Papers

Single-photon emitters (SPEs) are central to quantum communication, computing, and metrology, yet their development remains constrained by trade-offs in purity, indistinguishability, and tunability. This review presents a mechanism-based classification of SPEs, offering a physics-oriented framework to clarify the performance limitations of conventional sources, including quantum emitters and nonlinear optical processes. Particular attention is given to hybrid organic-inorganic perovskite quantum dots (HOIP QDs), which provide size- and composition-tunable emission with narrow linewidths and room-temperature operation. Through comparative analysis of physical mechanisms and performance metrics, we show how HOIP QDs may address key limitations of established SPE platforms. Recognizing the constraints of current deterministic sources, we introduce a performance framework to guide the development of scalable SPEs, and examine the theoretical potential of bright squeezed vacuum (BSV) states, discussing how BSV mechanisms could serve as a promising avenue for multiplexable, high-purity photon generation beyond conventional heralded schemes. The review concludes by outlining future directions for integrating HOIP- and BSV-based concepts into scalable quantum photonic architectures.

The hyperfine interaction in the ground state of a hydrogen atom of assumed radius $R$ is proportional to $-1/R^3$, raising the question of why the hyperfine interaction does not lead to collapse of hydrogen, or positronium. We approach the problem in terms of a minimax variational calculation based on the exact Gordon solution of the Dirac equation for the hydrogen atom ground state. The full Dirac treatment leads to the result that in an assumed variational state of size $R$, when $R$ minimizes the total energy the magnetic moment of the electron assumes its usual value, $e\hbar/2mc$, but when $R<\hbar/mc$, the effective electron magnetic moment becomes essentially $eR/2$, softening the hyperfine interaction and eliminating an energy minumum at small $R$. The magnetic moment of the proton is similarly suppressed, and the hyperfine interaction of a small size atom becomes bounded by the kinetic energy, thus assuring stability. We extend the Dirac variational calculation to positronium where we find simple results for the ground state energy and hyperfiine interaction, and then extend this variational calculation to Coulombic atoms of two fermions of arbitrary masses. This paper also lays out a framework for treating diquarks as relativistic Coulombic systems, in the presence of color electric and magnetic interactions.

We consider the thermochemical impact of post-CCSD(T) contributions to the total atomization energy (TAE, the sum of all bond energies) of first- and second-row molecules, and specifically their coupling with the subvalence correlation contribution. In particular, we find large contributions from (Q) when there are several neighboring second-row atoms. Otherwise, both higher-order triples $T_3$–(T) and connected quadruples (Q) are important in systems with strong static correlation. Reoptimization of the reference geometry for core-valence correlation increases the calculated TAE across the board, most pronouncedly so for second-row compounds with neighboring second-row atoms. %just slightly increases the calculated TAE for all species, but more pronouncedly so if strong static correlation is present, as well as for second-row compounds, again especially with neighboring second-row atoms. We present a first proposal for a `W5 theory' protocol and compare computed TAEs for the W4-08 benchmark with prior reference values. For some key second-row species, the new values represent nontrivial revisions. Our predicted TAE$_0$ values (TAE at 0 K) agree well with the ATcT (active thermochemical tables) values, including for the very recent expansion of the ATcT network to boron, silicon, and sulfur compounds.

We investigate the angle of repose ${\theta}_r$ of dense suspensions of colloidal silica particles ($d = 2$ $\mu m$ to $7$ $\mu m$) in water-filled microfluidic rotating drum experiments, to probe the crossover between the thermal (colloidal) and athermal (granular) regimes. For the smallest particles, thermal agitation promotes slow creep flows, and piles always flatten completely regardless of their initial inclination angle, resulting in ${\theta}_r = 0$. Above a critical particle size, piles of colloids stop flowing at a finite angle of repose, which increases with particle size but remains below the minimal value expected for athermal frictionless granular materials: $0 < {\theta}_r < {\theta}_{ath} \approx 5.8{\deg}$. We quantify the arrest dynamics as a function of the gravitational Péclet number $Pe_g$, which characterizes the competition between particle weight and thermal agitation. Our measurements are consistent with a recent rheological model [Billon et al., Phys. Rev. Fluids 8, 034302, 2023], in which the arrested state stems from a crossover between glass and jamming transitions as the granular pressure in the pile increases relative to the thermal pressure.

