Accessing the polarization of photon allows to understand the mechanisms behind its emission or scattering, revealing much about a peculiar environment or a probed object. For energy above $\sim$10~MeV, the pair production dominates the photon-matter interaction and the photon polarization is accessible via the azimuthal angle of the conversion. Unfortunately pair polarimeters have a low figure-of-merit for multi-GeV photons and are mostly used for beam characterization. In this paper, we report a new concept of a compact pair polarimeter associating monolithic active pixel sensors to low-density extended solid converters to reach simultaneously a high efficiency of $\sim$7\% and intrinsic analyzing power ranging from 0.2 to 0.5. This new concept will add a new obersvable to the multi-messenger physics, isolate the intrinsic strong force in nucleons and possibly reveal violations of Lorentz invariance.

We present semi-empirical evidence suggesting that weak and flavour mixing, at the most fundamental level, can be described in terms of the Euclidean geometry of regular polygons constructible with compass and straightedge, specifically, the pentagon and the heptadecagon, associated with Fermat primes -- a pattern referred to as Bi-Constructible. Our approach accurately reproduces quark and lepton mixing angles and offers indications that the Weinberg angle also fits naturally within this geometric framework. Concise Weak--Quark--Lepton Complementarity relations are derived. These findings suggest a semi-empirical unification pattern of weak and flavour mixing. The Standard Model gauge couplings g and g' admit elegant expressions involving the golden ratio, yielding a neat prediction for the fine-structure constant entirely in these terms.

The Schwarzschild solution was the first exact solution to Einstein's 1915 field equations, found by Karl Schwarzschild as early as 1916. And yet, physicists, mathematicians and philosophers have struggled for decades with the interpretation of the Schwarzschild solution and the two singularities appearing in it when it is written in polar coordinates. This article distinguishes between eight different ways in which the two singularities have been interpreted between 1916 and the late 1960s, when Penrose's first singularity theorem shed new and lasting light on the interpretation of the Schwarzschild solution.

We investigate a modified Einstein-Rosen wormhole model, made unidirectionally traversable through a bimetric geometry defined by two regular metrics, g(+) and g(-), and characterized by PT symmetry combining time reversal (t -> -t) and spatial inversion (x -> -x). In this framework, two distinct spacetime regions are identified at the wormhole throat (r = alpha) via PT symmetry, forming a single spacetime sheet. This model employs Eddington-Finkelstein coordinates to eliminate coordinate singularities at the throat, enabling traversability with a lightlike membrane of exotic matter at the junction to satisfy the Einstein field equations, similar to other traversable wormhole models. We extend this model by coupling two such wormholes to generate closed timelike curves (CTCs), made possible by the opposing causal orientations defined by the two metrics, while adhering to Novikov's self-consistency principle. An effective theory is developed for a scalar field crossing the wormhole, yielding PT-symmetric Klein-Gordon equations with a real energy spectrum ensured by pseudo-unitarity, consistent with quantum mechanical dynamics. These results open new avenues for exploring the effects of PT symmetry on causality and the quantization of scalar fields in traversable geometries.

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.

While aquaporin (AQP) gating dynamically regulates transmembrane water permeability for cellular homeostasis, its mechanisms remain poorly understood compared to ion channels. A central challenge is the lack of methods to measure water flow through AQPs with the spatiotemporal resolution and sensitivity equivalent to patch-clamp recordings of ion fluxes, a limitation stemming from the electrically silent nature of water transport. We introduce a technique to rapidly detect cytoplasmic flows induced by osmotic-gradient-driven transmembrane water transport in single adherent human cancer cells. This approach enables direct measurement of AQP-mediated water transport and provides a powerful tool to investigate AQP function and regulation and cytoplasmic flow dynamics at the single-cell level.

Recent theoretical and experimental investigations have revealed that flapping compliant membrane wings can significantly enhance propulsive performance (e.g. Tzezana and Breuer, 2019, J. Fluid Mech., 862, 871-888) and energy harvesting efficiency (e.g. Mathai et al., 2022, J. Fluid Mech., 942, R4) compared to rigid foils. Here, we numerically investigate the effects of the stretching coefficient (or aeroelastic number), $K_S$, the flapping frequency, $St_c$, and the pitching amplitude, $\theta_0$, on the propulsive performance of a compliant membrane undergoing combined heaving and pitching in uniform flow. Distinct optimal values of $K_S$ are identified that respectively maximize thrust and efficiency: thrust can be increased by 200%, and efficiency by 100%, compared to the rigid case. Interestingly, these optima do not occur at resonance but at frequency ratios (flapping to natural) below unity, and this ratio increases with flapping frequency. Using a force decomposition based on the second invariant of the velocity gradient tensor $Q$, which measures the relative strength between the rotation and deformation of fluid elements, we show that thrust primarily arises from $Q$-induced and body-acceleration forces. The concave membrane surface can trap the leading-edge vortex (LEV) from the previous half-stroke, generating detrimental $Q$-induced drag. However, moderate concave membrane deformation weakens this LEV and enhances body-acceleration-induced thrust. Thus, the optimal $K_S$ for maximum thrust occurs below resonance, balancing beneficial deformation against excessive drag. Furthermore, by introducing the membrane's deformation into a tangential angle at the leading edge and substituting it into an existing scaling law developed for rigid plates, we obtain predictive estimates for the thrust and power coefficients of the membrane.

An extension of Maxwell's original prescription for an ideal gas is adopted to derive a broad class of Kappa-type velocity distributions, encompassing both fat and short-tailed forms. Within this general framework, a physically consistent fat-tailed Kappa distribution is identified that accurately fits recent suprathermal data. In particular, a kinetic physical temperature $T$ emerges naturally from the model, eliminating the need to invoke an effective temperature $T_{\kappa\ell}$, as is commonly done in the literature. Finally, it is argued that only a particular value of $\ell$ ensures a satisfactory fit to the data when the physical kinetic temperature is employed.

The mechanisms controlling the relative heating and energization of electrons and protons during magnetic reconnection are explored. Simulations are carried out with the kglobal model, which produces bulk heating and the extended powerlaw distributions of both species that have been documented in observations. The simulations have been carried out with a range of proton-to-electron mass ratios and upstream temperatures to isolate the factors that control energy gain. The simulations reveal that when the upstream temperatures of the two species are equal, the proton heating and energization exceeds that of electrons and that this is a consequence of the much larger energy gain of protons on their first entry into the reconnection exhaust. The effective energy gain of protons on exhaust entry scales as $m_iC_A^2$ since the protons counterstream at the Alfvén speed $C_A$ while the initial electron energy gain is smaller by the factor $(\beta_{e0}m_e/m_i)^{1/2}$. Since Fermi reflection during flux rope merger dominates energy gain in large-scale reconnecting systems and the rate of energy gain is proportional to energy, protons continue to gain energy faster than electrons for the duration of the simulations, leading to temperature increments of protons exceeding that of electrons and the non-thermal energy content of protons also exceeding that of electrons.

Laser-plasma accelerators offer a compact means of producing high-energy electron beams, but their performance is fundamentally limited by dephasing between the accelerated electrons and the plasma wave. To overcome this limitation, we investigate the combination of plasma density tapering and optical guiding to extend the effective acceleration length. Using a Joule-class femtosecond laser coupled into an optical-field-ionized plasma waveguide with a controlled density gradient, we experimentally achieve electron beam energies exceeding 1.6 GeV, a 40% increase compared to the constant-density case. Particle-in-cell simulations reproduce the main experimental features and reveal the central roles of delayed injection, nonlinear laser evolution, and self-focusing in enhancing energy gain.

Solving the spray flamelet equations in composition space is very challenging, which is attributable to the fact that the maximum value of the mixture fraction, $Z_\mathrm{max}$, is a priori unknown in such flames. In this work, an analytical solution for this quantity is proposed, which allows its determination in spray flames subject to imposed quadratic evaporation profiles. It is then illustrated how the proposed approach allows to effectively cover the solution space of the spray flamelet equations. The employed strategy works very well for the considered cases and the generality of the evaporation profile definition provides flexibility for explorations of other parametric choices in the future.

We report the core binding energies of K-edge and L-edge transitions in simple semiconducting and insulating solids using periodic equation-of-motion coupled-cluster theory with single and double excitations (EOM-CCSD). In our all-electron calculations, we use triple zeta basis sets with core correlation, and we sample the Brillouin zone using up to 4x4x4 k-points. Our final numbers, which are obtained through composite corrections and extrapolation to the thermodynamic limit, exhibit errors of about 2 eV when compared to experimental values. This level of accuracy from CCSD is about the same as it is for molecules. A low-scaling approximation to EOM-CCSD performs marginally worse at lower cost, with errors of about 3 eV.

Breakthroughs in nanotechnology have enabled the large-scale fabrication of nanoparticles with varied compositions and structures. Yet, evaluating their electrical conductivities remains challenging due to high volume and individual variability. We report a rapid, wireless, and parallel method to characterize longitudinal nanostructures, including insulators, semiconductors, and conducting metal oxides by using MoO3, MoS2/MoO2, and MoS2 nanoribbons, produced at different fabrication stages, as a model system. Leveraging our semi-quantitative model based on Maxwell-Wagner and electrical double-layer polarization, electric conductivities of various nanoparticles are determined from their distinct electro-rotation behaviors in water, spanning six orders of magnitude. The results agree well with standard four-probe measurements. These findings highlight a non-destruction, rapid, simple characterization method promising to bring nanomaterials closer to practical applications in electronics, optics, sensing, catalysis, and robotics.

An experiment with short prototypes of helical undulators, comprised of longitudinally magnetized helices made from a single piece of a rare-earth magnet, is described. Wire electrical discharge machining (WEDM) in combination with a flat tool and the rotary movement of the workpiece made it possible to achieve high precision in the manufacture of NdFeB helices. An assembly of two oppositely longitudinally magnetized helices with a period of 20 mm and a relatively large inner diameter of 8 mm creates a field of 0.53 T on the axis of the system, which ensures the value of the undulator parameter K close to unity. According to the calculation, Halbach-type helical micro-undulators with periods of (3-6) mm of four helices can provide a field of 1 T and K = 0.28-0.6 at the axis.

In the U.S., introductory calculus-based courses often acts as a gatekeeper to STEM degrees. But access to the prerequisite math is far from equal--students from low-income and racially marginalized communities are far less likely to have the chance to take calculus before college. These disparities are then compounded by rigid placement systems that reward procedural fluency in algebra and trigonometry, while overlooking the conceptual quantitative reasoning that physics truly demands. Labeling students as "underprepared" ignores the structural causes of these gaps and places the burden of remediation on those least supported. This article challenges that broken-student narrative and advocates for instructional redesigns that meet students where they are. We spotlight Rutgers University's Extended Analytical Physics program, a long-running initiative that embeds physics quantitative literacy (PQL) into extended, credit-bearing courses, demonstrating how departments can expand access without compromising rigor. On a national scale, the TIPSSS network (Transforming Introductory Physics Sequences to Support all Students) is bringing together departments invested in similar reforms. Collectively, these efforts point toward a future where calculus-based physics becomes a pathway to opportunity, not a barrier.

By theoretical derivation, we constructed an inhomogeneous coefficient equation to correctly describing harmonic radiation in solids induced by a spatially inhomogeneous field, where the widely used semiconductor Bloch equation fails. This equation has superiority over the semiconductor Bloch equation with good applicability to both homogeneous and inhomogeneous fields. Using graphene as an example, it is found that under inhomogeneous field driving, even-order harmonics occur with an enhancing tendency as the field inhomogeneity increases. As for the second-order harmonic, its intensity dependence is consistent with the prediction from the perturbation theory, and its wavelength dependence can use to directly distinguish the relative contribution of intraband and interband transitions. The inhomogeneous coefficient equation provides a direct theoretical analysis tool for elucidating the physical mechanism of inhomogeneous field induced harmonic radiation in solids.

Quasi-zero stiffness (QZS) metamaterials are highly effective in isolating objects from low-frequency external vibrations, due to their high static stiffness but low dynamic stiffness characteristics. Traditionally, QZS metamaterials are designed by combining a negative-stiffness part with a positive-stiffness counterpart. Here, we present a novel QZS metamaterial design without relying on combining two components. The QZS characteristic is achieved solely through monolithic shell elements' unique geometry and nonlinear deformation. Using experimental and numerical approaches, we investigate the static and dynamic responses of the proposed metamaterials as a function of their geometric parameters. We then tune the structure's geometry to achieve ideal zero-stiffness behaviors and experimentally demonstrate an exceptional low-frequency vibration isolation mechanism. This concept can be further utilized as a building block for constructing metamaterials with multiple zero-stiffness features, enabling a broad range of applications.

Scandium-doped aluminum nitride has recently emerged as a promising material for quantum photonic integrated circuits (PICs) due to its unique combination of strong second-order nonlinearity, ferroelectricity, piezoelectricity, and complementary metal-oxide-semiconductor (CMOS) compatibility. However, the relatively high optical loss reported to date-typically above 2.4 dB/cm-remains a key challenge that limits its widespread application in low-loss PICs. Here, we present a monolithically integrated $\mathrm{Si}_3\mathrm{N}_4$-ScAlN waveguide platform that overcomes this limitation. By confining light within an etched $\mathrm{Si}_3\mathrm{N}_4$ waveguide while preserving the functional properties of the underlying ScAlN layer, we achieve an intrinsic quality factor of $Q_{\mathrm{i}} = 3.35 \times 10^5$, corresponding to a propagation loss of 1.03 dB/cm-comparable to that of commercial single-mode silicon-on-insulator (SOI) waveguides. This hybrid architecture enables low-loss and scalable fabrication while retaining the advanced functionalities offered by ScAlN, such as ferroelectricity and piezoelectricity. Our results establish a new pathway for ScAlN-based PICs with potential applications in high-speed optical communication, modulation, sensing, nonlinear optics, and quantum optics within CMOS-compatible platforms.

Traveling fronts ubiquitous in physics, chemistry, and biology are prone to transverse cellular deformations due to diffusive or convective instabilities. Here we show both theoretically and experimentally that new patterns can be obtained if the destabilization is triggered around a front locked radially by advection. Specifically, angularly shifting sun-ray-like patterns can develop around radially advected autocatalytic fronts due to a diffusive instability developing when the autocatalyst X and the reactant Y diffuse at different rates. The properties of these shining-star structures can be controlled by tuning the flow rate $Q$ and the ratio of diffusion coefficients $\delta$ as evidenced by linear stability analysis, nonlinear simulations, and experiments on the chlorite-tetrathionate reaction.

Model Order Reduction (MOR) based on Proper Orthogonal Decomposition (POD) and Smooth Particle Hydrodynamics (SPH) has proven effective in various applications. Most MOR methods utilizing POD are implemented within a pure Eulerian framework, while significantly less attention has been given to POD in a Lagrangian context. In this paper, we present the POD-MOR of SPH simulations applied to a mass-spring-damper system with two primary objectives: 1. To evaluate the performance of the data-driven POD-MOR approach. 2. To investigate potential methods for accelerating POD-MOR computations. Although the mass-spring-damper system is linear, its SPH implementations are nonlinear, and POD-MOR does not automatically lead to faster computations. Our findings indicate that (1) the POD-MOR effectively reduces the degrees of freedom in the SPH simulations by capturing the essential modes, and (2) in various cases, the acceleration of POD-MOR can be achieved without compromising accuracy. We hope that our results will motivate further investigations into the design of POD-MOR algorithms for nonlinear Lagrangian systems.

