An analytical model is developed to investigate the interaction of quasi longitudinal (qP) waves with a perfectly bonded interface between a thermo piezoelectric half space and a functionally graded piezoelectric half space. The formulation is based on the fractional order Lord Shulman generalized thermoelasticity theory, which provides an enhanced description of coupled thermo electro mechanical wave behaviour. Rotational effects are incorporated into the constitutive relations and equations of motion for both media, while the lower half space is assumed to be subjected to initial stress. Closed form solutions for reflection and transmission coefficients are obtained, together with associated energy partition factors, allowing a comprehensive assessment of interface wave characteristics. Numerical simulations carried out using MATLAB demonstrate that the reflection and transmission responses are strongly influenced by initial stress, fractional order parameter, and thermal relaxation time. The calculated energy ratios of scattered waves satisfy the energy conservation condition, confirming the mathematical consistency of the formulation. The findings of this study are relevant to the design and analysis of smart sensors, rotating and aerospace structures, vibration control systems, and energy harvesting devices employing functionally graded thermo piezoelectric materials under fractional order effects.

We present the first empirical derivation of a continuous-time stochastic model for real-world opinion dynamics. Using longitudinal social-media data to infer users opinion on a binary climate-change topic, we reconstruct the underlying drift and diffusion functions governing individual opinion updates. We show that the observed dynamics are well described by a Langevin-type stochastic differential equation, with persistent attractor basins and spatially sensitive drift and diffusion terms. The empirically inferred one-step transition probabilities closely reproduce the transition kernel generated from the D-MODD model we introduce. Our results provide the first direct evidence that online opinion dynamics on a polarized topic admit a Markovian description at the operator level, with empirically reconstructed transition kernels accurately reproduced by a data-driven Langevin model, bridging sociophysics, behavioral data, and complex-systems modeling.

Diffusion model, the state-of-the-art generative machine learning architecture, has shown promising results airfoil inverse designs. In this study, we implemented and trained a series of diffusion models on three different airfoil geometry data encoding formats -- principal component weights, ordered $x$-$y$ coordinates, and 2D signed distance functions (SDF) -- to generate 2D airfoils. By systematically comparing the performance of diffusion models trained on different data structures, it is found that for 2D airfoil design problems, the diffusion model performs the best when directly trained with coordinates. Training with latent space (PCA weights in this study) limits the model's design freedom, and decreases the training effectiveness. Although the 2D SDF data appears to result in the least performing model, it proves its feasibility in aerodynamic shape generation, paving the way towards 3D problems where SDF is more favored. This study also investigated deploying the diffusion model in practical engineering applications. A multi-target optimization procedure is proposed based on the stochastic nature of the diffusion process, which drastically simplifies the procedure compared to conventional methods. The extrapolation performance of the model is also investigated by tasking the model with both aerodynamic and flow condition labels that are extrapolated beyond the training set boundaries.

The historical and conceptual foundations of General Relativity are revisited, putting the main focus on the physical meaning of the invariant ds, the Equivalence Principle, and the precise interpretation of spacetime geometry. It is argued that Albert Einstein initially sought a dynamical formulation in which ds encoded the gravitational effects, without invoking curvature as a physical entity. The now more familiar geometrical interpretation (identifying gravitation with spacetime curvature) gradually emerged through his collaboration with Marcel Grossmann and the adoption of the Ricci tensor in 1915. Anyhow, in his 1920 Leiden lecture, Einstein explicitly reinterpreted spacetime geometry as the state of a physical medium (an ether endowed with metrical properties but devoid of mechanical substance) thereby actually rejecting geometry as an independent ontological reality. Building upon this mature view, gravitation is reconstructed from the Weak Equivalence Principle, understood as the exact compensation between inertial and gravitational forces acting on a body under a uniform gravitational field. From this fundamental principle, together with an extension of Fermat Principle to massive objects, the invariant ds is obtained, first in the static case, where the gravitational potential modifies the flow of proper time. Then, by applying the Lorentz transformation to this static invariant, its general form is derived for the case of matter in motion. The resulting invariant reproduces the relativistic form of Newton second law in proper time and coincides with the weak field limit of General Relativity in the harmonic gauge.

We report on the operation of a 13 g PbWO$_4$ crystal, grown from archaeological Pb and operated as a cryogenic calorimeter in an underground environment. Read out with a Ge thermistor, the detector achieves a low energy threshold and, for the first time, enables the derivation of a dark matter exclusion limit using PbWO$_4$ as target material, for both spin-dependent interactions on neutrons and spin-independent interactions. Although limited in mass and not representative of the final RES-NOVA detector design, this prototype demonstrates effective control of mechanical vibrations and low-energy noise in a cryogenic system, which is a key requirement for rare-event searches. The experiment therefore provides a proof of principle for the RES-NOVA detection concept, validating the use of archaeological Pb-based PbWO$_4$ crystals, low-background operation, and robust data-analysis procedures. These results establish a solid technological and methodological foundation for future RES-NOVA detectors employing larger target masses and advanced thermal readout technologies.

The Superconducting Quasiparticle-Amplifying Transmon (SQUAT) is a new sensor architecture for THz (meV) detection based on a weakly charge-sensitive transmon directly coupled to a transmission line. In such devices, energy depositions break Cooper pairs in the qubit capacitor islands, generating quasiparticles. Quasiparticles that tunnel across the Josephson junction change the transmon qubit parity, generating a measurable signal. In this paper, we present the design of first-generation SQUATs and demonstrate an architecture validation. We summarize initial characterization measurements made with prototype devices, comment on background sources that influence the observed parity-switching rate, and present experimental results showing simultaneous detection of charge and quasiparticle signals using aluminum-based SQUATs.

Active learning for photonic crystals explores the integration of analytic approximate Bayesian last layer neural networks (LL-BNNs) with uncertainty-driven sample selection to accelerate photonic band gap prediction. We employ an analytic LL-BNN formulation, corresponding to the infinite Monte Carlo sample limit, to obtain uncertainty estimates that are strongly correlated with the true predictive error on unlabeled candidate structures. These uncertainty scores drive an active learning strategy that prioritizes the most informative simulations during training. Applied to the task of predicting band gap sizes in two-dimensional, two-tone photonic crystals, our approach achieves up to a 2.6x reduction in required training data compared to a random sampling baseline while maintaining predictive accuracy. The efficiency gains arise from concentrating computational resources on high uncertainty regions of the design space rather than sampling uniformly. Given the substantial cost of full band structure simulations, especially in three dimensions, this data efficiency enables rapid and scalable surrogate modeling. Our results suggest that analytic LL-BNN based active learning can substantially accelerate topological optimization and inverse design workflows for photonic crystals, and more broadly, offers a general framework for data efficient regression across scientific machine learning domains.

An introduction into techniques that lead to effective communication techniques of scientific concepts to general audiences.

A general, compact way of achieving second-order in finite-volume numerical methods is to perform a MUSCL-like, piecewise linear reconstruction of flow properties at each cell interface. To avoid the surge of spurious oscillations in the discrete solution, a limiter function is commonly employed. This strategy, however, can add a series of drawbacks to the overall numerical scheme. The present paper investigates this behavior by considering three different limiter formulations in the context of a second-order, finite volume scheme for the simulation of steady, turbulent flows on unstructured meshes. Three limiter formulations are considered: the original Venkatakrishnan limiter, Wang's modification to the Venkatakrishnan limiter and Nishikawa's recently introduced R3 limiter. Three different configurations of the fully-developed, two-dimensional, transonic NACA 0012 airfoil are analyzed, configured with different angles of attack and similar freestream properties. The gas dynamics are modeled using the Reynolds-averaged Navier-Stokes (RANS) equations, where the negative Spalart-Allmaras turbulence model is used to solve the closure problem. All limiters are shown to yield similar results for all configurations of this case, although with different dissipative characteristics, provided their control constants are used within appropriate intervals. The presented numerical results are in good agreement with experimental data available in the literature.

Accurate knowledge of material reflectance in the vacuum ultraviolet (VUV) range is crucial for optimizing photon detection in noble gas detectors such as DUNE. Despite its importance, reflectance values for detector materials in the VUV region remain poorly characterized, with literature values showing significant variation depending on surface termination and finish. We present an angular-resolved reflectance measurement system developed at IFIC that operates in a gaseous argon atmosphere, enabling realistic measurements of detector materials under controlled conditions. The setup couples a deuterium lamp to a monochromator and employs a motorized PMT rotating around the sample to measure reflected light distributions across a wide angular range. We have characterized two key DUNE materials -- aluminum field cage profiles and stainless steel cryostat membranes -- in both the UV-VIS (300-500 nm) and VUV (128-200 nm) ranges. In the UV-VIS region, we confirm literature values of approximately 60% reflectance for aluminum and 40% for stainless steel. Preliminary VUV measurements at 45° angle of incidence yield reflectance values of 10-15% for both materials, significantly lower than their UV-VIS counterparts. The reflected light distributions exhibit a mixed character between specular and diffuse reflection. These results have direct implications for detector simulations and light yield predictions in next-generation experiments.

Hybrid light-matter states, known as molecular polaritons, arise from electronic or vibrational strong coupling (ESC and VSC) with confined electromagnetic fields. While these have been widely studied, the influence of electron-nuclear dynamics in driven cavities remains largely unknown. Here, we report a previously unrecognized mechanism of vibrational activation that emerges under collective ESC in driven optical cavities. Using semiclassical simulations that self-consistently combine Maxwell's equations with quantum molecular dynamics, we show that collective electronic Rabi oscillations coherently drive nuclear motion. This effect is captured using both vibrational wave-packet dynamics in a minimal two-level model and atomistic simulations based on time-dependent density-functional tight-binding with Ehrenfest dynamics. Vibrational activation depends non-monotonically on the Rabi frequency and is maximized when the collective polaritonic splitting resonates with a molecular vibrational mode. The mechanism exhibits features consistent with a stimulated Raman-like relaxation mechanism. Our results establish a self-consistent framework for realistic cavity-electron-nuclear dynamics.

We present a detailed characterization of a new generation of athermal-phonon single-charge sensitive Si HVeV detectors, the best of which achieved 612 meV $\pm$ 4 meV baseline resolution. Our sub-eV energy resolution enables precise measurements of single-photon events and reveal consistent energy losses of 0.81 eV $\pm$ 0.03 eV per charge excitation across two facilities. We demonstrate that the noise for these detectors is well described using a standard Transition Edge Sensor noise model. We also place upper bounds on the nominal phonon collection efficiency of 45\%, establishing these detectors as the most efficient athermal phonon detectors to date, limited only by intrinsic limitations of quasiparticle generation.

Neutral atoms have become a competitive platform for quantum metrology, simulation, sensing, and computing. Current magic trapping techniques are insufficient to engineer magic trapping conditions for qudits encoded in hyperfine states with $J \neq 0$, compromising qudit coherence. In this paper we propose a scheme to engineer magic trapping conditions for qudits via bichromatic tweezers. We show it is possible to suppress differential light shifts across all magnetic sublevels of the $5s5p$ $\mathrm{^{3}P_2}$ state by using two carefully chosen wavelengths (with comparable tensor light shift magnitude and opposite sign) at an appropriate intensity ratio, thus suppressing light-shift induced dephasing, enabling scalar magic conditions between the ground state and $5s5p$ $\mathrm{^{3}P_2}$, and tensor magic conditions for qudits encoded within it. Furthermore, this technique enables robust operation at the tensor magic angle 54.7$^\circ$ with linear trap polarization via reduced sensitivity to uncertainty in experimental parameters. We expect this technique to enable new loading protocols, enhance cooling efficiency, and enhance nuclear spins' coherence times, thus facilitating qudit-based quantum computing in ${}^{87}$Sr in the $5s5p$ $\mathrm{^{3}P_2}$ manifold.

As quantum technologies (QT) move from foundational research toward industrial and societal deployment, national strategies have become critical instruments for shaping the future of this emerging field. In this study, we conduct the first large-scale, data-driven analysis of 62 national quantum strategic documents (QSDs) from 20 countries. Using AI-based natural language processing (topic modeling), we identify 12 topics present in the text, ranging from technical development areas to transversal aspects such as workforce development and governance. Temporal analysis reveals a distinct shift in policy discourse toward applications of QT and commercialisation, and relatively away from basic science. Our findings highlight the increasing diversification of the QT field, and contribute to the growing area of quantum policy studies. We advocate for more AI and data-driven analyses of the quantum ecosystem, to work toward a scalable framework for understanding the technological and societal challenges of the second quantum revolution.

Volumetric additive manufacturing promises near-instantaneous fabrication of 3D objects, yet achieving high fidelity at the micro-scale remains challenging due to the complex interplay between optical diffraction and chemical effects. We present \emph{Single-View Holographic Volumetric Additive Manufacturing} (SHVAM), a mechanically static system that shapes volumetric dose distributions using time-multiplexed, phase-only holograms projected from a single optical axis. To achieve high resolution with SHVAM, we formulate hologram synthesis as a coupled inverse problem, integrating a differentiable wave-optical forward model with a simplified photochemical model that explicitly captures inhibitor diffusion and non-linear dose response. Optimizing hologram sequences under these coupled constraints allows us to pre-compensate for chemical blur, yielding higher print fidelity than optical-only optimization. We demonstrate the efficacy of SHVAM by fabricating simple 2D and 3D structures with lateral feature sizes of approximately \SI{10}{\micro\meter} within a $\SI{0.8}{\milli\meter} \times \SI{0.8}{\milli\meter} \times \SI{3}{\milli\meter}$ volume in seconds.

The rapid development of pretrained Machine Learning Interatomic Potentials (MLIPs) that cover a wide range of molecular species has made it challenging to select the best model for a given application. We benchmark 15 pretrained MLIPs, evaluating each one on accuracy, speed, memory use, and ability to produce stable simulations. This provides an objective basis for practitioners to select the most appropriate MLIP for their own simulations, and offers insight into which factors most strongly influence model accuracy. We find that the number of model parameters and the size of the training set are both strongly correlated with accuracy, while training on charged molecules and including explicit Coulomb energy terms are less essential than one might expect. Speed and memory use are determined as much by the model architecture as by the size of the model.

Learning the full family of solutions to parameterized partial differential equations (PDEs) is a central challenge to our ability to model the behavior of heterogeneous systems, with a variety of fundamental and application-oriented implications in fields such as hydrogeology where system properties exhibit significant (and often uncertain) spatial heterogeneity. We address this by reformulating a Physics-Informed Neural Network (PINN) as a differentiable solver that learns the continuous solution manifold for steady-state Darcy flow. Our framework requires only a single training run, circumventing the need for costly re-training for each new parameter instance. Its versatility is demonstrated through two representations of spatially heterogeneous hydraulic conductivity fields: a direct analytical form and a novel data-driven formulation resting on an autoencoder to create a low-dimensional latent encoding. A key innovation is the integration of the differentiable decoder into the physics-informed loss function, enabling on-the-fly reconstruction of complex conductivity fields via automatic differentiation. The approach yields accurate, mass-conserving flow solutions and supports efficient uncertainty quantification, providing a general methodology for physics-constrained data-driven modeling of heterogeneous systems.