In the quest to understand large-scale collective behavior in active matter, the complexity of hydrodynamic and phoretic interactions remains a fundamental challenge. To date, most works either focus on minimal models that do not (fully) account for these interactions, or explore relatively small systems. The present work develops a generic method that combines high-fidelity simulations with symmetry-preserving descriptors and neural networks to predict hydro-phoretic interactions directly from particle coordinates (effective interactions). This method enables, for the first time, self-contained particle-only simulations and theories with full hydro-phoretic two-body interactions.

In a staged, beam-driven, plasma wakefield accelerator, electrons are accelerated in a sequence of plasma stages, each powered by a driver electron bunch. Between each stage, a magnetic chicane is used to dispose of the spent driver and to inject the new fresh one, while transporting the witness bunch. We discuss the effect of synchrotron radiation in the interstage section on the accelerating gradient and on the final energy reach of such a machine.

In fully developed homogeneous and isotropic turbulence, the Lagrangian and Eulerian descriptions of motion, although formally equivalent, become statistically decoupled. In this work, by invoking Liouville theorem, we show that the joint probability density function (PDF) of the Eulerian and Lagrangian fields, evolving from arbitrary initial conditions, relaxes exponentially toward a factorized form given by the product of the corresponding marginal PDFs. This relaxation is governed by a genuine spectral gap of the Liouville operator, whose magnitude is primarily set by the bifurcation rate of the velocity gradient dynamics, whereas the contribution of Lyapunov exponents is shown to be significantly smaller. As a consequence, Eulerian Lagrangian correlations decay rapidly, and if the joint PDF is initially factorized, its factorized structure is preserved at all subsequent times, with each marginal evolving independently under the corresponding dynamics. We further show that the formal equivalence between the two descriptions implies the invariance of the relative kinetic energy between arbitrarily chosen points. When combined with the asymmetric statistics of instantaneous finite scale Lyapunov exponents in incompressible turbulence, this property provides a quantitative interpretation of particle pair separation and of the turbulent energy cascade. Finally, these results naturally lead to nondiffusive closure relations for the von Kármán Howarth and Corrsin equations, which coincide with those previously proposed by the author, thereby providing an independent theoretical validation of those closures.

Benign termination, in which magnetohydrodynamic (MHD) instabilities deconfine runaway electrons (REs) following hydrogenic injections, is a promising strategy for mitigating dangerous RE loads after disruptions. Recent experiments on the Joint European Torus (JET) have explored this scenario at higher pre-disruptive plasma currents than are achievable on other devices, revealing challenges in obtaining benign terminations at $I_p \geq 2.5$ MA. This work analyzes the evolution of these high-current RE beams and their terminating MHD events using fast magnetic sensor measurements and EFIT equilibrium reconstructions for approximately $40$ JET and $20$ DIII-D tokamak discharges. On JET, unsuccessful non-benign terminations occur at low edge safety factor ($q_{\text{edge}} \approx 2$), and are preceded by intermittent, non-terminating MHD events at higher rational $q_{\text{edge}}$. Trends in the internal inductance $l_i$ indicate more peaked RE current profiles in the high-$I_p$ non-benign population, which may hinder successful recombination through re-ionization. In contrast, benign terminations on JET typically occur at higher $q_{\text{edge}} \geq 3$ and exhibit less peaked RE current profiles. DIII-D displays a range of terminating edge safety factors, correlated with the measured $l_i$ values. Across both tokamaks, the RE current peaking is therefore found to determine which MHD instability boundary is encountered, confirmed by linear resistive MHD modeling with the CASTOR3D code. Measured growth rates are similar for benign and non-benign cases, indicating that ideal MHD timescales at low density after hydrogenic injection do not alone explain efficient RE deconfinement. Instead, non-benign cases are characterized by their lower MHD perturbation amplitudes $\delta B$. These observations suggest that the interplay between ideal and resistive dynamics governs the termination process.