Enhancement of the scrape-off layer (SOL) heat flux width has been observed in the ADITYA-U Tokamak following the injection of short fuel gas pulses. A notable reduction in parallel heat flux near the last closed flux surface (LCFS) is observed after each pulse. Comparative analysis indicates that pulsed fuelling is more effective in mitigating heat flux with improved core confinement than continuous gas feeding via real-time density control. Analytical and simulation works are also carried out for validation of experimental results. The analytical model shows that SOL width modification cannot be attributed solely to the decrease of temperature due to gas pulse injection; cross-field plasma diffusion also needs to increase. Simulations with the UEDGE code suggest that an increase in both the cross-field diffusion coefficient and inward pinch velocity is necessary to replicate the experimentally observed broadening of the heat flux SOL width. These findings provide insights into efficient SOL heat flux control strategies for future fusion devices.

The concept of inverse energy cascades has played a central role in the development of turbulence theory, with applications in two-dimensional and quasi-two-dimensional flows. We examine the presence or absence of inverse energy cascades in rotating stably stratified flows constrained to anisotropic yet fully three-dimensional domains, in a range of parameters that are relevant for planetary atmospheres. In particular, we focus on regimes with aspect ratios, Rossby, and Froude numbers similar to those found in the Earth's and other planets atmospheres. Our results show that, under certain conditions, inverse energy cascades can indeed emerge from the dry fluid dynamics solely, suggesting that this process can play a role in intermediate-scale atmospheric self-organization processes.

Learning from experience is a key feature of decision-making in cognitively complex organisms. Strategic interactions involving Bayesian inferential strategies can enable us to better understand how evolving individual choices to be altruistic or selfish can affect collective outcomes in social dilemmas. Bayesian strategies are distinguished, from their reactive opponents, in their ability to modulate their actions in the light of new evidence. We investigate whether such strategies can be resilient against reactive strategies when actions not only determine the immediate payoff but can affect future payoffs by changing the state of the environment. We use stochastic games to mimic the change in environment in a manner that is conditioned on the players' actions. By considering three distinct rules governing transitions between a resource-rich and a resource-poor states, we ascertain the conditions under which Bayesian tit-for-tat strategy can resist being invaded by reactive strategies. We find that the Bayesian strategy is resilient against a large class of reactive strategies and is more effective in fostering cooperation leading to sustenance of the resource-rich state. However, the extent of success of the Bayesian strategies depends on the other strategies in the pool and the rule governing transition between the two different resource states.

Converting light into matter has been a longstanding goal in physics, particularly the creation of electron-positron pairs through quantum electrodynamic (QED) processes. While current approaches using multiple colliding laser pulses can achieve this conversion, they struggle to produce well-collimated particle beams - a crucial requirement for practical applications. Here we demonstrate that a single ultra-intense laser pulse, when reflected from a curved plasma mirror, can generate highly collimated electron-positron pairs with unprecedented efficiency. By focusing the laser to field strengths exceeding $a_0 > 2000$, our method triggers QED cascades that produce tightly focused particle beams, distinctly different from the diffuse plasmas created by conventional multi-laser setups. The technique works even at relatively modest laser powers of 13PW, making it immediately testable at existing facilities. This breakthrough opens new possibilities for studying fundamental QED processes and generating controlled matter-antimatter plasmas.

Developing an organoid computing platform from neurons in vitro demands stable, precisely controlled microenvironments. To address this requirement, we designed, simulated, and fabricated a microfluidic device featuring hexagonal wells ($34.64\,\mathrm{\mu m}$ side length) in a honeycomb array connected by $20\,\mathrm{\mu m}$ channels. Computational fluid dynamics (CFD) modeling, validated by high mesh quality ($0.934$ orthogonal quality) and robust convergence, confirmed the architecture supports flow regimes ideal for ensuring cell viability. At target flow rates of $0.1$ - $1\,\mathrm{\mu L/min}$, simulations revealed the extrapolated pressure differential across the full $50{,}000\,\mathrm{\mu m}$ device remains within stable operating limits at $177\,\mathrm{kPa}$ (average) and $329\,\mathrm{kPa}$ (maximum). Photolithography successfully produced this architecture, with only minor corner rounding observed at feature interfaces. This work therefore establishes a computationally validated and fabricated platform, paving the way for experimental flow characterization and subsequent neural integration.

Dissipative Kerr solitons (DKSs) generated in high-Q microresonators driven by continuous-wave (CW) lasers provide chip-scale optical frequency combs composed of mutually coherent CW lines. However, their small mode volume makes them highly susceptible to thermal fluctuations, and the resulting thermo-refractive noise (TRN) perturbs the repetition rate $f_{\rm rep}$. Here, we experimentally demonstrate a blue-detuned DKS in a coupled-ring microresonator. By employing avoided-mode-crossing (AMX)-induced dispersion engineering at the pump mode, DKSs are generated even when the pump laser is tuned to the higher-frequency (blue) side of the resonance. In this regime, the pump laser not only seeds DKS formation but also serves as a cooling laser for the thermally sensitive pumped mode. We observe a self-cooling effect that reduces the phase noise of $f_{\rm rep}$ by up to 14.5 dB, while achieving a pump-to-comb conversion efficiency as high as 37 %. These results establish blue-detuned DKSs as a thermally robust and power-efficient solution for integrated microcomb systems, eliminating the need for auxiliary lasers.

This paper proposes a simple, four-mirror, in-line projector for high-NA EUV lithography that eliminates the most troublesome mask 3D effect. The design consists of a two-stage concave-convex pair, where optical aberrations are cancelled within each stage and between them, in a manner similar to that of a double-Gauss lens. The light rays pass through the central aperture in each mirror with acceptable obscuration. The numerical aperture (NA) is 0.5 and 0.7 for Hyper-NA. It has a circular exposure field with a diameter of 26 mm. The residual radial distortion is rather high at a few microns at the field rim, and the scan motion causes image blurring. Thus, we need to revert to the stepper design, and the field becomes smaller, i.e. 18 mm x 18 mm square. However, this brings an important benefit: we can remove the scanning mechanism from the photomask side. It is important to note that both the wafer and the photomask remain stationary during the EUV exposure. This guarantees superior overlay control and results in enhanced productivity. This approach serves to simplify the system and reduce electrical consumption also. Illumination will be provided through two rectangular scan-mirrors located in front of the mask, providing dual line scan field, which matches with off-axis illumination enhancing the resolution and bypasses the central obscurations.

With the demonstrations of pseudo-magnetism in optical systems, the pursuits of its practical applications require not only the use of pseudomagnetic fields to create functional optical devices but also a reliable method to manipulate pseudo-magnetism-affected light waves. Here, we experimentally demonstrate an ultracompact Si-based cavity formed by triaxially deformed photonic honeycomb lattices. The triaxial deformation could lead to Landau quantization, showing the possibilities of realizing the localization and resonating of photons with pseudomagnetic fields. Through adopting the Si waveguides for directional coupling, we successfully obtain the transmission spectra for the proposed cavities in the photonic integrated circuits. This opens a novel avenue for highly efficient excitations and detections of Landau-quantized photonic density of states, totally on chip. Moreover, we verify a linear electrical tunability of -0.018 THz/mW for the pseudo-magnetism-induced optical resonant states, fulfilling the manipulation of photons without varying deformations. Our work introduces a mechanism for performing tunable light waves in triaxial deformation-engineered systems, which enriches the design principles of integrated optical devices.

The ability to arbitrarily dial in amplitudes and phases enables the fundamental quantum state operations pioneered for microwaves and then infrared and visible wavelengths during the second half of the last century. Self-seeded X-ray free-electron lasers (FELs) routinely generate coherent, high-brightness, and ultrafast pulses for a wide range of experiments, but have so far not achieved a comparable level of amplitude and phase control. Here we report the first tunable phase-locked, ultra-fast hard X-ray (PHLUX) pulses by implementing a recently proposed method: A fresh-bunch self-seeded FEL, driven by an electron beam that was shaped with a slotted foil and a corrugated wakefield structure, generates coherent radiation that is intensity-modulated on the femtosecond time scale. We measure phase-locked (to within a shot-to-shot phase jitter corresponding to 0.1 attoseconds) pulse triplets with a photon energy of 9.7 keV, a pulse energy of several tens of microjoules, a freely tunable relative phase, and a pulse delay tunability between 4.5 and 11.9 fs. Such pulse sequences are suitable for a wide range of applications, including coherent spectroscopy, and have amplitudes sufficient to enable hard X-ray quantum optics experiments. More generally, these results represent an important step towards a hard X-ray arbitrary waveform generator.

In recent years, Born-Markov master equations based on tracing out the electromagnetic degrees of freedom have been extensively employed in the description of quantum optical phenomena originating from photon-mediated interactions in quantum emitter ensembles. The breakdown of these effective models, built on assumptions such as ensemble spectral homogeneity, an unstructured photonic density of states, and weak light-matter coupling, has also recently attracted considerable attention. Here, we investigate the accuracy of this well-established framework beyond the most conventional, and extensively explored, spontaneous emission configuration. Specifically, we consider a system comprising two coherently driven and detuned quantum emitters, embedded within a hybrid photonic-plasmonic cavity, formed by a metallic nanorod integrated into a high-refractive-index dielectric microresonator. The local density of photonic states in this structure exhibits a complex frequency dependence, making it a compelling platform for exploring photon-mediated interactions beyond the assumptions above. We benchmark this modeling approach for the quantum dynamics of the emitter pair against exact calculations based on a macroscopic field quantization formalism, providing an illustrative assessment of its validity in significantly structured and dispersive photonic environments. Our analysis reveals four distinct regimes of laser driving and frequency splitting that lead to markedly different levels of accuracy in the effective model.

By reentering into laser interferometers, scattered or stray light introduces non-linear noise. This is a major limitation of precision interferometers as preventing such parasitic light is nearly impossible. Thus, substantial effort is put into mitigating the reentering of these fields in various ways. Ground-based laser interferometric gravitational wave detectors employ such mitigation techniques to reduce otherwise restrictive stray light noise. However, they are now reaching sensitivities where conventional mitigation techniques reach limitations. Further improvements planed for future observatories are placing even more demanding constraints on tolerable stray light power. We previously presented tunable coherence as a possible technique to ease these constraints and suppress unwanted coherent interference. For these promising demonstrations, the remaining coherence length and achievable suppression in length-constrained layouts was limited, among other things, by the used pseudo-random-noise phase modulation frequency. In this work, we demonstrate stray light suppression and cavity performance at modulation frequencies up to 10 GHz. This reduces the remaining coherence to a few centimeter in an interferometer, and even to the scale of the laser wavelength in a cavity. We further present a first demonstration of tunable coherence in a power-recycled Michelson interferometer, successfully suppressing stray light in a more complex topology.

Accurate in situ characterization of plasmonic materials dispersion and efficiency remains a key challenge for next generation nanophotonic devices. To this end, we introduce a platform leveraging extraordinary optical transmission (EOT) through plasmonic gratings comprised of subwavelength Fabry Perot (FP) resonators to interrogate the optical response of plasmonic materials. We implement direct E k dispersion mapping across a well defined set of optical momenta by systematically varying the grating size, with each grating serving as a discrete momentum-space probe. Non Hermitian modal decomposition is carried out by means of the finite element method (FEM) and validated with finite difference time domain (FDTD) to examine the eigenstates of the plasmonic systems and analyze the modal hybridization within the aperture. The interplay between the resonant mechanisms involved in the enhanced transmitted field is investigated in both an idealized perfect electric conducting metal and a realistic dispersive metal, emphasizing the aperture s role in mode confinement and resonance shift. This approach provides an angle insensitive platform for reliable, in situ and real time characterization of established and emerging plasmonic materials.

This study explores the use of charge-coupled devices (CCDs) for detecting low-energy beta particles from tritium decay - a critical signal for nuclear safety, nuclear nonproliferation, and environmental monitoring. We employ a dual approach utilizing both measured CCD data and detailed Geant4 simulations. Our analysis compares classical techniques with advanced deep learning methods, including convolutional neural networks (CNNs), autoencoders trained exclusively on tritium data, and preliminary studies on boosted decision trees (BDTs). The CNN, trained on mixed signal/background datasets, demonstrates superior classification performance, while the autoencoder shows the potential of unsupervised, background-agnostic strategies. These results highlight the excellent sensitivity achievable thanks to the background rejection made possible by information-rich CCD data, paving the way for improved portable tritium monitoring.

Both the future CBM-RICH and the recently upgraded HADES-RICH use Hamamatsu H12700 Multi-Anode Photomultipliers (MAPMTs) as their photon detectors. To test the MAPMTs thoroughly before using them in the detectors, the photon sensors were qualified well in advance and a test bench was built to efficiently characterize each MAPMT. The test bench measures the single photoelectron gain, dark rate, relative efficiency, and afterpulse probability of each channel. This article describes the operating principle of the test bench and discusses the distributions of each measured quantity over 1,100 MAPMTs. Additionally, the long-term aging effects of the H12700 MAPMT are investigated based on repeated measurements of individual MAPMTs.

Many complex systems - be they financial, natural or social - are composed by units - such as stocks, neurons or agents - whose joint activity can be represented as a multivariate time series. An issue of both practical and theoretical importance concerns the possibility of inferring the presence of a static relationships between any two units solely from their dynamic state. The present contribution aims at providing an answer within the frame of traditional hypothesis testing. Briefly speaking, our suggestion is that of linking any two units if behaving in a sufficiently similar way. To achieve such a goal, we project a multivariate time series onto a signed graph, by i) comparing the empirical properties of the former with those expected under a suitable benchmark and ii) linking any two units with a positive (negative) edge in case the corresponding series share a significantly large number of concordant (discordant) values. To define our benchmarks, we adopt an information-theoretic approach that is rooted into the constrained maximisation of Shannon entropy, a procedure inducing an ensemble of multivariate time series that preserves some of the empirical properties on average while randomising everything else. We showcase the possible applications of our method by addressing one of the most timely issues in the domain of neurosciences, i.e. that of determining if brain networks are frustrated or not - and, in case, to what extent. As our results suggest, this is indeed the case, the structure of the negative subgraph being more prone to inter-subject variability than the complementary, positive subgraph. At the mesoscopic level, instead, the minimisation of the Bayesian Information Criterion instantiated with the Signed Stochastic Block Model reveals that brain areas gather into modules aligning with the statistical variant of the Relaxed Balance Theory.

Holotomography (HT) has revolutionized quantitative label-free 3D imaging, yet conventional lens-based implementations are fundamentally constrained in field-of-view (FOV) and imaging depth, limiting their utility for critical high-throughput applications in material and life sciences. Lensless HT (LHT) offers a promising alternative for large-volume examination, however existing approaches fail to accurately reconstruct highly scattering samples over extended depths, which remains a critical challenge in optical imaging field. Here, we introduce a gigavoxel-scale, multiple-scattering-aware LHT with a large FOV (surpassing 0.6 cm2), millimeter-scale axial range and pixel level (~2.4 micron) resolution. Our approach leverages a multi-wavelength, oblique-illumination hologram reconstruction and a robust, automatic illumination angle calibration, which are necessary for precise large-volume 3D holographic reconstruction. Moreover, we propose optimization-driven multi-slice tomographic framework to accurately capture multiple-scattering effects outperforming first order Born/Rytov-based inversions. To rigorously validate our method, we reconstruct bespoke multi-layer two-photon polymerized test structure over a 1.7 mm imaging depth and 25 mm2 FOV, yielding an unprecedented 3D space-bandwidth product exceeding a gigavoxel level. Furthermore, we demonstrate for the first time on-chip label-free imaging of entire 500-micron-thick tissue slice of optically-cleared mouse brain. With the proposed method, we aim to unlock powerful new capabilities for large-scale, quantitative, label-free 3D imaging across biomedicine, neuroscience, material sciences and beyond.

