Papers

Polar molecules represent a promising platform for quantum simulation and computation protocols. Highly controllable arrays of optical tweezers are now accessible in experiments, allowing for unprecedented control of individual molecules. Motional dephasing is typically seen as an obstacle in quantum computing scenarios. Here, we instead consider using the trap structure as a resource for implementing efficient quantum gates. By numerically solving the two-body problem of dipoles trapped in separate tweezers, we identify trap-induced resonances that can serve as the mechanism for achieving state-dependent dynamics and can be further utilized for quantum sensing.

The instability characteristics of lean hydrogen/air flames have attracted considerable research attention, yet the effect of gravity remains insufficiently understood. In this study, time-resolved two-dimensional simulations with detailed chemistry and transport are conducted to investigate the influence of gravity-induced Rayleigh-Taylor (RT) instability on the linear growth rate of disturbances and nonlinear morphology evolution of cellular flame fronts at different length scales. In the linear regime, a parametric study is performed across various equivalence ratios, initial temperatures and pressures; in each case, the dispersion relation is calculated for various gravity levels. The influence of gravity is most pronounced under ultra-lean, low-temperature, and high-pressure conditions, and a universal scaling law between gravity sensitivity and the Froude number is established. In the nonlinear regime, gravity has opposite effects on the large-scale and small-scale structures of lean hydrogen flames. On the one hand, gravity inhibits the splitting of small-scale cellular structures through a baroclinic torque mechanism; on the other hand, it promotes the development of large-scale finger-like structures, thereby increasing the total surface area and the global consumption speed of the flame. The effects of gravity on the probability distributions of cell size, displacement speed, Karlovitz number, and local curvature are also analyzed. The results and findings of the present study should advance the fundamental understanding of hydrogen flame dynamics under varying gravity conditions and provide insight for relevant applications, including fire safety and space propulsion.

Topological photonics has emerged as a powerful platform for terahertz on-chip systems due to its robust waveguiding capabilities. However, directly extracting topological valley-locked edge states into directional free-space radiation without auxiliary couplers while preserving guided-wave functionality remains a fundamental challenge. In this work, we propose and experimentally demonstrate a bifunctional topological valley-reshaped device. By introducing an angular truncation and a spatial displacement to a complete topological waveguide (TW), the resulting structure inherently retains its waveguiding capabilities. Furthermore, when operated as an isolated section, it functions as a topological leaky-wave antenna (TLWA) that exhibits directional single-lobe radiation. The TW shows low-loss guided-wave performance with an 18 GHz operating bandwidth, supporting error-free transmission up to 60 Gbps. For the TLWA, by gradually reducing the number of protective lattices that are orthogonal to the propagation direction, the valley-locked edge state becomes momentum-matched to the free-space light line, generating leaky-wave radiation. Simultaneously, reshaping of the opposite valley-locked edge state suppresses far-field side lobes and reduces reflection, yielding a clean single-beam radiation pattern with a side-lobe suppression ratio (SLSR) exceeding 15 dB. The TLWA realizes a measured peak gain of 12.5 dBi and a 19 GHz operating bandwidth. Notably, the low-dispersion property of the K-valley radiation allows the main-lobe direction to vary by only 2 degrees across the entire operating band, enabling error-free free-space reception at 24 Gbps. This bifunctional design represents a key step toward highly integrated and modular terahertz on-chip systems.

We report measurements of the electron drift mobility in dense argon gas over an extended range of densities, temperatures, and electric fields, supplementing our earlier work. The measurements confirm the validity of the heuristic model we previously developed by introducing multiple scattering effects in the classical kinetic theory description of the electron mobility in a dilute gas. We definitively show that, in the argon gas, because of the particular energy dependence of its electron-atom momentum-transfer scattering cross section, none of the multiple scattering effects we have identified in the past can be neglected if the mobility behavior is to be accurately rationalized over the whole investigated parameter range.

