Papers

Condensates can accelerate reactions by concentrating reactants, yet product accumulation can eliminate this advantage. We show that product-induced suppression of condensation generates rotating, deforming, and propagating condensate domains through spontaneous symmetry breaking. Their reorganization shifts reaction sites away from accumulated products while retaining reactant enrichment, yielding rates above those of static condensates and homogeneous states. Reaction flows can thus self-organize condensates into dynamic reactors, with implications for biological and synthetic systems.

Twocolor laser filamentation is considered a promising strategy for deep transformations of the spectrum and generating ultrashort light pulses, which is critically important for developing effective methods to control broadband coherent radiation in attosecond physics and ultrafast spectroscopy. We present the results of our experiments on the study of the spectral dynamics of supercontinuum generated during collinear twocolor filamentation of femtosecond pulses at the fundamental (800 nm) and second harmonic (400 nm) frequencies in Ar, N2, and CO2 gases at pressures up to 11 atm. We demonstrate that the transition to multiple filamentation in high pressure gases qualitatively changes the interaction dynamics between the color components. We demonstrate that instead of a simple merging of spectra broadened during filamentation, a complex, nonmonotonic dependence of the shape of the twocolor supercontinuum on the time delay between pulses is observed, including selective suppression of radiation in the 400 nm region. For the first time, significant differences are identified between the dynamics of twocolor filamentation in atomic and molecular gases. Particularly, in CO2, spectral transformation is preserved at picosecond interpulse delays, which is explained by the strong contribution of molecular rotational wave packets to the effective nonlinear polarizability. Our results demonstrate the possibility of controlled redistribution of spectral energy and dynamic switching off of individual regions of supercontinuum in atomic and molecular gases as their pressure changes, which fills the gap in understanding the spatiotemporal evolution of multiple two color filaments and paves the way for the controlled formation of ultrashort laser pulses.

Spinel chalcogenides provide a platform for investigating the interplay among metallic, superconducting, and pressure-induced insulating states. Here, we combine synchrotron powder X-ray diffraction and electrical-resistivity measurements to investigate the pressure evolution of CuIr2Se4, CuRh2S4, and CuRh2Se4 over pressure ranges extending beyond those previously explored. High-pressure diffraction reveals closely related monoclinic supercells in all three compounds. For CuIr2Se4 and CuRh2S4, constrained profile fits based on structural models relaxed using density functional theory are compatible with Phase-IV-type bond-disproportionated structures, whereas the data for CuRh2Se4 establish a compatible monoclinic unit cell without resolving its atomic-scale ordering pattern. Insulating-like transport develops abruptly over a narrow pressure range in CuIr2Se4 but more gradually over broader pressure ranges in the Rh-based compounds, in close correspondence with their respective structural transformations. We also establish previously unreported bulk superconductivity in CuIr2Se4 at ambient pressure: zero resistance is attained at 0.29 K, and the accompanying ac diamagnetic response is consistent with nearly complete superconducting shielding. These results establish a close relationship between the formation of the high-pressure monoclinic phases and the evolution toward insulating transport, and demonstrate that transition-metal and chalcogen substitutions tune the characteristic pressure scales and the competition with superconductivity within a closely related structural framework.

We investigate the nonlinear coalescence of two current-carrying ELM filaments using a three-dimensional electromagnetic fluid model. In the flat-density limit, the coalescence exhibits magnetic island-like reconnection, characterized by X-point formation, current-sheet development, and Sweet-Parker-like resistive scaling. Introducing a blob-like density perturbation modifies the reconnection dynamics: while the peak reconnection rate remains nearly unchanged for weak perturbations, it decreases and is increasingly delayed for larger density amplitudes. Analysis of the induction equation reveals a transition from resistive to increasingly density-dependent advective dynamics. Finite density perturbations also enhance the post-compression rebound, or sloshing, of the filaments. The sloshing amplitude increases with the density-gradient pressure force, establishing density perturbation as an additional control parameter for both reconnection and filament sloshing. These results highlight the coupled electromagnetic and pressure-driven dynamics governing the nonlinear evolution of ELM filaments in the tokamak edge.

Excitons provide a sensitive optical probe of electronic correlations in nearby two-dimensional materials, yet their coupling to intrinsic flat-band Mott systems remains largely unexplored. Here we combine gate-tunable optical spectroscopy with first-principles calculations to study monolayer WSe$_2$ in direct contact with the van der Waals Mott insulator Nb$_3$Cl$_8$. The gate evolution of WSe$_2$ excitonic resonances reveals signatures of a correlation-reconstructed Mott gap in Nb$_3$Cl$_8$ that is absent from the single-particle band picture. In the electron-doped regime, the WSe$_2$ 2s Rydberg exciton undergoes a multistage evolution and develops into interlayer attractive and repulsive polaron branches, showing that a Rydberg exciton can be dressed by strongly correlated flat-band electrons in an adjacent Mott layer. Under an out-of-plane magnetic field, spin-polarized Nb$_3$Cl$_8$ states further induce valley-selective exciton coupling, producing a strongly enhanced circular polarization of the WSe$_2$ exciton emission. These results extend exciton-based sensing and exciton-polaron physics to intrinsic flat-band Mott materials, providing an optical route to probe and engineer correlation-driven interfacial quasiparticles.