Emerging two-dimensional (2D) magnetic semiconductors represent transformative platforms to explore magneto-optics and opto-spintronic applications. Though 2D opto-spintronics has attracted tremendous research efforts in spin-dependent photodetectors and non-volatile memory components, the realization of one core application - spin-modulated light-emitting device (spin-LED) - remains elusive so far. Here we successfully realize prototype spin-LED integrated with a 2D semiconducting magnet CrSBr, demonstrating considerable electroluminescence (EL) down to bilayers. Intriguingly, the EL of the spin-LED is discovered to be directly manipulated by spin-flip and spin-canting transitions. Notably, spin-flip transitions enable unprecedented hysteretic behaviors of EL characteristics, while spin-canting transitions induce EL continuous modulation with robust anisotropy. This versatile manipulation is originated from the synergy of magnetic-order mediated excitonic transitions and spintronic transport. The prototype demonstration of spin-LED establishes an indispensable scheme of opto-spintronic devices leveraging 2D spin transitions and strong excitonic effects, presenting a critical step towards integrated 2D opto-spintronics.

Nonlinear idempotent operator instead of a linear projection is introduced to derive kinetic models for dense fluids. A new lattice Boltzmann model for compressible two-phase flow is derived based on the Enskog--Vlasov kinetic equation as an example of practical importance.

It is challenging to distinguish Floquet Chern insulator (FCI) and Floquet anomalous topological insulator (FATI) because of their common features of chiral edge states and far away from equilibrium. A hybrid straight-curved waveguide array is proposed to enable topological phase transitions from FCI to FATI and show how to diagnose the two phases using Bloch oscillations. As a proof of principle, the hybrid straight-curved waveguide array is designed as a straight honeycomb waveguide array nested in an asynchronous curved Kagome waveguide array. Under a two-dimensional (2D) tilted potential created by the spatial gradient of refractive indices, an initial Gaussian-like wavepacket undergoes 2D Bloch oscillations, displaying quasi-quantized displacement in the FCI and no drift in the FATI. This approach offers a direct and unambiguous method to diagnose Floquet topological phases from the bulk response.

This paper presents an Output-Recurrent Gated State Space Model (OR-GSSM) for complex multiphase flows modeling and uncertainty quantification of exhaust vehicles during motion. By establishing the state-space formulation of the gas-liquid Navier-Stokes equations applying semigroup theory and Galerkin projection, explicitly characterizing the dynamic coupling evolution between the velocity, pressure, and volume fraction fields. A novel Gated State Space Transition (GSST) unit is designed to learn parameterized transition and input matrices with adaptive timescales, enhancing physical interpretability and computational efficiency. The output recursion mechanism aligns with the numerical solution characteristics of state-space equations, mitigating long-term error accumulation and addressing training-inference pattern mismatch issues inherent in teacher forcing and scheduled sampling. Validations on the underwater cone-head and water-exit hemisphere-head vehicles demonstrate that: OR-GSSM outperforms OR-ConvLSTM and OR-ConvGRU baselines in accuracy and computational efficiency through its physics-informed adaptive state-space unit design and parallel matrix operations; The output recursion mechanism ensures more stable training, better generalization, and higher prediction accuracy than teacher forcing and scheduled sampling; OR-GSSM accurately captures the gas-phase expansion, gas-liquid mixing formation, backflow jet generation, bubble shedding, and entire water-exit process, etc, showcasing outstanding modeling capability; Its uncertainty quantification effectively characterizes flow features and uncertainty distributions, validating prediction reliability. The proposed method resolves the accuracy-real-time trade-off in traditional computational fluid dynamics, advancing machine learning for multiphase flow modeling and uncertainty quantification in exhaust vehicles.

High-order harmonic generation (HHG) provides a powerful optical tool for probing ultrafast dynamics on the attosecond timescale. While its mechanisms in gases and solids are well-established, understanding nonlinear optical responses in liquids remains challenging. The absence of long-range order in liquids questions the applicability of the existing HHG models developed in other media. Through combined experimental and theoretical investigations, we identify unique characters of liquid-phase HHG -- spectral redshift and broadening, which are fundamentally distinct from both the gaseous and solid-state counterparts. Quantitative measurements and simulations of HHG in liquids illustrate a near linear dependence of harmonic redshift and broadening on the laser intensity, with the nonlinear response of water exceeding that of ethanol. The simulations reveal that these features arise from delocalized electronic states with energy loss in multiple scatterings and transient Stark shift during their transitions in laser fields. Meanwhile, we find that liquid polarity or hydrogen bond exerts decisive control over the transition dipole momentum distributions of delocalized states. Our findings establish a nonlinear spectral method for probing the internal network in liquids, paving the way for studying its role in chemical and biological processes.

This work investigates projection-based Reduced-Order Models (ROMs) formulated in the frequency domain, employing a space-time basis constructed with Spectral Proper Orthogonal Decomposition to efficiently represent dominant spatio-temporal coherent structures. Although frequency domain formulations are well suited to capturing time-periodic solutions, such as unstable periodic orbits, this study focusses on modelling statistically stationary flows by computing long-time solutions that approximate their underlying statistics. In contrast to traditional ROMs based solely on spatial modes, a space-time formulation achieves simultaneous reduction in both space and time. This is accomplished by Galerkin projection of the Navier-Stokes equations onto the basis using a space-time inner product, yielding a quadratic algebraic system of equations in the unknown amplitude coefficients. Solutions of the ROM are obtained by identifying amplitude coefficients that minimise an objective function corresponding to the sum of the squares of the residuals of the algebraic system across all frequencies and modes, quantifying the aggregate violation of momentum conservation within the reduced subspace. A robust gradient-based optimisation algorithm is employed to identify the minima of this objective function. The method is demonstrated for chaotic flow in a two-dimensional lid-driven cavity at $Re=20{,}000$, where solutions with extended temporal periods approximately fifteen times the dominant shear layer time scale are sought. Even without employing closure models to represent the truncated spatio-temporal triadic interactions, multiple ROM solutions are found that successfully reproduce the dominant dynamical flow features and predict the statistical distribution of turbulent quantities with good fidelity, although they tend to overpredict energy at spatio-temporal scales near the truncation boundary.

Objective: Time-difference electrical impedance tomography (EIT) is gaining widespread use for bedside lung monitoring in intensive care patients suffering from lung-related diseases. It involves collecting voltage measurements from electrodes placed on the patient's thorax, which are then used to reconstruct impedance images. This study investigates how incorporating anatomical information from CT data into the widely used GREIT reconstruction algorithm affects EIT images and improves their interpretability. Approach: Based on clinically motivated lung state scenarios, we simulated EIT measurements to assess how the GREIT parameters influence the result of EIT image reconstruction, particularly with respect to noise performance and image accuracy. We introduce quality measures that allow us to perform a quantitative assessment of reconstruction quality. Anatomical features from CT data were included by customizing background conductivity and GREIT training target distribution. Main results: Our analysis confirmed that unphysiological background conductivity assumptions can lead to misleading EIT images, whereas physiological values, although more accurate, come with higher noise sensitivity. By increasing the number of GREIT training targets inside the lung and adapting the respective weighting radius, we significantly improved the anatomical accuracy of the EIT images. When applied to clinical EIT data from a representative ARDS patient, these adjustments in the reconstruction setup substantially enhanced the interpretability of the resulting EIT images. Significance: Integrating CT-based anatomical data in the GREIT reconstruction significantly enhances the clinical applicability of EIT in lung monitoring. The improved interpretability of EIT images facilitates better-informed clinical decisions and the individualized adjustment of ventilation strategies for critically ill patients.

Deep reinforcement learning (DRL) is emerging as a powerful tool for fluid-dynamics research, encompassing active flow control, autonomous navigation, turbulence modeling and discovery of novel numerical schemes. We introduce SmartFlow, a CFD-solver-agnostic framework for both single- and multi-agent DRL algorithms that can easily integrate with MPI-parallel CPU and GPU-accelerated solvers. Built on Relexi and SmartSOD2D, SmartFlow uses the SmartSim infrastructure library and our newly developed SmartRedis-MPI library to enable asynchronous, low-latency, in-memory communication between CFD solvers and Python-based DRL algorithms. SmartFlow leverages PyTorch's Stable-Baselines3 for training, which provides a modular, Gym-like environment API. We demonstrate its versatility via three case studies: single-agent synthetic-jet control for drag reduction in a cylinder flow simulated by the high-order FLEXI solver, multi-agent cylinder wake control using the GPU-accelerated spectral-element code SOD2D, and multi-agent wall-model learning for large-eddy simulation with the finite-difference solver CaLES. SmartFlow's CFD-solver-agnostic design and seamless HPC integration is promising to accelerate RL-driven fluid-mechanics studies.

Evacuation is critical for disaster safety, yet agencies lack timely, accurate, and transparent tools for evacuation prediction. This study introduces Evac-Cast, an interpretable machine learning framework that predicts tract-level evacuation rates using over 20 features derived from four dimensions: hazard intensity, community vulnerability, evacuation readiness, and built environment. Using an XGBoost model trained on multi-source, large-scale datasets for two hurricanes (Ian 2022, Milton 2024) and two wildfires (Kincade 2019, Palisades--Eaton 2025), Evac-Cast achieves mean absolute errors of 4.5% and 3.5% for hurricane and wildfire events, respectively. SHAP analysis reveals a consistent feature importance hierarchy across hazards, led by hazard intensity. Notably, the models perform well without explicit psychosocial variables, suggesting that macro-level proxies effectively encode behavioral signals traditionally captured through time-consuming surveys. This work offers a survey-free, high-resolution approach for predicting and understanding evacuation in hazard events, which could serve as a data-driven tool to support decision-making in emergency management.

The future of ART in head and neck cancer is just beginning. Novel technologies have pushed the boundary of what is possible in terms of techniques to identify biomarkers for adaptation as well as innovative devices specialized to respond to these adaptations, sometimes in real-time. Important interdisciplinary steps must be taken moving forward to ensure the safe deployment of these new techniques, such as rigorous quality assurance evaluations from medical physicists, clinical trials from physicians, and comprehensive testing from vendors prior to release. In summary, we aimed not to provide a single correct answer for the optimal implementation of ART in the era of imaging biomarkers, but to encourage the field to collaborate and bring each idea discussed here together to overcome current barriers and deliver the best treatment possible to the patient.

Hybrid oscillator architectures that combine feedback oscillators with self-sustained negative resistance oscillators have emerged as a promising platform for artificial neuron design. In this work, we introduce a modeling and analysis framework for amplifier-assisted organic electrochemical neurons, leveraging nonlinear dynamical systems theory. By formulating the system as coupled differential equations describing membrane voltage and internal state variables, we identify the conditions for self-sustained oscillations and characterize the resulting dynamics through nullclines, phase-space analysis, and bifurcation behavior, providing complementary insight to standard circuit-theoretic arguments of the operation of oscillators. Our simplified yet rigorous model enables tractable analysis of circuits integrating classical feedback components (e.g., operational amplifiers) with novel devices exhibiting negative differential resistance, such as organic electrochemical transistors (OECT). This approach reveals the core mechanisms behind oscillation generation, demonstrating the utility of dynamic systems theory in understanding and designing complex hybrid circuits. Beyond neuromorphic and bioelectronic applications, the proposed framework offers a generalizable foundation for developing tunable, biologically inspired oscillatory systems in sensing, signal processing, and adaptive control.

As quantum information science advances and the need for pre-college engagement grows, a critical question remains: How can young learners be prepared to participate in a field so radically different from what they have encountered before? This paper argues that meeting this challenge will require strong interdisciplinary collaboration with the Learning Sciences (LS), a field dedicated to understanding how people learn and designing theory-guided environments to support learning. Drawing on lessons from previous STEM education efforts, we discuss two key contributions of the learning sciences to quantum information science (QIS) education. The first is design-based research, the signature methodology of learning sciences, which can inform the development, refinement, and scaling of effective QIS learning experiences. The second is a framework for reshaping how learners reason about, learn and participate in QIS practices through shifts in knowledge representations that provide new forms of engagement and associated learning. We call for a two-way partnership between quantum information science and the learning sciences, one that not only supports learning in quantum concepts and practices but also improves our understanding of how to teach and support learning in highly complex domains. We also consider potential questions involved in bridging these disciplinary communities and argue that the theoretical and practical benefits justify the effort.

We report on the sticking time of the AlF dimer in the ultracold regime. We employ a full-dimensional potential energy surface for AlF-AlF, constructed using a machine learning approach [X. Liu et al., J. Chem. Phys. 159, 144103 (2023)], to compute the density of states using a semi-classical counting method. Next, using the Rice-Ramsperger-Kassel-Marcus (RRKM) theory, we determine a sticking time of 216.3 ns, which is shorter than that of other previously reported dimers. We explain these results in light of the ratio of the dissociation energy of the complex to the dissociation energy of the molecule, yielding a computationally inexpensive scheme to estimate the sticking time of collisional complexes.

The use of high-precision measurements of the $g$ factor of single-electron ions is considered as a detailed probe for physics beyond the Standard Model. The contribution of the exchange of a hypothetical force-carrying scalar boson to the $g$ factor is calculated for the ground state of H-like ions and used to derive bounds on the parameters of that force. Similarly to the isotope shift, we employ the nuclide shift, i.e. the difference for elements with different proton and/or neutron numbers, in order to increase the experimental sensitivity to the new physics contribution. In particular we find, combining available measurements with current precision with different ions, that the coupling constant for the interaction between an electron and a proton can be constrained up to three orders of magnitude better than with the best current atomic data and theory.

A robust and flexible architecture capable of providing real-time analysis on diagnostic data in experimental physics is of crucial importance to physics experiments. In this paper, we present such an online framework, used in June 2025 as part of the HRMT-68 experiment, performed at the HiRadMat facility at CERN, using the Super Proton Synchrotron (SPS) beam line. HRMT-68 was a fixed-target laboratory astrophysics experiment aiming to identify plasma instabilities generated by a relativistic electron-positron beam during traversal of an argon plasma. This framework was essential for experimental data acquisition and analysis, and can be adapted for a broad range of experiments with a variety of experimental diagnostics. The framework's modular and customizable design enabled us to rapidly observe and extract emergent features from a diverse range of diagnostic data. Simultaneously, it allowed for both the introduction of new diagnostic devices and the modification of our analysis as features of interest were identified. As a result, we were able to effectively diagnose equipment malfunction, and infer the beam's response to varying bunch duration, beam intensity, and the plasma state without resorting to offline analysis, at which time adjustment or improvement would have been impossible. We present the features of this agile framework, whose codebase we have made publicly available, which can be adapted for future experiments with minimal modification.