Nominal two-dimensional (2D) shear layers have been studied extensively, and their principal dynamics are well understood. In practical configurations, however, the behavior of such shear layers is affected by proximal surfaces. In this study, we investigate three-dimensional (3D) coherent structures developing downstream of a relatively thick splitter plate in a realistic nozzle featuring sidewalls, an upper boundary formed by a single expansion ramp, and a lower boundary defined by a protruding deck. As a result, in addition to the primary splitter plate shear layer (SPSL) arising from mixing between the core and bypass streams, the flow contains upper (USL) and lower (LSL) shear layers with the ambient. Large-eddy simulation data are analyzed to characterize the unsteady flow dynamics, while the mean flow provides insight into the underlying amplification mechanisms. Spectral proper orthogonal decomposition reveals a clear separation of broadband and tonal dynamics across frequency bands. The broadband low-frequency modes are highly 3D and originate in the USL and LSL. In contrast, tonal high-frequency content is associated with a 2D instability in the SPSL. Both the broadband and tonal signatures also appear in the nonlinear energy transfer mechanisms. Triglobal resolvent analysis further clarifies the amplification mechanisms within the USL and LSL. Low-frequency response modes are excited by forcing localized near the nozzle geometry and are governed by 3D Kelvin-Helmholtz dynamics. The low-frequency streamwise vortices generated at the nozzle corners drive the axis-switching behavior characteristic of rectangular jets. Wavemaker analysis further demonstrates that these corner vortices are part of self-sustaining low-frequency dynamics.

Neuronal electrical activity underlies human cognition, yet its direct, noninvasive measurement in the living human brain remains a fundamental challenge. Existing neuroimaging techniques, including EEG, MEG, and fMRI, are limited by trade-offs in sensitivity and spatial or temporal resolution. Here we propose quantum sensing MRI (qsMRI), a noninvasive approach that enables direct detection of neuronal firing-induced magnetic fields using a clinical MRI system. qsMRI exploits endogenous proton (1H) nuclear spins in water molecules as intrinsic quantum sensors and decodes time-resolved phase information from free induction decay (FID) signals to infer neuronal magnetic fields. We validate qsMRI through simulations, phantom experiments, and human studies at rest and during motor tasks, and provide open experimental procedures to facilitate independent validation. We further present a case study demonstrating potential applications to neurological disorders. qsMRI represents a first-in-human application of quantum sensing on a clinical MRI platform, establishes a non-BOLD functional imaging modality, and enables interrogation of neuronal firing dynamics in both cortical and deep brain regions.

In homogeneous and isotropic turbulence, measurements of the longitudinal velocity derivative, $\partial_1 u_1$, make it possible to estimate a surrogate of the rate of energy dissipation per unit mass, $\epsilon$: $\epsilon_s = 15 \nu (\partial_1 u_1)^2 $, where $\nu$ is the fluid viscosity, in the sense that the averages of $\epsilon$ and $\epsilon_s$ are equal. We show here that the $n^{th}$ moments of the fluctuations $\epsilon$ and $\epsilon_s$, for $n > 2$, are not exactly proportional to each other, and that the expression for the moment $\langle \epsilon_s^n \rangle$ for $ n \ge 3$ involves in addition to a term proportional to $\langle \epsilon^n \rangle$, other contributions involving the invariant of the strain tensor, $\SSs$: ${\rm tr}( \SSs^3)$. The contribution of this term depends on the distribution of the dimensionless ratio $\mathcal{R} \equiv {\rm tr}(\SSs^3)/{\rm tr}(\SSs^2)^{3/2}$. We find, however, that the relation obtained by assuming that $\mathcal{R}$ is uniformly distributed in the interval $-1/\sqrt{6} \le \mathcal{R} \le 1/\sqrt{6}$, which is obtained when the matrix $\SSs$ has a Gaussian distribution, differs by no more than a few percents from the exact distribution.

Electromagnetic hyperbolicity has driven key functionalities in nanophotonics, including super-resolution imaging, efficient energy control, and extreme light manipulation. Central to these advances are hyperbolic polaritons - nanometer-scale light-matter waves - spanning multiple energy-momentum dispersion orders with distinct mode profiles and incrementally high optical momenta. In this work, we report the mode conversion of hyperbolic polaritons across different dispersion orders by breaking the structure symmetry in engineered step-shape van der Waals (vdW) terraces. The mode conversion from the fundamental to high-order hyperbolic polaritons is imaged using scattering-type scanning near-field optical microscopy (s-SNOM) on both hexagonal boron nitride (hBN) and alpha-phase molybdenum trioxide (alpha-MoO3) vdW terraces. Our s-SNOM data, augmented with electromagnetic simulations, further demonstrate the alteration of polariton mode conversion by varying the step size of vdW terraces. The mode conversion reported here offers a practical approach toward integrating previously independent different-order hyperbolic polaritons with ultra-high momenta, paving the way for promising applications in nano-optical circuits, sensing, computation, information processing, and super-resolution imaging.

Transition states (TSs) govern the rates and outcomes of chemical reactions, making their accurate prediction a central challenge in computational chemistry. Although recent machine-learning models achieve near chemical accuracy in the prediction of TS structures and the associated reaction barriers for small organic reactions, their ability to generalize beyond the training domain remains largely unexplored. Here, we introduce targeted benchmarks to probe chemical and structural novelty in generative TS prediction. Building on Transition1x, a large-scale dataset of reactions involving small organic molecules, we construct curated extensions incorporating controlled elemental substitutions and diverse transition-metal complexes (TMC). These benchmarks reveal fundamental limitations of generative models in the generalization to previously unseen elements. As a result, they produce unphysical geometries and large energetic errors, even for reactions structurally similar to well-predicted organic systems. To address this challenge, we introduce a self-supervised pretraining strategy based on equilibrium conformers that exposes generative TS models to novel chemical environments prior to targeted fine-tuning. Across the newly proposed benchmarks, self-supervised pretraining substantially improves TS prediction for previously unseen systems, lowering the median RMSD of TS geometries on T1x-TMC reactions from 0.39 to 0.19 $\mathring{A}$ and reducing fine-tuning data requirements by up to 75%, enabling reliable performance even in low-data regimes. Overall, the integration of generative TS models with self-supervised pseudo-reaction pretraining provides an efficient, scalable, and chemically robust framework for elucidating TSs well beyond the small organic molecule domain, establishing a foundation for investigating complex and catalytically relevant reaction landscapes.

Quantum technologies have surpassed classical systems by leveraging the unique properties of superposition and entanglement in photons and matter. Recent advancements in integrated quantum photonics, especially in silicon-based and lithium niobate platforms, are pushing the technology toward greater scalability and functionality. Silicon circuits have progressed from centimeter-scale, dual-photon systems to millimeter-scale, high-density devices that integrate thousands of components, enabling sophisticated programmable manipulation of multi-photon states. Meanwhile, lithium niobate, thanks to its wide optical transmission window, outstanding nonlinear and electro-optic coefficients, and chemical stability, has emerged as an optimal substrate for fully integrated photonic quantum chips. Devices made from this material exhibit high efficiency in in generating, manipulating, converting, storing, and detecting photon states, thereby establishing a basis for deterministic multi-photon generation and single-photon quantum interactions, as well as comprehensive frequency-state control. This review explores the development of integrated photonic quantum technologies based on both silicon and lithium niobate, highlighting invaluable insights gained from silicon-based systems that can assist the scaling of lithium niobate technologies. It examines the functional integration mechanisms of lithium niobate in electro-optic tuning and nonlinear energy conversion, showcasing its transformative impact throughout the photonic quantum computing process. Looking ahead, we speculate on the developmental pathways for lithium niobate platforms and their potential to revolutionize areas such as quantum communication, complex system simulation, quantum sampling, and optical quantum computing paradigms.

We analyze a class of inner-shell x-ray laser systems in which the initial conditions of the emission are set by a resonant x-ray difference-frequency drive. Using a microscopic density-matrix framework, we show that two coherent x-ray fields at frequencies $\omega_1$ and $\omega_2$, with $\omega_1-\omega_2=\omega_0$, can induce a phase-locked coherence on a core-level transition at $\omega_0$ without requiring an external field or nonlinear susceptibility at that frequency. In the presence of population inversion, this driven coherence sets the phase and temporal onset of the amplified field, while gain remains governed by conventional inner-shell lasing mechanisms. We refer to this operating regime as a resonant x-ray difference-frequency laser (re-XDFL). The analysis demonstrates that difference-frequency-driven coherence provides a physically consistent route to controlled inner-shell x-ray laser emission beyond purely ASE-initiated operation.

Neutral atoms for quantum computing applications show promise in terms of scalability and connectivity. We demonstrate the realization of a versatile apparatus capable of stochastically loading a 5x5 array of optical tweezers with single $^{88}$Sr atoms featuring flexible magnetic field control and excellent optical access. A custom-designed oven, spin-flip Zeeman slower, and deflection stage produce a controlled flux of Sr directed to the science chamber. In the science chamber, featuring a vacuum pressure of $3 \times 10^{-11}$ mbar, the Sr is cooled using two laser cooling stages, resulting in $\sim 3 \times 10^5$ atoms at a temperature of 5(1) $\mu$K. The optical tweezers feature a $1/e^2$ waist of 0.81(2) $\mu$m, and loaded atoms can be imaged with a fidelity of $\sim 0.997$ and a survival probability of $0.99^{+0.01}_{-0.02}$. The atomic array presented here forms the core of a full-stack quantum computing processor targeted for quantum chemistry computational problems.

Hydropower is an important source of renewable energy that provides clean energy. Pump-turbine type hydraulic turbine is widely used to mitigate the intermittent energy demand and store a large-scale energy. Pump-turbine operates in reverse mode in pump mode to store energy. Flow conditions in turbine mode and pump mode operations is substantially different. This study investigates the unsteady flow field in the model pump-turbine. A computational model of the pump-turbine was created, and the model included hexahedral mesh of 58.19 million nodes. Total verification and validation error was 7.7%. Three operating conditions in turbine mode and four in pump mode were simulated. Flow characteristics, such as blade loading, time-dependent pressure fluctuations, frequency spectra, radial and tangential velocity were investigated. The frequency spectra revealed amplitude of frequencies up to tenth harmonics of the blade passing frequency in pump mode. The higher harmonic frequencies can potentially reach the high mode eigen frequencies and increase the risk of resonance. Flow field analysis in the draft tube indicted the strong presence of Dean vortices causing highly asymmetric flow at the runner inlet in pump mode operation. This study provides essential insights into the complex flow phenomena and advances the understanding of unsteady flow behaviour in pump-turbines.

Cooperation underlies many natural and artificial systems. While voluntary participation can sustain cooperation without informational assumptions, real interactions are rarely anonymous, leaving the joint effects of participation and reputation insufficiently understood. We propose a reputation-based voluntary Prisoner's Dilemma in which agents incur a monitoring cost to inspect opponents and decide whether to exit an interaction for a fixed incentive to avoid exploitation or to default to cooperation or defection. We show that reputation-conditioned exit generates multiple coexistence pathways that sustain cooperation across population structures. In well-mixed populations, cooperation persists through stable mixed coexistence, whereas in structured populations, exit-incentive-dependent regimes emerge, including local cyclic dominance and persistent oscillations. Together, these results extend voluntary participation frameworks and underscore the role of exit-incentive design in cooperative multi-agent systems.

We develop and numerically analyze an algebraic-geometric framework for spin-adapted coupled-cluster (CC) theory. Since the electronic Hamiltonian is SU(2)-invariant, physically relevant quantum states lie in the spin singlet sector. We give an explicit description of the SU(2)-invariant (spin singlet) many-body space by identifying it with an Artinian commutative ring, called the excitation ring, whose dimension is governed by a Narayana number. We define spin-adapted truncation varieties via embeddings of graded subspaces of this ring, and we identify the CCS truncation variety with the Veronese square of the Grassmannian. Compared to the spin-generalized formulation, this approach yields a substantial reduction in dimension and degree, with direct computational consequences. In particular, the CC degree of the truncation variety -- governing the number of homotopy paths required to compute all CC solutions -- is reduced by orders of magnitude. We present scaling studies demonstrating asymptotic improvements and we exploit this reduction to compute the full solution landscape of spin-adapted CC equations for water and lithium hydride.

Heat generation and temperature reading at the nanoscale have attracted increasing attention due to their direct relevance in thermal therapeutic approaches. Consequently, huge progress has been made toward the design of dual-function nanoplatforms that integrate heating and thermometry capabilities at the nanoscale. However, in most cases, dual nanoheater nanothermometer platforms rely either on specifically engineered materials or on complex readout schemes, which limits translational potential due to complex implementation procedures. To overcome these challenges, we present a methodology for directly extracting temperature information based on dynamical magnetization measurements of cobalt ferrite magnetic nanoflowers. We demonstrate that these nanocrystals monitor temperature changes through variations in their magnetization cycles measured under alternating magnetic fields. Importantly, this thermometric functionality is preserved after surface functionalization and under chemical variations in the nanoparticle environment. Interestingly, we show that we can simultaneously generate heat and report temperature changes within the same agent. This is thanks to the photothermal conversion of cobalt ferrite nanoparticles subjected to near infrared irradiation and the tight reported relationship between magnetization dynamics and Brownian relaxation. Together, these results establish cobalt ferrite magnetic nanoparticles as a label-free platform for simultaneous heat generation and intrinsic temperature readout, enabling real-time nanoscale thermal control.

Pure-quartic solitons (PQSs) supported by negative fourth-order dispersion have recently attracted considerable interest. In this work, we study both adiabatic and nonadiabatic compression of PQSs in nonlinear optical fibers with pure quartic dispersion in the presence of distributed gain and loss. Within a variational framework, we show that, for weak constant gain, the adiabatic compression dynamics can be mapped onto the motion of an effective particle in a slowly deformed potential, providing an intuitive physical picture. To overcome the long propagation distance required by conventional adiabatic condition, we exploit shortcuts to adiabaticity (STA) based on inverse engineering and derive analytical gain-loss profiles, with appropriate boundary conditions that realize a prescribed fast compression over a shorter propagation distance. Numerical simulations confirm the theoretical predictions and indicate a minimum propagation distance below which noticeable waveform distortion emerges. Compared with standard adiabatic references, the STA design significantly reduces the required compression distance while maintaining high-fidelity PQS evolution.