Thermoacoustic instabilities remain a major challenge in the operation and development of modern gas turbine combustors for power generation and propulsion. In laboratory environments, such instabilities can also hinder the accurate characterization of key thermoacoustic properties of the flames. Many modern combustors therefore employ wall-mounted acoustic dampers, such as Helmholtz or quarter-wave resonators; however, these devices are typically effective only over narrow frequency ranges. In this study, the application of perforated acoustic black holes (ABHs) as broadband passive dampers for thermoacoustic instability mitigation is investigated. Several ABH designs are additively manufactured and experimentally characterized through scattering matrix measurements. A reduced-order model based on the transfer matrix method is developed and is shown to be in good agreement with the experimental results. Using this validated model, a damper design is optimized to maximize acoustic dissipation over the frequency range 500-2000 Hz. The optimized perforated ABH damper is installed in the plenum section of the combustor test rig, and its thermoacoustic performance is evaluated over a range of equivalence ratios and outlet boundary conditions. Across all operating conditions considered, the ABH damper leads to a substantial reduction of the amplitude of the acoustic pressure oscillations. These results demonstrate the potential of perforated ABH-based dampers as a robust and broadband passive solution for mitigating thermoacoustic instabilities in hydrogen-fueled combustors.

Wildfire spread is strongly influenced by the transport and ignition of embers. While long-range spotting driven by plume lofting has received significant attention, embers transported near the surface by turbulent winds can also influence fire propagation. We develop a stochastic model for near-surface ember transport, referred to as ember wash. The model represents ember motion as a sequence of short displacements analogous to saltation-like transport and incorporates a probabilistic ignition process that depends on ember survival during transport. This formulation leads to an exponential distribution of ember flight times. The model is implemented within a simplified fire spread model to examine burn patterns and growth dynamics. Simulations demonstrate that ember wash produces spread behavior that differs fundamentally from classical plume-driven spotting. These results suggest that ember wash provides a plausible mechanism for wildfire spread regimes that differ from those predicted by geometric or plume-driven spread models.

Modeling the Scape-off layer (SOL) of stellarator fusion devices is challenging due to the complicated magnetic topology, requiring numerical tools to solve transport equations for realistic geometries. Previously the flux coordinate independent (FCI) method has been successfully applied to model the SOL in simplified geometries. The current work presents some of the recent improvements for the BOUT++ modeling implemented to simulate the SOL in realistic geometries with the example of Wendelstein 7-X. The changes include improvements for the grid generation tool, the physics model as well as the BOUT++ library itself. A short outlook is given on current modeling work using the new features.

Free-electron lasers (FELs) provide a revolutionary tool for capturing the structure and dynamics of matter in real time at the atomic scale. The size and cost of FELs can be substantially reduced by using laser wakefield acceleration (LWFA), which offers acceleration gradients orders of magnitude beyond radiofrequency technology, producing multi-GeV electron beams within tens of centimeters. This compactness opens the possibility of integrating multiple operating modes - from the EUV to X-rays including broadband operation - into one facility. Realizing this vision, however, faces key challenges: current LWFA bunches are too short to sustain sufficient radiation slippage, limiting FEL pulse energy at EUV wavelengths, while the large energy spread and emittance make X-ray lasing even more demanding. Here we present a LWFA-driven FEL scheme that addresses these challenges, enabling multi-mode operation spanning different wavelengths and bandwidths within a single facility. The scheme employs staged acceleration to reach multi-GeV energies while preserving beam quality, combined with a dual-chicane beamline that stretches the bunch to mitigate the radiation slippage for EUV FEL and tailors the energy chirp for diverse FEL bandwidth modes. Simulations demonstrate that the scheme can generate high-quality electron beams with energies up to 7 GeV and tunable energy chirp, enabling both FEL saturation from the EUV to X-ray wavelengths and large bandwidth operation with a bandwidth of up to 11%. This work provides a roadmap for compact, multi-mode FELs based on plasma acceleration, and the high-energy, high-quality beams achieved also point toward compact injectors for next-generation storage-ring light sources.