Spontaneous parametric down-conversion (SPDC) in ultrathin optical resonant metasurfaces offers a promising platform for integrated quantum light sources. However, conventional resonances suffer from steep momentum dispersion, limiting photon-pair generation to narrow excitation and emission angles. Here, we demonstrate a flatband-resonant 3R-MoS$_{2}$ metasurface for high-efficiency, broad-angle SPDC photon-pair generation. By implementing a supercell symmetry-breaking strategy via Brillouin zone folding, we achieve a flatband quasi-bound states in the continuum (quasi-BICs) resonance in the near-infrared regime that maintains a robust quality factor across a wide range of incident angle. Exploiting the strong local electric field enhancement of resonance and the large second-order susceptibility of 3R-MoS$_{2}$, the metasurface boosts the degenerate SPDC pair-generation rate with orders of magnitude enhancement over an unpatterned film of identical thickness. Crucially, the near-zero band dispersion extends the effective emission angle to $\pm 5^{\circ}$ with negligible spectral drift $\sim 0.1$ nm and high directional tolerance. Under pulsed excitation with a finite spectral bandwidth, the photon pair yield remains high, demonstrating strong spectral locking to the quasi-BIC resonance against pump detunings. This work establishes a versatile strategy for momentum-independent quantum light sources, opening new avenues for scan-free quantum ghost imaging and spatial-frequency quantum processing.

Flapping insect wings generate leading-edge vortices (LEVs) that produce much of the lift required for hovering, yet the frequency governing their formation has not been connected to classical vortex shedding. Roshko scaling for bluff-body wakes, together with the Sigurdson extension to wall-bounded separated flows, identifies a universal vortex-formation Strouhal-number range of approximately 0.15 to 0.17. Here we show that this same range governs LEV formation on both stationary wings and hovering insect wings. We first develop an approximate scaling for stationary flat-plate wings in which discrete LEVs are generated by periodic leading-edge perturbations, and show that maximum lift consistently occurs within the universal Strouhal range. In hovering insects, one LEV forms during each translational half-stroke, allowing the same framework to be applied to published flight data. Despite large differences in morphology, kinematics and Reynolds number, the insect data recover the same Strouhal range. These results reveal a common vortex-formation timescale linking classical separated flows, artificially generated LEVs and the natural LEVs that sustain insect hovering.

Physics-Informed Neural Networks (PINNs) embed PDE residuals into neural network training, but their internal representations remain opaque: it is unknown what physical features their hidden layers encode or whether those features have a localized causal role. We present PhysSAE, a mechanistic interpretability framework that trains overcomplete sparse autoencoders (SAEs) on PINN penultimate-layer activations and evaluates dictionary atoms through direct causal intervention in the original frozen hidden state: $h_{\mathrm{cf}} = h - \alpha z_k d_k$, bypassing the SAE decoder entirely. Across six PDE families, with 3 PINN seeds and 3 SAE seeds each—we show that (i) Our discovered SAE atoms align with independently-defined physical observables (max Pearson $|r|=0.951$, always $\gg$ permutation null), (ii) the causal footprint of top-aligned atom ablation is 1.2–4.2$\times$ more spatially concentrated canonical than PCA or ICA interventions, and (iii) top-aligned atoms outperform matched random controls on causal localization for structured physical concepts (ESF$_{80}$ advantage 0.04-0.44). Two-atom bilateral representations improve concept regression R$^2$ by $\Delta R^2\!=\!0.05\text{-}0.15$ over single atoms, while random pairs decrease it by up to 0.60. These results demonstrate that PINNs develop sparse, physically structured latent representations that can be identified and causally interrogated post-hoc, opening a path toward interpretability-aware scientific machine learning.

Timing jitter management is crucial in asynchronous optical sampling (ASOPS) and dual-comb spectroscopy to maintain spectral bandwidth and fidelity. However, a quantitative framework predicting jitter correction efficiency from laser phase noise has been lacking. Here, we present formulation and simulation models that evaluate jitter-suppression dynamics based on phase noise spectra. Our model quantifies the contrast between conventional triggering and software-based jitter correction. It also enables retrieval of a jitter-free spectrum by compensating for jitter-induced power degradation. This work provides a universal design framework for optimizing diverse ASOPS systems by predicting residual jitter prior to system construction.