The properties of the solution to the adjoint two-dimensional boundary layer equations on a flat plate are investigated from the viewpoint of Libby-Fox theory that describes the algebraic perturbations to the Blasius boundary layer. The adjoint solution is obtained from the Green's function of the perturbation equation as a sum over the infinite perturbation modes of the Blasius solution. The analysis of the solution allows us to obtain constraints on the eigenvalues and eigenfunctions. The extension of the analysis to the case with non-zero pressure gradient, corresponding to the Falkner-Skan solution, is also briefly discussed.

Upconversion luminescence consists of the absorption of low-energies photons followed by the emission of a higher energy photon. The process has mainly been studied in lanthanides to upconvert monochromatic near-infrared excitation to near-infrared or visible light, and has been exploited only to a limited extent to upconvert broad excitations to ultra-violet. In addition, upconverting near-infrared and visible light to ultra-violet is crucial for applications such as solar-to-fuel conversion or environmental remediation. However, upconversion luminescence is limited by the low absorption cross-sections of lanthanides. In this work, we engineered Bloch modes in a photonic crystal to assist a multi-wavelength upconversion mechanism and demonstrated a 28-fold enhancement of ultra-violet upconversion luminescence of Yb3+-Tm3+ doped thin films. Materials were selected and optimized to design nanostructures without parasitic absorption losses. The geometric parameters of the photonic crystals were scanned to match a slow-light resonance with an excited-state transition of Tm3+ and thus enhance incident visible light absorption. Ultra-violet light extraction was also enhanced by photonic crystal Bloch modes. Each of these two contributions were quantified and the measured photonic band structures were well reproduced by electromagnetic simulations.

Extending the high-harmonic cutoff with experimentally accessible fields is essential for advancing tabletop coherent extreme ultraviolet (EUV) and soft X-ray sources. Although terahertz (THz) assistance offers a promising route, cutoff extension at weak, laboratory-accessible THz strengths remain poorly understood. In this report, we comprehensively investigate THz-assisted high-order harmonic generation (HHG) using time-dependent Schrödinger equation simulations supported by classical trajectory analysis and Bohmian-based quantum dynamics. By mapping the plateau evolution versus THz strength, we show that even weak THz fields can extend the cutoff, producing a pronounced ``fish-fin'' structure whose prominent rays saturate near $I_p + 8 U_p$. We trace this extension to long electron excursions spanning several optical cycles before recombination, and provide a fully consistent explanation using both classical analysis and Bohmian trajectories flow. Our findings reveal that this cutoff-extension mechanism is remarkably robust, persisting across different atomic species and remaining insensitive to variations in the driving parameters. These results demonstrate that cutoff control is achievable with laboratory-scale THz fields, offering practical guidelines for engineering coherent high-energy HHG, and providing a robust pathway for tracking ultrafast electron motion in real time.

The most stringent laboratory-based experimental limits on the existence of sub-MeV sterile neutrinos are currently set by decay spectroscopy of radioactive $^7$Be embedded into superconducting sensors. The systematic uncertainties are dominated by the modeling of the electron shake-up and shake-off spectra that are not based on state-of-the-art atomic theory and do not include electron correlations or relativistic effects. We have used the multiconfiguration Dirac-Fock formalism to obtain correlated wavefunctions ab initio and compute all single and double shake processes in the electron capture decay of atomic $^7$Be. The simulations can explain some but not all of the observed spectral features, likely because the wave functions are modified by the Ta sensor material that the $^7$Be is embedded into. The new models also show that the L/K electron capture ratio of $^7$Be in Ta has previously been slightly underestimated revising the previous value of 0.070(7) to a new value of 0.0756(20).

Radiofrequency (RF) systems play a critical role in particle accelerators by enabling the acceleration, manipulation, and diagnosis of charged particle beams. At the heart of many of these systems lies the RF cavity, whose effective design requires close collaboration among RF designers, beam physicists, and mechanical engineers. This chapter presents the fundamental principles of RF systems, with particular emphasis on RF cavities, and underscores the interdisciplinary effort involved in their development. The SwissFEL X-ray free-electron laser at the Paul Scherrer Institut serves as a key example to illustrate these concepts.

Flat-band bound states in the continuum (BICs) are topological states with suppressed group velocity and robustness against radiation loss, offering a powerful platform for the exploration of non-Hermitian, nonlinear, topological phenomena and device applications. Van der Waals (vdW) metasurfaces have recently emerged as promising candidates for sustaining BICs and hybridizing with material transitions. However, the realization of flat-band BICs remains elusive. Here, we experimentally demonstrate polaritonic high-order BICs on a wide-angle flat band utilizing a subwavelength metasurface made of a vdW magnet CrSBr. The large oscillator strength of direct excitons in CrSBr enables near ultrastrong coupling with BICs, leading to strongly suppressed polaritonic angular dispersions. Remarkably, second-order polaritonic BICs become flat-band across a wide angular range, with corresponding Q factors exceeding 1500. Additionally, we find that these polaritonic BICs vanish in the transverse magnetic configuration, while leading to fascinating surface hyperbolic exciton-polaritons within the Reststrahlen band. Our findings underscore CrSBr as an exceptional platform for exploring flat-band photonics and polaritonics, paving the new avenue for advances in next-generation optical and quantum technologies.

In this work, we present a general form for the electron-ion diffractive potential derived from the quantum pair density matrix and fit to the improved Kelbg potential for atomic numbers up to $Z = 54$. We apply classical molecular dynamics using the improved Kelbg potential for carbon with various forms of the Pauli potential to compute internal energies and pressures for hot, dense plasma conditions. Our results are compared to an equation of state model based on path integral Monte Carlo and density functional theory simulations to examine the extent to which the improved Kelbg potential reproduces the internal energy and pressure of carbon plasmas. The regions of validity for carbon agree generally with those derived previously for hydrogen once pressure ionization effects are incorporated. Based on our carbon results and previously published hydrogen studies, we discuss the general applicability and limitations of these potentials for equation of state studies in warm dense matter and high energy density plasmas.

Accurate and fast calculations of localized surface plasmon resonances (LSPR) in metallic nanoparticles is essential for applications in sensing, nano-optics, and energy harvesting. Although full-wave numerical techniques such as the boundary element method (BEM) or the discrete dipole approximation (DDA) provide high accuracy, their computational cost often hinders rapid parametric studies. Here it is presented an ultrafast method that avoids solving large eigenproblems. Instead, only the dipolar component of the induced surface charge density \((\sigma_{dipolar})\) is retained through a expansion into Cartesion dipole basis, yielding a compact $3\times3$ geometric formulation that avoids full boundary-integral solves. The spectral response is obtained in a similar way, by projecting the Neumann--Poincaré surface operator onto the dipole subspace and evaluating a Rayleigh quotient, giving geometry-only eigenvalues again without an $N\times N$ eigenproblem. A major advantage of this method is that all geometry-dependent quantities are computed once per nanoparticle, while material dispersion and environmental changes enter only through simple algebraic expressions for the polarizability, enabling rapid evaluation across wavelengths. Retardation effects are incorporated through the modified long-wavelength approximation (MLWA), extending accuracy into the weakly retarded regime. The resulting framework provides a valuable tool for fast modelling and optimization of plasmonic nanoparticles at a significant lesser computational cost than BEM, DDA, and other standard tools.

The NA64 experiment at CERN H4 beamline recently started a high-energy positron-beam program to search for light dark matter particles through a thick-target, missing-energy measurement. To fulfil the energy resolution requirement of the physics measurement $\sigma_E/E\simeq2.5\%/\sqrt{E\mathrm{[GeV}]} \oplus 0.5\%$ and cope with the constraints and performance requests of the NA64 setup, a new high-resolution homogeneous electromagnetic calorimeter PKR-CAL has been designed. The detector is based on PbWO$_4$ crystals, each read by multiple SiPM sensors to maximize the light collection. The PKR-CAL design has been optimized to mitigate and control unavoidable SiPM saturation effects at high light levels, as well as to minimize the gain fluctuations induced by instantaneous variations of the H4 beam intensity. The $R\&D$ program culminated in the construction of a small-scale prototype, POKERINO. In this work, we present the results from the experimental characterization campaign of the POKERINO aiming at demonstrating that the obtained performances are compatible with the application requirements.

We present a rapid, large-field bimodal imaging platform that integrates conventional brightfield microscopy with a lensless IR imaging scanner, enabling whole-slide IR image stack acquisition in minutes. Using a dedicated deep learning model, we implement an optical HE staining strategy based on subcellular morpho-spectral fingerprinting.

Quantum geometrical molecular dynamics provides a quantum geometric picture for understanding reactive dynamics, especially excited-state conical intersection dynamics, and also a numerically exact method for strongly correlated electron-nuclear dynamics. However, there are substantial challenges in describing medium-sized molecules with tens of nuclear degrees of freedom. The main challenge is that it uses a discrete variable representation to discretize the molecular configuration space, and thus requires a tremendous number of quantum chemistry calculations to construct the electronic overlap matrix. Moreover, the expansion coefficients scale exponentially with molecular size for direct-product basis sets. We address these challenges by first introducing a coarse-grained local diabatic ansatz, followed by a tensor network representation of the expansion coefficients and the molecular time-evolution operator. With a full 24-dimensional demonstration using the pyrazine molecule, we show that such developments provide a highly accurate and computationally tractable method for high-dimensional, fully quantum, strongly coupled electron-nuclear dynamics from first principles.

The Low Gain Avalanche Diodes (LGADs) are promising particle detectors for timing resolution better than $50$ ps under a high radiation environment. This study investigates n-in-p LGAD architecture, focusing on ultra-thin sensors of thickness less than $50\ \mu$m using the WeightField2 program. The capabilities of WeightField2 are demonstrated by comparing its results with irradiation measurements from an FBK LGAD wafer, showing good agreement across unirradiated and neutron-irradiated conditions. This paper presents device simulations in High Luminosity LHC conditions (lifetime integrated fluence $ \mathcal{O} (10^{14})\ \mathrm{n_{eq}~cm^{-2}}$, temperature $ \approx 243\ \mathrm{K} $), and taking into account radiation damage, gain reduction due to fluence, and lattice defects. It is shown that a 20 $\mu$m thick sensor achieves the best timing performance. Among Silicon (Si), Diamond (C), and 4H-Silicon Carbide (4H-SiC), we found 4H-SiC to be the most promising: it provides the highest gain value for a fixed thickness and gain implant layer configuration, and best retains high charge collection value and timing capability under increasing fluence up to $50\times10^{14}\ \mathrm{n_{eq}~cm^{-2}}$. A time resolution less than 25 ps is reported with different gain implant concentrations for a $20 \mu$m 4H-SiC sensor. This work presents the potential of SiC-based LGADs in high-radiation collider environments.

We report on the measurement of neutron energy spectra at the newly established Yemi Underground Laboratory (Yemilab) in the Republic of Korea, designed to host dark matter and rare-event search experiments. A high-sensitivity neutron spectrometer was employed, consisting of ten cylindrical {}^{3}He proportional counters, eight of which were embedded in cylindrical high-density polyethylene moderators of various sizes. To quantify and mitigate contributions from internal \alpha-backgrounds, each detector underwent a dedicated background measurement using a cadmium-shielded box. These backgrounds, primarily originating from trace amounts of U and Th in the stainless-steel housings, were characterized and subtracted during data analysis. Neutron measurements were carried out at three locations within the Yemilab between March to October 2023. After waveform-based event selection and correction for \alphasym-backgrounds, neutron count rates were estimated and corresponding energy spectra were reconstructed using the unfolding method. The total neutron fluence rates were measured ranged from (3.24 $\pm$ 0.11) to (4.01 $\pm$ 0.10) $\times~10^{-5}~ {cm}^{-2}~{s}^{-1}$, with thermal and fast neutron components (1 - 10 MeV) ranging from (1.32 $\pm$ 0.05) to (1.51 $\pm$ 0.05) $\times 10^{-5}~{cm}^{-2}~{s}^{-1}$ and (0.27 $\pm$ 0.03) to (0.34 $\pm$ 0.10) $\times~10^{-5}~{cm}^{-2}~{s}^{-1}$, respectively.

Time-dependent electronic structure methods provide an efficient, accurate, and robust alternative to traditional time dependent methods for computing both linear and non-linear optical properties. With this in mind, we have developed the real-time Tamm-Dancoff approximation (RT-TDA). This is an approach to model electron dynamics by propagating the linear-response time-dependent density functional theory (LR-TDDFT) amplitudes within the Tamm-Dancoff approximation (TDA) and adiabatic approximation. Because the electronic structure is propagated in real-time in a many-electron basis, RT-TDA overcomes known limitations of adiabatic Kohn-Sham RT-TDDFT for describing dynamics in intense fields. Acceleration by graphic processing units (GPUs) enables simulations of larger molecules and on longer timescales. To demonstrate the utility of our approach, we present the calculations of the linear absorption spectrum of a large organic molecule (120 heavy atoms), of Rabi oscillations, and of nonlinear 2-photon absorption, in which we observe the AC Stark effect.

Filtered budgets for anelastic turbulence and a general expression of the turbulent sensible heat flux are derived for a multicomponent fluid with an arbitrary equation of state. A family of subgrid-scale closures is then found under the constraint of consistency with (i) the first and second laws of thermodynamics and (ii) invariance with respect to irrelevant thermodynamic constants. A similar family of fully compressible models is also constructed heuristically. These models predict turbulent kinetic energy, assume down-gradient closures for three-dimensional turbulent fluxes and impose certain relationships between the closures for the turbulent fluxes of heat, matter, entropy, and the work of buoyancy forces. A key finding is the explicit derivation of the local rate of entropy production in the filtered model. Positive entropy production is guaranteed whenever the turbulent diffusions of heat and composition are positive and no cross-diffusion occurs. Cross-diffusivities are admissible provided their magnitude is within the bounds of an explicit criterion. The filtered model is invariant under a wider class of transformations than the unfiltered model. Furthermore, in the special case of a single turbulent diffusivity, an arbitrary conservative variable can be prognosed while ignoring its precise relationship to entropy. These findings show that down-gradient closures are consistent with the first and second law of thermodynamics even when they lead to a turbulent sensible heat flux up the temperature gradient. Indeed, while molecular conduction/diffusion is spontaneous and energy-conserving, stratified turbulent mixing is driven by mechanical turbulence and enabled by the consumption of turbulent kinetic energy.