This study investigates the role of inertia in moving contact lines using experiments, theoretical analysis, and numerical simulations. Experiments are conducted using a plate immersion configuration over a wide range of Reynolds numbers from $O(10^{-3})$ to $O(10)$. Flow configurations and quantitative measurements are obtained using high-speed imaging and particle image velocimetry. The streamfunction contours reconstructed from the experimental velocity fields are compared with the viscous modulated wedge solution (viscous-MWS) and inertial-MWS theory. Experimental observations show that the streamfunction contours agree well with viscous predictions at low Reynolds numbers; however, systematic deviations emerge as the Reynolds number increases. The inertial-MWS theory, an inertial extension of the Huh and Scriven framework, accounts for these deviations, but only within a narrow range of Reynolds numbers $10^{-1} < Re < 1$. At higher Reynolds numbers, inertial theory fails to accurately capture the deviations in the streamfunction contours observed in the experiments. Moreover, simulations conducted using the volume of fluid method support our findings, exhibiting deviations in streamfunction contours consistent with experimental observations. We demonstrate that inertia does not fundamentally alter the underlying flow configuration but instead induces a systematic deviation in the streamfunction contours. At finite $Re$, the interfacial speed transitions from a nearly constant value in the viscous regime to a monotonic decay along the interface. These findings expose the need for more sophisticated models of moving contact lines.

Iterative model-based image reconstruction in photoacoustic tomography (PAT) enables principled incorporation of detector physics, object-related priors, and complex acquisition strategies. However, for three-dimensional (3D) imaging scenario, the computational cost is often dominated by repeatedly solving wave equations. We propose a fast forward model for planar detection geometries that exploits transverse shift invariance. This symmetry enables to compute the full acoustic field from a 3D object, as a result of a set of 2D convolutions with depth-dependent impulse responses. This formulation yields a FFT-based forward operator and its corresponding discrete adjoint operator, making iterative reconstruction faster without calling partial differential equation (PDE) solvers at each iteration. We validate the model against commonly used PDE solver under matched discretization and boundary settings, and demonstrate accelerations of up to 2 orders of magnitude for iterative reconstructions from experimental all-optical photoacoustic datasets.

Powder snow avalanches are highly dynamic, multiphase gravity-driven flows typically composed of a dense basal layer overlain by airborne layers in which snow particles are suspended within a turbulent air phase. Despite extensive work on related systems such as pyroclastic density currents and turbidity currents, all gravity current communities face a fundamental limitation: the lack of direct, high-resolution particle-scale field data. Here, we present the first direct optical observations of individual particle motion inside the airborne layers of a natural powder snow avalanche using high-speed imaging. The flow is segmented into three regions: an initial short living surge, a highly dynamic suspension phase, and a final wake. Across these phases, we quantify flow velocity and turbulence characteristics, including integral length scales, and use image intensity as a proxy for particle concentration. We identify fluctuations exceeding the turbulent integral scale and use linear stability analysis to link them to Kelvin-Helmholtz-type shear instabilities. Observed changes in clustering behavior, stratification, and the decoupling of particles from the flow mark the transition from an unstable suspension layer characterized by a high level of turbulent activity to a stable one dominated by passive snow settling. Together, these findings provide the first empirical constraints on turbulence and instability dynamics in airborne avalanche layers, with direct implications for the refinement of numerical avalanche models and closure schemes in multiphase gravity current simulations.

The dependence of surface-enhanced Raman scattering (SERS) spectra on the precise axial position of the laser focus relative to a solid nanostructured substrate has received little to no attention in the literature. Here we show this dependence is both real and physically meaningful. Through vertical (Z-axis) scans varying the distance between the laser focus and a planar SERS substrate, we find that the SERS signal intensity follows a Lorentzian axial profile that peaks consistently above the physical sample surface. More significantly, the relative intensities of different spectral regions, i.e. SERS bands and background, vary non-monotonically and non-uniformly along the Z axis, meaning that band intensity ratios are focus-dependent. Finite-Difference Time-Domain (FDTD) simulations attribute these effects to plasmonic near-field responses specific to the focused and defocused beam interacting with the nanostructured metal surface. These findings reveal a previously overlooked source of spectral distortion in solid-substrate SERS measurements, with direct implications for the design and interpretation of quantitative assays based on band intensity ratios.