Laws from physics to social science rest on a small, enumerable number of factors explaining variation in the systems they describe. While the orthodoxy of economics, physics and chemistry relies on a small number of analytically interpretable laws, large data models pursuing universality throw in all available data. Both fail in their extremes through the same flaw, an incorrect basis: too few orthogonal variables to describe system variation, or an explosion of dimensionality around fundamentally low dimensional but unknown dynamics. We propose and validate a framework that extracts an interpretable, mathematically sound, low dimensional and dynamically informed basis directly from arbitrary timeseries. Combining Takens' Delay Embedding Theorem, dimensionality reduction and optimal transport, we produce a phase space of phase spaces (PSoPS) on which all interrelated dynamical series exist: barycenters formed from many phase spaces, together with the families of transport plans between individual phase spaces and the barycenter, yield a basis describing the relational geometry of each timeseries and the topology of economic relationships. The macroeconomic PSoPS built from 281,536 series is a single connected object of effective dimension 7.3 +/- 0.2, into which the canonical laws (Phillips, Okun, Solow) embed as roughly 2-dimensional looped sub-attractors. The coupling between concept barycenters is directed, with the price of money and productivity as net sources and credit and output as sinks, recovering monetary transmission from geometry alone. IAAFT phase-randomized surrogates destroy 36-43% of the dimension across the three laws and 38% on the full corpus, so the basis is irreducibly nonlinear. We close with proposed extensions to fields whose dynamics share attractor geometries and to systems that are intrinsically low dimensional yet hard to navigate in raw coordinates.

Large plastic deformation can drive an initially uniform single crystal to spontaneously subdivide into misoriented grains separated by thin dislocation walls – a pattern-forming instability rooted in the loss of convexity of the crystal's elastic energy at large strain. We study this phenomenon for a ductile crystal in plane-strain simple shear within continuum dislocation theory, using a polyconvex (Ciarlet–Geymonat) elastic energy that guarantees existence of minimizers for the coupled deformation–slip problem. Minimizing over the plastic slip yields a condensed energy of double-well form whose non-quasiconvexity favours a lamellar microstructure; the gradient of the geometrically necessary dislocation density regularizes it, giving the grain boundaries a finite thickness and energy as functions of the misorientation angle. A block-coordinate finite element scheme – alternating a convex non-smooth solve for the slip with a Levenberg-regularized Newton solve for the deformation – resolves this microstructure numerically and detects its spontaneous onset, reproducing the lamellar grain structure in agreement with the closed-form analysis.

Infectious diseases may interact by competing for the same hosts or by facilitating subsequent infections. Understanding the dynamics of such multi-pathogen systems, particularly those subject to endemic seasonality and mitigation, is essential for designing robust public health interventions. We propose a three-stage modeling framework to disentangle the interplay between seasonality and behavioral feedback as a way of mitigation. First, we analyze a coupled susceptible-infectious-recovered-susceptible (SIRS) system without external forcing and show that the abrupt transition between the disease-free and endemic equilibria arises from a backward bifurcation-induced first-order phase transition. Second, we independently examine seasonality and behavioral feedback, characterizing where and when oscillatory behavior is induced near critical tipping points. Third, we demonstrate that their combination generates complex multi-annual wave patterns, with high-incidence cycles driven by seasonality and low-incidence intervals driven by behavioral feedback. By mapping stability as a function of seasonal forcing, mitigation strength, and cooperativity, we identify distinct period-doubling cascades with chaotic signatures arising from different mechanisms: the interplay between seasonality and behavior, and inter-pathogen cooperativity with the backward bifurcation it induces. We then analyze how these mechanisms interact across parameter ranges. Altogether, we show that cooperation fundamentally expands the spectrum of possible epidemic patterns, highlighting the importance of considering multi-pathogen interactions in epidemic modeling and control strategies.