Quantum computation is inherently hybrid, and fast classical manipulation of qubit operators is necessary to ensure scalability in quantum software. We introduce PauliEngine, a high-performance C++ framework that provides efficient primitives for Pauli string multiplication, commutators, symbolic phase tracking, and structural transformations. Built on a binary symplectic representation and optimized bit-wise operations, PauliEngine supports both numerical and symbolic coefficients and is accessible through a Python interface. Runtime benchmarks demonstrate substantial speedups over state-of-the-art implementations. PauliEngine provides a scalable backend for operator-based quantum software tools and simulations.

Turbulent flows have high requirements for very fine meshes near the boundary to ensure accuracy. In the context of topology optimization (TO), such fine meshes become unrealistic and common approaches are hampered by low accuracy and overestimation of boundary layer thickness. Wall-functions are a natural way to ease the computational requirements, but they are not naturally imposed in density-based TO due to the diffuse design parametrization. We propose an implicit wall-function formulation for the Reynolds-Averaged Navier-Stokes (RANS), standard k-epsilon model that extracts wall-normal information directly from the gradient of the design variable and enables a penalty-based formulation for imposing wall-functions to the RANS equations, without the need for body-fitted meshes. The method provides a reliable route to high Reynolds number turbulent topology optimization, delivering boundary layer accuracy comparable to explicit-wall body-fitted analyses, while retaining the flexibility of density-based TO. Furthermore, because wall effects are modeled using wall-functions, accurate solutions are obtained on substantially coarser meshes, leading to significant reductions in computational cost. The approach is validated on three canonical benchmarks over Reynolds numbers up to Re = 2e5: a pipe-bend; a U-bend; and a Tesla-valve. Across all cases, the proposed method accurately recovers near-wall velocity profiles, closely matching verification simulations on body-fitted meshes with explicit wall-functions. In contrast, a conventional turbulent TO formulation, without the proposed wall-function treatment, mispredicts boundary-layer development and yields sub-optimal results.

The interplay between topology and nonlinearity represents a central challenge in modern physics. Here, we investigate this interplay by considering a synthetic Su-Schrieffer-Heeger lattice with all-to-all nonlocal interactions. We find that the distinctive nonlinearity maintains an effective chiral symmetry and leads to a quantized nonlinear winding and Berry phase, as corroborated by the developed Bogoliubov nonlinear adiabatic theory. Increasing nonlinearity drives a sequence of topological transitions signaled by the appearance of characteristic swallowtail band structures at intermediate interaction strengths and band swapping in the strong nonlinear regime. The band swapping results in quantized fractional windings and double-period Bloch oscillations that are closely related to discrete time crystals. Remarkably, even starting from a topologically trivial linear system, nonlocal nonlinearity can induce an emergent topological phase with fractional windings. Experimentally, our model can be realized using photons in a degenerate optical cavity with Rydberg-mediated interactions. Our results establish a rigorous framework and pave the way for exploring nonlinear topological phenomena and their applications in synthetic quantum platforms.

The light deflection under a strong gravitational field, referred to as strong gravitational lensing, provides a powerful probe of spacetime geometry. Besides, laboratory analogue models are employed to study the effects of curved spacetime and explore the design of optical devices. Here, applying the framework of analogue gravity, we reveal the behavior of the optical echo from a pulsed point-like source near a black hole, which is strongly dependent on the interplay of the black hole's photon sphere and the source's duration. We model the Schwarzschild spacetime as a Flamm paraboloid and calculate the echo response, using analytical geodesic solutions and the Huygens-Fresnel principle. Particularly, when the spatial scale of pulse duration is comparable to the photon sphere, continuous “echo tails" appear along bright interference fringes in temporal response. Analysis in both the temporal and frequency domains reveals that these echo tails are a signature of resonance between the incoming pulse and the photon sphere. This work provides a wave-optics perspective on the interaction between dynamic sources and black holes, offering a table top window on strong gravitational lensing.