We present an extension of recently discovered collisionlessly damped yet topologically protected surface plasma waves (TSPWs), from a simplified slab geometry to a cylindrical plasma-vacuum interface. A distinctive feature of these modes-emerging above the electron cyclotron frequency-is their collisionless damping, which arises from resonant coupling to a continuum of upper-hybrid modes localized within a smooth plasma-vacuum transition layer. We demonstrate both temporal and spatial damping of TSPWs at the boundary of a cylindrical magnetized plasma. Furthermore, we show that in the presence of a non-uniform magnetic field, the TSPW exhibits a smooth and continuous transition, highlighting its topological robustness under magnetic inhomogeneity.

We present the design and optical characterization of a plasmonic metasurface engineered to exhibit strong polarization anisotropy under both linearly and circularly polarized light. The metasurface consists of geometrically asymmetric gold nanostructures arranged periodically on a glass substrate. Each nanostructure is formed by the fusion of three equilateral triangles. The nanostructures simultaneously break mirror and inversion symmetries, resulting in chiral and pseudo-chiral optical responses that manifest as linear and circular polarization-dependent spectral features. Our numerical and experimental results reveal clear chiroptical effects in both near- and far-field. Near-field scanning optical microscopy confirms the excitation of polarization-selective localized plasmonic modes, with spatially distinct hot-spots lighting up under different incident polarizations. Furthermore, we demonstrate that the metasurface exhibits a measurable enantiospecific optical response when coated with thin left- or right-handed chiral overlayers. The differential circular dichroism signals observed in the presence of opposite enantiomers highlight the potential of the metasurface for label-free chiral sensing. These findings provide new insights into the interplay between structural anisotropy, pseudo-chirality, and enantioselective interactions in planar plasmonic systems. Our findings highlight the ability of planar metasurfaces to emulate chiral optical behavior without requiring volumetric 3D structures.

Quantum enhanced sensing exploits the coherent dynamics of two-level systems (TLSs) to achieve exceptional sensitivities and measurement precision that surpass classical detection limits. While platforms such as nitrogen vacancy centers in diamond and rare earth doped crystals have shown excellent performance, their integration with surfaces and external targets remains limited by bulk geometries. Two dimensional (2D) van der Waals materials, particularly hexagonal boron nitride (hBN), offer a compelling alternative, providing atomically thin hosts for spin defects with intrinsic surface proximity and environmental accessibility. These attributes enable high resolution sensing of magnetic fields, strain, and temperature at the nanoscale. In this Perspective, we review recent progress in quantum sensing using spin defects in hBN, including the widely studied boron vacancy (VB-) and emerging carbon related single spin centers. We summarize protocols for spin initialization, coherent manipulation, and optical readout, and highlight demonstrated applications in hybrid architectures and extreme environments and discuss advances in deterministic defect engineering, coherence preservation at the 2D limit. Finally, we discuss future opportunities and challenges in realizing scalable, robust, and multifunctional quantum sensors based on 2D materials.

The quantitative measurement of energy deposits in particle detectors, particularly in calorimeters, is usually accomplished with the help of Analog-to-Digital converters (ADCs) due to their precision, wide measurement range, and good linearity. However, drawbacks such as power consumption, data volume, and bandwidth limit their use in the next generation of high-energy physics experiments. Time-over-threshold (ToT) systems offer simplicity, low power consumption, easy integrability, and wide bandwidth, but they lack precision, linearity, and dynamic range. In this work, we propose a shaper circuit that improves the weaknesses of ToT systems without sacrificing performance. We simulated and implemented the concept in the readout system of the Ring Imaging Cherenkov detector of the Compressed Baryonic Matter experiment at FAIR.

There is an unmet need for artificial intelligence techniques that can speed up the design of growth strategies for cultured tissues. Cultured tissue is increasingly important for a range of applications such as cultivated meat, pharmaceutical assays and regenerative medicine. In this paper, we introduce a method based around evolutionary strategies, machine learning and biophysical simulations that can be used to speed up the process of identifying new tissue growth strategies for these diverse applications. We demonstrate the method by designing tethering strategies to grow tissues containing various cell types with desirable properties such as high cellular alignment and uniform density.

The growing challenges of scaling digital computing motivate new approaches, especially through the dynamical evolution of physical systems that mimic neural networks and combinatorial optimization problems. While light is a hyper efficient information carrier, intrinsically weak light interactions make direct information processing difficult to implement. Recently, specialized nonlinear photonics have opened new controls over light fields with extraordinary bandwidth, coherence, and the emergence of strong interactions among nonlinear eigenstates like solitons. We harness an ensemble of hundreds of Kerr-nonlinear microresonator solitons and implement an analog feedback network to create an Ising machine with fully programmable all-to-all interactions. By increasing the feedback for self, on-diagonal interactions, each soliton exhibits a universal spin-like bifurcation. Using this palette of interactions amongst the entire soliton ensemble, we encode the Ising machine to solve the benchmark Boolean satisfiability problem (SAT). The combination of uniform soliton interactions and the compatibility of our Ising machine with high-speed data interconnects enables rapid and precise solutions of complex SAT problems. Indeed, the soliton properties bound the tradeoff of optical power and time use by the machine at approximately 10 mW and 1 $\mu$s for a single feedback step. We performed >10,000 trials on more than 100 randomly generated SAT instances to evaluate the Ising machine, demonstrating the potential to exceed the performance of benchmark digital SAT solvers. Our work highlights the convergence of optical nonlinearity, ultralow loss photonics, and optoelectronic circuits, which can be combined for a wide range of computation-acceleration tasks.

Antiferromagnets (AFs) are prospective for next-generation high-density and high-speed spintronic applications due to their negligible stray field and ultrafast spin dynamics, notwithstanding the challenges in detecting and manipulating AF order with no magnetization (M = 0). Among the AFs, non-collinear AFs are of particular interest because of their unique properties arising from the non-collinear spin structure and the small magnetization M. In this work, we describe the recently observed vector spin Seebeck effect in non-collinear LuFeO$_3$, where the magneto-thermovoltage under an in-plane temperature gradient, not previously observed, is consistent with the predicted spin swapping effect. Our results shed light on the importance of the non-collinear spin structure in the emerging spin phenomena in non-collinear AFs and offer a new class of materials for AF spintronics and spin caloritronics.

Manipulation of directional magnon propagation, known as magnon spin current, is essential for developing magnonic memory and logic devices featuring nonvolatile functionalities and ultralow power consumption. Magnon spin current can usually be modulated by magnetic field or current-induced spin torques. However, these approaches may lead to energy dissipation caused by Joule heating. Electric-field switching of magnon spin current without charge current is highly desired but very challenging to realize. By integrating magnonic and piezoelectric materials, we demonstrate manipulation of the magnon spin current generated by the spin Seebeck effect in the ferrimagnetic insulator Gd3Fe5O12 (GdIG) film on a piezoelectric substrate. We observe reversible electric-field switching of magnon polarization without applied charge current. Through strain-mediated magnetoelectric coupling, the electric field induces the magnetic compensation transition between two magnetic states of the GdIG, resulting in its magnetization reversal and the simultaneous switching of magnon spin current. Our work establishes a prototype material platform that pave the way for developing magnon logic devices characterized by all electric field reading and writing and reveals the underlying physics principles of their functions.

Convective flow in Earth's iron-rich liquid core drives self-sustained dynamo action, generating Earth's magnetic field, which is strongest among all terrestrial planets of the solar system. Rock records show that this magnetic field has been operative in Earth for at least 3.4 billion years (b.y). However, advanced high pressure experiments have revised the value of the thermal conductivity of the outer core, which implies an age for the inner core of less than 1 b.y., when compositional convection begins. This creates a puzzle, with a gap between the observations of an early magnetic field on Earth and the young inner core. Previous work has suggested that the pre-inner core dynamo could have been generated in a magma ocean (MO) at the base of the mantle; however, the fluid dynamics of this scenario have received little attention. Here we numerically model the non-magnetic rotating flow in a MO above a convectively stable core in a configuration representing the pre-inner core days of Earth's evolution. Simulations here explore the importance of several dimensionless parameters on coupled core-MO convection -- the Rayleigh number, the ocean/core thermal diffusivity ratio, thermal expansion coefficient ratio, viscosity ratio, and layer thickness ratio. It is found that the MO can easily drive a flow of comparable magnitude in the core, and an approximately linear relationship is observed between the ratio of root-mean-square velocities in the core and the ocean, $(u_c^{RMS}/u_o^{RMS})$, and $(\Nu_o-1)$, where $\Nu_o$ is the Nusselt number for the MO, for the $\Nu_o$ of order 1 to 10 considered. Radial and azimuthal components of the core flow are of similar magnitude, so that, with comparable toroidal and poloidal components, we speculate that the MO-driven core flow could drive an early dynamo.

We introduce a variational scheme inspired by classical shadow tomography to compute ground state correlations of quantum spin Hamiltonians. Shadow tomography allows for efficient reconstruction of expectation values of arbitrary observables from a bag of repeated, randomized measurements, called snapshots, on copies of the state $\rho$. The prescription allows one to infer expectation values of $M$ $k-$local observables to accuracy $\epsilon$ using just $N \sim 3^k \text{log}M /\epsilon^2$ snapshots when measurements are performed in locally random bases. Turning this around, a bag of snapshots can be considered an efficient representation of the state $\rho$, particularly for estimating low-weight observables, such as terms in a local Hamiltonian needed to estimate the energy. Inspired by this, we consider a variational scheme wherein a bag of $N$ parametrized snapshots is used to represent the putative ground state of a desired local spin Hamiltonian and optimized to lower the energy with respect to it. Additional constraints in the form of positivity of reduced density matrices, motivated by work in quantum chemistry, are employed to ensure compatibility of the predicted correlations with the underlying Hilbert space. Unlike reduced density matrix approaches, learning the underlying distribution of measurement outcomes allows one to further correlations beyond those in the constrained density matrix. We show, with numerical results, that the proposed variational method can be parallelized, is efficiently simulable, and yields a more complete description of the ground state.

The dynamics of a rigid particle above a fluid-fluid interface in shear flow is studied here numerically and analytically as a function of the downward force applied on the particle. It is found here that the particle goes below the equilibrium level of the interface for a strong enough downward force. Such states remain stable under flow, with a fluid film of a well-defined thickness separating the particle from the indented interface. This result contradicts the classical lubrication theory, which predicts an infinitely large downward force being necessary for the particle to approach the equilibrium level of the interface. It is found that the classical lubrication approximation is only valid in a narrow range of shear rates, which shrinks to a point when the particle approaches the equilibrium level of the interface. The gap renormalization model, proposed here, cures this limitation of the classical lubrication theory, showing quantitative agreement with the numerical results when the particle touches the equilibrium level of the interface. It is found that the gap renormalization model provides a quantitative interpretation of the recent experimental results, including the range of particle heights above the interface for which the classical lubrication approximation breaks down.

Large-scale human mobility datasets play increasingly critical roles in many algorithmic systems, business processes and policy decisions. Unfortunately there has been little focus on understanding bias and other fundamental shortcomings of the datasets and how they impact downstream analyses and prediction tasks. In this work, we study `data production', quantifying not only whether individuals are represented in big digital datasets, but also how they are represented in terms of how much data they produce. We study GPS mobility data collected from anonymized smartphones for ten major US cities and find that data points can be more unequally distributed between users than wealth. We build models to predict the number of data points we can expect to be produced by the composition of demographic groups living in census tracts, and find strong effects of wealth, ethnicity, and education on data production. While we find that bias is a universal phenomenon, occurring in all cities, we further find that each city suffers from its own manifestation of it, and that location-specific models are required to model bias for each city. This work raises serious questions about general approaches to debias human mobility data and urges further research.

Chiral two-dimensional (2D) halide perovskites are formed by embedding chiral organic cations in a perovskite crystal structure. The chirality arises from distortions of the 2D metal halide layers induced by the packing of these organic cations. Sn-based octahedra spontaneously distort, but it remains unclear whether this intrinsic structural instability enhances the chirality. We investigate the effect of the metal cation on structural and phonon chirality in MBA$_{2}$Sn$_{\mathrm{x}}$Pb$_{1-\mathrm{x}}$I$_{4}$ (x = 0, 1/2, and 1). Incorporating Sn does distort the metal halide octehedra, yet it only has a minor impact on the structural chirality. In contrast, the phonons in MBA$_{2}$SnI$_{4}$ are substantially more chiral than in MBA$_{2}$PbI$_{4}$, especially the in-plane acoustic modes. However, this enhanced phonon chirality does not lead to a generation of a larger angular momentum under a temperature gradient, because the contributions of different chiral phonons tend to compensate one another.

Atomic interface engineering (AIE) is critical for advancing technologies in energy storage, catalysis, and microelectronics. In anode-less lithium metal batteries (ALLMBs), AIE is essential for controlling interfacial chemistry governing lithium deposition and solid electrolyte interphase (SEI) formation on copper current collectors. However, native copper surfaces readily oxidize, forming electronically insulating oxides that degrade performance and obscure failure mechanisms. Here, we report a scalable ion implantation strategy to create an atomically clean and robust copper interface. By implanting copper ions into commercial foils, we simultaneously remove the native oxide and introduce subsurface vacancy clusters that act as oxygen traps, yielding an oxidation-resistant and conductive surface. Experimental characterization and multiscale simulations reveal that these engineered vacancies suppress reoxidation and guide the formation of an ultrathin Li2O-enriched solid electrolyte interphase. When applied in ALLMBs, the current collectors enable uniform lithium deposition, suppress parasitic reactions, and deliver a Coulombic efficiency of 99.0% over 400 cycles under lean electrolyte conditions. This work presents a generalizable and industry-compatible approach for stabilizing electrochemical interfaces.

This study presents a refined approach to computing the electronic structure of indium antimonide (InSb) using advanced \textit{ab initio} techniques with the In and Sb $4d^{10}$ semicore electrons included in the valence states. These states are modeled using fully relativistic projector augmented waves (PAW) and optimized norm-conserving Vanderbilt (ONCV) pseudopotentials. However, standard Kohn-Sham density-functional theory (DFT) calculations with these pseudopotentials often produce non-physical band inversions and incorrect band gaps at the $\Gamma$-point due to $5p$-$4d$ repulsion and self-interaction errors (SIE). To resolve these issues, we apply a combination of hybrid Heyd-Scuseria-Ernzerhof (HSE) exchange-correlation (XC) functionals, many-body perturbation theory (MBPT) via quasiparticle $G_0W_0$, and DFT+$U$, significantly improving the accuracy of the band structure over previous studies. A Bayesian optimization framework is used to refine key parameters, including the inverse screening length ($\mu$) and Hartree-Fock (HF) exchange fraction ($\alpha$) in HSE-based XC functionals, as well as the Hubbard $U$ parameters in DFT+$U$, leading to significantly improved band structure predictions. This approach yields highly precise band gaps, bulk moduli, effective masses, Luttinger parameters, valence bandwidth, and $4d$ band positions, achieving unprecedented agreement with experimental data. The resulting model resolves the long-standing incomplete description of InSb's electronic band structure and provides a transferable computational framework for accurate electronic structure predictions across diverse material systems, offering valuable insights for future electronic, optoelectronic, energy, and quantum applications.