We study the operator Michelson contrast Delta(A) = (kappa(A)-1)/(kappa(A)+1), with kappa(A)=||A|| ||A^{-1}||, for positive invertible operators, and introduce its logarithmic parametrization via arctanh(Delta(A)) = (1/2) ln kappa(A). This defines the Operator Logarithmic Contrast (OLC), extended to all invertible operators via polar decomposition. We prove OLC is subadditive under multiplication: OLC(AB) <= OLC(A)+OLC(B). For positive invertible A, we identify OLC(A) exactly as the projective Thompson distance from the identity ray to the ray of A. We establish a sharp dimensional threshold for equality in subadditivity: for dimensions 2 and 3, equality forces commutativity, while n=4 is the smallest dimension admitting noncommuting equality examples. Moreover, equality still forces commutativity in any total dimension if the operators factor as pure Kronecker products X = tensor X_i and Y = tensor Y_i with each factor dimension <= 3. We derive contraction inequalities, sum bounds, trace estimates for density operators, Lipschitz continuity (including singular limits), and limiting behavior for Cesaro means and infinite products. For density operators, we obtain an exact closed-form bijection between OLC and von Neumann entropy for qubits; prove this bijection does not exist for d >= 3; and establish upper/lower entropy envelopes at fixed condition number, showing the admissible range [S_min(kappa), S_max(kappa)] satisfies ln 2 <= S_max(kappa) < ln 3 for every kappa > 1. The qubit case recovers, as an operator lift, the classical Michelson-Jensen-Shannon equivalence of Bruni, Rossi, and Vitulano, while a counterexample shows this equivalence fails for d > 2 as a direct consequence of the non-bijectivity.

This study proposes a hybrid reduced-order modeling (ROM) framework for the simulation of turbulent flow. The central idea is to decompose flow dynamics according to their temporal characteristics and predict the resulting components individually. The full field is first divided into a sub-field represented by a limited number of proper orthogonal decomposition (POD) modes (named as POD-retained field) and the corresponding residual sub-field (named as POD-truncated field). A frequency-informed POD strategy identifies the retained modes by considering both modal energy and dominant frequency. The evolution of retained POD coefficients, which feature similar temporal scales, is described using a vector autoregressive (VAR) model. In parallel, the POD-truncated field is compressed into a low-dimensional latent space using a Fourier-neural-operator-based Koopman $\beta$-variational autoencoder (FK-$\beta$-VAE), with the latent variables subsequently predicted by a switching-VAR model. Turbulent statistics of the full field are recovered by combining the contributions from the two components. The framework is assessed using turbulent channel flow at a friction Reynolds number of $110$. The predicted Reynolds-stress components, turbulent kinetic energy (TKE), and dominant wavenumber spectra show good agreement with the reference. Moreover, in comparison with an alternative framework of full-field modeling (i.e., without field decomposition), the proposed framework yields more accurate and robust long-term statistical predictions.

The Hofmann stability chart is used to screen working points in space-charge-dominated linacs. We identify two errors in its published higher-order dispersion relations: missing $(1\mp2\hat\eta^2/\alpha)$ factors in the third-order $S^4$ coupling residues, and a sign error in the stated isotropic reduction of the fourth-order relation. Both corrections follow from Hofmann's Vlasov-Poisson equations without fitted parameters. They reproduce coherent tune-shift coefficients in the author's later monograph that the printed forms miss by 24% and 127%. Mode-resolved figures from a published application agree with the corrected relations and reject the printed forms, indicating an inconsistency between the 1998 equations and the calculations underlying those tested figures. We quantify the effect on the non-oscillatory stability chart. Inside the adopted $S^2\le10$ comparison domain, printed and corrected forms disagree on 0.73-2.11% of cells, with no preferred direction. Among excluded cells, disagreement reaches 22%, and the printed relation over-predicts instability at every sampled anisotropy. This concentration may help explain why the errors persisted, although it does not establish their historical cause. For PIP-II, the corrected chart flags four of thirty-two evaluable periods, including one on a third-order odd branch missed by a second-order screen. This count covers non-oscillatory modes only and remains conditional on an unresolved factor-five disagreement between two codes on transverse emittance growth.

Spectral sensing in the extended shortwave infrared (e-SWIR) is important for molecular analysis, infrared imaging, and machine vision, motivating the development of compact spectrometers for broader applications. However, conventional commercial off-the-shelf spectrometers in this wavelength region are expensive and bulky due to their reliance on external dispersive optics/filters and/or cryogenic accessories. Other emerging computational spectrometers are based on Si and InGaAs photodetectors that remain focused on the visible and near-infrared, with few detector platforms operating in the e-SWIR regime that simultaneously provide broadband sensitivity, low-noise room-temperature operation, and diverse spectral signatures for accurate identification and reconstruction. Here, we report a room-temperature e-SWIR computational spectrometer based on InAs/InP core-shell nanowire photodetector arrays with geometry-encoded spectral responses. The detectors exhibit self-powered broadband photoresponse across the 1–3 $\mu$m range, with responsivity up to 0.215 A W$^{-1}$, detectivity up to $1.6 \times 10^{9}$ cm Hz$^{1/2}$ W$^{-1}$, and microsecond response times. The excellent detector performance is leveraged to demonstrate filter-free spectral reconstruction using a compact multipixel photodetector array device. This enables high-accuracy molecular absorption spectrum reconstruction and hyperspectral imaging. Our results indicate that InAs nanowire arrays are a promising platform for compact computational spectrometry and imaging in the e-SWIR at room temperature.