Black carbon (BC) contributes to Arctic warming by absorbing sunlight and darkening snow. Its atmospheric lifetime critically determines its concentration and climate impact, yet the processes controlling its removal remain poorly constrained in the Arctic. From 18 months of single-particle measurements at the Zeppelin Observatory (Svalbard), we analysed 37 liquid cloud events (~200 hours) to investigate the link between BC properties and in-cloud scavenging, providing the most extensive in-cloud single particle BC dataset to date. While large BC cores (DrBC>200 nm) were consistently scavenged, smaller cores were only partly removed. However, even thin soluble coatings significantly enhanced their scavenging, showing that mixing state modulates BC scavenging in the CCN-limited regime typical of Arctic low-level clouds. Seasonal variability in clear sky BC mixing state further suggests corresponding changes in scavenging efficiency. Our results demonstrate that besides size, the size-resolved BC mixing state is a key variable for BC scavenging in the Arctic and models should take it into consideration to accurately predict BC-cloud interaction.

Atom interferometers require both high efficiency and robust performance in their mirror pulses under experimental inhomogeneities. In this work, we demonstrated that quantum optimal control designed mirror pulse significantly enhance interferometer performance by using novel adaptive sliced structure. Using gradient ascent pulse engineering (GRAPE), optimized mirror pulse for a Mach-Zehnder light-pulse atom interferometer was designed by discretizing the control into non-uniform phase slices. This design broadened the tolerence to experimentally relevant variations in detuning $[-\Omega_0,\Omega_0]$ and Rabi frequency $[0.1\times\Omega_0,1.9\times\Omega_0]$ ($\Omega_0=2\pi\times25$ kHz), while maintaining high transfer efficiency even when the response-time delays up to 1.6 $\rm{\mu s}$. The optimized pulse was found to be robust to coupling inhomogeneity and velocity spread, offering a significant improvement in robustness over conventional pulse. The adaptive pulse slicing method provides a minimalist strategy that reduces experimental complexity while enhancing robustness and scalability, offering an innovative scheme for quantum optimal control in high precision atom interferometry.

Quartz tuning fork-based atomic force microscopy (QTF-AFM) has become a powerful tool for high-resolution imaging of both conductive and insulating samples, including semiconductor structures and metal-coated surfaces as well as soft matter under ambient conditions, while also enabling measurements in more demanding environments including ultrahigh vacuum and cryogenic conditions where conventional cantilever-based AFM often encounters limitations. However, the broader adoption of QTF-AFM has been constrained by the difficulty of attaching a cantilever tip to a quartz tuning fork (QTF) with the positional and angular precision required for repeatable and reproducible probe fabrication. For stable operation, the tip must be placed precisely at the midline of a single tine, aligned parallel to the prong axis, and rigidly secured. Even slight lateral offsets or angular deviations disrupt the intrinsic antisymmetric flexural mode, induce torsional coupling, and ultimately lead to systematic image distortions and reduced measurement integrity. In this work, we present a comprehensive, semi-automated QTF-tip fabrication system that integrates precision alignment, real-time frequency-sweep monitoring, and controlled Q-factor tuning within a single workflow. Experimental characterization demonstrates consistent probe preparation across multiple trials, preservation of sharp and well-defined resonance responses with deliberately adjustable damping, and high-fidelity, high-resolution imaging in practical scanning tests. This integrated approach provides a reproducible framework to QTF-based probe fabrication, lowering the technical barrier to QTF-AFM implementation and broadening its applicability across diverse sample types and operating environments.

We introduce a quantitative method to compare arbitrary pairs of graph centrality measures, based on the ordering of vertices induced by them. The proposed method is conceptually simple, mathematically elegant, and allows for a quantitative restatement of many conjectures that were previously cumbersome to formalize. Moreover, it produces an approximation scheme useful for network scientists. We explore some of these uses and formulate new conjectures that are of independent interest.

We combined experimental and theoretical investigations of an effective two-level atomic system operating in the near-infrared telecom wavelength regime, realized using hot rubidium vapor confined within a sub-micron-thick cell. In this strongly confined geometry, atomic coherence is profoundly influenced by wall-induced relaxation arising from frequent atom-surface collisions. By analyzing both absorption and fluorescence spectra, we demonstrate that the optical response is dominated by a closed cycling transition, which effectively isolates the atomic dynamics to a two-level configuration despite the presence of multiple hyperfine states. This confinement-induced selection suppresses optical pumping into uncoupled states and enables robust, controllable light-matter interaction at telecom wavelengths within a miniature atomic platform. Our results establish a practical route to realizing near-infrared atomic two-level systems in compact vapor-cell devices, opening new opportunities for integrated quantum photonic technologies, including on-chip quantum memories, telecom-band frequency references, and scalable quantum information processing.

Conformal field theories (CFTs) feature prominently in high-energy physics, statistical mechanics, and condensed matter. For example, CFTs govern emergent universal properties of systems tuned to quantum phase transitions, including their entanglement, correlations, and low-energy excitation spectra. Much of the rich structure predicted by CFTs nevertheless remains unobserved in experiment. Here we directly observe the energy excitation spectra of emergent CFTs at quantum phase transitions -- recovering universal energy ratios characteristic of the underlying field theories. Specifically, we develop and implement a modulation technique to resolve a Rydberg chain's finite-size spectra, variably tuned to quantum phase transitions described by either Ising or tricritical Ising CFTs. We also employ local control to distinguish parities of excitations under reflection and, in the tricritical Ising chain, to induce transitions between distinct CFT spectra associated with changing boundary conditions. By utilizing a variant of the modulation technique, we furthermore study the dynamical structure factor of the critical system, which is closely related to the correlation of an underlying Ising conformal field. Our work not only probes the emergence of CFT features in a quantum simulator, but also provides a technique for diagnosing a priori unknown universality classes in future experiments.

We report temperature-dependent switching between lower and upper polariton condensation in a GaAs/AlGaAs microcavity when both of these species have comparable populations in a mixture. Using angle-resolved photoluminescence, we observe that at low temperatures, condensation occurs in the lower polariton branch, while at elevated temperatures, the upper polariton branch can become favored. At an intermediate temperature, we observe instability in the condensate formation, characterized by metastable correlations of the fluctuations in intensity and linewidth of the lower and upper polariton branches.

Does gravity care about electric charge? Precision tests of the weak equivalence principle achieve remarkable sensitivity but deliberately minimize electric charge on test masses, leaving this fundamental question experimentally open. We present a minimalist framework coupling electromagnetism to linearized gravity through conservation of a complex charge-mass current, predicting charge-dependent violations $\Delta a/g = \kappa(q/m)$. Remarkably, this prediction occupies unexplored experimental territory precisely because precision gravity tests avoid charge variation. We identify this as a significant gap and propose a modified torsion balance experiment where $q/m$ is treated as a controlled variable. Such an experiment could test whether gravitational acceleration depends on electric charge, probing physics in genuinely new parameter space. This work exemplifies how theoretical minimalism can reveal overlooked opportunities in fundamental physics.

When parameters of a dynamical system change sufficiently fast, critical transitions can take place even in the absence of bifurcations. This phenomenon is known as rate-induced tipping and has been reported in a variety of systems, from simple ordinary differential equations and maps to mathematical models in climate sciences and ecology. In most examples, the transition happens at a critical rate of parameter change, a rate-induced tipping point, and is associated with a simple unstable orbit (edge state). In this work, we show how this simple picture changes when non-attracting fractal sets exist in the autonomous system, a ubiquitous situation in non-linear dynamics. We show that these fractals in phase space induce fractals in parameter space, which control the rates and parameter changes that result in tipping. We explain how such rate-induced fractals appear and how the fractal dimensions of the different sets are related to each other. We illustrate our general theory in three paradigmatic systems: a piecewise linear one-dimensional map, the two-dimensional Hénon map, and a forced pendulum.

We present an efficient implementation of ab initio many-body quantum embedding and local correlation methods for infinite periodic systems through translational symmetry adapted interpolative separable density fitting, an approach which reduces the scaling of the calculations to only linear with the number of k-points. Employing this methodology, we compute correlated ground-state coupled cluster energies within density matrix embedding and local natural orbital correlation frameworks for both weakly and strongly correlated solids, using up to 1000 k-points. By extrapolating the local correlation domains and k-point sampling we further obtain estimates of the full coupled cluster with singles, doubles, and perturbative triples ground-state energies in the thermodynamic limit.

Jet tagging, identifying the origin of jets produced in particle collisions, is a critical classification task in high-energy physics. Despite the revolutionary impact of deep learning on jet tagging over the past decade, the paradigm has remained unchanged. In particular, jets are classified independently, one at a time. This single-jet approach ignores correlations, overlaps, and wider event context between jets. We introduce PanopTag, a new paradigm for jet tagging that departs from traditional single-jet tagging approaches. Rather than classifying jets independently, PanopTag simultaneously tags all jets by employing an encoder-decoder architecture that uses jet kinematics as queries to cross-attend to particle flow object embeddings. We evaluate PanopTag on heavy-flavor $(b/c)$-tagging and demonstrate remarkable performance improvements over state-of-the-art single-jet baselines that are only accessible by exploiting event-level features and correlations between jets.

Social media platforms facilitate echo chambers through feedback loops between user preferences and recommendation algorithms. While algorithmic homogeneity is well-documented, the distinct evolutionary pathways driven by content-based versus link-based recommendations remain unclear. Using an extended dynamic Bounded Confidence Model (BCM), we show that content-based algorithms--unlike their link-based counterparts--steer social networks toward a segregation-before-polarization (SbP) pathway. Along this trajectory, structural segregation precedes opinion divergence, accelerating individual isolation while delaying but ultimately intensifying collective polarization. Furthermore, we reveal a paradox in information sharing: Reposting increases the number of connections in the network, yet it simultaneously reinforces echo chambers because it amplifies small, latent opinion differences that would otherwise remain inconsequential. These findings suggest that mitigating polarization requires stage-dependent algorithmic interventions, shifting from content-centric to structure-centric strategies as networks evolve.

Nonlinear stochastic motion presents significant challenges for Bayesian particle tracking. To address this challenge, this paper proposes a framework to construct an invertible transformation that maps the nonlinear state-space model (SSM) into a higher-dimensional linear Gaussian SSM. This approach allows the application of standard linear-Gaussian inference techniques while maintaining a connection to the dynamics of the original system. The paper derives the necessary conditions for such transformations using Ito's lemma and variational calculus, and illustrates the method on a bistable cubic motion model, radial Brownian process model, and a logistic model with multiplicative noise. Simulations confirm that the transformed linear systems, when projected back, accurately reconstruct the nonlinear dynamics and, in distinct regimes of stiffness and singularity, yield tracking accuracy competitive with conventional filters, while avoiding their structural instabilities.

We investigate the thermodynamics and phase stability of the AlTiVCr lightweight high-entropy alloy using a combination of ab initio electronic structure calculations, a concentration wave analysis, and atomistic Monte Carlo simulations. In alignment both with experimental data and with results obtained using other computational approaches, we predict a $\textrm{B2}$ (CsCl) chemical ordering emerging in this alloy at comparatively high temperatures, which is driven by Al and Ti moving to separate sublattices, while V and Cr express weaker site preferences. The impact of this $\textrm{B2}$ chemical ordering on the electronic transport properties of the alloy is investigated within a Kubo-Greenwood linear response framework and it is found that, counter-intuitively, the alloy's residual resistivity increases as the material transitions from the $\textrm{A2}$ (disordered bcc) phase to our predicted $\textrm{B2}$ (partially) ordered structure. This is understood to result primarily from a reduction in the density of electronic states at the Fermi level induced by the chemical ordering. At low temperatures, our atomistic Monte Carlo simulations then reveal subsequent sublattice orderings, with the ground-state configuration predicted to be a fully-ordered, single-phase structure with vanishing associated residual resistivity. These results give fresh, insight into the atomic-scale structure and consequent physical properties of this well-studied, technologically relevant material.

Multiphase gas -- ranging from cold molecular clouds ($\lesssim 100\,$K) to hot, diffuse plasma ($\gtrsim 10^6\,$K) is a defining feature of the interstellar, circumgalactic, intracluster, and intergalactic media. Accurately simulating its dynamics is critical to improving our understanding of galaxy formation and evolution, however, due to their multi-scale and multi-physics nature, multiphase systems are highly challenging to model. In this review, we provide a comprehensive overview of numerical simulations of multiphase gas in and around galaxies. We begin by outlining the environments where multiphase gas arises and the physical and computational challenges associated with its modeling. Key quantities that characterize multiphase gas dynamics are discussed, followed by an in-depth look at idealized setups such as turbulent mixing layers, cloud-wind interactions, thermal instability, and turbulent boxes. The review then transitions to less idealized and/or larger-scale simulations, covering radiative supernovae bubbles, tall box simulations, isolated galaxy models including dwarf and Milky Way-mass systems, and cosmological zoom-in simulations, with a particular focus on simulations that enhance resolution in the halo. Throughout, we emphasize the importance of connecting scales, extracting robust diagnostics, and comparing simulations to observations. We conclude by outlining persistent challenges and promising directions for future work in simulating the multiphase Universe.

Gravitational waves (GWs) in the $10^{-3}-0.1$ Hz band encode unique signatures of the early universe and merging compact objects, but they are beyond the reach of existing observatories. Theoretical models suggest that the Moon could act as a resonant detector, but the unknown influence of its rugged surface and heterogeneous interior has cast doubt on this prospect. Here, we resolve this long-standing uncertainty by constructing the first high-resolution, structurally realistic model of the lunar GW response. We achieve this by combining high-fidelity spectral-element simulations with the analytical power of normal-mode perturbation theory, thereby resolving topographical effects down to $3.7$ km grid spacing while maintaining the capacity to discern global free-oscillation patterns. This dual-methodology approach not only recovers the expected predominant quadrupole ($l=2$) oscillation mode, but also exposes a systematic signal amplification of $(10-20)\%$ in thick-crust regions. This enhancement is traced by our normal-mode analysis to a mode-coupling process, in which the original quadrupolar oscillation induced by the passing GWs distributes energy into a series of higher-order modes, the hybridized eigenmodes of the laterally heterogeneous Moon. Near certain eigen-frequencies and at specific locations, we observe up to tenfold amplification, highlighting the power of numerical simulations in resolving these structurally fine-tuned features. Our work establishes the Moon as an accurately calibrated resonant GW detector, and the resulting amplification maps provide quantitative guide for the optimal landing site selection.

The deep neural network multigrid solver (DNN-MG) combines a coarse-grid finite element simulation with a deep neural network that corrects the solution on finer grid levels, thereby improving the computational efficiency. In this work, we discuss various design choices for the DNN-MG method and demonstrate significant improvements in accuracy and generalizability when applied to the solution of the instationary Navier-Stokes equations. We investigate the stability of the hybrid simulation and show how the neural networks can be made more robust with the help of replay buffers. By retraining on data derived from the hybrid simulation, the error caused by the neural network over multiple time-steps can be minimized without the need for a differentiable numerical solver. Furthermore, we compare multiple neural network architectures, including recurrent neural networks and Transformers, and study their ability to utilize more information from an increased temporal and spatial receptive field. Transformers allow us to make use of information from cells outside the predicted patch even with unstructured meshes while maintaining the locality of our approach. This can further improve the accuracy of DNN-MG without a significant impact on performance.