The growing demand for ultra low power computing and the emergence of quantum technologies have intensified interest in cryogenic electronics, particularly superconducting this http URL their promise, current controlled superconducting components face fundamental challenges in cascadability, limiting their effectiveness in complex logic this http URL overcome this, recent efforts have focused on developing gate tunable superconducting devices, such as Josephson Junction Field Effect Transistors (JJFETs).However, achieving robust control and sufficient supercurrent gain, both critical for transistor-like performance in logic circuits remains a key challenge.A recent advancement in JJFET design, based on InAs and GaSb heterostructures, demonstrates enhanced gain and favorable device characteristics suitable for circuit this http URL on this innovation, we propose and analyze fundamental voltage controlled logic topologies using the quantum enhanced JJFET. We develop a Verilog A based circuit compatible compact model of the quantum enhanced JJFET which accurately captures the experimentally observed device this http URL ensure cascadability, our logic circuits incorporate the multilayered Heater Nanocryotron (nTron), a superconducting nanowire-based thermal this http URL simulation based analysis, we demonstrate the successful implementation of fundamental logic gates, including NOT, NAND, and NOR. Furthermore, we design a 3 input majority gate, which plays a pivotal role in quantum and reversible computing due to its this http URL, to demonstrate the cascadability of our proposed logic topology, we demonstrate the operation of a 2 input XOR gate based on our designed JJFET based NOT, NAND, and NOR gate.

Solids in an intense laser field show high-harmonic generation (HHG), which can provide information on carrier dynamics and band structures in weakly correlated systems. In strongly correlated systems, a laser field can induce a transition between the various electronic phases formed by the entanglement of charge, spin, and orbital degrees of freedom via carrier generation. The HHG accompanying this process should contain information on the nonequilibrium electronic-state dynamics along the oscillating field - an aspect that remains unresolved to date. Here, we show that an intense mid-infrared (MIR) pulse induces a Mott insulator-metal transition in a one-dimensional cuprate, Sr2CuO3, the evolution of which is reflected by the spectral features of HHs. When the electric-field amplitude exceeds 6 MV/cm, carriers are efficiently generated and each harmonic frequency decreases from odd multiples of the MIR frequency. Dynamical mean-field theory indicates that these redshifts originate from a series of electronic-structure reconstructions in each electric-field cycle during the melting of the Mott-insulator state, which modifies the radiation phase from carrier recombination cycle-by-cycle. This phenomenon is negligible in rigid-band systems. This experimental-theoretical study confirms that HH spectroscopy research can potentially unravel the sub-cycle dynamics of nonequilibrium phase transitions in correlated materials.

The Habitable Worlds Observatory (HWO), a flagship ultraviolet/optical/infrared space telescope recommended by the National Academies' Pathways to Discovery in Astronomy and Astrophysics, will require detector technologies capable of supporting significantly larger pixel-count arrays than previous missions. Microwave Kinetic Inductance Detectors (MKIDs), naturally suited to microwave multiplexing readout, are already in use across several balloon-borne missions with FPGA-based systems. To transition this capability to space, we are developing a radiation-hardened detector readout system that builds directly on the technical and environmental requirements defined by the PRIMA mission. PRIMA serves as a critical pathfinder, informing the radiation tolerance, resource constraints, and on-board processing capabilities needed for HWO. In this work, we present our current results on algorithm implementation, hardware architecture, and firmware development using the radiation-hardened AMD Kintex Ultrascale FPGA, aligning with PRIMA's stringent specifications to ensure compatibility with future space-based observatories like HWO.

Earth system modeling presents a fundamental challenge in scientific computing: capturing complex, multiscale nonlinear dynamics in computationally efficient models while minimizing forecast errors caused by necessary simplifications. Even the most powerful AI- or physics-based forecast system suffer from gradual error accumulation. Data assimilation (DA) aims to mitigate these errors by optimally blending (noisy) observations with prior model forecasts, but conventional variational methods often assume Gaussian error statistics that fail to capture the true, non-Gaussian behavior of chaotic dynamical systems. We propose PnP-DA, a Plug-and-Play algorithm that alternates (1) a lightweight, gradient-based analysis update (using a Mahalanobis-distance misfit on new observations) with (2) a single forward pass through a pretrained generative prior conditioned on the background forecast via a conditional Wasserstein coupling. This strategy relaxes restrictive statistical assumptions and leverages rich historical data without requiring an explicit regularization functional, and it also avoids the need to backpropagate gradients through the complex neural network that encodes the prior during assimilation cycles. Experiments on standard chaotic testbeds demonstrate that this strategy consistently reduces forecast errors across a range of observation sparsities and noise levels, outperforming classical variational methods.

In biosensing and diagnostic applications, a key objective is to design detection systems capable of identifying targets at very low concentrations, i.e., achieving high sensitivity. Here, we propose a linker-mediated detection scheme in which the presence of target molecules (linkers) facilitates the adsorption of ligand-coated guest nanoparticles onto a receptor-coated host substrate. Through a combination of computer simulations and mean-field theory, we demonstrate that, at fixed overall binding strength, increasing the valency of linkers exponentially lowers the concentration threshold for detection. This enables the identification of targets at extremely low concentrations, which is critical for early-stage disease and pathogen diagnostics. Furthermore, superselectivity with respect to binding strength is preserved for multivalent linkers, allowing for effective discrimination between targets and non-targets. Our findings highlight multivalency engineering of linkers as a powerful strategy to dramatically enhance the sensitivity of biodetection systems.

The detection of gravitational waves from extreme-mass-ratio inspirals (EMRIs) in space-borne antennas like LISA and Taiji promises deep insights into strong-field gravity and black hole astrophysics. However, the complex, non-convex likelihood landscapes of EMRI signals (compounded by instrumental noises) have long hindered reliable parameter estimation based on traditional Markov Chain Monte Carlo (MCMC) methods, which often fail to escape local optima or require impractical computational costs. To address this critical bottleneck, we introduce Flow-Matching Markov Chain Monte Carlo (FM-MCMC), a pioneering Bayesian framework that synergizes continuous normalizing flows (CNFs) with parallel tempering MCMC (PTMCMC). By leveraging CNFs to rapidly explore high-dimensional parameter spaces and PTMCMC for precise posterior sampling, FM-MCMC achieves unprecedented efficiency and accuracy in recovering EMRI intrinsic parameters. By enabling real-time, unbiased parameter inference, FM-MCMC unlocks the full scientific potential of EMRI observations, and would serve as a scalable pipeline for precision gravitational-wave astronomy.

We present a case study of how a software framework (Chombo) supported the specific needs of a scientific application (COGENT). Since its inception in 2000, the Chombo framework has supported various applications. One example of such support has been the collaboration with the Edge Simulation Laboratory to build the COGENT model. The specific needs of the COGENT effort required the design and implementation of a set of new capabilities in the Chombo framework, such as higher-order mapped-multiblock discretizations and multi-dimensional code organization. These capabilities allowed COGENT to develop a unique simulation capability for modeling the edge layers in tokamaks. Once developed, these capabilities were able to support other applications which had similar needs.

The interface between semiconductors and ion-conducting electrolytes is characterised by charge distributions and potential drops that vary substantially with the evolution of surface states. These surface states at the very interface to the liquid can form or be passivated, depending on the applied potential between electrode and electrolyte, and hereby fundamentally impact properties such as charge transfer. Characterisation and understanding of such potential-dependent surface states with high spatial and temporal resolution is a significant challenge for the understanding and control of semiconductor-electrolyte interfaces. Here, we show that the optical anisotropy of InP(100) can be used to detect the potential-dependent formation of highly ordered surface states under operating conditions. Upon formation of a surface state in the bandgap of the semiconductor, the potential drop and hence the electric field is shifted away from the semiconductor to the Helmholtz-layer of the electrolyte. This modifies the instantaneous response of the optical anisotropy to disturbances of the applied potential. We propose an electrochemical variant of the linear electro-optical effect and our findings open a novel route for understanding these interfaces. The results show how surface states from surface reconstructions at this reactive interface can be switched on or off with the applied potential.

Studying stream interaction regions (SIRs), from their inception and the dynamics of their development, can provide insight into solar-terrestrial connections. Some in-situ instruments on the Solar Orbiter (SolO) space mission are designed to measure solar wind (SW) and interplanetary magnetic field parameters along the flight path. These instruments are ideal for studying the dynamics of SIR evolution at heliocentric distances of 0.28-1.0 AU and with changes in heliolatitude of 0^{\circ} - 33^{\circ}. To address the challenges of promptly identifying SIRs and predicting their arrival time on Earth, we consider using trigger events from the Radio and Plasma Wave (RPW)/SolO instrument, which are transmitted in telemetry data packages. We suggest that multiple activations of the trigger mode (SBM1 mode) in the RPW instrument over an interval of up to four hours may reflect the fine structure of large-scale events in SW. Such events can serve as markers for the spacecraft's location within the SIR. In this regard, the 2023 analysis revealed that multiple activations of the SBM1 trigger mode throughout the day accounted for more than 50\% of the total number of days for which such events were recorded. Of this number, 63\% were events when the trigger algorithm was prompted repeatedly within a time interval of up to four hours. A comparison of the registration times of SBM1 trigger events with the SW parameters obtained from the SWA-PAS and MAG instruments showed that repeated activations of the trigger algorithm occurred at the stream interface surface when a high-speed SW stream and a formed compression region were present.

Populations evolving in fluctuating environments face the fundamental challenge of balancing adaptation to current conditions against preparation for uncertain futures. Here, we study microbial evolution in partially predictable environments using proteome allocation models that capture the trade-off between growth rate and lag time during environmental transitions. We demonstrate that evolution drives populations toward an evolutionary stable allocation strategy that minimizes resource depletion time, thereby balancing faster growth with shorter adaptation delays. In environments with temporal structure, populations evolve to learn the statistical patterns of environmental transitions through proteome pre-allocation, with the evolved allocations reflecting the transition probabilities between conditions. Our framework reveals how microbial populations can extract and exploit environmental predictability without explicit neural computation, using the proteome as a distributed memory system that encodes environmental patterns. This work demonstrates how information-theoretic principles govern cellular resource allocation and provides a mechanistic foundation for understanding learning-like behavior in evolving biological systems.

We introduce a quantum algorithm for ground-state preparation based on a Chebyshev series approximation to the wall function. This projector can be efficiently implemented as a product of Hamiltonian operators, enabling a straightforward realization via the linear combinations of unitaries method. We analyze the asymptotic scaling and provide numerical benchmarks, demonstrating that the wall-Chebyshev projector achieves competitive performance with leading methods based on imaginary time evolution and alternative projector function approximations. Notably, our approach exhibits superior robustness and convergence in scenarios where accurate ground-state energy estimates are unavailable, showing promise for realistic chemistry problems.

Many combinatorial optimization problems (COPs) are naturally expressed using variables that take on more than two discrete values. To solve such problems using Ising machines (IMs) - specialized analog or digital devices designed to solve COPs efficiently - these multi-valued integers must be encoded using binary spin variables. A common approach is one-hot encoding, where each variable is represented by a group of spins constrained so that exactly one spin is in the "up" state. However, this encoding introduces energy barriers: changing an integer's value requires flipping two spins and passing through an invalid intermediate state. This creates rugged energy landscapes that may hinder optimization. We propose a higher-order Ising formulation for Max-3-Cut, which is the smallest fundamental COP with multi-valued integer variables. Our formulation preserves valid configurations under single-spin updates. The resulting energy landscapes are smoother, and we show that this remains true even when the binary variables are relaxed to continuous values, making it well-suited for analog IMs as well. Benchmarking on such an IM, we find that the higher-order formulation leads to significantly faster solutions than the Ising baseline. Interestingly, we find that an empirical rescaling of some terms in the Ising formulation - a heuristic proposed in prior work - approaches the performance of the higher-order Ising formulation, underscoring the importance of empirical parameter tuning in COP encodings.

Hydrogen atom transfer (HAT) reactions are essential in many biological processes, such as radical migration in damaged proteins, but their mechanistic pathways remain incompletely understood. Simulating HAT is challenging due to the need for quantum chemical accuracy at biologically relevant scales; thus, neither classical force fields nor DFT-based molecular dynamics are applicable. Machine-learned potentials offer an alternative, able to learn potential energy surfaces (PESs) with near-quantum accuracy. However, training these models to generalize across diverse HAT configurations, especially at radical positions in proteins, requires tailored data generation and careful model selection. Here, we systematically generate HAT configurations in peptides to build large datasets using semiempirical methods and DFT. We benchmark three graph neural network architectures (SchNet, Allegro, and MACE) on their ability to learn HAT PESs and indirectly predict reaction barriers from energy predictions. MACE consistently outperforms the others in energy, force, and barrier prediction, achieving a mean absolute error of 1.13 kcal/mol on out-of-distribution DFT barrier predictions. This accuracy enables integration of ML potentials into large-scale collagen simulations to compute reaction rates from predicted barriers, advancing mechanistic understanding of HAT and radical migration in peptides. We analyze scaling laws, model transferability, and cost-performance trade-offs, and outline strategies for improvement by combining ML potentials with transition state search algorithms and active learning. Our approach is generalizable to other biomolecular systems, enabling quantum-accurate simulations of chemical reactivity in complex environments.

The space-based CUbesat Solar Polarimeter (CUSP) mission aims to measure the linear polarization of solar flares in the hard X-ray band by means of a Compton scattering polarimeter. CUSP will allow to study the magnetic reconnection and particle acceleration in the flaring magnetic structures of our star with its unprecedented sensitivity to solar flare polarization. CUSP is a project in the framework of the Alcor Program of the Italian Space Agency aimed to develop new CubeSat missions. It has been proposed as a constellation of a two Cubesat mission to monitor the Sun for Space Weather, and will proceed with a single-satellite asset in its baseline implementation. In the frame of CUSP's Phase B study, that started in December 2024 for a 1-year period, we present the development status of this dual-phase polarimeter. Preliminary laboratory results using two chains of acquisition will be discussed. The first chain of acquisition, based on the Hamamatsu R7600 multi-anode photomultiplier tubes coupled to plastic scintillator bars and read out by the MAROC-3A ASIC, is used to detect the Compton scattering of incoming photons. On the other hand, GAGG crystals coupled to avalanche photo-diodes with a readout based on the SKIROC-2A ASIC are used to absorb the scattered photons. By reconstructing the azimuthal scattering direction for many incoming photons, one can infer the linear polarization degree and angle of the source. We will discuss the calibration results obtained with our prototype detector by using well-known radioactive isotopes, allowing us to assess the performances of our detector over the full 25-100 keV energy range.

The CUbesat Solar Polarimeter (CUSP) project is a CubeSat mission orbiting the Earth aimed to measure the linear polarization of solar flares in the hard X-ray band by means of a Compton scattering polarimeter. CUSP will allow to study the magnetic reconnection and particle acceleration in the flaring magnetic structures of our star. CUSP is a project in the framework of the Alcor Program of the Italian Space Agency aimed to develop new CubeSat missions. CUSP undergoing the Phase B started in December 2024 that will last for 12 month. The Compton polarimeter of the CUSP payload performs coincidence measurements between plastic scintilaltors and GaGG(Ce) crystals to derive the polarization of X-rays. These sensors are readout by Multi Anode Photomultiplier Tubes (MAPMTs) and Avalanche Photodiodes (APDs) respectively. Both sensors need an HV power supply up to -1~kV (for the MAPMT) and +500~V (for the APD). We tested precision regulated High Voltage DC/DC Converters by HVM Technology Inc. with Sub-Miniature Case Size ($0.85''\times0.85''\times0.60''$) of the SMHV series. These modules are compact and suited for CubeSat missions.