Medium-range precipitation forecasts are impaired by persistent systematic biases, lead-time-dependent error accumulation, and coarse spatial resolution, restricting their reliability for flood-drought risk assessment. Existing AI correction techniques lack dedicated modeling for multi-day dynamic bias evolution and proper meteorological constraints, often generating over-smoothed rainfall structures, and cannot meet operational deployment demands. This work introduces PCSDiff, a cascaded task-decoupled diffusion framework targeting 10-day precipitation bias correction and downscaling. To jointly counteract temporal error drifts and reconstruct physically plausible local precipitation details, PCSDiff integrates the Precipitation Intensity-aware Multi-branch Decoder (PIMD) module for dynamic multi-day error mitigation using synoptic-temporal features, followed by a two-phase conditional diffusion super-resolution module to restore fine-scale precipitation patterns. Evaluated against CMA-CRA observations over China after global-data training, PCSDiff cuts RMSE by 16.1% and lifts ACC by 13.9% relative to raw ECMWF forecasts at 3-10-day lead times, and consistently outperforms mainstream deep-learning baselines on both general and extreme-precipitation metrics. Benefiting from a streaming inference pipeline, our method achieves low-latency rolling forecasting for practical meteorological operations.

Spatially resolved stress proxies and rate-based seismicity models are increasingly combined for regional earthquake forecasting, yet formally testing their non-redundancy remains largely unaddressed. We present the Nested-Permutation Alarm for Dual-Parameter Earthquake Forecasting (ANADEF) pipeline, integrating a stress-sensitive Gutenberg–Richter $b$-value field, estimated via a penalized 2D B-spline inversion, with a stationary background rate ($\mu$) from space–time ETAS stochastic declustering. Applied to the Zagros Fold–Thrust Belt using an 18-year catalog ($n=40{,}731$, $M_{\mathrm{N}}\geq1.5$, 2006–2024) under a two-stage protocol with non-overlapping training (2006–2014) and target (2015–2024) windows, the model achieves, for $M_w\geq5.0$ ($N=55$), a retrospective Area Skill Score $S=0.69$ (95% CI: 0.64–0.73), reducing alarmed area from $\tau\approx0.38$ ($\mu$-only) to $\tau\approx0.28$ while retaining hit rate $\nu=92.7\%$; since thresholds are calibrated on the evaluation catalog, these are in-sample, not out-of-sample, estimates. A nested permutation procedure re-optimizing thresholds within each null realization tests whether $b$-value adds information beyond $\mu$, absorbing the optimization bias. Significance is null-model dependent: cell-wise randomization yields $p=0.012$, while a conservative spatial-structure-preserving null gives weaker, non-significant evidence ($p=0.057$)—incremental stress information is suggestive but not unambiguously established. Calibrated thresholds were frozen and applied to updated 2015–2024 fields, generating an unvalidated, forward-looking spatial alarm template for 2025–2029. These results establish a reproducible, statistically transparent methodology for testing, rather than assuming, complementarity between stress-sensitive and rate-based predictors, offering a candidate operational template pending prospective validation.

We solve in closed form a one-dimensional relativistic system: two masses interacting only through elastic collisions with a massless mediator bouncing between them. Momenta, times, and positions are hyperbolic functions of the collision index. The mediator energy, interpreted as the pair's effective potential, obeys an exact discrete Coulomb law, $V\propto 1/r$, with a Lorentz-invariant action as coupling. A massive Newtonian mediator instead transmits a $1/r^{3}$ force; one adiabatic invariant traces both laws to the mediator's dispersion relation. Continued to negative mediator energy, the closed forms turn trigonometric, binding a one-dimensional mechanical analog of the Coulomb atom.

The advent of X-ray free-electron lasers and high-harmonic generation has made time-resolved X-ray spectroscopy a powerful tool for probing local atomic environments, yet whether localized core excitations can report on global collective distortions remains open. Cyclo[18]carbon (C$_{18}$), with its polyynic ground state (D$_\text{9h}$) and cumulenic transition state (D$_\text{18h}$), provides an ideal model to address this long-standing issue in bond-length alternation (BLA) dynamics. Mapping two-dimensional potential energy surfaces by first-principles simulations, we find that core ionization symmetrizes the ground-state double-well potential along the BLA coordinate. Our calculated X-ray spectra reveal remarkable sensitivity to bond-length variations: C1s ionization potentials vary by up to 2.4~eV across the BLA coordinate (1.1–1.4~Å), with a 0.9~eV variation for minima predicted by different functionals, while NEXAFS $\pi^*$ peaks shift by up to 4~eV across the same coordinate. These predicted signatures provide a quantitative spectroscopy–structure dictionary for decoding transient structures in future ultrafast X-ray experiments and monitoring bond-alternation dynamics in real time.