Emergent Dirac fields, exhibiting effective relativistic physics, are most commonly associated with bulk and surface states in materials such as graphene and topological insulators. Here we show that phase dislocations of a complex Dirac mass support a distinct family of propagating Dirac defect states that behave as massless or massive one-dimensional relativistic modes. These traveling-wave defect states, termed Dirac branch-cut (DBC) modes, are governed by a one-dimensional Dirac Hamiltonian whose effective mass is controlled by the phase difference across the branch cut. Using acoustic metamaterials, we experimentally demonstrate a range of relativistic phenomena exhibited by DBC modes, including relativistic dispersion, energy-independent confinement, Klein tunnelling, and transport along freeform (e.g., spiral) trajectories. Our results establish branch-cut defects as a distinct platform for realizing propagating Dirac defect states, extending relativistic Dirac physics from bulk and surface states to propagating modes confined to defect boundaries.

Intrinsic instabilities of laminar premixed flames play an important role in the dynamics of hydrogen combustion and in the development of predictive models for reacting flows. However, determining their dispersion relations typically relies either on simplified analytical descriptions of the flame front or on computationally expensive direct numerical simulations (DNS). This work develops a generalized eigenvalue problem-based linear stability analysis (GEVP-LSA) framework that predicts the growth rates and spatial structure of intrinsic flame instabilities directly from the linearized governing equations of a 1D base flame. The approach is first validated using the classical Darrieus-Landau configuration, where the numerical results reproduce the analytical dispersion relation and eigenmode structure. The framework is then applied to a model flame of finite thickness governed by the reactive Navier-Stokes equations. The resulting dispersion relations and perturbation fields show excellent agreement with corresponding DNS results while reducing the computational effort by a factor of 1e8. The proposed method therefore provides an efficient and accurate tool for studying intrinsic flame instabilities and offers a scalable foundation for future stability analyses of more complex reacting-flow configurations relevant to combustion modeling and large-eddy simulations.

Reconstructing high-resolution turbulent flow fields from severely under-resolved observations is a fundamental inverse problem in computational fluid dynamics and scientific machine learning. Classical interpolation methods fail to recover missing fine-scale structures, while existing deep learning approaches rely on convolutional architectures that lack the spectral and multiscale inductive biases necessary for physically faithful reconstruction at large upscaling factors. We introduce the Spectrally-Informed Multi-Resolution Neural Operator (SIMR-NO), a hierarchical operator learning framework that factorizes the ill-posed inverse mapping across intermediate spatial resolutions, combines deterministic interpolation priors with spectrally gated Fourier residual corrections at each stage, and incorporates local refinement modules to recover fine-scale spatial features beyond the truncated Fourier basis. The proposed method is evaluated on Kolmogorov-forced two-dimensional turbulence, where $128\times128$ vorticity fields are reconstructed from extremely coarse $8\times8$ observations representing a $16\times$ downsampling factor. Across 201 independent test realizations, SIMR-NO achieves a mean relative $\ell_2$ error of $26.04\%$ with the lowest error variance among all methods, reducing reconstruction error by $31.7\%$ over FNO, $26.0\%$ over EDSR, and $9.3\%$ over LapSRN. Beyond pointwise accuracy, SIMR-NO is the only method that faithfully reproduces the ground-truth energy and enstrophy spectra across the full resolved wavenumber range, demonstrating physically consistent super-resolution of turbulent flow fields.

For strongly quantum-degenerate systems at finite temperatures, the fermion sign problem remains the major obstacle to first-principles simulations. In this work, we apply the recently proposed pseudo-fermion method - designed to overcome the sign problem - to strongly quantum-degenerate uniform electron gases. We find that the pseudo-fermion method can efficiently and highly accurately infer the energy of the uniform electron gas while being free from the fermion sign problem. For example, in the strongly quantum-degenerate regime where RPIMC fails (33 spin-polarized electrons at the density parameter $r_s = 0.5$), the relative deviation between the pseudo-fermion method and the exact CPIMC result is only 0.6%. In particular, the pseudo-fermion method bridges the gap where neither CPIMC nor RPIMC can accurately simulate the regime $1 \le r_s \le 2$ at the reduced temperature $\theta = 0.0625$. This work demonstrates that the pseudo-fermion method opens a new pathway for studying strongly quantum-degenerate systems in a sign-problem-free manner.