We study an out-of-equilibrium quantum system in which a state connecting two reservoirs is also coupled by stimulated and spontaneous emission of photons to an antitrapped state, thus implementing particle loss. After revisiting the spontaneous emission process, we show that the proper effective description of such a system requires one to go beyond the usual Lindbladian formalism and includes a nonreciprocal (``non-Hermitian'') coupling to the reservoir modeling the untrapped state. The presence of both, the reservoirs and the nonreciprocal coupling, have observable consequences that we compute, for example, by looking at the quantum Zeno effect in the loss current. We discuss the connection of our findings to possible experiments in cold atomic gases.

Theoretical accounts of ultrastrongly coupled light-matter systems commonly assume that it arises from the interaction of an emitter with propagating photon modes supported by a structure, understanding photons as the excitations of the transverse electromagnetic field. This description discards the Coulomb interaction between the emitter and structure charges. Here, we show with a general argument based on electromagnetic constraints that the emitter-photon coupling strength is fundamentally limited. Accordingly, we conclude that the ultrastrong coupling regime cannot be reached with photons. Instead, it must originate from the Coulomb interactions between charges. A further corollary is that the so-called polarization self-energy term does not need to be included. We illustrate our claims by solving an analytical model of the paradigmatic case of an emitter next to a metallic nanosphere. These findings shed light on the fundamental processes underlying ultrastrong coupling, clarify the role of the polarization self-energy term and compel a reevaluation of previous literature.

Frictional instabilities in fluid-saturated granular materials underlie critical natural hazards such as submarine landslides and earthquake initiation. Distinct failure behaviors emerge under subaerial and subaqueous conditions due to the coupled effects of mechanical deformation, interparticle friction, and fluid interactions. This study employs three-dimensional coupled Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) simulations to investigate the collapse and runout dynamics of dense and loose granular assemblies across these settings. Parametric analyses reveal that pore pressure evolution plays a central role in governing failure mechanisms: dense assemblies stabilize through dilation, while loose assemblies undergo rapid compaction and fluidization, particularly under subaqueous conditions. Spatiotemporal analyses of coarse-grained fields further highlight strain-rate-dependent behavior driven by evolving porosity and effective stress. Both environments exhibit rate-strengthening behavior that scales with the inertial number (In) and viscous number (Iv), though driven by distinct mechanisms: subaerial systems are dominated by interparticle contact networks, whereas subaqueous systems are influenced by fluid drag, pore-pressure buildup, and lubrication. An analytical solution for excess pore pressure is compared with breaching-induced pressure distributions from CFD-DEM simulations, using input parameters derived from numerical triaxial DEM tests. The model captures fluid-particle coupling effectively, reproducing comparable excess pore pressures at steady state, while early-time discrepancies underscore the complexities of transient interactions. These findings advance the understanding of failure mechanics in saturated granular media and support the development of physics-based models for mitigating hazards associated with subaqueous granular flows.

The Earth's earliest magnetic field may have originated in a basal magma ocean, a layer of silicate melt surround the core that could have persisted for billions of years. Recent studies show that the electrical conductivity of liquid with a bulk silicate Earth composition exceeds 10000 S/m at basal magma ocean conditions, potentially surprising the threshold for dynamo activity. Over most of its history however, the basal magma ocean is more enriched in iron than the bulk silicate Earth, due to iron's incompatibility in the mineral assemblages of the lower mantle. Using ab-initio molecular dynamics calculations, we examine how iron content affects the silicate dynamo hypothesis. We investigate how the electrical conductivity of silicate liquid changes with iron enrichment, at pressures and temperatures relevant for Earth's basal magma ocean. We also compute the electronic contribution to the thermal conductivity , to evaluate convective instability of basal magma oceans. Finally, we apply our results to model the thermal and magnetic evolution of Earth's basal magma ocean over time.

Drylands forestation offers the potential for significant long-term sequestration of atmospheric CO$_2$. Consider sequestration of both organic and inorganic carbon by a planted semi-arid forest, based on carbon that originates from atmospheric CO$_2$. Measurements at Israel's Yatir forest give a sequestration rate of $\sim$550 gram CO$_2$ m$^{-2}$ yr$^{-1}$ as organic carbon in the tree's biomass. In addition, $\sim$216 gram CO$_2$ m$^{-2}$ yr$^{-1}$ precipitates as calcite (CaCO$_3$) in the soil due to a combination of microbial activity on organic soil carbon, and the reaction of soil water with CO$_2$ exhaled from tree roots. The exhaled CO$_2$ is formed when glucose (produced by photosynthesis) is oxidized to supply energy for the trees' cellular processes. Significantly, low rainfall in drylands precludes dissolving precipitated calcite. Published estimates restrict the potential drylands surface available for sustainable forestation to $\sim$4.5 million km$^2$, only $\sim$10$\%$ of the global drylands. The dominant limitation is the apparent lack of water. However, immediately under many drylands, there are fossil waters that had recharged underlying aquifers during prior wetter climatic regimes. Conservatively, including this water, at least 9.0 million km$^2$ is available for afforestation. Such an area may yield a potential total annual sequestration rate of $\sim$7.0 Gt CO$_2$ yr$^{-1}$, divided between $\sim$5.0 Gt CO$_2$ yr$^{-1}$ (organic) and $\sim$2.0 Gt CO$_2$ yr$^{-1}$ (inorganic); a respectable $\sim$35$\%$ of the annual rate of atmospheric CO$_2$ increase. However, considering the reduction in land surface albedo (reflectivity), the effective cooling would be $\sim$5.0 Gt CO$_2$ yr$^{-1}$. Drylands reforestation would provide additional area for sequestration.

Traditional methods for biological shape inference, such as deep learning (DL) and active contour models, face important limitations in 3D. DL approaches require large annotated datasets, which are often impractical to obtain, while active contour methods depend on carefully tuned heuristics for intensity attraction and shape regularization. We introduce deltaMic, a novel differentiable 3D renderer for fluorescence microscopy that formulates shape inference as an inverse problem. By leveraging differentiable convolutions, deltaMic simulates the image formation process, integrating a parameterized point spread function (PSF) with a triangle mesh-based representation of biological structures. Unlike DL- or contour-based segmentation, deltaMic directly optimizes both shape and optical parameters to align synthetic and real microscopy images, removing the need for large datasets or sample-specific fine-tuning. To ensure scalability, we implement a GPU-accelerated Fourier transform for triangle meshes along with narrow-band spectral filtering. We show that deltaMic accurately reconstructs cell geometries from both synthetic and diverse experimental 3D microscopy data, while remaining robust to noise and initialization. This establishes a new physics-informed framework for biophysical image analysis and inverse modeling.

The simulation of turbulent flow requires many degrees of freedom to resolve all the relevant times and length scales. However, due to the dissipative nature of the Navier-Stokes equations, the long-term dynamics are expected to lie on a finite-dimensional invariant manifold with fewer degrees of freedom. In this study, we build low-dimensional data-driven models of pressure-driven flow through a circular pipe. We impose the `shift-and-reflect' symmetry to study the system in a minimal computational cell (e.g., smallest domain size that sustains turbulence) at a Reynolds number of 2500. We build these models by using autoencoders to parametrize the manifold coordinates and neural ODEs to describe their time evolution. Direct numerical simulations (DNS) typically require on the order of O(105) degrees of freedom, while our data-driven framework enables the construction of models with fewer than 20 degrees of freedom. Remarkably, these reduced order models effectively capture crucial features of the flow, including the streak breakdown. In short-time tracking, these models accurately track the true trajectory for one Lyapunov time, while at long-times, they successfully capture key aspects of the dynamics such as Reynolds stresses and energy balance. Additionally, we report a library of exact coherent states (ECS) found in the DNS with the aid of these low-dimensional models. This approach leads to the discovery of seventeen previously unknown solutions within the turbulent pipe flow system, notably featuring relative periodic orbits characterized by the longest reported periods for such flow conditions.

Multiphase flows are an important class of fluid flow and their study facilitates the development of diverse applications in industrial, natural, and biomedical systems. We consider a model that uses a continuum description of both phases in which separate momentum equations are used for each phase along with a co-incompressibility condition on the velocity fields. The resulting system of equations poses numerical challenges due to the presence of multiple non-linear terms and the co-incompressibility condition, and the resulting fluid dynamics motivate the development of an adaptive mesh refinement (AMR) technique to accurately capture regions of high stresses and large material gradients while keeping computational costs low. We present an accurate, robust, and efficient computational method for simulating multiphase mixtures on adaptive grids, and utilize a multigrid solver to precondition the saddle-point system. We demonstrate that the AMR discretization asymptotically approaches second order accuracy in $L^1$, $L^2$ and $L^\infty$ norms. The solver can accurately resolve sharp gradients in the solution and, with the multigrid preconditioning strategy introduced herein, the linear solver iterations are independent of grid spacing. Our AMR solver offers a major cost savings benefit, providing up to ten fold speedup over a uniform grid in the numerical experiments presented here, with greater speedup possible depending on the problem set-up.

As the ring-based light source community is moving towards fourth-generation light sources, many facilities plan to upgrade their boosters in parallel to meet the more demanding beam properties for the storage ring, especially in terms of a much lower emittance. Concerns over collective effects have, therefore, risen, particularly in the transverse planes, since the vacuum chamber dimensions tend to be reduced as a way to achieve a stronger focusing force on the beam. In this article, we present numerical studies on transverse beam instabilities, both in the single- and multibunch regimes, in the SOLEIL II booster as an example of a low-emittance booster. We show that Landau damping is an efficient mechanism for suppressing both transverse single-bunch and coupled-bunch instabilities. We also prove that the damping in the longitudinal plane can diffuse to the transverse plane and limit the transverse emittance growth. Moreover, we have discovered that the beam can exhibit sawtooth instability at high energy and that broad-band impedance is one of the key factors in suppressing transverse coupled-bunch instability.

Recent research indicates that the use of multiple external representations MERs has the potential to support learning, especially in complex scientific areas, such as quantum physics. In particular, the provision of informationally redundant external representations can have advantageous effects on learning outcomes. This is of special relevance for quantum education, where various external representations are available and their effective use is recognised as crucial to student learning. However, research on the effects of informationally redundant external representations in quantum learning is limited. The present study aims to contribute to the development of effective learning materials by investigating the effects of learning with informationally redundant external representations on students' learning of quantum physics. Using a between-subjects design, 113 students were randomly assigned to one of four learning conditions. The control group learnt with a traditional multimedia learning unit on the behaviour of a single photon in a Mach-Zehnder interferometer. The three intervention groups received redundant essential information in the Dirac formalism, the Bloch sphere, or both. The use of eye tracking enabled insight into the learning process depending on the external representations provided. While the results indicate no effect of the study condition on learning outcomes (content knowledge and cognitive load), the analysis of visual behaviour reveals decreased learning efficiency with the addition of the Bloch sphere to the multimedia learning unit. The results are discussed based on current insight in learning with MERs. The study emphasises the need for careful instructional design to balance the associated cognitive load when learning with informationally redundant external representations.

We investigated the $\alpha$ and $\beta$ effects in a rotating spherical plasma system relevant to astrophysical environments. These coefficients were derived using three different approaches based on the large-scale magnetic field $\overline{\mathbf{B}}$, turbulent velocity $\mathbf{u}$, and turbulent magnetic field $\mathbf{b}$, yielding $\alpha_{\mathrm{EM-HM}}$, $\beta_{\mathrm{EM-HM}}$, $\beta_{\mathrm{vv-vw}}$, and $\beta_{\mathrm{bb+jb}}$. Using raw data from direct numerical simulations (DNS), we constructed the magnetic induction equation incorporating the $\alpha$ and $\beta$ coefficients. We then reproduced the $\overline{\mathbf{B}}$ field and compared the results with the DNS data. In the kinematic regime, where $\overline{\mathbf{B}}$ is weak, all models exhibit good agreement with the DNS results. However, in the nonlinear regime, the $\overline{\mathbf{B}}$ field, reproduced using $\beta_{\mathrm{vv-vw}}$, deviates from the DNS and exhibits unbounded growth. To address this discrepancy, we added $\beta_{\mathrm{bb+jb}}$, which represents the contribution of turbulent magnetic fields, to $\beta_{\mathrm{vv-vw}}$. This addition suppresses the divergent growth of $\overline{\mathbf{B}}$ in the nonlinear regime. We then assessed the actual influence of $\alpha$ and $\beta$ on the evolution of $\overline{\mathbf{B}}$ by applying weighted combinations of the two coefficients. Our results show that magnetic $\beta$ diffusion plays a dominant role throughout the entire process. In contrast, the $\alpha$ effect is minor in the kinematic regime but becomes essential for sustaining the $\overline{\mathbf{B}}$ field in the nonlinear regime. We also discussed the underlying physical mechanism responsible for this behavior.

Beliefs are central to individual decision-making and societal dynamics, and they are shaped through complex interactions between personal cognition and social environments. Traditional models of belief dynamics often fail to capture the interplay between internal belief systems and external influences. We present a meta-model that represents belief dynamics through three belief types: Personal beliefs, Expressed beliefs, and Social beliefs about others (PES). This distinction allows the model to account for the potential misperception of others' beliefs as well as distortions in the belief expression, and it permits the formalization of psychological processes such as ego projection, social influence, authenticity, and conformity. These processes have been studied extensively in social psychology but are rarely integrated into a comprehensive formal model. The PES meta-model also encompasses many existing belief dynamics models, such as versions of the Voter, Ising, DeGroot, and bounded confidence models. Its nested structure enables comparative analyses between different models and supports the construction of new models by recombining its components, providing a flexible framework for cumulative theory development.

Unknown node attributes in complex networks may introduce community structures that are important to distinguish from those driven by known attributes. We propose a block-corrected modularity that discounts given block structures present in the network to reveal communities masked by them. We show analytically how the proposed modularity finds the community structure driven by an unknown attribute in a simple network model. Further, we observe that the block-corrected modularity finds the underlying community structure on a number of simple synthetic network models while methods using different null models fail. We develop an efficient spectral method as well as two Louvain-inspired fine-tuning algorithms to maximize the proposed modularity and demonstrate their performance on several synthetic network models. Finally, we assess our methodology on various real-world citation networks built using the OpenAlex data by correcting for the temporal citation patterns.

This study benchmarks hybrid quantum physics-informed neural network (HQPINN) to model high-speed flows, compared against classical physics-informed neural networks (PINNs) and fully quantum neural networks (QNNs). The HQPINN architecture integrates a parameterized quantum circuit (PQC) with a classical neural network in parallel, trained via a physics-informed loss. Across harmonic, non-harmonic, and transonic benchmarks, HQPINNs demonstrate balanced performance, offering competitive accuracy and stability with reduced parameter cost. Quantum PINNs are highly efficient for harmonic problems achieving the lowest loss with minimal parameters due to their Fourier structure, but struggle to generalize in non-harmonic settings involving shocks and discontinuities. HQPINNs mitigate such artifacts, and with sufficient parameterization, can match the performance of classical models in more complex regimes. Although constrained by current quantum emulation costs and scalability, HQPINNs show promise as general-purpose solvers, offering parameter efficiency with robust fallback behavior, particularly suited for problems where the nature of the solution is not known a-priori.