We theoretically propose a nonlinear spin-wave Doppler effect, in which the time-dependent motion of a magnetic energy boundary acts as an active frequency modulator, directly converting boundary-induced phase dynamics into instantaneous spectral synthesis for propagating spin-wave modes. In contrast to the conventional linear Doppler effect governed by constant relative velocity, this mechanism enables dynamic phase-to-frequency transduction, generating high-order harmonics, magnonic frequency combs, and coherent chirped sidebands, without requiring nonlinear magnon-magnon coupling or multi-magnon scattering. Micromagnetic simulations on voltage-controlled anisotropy boundaries in ferroelectric/ferromagnetic (FE/FM) heterostructures demonstrate that the comb spacing and spectral topology are determined solely by boundary kinematics, confirming direct Doppler phase coupling between boundary motion and spin-wave propagation. These results establish moving magnetic-energy boundaries as a new class of on-chip spectral synthesizers and define a coherent and energy-efficient framework for flexible tuning of magnonic frequencies, fundamentally distinct from traditional passive scattering or nonlinear multi-magnon mechanisms.

Astrophysical dynamos feature various spatial structures and dynamical regimes, ranging from hemispherical magnetic fields to the random reversals of the geodynamo. The Tayler-Spruit dynamo, recently confirmed in direct numerical simulations, has been invoked to explain angular momentum transport in stellar radiative zones and magnetar formation in a proto-neutron star spun up by fallback accretion. Whether this dynamo mechanism can lead to different dynamical regimes remains an open question. Using three-dimensional direct numerical simulations, we model the dynamics of a stably stratified spherical Couette flow, with the outer sphere rotating faster than the inner one. While the generation of strong stationary and hemispherical dynamos has been observed in our previous studies, we report for the first time the existence of reversals and complex temporal dynamics. We observe that the dynamics is strongly correlated with the equatorial symmetry breaking of the flow. Focusing on a fiducial dynamo simulation, we propose an interpretation of its dynamics that consists in the coupling of two large-scale magnetic modes with opposite equatorial symmetries by the flow symmetry breaking. While this interpretation captures the simplest observed dynamics, the nonlinear interaction between a higher number of magnetic modes is certainly required to describe more complex regimes. The wide diversity of dynamical regimes generated by the Tayler-Spruit dynamo may have interesting implications for the geometry of the neutron star magnetic fields, and therefore neutron star emissions.

Recurrent fluorescence (RF) is an important relaxation mechanism in polycyclic aromatic hydrocarbons (PAHs), which could stabilize them and contribute to the production of aromatic infrared bands that are observed in the infrared spectra of the interstellar medium (ISM). In this theoretical work, a statistical model of relaxation by recurrent fluorescence is formally developed, including Herzberg-Teller and Duschinsky rotation effects as well as a full account of vibrational progressions. Using canonical and harmonic approximations, the RF rate constants can be determined from the transition dipole moment time autocorrelation functions. Application to the naphthalene, anthracene, and pyrene cations is presented based on quantum chemical inputs obtained from time-dependent density-functional theory. For these highly symmetric molecules, the low-lying, symmetry-forbidden electronic transitions are predicted to contribute possibly even more than higher energy, non-forbidden transitions. Such an unexpected contribution could increase the cooling efficiency of PAHs and, in turn, stabilize them further under the highly ionized environments of the ISM.