We introduce a spin $z$-component ($S_{z}$) conserving symmetry-preserving ansatz and a shallow quantum phase estimation (QPE) routine of spin $x$ ($S_x$), and combine them into a spin-filtering variational quantum deflation (sfVQD) scheme for noisy intermediate-scale quantum (NISQ) computing era excited state calculations. The scheme encodes the spin information into a small ancilla register through controlled rotations under $\mathrm{exp} (i\theta\hat{S}_{x})$ with only modest circuit overhead. The encoded information is then utilized to suppress spin contamination by screening, avoiding costly explicit evaluation on the total spin $\langle\hat{S}^{2}\rangle$. Because the screening module operates independently of the variational ansatz, it can also be employed with other excited-state calculation schemes based on variational quantum eigensolvers. As a demonstration, we apply sfVQD to LiH and BeH$_2$ with varying geometries to show markedly improved separation of singlet and triplet manifolds over conventional VQD without QPE-derived screening. These results suggest that ancilla-assisted symmetry screening provides a modular and NISQ-compatible route to securing excited state calculations of physically meaningful properties. We discuss how our scheme may naturally be extended to computing other conserved quantities.

Investment in artificial intelligence (AI) has grown rapidly, yet its returns to scientific research remain poorly understood. We study how AI reshapes the production of science using a comprehensive dataset of research proposals submitted to a large international funding agency, including both funded and unfunded projects. Combining keyword extraction with large language model classification, we identify the presence, type, and functional role of AI within each proposal and link these measures to detailed budget allocations, team structure, and subsequent publication outcomes. We find that, in the short run, AI adoption is associated with modest improvements in scientific outcomes concentrated in the upper tail. Instead, its primary effects arise in the organization of research: AI-enabled projects reallocate resources toward human capital, involve larger teams, and undertake a broader set of tasks. These patterns are consistent with a reorganization of the scientific production process rather than immediate efficiency gains, in line with theories of general-purpose technologies. Task-level analyses further show that activities expanded in AI-enabled projects, particularly ideation and experimentation, are increasingly compatible with large language model capabilities, suggesting potential for future productivity gains as these technologies mature.

Trees are key roughness elements in urban environments, shaping airflow, microclimates, and pollutant dispersion. Yet the aerodynamic drag of complex tree-like structures at high Reynolds numbers remains poorly characterized compared with the well-studied drag crisis of simple bluff bodies. We combine large-scale lattice Boltzmann simulations with an analytical branch-wise drag model to examine fractal trees over a wide range of height-based Reynolds numbers, $Re_H$. Direct numerical simulations using a cumulant lattice Boltzmann method with adaptive mesh refinement cover $2.5\times10^3 \le Re_H \le 1.2\times10^5$, and the analytical model extends predictions to $Re_H \sim 10^9$. Under uniform inflow, the analysis indicates a drag-crisis transition near $Re_H \approx 3\times10^6$, with increasing structural complexity smoothing this transition because smaller branches remain subcritical. Introducing inflow turbulence with streamwise intensity $I_u \approx 8\%$, representative of atmospheric-boundary-layer winds, shifts the apparent onset to $Re_H \approx 1.5\times10^5$ and further moderates the drag reduction. Interpreted at full scale, this suggests that urban trees of order $10$–$30$ m exposed to winds of $1$–$10~\mathrm{m/s}$ generally operate in the crisis or post-crisis regime. In both uniform and turbulent inflow, the framework predicts a reversal in drag-coefficient ordering across geometries: simplified trees show lower drag in the subcritical regime but may exhibit higher drag in the supercritical regime, whereas more complex trees undergo a smoother, moderated crisis. These results challenge the common assumption that pruning always reduces aerodynamic loading and highlight the need to reassess vegetation-drag parameterizations and pruning strategies in high-$Re_H$ conditions.