Photon-counting computed tomography (PCCT) has emerged as a promising imaging technique, enabling spectral imaging and material decomposition (MD). However, images typically suffer from a low signal-to-noise ratio due to constraints such as low photon counts and sparse-view settings which provoke artifacts. To prevent this, variational methods minimize a data-fit function coupled with handcrafted regularizers which mimics a prior by enforcing image properties such as the sparsity of the gradient. In the last few years, diffusion models (DMs) became predominant in the field of generative models and have been used as a learned prior for image reconstruction. This work investigates the use of DMs as regularizers for MD tasks in PCCT, specifically using diffusion posterior sampling (DPS) guidance. Three DPS-based approaches, i.e., image-domain two-step DPS (im-TDPS), projection-domain two-step DPS (proj-TDPS), and one-step DPS (ODPS), are evaluated. The first two methods perform MD in two steps by performing reconstruction and MD separately. The last method, ODPS, samples the material images directly from the measurement data. The results indicate that ODPS achieves superior performance compared to im-TDPS and proj-TDPS, providing sharper, noise-free and crosstalk-free images. Furthermore, we introduce a novel hybrid ODPS method for scenarios involving materials absent from the training dataset. This methods combine DM priors with standard variational handcrafted regularizers for the materials unknown to the DM. This hybrid method demonstrates improved MD quality compared to a standard variational method and does not require additional training of the DM neural network (NN).

Recent studies have applied variational calculus, conformal mapping, and point transformations to extend the one-dimensional SCLC from planar gaps to more complicated geometries. However, introducing a magnetic field orthogonal to the diode's electric field complicates these calculations due to changes in the electron trajectory. This paper extends a recent study that applied variational calculus to determine the SCLC for a cylindrical crossed-field diode to derive an equation that is valid for any orthogonal coordinate system. We then derive equations for the SCLC for crossed-field gaps in spherical, tip-to-tip, and tip-to-plane geometries that can be solved numerically. These calculations exhibit a discontinuity at the Hull cutoff magnetic field $B_H$ corresponding to the transition to magnetic insulation as observed analytically for a planar geometry. The ratio of the crossed-field SCLC to the nonmagnetic SCLC becomes essentially independent of geometry when we fix $\delta = D/D_M > 0.6$, where $D$ is the canonical gap distance accounting for geometric effects on electric potential and $D_M$ is the effective gap distance that accounts for magnetic field and geometry. The solutions for these geometries overlap as $\delta \to 1$ since the geometric corrections for electric potential and magnetic field match. This indicates the possibility of more generally accounting for the combination of geometric and magnetic effects when calculating $B_H$ and SCLC.

Superconducting devices have enabled breakthrough performance in quantum sensing and ultra-low-power computing. Nevertheless, the need for a cryo-electronics platform that can interface superconducting electronics with Complementary Metal-Oxide-Semiconductor (CMOS) devices has become increasingly evident in many cutting-edge applications. In this work, we present a three-terminal micrometer-wide superconducting wire-based cryotron switch (wTron), fabricated using photolithography, that can directly interface with CMOS electronics. The wTron features an output impedance exceeding 1 k$\Omega$ and exhibits reduced sensitivity to ambient magnetic noise, similar to its nanoscale predecessor, the nanocryotron. In addition, its micrometer-wide wires support switching currents in the mA range, making wTrons well-suited for driving current-hungry resistive loads and highly capacitive CMOS loads. We demonstrate this capability by using the wTron to drive room-temperature CMOS electronics, including an LED and a MOSFET with a gate capacitance of 500 pF. We then examine the optimal design parameters of wTrons to drive CMOS loads, such as MOSFETs, HEMTs, and electro-optic modulators. Furthermore, to demonstrate the foundry readiness of the wTron, we fabricated wTrons using MIT Lincoln Laboratory's SFQ5ee superconducting process and characterized their switching behavior. Our work shows that wTron will facilitate the interface between superconducting electronics and CMOS, thereby paving the way for the development of foundry-compatible cryo-electronic ecosystems to advance next-generation computing and quantum applications.

Optical computing systems provide an alternate hardware model which appears to be aligned with the demands of neural network workloads. However, the challenge of implementing energy efficient nonlinearities in optics -- a key requirement for realizing neural networks -- is a conspicuous missing link. In this work we introduce a novel method to achieve nonlinear computation in fully linear media. Our method can operate at low power and requires only the ability to drive the optical system at a data-dependent spatial position. Leveraging this positional encoding, we formulate a fully automated, topology-optimization-based hardware design framework for extremely specialized optical neural networks, drawing on modern advancements in optimization and machine learning. We evaluate our optical designs on machine learning classification tasks: demonstrating significant improvements over linear methods, and competitive performance when compared to standard artificial neural networks.

Calculating the dynamics of charged particles in electromagnetic fields (i.e. the particle pushing problem) is one of the most computationally intensive components of particle-in-cell (PIC) methods for plasma physics simulations. This task is especially challenging when the plasma is strongly magnetized, since in this case the particle motion consists of a wide range of temporal scales from highly oscillatory fast gyromotion to slow macroscopic behavior and the resulting numerical model is very stiff. Current state-of-the-art time integrators used to simulate particle motion have limitations given the severe numerical stiffness of the problem and more efficient methods are of interest. Recently, exponential integrators have been proposed as a promising new approach for these simulations and shown to offer computational advantages over commonly used schemes. Exponential methods can solve linear problems exactly and are A-stable. In this paper, the standard exponential algorithms framework is extended to derive Nyström-type exponential methods that integrate the Newtonian equations of motion as a second-order differential equation. Specific Nyström-type schemes of second and third orders are derived and applied to strongly magnetized particle pushing problems. Numerical experiments are presented to demonstrate that the Nyström-type exponential integrators can provide significant improvement in computational efficiency over the standard exponential methods.

The COMET Phase-$\alpha$ experiment aims to evaluate the novel muon transport beamline for the muon-to-electron conversion search at J-PARC, Japan. A dedicated Range Counter (RC) was developed to measure the momentum spectrum of transported negative muons with momenta of 30--100 MeV/$c$. The RC consists of graphite momentum degraders, a muon absorber, and plastic scintillation counters ($\rm T_0$, $\rm T_1$, and $\rm T_2$) to detect decay-in-orbit (DIO) electrons from stopped muons. The number of muons stopped in the absorber is reconstructed from the decay time distribution. A copper absorber was selected due to the short lifetime of muonic atoms in copper, which enhances signal separation. The counters' performance was evaluated experimentally. The $\rm T_0$ Counter, made of a $200\times 200\times 0.5~{\rm mm^3}$ scintillator plate, achieved a muon-trigger efficiency exceeding 99.9%. The $\rm T_1$ and $\rm T_2$ Counters also demonstrated high electron-detection efficiencies of $>99$%. Based on these results, simulation studies estimate the acceptance for reconstructing the number of DIO electrons from the absorber to be approximately 47% with a corresponding signal purity of 60% against muon capture-induced backgrounds.

Novel electronic devices can often be operated in a plethora of ways, which makes testing circuits comprised of them difficult. Often, no single tool can simultaneously analyze the operating margins, maximum speed, and failure modes of a circuit, particularly when the intended behavior of subcomponents of the circuit is not standardized. This work demonstrates a cost-effective time-domain data acquisition system for electronic circuits that enables more intricate verification techniques than are practical with conventional experimental setups. We use high-speed digital-to-analog converters and real-time multi-gigasample-per-second waveform processing to push experimental circuits beyond their maximum operating speed. Our custom time-tagging data capture firmware reduces memory requirements and can be used to determine when errors occur. The firmware is combined with a thermal-noise-limited analog frontend with \ensuremath{{50}\,\mathrm{dB}} of dynamic range. Compared to currently available commercial test equipment that is seven times more expensive, this data acquisition system was able to operate a superconducting shift register at a nearly three-times-higher clock frequency (${200}\,\mathrm{MHz}$ vs. ${80}\,\mathrm{MHz}$)

The pseudopotential model within the Lattice Boltzmann Method (LBM) framework has emerged as a prominent approach in computational fluid dynamics due to its dual strengths in physical intuitiveness and computational tractability. However, when modeling wettability phenomenon, existing solid-fluid interaction schemes exhibit persistent challenges in multi-component immiscible fluid systems, notably manifested through spurious velocity artifacts and unphysical mass-transfer boundary layers. This study presents an improved interaction scheme that preserves implementation simplicity while effectively mitigating these numerical artifacts. Furthermore, leveraging the enhanced isotropy characteristics of eighth-order discrete schemes, we develop a novel boundary treatment methodology addressing second-layer lattice data reconstruction at complex interfaces. To verify the universality of the proposed optimization scheme, four benchmark scenarios, including static contact angle measurement on cylindrical surface, droplet dynamics through confined geometries, immiscible displacement processes, and co-current flow in microchannels, are simulated to demonstrate the proposed scheme's capability. The results show that the improved scheme can well simulate various complex immiscible multiphase flows.

We propose a Hybrid High-Order (HHO) formulation of the incompressible Navier--Stokes equations, that is well suited to be employed for the simulation of turbulent flows. The spatial discretization relies on hybrid velocity and pressure spaces and the temporal discretization is based on Explicit Singly Diagonal Implicit Runge-Kutta (ESDIRK) methods. The formulation possesses some attractive features that can be fruitfully exploited when high-fidelity computations are required, namely: pressure-robustness, conservation of mass enforced cell-by-cell up to machine precision, robustness in the inviscid limit, implicit high-order accurate time stepping with local time step adaptation, reduced memory footprint thanks to static condensation of both velocity and pressure, possibility to exploit inherited $p$-multilevel solution strategies to improve performance of iterative solvers. After demonstrating the relevant properties of the scheme in practice, performing challenging 2D and 3D test cases, we consider the simulation of the Taylor--Green Vortex flow problem at Reynolds 1600.

Here, we report a characterization setup for low-loss integrated photonic waveguides in aluminum oxide, tailored for deep UV applications. The setup includes a 261nm UV light source, objective lenses for free-space coupling, an automated stage, and a UV camera. The measured losses for the aluminum oxide waveguides in the UV-261nm range were calculated to be 4.9485 dB/cm. These results demonstrate the effectiveness of the setup in achieving low-loss characterization of integrated photonics waveguides for deep UV applications.

Ion-acoustic waves in a dusty plasma are investigated where it is assumed that the ions follow a Cairns distribution and the electrons are Boltzmann distributed. Two theoretical methods are applied: Sagdeev pseudopotential analysis (SPA) and reductive perturbation theory (RPT). Since SPA incorporates all nonlinearities of the model it is the most accurate but deriving soliton profiles requires numerical integration of Poisson's equation. By contrast, RPT is a perturbation method which at second order yields the Gardner equation incorporating both the quadratic nonlinearity of the KdV equation and the cubic nonlinearity of the modified KdV equation. For consistency with the perturbation scheme the coefficient of the quadratic term needs to be at least an order of magnitude smaller than the coefficient of the cubic term. Solving the Gardner equation yields an analytic expression of the soliton profile. Selecting an appropriate set of compositional parameters, the soliton solutions obtained from SPA and RPT are analyzed and compared.

This work begins the development of fast, scalable solvers for scientific and engineering-relevant magnetohydrodynamics (MHD) models of tokamaks using multigrid methods. These models are characterized by a distinguished direction following the magnetic field around the torus, which dominates the plasma dynamics. All tokamak models exploit this structure, for example, NIMROD uses 2D, unstructured, high-order finite elements in the poloidal plane with Fourier modes in the toroidal coordinate, and the 3D, extended MHD code M3D-C1 uses 2D, unstructured C1 elements in the poloidal plane with cubic Hermite functions in the toroidal direction and a regular grid that is partially aligned with the magnetic field. This structure suggests addressing the toroidal coordinate first, which NIMROD does at the formulation level, but M3D-C1 uses a full 3D discretization. The resulting algebraic system is solved at each time step in an implicit time integrator. This work addressed the toroidal coordinate in the M3D-C1 velocity solve by adding semi-coarsening multigrid to the existing PETSC - Portable, Extensible Toolkit for Scientific Computation - block Jacobi solver, with the addition of little new code. Competitive performance of this new solver configuration is demonstrated on a self-consistent runaway electron model of a SPARC4 disruption, and the next steps in the development of this solver are outlined.

As the terahertz (THz) band emerges as a pivotal technology for next-generation wireless communications, accurate channel modeling in dynamic environments becomes increasingly critical, particularly for scenarios involving reflective interactions with water surfaces. This article presents comprehensive experimental and theoretical investigations into THz channel (120-320 GHz) performance under dynamic water surface reflections. By developing and validating a modified dual-scale scattering model based on the improved integral equation model (I2EM), this work systematically evaluates channel characteristics, such as signal power loss and bit error rate (BER), across various dynamic aquatic scenarios. Laboratory experiments and real-world natatorium measurements demonstrate the model's efficacy in capturing complex temporal and spatial scattering behaviors, offering vital insights and robust predictive capabilities essential for deploying possible THz communication systems in aquatic and sports environments.

Spatial filtering of optical fields has widespread applications ranging from beam shaping to optical information processing. However, conventional spatial filters are bulky and alignment-sensitive. Here, we present nonlocal non-Hermitian metasurfaces that can act as exceptionally effective optical spatial filters while being highly compact and insensitive to both lateral and longitudinal displacements. The metasurface design is based on a resonant waveguide grating in which radiative losses of the modes are tailored to realize a symmetry-protected bound state in the continuum in the middle of a non-Hermitian flat band. Using this design, we propose a compact spatial filtering device operating over an angular range of approximately 1 degree around normal incidence. In addition to being ultrathin and robust against translational misalignment, the proposed metasurfaces are easy to manufacture, which makes them an attractive alternative to conventional spatial filters, holding a potential to become a widely used optical component.

Topological photonics was embarked from realizing the first-order chiral edge state in gyromagnetic media, but its higher-order states were mostly studied in dielectric lattice instead. In this paper in a series of gyromagnetic Lieb photonic crystals, we theoretically unveil topological phases which include the first-order Chern, and the second-order dipole, quadrupole phases. Concretely, for the primitive Lieb lattice, and for its deformation by breaking spatial symmetry through unit-cell deformation, versatile topological phases can be established to transit around, with bandgap closures marking the phase boundaries. Our results on gyromagnetic Lieb photonic crystals may contribute to broadening the scope of sublattice engineering design for topological phase manipulation, potentially enabling multifunctional disorder-resistant waveguides and integrated photonic circuits for information communication.

Inspired by the design concept of negative curvature hollow-core fibers, this paper presents an innovative negative curvature suspended-core THz fiber. Compared to traditional suspended-core fibers, all structural units of this fiber are designed with circular boundaries, effectively avoiding the issues of insufficient mechanical strength and manufacturing difficulties associated with the wide and thin rectangular support arms in traditional structures. The numerical simulation using the full-vector finite element method shows that the optical fiber loss is as low as 0.02cm-1 in 0.66-1.09THz, and the low loss bandwidth is 0.43THz. In addition, near-zero flat dispersion of -0.08-0.74 ps/THz/cm can be achieved. The fiber exhibits excellent characteristics of low loss, wide bandwidth, and low dispersion, theoretically opening a new research path for the design of low-loss THz fibers.

