS-NN, trained on the subset of the database with projectile charge $|Z_\mathrm{P}|\leq 8$. The network interpolates the CDW-EIS results to additional projectile–target systems and impact energies, reproducing both held-out CDW-EIS calculations and the experimental benchmarks considered in this work with satisfactory accuracy.