This study investigates the characteristics of X-pinch plasmas driven under low current rise rate ($dI/dt$) conditions using soft x-ray spectroscopy combined with the Bennett relation. X-pinch experiments were conducted on the SNU X-pinch device using copper wires at a low $dI/dt$ of 0.2-0.3 kA/ns. The resulting 1-10 keV soft x-ray signals, measured by an x-ray filtered AXUV photodiode array (XFPA), exhibit significant nonlinear effects due to the high intensity of the soft x-ray pulses. This work characterizes the nonlinear behavior of the AXUV-HS5 Si PIN photodiodes under intense pulsed radiation using a pulsed laser. We identified a charge conservation property that the total collected charge remains proportional to the incident pulse energy despite temporal profile distortion. Based on this diagnostic finding, we developed and applied a new framework for plasma parameter estimation. By combining a spherical emission model with the Bennett equilibrium, this approach determines that the soft x-ray source plasma is a 'bright spot', characterized by a plasma density $n_e \sim 10^{21} \text{ cm}^{-3}$, size $d \sim 30-40\ \mu\text{m}$, electron temperature $T_e \sim 1 \text{ keV}$, and an emission duration $t_B \sim 1 \text{ ns}$, rather than an extremely compressed 'hot spot'.

The bulk-edge correspondence principle, a cornerstone of topological physics, ensures that first-order topological systems host robust chiral edge states in two dimension. This was later extended to higher-order phases, where second-order topological insulators exhibit localized, topologically protected corner states. While the transition between these distinct phases has been demonstrated in periodic systems, its existence in disordered platforms remains an open question. Here, we demonstrate a controllable topological phase transition between a second-order topological phase and a first-order topological phase in a two-dimensional photonic alloy. By tuning the magnetic doping concentration - implemented by attaching permanent magnets randomly to nonmagnetized yttrium iron garnet rods in an alternately magnetized honeycomb lattice with C3 rotational symmetry - we flexibly control the system's topology. At zero doping, we observe higher-order corner states, confirmed by a trivial Chern number and non-zero bulk polarizations of 1/3. As doping concentration increases, these corner states progressively merge with the bulk states, culminating in the closure of the bulk transmission gap. After the bulk transmission gap reopens with further increased doping, the system transitions to a first-order topological phase, characterized by a nontrivial Chern number of -1 and the emergence of a chiral edge state. This transition is reversible, providing a highly tunable and experimentally simple platform for flexibly switching between localized corner states and delocalized chiral edge states within a single photonic system.

New symmetries have been found in Einstein-Maxwell spacetimes. New symmetries have also been found in imperfect fluid curved spacetimes. We will prove in this paper that we can extend these symmetries to spacetimes with higher curvature terms. Higher curvature theories are in many cases associated to dark energy for instance. We provide further justification for these higher curvature formulations through the existence of a new symmetry.

We develop a bicategorical formulation of entropy and Landauer's principle in which deterministic monotone maps between logical and thermodynamic state spaces are embedded into a broader theory of open, many-to-many feasibility interfaces. Entropy-ordered state spaces form the objects of a locally posetal bicategory, Boolean profunctors encode feasible implementations, and refinements define 2-morphisms. Although this bicategory admits genuinely non-representable interfaces, every exact Landauer adjunction lies in its representable sector and is generated by a unique monotone implementation map. Such an adjunction also induces an idempotent boundary monad, together with a dual idempotent bulk comonad. Our main result identifies the Eilenberg-Moore object of the boundary monad with a universal boundary-stable quotient determined by the bulk-mediated round-trip relation. This provides a canonical reconstruction principle: the quotient captures exactly those boundary distinctions that remain observable after implementation in the bulk and subsequent readout. In the representable case, two boundary states are identified precisely when they have the same image under the implementation map. Therefore, the result turns irreversible information loss into an explicit universal object and provides a compositional framework for studying reconstruction, coarse-graining, and bulk-boundary visibility in open thermodynamic systems. Finally, we outline a potential cost-enriched extension in which interfaces carry dissipation costs and composition minimizes accumulated dissipation over admissible intermediate realizations, opening a route from Boolean feasibility to quantitative thermodynamic implementation.