We consider here several aspects of the following challenging question: is it possible to use a passive cloak to make invisible a dielectric inclusion on a finite frequency interval in the quasistatic regime of Maxwell's equations for an observer close to the object? In this work, by considering the Dirichlet-to-Neumann (DtN) map, we not only answer negatively this question, but we go further and provide some quantitative bounds on this map that provide fundamental limits to both cloaking as well as approximate cloaking. These bounds involve the following physical parameters: the length and center of the frequency interval, the volume of the cloaking device, the volume of the obstacle, and the relative permittivity of the object. Our approach is based on two key tools: i) variational principles from the abstract theory of composites and ii) the analytic approach to deriving bounds from sum rules for passive systems. To use i), we prove a new representation theorem for the DtN map which allows us to interpret this map as an effective operator in the abstract theory of composites. One important consequence of this representation is that it allows one to incorporate the broad and deep results from the theory of composites, such as variational principles, and to apply the bounds derived from them to the DtN map. These results could be useful in other contexts other than cloaking. Next, to use ii), we show that the passivity assumption allows us to connect the DtN map (as function of the frequency) with two important classes of analytic functions, namely, Herglotz and Stieltjes functions. The sum rules for these functions, combined with the variational approach, allows us to derive new inequalities on the DtN map which impose fundamental limitations on passive cloaking, both exact and approximate, over a frequency interval. We consider both cases of lossy and lossless cloaks.

The accurate modeling of non-covalent interactions between helium and graphitic materials is important for understanding quantum phenomena in reduced dimensions, with the helium-benzene complex serving as the fundamental prototype. However, creating a quantitatively reliable potential energy surface (PES) for this weakly bound system remains a significant computational challenge. In this work, we present a comprehensive, multi-level investigation of the He-Bz interaction, establishing benchmark energies using high-level coupled-cluster singles-and-doubles with perturbative triples (CCSD(T)) methods extrapolated to the complete basis set limit and assessing higher-order (CCSDT(Q)) contributions. We use symmetry-adapted perturbation theory (SAPT) to benchmark it against CCSD(T) and to decompose the interaction into its physical components-confirming it is dominated by a balance between dispersion and exchange-repulsion. A continuous, three-dimensional PES is constructed from discrete ab initio points using multifidelity Gaussian process regression that combines density functional theory results with sparse coupled-cluster energies. The result is a highly accurate PES with sub-cm-1 accuracy that obeys physical laws. This new PES is applied to path integral Monte Carlo (PIMC) simulations to study the solvation of 4He atoms on benzene at low temperatures. Our PIMC results reveal qualitatively different solvation behavior, particularly in the filling of adsorption layers, when compared to simulations using commonly employed empirical Lennard-Jones potentials. This work provides a benchmark PES essential for accurate many-body simulations of helium on larger polycyclic aromatic hydrocarbons towards graphene.

Actin filaments, crosslinkers and myosin molecular motors form contractile networks. For instance, the cell cortex is a thin network below the cell membrane ; contraction of the cell cortex allows cells to round up during cell division. Contractile actin-myosin networks are often represented at large scale by continuous theories such as active gel models. However, experimental perturbations are microscopic while parameters in continuous models are macroscopic, thus making those models hard to falsify experimentally. Here we use numerical simulations, in which we can access both microscopic and macroscopic quantities, to show that active gel models can indeed be applied to describe contractile actin. We predict that contractile stress should scale linearly with actin density, which is confirmed by numerical simulations. Moreover, we can accurately predict how the contractile stress depends on motor properties such as unloaded speed and stall force.

This paper presents FormationEval, an open multiple-choice question benchmark for evaluating language models on petroleum geoscience and subsurface disciplines. The dataset contains 505 questions across seven domains including petrophysics, petroleum geology and reservoir engineering, derived from three authoritative sources using a reasoning model with detailed instructions and a concept-based approach that avoids verbatim copying of copyrighted text. Each question includes source metadata to support traceability and audit. The evaluation covers 72 models from major providers including OpenAI, Anthropic, Google, Meta and open-weight alternatives. The top performers achieve over 97% accuracy, with Gemini 3 Pro Preview reaching 99.8%, while tier and domain gaps persist. Among open-weight models, GLM-4.7 leads at 98.6%, with several DeepSeek, Llama, Qwen and Mistral models also exceeding 93%. The performance gap between open-weight and closed models is narrower than expected, with several lower-cost open-weight models exceeding 90% accuracy. Petrophysics emerges as the most challenging domain across all models, while smaller models show wider performance variance. Residual length bias in the dataset (correct answers tend to be longer) is documented along with bias mitigation strategies applied during construction. The benchmark, evaluation code and results are publicly available.

