Although some of the human senses can nowadays be replaced by low-cost electronic sensors such as microphones and image sensors, a compact low-cost electronic nose (E-nose) remains elusive. In this work, an E-nose is presented that can capacitively detect volatile organic compounds (VOCs). The E-nose consists of an array of 1024 capacitive microelectrodes on a complementary metal-oxide-semiconductor (CMOS) chip, functionalized by inkjet printing. The pixels are coated with a UV-curable ink and metal-organic frameworks (MOFs: ZIF-8, MIL-101(Cr), MIL-140A) to create chemically diverse microdomains that generate gas-specific response patterns through adsorption-driven dielectric loading. ZIF-8 exhibits the highest response to 2-butanone, whereas the UV-curable layer responds most strongly to toluene; both show low cross-sensitivity to water vapor, enabling operation under humid conditions. After calibration in pure gases, reproducible responses to controlled binary mixtures of toluene and 2-butanone are observed. The device operates at low power, combines a large 1024-pixel array with CMOS integration, and offers application-specific functionalization by inkjet printing, providing both low cost and versatility. By further extending the range of functionalization materials, the E-nose can be applied to analyze a wide variety of gases, with potential applications in safety monitoring, health, agriculture, and robotics.

Magneto-optic photonic systems can violate Kirchhoff's law of thermal emission by breaking Lorentz reciprocity. We develop a dispersive perturbation theory yielding an analytical expression for magneto-optical resonance frequency shifts in plasmonic semiconductors under applied magnetic fields. This expression shows the shift is governed by the overlap of the mode's optical spin density with the magneto-optical material. We use this expression to design a III-V metasurface that achieves nonreciprocal emissivity contrast of 0.8 at only 0.1 T, and demonstrate that the theory can explain order-of magnitude differences in magnetic field sensitivity between different photonic structures.

Certain radiation detectors are `paralyzed' with high input count rates. When applied to count rates close to the event discriminator working rate the one-parameter dead time model fails. Here we present a corrected paralyzable detector model accounting for the event discriminator's finite response time. This two-parameter analytical model, when compared to the experimental data from a commercial x-ray detector, gives an improved description of the input and output count rate relations. Furthermore, it can independently determine the discriminator response time and the pulse shaper dead time, critical parameters for understanding a detector's performance. Finally, this model also provides a post-acquisition pile-up correction that greatly reduces artifacts in high-throughput spectra. In some situations, applying this model to optimize the acquisition and post-acquisition correction allows a user to acquire data an order of magnitude faster without compromising accuracy.

I present a reanalysis of temperature data from a publicly available certified laboratory report that documented the self-discharging behavior of an energy-storage device during 10 days. Graphs of temperature variations of both the tested device itself and the test chamber (fume hood) were given mainly for monitoring without further analysis, and variations in the ambient temperature signal were attributed to "other cells being cycled simultaneously in the same fume hood". I show that the ambient temperature signal alone -- together with some quite mild and reasonable assumptions -- allow to extract previously unpublished information on the simultaneously run test on the other cells: 1) the number of charge/discharge cycles 2) the cycle period, 3) the charge/discharge half-cycle asymmetry, and -- most significantly -- evidence that 4) the mentioned "other device" completed 338 full charge/discharge cycles at 3C rate at room temperature without any detectable thermal degradation signature.

A dichroic dual-angle refractor based on a 2D Huygens' metasurface model is treated by means of resonant-circuit approximations of multiple cells. The whole parameter space of refraction angles is tested for the analytic approximations involved, whereas the realised refraction guides the choice of optimal such angles. Our results indicate the possibility of sythesising a rectangular array of a number of cells which eventually depends on the low-frequency band diffraction limit. Aberrations from the ideal performance are also examined and attributed to certain approximation caveats.

We explore the capabilities of Large Language Models (LLMs) by comparing the way they gather data with the way humans build knowledge. Here we examine how scientific knowledge is made and compare it with LLMs. The argument is structured by reference to two figures, one representing scientific knowledge and the other LLMs. In a 2014 study, scientists explain how they choose to ignore a 'fringe science' paper in the domain of gravitational wave physics: the decisions are made largely as a result of tacit knowledge built up in social discourse, most spoken discourse, within closed groups of experts. It is argued that LLMs cannot or do not currently access such discourse, but it is typical of the early formation of scientific knowledge. LLMs 'understanding' builds on written literatures and is therefore insecure in the case of the initial stages of knowledge building. We refer to Colin Fraser's 'Dumb Monty Hall problem' where in 2023 ChatGPT failed though a year later or so later LLMs were succeeding. We argue that this is not a matter of improvement in LLMs ability to reason but in the change in the body of human written discourse on which they can draw (or changes being put in by humans 'by hand'). We then invent a new Monty Hall prompt and compare the responses of a panel of LLMs and a panel of humans: they are starkly different but we explain that the previous mechanisms will soon allow the LLMs to align themselves to humans once more. Finally, we look at 'overshadowing' where a settled body of discourse becomes so dominant that LLMs fail to respond to small variations in prompts which render the old answers nonsensical. The 'intelligence' we argue is in the humans not the LLMs