Metal Laser Powder Bed Fusion (PBF-LB/M) is a leading additive manufacturing technique in which part quality and grain morphology are highly dependent on process parameters. Numerous studies of process variations, such as laser power, scan speed, and spot diameter, have demonstrated that they strongly influence melt pool dynamics; however, the effects of powder layer height and geometric variations remain less well understood. In this article, we focus on variations in powder layer height and part geometry to study their influence on melt pool dynamics. We employed a high-fidelity multiphysics simulation framework based on the open source finite volume method (FVM) solver package `LaserBeamFoam' built on `OpenFOAM' to study the variations in different melt pool metrics – melt pool depth, width, bead height, overlap depth, overlap width, solidified area, and dilution area. The solver captures coupled phenomena of heat transfer, fluid flow, vaporization, recoil pressure, Marangoni convection, and realistic laser reflection behavior to accurately model the melt pool dynamics. Simulations are performed for different powder layer heights and geometric dimensions for direct comparison with benchmark experiments conducted at the National Institute of Standards and Technology (NIST) in 2025. Quantitative validation against NIST experiment demonstrates excellent agreement in all the melt pool metrics. These results highlight the predictive capability of physics-based PBF-LB models, paving the way for process optimization, defect mitigation, and the integration of simulation into digital twin frameworks for additive manufacturing.

We introduce AIFS-COMPO, a skilful medium-range data-driven global forecasting system for aerosols and reactive gases. Building on the ECMWF Artificial Intelligence Forecast System (AIFS), AIFS-COMPO employs a transformer-based encoder-processor-decoder architecture to jointly model meteorological and atmospheric composition variables. The model is trained on Copernicus Atmosphere Monitoring Service (CAMS) reanalysis, analysis, and forecast data to learn the coupled dynamics of weather, emissions, transport, and atmospheric chemistry. We evaluate AIFS-COMPO against a range of atmospheric composition observations and compare its performance with the operational CAMS global forecasting system IFS-COMPO. The results show that AIFS-COMPO achieves comparable or improved forecast skill for several key species while requiring only a fraction of the computational resources. Furthermore, the efficiency of the approach enables forecasts beyond the current operational horizon, demonstrating the potential of AI-based systems for fast and accurate global atmospheric composition prediction.

We study the emission of Cherenkov plasmons by the gas of neutrinos with a nonzero temperature and a chemical potential. The background plasma, consisting of charged leptons, is taken to be nonrelativistic. The energy emission rate is obtained for longitudinal plasmons. To get the neutrino emissivity we average quantum field theory matrix element over the distribution functions of incoming and outgoing particles. Our results are applied for the description of the cooling down of a neutrino cluster formed in the early universe. Such clusters can exist owing to the neutrino interaction with a hypothetical light scalar boson. Using particular cluster parameters, we demonstrate that the considered cooling mechanism is efficient for some clusters. We find the temperature range where the proposed cooling channel is valid. Some useful calculations of the polarization tensor, as well as the plasmon form factors and their dispersion relations are also provided.

We present a quantum-mechanical treatment of positron channeling radiation in a planar harmonic potential that explicitly accounts for interference between transition amplitudes from different transverse energy levels. Because the planar channel potential for positrons in diamond~(110) is well approximated by a parabola, the transverse spectrum is equidistant, $\varepsilon_n = \Omega(n+\tfrac{1}{2})$, and all $n \to n{-}j$ transitions radiate at the same Doppler-shifted frequency. The sudden-approximation entry of the positron into the crystal produces a Glauber coherent state~\cite{Glauber1963} with Poisson-distributed level populations $|c_n|^2 = e^{-n_0}n_0^n/n!$ and mean occupation $n_0 \propto \theta_{\rm in}^2$. Phase synchronization between the $c_n$ and the dipole matrix elements ensures constructive interference of all contributing amplitudes. Three exact scaling laws follow: (i)~$I_{\rm incoh}\propto n_0\propto\theta_{\rm in}^2$; (ii)~$I_{\rm coh}\propto n_0^2\propto\theta_{\rm in}^4$; (iii)~$\mathcal{G}\equiv I_{\rm coh}/I_{\rm incoh}\approx n_0 \propto\theta_{\rm in}^2$. Numerically, $\mathcal{G} = 12\text{--}31$ for positron energies of $4\text{--}14$~GeV in diamond~(110) at $\theta_{\rm in}=31\;\mu$rad, in agreement with the experimental first-harmonic peak positions of Avakyan et al.~\cite{Avakyan1982} to within 15%. The transition from $N$- to $N^2$-scaling of radiated intensity, driven by quantum coherence, opens a route toward high-intensity monochromatic gamma-ray sources.