We propose a graph-based, multi-fidelity learning framework for the prediction of stationary Navier–Stokes solutions in non-parametrized two-dimensional geometries. The method is designed to guide the learning process through successive approximations, starting from reduced-order and full Stokes models, and progressively approaching the Navier–Stokes solution. To effectively capture both local and long-range dependencies in the velocity and pressure fields, we combine graph neural networks with Transformer and Mamba architectures. While Transformers achieve the highest accuracy, we show that Mamba can be successfully adapted to graph-structured data through an unsupervised node-ordering strategy. The Mamba approach significantly reduces computational cost while maintaining performance. Physical knowledge is embedded directly into the architecture through an encoding-processing-physics informed decoding pipeline. Derivatives are computed through algebraic operators constructed via the Weighted Least Squares method. The flexibility of these operators allows us not only to make the output obey the governing equations, but also to constrain selected hidden features to satisfy mass conservation. We introduce additional physical biases through an enriched graph convolution with the same differential operators describing the PDEs. Overall, we successfully guide the learning process by physical knowledge and fluid dynamics insights, leading to more regular and accurate predictions

Electrochemical creatinine sensors offer great promise towards rapid, reagent-free and point-of-care (POC) kidney-function monitoring. However, challenges related to analyte binding, data reproducibility, sensitivity, fouling and device degradation deter its widespread implementation. Here, we show how a carbon electrode modified with a combination of poly(3,4-ethylene dioxythiophene): poly(styrene sulfonate) (PEDOT:PSS) and copper nanoparticles (CuNPs) can rapidly and accurately detect creatinine (CT) in artificial urine media employing electrochemical techniques. Applying redox potential sweeps (vs Ag/AgCl) using copper sulfate (CuSO4) solution on such sensor facilitates the CuNP embedding process inside the conjugated polymer matrix which has been further validated by supporting techniques. We predicted and validated the formation and contribution of two Cu-CT coordination complexes corresponding to Cu(I) and Cu(II) states, which are responsible for CT detection. The fabricated CT sensor exhibits high selectivity against major artificial urine interferents and is stable for a month showing minimal degradation (0.53%) in peak current value. Such sensors can be utilized to detect and monitor different stages of renal failure in real-time patient samples.

In this study, we investigate feature-based 2.5D controlled source marine electromagnetic (mCSEM) data inversion using generative priors. Two-and-half dimensional modeling using finite difference method (FDM) is adopted to compute the response of horizontal electric dipole (HED) excitation. Rather than using a neural network to approximate the entire inverse mapping in a black-box manner, we adopt a plug-andplay strategy in which a variational autoencoder (VAE) is used solely to learn prior information on conductivity distributions. During the inversion process, the conductivity model is iteratively updated using the Gauss Newton method, while the model space is constrained by projections onto the learned VAE decoder. This framework preserves explicit control over data misfit and enables flexible adaptation to different survey configurations. Numerical and field experiments demonstrate that the proposed approach effectively incorporates prior information, improves reconstruction accuracy, and exhibits good generalization performance.

Understanding the spatio-temporal evolution of radiolytic species created by high-energy electrons in water underpins key applications from radiotherapy and nuclear safety to environmental processing and electron microscopy. Here, using the Manchester Inhomogeneous Radiation Chemistry by Linear Expansions (MIRaCLE) toolkit, we introduce and benchmark a novel approach to simulating these processes. Although the initial conditions are determined stochastically, the subsequent time evolution is calculated deterministically using a continuum representation, derived from those initial conditions. This hybrid approach essentially averages over many chemistry “trajectories” simultaneously, often converging to the 1% level in one shot, not requiring multiple runs. We demonstrate this new approach through the calculation of time-dependent G-values for e_{aq}^-$, \dot{\mathrm{OH}} and other radiolytic products, including at unprecedented dose rates where calculations which would take years with a conventional Monte Carlo approach can be performed in mere hours on a commercial laptop. We demonstrate that the main artifact of continuum modelling can be mitigated by a correction term. These results establish MIRaCLE as a flexible and efficient platform for modelling long-timescale radiolysis, providing a bridge between Monte Carlo approaches and macroscopic reaction–diffusion schemes, with broad implications for radiation chemistry in medicine, energy, and materials science.