Precise control and nanoscale confinement of terahertz (THz) fields are essential requirements for emerging applications in photonics, quantum technologies, wireless communications, and sensing. Here, we demonstrate a polaritonic cavity enhanced THz photoresponse in an antenna coupled device based on chemical vapor deposited (CVD) monolayer graphene. The dipole antenna lobes simultaneously serve as two gate electrodes, concentrate the impinging THz field, and efficiently launch acoustic graphene plasmons (AGPs), which drive a strong photo-thermoelectric (PTE) signal. Between 6 and 90 K, the photovoltage exhibits pronounced peaks, modulating the PTE response by up to 40\%, that we attribute to AGPs forming a Fabry Pérot THz cavity in the full or half graphene channel. Combined full wave and transport thermal simulations accurately reproduce the gate controlled plasmon wavelength, spatial absorption profile, and the resulting nonuniform electron heating responsible for the PTE response. The lateral and vertical maximum confinement factors of the AGP wavelength relative to the incident wavelength are 165 and 4000, respectively, for frequencies from 1.83 to 2.52 THz. These results demonstrate that wafer scalable CVD graphene, without hBN encapsulation, can host coherent AGP resonances and exhibit an efficient polaritonic enhanced photoresponse under appropriate gating, antenna coupling, and AGP cavity design, opening a route to scalable, polarization and frequency selective, liquid nitrogen cooled, and low power consumption THz detection platforms based on plasmon thermoelectric transduction.

In this HYP2025 talk I pay tribute to Tullio Bressani (1940-2024), Bogdan Povh (1932-2024) and Toshimitsu Yamazaki (1934-2025), all of whom made lasting contributions to shaping up Strangeness Nuclear Physics. Yoshinori Akaishi's (1941-2025) record is also noted.

It is well known that a solid (e.g. wood or rubber) can be put under tensile stress by pulling on it. Once a critical stress is overcome, the solid breaks, leaving an empty space. Similarly, due to internal cohesion, a liquid can withstand tension (i.e. negative pressure), up to a critical point where a large bubble spontaneously forms, releasing the tension and leaving a void (the bubble). This process is known as cavitation. While water at negative pressure is metastable, such a state can be long-lived. In fact, water under tension is found routinely in the plant kingdom, as a direct effect of dehydration, e.g. by evaporation. In this chapter, we provide a brief overview of occurrences of water stress and cavitation in plants, then use a simple thermodynamic and fluid mechanical framework to describe the basic physics of water stress and cavitation. We focus specifically on situations close to those in plants, that is water at negative pressure nested within a structure that is solid, but porous and potentially deformable. We also discuss insights from these simple models as well as from experiments with artificial structures mimicking some essential aspects of the structures found within plants.

Forming planetary systems are populated by large numbers of gravitationally interacting planetary bodies, spanning from massive giant planets to small planetesimals akin to present-day asteroids and comets. All these planetary bodies are embedded in the gaseous embrace of their native protoplanetary disks, and their interactions with the disk gas play a central role in shaping their dynamical evolution and the outcomes of planet formation. These factors make realistic planet formation simulations extremely computationally demanding, which in turn means that accurately modeling the formation of planetary systems requires the use of high-performance methods. The planet formation code Mercury-Ar$\chi$es was developed to address these challenges and, since its first implementation, has been used in multiple exoplanetary and Solar System studies. Mercury-Ar$\chi$es is a parallel n-body code that builds on the widely used Mercury code and is capable of modeling the growth and migration of forming planets, the interactions between planetary bodies and the disk gas, as well as the evolving impact flux of planetesimals on forming planets across the different stages of their formation process. In this work we provide the up-to-date overview of its physical modeling capabilities and the first detailed description of its high-performance implementation based on the OpenMP directive-based parallelism for shared memory environments, to harness the multi-thread and vectorization features of modern processor architectures.

The growing body of atmospheric observations of exoplanets from space and ground-based facilities showcases how the great diversity of the planetary population is not limited to their physical properties but extends to their compositions. The ESA space mission Ariel will observe and characterise hundreds of exoplanetary atmospheres to explore and understand the roots of this compositional diversity. To lay the foundations for the Ariel mission, the OPAL Key Science Project is tasked with creating an unprecedented library of realistic synthetic atmospheres spanning tens of elements and hundreds of molecules on which the Ariel consortium will test and validate its codes and pipelines ahead of launch. In this work we describe the aims and the pipeline of codes of the OPAL project, as well as the process through which we trace the genetic link connecting planets to their native protoplanetary disks and host stars. We present the early results of this complex and unprecedented endeavour and discuss how they highlight the great diversity of outcomes that emerge from the large degeneracy in the parameter space of possible initial conditions to the planet formation process. This, in turn, illustrates the growing importance of interdisciplinary modelling studies supported by high-performance computing methods and infrastructures to properly investigate this class of high-dimensionality problems.

Understanding how cosmological parameters influence the cosmic microwave background (CMB) power spectra is a central component of modern cosmology education, but interactive exploration is often limited by computational cost or technical complexity. We present CosmoSlider, a lightweight visualization tool that enables real-time exploration of CMB power spectra as multiple cosmological parameters are varied simultaneously. The tool employs a neural-network emulator implemented using TensorFlow Lite, allowing rapid evaluation of spectra without relying on large grids of precomputed models or on-demand execution of Einstein--Boltzmann solvers. CosmoSlider is available both as an iOS application and as a web-based tool, making it accessible across platforms and suitable for use in classrooms, lectures, and self-guided study. By providing immediate visual feedback, CosmoSlider supports the development of intuition for the physical processes underlying CMB anisotropies and serves as a complementary resource to traditional theoretical instruction.

We theoretically compare the quantum Fisher information (QFI) for three configurations of absorption spectroscopy with undetected idler photons: an SU(1,1) interferometer with inter-source idler loss, an induced-coherence (IC) setup in which the idler partially seeds a second squeezer together with a vacuum ancilla, and a distributed-loss (DL) scheme with in-medium attenuation. We calculate the QFI as a function of parametric gain for both full and signal-only detection access. For losses below 99% and low to moderate gain, the SU(1,1) configuration provides the largest QFI. At high gain and intermediate loss, the IC scheme performs best, while under extreme attenuation (transmission $<$ 1%) the DL model becomes optimal. These results delineate the measurement regimes in which each architecture is optimal in terms of information theory.

We introduce a method for engineering discrete local dynamics in globally-driven dual-species neutral atom experiments, allowing us to study emergent digital models through uniform analog controls. Leveraging the new opportunities offered by dual-species systems, such as species-alternated driving, our construction exploits simple Floquet protocols on static atom arrangements, and benefits of generalized blockade regimes (different inter- and intra-species interactions). We focus on discrete dynamical models that are special examples of Quantum Cellular Automata (QCA), and explicitly consider a number of relevant examples, including the kicked-Ising model, the Floquet Kitaev honeycomb model, and the digitization of generic translation-invariant nearest-neighbor Hamiltonians (e.g., for Trotterized evolution). As an application, we study chaotic features of discretized many-body dynamics that can be detected by leveraging only demonstrated capabilities of globally-driven experiments, and benchmark their ability to discriminate chaotic evolution.

We discuss a model of the universe where dark energy is replaced by electrically-charged extremely-massive dark matter. The cosmological constant has a value of the same order as the mean matter density, consistent with observations, and is obtained classically without fine-tuning.

Real-world networks have a complex topology comprising many elements often structured into communities. Revealing these communities helps researchers uncover the organizational and functional structure of the system that the network represents. However, detecting community structures in complex networks requires selecting a community detection method among a multitude of alternatives with different network representations, community interpretations, and underlying mechanisms. This tutorial focuses on a popular community detection method called the map equation and its search algorithm Infomap. The map equation framework for community detection describes communities by analyzing dynamic processes on the network. Thanks to its flexibility, the map equation provides extensions that can incorporate various assumptions about network structure and dynamics. To help decide if the map equation is a suitable community detection method for a given complex system and problem at hand - and which variant to choose - we review the map equation's theoretical framework and guide users in applying the map equation to various research problems.

The demand for integrated photonic chips combining the generation and manipulation of quantum states of light is steadily increasing, driven by the need for compact and scalable platforms for quantum information technologies. While photonic circuits with diverse functionalities are being developed in different single material platforms, it has become crucial to realize hybrid photonic circuits that harness the advantages of multiple materials while mitigating their respective weaknesses, resulting in enhanced capabilities. Here, we demonstrate a hybrid III-V/Silicon quantum photonic device combining the strong second-order nonlinearity and direct bandgap of the III-V semiconductor platform with the high maturity and CMOS compatibility of the silicon photonic platform. Our device embeds the spontaneous parametric down-conversion (SPDC) of photon pairs into an AlGaAs source and their vertical routing to an adhesively-bonded silicon-on-insulator circuitry, within an evanescent coupling scheme managing both polarization states. This enables the on-chip generation of broadband (> 40 nm) telecom photons by type 0 and type 2 SPDC from the hybrid device, at room temperature and with internal pair generation rates exceeding $10^5$ $s^{-1}$ for both types, while the pump beam is strongly rejected. Two-photon interference with 92% visibility (and up to 99% upon 5 nm spectral filtering) proves the high energy-time entanglement quality of the produced quantum state, thereby enabling a wide range of quantum information applications on-chip, within an hybrid architecture compliant with electrical pumping and merging the assets of two mature and highly complementary platforms in view of out-of-the-lab deployment of quantum technologies.

The High-Granularity Calorimeter (HGCAL) will replace the current CMS Endcap Calorimeter during Long-Shutdown 3. The Endcap Concentrator (ECON) ASICs represent key elements in the readout chain, processing trigger (ECON-T) and data (ECON-D) streams from the HGCROC to the lpGBT. The ECONs will operate in a radiation environment with a High-Energy Hadron (${E\geq20MeV}$) flux up to $2\cdot10^{7} cm^{-2}s^{-1}$. This contribution describes the Universal Verification Methodology (UVM)-based functional verification of the ECON ASICs focusing on the re-use of existing components to manage the complexity of the verification environment.

We present an all-silicon source of near-infrared linearly-polarized single photons, fabricated by nanoscale positioning of a color center in a silicon-on-insulator microcavity. The color center consists of a single W center, created at a well-defined position by Si$^{+}$ ion implantation through a 150 nm-diameter nanohole in a mask. A circular Bragg grating cavity resonant with the W's zero-phonon line at 1217 nm is fabricated at the same location as the nanohole. By Purcell enhancement of zero-phonon emission, we obtain a photon count rate of $1.29 \pm 0.01$ Mcounts/s at saturation under above-gap continuous-wave excitation with a Debye-Waller factor of $98.6\pm1.4 \%$. A clean photon antibunching behavior is observed up to pump powers ensuring saturation of the W's emission ($g^{(2)}(0)=0.06\pm0.02$ at $P=9.2P_{sat}$), evidencing that the density of additional parasitic fluorescent defects is very low. We also demonstrate the triggered emission of single photons with $93\pm2 \%$ purity under weak pulsed laser excitation. At high pulsed laser power, we reveal a detrimental effect of repumping processes, that could be mitigated using selective pumping schemes in the future. These results represent a major step towards on-demand sources of indistinguishable near-infrared single photons within silicon photonics chips.

Evaporation from porous media is a key phenomenon in the terrestrial environment and is linked to accumulation of solutes at or near the evaporative surface. The current study aims at improved understanding of solute accumulation near evaporating surfaces. Analytical and numerical modelling studies have suggested the development of local instabilities due to density differences during evaporation in case of saturated porous media with high permeability. These instabilities lead to density-driven downward flow through fingering, and thus a redistribution of solutes. To experimentally investigate this, we performed evaporation experiments on two types of porous media (F36 and W3) with intrinsic permeabilities that differed by two orders of magnitude. Using magnetic resonance imaging (23Na-MRI), we monitored the development of solute accumulation and subsequent redistribution during evaporation with a continuous supply of water at the bottom of the samples. Significant differences between the Na enrichment patterns were observed for the two porous media. The F36 sample showed an initial enrichment at the surface within the first hour, but soon after a downwards moving plume developed that redistributed NaCl back into the column. Average depth profiles of Na concentrations showed that the surface concentration reached only 2.5 M, well below the solubility limit. In contrast, the W3 sample with lower permeability showed enrichment in a shallow near-surface zone where a concentration of over 6 M was reached. Comparison of experimental results with numerical simulations using DuMux showed qualitative agreement between measured and modelled solute concentrations. This study experimentally confirms the importance of density-driven redistribution of solutes in case of saturated porous media, which has implications for predicting evaporation rates and the time to the start of salt crust formation.

The classical problem of steady rarefied gas flow past an infinitely thin circular disk is revisited, with particular emphasis on the gas behavior near the disk edge. The uniform flow is assumed to be perpendicular to the disk surface. An integral equation for the velocity distribution function, derived from the linearized Bhatnagar-Gross-Krook (BGK) model of the Boltzmann equation and subject to diffuse reflection boundary conditions, is solved numerically. The numerical method fully accounts for the discontinuity in the velocity distribution function that arises due to the presence of the edge. It is found that a kinetic boundary layer forms near the disk edge, extending over several mean free paths, and that its magnitude scales as $\mathrm{Kn}^{1/2}$ as the Knudsen number $\mathrm{Kn}$ (defined with respect to the disk radius) tends to zero. A thermal polarization effect, previously studied for spherical geometries, is also observed in the disk case, with a more pronounced manifestation near the edge that exhibits the same $\mathrm{Kn}^{1/2}$ scaling. The drag force acting on the disk is computed over a wide range of Knudsen numbers and shows good agreement with existing results for a hard-sphere gas and in the near-free-molecular regime.

The MIST experiment aims to detect the cosmological 21-cm signal through sky observations at 25-125 MHz using a wide-beam antenna. The antenna is mounted above the soil and the beam characteristics are highly dependent on the soil's electrical properties. Accurate models for the beam obtained from electromagnetic simulations are crucial for detecting the 21-cm signal. Determining the soil properties to inform the beam simulations is therefore a very high priority for MIST. Here we report the first electrical characterization of the MIST observation site in the Canadian High Arctic, which was conducted in July, 2022. The electrical parameters were estimated using impedance measurements of the instrument's antenna, which is a very advantageous approach for MIST. Our best-fit soil model is consistent with a thawed active layer underlain by permafrost, and the parameters were estimated with a precision close to the requirements for the detection of the cosmological 21-cm signal.