The particle in an expanding/contracting 1-dimension box is revisited in action-angle like variables with direct thermodynamic interpretation. An angle dependent potential is proposed accurately describing the mechanical behavior while also capturing thermodynamic evolution – entropy production – within a canonical Hamiltonian framework. Heat transfer at constant volume is analyzed, and the derived thermal conductance matches the universal quantum of heat conductance $G_{Q}$ in the quantum limit. Having a Hamiltonian scheme interpretable in thermodynamic terms, a Schrödinger-like wave equation is formulated whose wavefunction solutions contain the information about the entropy evolution. The results show exact agreement with 'classical' results for non abrupt changes. Finally, comparisons with a pure quantum mechanical treatment of the wave function in an expanding box confirm consistency in quasi-static regimes and reveal adiabaticity breakdown under far-from-equilibrium conditions.

Diffusion exchange spectroscopy (DEXSY) is a method to probe exchange between domains of varying confinement. Analyses of DEXSY signals typically assume Gaussian diffusion within distinct compartments and first-order exchange kinetics between them. Other situations can yield DEXSY signal contrast with respect to mixing time, however, leading to potentially erroneous interpretation. Here, we demonstrate that a one-dimensional compartment with reflecting boundaries and without relaxation can by itself produce such contrast in certain experimental regimes. The origin of this contrast is the diffusive mixing of spin isochromats initially near versus far from either boundary, as the former can be relatively coherent in an effect known as edge enhancement or signal localization. We consider DEXSY signals in the case of extended field gradients and identical encodings. Signals were generated via a numerical approach that solves the Bloch-Torrey equation in discrete space and time using matrix operators. We find that in the localization regime, an apparent first-order rate constant of exchange, $k$, can be extracted from DEXSY signals even in this minimal system. The measured $k$ is approximately proportional to $D/L^2$, where $D$ is the diffusivity and $L$ is the domain size. Typically, $k \approx \pi^2 D/L^2$. We attribute this localization-driven exchange to the relaxation of spatial magnetization modes with mixing time, noting that $\pi^2 D/L^2$ is the first non-zero eigenvalue of the Laplacian basis. These results demonstrate that DEXSY and related methods such as filter exchange spectroscopy (FEXSY) may not be specific to genuine barrier permeation.

Artificial intelligence (AI) and machine learning (ML) models in materials science are predominantly trained on ideal bulk crystals, limiting their transferability to real-world applications where surfaces, interfaces, and defects dominate. We present Mat3ra-2D, an open-source framework for the rapid design of realistic two-dimensional materials and related structures, including slabs and heterogeneous interfaces, with support for disorder and defect-driven complexity. The approach combines: (1) well-defined standards for storing and exchanging materials data with a modular implementation of core concepts and (2) transformation workflows expressed as configuration-builder pipelines that preserve provenance and metadata. We implement typical structure generation tasks, such as constructing orientation-specific slabs or strain-matching interfaces, in reusable Jupyter notebooks that serve as both interactive documentation and templates for reproducible runs. To lower the barrier to adoption, we design the examples to run in any web browser and demonstrate how to incorporate these developments into a web application. Mat3ra-2D enables systematic creation and organization of realistic 2D- and interface-aware datasets for AI/ML-ready applications.