Understanding how crustal faults accumulate strain, nucleate ruptures, and redistribute post-seismic stress is fundamental to seismic hazard assessment. The 16 April 2016 $M_w$ 7.0 Kumamoto earthquake, rupturing the Futagawa–Hinagu fault zone in the Beppu–Shimabara graben, central Kyushu, Japan, offers a premier dataset for tracking these processes across a full earthquake cycle. Using the JMA catalog (2014–2018) with dynamic completeness estimation ($M_c \approx 2.15$–$2.25$), we examine the Gutenberg–Richter $b$-value, the 3-D hypocentral fractal dimension ($D_c$), and the seismicity-rate anomaly ($Z$-value). During the one- to two-year preparatory phase, $b$ declined from a baseline of $1.35 \pm 0.10$ to a precursory minimum of $0.59$, while $D_c$ contracted from $0.85 \pm 0.15$ to $0.70$–$0.80$, recording stress concentration and microfracture coalescence onto a narrow nucleation zone. A coherent negative $Z$-value anomaly (-1.6 to -2.0) developed along the graben, strengthening with integration time—consistent with, though not proof of, progressive fault locking. Depth-sliced volumes show the low-$b$ locked core ($b \le 0.65$) was stratified at $10$–$12.5$~km depth and sharpened within the final four months before failure. Rupture reversed this within weeks: $b$ surged to $1.25$–$1.35$ and $D_c$ expanded to $2.04$–$2.11$, consistent with coseismic stress drop and aftershock activation, followed by recovery over 2.5 years. Cross-sections reveal sharp aftershock localization alongside two unrelaxed asperities ($b \approx 0.80$–$0.95$) near the Hinagu termination and Mount Aso, consistent with positive Coulomb stress loading. This framework resolves asperity locking, release, and healing better than any single metric; however, since anomalies were identified retrospectively, they should be read as evidence of coherent behavior rather than a validated forecast tool alone.

We test whether the Kuramoto model quantitatively describes spatial synchronization in regional epidemic waves, using daily COVID-19 incidence for 400 German Kreise (2021–2023) and weekly ILI rates for 12 European countries (ECDC, 2021–2026). Bandpass filtering and Hilbert-transform phase extraction yield high global order parameters ($\langle r\rangle\approx0.87$ for Germany; $\langle r\rangle\approx0.97$ for Europe), yet both are dominated by a common-mode driver rather than pairwise local coupling. Removing the common mode reveals genuine short-range synchronization: an exponentially decaying residual local order with correlation length $\xi=152\pm6$~km in Germany, and a significant neighbor-vs.-non-neighbor contrast (Mann–Whitney $p=0.005$) in the European panel. We interpret this convergent pattern across two diseases and two spatial scales as a universal two-scale structure: a long-range common-mode field superimposed on shorter-range diffusive coupling. Cross-validation against a companion gauge-mediated Doi–Peliti framework shows that district-level effective screening mass $m_R$ is uncorrelated with local~$r$ (null result), the national $(m_R,r)$ phase portrait independently reproduces a previously reported hysteresis loop, and pairwise phase lags quantitatively match fractional-calculus predictions. A frequency–degree correlation provides the structural signature of explosive synchronization; excess synchronization between districts sharing the same Bundesland implicates shared school-holiday calendars and state-level policy as local coupling drivers.

Programmable control of individual pulses in a high-repetition-rate (typically MHz) ultrafast pulse train is a long-standing goal for optical arbitrary waveform synthesis. Here, we report a programmable pulse-by-pulse shaper that enables deterministic spectral-temporal control of ultrafast pulses at a repetition rate of $\sim$ 20~MHz. By synchronizing an FPGA-driven electro-optic modulation on the stretched waveform in a temporal 4$f$ shaping system, the regime writes pulse-index-dependent spectral phase profiles onto individual pulses. We demonstrate three levels of programmable ability: zero-order phase coding that maps pulse-by-pulse phase sequences into double-slit-like spectral-temporal interference; first-order phase programming that produces arbitrary temporal trajectories with deterministic delay; and fractional-order phase engineering that generates programmable temporal breathing. By launching the shaped pulse train into a nonlinear fiber stage, the programmed temporal breathing is converted into one spectral breathing. We further construct a phase-level-dependent regime map of nonlinear spectral breathing, revealing transitions from weak single-envelope breathing to multi-peak spectral splitting and strongly breathing merged-spectrum dynamics, in agreement with numerical simulations. This pulse-by-pulse spectral-temporal synthesis platform establishes pulse index as a programmable degree of freedom for ultrafast pulse shaping and provides a route toward real-time and pulse resolved optical arbitary waveform synthesis.