We perform narrowline cooling of single dysprosium atoms trapped in a 1D optical tweezers array, employing the narrow single-photon transition at 741 nm. At the trapping wavelength of 532 nm, the excited state is less trapped than the ground state. To obtain efficient cooling performances, we chirp the frequency of the cooling beam to subsequently address the red sidebands of different motional states. We demonstrate the effectiveness of the cooling protocol through Raman thermometry, which we characterize for our experimental conditions. We obtain an array of 75 atoms close to the motional ground state in the radial direction of the tweezers. Our results demonstrate the possibility to manipulate the motional degree of freedom of dysprosium in optical tweezers arrays, a key ingredient to exploit the potential of lanthanide-based tweezers platforms for quantum science.

Molecular dynamics (MD) provides insights into atomic-scale processes by integrating over time the equations that describe the motion of atoms under the action of interatomic forces. Machine learning models have substantially accelerated MD by providing inexpensive predictions of the forces, but they remain constrained to minuscule time integration steps, which are required by the fast time scale of atomic motion. In this work, we propose FlashMD, a method to predict the evolution of positions and momenta over strides that are between one and two orders of magnitude longer than typical MD time steps. We incorporate considerations on the mathematical and physical properties of Hamiltonian dynamics in the architecture, generalize the approach to allow the simulation of any thermodynamic ensemble, and carefully assess the possible failure modes of such a long-stride MD approach. We validate FlashMD's accuracy in reproducing equilibrium and time-dependent properties, using both system-specific and general-purpose models, extending the ability of MD simulation to reach the long time scales needed to model microscopic processes of high scientific and technological relevance.

Foundation models, or large atomistic models (LAMs), aim to universally represent the ground-state potential energy surface (PES) of atomistic systems as defined by density functional theory (DFT). The scaling law is pivotal in the development of large models, suggesting that their generalizability in downstream tasks consistently improves with increased model size, expanded training datasets, and larger computational budgets. In this study, we present DPA3, a multi-layer graph neural network founded on line graph series (LiGS), designed explicitly for the era of LAMs. We demonstrate that the generalization error of the DPA3 model adheres to the scaling law. The scalability in the number of model parameters is attained by stacking additional layers within DPA3. Additionally, the model employs a dataset encoding mechanism that decouples the scaling of training data size from the model size within its multi-task training framework. When trained as problem-oriented potential energy models, the DPA3 model exhibits superior accuracy in the majority of benchmark cases, encompassing systems with diverse features, including molecules, bulk materials, surface and cluster catalysts, two-dimensional materials, and battery materials. When trained as a LAM on the OpenLAM-v1 dataset, the DPA-3.1-3M model exhibits lowest overall zero-shot generalization error across 12 downstream tasks spanning a diverse array of research domains. This performance suggests superior accuracy as an out-of-the-box potential model, requiring minimal fine-tuning data for downstream scientific applications.

We propose a fully analytical method for describing high-order harmonic generation (HHG). This method is based on the strong-field approximation (SFA) and electron-trajectory theory, but utilizes the perturbation expansion on the Keldysh parameter $\gamma$. This expansion allows us to clearly differentiate the nonadiabatic and adiabatic effects on HHG. We show that the nonadiabatic effect relating to high-order expansion depends on the laser wavelength and remarkably enhances the HHG yields for cases of short wavelengths, providing deeper insights into wavelength-dependent HHG yields which are important in producing attosecond pulses. Especially, our method provides the analytical and accurate descriptions of nonadiabatic exit velocity and position of the tunneling electron at the tunnel exit. These descriptions are meaningful for constructing a fully analytical and quantitative Coulomb-included HHG model, which is crucial in HHG-based attosecond measurement.

Populations of cells regulate gene expression in response to external signals, but their ability to make reliable collective decisions is limited by both intrinsic noise in molecular signaling and variability between individual cells. In this work, we use optogenetic control of the canonical Wnt pathway as an example to study how reliably information about an external signal is transmitted to a population of cells, and determine an optimal encoding strategy to maximize information transmission from Wnt signals to gene expression. We find that it is possible to reach an information capacity beyond 1 bit only through an appropriate, discrete encoding of signals: using either no Wnt, a short Wnt pulse, or a sustained Wnt signal. By averaging over an increasing number of outputs, we systematically vary the effective noise in the pathway. As the effective noise decreases, the optimal encoding comprises more discrete input signals. These signals do not need to be fine-tuned to achieve near-optimal information transmission. The optimal code transitions into a continuous code in the small-noise limit, which can be shown to be consistent with the Jeffreys prior. We visualize the performance of different signal encodings using decoding maps. Our results suggest optogenetic Wnt signaling allows for regulatory control beyond a simple binary switch, and provides a framework to apply ideas from information processing to single-cell in vitro experiments.

Are your perceptions, memories and observational data, a statistical fluctuation out of the thermal equilibrium of the universe, having no correlation with the actual past state of the universe? Arguments are given in the literature for and against this "Boltzmann brain" hypothesis. Complicating these arguments have been the many subtle -- and very often implicit -- joint dependencies among these arguments and others that have been given for the past hypothesis, the second law, and even for Bayesian inference of the reliability of experimental data. These dependencies can easily lead to circular reasoning. To avoid this problem, since all of these arguments involve the stochastic properties of the dynamics of the universe's entropy, we begin by formalizing that dynamics as a time-symmetric, time-translation invariant Markov process, which we call the entropy conjecture. Crucially, like all stochastic processes, the entropy conjecture does not specify any time(s) which it should be conditioned on in order to infer the stochastic dynamics of our universe's entropy. Any such choice of conditioning times and associated entropy values must be introduced as an independent assumption. This observation allows us to disentangle the standard Boltzmann brain hypothesis, its "1000CE" variant, the past hypothesis, the second law, and the reliability of our experimental data, all in a fully formal manner. In particular, we show that these all make an arbitrary assumption that the dynamics of the universe's entropy should be conditioned on a single event at a single moment in time, differing only in the details of their assumptions. In this aspect, the Boltzmann brain hypothesis and the second law are equally legitimate (or not).

In positron emission tomography (PET), a clear theoretical model describing how system sensitivity varies as a source is moved trans-axially within the field of view (FOV) is lacking. The current understanding and practical intuition often suggest that sensitivity is maximum at the center of the FOV, an assumption reflected in standardized protocols. In this work, we derive an analytic model for the trans-axial-plane sensitivity distribution in a cylindrical PET scanner based on solid angle. The model, formulated as a function of trans-axial offset from the center, is validated through both Monte Carlo simulations and physical experiments on a representative system. We find that the derived theoretical distribution is essentially consistent with simulation and experimental results, revealing a non-intuitive feature: sensitivity increases with trans-axial offset, peaks at the edge of the FOV, and drops off sharply beyond it. This study provides the first closed-form model of trans-axial geometric sensitivity in cylindrical PET scanners, offering a vital benchmark for isolating detector technology improvements and revealing a non-intuitive, offset-dependent sensitivity profile that enables new protocol optimization strategies.

Turbulent flows, despite their apparent randomness, exhibit coherent structures that underpin their dynamics. Proper orthogonal decomposition (POD) has been widely used to extract these structures from experimental data. While periodic features like vortex shedding can be identified using POD mode pairs when periodicity dominates the flow, detecting such structures in complex flows is more challenging. The Hilbert proper orthogonal decomposition (HPOD) addresses this by applying POD to the analytic signal of the turbulent fluctuations, yielding complex modes with a $90^\circ$ phase shift between the real and imaginary components. These modes capture propagating structures effectively but introduce filtering artefacts from the Hilbert transform that is used to derive the analytic signal. The current work investigates the relationship between the modes of the POD and those of the HPOD on the velocity fluctuations in the wake of a sphere. By comparing their outputs, POD mode pairs that correspond to the same propagating structures revealed by HPOD are identified. Furthermore, this study explored whether computing the analytic signal of the POD modes can replicate the HPOD modes, offering a more computationally efficient method for determining the pairs of POD modes that represent propagating structures. The results show that the pairs of POD modes identified by the HPOD can be more efficiently determined using the Hilbert transform directly on the POD modes. This method enhances the interpretive power of POD, enabling more detailed analysis of turbulent dynamics without introducing the filtering from the Hilbert transform.

Quantum mechanics is widely regarded as a complete theory, yet we argue it is a tractable projection of a deeper, computationally-inaccessible classical variational structure. By analyzing the coupled partial differential equations of the Hamilton type 1 principal function, we show that classical action-based dynamics are generally undecidable, paralleling spectral gap undecidability in quantum systems. In near Kolmogorov-Arnold-Moser systems, stability hinges on Diophantine conditions that are themselves undecidable, limiting predictability via arithmetic logic rather than randomness. Phenomena like spin 3/2 systems and larger, quantum scars and Leggett inequality violations support this view, naturally explained by time symmetric classical action. This framework offers a principled resolution to the long standing dichotomy between unitarity and entanglement by deriving both as emergent features of a tractable rendering from a fundamentally non-separable classical variational geometry. Collapse and decoherence arise from representational limits, not ontological indeterminism. We propose an explicit experimental test using lateral double quantum dots to detect predicted deviations from standard quantum coherence at the classical chaos threshold. This reframing suggests the classical quantum boundary is set by computability and not by the Planck constant. Implications for quantum computing and quantum encryption are discussed.

Magnetically confined fusion plasmas exhibit predator-prey-like cyclic oscillations through the self-regulating interaction between drift-wave turbulence and zonal flow. To elucidate the detailed mechanism and causality underlying this phenomenon, we construct a simple stochastic predator-prey model that incorporates intrinsic fluctuations and analyze its statistical properties from an information-theoretic perspective. We first show that the model exhibits persistent fluctuating cyclic oscillations called quasi-cycles due to amplification of intrinsic noise. This result suggests the possibility that the previously observed periodic oscillations in a toroidal plasma are not limit cycles but quasi-cycles, and that such quasi-cycles may be widely observed under various conditions. For this model, we further prove that information of the zonal flow is propagated to turbulence. This result suggests that turbulence behavior may be predictable to a certain extent based on zonal flow characteristics.

Vibrational sum- and difference-frequency generation (SFG and DFG) spectroscopy probes the nonlinear response of interfaces at mid-infrared (MIR) wavelengths while detecting upconverted signals in the visible. Recent work has moved from large-area films and colloids to nanoscale structures using dual-resonant plasmonic nanocavities that co-confine light and matter in deep-subwavelength volumes. Here we implement high-resolution ($<1$~cm$^{-1}$), continuous-wave ultrabroadband vSFG, vDFG, and four-wave mixing (FWM) coherent spectroscopy from 860 to 1670~cm$^{-1}$ on dual-resonant antennas under ambient conditions. Using a commercial, broadly tunable quantum-cascade laser and eliminating geometric phase matching simplify acquisition and expand spectral reach. The resulting spectra exhibit coherent interference between resonant (vibrational) and nonresonant (electronic) contributions to the effective $\chi^{(2)}$, previously accessible only under fs/ps excitation. Simultaneous measurement of SFG and DFG enables a {ratiometric} analysis that suppresses common-mode drifts and helps reveal vibrational resonances. We demonstrate versatility and reproducibility across several analytes that span distinct relative strengths of vibrational vs. electronic nonlinearities. Together, these capabilities position our approach as a scalable route to multiplexed, high-resolution MIR sensing and a practical basis for chip-level, label-free coherent spectroscopy. It opens a feasible path toward single- and few-molecule optomechanical studies using nanoscale trapping strategies.

Oscillator Ising Machines (OIMs) and probabilistic bit (p-bit)-based computing platforms have emerged as promising paradigms for tackling complex combinatorial optimization problems. Although traditionally viewed as distinct approaches, this work presents a theoretically grounded framework for configuring OIMs as p-bit engines. We demonstrate that this functionality can be enabled through a novel interplay between first- and second harmonic injection to the oscillators. Our work identifies new synergies between the two methods and broadens the scope of applications for OIMs. We further show that the proposed approach can be applied to other analog dynamical systems, such as the Dynamical Ising Machine.

We present a theoretical proposal and simulation study of a digital closed-loop thermal atomic-beam interferometer for inertial navigation applications. The scheme synchronizes phase biasing with momentum-kick reversal through the atomic transit time, extracting four interferometric phases to suppress Raman beam path-length errors, while two-photon detuning feedback maintains a pseudo-inertial frame and eliminates cross-coupling. The interferometer enables simultaneous measurements of acceleration and rotation based on an absolute, atom-interferometric reference, with high bandwidth and a wide dynamic range. Numerical simulations verify that acceleration and angular velocity can be measured simultaneously and independently in real time without cross-coupling, demonstrating the absolute, decoupled nature of the proposed measurement scheme. We further evaluate the noise-limited performance of the sensor and obtain sensitivities of $3{\rm \mu m / s^2 / \sqrt{Hz}}$ (velocity random walk) and $15{\rm \mu deg / \sqrt{h}}$ (angular random walk) for a ${170}^{\circ}$ $^{85}$Rb beam and an interferometer arm length of 100~mm, surpassing the performance of sensors currently used in state-of-the-art inertial navigation systems.

Turbulence remains one of the last unresolved problems of classical physics and a major bottleneck to accurate flow prediction in climate, aerospace, and energy systems. Industrial simulations therefore rely on averaged representations of turbulence, which often struggle to predict flows governed by multiple interacting mechanisms. We present a unified, data-driven turbulence modeling framework designed to learn robustly from sparse, indirect observations across diverse flow regimes. The framework embeds physical consistency into a flexible, frame-invariant closure, automatically selects representative training cases based on similarity of flow-feature distributions, and learns a single, unified model through a multi-objective ensemble strategy that balances competing objectives across flows and quantities of interest. The resulting unified foundation model adapts seamlessly across regimes without manual intervention. It outperforms existing turbulence models across a broad spectrum of canonical flows and maintains improved performance in complex three-dimensional configurations of industrial relevance, including a generic car and a gas turbine diffuser. When application-specific accuracy is required, the framework further enables specialist models through additive fine-tuning on targeted flow datasets. The results demonstrate the feasibility of a deployable and generalized turbulence modeling approach that unifies multiple flow mechanisms within a single architecture for a broad range of natural and industrial flows.

Machine learning (ML) has achieved remarkable success in climate and marine science. Given that greenhouse warming fundamentally reshapes ocean conditions such as stratification, circulation patterns and eddy activity, evaluating the climate adaptability of the ML models is crucial. While physical constraints have been shown to enhance the performance of ML models, kinetic energy (KE) cascade has not been used as a constraint despite its importance in regulating multi-scale ocean motions. Here we develop two sea surface height (SSH) prediction models (with and without KE cascade constraint) and quantify their climate adaptability at the Kuroshio Extension. Both models exhibit only slight performance degradation under greenhouse warming conditions. Incorporating the KE cascade as a physical constraint significantly improves the model performance, reducing eddy kinetic energy errors by 14.7% in the present climate and 15.9% under greenhouse warming. Additional validations using satellite observations and in the Gulf Stream region further confirm the robustness of the proposed models. Compared with the KE spectrum constraint, both constraints improve the cross-scale transfer and spectrum of KE, but the KE cascade constraint yields larger improvements in the cross-scale transfer. This work presents the first application of the KE cascade as a physical constraint for ML-based ocean state prediction and demonstrates its robust adaptability across climates, offering guidance for the further development of global ML models for both present and future conditions.