The design and evaluation on the NSLS-II beamline of the 3FI application specific integrated circuit (ASIC) bump-bonded to a simply, planar, two-dimensionally segmented silicon sensor is presented. The ASIC was developed for Full-Field Fluorescence spectral X-ray Imaging (3FI). It is a small-scale prototype that features a square array of 32x32 pixels with a pitch of 100 {\mu}m. The ASIC was implemented in a 65 nm CMOS process. Each pixel incorporates a charge-sensitive amplifier, shaping filter, discriminator, peak detector, and sample-and-hold circuit, allowing detection of events and storing signal amplitudes. The system operates in an event-driven readout mode, outputting analog values for threshold-triggered events, allowing high-speed multi-element X-ray fluorescence imaging. At power consumption of 200 {\mu}W per pixel, consisting almost uniquely of power dissipated in analog blocks, 308 eV full width at half maximum (FWHM) energy resolution at 8.04 keV, that corresponds to 30 e- rms equivalent noise charge (ENC) and 138 eV FWHM energy resolution at 3.69 keV (16 e- rms ENC) were obtained, for Cu and Ca K{\alpha} lines, respectively. Each pixel operates independently, and the detector enables in situ trace element microanalysis in biological and environmental research. Its architecture addresses limitations of X-ray Fluorescence Microscopy (XFM), typically requiring mechanical scanning, by offering frame-lees data acquisition, translating to high-throughput operation. The 3FI ASIC is suitable for example for studies of nutrient cycling in the (mycor)rhizosphere, microbial redox processes, and genotype-phenotype correlations in bio-energy crops. Additional performances, such as enhanced spatial resolution can be further improved with coded-aperture and Wolt, extending the use to environmental, biomedical, and material science studies.

Single-shot coherent diffractive imaging (CDI) using intense XUV and soft X-ray pulses holds the promise to deliver information on the three dimensional shape as well as the optical properties of nano-scale objects in a single diffraction image. This advantage over conventional X-ray diffraction methods comes at the cost of a much more complex description of the underlying scattering process due to the importance of wide-angle scattering and propagation effects. The commonly employed reconstruction of the sample properties via iterative forward fitting of diffraction patterns requires an accurate and fast method to simulate the scattering process. This work introduces the propagation multi-slice Fourier transform method (pMSFT) and demonstrates its superior performance and accuracy against existing methods for wide-angle scattering. A derivation from first principles, a unified physical picture of the approximations underlying pMSFT and the existing methods, as well as a systematic benchmark that provides qualified guidance for the selection of the appropriate scattering method is presented.

The results of direct numerical simulation of plane-symmetric turbulence of water waves for potential flows within the framework of conformal variables taking into account low-frequency pumping and high-frequency viscous dissipation are presented. In this model, for a wide range of pumping amplitudes, the weak turbulence regime was not detected. It is shown that for typical turbulence parameters, the main effects are the processes of wave breaking, the formation of cusps on wave crests, which make the main contribution to the turbulence spectra with a dependence on frequency and wavenumber with the same exponent equal to $-4$. In this strongly nonlinear regime, the probability density of wave steepness at large deviations has power-law tails responsible for the intermittency of turbulence.

We report the realization of Zinc Sulfide (ZnS) nanowaveguides and the experimental observation of second harmonic generation (SHG) in such structures, demonstrating their potential for integrated nonlinear photonics. ZnS thin films were deposited via RF magnetron sputtering and characterized using atomic force microscopy (AFM), scanning electron microscopy (SEM), X-ray diffraction (XRD), and ellipsometry. The nonlinear optical properties of these films were theoretically analyzed to assess their suitability for second-order nonlinear processes. We detail the fabrication and optical characterization of ZnS nanowaveguides, leading to the experimental observation of SHG in such structures. These findings establish ZnS as a promising platform for nonlinear photonic applications, particularly in compact and integrated frequency conversion devices. This work represents a significant step toward expanding the scope of wide bandgap semiconductors in advanced photonic technologies.

An optimal local quantum thermometer is a quantum many-body system that saturates the fundamental lower bound for the thermal state temperature estimation accuracy [L. Correa, et. al., Phys. Rev. Lett. 114, 220405 (2015)]. Such a thermometer has a particular energy level structure with a single ground state and highly degenerated excited states manifold, with an energy gap proportional to the estimated temperature. In this work, we show that the optimal local quantum thermometer can be realized in an experimentally feasible system of spinless fermions confined in a one-dimensional optical lattice described by the Rice-Mele model. We characterize the system's sensitivity to temperature changes in terms of quantum Fisher information and the classical Fisher information obtained from experimentally available site occupation measurements.

Quantum information scrambling, which describes the propagation and effective loss of localinformation, is crucial for understanding the dynamics of quantum many-body systems. We report the observation of anomalous information scrambling in an atomic tweezer array with dominant van der Waals interaction. We characterize information spreading by an out-of-time-order correlator and observe persistent oscillations inside a suppressed linear light cone for the initial Neel state. Such an anomalous dynamic, which differs from both generic thermal and many-body localized scenarios, originates from weak ergodicity breaking in quantum many-body scarred systems.

In nonlinear topological systems, edge solitons either originate from linear topological edge modes or emerge as nonlinearity-induced localized states without topological protection. While electric circuits (ECs) provide a platform for realizing various types of topological insulators, observation of edge solitons and transitions between them in EC lattices remains a challenging problem. Here, we realize quench dynamics in nonlinear ECs to experimentally demonstrate both topologically nontrivial and trivial edge solitons in a trimer EC lattice and transitions between them. In the weakly nonlinear regime, we observe two types of topologically nontrivial edge solitons that originate from the corresponding linear topological edge states, characterized by the presence of mutually antisymmetric or symmetric peaks at two edge sites. Under strong nonlinearity, topologically trivial edge solitons with antisymmetric, symmetric, and asymmetric internal structures are discovered. The work suggests possibilities for exploring sophisticated nonlinear states and transitions between them in nonlinear topological systems.

Rydberg Atomic REceiver (RARE) is driving a paradigm shift in electromagnetic (EM) wave measurement by harnessing the electron transition phenomenon of Rydberg atoms. Operating at the quantum scale, such receivers have the potential to breakthrough the performance limit of classic receivers, sparking a revolution in physical-layer wireless communications. The objective of this paper is to offer insights into RARE-empowered communications. We first provide a comprehensive introduction to the fundamental principles of RAREs. Then, a thorough comparison between RAREs and classic receivers is conducted in terms of the antenna size, sensitivity, and bandwidth. Subsequently, we overview the recent progresses in RARE-aided wireless communications, covering the frequency-division multiplexing, multiple-input-multiple-output, wireless sensing, and quantum many-body techniques. Moreover, the unique application of RARE in multiband sensing and communication is introduced. Finally, we conclude by providing promising research directions.

Electron spin-qubits in silicon-germanium (SiGe) heterostructures are a major candidate for the realization of scalable quantum computers. A critical challenge in strained Si/SiGe quantum wells (QWs) is the existence of two nearly degenerate valley states at the conduction band minimum that can lead to leakage of quantum information. To address this issue, various strategies have been explored to enhance the valley splitting (i.e., the energy gap between the two low-energy conduction band minima), such as sharp interfaces, oscillating germanium concentrations in the QW (known as wiggle wells) and shear strain engineering. In this work, we develop a comprehensive envelope-function theory augmented by an empirical nonlocal pseudopotential model to incorporate the effects of alloy disorder, strain, and non-trivial resonances arising from interactions between valley states across neighboring Brillouin zones. We apply our model to analyze common epitaxial profiles studied in the literature with a focus on wiggle well type structures and compare our results with previous work. Our framework provides an efficient tool for quantifying the interplay of these effects on the valley splitting, enabling complex epitaxial profile optimization in future work.

Cosmic voids are large, nearly empty regions that lie between the web of galaxies, filaments and walls, and are recognized for their extensive applications in the field of cosmology and astrophysics. Despite their significance, a universal definition of voids remains unsettled as various void-finding methods identify different types of voids, each differing in shape and density, based on the method that were used. In this paper, we present VEGA, a novel algorithm for void identification. VEGA utilizes Voronoi tessellation to divide the dataset space into spatial cells and applies the Convex Hull algorithm to estimate the volume of each cell. It then integrates Genetic Algorithm analysis with luminosity density contrast to filter out over-dense cells and retain the remaining ones, referred to as void block cells. These filtered cells form the basis for constructing the final void structures. VEGA operates on a grid of points, which increases the algorithm's spatial accessibility to the dataset and facilitates the identification of seed points around which the algorithm constructs the voids. To evaluate VEGA's performance, we applied both VEGA and the Aikio Mähönen method to the same test dataset. We compared the resulting void populations in terms of their luminosity and number density contrast, as well as their morphological features such as sphericity. This comparison demonstrated that the VEGA void finding method yields reliable results and can be effectively applied to various particle distributions.

This work provides a proof of concept for the computation of pure gluonic amplitudes in quantum chromodynamics (QCD) on graphics processing units (GPUs). The implementation relies on the Berends-Giele recursion algorithm and, for the first time on a GPU, enables the numerical computation of amplitudes in an arbitrary number of space-time dimensions and over finite fields. This demonstrates the advantages of hardware acceleration, not only for the computation of tree-level amplitudes for real-radiation processes in four dimensions over complex numbers but also for generating loop integrands for virtual corrections in $d$ dimensions over finite fields. The associated computer program is publicly available.

Exceptional points (EPs) are prominent non-Hermitian band degeneracies that give rise to a variety of intriguing and unconventional phenomena. Similar to Weyl and Dirac points, EPs carry topological charges and comply with the celebrated fermion doubling theorems in lattices. Beyond these characteristics, EPs exhibit more exotic topological properties, particularly non-Abelian braiding topologies not seen in conventional degeneracies. Here, we investigate these foundational concepts of EPs in two-dimensional non-Hermitian lattices where the fundamental domain of the Brillouin zone is a Klein bottle, rather than a torus assumed in previous studies. We find that EPs do not necessarily appear in pairs with opposite topological charges in the Brillouin Klein bottle, thus violating the fermion doubling theorem. The violation occurs because, without crossing the boundary, the sum of the topological charges of EPs is in fact an even number rather than zero. Moreover, we uncover unique braiding topologies of EPs that cannot be captured by existing theories. Specifically, the composite braidings around all EPs equals the braiding along the boundary of the Brillouin Klein bottle. This novel braiding topology further confirms the failure of the fermion doubling theorem, and allows us to explore the non-Abelian braidings of EPs beyond the scope of topological charges. Our work highlights the fundamental role of Brillouin-zone topology in non-Hermitian systems.

Material processing with femtosecond lasers has attracted enormous attention because of its potential for technology and industrial applications. In parallel, time-resolved x-ray diffraction has been successfully used to study ultrafast structural distortion dynamics in semiconductor thin films or surface layers. However, real-world processing applications mostly are concerned with bulk materials, which prevents the use of x-ray surface based techniques. For processing applications, a fast and depth-sensitive probe is needed. To address this, we present a novel technique based on ultrafast x-ray dynamical diffraction (UDD) capable of imaging transient strain distributions inside bulk crystals upon laser excitation. This pump-probe technique provides a complete picture of thetemporal evolution of ultrafast distorted lattice depth profiles. We demonstrate the potential of UDD by studying a thin Si single crystal upon single pulse femtosecond optical excitation. Our study reveals that below the melting threshold strong lattice distortions not only longitudinal, but also transversal to the propagation of the strain wave appear on picosecond time scales along the single crystal. The observation of this transversal deformation after laser excitation contradicts previous work that were not able to observed it, what could be related to the high sensitivity of dynamical diffraction with respect to the lattice distortions. The speed of propagation of this ultrafast transversal strain deformation is observed to be slower to the longitudinal sound speed for Si as described in the bibliography.

Single-particle methods based on Kohn-Sham unoccupied states to describe near-edge X-ray absorption (XAS) spectra are routinely applied for the description of K-edge spectra, as there is no complication due to spin-orbit (SO) coupling. L- and M-edge spectra are often addressed via variants of time-dependent density functional theory (TDDFT) based on SO calculations. Here, we present a computationally efficient implementation based on single-particle calculations with core holes within the frozen-core approximation. Combined with a semiempirical energy shift and a fixed spin-orbit splitting for each core level, this allows for a computationally cheap, while overall accurate prediction of experimental spectra on the absolute energy scale. The spectra are compared to about 40 times slower linear-response TDDFT calculations for molecules and show similar or even better match with experiment. An exception are multiplet effects that we analyze in detail and show that they cannot be covered by a single-particle approximation. A similar picture emerges for solids, where good qualitative and sometimes even quantitative agreement to experimental XAS and electron energy-loss spectra is achieved.

Measurement-device-independent quantum key distribution (MDI-QKD), which eliminates all the attacks from the eavesdropper to the measurement party, has been one of the most promising technology for the implementation of end-to-end quantum networks. In practice, the asymmetry of both sources and channels is generally inevitable. Therefore, we propose a theory to analyze the performance when any two MDI users in networks communicates using asymmetric sources in distinct single or multiple temporal modes. As a specific application, we model to obtain the key rate of MDI-QKD with weak coherent pulse source and spontaneous parametric down-conversion source, and compare the performance to the cases with symmetric (i.e. identical) sources. The result demonstrates that the actual performance does not degrade due to the asymmetry of sources. In contrary, it maintains at a good level over the entire distance we study. This work provides a theoretical basis for analyzing and optimizing MDI-QKD networks with asymmetric sources, and thus paving the way for the practical deployment of completely asymmetric MDI-QKD networks.

The main objective of this paper is to introduce a transfer learning-enhanced deep reinforcement learning (DRL) methodology that is able to optimise the geometry of any airfoil based on concomitant aerodynamic and structural integrity criteria. To showcase the method, we aim to maximise the lift-to-drag ratio $C_L/C_D$ while preserving the structural integrity of the airfoil -- as modelled by its maximum thickness -- and train the DRL agent using a list of different transfer learning (TL) strategies. The performance of the DRL agent is compared with Particle Swarm Optimisation (PSO), a traditional gradient-free optimisation method. Results indicate that DRL agents are able to perform purely aerodynamic and hybrid aerodynamic/structural shape optimisation, that the DRL approach outperforms PSO in terms of computational efficiency and aerodynamic improvement, and that the TL-enhanced DRL agent achieves performance comparable to the DRL one, while further saving substantial computational resources.

This work introduces a Hamiltonian approach to regularization and linearization of central force particle dynamics through a new canonical extension of the so-called ``projective decomposition''. The regularization scheme is formulated within the framework of classic analytical Hamiltonian dynamics as a redundant-dimensional canonical/symplectic coordinate transformation, combined with an evolution parameter transformation, on extended phase space. By considering a generalized version of the standard projective decomposition, we obtain a family of such canonical transformations which differ at the momentum level. From this family of transformations, a preferred canonical coordinate set is chosen that possesses a simple and intuitive connection to the particle's local reference frame. Using this transformation, closed-form solutions are readily obtained for inverse square and inverse cubic radial forces, or any superposition thereof. From these solutions, a new set of orbit elements for Kepler-Coulomb dynamics is derived, along with their variational equations for arbitrary perturbations (singularity-free in all cases besides rectilinear motion). Governing equations are numerically validated for the classic two-body problem incorporating the $J_2$ gravitational perturbation.

This paper presents a search for underlying analytic structures among the fundamental parameters of the Standard Model (SM) using symbolic regression and genetic programming. We identify the simplest analytic relationships connecting pairs of these constants and report several notable expressions obtained with relative precision better than 1%. These results may serve as valuable inputs for model builders and artificial intelligence methods aimed at uncovering hidden patterns among the SM constants, or potentially used as building blocks for a deeper underlying law that connects all parameters of the SM through a small set of fundamental constants.