The present paper sketches a framework for solving the wave and Fokker-Planck equations in the ion cyclotron resonance frequency domain fully selfconsistently. It illustrates this can be done by first constructing "building blocks" that are commonly needed by the wave and Fokker-Planck equations, allowing e.g. to account for wave coupling in plasmas containing non-Maxwellian distributions. Up to details, the paper exploits known expressions and methods to solve the two intimately connected aspects of the description of the wave-particle interaction underlying ion cyclotron resonance heating. Two cases are presented: the case where the guiding centre motion is limited to just following magnetic field lines, and the extended case accounting for drifts away from magnetic surfaces but assuming axisymmetry. A limited set of analytical results is included. As combining wave and Fokker-Planck solving is the focus, the computation of the dielectric response for arbitrary distribution functions is illustrated as well.

Recent asteroseismic observations constitute a great challenge for rotating stellar evolution models, which predict overly fast internal rotation rates when only hydrodynamic processes are included. This suggests the absence of one or several unidentified angular momentum (AM) transport processes in these models. Transport by large-scale and strong magnetic fields in the radiative zone is a promising candidate to explain the observations. While these fields might be characterised by a fossil origin, the Tayler-Spruit dynamo constitutes a primary mechanism to form the necessary magnetic fields. Despite recent numerical studies, this mechanism remains poorly known. Motivated by this scenario, we investigated the Tayler-Spruit dynamo through a new set of 3D numerical simulations. We modelled the radiative zone as a Boussinesq stably stratified fluid whose differential rotation is maintained by a volumetric body force. Here, we report, for the first time, the coexistence of two dynamo solutions, which mainly differ by the magnetic field location (near the equator and the polar axis). While the equatorial dynamo is driven by an instability sharing both characteristics of the magnetorotational and Tayler instabilities, we focus mainly on the newly identified polar dynamo, which is driven by the standard Tayler instability. We show that this dynamo can still operate and transport AM efficiently in a strong stratification regime, with a Brunt-Väisälä frequency that is 130 times larger than the rotation rate. We extracted new scaling laws for the magnetic field, AM transport, and the minimum shear to trigger the dynamo. Finally, we were able to roughly constrain the signature of the generated magnetic fields on asteroseismic modes propagating in main sequence and evolved stars.

The development of accurate tools to characterize Intramolecular Vibrational Redistribution (IVR) is of major interest in chemistry. In this context, surface-enhanced vibrational spectroscopies stand up as well-established techniques to study molecular vibrational lines and populations with a sensitivity that can reach the single-molecule level. However, to date, this possibility has not been fully developed to address IVR. Here, we establish a quantum mechanical framework based on molecular optomechanics that accounts for IVR, and we adopt it to analyze strategies to optimize IVR characterization by surface-enhanced vibrational spectroscopy. In particular, we model two different pump-and-probe configurations where the vibrational pumping is provided either by infrared laser illumination or by Stokes scattering processes in surface-enhanced Raman spectroscopy (SERS). We show for the two pumping configurations the existence of clear signatures on the anti-Stokes SERS spectra of population transfer between coupled vibrational modes in a molecule. Our calculations adopt realistic molecular and SERS parameters, suggesting that these signatures of IVR could be accessible at the single-molecule level within realistic experimental platforms.