In this paper, we study two years of access card validation records from Bogotá's multimodal public transport system, comprising over 2.3 billion trips across bus rapid transit, feeder buses, dual-service buses, and an aerial cable network. From user trajectories constructed exclusively from access records, we derive motifs that reveal recurrent mobility patterns extending beyond simple two-location visits. This approach enables the construction of an integrated origin-destination (OD) matrix covering 2,828 urban zones. Similarity analysis using the Jensen-Shannon divergence confirms the temporal stability of mobility structures across semesters, despite infrastructure changes and fare policy adjustments. From the obtained OD matrices, we derive transition probabilities between zones and uncover a robust power-law relationship with geographical distance, consistent with Lévy flight dynamics. We validate our model using Monte Carlo simulations showing that reproduces both local and long-range displacements, with similar scaling exponents across time. These findings demonstrate that Bogotá's public transport mobility can be effectively modeled through Lévy processes, providing a novel framework for analyzing complex transportation systems based solely on user access records.

Over 25,600 3-inch photomultiplier tubes (PMTs) have been instrumented for the central detector of the Jiangmen Underground Neutrino Observatory. Each PMT is equipped with a high-voltage divider and a frontend cable with waterproof sealing. Groups of sixteen PMTs are connected to the underwater frontend readout electronics via specialized multi-channel waterproof connectors. This paper outlines the design and mass production processes for the high-voltage divider, the cable and connector, as well as the waterproof potting of the PMT bases. The results of the acceptance tests of all the integrated PMTs are also presented.

Elastic turbulence (ET), observed in flows of sufficiently elastic polymer solution at small inertia, is characterized by chaotic motions and power-law scaling of energy spectrum ($E$) in both wavenumber ($k$) and frequency ($\omega$): $E(k) \sim k^{-\alpha}$ and $E(\omega) \sim \omega^{-\beta}$. Experiments of ET have obtained a vast range of values for the exponent $\beta$. In inertial turbulence, Taylor's frozen-flow hypothesis implies $\alpha = \beta$, i.e., spatial and temporal scales are linearly related to each other. In contrast, from high-resolution simulation in three different setups, a tri-periodic box, a channel, and a planar jet, we show that in ET $\alpha \approx 4$ while $\beta$ varies significantly. Our analysis shows that in general Taylor's hypothesis does not hold in ET as there is no universal relation, linear or otherwise, between space and time. We thus clear the confusion of the different scaling exponents found in ET, and focus the attention of future research on understanding $\alpha$. Our analysis also implies that waves-like dynamics with a linear dispersion relation (e.g., Alfvén waves) can not play a role in determining the scaling behavior of ET. The techniques introduced here can be useful for studying smooth chaotic flows in general, e.g., active turbulence.

This paper considers the problem of water wave scattering by a rectangular anisotropic elastic plate mounted on the ocean surface, with either free, clamped or simply-supported edges. The problem is obtained as an expansion over the dry modes of the elastic plate, which are computed using a Rayleigh--Ritz method. In turn, the component diffraction and radiation problems are solved by formulating a boundary integral equation and solving numerically using a constant panel method. The results are presented to highlight the resonant responses of the plate under different forcing scenarios. In particular, we illustrate how the excitation of certain modes can be forbidden due to symmetry.

The global ocean model NEMO is run in a series of stand-alone configurations (2015-2022) to investigate the potential for improving global medium-range storm surge forecasts by including the inverse barometer effect. The analysis focus on the residual water level, i.e. the water level variations not due to tides. Here, we compare a control experiment, where the inverse barometer effect was not included, against a run dynamically forced with mean sea level pressure. In the control experiment, the inverse barometer effect was then calculated diagnostically and added to the ocean model sea surface elevation, resulting in a total of three experiments to investigate. We compare against the global GESLA3 water level data set and find that the inclusion of the inverse barometer effect reduces the root-mean-square error by $\sim 1~cm$ on average. When we mask out all data where the observed residual water level is less than $\pm1$ or $\pm2$ standard deviations, including the inverse barometer effect reduces the RMS error by $4-5$ cm. While both methods reduce water level errors, there are regional differences in their performance. The run with dynamical pressure forcing is seen to perform slightly better than diagnostically adding the inverse barometer effect in enclosed basins such as the Baltic Sea. Finally, an ensemble forecast experiment with the Integrated Forecast System of the European Centre for Medium-range Weather Forecasts demonstrates that when the diagnostic inverse barometer effect is included for a severe storm surge event in the North Sea (Storm Xaver, December 2013), the ensemble spread of water level provides a stronger and earlier indication of the observed maximum surge level than the when the effect is excluded.

Molecular dynamics simulations are an integral tool for studying the atomistic behavior of materials under diverse conditions. However, they can be computationally demanding in wall-clock time, especially for large systems, which limits the time and length scales accessible. Coarse-grained (CG) models reduce computational expense by grouping atoms into simplified representations commonly called beads, but sacrifice atomic detail and introduce mapping noise, complicating the training of machine-learned surrogates. Moreover, because CG models inherently include entropic contributions, they cannot be fit directly to all-atom energies, leaving instantaneous, noisy forces as the only state-specific quantities available for training. Here, we apply a knowledge distillation framework by first training an initial CG neural network potential (the teacher) solely on AA-to-CG mapped forces to denoise those labels, then distill its force and energy predictions to train refined CG models (the student) in both single- and ensemble-training setups while exploring different force and energy target combinations. We validate this framework on a complex molecular fluid, a deep eutectic solvent, by evaluating two-, three-, and many-body properties and compare the CG and all-atom results. Our findings demonstrate that training a student model on ensemble teacher-predicted forces and per-bead energies improves the quality and stability of CG force fields.

The FastRICH ASIC provides high-precision, triggerless readout for the LS3 Enhancements and Upgrades II of the LHCb RICH detector. The demands of continuous data acquisition and varying hit rates across the detector impose unique challenges on the ASIC's design and verification. This work presents the verification strategy for FastRICH, focusing on functional correctness, timing performance, and operational robustness. The methodology includes simulations across occupancy scenarios, validation of timing precision, and stress testing under pile-up and high-rate conditions. Results demonstrate that FastRICH meets its performance requirements over the full range of expected occupancies. Key design and verification challenges specific to triggerless, fast-timing ASICs are discussed, along with lessons learned for future developments.

The resolving ability of widefield fluorescence microscopy is fundamentally limited by out-of-focus background owing to its low axial resolution, particularly for densely labeled biological samples. Although total internal reflection fluorescence (TIRF) microscopy provides strong near-surface sectioning, they are intrinsically restricted to shallow imaging depths. Here we present computational TIRF (cTIRF), a deep learning-based imaging modality that generates TIRF-like sectioned images directly from conventional widefield epifluorescence measurements without any optical modification. By integrating a physics-informed forward model into network training, cTIRF achieves effective background suppression and axial resolution enhancement while maintaining consistency with the measured widefield data. We demonstrate that cTIRF recovers near-surface structures with performance comparable to experimental TIRF, and further enables both single-frame and volumetric sectioned reconstruction in densely labeled samples where conventional TIRF fails. This work establishes cTIRF as a practical and deployable alternative to hardware-based optical sectioning in fluorescence microscopy, enabled by rapid adaptation to new imaging systems with minimal calibration data.

In collisionless plasmas, relaxation of the deviations of ion velocity distribution functions (VDFs) from local thermodynamic equilibrium occurs through particle interactions with electromagnetic fields. In particular, in the Earth's magnetotail, the deviations of the ion VDFs, typically consisting of multiple components, from the equilibrium must be limited to maintain stability of the current sheet. Curvature scattering is a leading candidate mechanism to limit such deviations, but its role remains insufficiently understood. We investigate the limits of ion temperature anisotropy in a magnetotail-like configuration by modeling a quasi-1D current sheet with a finite magnetic field curvature and three ion populations. We derive analytical thresholds for anisotropy based on current sheet stability and validate against spacecraft observations and numerical simulations. Our findings demonstrate that curvature scattering imposes limits on ion anisotropies, thereby maintaining the stability of the current sheet.

$\Lambda$-doubling of diatomic molecules is a subtle microscopic phenomenon that has long attracted the attention of experimental groups, insofar as rotation of molecular $\textit{nuclei}$ induces small energetic changes in the (degenerate) $\textit{electronic}$ state. A direct description of such a phenomenon clearly requires going beyond the Born-Oppenheimer approximation. Here we show that a phase space theory previously developed to capture electronic momentum and model vibrational circular dichroism -- and which we have postulated should also describe the Einstein-de Haas effect, a macroscopic manifestation of angular momentum conservation -- is also able to recover the $\Lambda$-doubling energy splitting (or $\Lambda$-splitting) of the NO molecule nearly quantitatively. The key observation is that, by parameterizing the electronic Hamiltonian in terms of both nuclear position ($\mathbf{X}$) and nuclear momentum ($\mathbf{P}$), a phase space method yields potential energy surfaces that explicitly include the electron-rotation coupling and correctly conserve angular momentum (which we show is essential to capture $\Lambda-$doubling). The data presented in this manuscript offers another small glimpse into the rich physics that one can learn from investigating phase space potential energy surfaces $E_{PS}(\mathbf{X},\mathbf{P})$ as a function of both nuclear position and momentum, all at a computational cost comparable to standard Born-Oppenheimer electronic structure calculations.

Polymorphism has been observed in viral capsid assembly, demonstrating the ability of identical protein dimers to adopt multiple geometries under the same solution conditions. A well-studied example is the hepatitis B virus (HBV), which forms two stable capsid morphologies both in vivo and in vitro. These capsids differ in diameter, containing either 90 or 120 protein dimers. Experiments have shown that their relative prevalence depends on the ionic conditions of the solution during assembly. We developed a model that incorporates salt effects by altering the intermolecular binding free energy between capsid proteins, thereby shifting the relative thermodynamic stability of the two morphologies. This model reproduces experimental results on the prevalence ratios of the large and small HBV capsids. We also constructed a kinetic model that captures the time-dependent ratio of the two morphologies under subcritical capsid concentrations, consistent with experimental data.

In photo injectors, the transverse emittance is one of the key measures of beam quality as it defines the possible performance of the whole facility. As such it is important to measure the emittance in photo injectors and ensure the accuracy of these measurements. While there are many different methods of measuring the emittance, this paper focuses on quantifying the systematic errors present in transverse phase space measurements taken with slit-profiler methods, i.e. scanning a narrow slit over the beam and continually measuring the passed beamlets' divergence with a downstream profiler. The measurement errors include effects of the slit size, beamlet imaging, and residual space charge. While these effects are generally small, they can have significant impact on the measured emittance when the 2D phase space is strongly coupled. The systematic effects studied and corrections are demonstrated with simulations and measurements from the Photo Injector Test facility at DESY in Zeuthen (PITZ) using a slit-screen emittance scanner.

Surface acoustic wave platforms based on piezoelectric thin-film heterostructures provide sub-wavelength acoustic confinement, making them attractive for compact nonlinear phononic systems with applications including frequency conversion, parametric interactions, and nonlinear signal processing. Here, we investigate guided surface acoustic wave phononic four-wave mixing at gigahertz frequencies in an aluminum scandium nitride/4H-silicon carbide heterostructure operated at both room temperature (295 K) and cryogenic temperature (4 K). The 500 nm thick aluminum scandium nitride film supports guided Rayleigh and Sezawa modes with distinct displacement and strain energy density distributions, allowing a direct comparison of mode-dependent nonlinear behavior within the same device. Continuous-wave four-wave mixing measurements reveal an enhancement in the extracted modal nonlinear coefficient at 4 K relative to 295 K for both modes. In addition, the Rayleigh mode exhibits a modal nonlinearity approximately two orders of magnitude larger than that of the Sezawa mode across both temperature regimes. These results demonstrate that phononic four-wave mixing is strongly influenced by temperature, mode confinement, and strain localization while establishing aluminum scandium nitride on silicon carbide heterostructures as a promising platform for engineering enhanced nonlinear phononic interactions for future classical and quantum acoustic on-chip signal processing systems.

Contagion processes in social systems often involve interactions that go beyond pairwise contacts. Higher-order networks, represented as hypergraphs, have been widely used to model multi-body interactions, and their presence can drastically alter contagion dynamics compared to traditional network models. However, existing analytical approaches typically assume independence between pairwise and higher-order degrees, and thus study their roles in isolation. In this paper, we develop an effective hyperdegree model (EHDM) to describe Susceptible-Infected-Susceptible (SIS) dynamics on hypergraphs that explicitly captures correlations between the distribution of groups with different sizes. Our effective hyperdegree model shows excellent agreement with stochastic simulations across different types of higher-order networks, including those with heterogeneous degree distributions. We explore the critical role of cross-order degree correlations, specifically, whether nodes that are hubs in pairwise interactions also serve as hubs in higher-order interactions. We show that positive correlation decreases the epidemic threshold and anti-correlation temporally desynchronizes infection pathways (pairwise and group interactions). Finally, we demonstrate that, depending on the level of correlation, the optimal control strategy shifts -- from one that is purely pairwise- or higher-order-focused to one in which a mixed strategy becomes optimal.

We use the high-resolution Swarm faceplate plasma density data at 16 Hz to develop a set of parameters that can characterize multi-scale ionospheric structures and irregularities along the Swarm orbit. We present the methods for calculating density gradients over different window sizes, rate of change of density index, power spectral density and the spectral slope at both low and high latitudes. The faceplate plasma data are not continuously available through the years. However, about 8 years of data from Swarm A are processed from late 2014 to the end of 2025. Some statistical results from Swarm A are presented. The variations of plasma structures and irregularities are dependent on solar activity, season, local time and geomagnetic activities, and the variations show different patterns between low and high latitudes. For example, the high-latitude ionosphere is characterized by persistent ionospheric structures and irregularities poleward of 60 magnetic latitude, while the low-latitude ionospheric irregularities are only dominant during 19-01 local time near the magnetic equator. The occurrence of steep spectral slope at high latitudes shows clear seasonal variations, i.e., it maximizes during local summer and minimizes during local winter in both hemispheres. However, the occurrence of steep spectral slope at low latitudes is only sensible when significant plasma structures and irregularities are present. We further calculate the histogram of spectral slopes at low latitudes when the rate of change of density index is enhanced. The histogram resembles a Gaussian distribution with an expected value of 1.97. The processed data are available to the wider community. Given the high resolution, this new data product will be useful for the scientific communities that are interested in the magnetosphere-ionosphere-thermosphere coupling and near-Earth space environment.