In viscous flow, the circulation enclosed by a deterministic material loop generally changes through vorticity diffusion. It is shown that the source-bearing unsteady Kiknadze–Krasnov vortex possesses a distinguished cylindrical material surface on which this viscous circulation transfer vanishes identically. For a single distributed mode, the scaled squared radius $x=\beta(t)r^2$ satisfies \[ \frac{\mathrm D x}{\mathrm D t} = 4\nu\beta(t)\big[(s-1)-x\big], \] where $\beta(t)$ is the inverse squared radial scale, $\nu$ is the kinematic viscosity, and $s$ is the incomplete-gamma shape parameter. For $s>1$, the cylinder $x=s-1$ is transported exactly by the radial flow and preserves the inside/outside ordering of fluid trajectories. The same factor $(s-1)-x$ governs the radial vorticity gradient and the material circulation-transfer rate. Consequently, the magnitude of the distributed axial vorticity $|\omega_z|$ attains its annular maximum on this material cylinder, $\partial_r\omega_z=0$ there, and the enclosed circulation remains exactly $\Gamma P(s,s-1)$ for every admissible prescribed strain history. Neighbouring material cylinders continue to exchange circulation viscously, while the distinguished moving cylinder carries a fixed fraction of the distributed circulation throughout the admissible evolution. In the steady source–strain limit, the surface reduces to the radial stagnation cylinder. The complete velocity and pressure fields, exact radial-scale evolution, admissible parameter ranges, and qualifications for multiple radial scales are also derived. The resulting solution-specific material-circulation invariant provides an exact benchmark for numerical material-surface tracking, vortex-core evolution, and viscous circulation transport, with the principal analytical results additionally supported by machine-checked formal verification.

Aero-optic effects distort light wave propagation near a high-speed aircraft, thereby degrading performance in airborne imaging and communication systems. Measuring aero-optic data through experiment is costly and the resulting data often has a limited spatial size. Further, alternative methods for simulating this data, including computational fluid dynamics and conventional phase screen generation algorithms (e.g., boiling flow), face drawbacks such as large computation time or inaccurate statistics. More recently, data-driven algorithms have been proposed that can synthesize data that matches relevant statistics of measured aero-optic data. However, these methods cannot spatially extend aero-optic data. In this paper, we introduce ReVAR-ext (Re-whitened Vector AutoRegression-extender), an algorithm that builds on an existing data-driven approach, ReVAR, to spatially extend measured aero-optic data (a process called outpainting) and match the spatial and temporal correlations of the measured data. ReVAR-ext generalizes the generation process of ReVAR by combining multiple sets of synthetic data with the input measured data. This approach generates multiple fixed-sized synthetic images, each of which overlaps with the input data, and then stitches them together. When paired with ReVAR, the ReVAR-ext algorithm can generate aero-optic data with arbitrary temporal duration and arbitrary spatial size. Our experiments show that extended data generated by ReVAR-ext closely matches the temporal power spectrum of two measured aero-optic data sets. Further, the extended data approximately matches the spatial autocorrelation, with reduced accuracy at large spatial lags and at vertical lags.

We explore two scenarios of $f(Q,\mathcal{L}_m)$ gravity: linear and non-linear gravity models. The dynamical system analysis identifies two critical points for each of the proposed linear and nonlinear matter–geometry coupling models. These equilibrium points correspond to distinct phases of cosmic evolution. Depending on the model parameters, the resulting critical points successfully reproduce the observed sequence of cosmic evolution, from a decelerated matter-dominated Universe to the present epoch of accelerated expansion. The effective equation of state parameter ($\omega_{\text{eff}}$) and the deceleration parameter ($q$) exhibit smooth transitions from decelerated to accelerated expansion, with transition epochs around $N_{\text{tr}} \approx -0.27$ for linear Model and $N_{\text{tr}} \approx -0.32$ for non-linear Model, consistent with late-time cosmic acceleration. Statistical constraints derived from CC+BAO, DESI DR II, and Pantheon$^+$ datasets provide best-fit values for the model parameters ($\alpha, \beta, \gamma, H_0$), showing compatibility with current cosmological observations. The analysis employs Akaike (AIC) and Bayesian (BIC) information criteria to evaluate model performance. Our results demonstrate that $f(Q,\mathcal{L}_m)$ gravity provides a viable alternative framework for explaining late-time acceleration, with rich dynamical features that merit further exploration in view of upcoming high-precision surveys.