Neuromorphic computing targets energy-efficient event-driven information processing by placing artificial spiking-neurons at its core. Artificial neuron devices and circuits have multiple operating modes and produce region-dependent nonlinear dynamics that are not captured by system analysis methods for linear systems, such as transfer functions, Fourier/Laplace transform, Bode diagram, etc. Thus, new tools are needed to evaluate the nonlinear behavior of neurons and to guide the design and optimization of artificial neuron implementations. Here we present a generalized bifurcation analysis framework based on nonlinear dynamical systems theory. A CMOS axon-hillock neuron, memristor neuron, and the FitzHugh-Nagumo biological neuron model are selected for demonstration. We evaluate Hopf bifurcation conditions to define the rest and firing domains in parameter space of the system, and predict the near-onset firing rate. The results are further compared with numerical simulations. The framework standardizes the analysis for various neuron models and physical realizations. It yields practical design and optimization guidelines for artificial neurons for neuromorphic computing systems, including parameter combinations to make a neuron fire/rest and to control the corresponding firing properties, e.g., firing rate and amplitude.

The global transition from traditional power plants to renewable energy sources introduces new challenges in grid stability, primarily because inverter-based technologies provide insufficient inertia. To address this, we introduce an artificial intelligence algorithm that autonomously stabilizes power grids by adaptively tuning admittance regulators in response to disturbances. This Adaptive Admittance Controller (AAC) algorithm not only stabilizes the system in real time but also identifies the best regulator locations, thereby unifying grid planning and real time control within a single framework. When tested on a real UK power grid, the AAC markedly reduces frequency deviations and rapidly restores nominal operation. In addition, the algorithm isolates a small number of key regulators and intervenes only on these, lowering both system complexity and cost. The AAC algorithm further reduces the nonlinearity effect, quickly stabilizing the frequency and power flow. This intelligent control scheme enables power grids to reliably return to stable operating conditions under a broad spectrum of fault scenarios. The proposed framework can also be used to mitigate cascading failures by adaptively controlling critical links in a variety of networked infrastructures, such as cascades of traffic congestion on road networks or fuse failures in energy-saving systems.

We have developed an analytical framework for magnetically induced transparency-absorption (MITA) and normal-anomalous dispersion (MINAD) in a weakly driven ${}^{87}\text{Rb}$ vapor, or any J-type three-level system, under a vector magnetic field. By solving the Bloch equations in the stationary, quasi-stationary, and short-pulse regimes, we obtained closed-form expressions for the atomic populations and coherences and identified a bifurcation in the oscillatory dynamics at zero longitudinal Zeeman splitting. The Fourier-domain analysis reveals alternating transparency/absorption and normal/anomalous dispersion with frequency-dependent sign reversals, enabling spectrally selective filtering and group-delay effects. Slow oscillatory behavior in the radio-frequency range makes the system suitable for weak magnetic-field sensing, while fast oscillations at optical frequencies suggest applications in spectral filtering and frequency-comb-like signal shaping. The results provide a theoretical basis for experimental observation of MITA/MINAD and for optimizing atomic-vapor platforms for precision magnetometry and related photonic functionalities.

We present Flo, a data-driven storm surge model, covering the North Sea, Norwegian Sea and Barents Sea. The model is built using the Anemoi framework for creating machine learning weather forecasting systems, developed by the European Centre for Medium-Range Weather Forecasts and partners. The model is based on a graph neural network, and is capable of simulating water level due to atmospheric effects (wind stress and inverse barometer effect, i.e. the non-tidally induced part of the total water level; the residual water level) at a horizontal resolution of 4 km and a temporal resolution of 1 hour with a quality comparable to the numerical model on which it was trained. The model was trained using a dataset consisting of 43 years of atmospheric data from the 3-km Norwegian Reanalysis hindcast for mean sea level pressure and winds, and the NORA-Surge hindcast for water level. Evaluation was done by comparing results from hindcast runs of the Flo model against independent observations of more than 90 water level gauges along the European coast, and against the NORA-Surge hindcast. The evaluation shows that Flo produces hindcasts with accuracy similar to the NORA-Surge hindcast, and it is shown that the model can resolve key physical processes. As the NORA-Surge hindcast used for training does not include data assimilation, Flo is not expected to systematically outperform the numerical model when evaluated against observations. Nevertheless, the present work represents an important step towards complementing traditional physics-based storm surge modelling with machine learning approaches and the framework establishes a strong foundation for future developments, particularly for training storm surge models that offer more flexibility for incorporating observations and other additional data sources.