We investigate the well-known Shin-Metiu model for an electronic crossing, using both a standard Born-Huang (BH) framework and a novel phase space (PS) electronic Hamiltonian framework. We show that as long as we are not in the strongly nonadiabatic region, a phase space framework can obtain a relative error in vibrational energy gap which is consistently one order of magnitude smaller than what is found within a BH framework. In line with recent results showing that dynamics on one phase space surface can outperform dynamics on one Born-Oppenheimer surface, our results indicate that the same advantages should largely hold for curve crossings and dynamics on two or a handful of electronic surfaces, from which several implications can be surmised as far as the possibility of spin-dependent electron transfer dynamics.

Verification is a critical aspect of designing front-end (FE) readout ASICs for High-Energy Physics (HEP) experiments. These ASICs share several similar functional features, resulting in similar verification objectives, which can be addressed using comparable verification strategies. This contribution presents a set of re-usable verification components for addressing common verification tasks, such as clock generation, reset handling, configuration, as well as hit and fault injections. The components were developed as part of the CHIPS initiative and they have been successfully used in the verification of multiple HEP ASICs.

VN precipitates used to strengthen ARAFM steels for fusion applications dissolve under high Fe ion irradiation (100 dpa at 10^-3 dpa s^-1, 600 C). This study examined point defects and solute substitutions using atom probe tomography, machine learning interatomic potentials, and density functional theory. Combined with transmission electron microscopy, results show N-vacancies and substitutional Cr exist in VN precipitates before irradiation. Monte Carlo simulations and collision cascade simulations confirm ordered vacancies at operating temperatures help mitigate irradiation damage. However, solute additions disrupt vacancy ordering and enhance irradiation-induced damage, potentially accelerating dissolution.

Parton distribution functions (PDFs) describe the inner, non-perturbative structure of hadrons. Their computation involves matrix elements with a Wilson line along a direction on the light cone, posing significant challenges in Euclidean lattice calculations, where the time direction is not directly accessible. We propose implementing the light-front Wilson line within the Hamiltonian formalism using tensor network techniques. The approach is demonstrated in the massive Schwinger model (quantum electrodynamics in 1+1 dimensions), a toy model that shares key features with quantum chromodynamics. We present accurate continuum results for the fermion PDF of the vector meson at varying fermion masses, obtained from first-principle calculations directly in Minkowski space. Our strategy also provides a useful path for quantum simulations and quantum computing.

Quantum sensors driven into the quantum chaotic regime can have dramatically enhanced sensitivity, which, however, depends intricately on the details of the underlying classical phase space. Here, we develop an accurate semiclassical approach that provides direct and efficient access to the phase-space-resolved quantum Fisher information (QFI), the central quantity that quantifies the ultimate achievable sensitivity. This approximation reveals, in very concrete terms, that the QFI is large whenever a specific dynamical quantity tied to the sensing parameter displays a large variance over the course of the corresponding classical time evolution. Applied to a paradigmatic system of quantum chaos, the kicked top, we show that the semiclassical description is accurate already for modest quantum numbers, i.e., deep in the quantum regime, and it extends seamlessly to very high quantum numbers that are beyond the reach of other methods.

Information processing abilities of active matter are studied in the reservoir computing (RC) paradigm to infer the future state of a chaotic signal. We uncover an exceptional regime of agent dynamics that has been overlooked previously. It appears robustly optimal for performance under many conditions, thus providing valuable insights into computation with physical systems more generally. The key to forming effective mechanisms for information processing appears in the system's intrinsic relaxation abilities. These are probed without actually enforcing a specific inference goal. The dynamical regime that achieves optimal computation is located just below a critical damping threshold, involving a relaxation with multiple stages, and is readable at the single-particle level. At the many-body level, it yields substrates robustly optimal for RC across varying physical parameters and inference tasks. A system in this regime exhibits a strong diversity of dynamic mechanisms under highly fluctuating driving forces. Correlations of agent dynamics can express a tight relationship between the responding system and the fluctuating forces driving it. As this model is interpretable in physical terms, it facilitates re-framing inquiries regarding learning and unconventional computing with a fresh rationale for many-body physics out of equilibrium.

Large language models (LLMs) are used worldwide, yet exhibit Western cultural tendencies. Many countries are now building ``regional'' or ``sovereign'' LLMs, but it remains unclear whether they reflect local values and practices or merely speak local languages. Using India as a case study, we evaluate six Indic and six global LLMs on two dimensions -- values and practices -- grounded in nationally representative surveys and community-sourced QA datasets. Across tasks, Indic models do not align better with Indian norms than global models; in fact, a U.S. respondent is a closer proxy for Indian values than any Indic model. We further run a user study with 115 Indian users and find that writing suggestions from both global and Indic LLMs introduce Westernized or exoticized writing. Prompting and regional fine-tuning fail to recover alignment and can even degrade existing knowledge. We attribute this to scarce culturally grounded data, especially for pretraining. We position cultural evaluation as a first-class requirement alongside multilingual benchmarks and offer a reusable, community-grounded methodology. We call for native, community-authored corpora and thickxwide evaluations to build truly sovereign LLMs.

Quantum chemistry is a promising application of future quantum computers, but the requirements on qubit count and other resources suggest that modular computing architectures will be required. We introduce an implementation of a quantum chemistry algorithm that is distributed across several computational modules: the distributed unitary selective coupled cluster (dUSCC). We design a packing scheme using the pseudo-commutativity of Trotterization to maximize the parallelism while optimizing the scheduling of all inter-module gates around the buffering of inter-module Bell pairs. We demonstrate dUSCC on a 3-cluster (H$_4$)$_3$ chain and show that it naturally utilizes the molecule's structure to reduce inter-module latency. We show that the run time of dUSCC is unchanged with inter-module latency up to $\sim$20$\times$ slower than intra-module gates in the (H$_4$)$_3$ while maintaining chemical accuracy. dUSCC should be "free" in the weakly entangled systems, and the existence of "free" dUSCC can be found efficiently using classical algorithms. This new compilation scheme both leverages pseudo-commutativity and considers inter-module gate scheduling, and potentially provides an efficient distributed compilation of other Trotterized algorithms.

By performing Monte Carlo simulations of the three-dimensional Ising model, we apply the recently proposed Lee-Yang-zero ratio (LYZR) method to determine the location of the critical point in this model. We demonstrate that the LYZR method is as powerful as the conventional Binder-cumulant method in studying the critical point, while the LYZR method has the advantage of suppressing the violation of the finite-size scaling and non-linearity near the critical point. We also achieve a precise determination of the values of the LYZRs at the critical point, which are universal numbers. In addition, we propose an alternative method that uses only a single Lee-Yang zero and show that it is also useful for the search for the critical point.

Partial Differential Equations (PDEs) are the bedrock for modern computational sciences and engineering, and inherently computationally expensive. While PDE foundation models have shown much promise for simulating such complex spatio-temporal phenomena, existing models remain constrained by the pretraining datasets and struggle with auto-regressive rollout performance, especially in out-of-distribution (OOD) cases. Furthermore, they have significant compute and training data requirements which hamper their use in many critical applications. Inspired by recent advances in ``thinking" strategies used in large language models (LLMs), we introduce the first test-time computing (TTC) strategy for PDEs that utilizes computational resources during inference to achieve more accurate predictions with fewer training samples and smaller models. We accomplish this with two types of reward models that evaluate predictions of a stochastic based model for spatio-temporal consistency. We demonstrate this method on compressible Euler-equation simulations from the PDEGym benchmark and show that TTC captures improved predictions relative to standard non-adaptive auto-regressive inference. This TTC framework marks a foundational step towards more advanced reasoning algorithms or PDE modeling, inluding building reinforcement-learning-based approaches, potentially transforming computational workflows in physics and engineering.

Bifurcation phenomena in nonlinear dynamical systems often lead to multiple coexisting stable solutions, particularly in the presence of symmetry breaking. Deterministic machine learning models are unable to capture this multiplicity, averaging over solutions and failing to represent lower-symmetry outcomes. In this work, we formalize the use of generative AI, specifically flow matching, as a principled way to model the full probability distribution over bifurcation outcomes. Our approach builds on existing techniques by combining flow matching with equivariant architectures and an optimal-transport-based coupling mechanism. We generalize equivariant flow matching to a symmetric coupling strategy that aligns predicted and target outputs under group actions, allowing accurate learning in equivariant settings. We validate our approach on a range of systems, from simple conceptual systems to physical problems such as buckling beams and the Allen--Cahn equation. The results demonstrate that the approach accurately captures multimodal distributions and symmetry-breaking bifurcations. Moreover, our results demonstrate that flow matching significantly outperforms non-probabilistic and variational methods. This offers a principled and scalable solution for modeling multistability in high-dimensional systems.

Freeze-thaw cycles can be regularly observed in nature in water and are essential in industry and science. Objects present in the medium will interact with either an advancing solidification front during freezing or a retracting solidification front, i.e., an advancing melting front, during thawing. It is well known that objects show complex behaviours when interacting with the advancing solidification front, but the extent to which they are displaced during the retraction of the solid-liquid interface is less well understood. To study potential hysteresis effects during freeze-thaw cycles, we exploit experimental model systems of oil-in-water emulsions and polystyrene (PS) particle suspensions, in which a water-ice solidification front advances and retracts over an individual immiscible (and deformable) oil droplet or over a solid PS particle. We record several interesting hysteresis effects, resulting in non-zero relative displacements of the objects between freezing and thawing. PS particles tend to migrate further and further away from their initial position, whereas oil droplets tend to return to their starting positions during thawing. We rationalize our experimental findings by comparing them to our prior theoretical model of Meijer, Bertin & Lohse, Phys. Rev. Fluids (2025), yielding a qualitatively good agreement. Additionally, we look into the reversibility of how the droplet deforms and re-shapes throughout one freeze-thaw cycle, which will turn out to be remarkably robust.

A key aspect of learned partial differential equation (PDE) solvers is that the main cost often comes from generating training data with classical solvers rather than learning the model itself. Another is that there are clear axes of difficulty--e.g., more complex geometries and higher Reynolds numbers--along which problems become (1) harder for classical solvers and thus (2) more likely to benefit from neural speedups. Towards addressing this chicken-and-egg challenge, we study difficulty transfer on 2D incompressible Navier-Stokes, systematically varying task complexity along geometry (number and placement of obstacles), physics (Reynolds number), and their combination. Similar to how it is possible to spend compute to pre-train foundation models and improve their performance on downstream tasks, we find that by classically solving (analogously pre-generating) many low and medium difficulty examples and including them in the training set, it is possible to learn high-difficulty physics from far fewer samples. Furthermore, we show that by combining low and high difficulty data, we can spend 8.9x less compute on pre-generating a dataset to achieve the same error as using only high difficulty examples. Our results highlight that how we allocate classical-solver compute across difficulty levels is as important as how much we allocate overall, and suggest substantial gains from principled curation of pre-generated PDE data for neural solvers. Our code is available at this https URL

The full characterization of quantum states of light is a central task in quantum optics and information science. Double homodyne detection provides a powerful method for the direct measurement of the Husimi Q quasi-probability distribution, offering a complete state representation in a simple experimental setting and a limited time frame. Here, we demonstrate that double homodyne detection can serve as more than a passive characterization tool. By intentionally unbalancing the input beamsplitter that splits the quantum signal, we show that the detection scheme itself performs an effective squeezing or anti-squeezing transformation on the state being measured. The resulting measurement directly samples the Q function of the input state as if it were acted upon by a squeezing operator whose strength is a tunable experimental parameter: the beamsplitter's reflectivity. We experimentally realize this technique using a robust polarization-encoded double homodyne detection to characterize a squeezed vacuum state. Our results demonstrate the controlled deformation of the measured Q function's phase-space distribution, confirming that unbalanced double homodyne detection is a versatile tool for simultaneous quantum state manipulation and characterization.

We present a first-principles study of the multimode Jahn-Teller (JT) effect in the exctied $^{3}E$ state of the negatively charged nitrogen-vacancy (NV) center in diamond. Using density functional theory combined with an intrinsic distortion path (IDP) analysis, we resolve the full activation pathways of the JT distortion and quantitatively decompose the distortion into contributions from individual vibrational modes. We find that multiple vibrational modes participate cooperatively in the JT dynamics, giving rise to a shallow adiabatic potential energy surface with low barriers, consistent with thermally activated pseudorotation. The dominant JT-active modes are found to closely correspond to vibrational features observed in two-dimensional electronic spectroscopy (2DES), in agreement with recent ab initio molecular dynamics simulations. Our results establish a microscopic, mode-resolved picture of vibronic coupling in the excited-state NV center and provide new insight into dephasing, relaxation, and optically driven dynamics relevant to solid-state quantum technologies.

Astronomy, often perceived as a distant or luxury science, holds immense potential as a driver for sustainable local socio-economic development. This paper explores how astronomy can create tangible benefits for communities through education, tourism, technology transfer, and capacity building. Using case studies from South Africa, Chile, Indonesia, and India, we demonstrate how astronomical facilities and initiatives have stimulated local economies, generated employment, supported small enterprises, and enhanced STEM participation, while simultaneously inspiring a sense of shared global heritage. The analysis identifies both successes and challenges, including unequal benefit distribution, limited local ownership, and sustainability gaps once external funding ends. Building on these lessons, we propose a practical framework/guidelines for designing, implementing, and evaluating astronomy-based community initiatives, rooted in participatory engagement and aligned with the UN Sustainable Development Goals (SDGs). This paper positions astronomy as a catalyst for inclusive growth, demonstrating that investment in the cosmos can translate into grounded, measurable benefits for people and places on Earth.

In the analysis of complex physical systems, the objective often extends beyond merely computing a numerical solution to capturing the precise crossover between different regimes and extracting parameters containing meaningful information. However, standard numerical solvers and conventional deep learning approaches, such as Physics-Informed Neural Networks (PINNs), typically operate as black boxes that output solution fields without disentangling the solution into its interpretable constituent parts. In this work, we propose GlueNN, a physics-informed learning framework that decomposes the global solution into interpretable, patchwise analytic components. Rather than approximating the solution directly, GlueNN promotes the integration constants of local asymptotic expansions to learnable, scale-dependent coefficient functions. By constraining these coefficients with the differential equation, the network effectively performs regime transition, smoothly interpolating between asymptotic limits without requiring ad hoc boundary matching. We demonstrate that this coefficient-centric approach reproduces accurate global solutions in various examples and thus directly extracts physical information that is not explicitly available through standard numerical integration.