Dark Energy Survey has shown that the emission light curve widths $\Delta t_{\rm em}$ of supernovae increase to $\Delta t_{\rm obs}$, obeying $\Delta t_{\rm obs}/\Delta t_{\rm em}=(1+z)^b$ with $b=1.003\pm0.011$, excluding a nondilating redshift. We test hybrid models with $1+z=(1+z_p)(1+z_t)$ and the phenomenological stretch $(1+z_p)R(z)$, where $z_p$ is the redshift associated with the expanding parent universe cosmology, $z_t$ is a tired-light (TL) contribution, and $R$ is the hybrid-to-parent lookback-time ratio. At $z=1$, $R$ and $1+z_t$ differ by $1.3\%$ for CCC+TL and $0.5\%$ for $\Lambda$CDM+TL, and their 95% bands overlap the observed relation. This compatibility shows that the observations do not exclude the hybrid models, only fully nonexpanding ones.

As increasingly-damaging floods can strike within hours of a storm and in ungauged reaches, hourly network-wide simulation has become critical societal infrastructure. Here we demonstrate a multi-timescale physics-embedded learning model which outperforms the United States' operational system and surpasses AI-based systems at flood peaks. Covering more than 800,000 river reaches of the conterminous U.S., the model elevates median hourly Nash-Sutcliffe efficiency at 2,831 gauges to 0.683 from 0.461 for the operational National Water Model v3.0, and narrows flood-peak timing errors from 7-8 hours to 4.5-6 hours. dHBV2.0MTS-MC captures 33% more >=50-year floods than NWM3.0 and 159% more than an operational LSTM baseline. Against recent AI models, overall hourly skill is comparable while rare-flood accuracy is distinctly higher, with relative peak-magnitude error reduced by 34% for >=100-year floods. It combines long-term hydrologic context, short-term shocks, and infiltration excess to resolve extraordinary hourly peaks not visible on a daily plot. Process-model parameters and hourly discharge are produced for every reach, seamlessly covering the continent at 7.2 km2 median resolution. This candidate for the next-generation National Water Model sets a new operational accuracy level for national-scale flood prediction.

A theory for the general circulation of the atmosphere must be based on a theory of its macroturbulence. A central component is the near-surface eddy heat flux, which is closely tied to the mass transport of the surface branch of the circulation. Existing scaling theories are largely formulated within the two-layer quasi-geostrophic framework, which assumes supercritical states and an inverse cascade of kinetic energy—conditions that are frequently not satisfied in planetary atmospheres, including Earth's. Nevertheless, such theories have shown some empirical success, including that the Rhines scale is associated with the mixing length even in the absence of any inverse energy cascade. Here, we analyze hundreds of idealized, dry general circulation model simulations spanning wide ranges of rotation rates, meridional temperature gradients, vertical stratification, and seasonality. Most simulations reside in the marginally critical regime where strongly nonlinear scaling theories fail. We propose a new theory that captures both domain-averaged and local eddy heat flux behavior across all simulations. The theory uses scaling arguments for the zonal momentum balance and accounts for kinematic effects on eddy mixing. The scaling depends on a non-dimensional thermal Rossby number and implicitly accounts for the role of nonlinear eddy momentum fluxes across jets. We demonstrate the skill of the proposed scaling in explaining global near-surface temperature distributions across a wide range of climate states in a simple energy balance model. Implications for general circulation theory and extending the scalings to moist atmospheres are discussed.

AETHER-P\textsuperscript{3} Nowcast v1.0 is a machine-learning-based global thermospheric neutral-density model developed for low-Earth-orbit applications and prepared for onboarding to NASA's Community Coordinated Modeling Center (CCMC). The model provides pointwise neutral-density estimates together with predictive uncertainty using a deep evidential regression framework driven by causal solar, solar-wind, geomagnetic, spatial, temporal, and empirical-model inputs. This report documents the released model configuration, training-data construction, software traceability, output products, validation strategy, benchmark performance, and known limitations. The training archive combines accelerometer- and mission-derived density observations from CHAMP, GRACE-A, GOCE, Swarm-C, and GRACE-FO spanning 2000–2023. More than 40 million eligible 30-s observations are available, but the archive is strongly dominated by consecutive quiet-time measurements. To preserve coverage of physically important regimes, the final 1.67-million-sample training set is constructed using deterministic regime-aware sampling that progressively subsamples quiet conditions while retaining all available extreme-condition observations. Validation uses temporally disjoint chronological blocks with exclusion guards to reduce information leakage. Evaluation across quiet, moderate, and extreme conditions shows competitive performance relative to HASDM, JB2008, NRLMSISE-00, and available WAM-IPE cases, while also identifying limitations associated with sparse training coverage, mission-dependent density products, and condition-dependent uncertainty calibration. The report provides a reproducible technical description of the AETHER-P\textsuperscript{3} Nowcast v1.0 research release and its current CCMC onboarding configuration.