Precision measurements of $\beta$ decay transitions offer a promising channel through which the Standard Model (SM) can be probed. There is currently an ongoing effort to increase the precision on measurements of $\mathcal{F}t$-values for superallowed $\beta$ decay transitions between mirror nuclides. These allow for a determination of $V_{ud}$ which is complementary to that obtained from pure Fermi $0^+ \rightarrow 0^+$ transitions. The Superallowed Transition BEta-NEutrino Decay Ion Coincidence Trap (St. Benedict), under construction at the Nuclear Science Laboratory (NSL) at the University of Notre Dame, seeks to measure the Fermi-to-Gamow-Teller mixing ratio for transitions between mirror nuclei in order to expand the list of nuclides from which $V_{ud}$ can be extracted. Production and selection of the species of interest will be done in-flight, using the \textit{TwinSol} magnetic separator system. The first element of St. Benedict will be a large volume gas catcher which will thermalize radioactive ion beams for low energy delivery to the rest of the system. Offline commissioning of this gas catcher has been completed using an internal potassium source, and the device demonstrated a transport efficiency upwards of 95\% for pressures of 66 mbar and lower.

Dual-phase time projection chamber (TPC) that employs a multi-ton-scale liquid xenon (LXe) target mass is a pioneering detector technology to search for dark matter. Beyond its advantage in dark matter direct detection efforts, the natural xenon target allows it to search for the neutrinoless double-beta decay ($0\nu\beta\beta$) process, which would violate lepton number conservation and indicate that neutrinos are Majorana particles. However, such $0\nu\beta\beta$ searches have been limited by gamma-ray backgrounds originating from the detector materials. In this work, we designed an augmented convolutional neural network (A-CNN) model to extract additional event-topology information from detector data. Using simulation and calibration data from XENONnT, a leading LXe TPC experiment, our model achieved over 60% background rejection while maintaining 90% signal acceptance. This rejection power improves XENONnT's projected sensitivity of the $^{136}$Xe $0\nu\beta\beta$ search by about 40%. The implementation of A-CNN in the data analysis of future liquid xenon observatories, such as XLZD, will further enhance their sensitivities for $0\nu\beta\beta$ with $^{136}$Xe.

The effective design and operation of electron emitters is the core of critical technologies such as high-resolution electron imaging and spectroscopy or X-ray production for medical imaging. Despite 100 years of theoretical development in thermo- and field-electron emission models, the analysis of experimental data and design of electron emitters remains an art more than a science. This is due to the many processes that are involved in electron emission, which result in an extremely complex phenomenon. Here we describe and develop the Methods for an Electron Emission Digital Twin (MEEDiT), which integrates state-of-the-art thermo-field electron emission models and experimental data characterisation. By applying MEEDiT to silicon electron emitters, we demonstrate an approach that bridges the gap between simple experimental measurements and 'hidden' physical quantities like temperature and field enhancement. MEEDiT provides the physical consistency of a 3D simulation with the speed of a neural network, enabling resource-effective, real-time characterization and the extraction of critical data that is otherwise inaccessible during operation.

Microswimmers in suspension exhibit collective swimming behaviour, forming various self-organised structures including ordered, aggregated, and turbulent-like structures. When mixed with passive particles phase-separation is known to occur, but due to the difficulty of accurately handling many-body hydrodynamic interactions, the formation of self-organised structures in mixed suspensions has remained unexplored so far. In this study, we investigate the dynamics of mixed dense suspensions of spherical bottom-heavy squirmers and obstacle spheres using Stokesian dynamics in three dimensions, taking hydrodynamic interactions into account. The results show that without an external orientating mechanism the formation of orientational order is in general disturbed by the presence of passive spheres. An initially phase-separated state is metastable for neutral or puller squirmers at high packing densities. When the squirmers are bottom-heavy, phase-separation can occur dynamically in some cases, notably a fibrillar kind of separation for neutral squirmers and pullers at medium densities. We also observed a novel form of lamellar phase-separation for pullers at high densities with strong bottom-heaviness, with a sandwich-like structure consisting of a layer of passive particles pushed by a layer of swimmers, followed by a gap. These results indicate that microstructure and particle transport undergo significant changes depending on the type of swimmer, highlighting the importance of hydrodynamic interactions. These insights allow for a deeper understanding of the behaviour of active particles in complex fluids and to control them using external torques.

[Abridged] In the late nineteenth century, Mars emerged as one of the most intensively reported astronomical objects in the popular press, driven by favourable oppositions, improved telescopic capabilities and growing speculation regarding planetary habitability. I examine how Mars was interpreted in Australian newspapers between the 1870s and 1899, focusing on the ways in which astronomical knowledge was framed, contextualised and debated within a colonial media environment. Drawing on a large collection of digitised newspaper articles, I analyse how observational authority, instrumental credibility and individual expertise were harnessed in press reporting. The paper situates Australian Mars coverage within a global network of scientific communication dominated by metropolitan centres in Europe and North America, while highlighting the distinctive role played by southern-hemisphere visibility. Australian observatories and observers were frequently positioned as contributors of confirmatory observation rather than interpretive leadership, reinforcing a pattern of locally grounded but internationally oriented scientific engagement. The analysis traces a shift from early emphasis on disciplined observation and measurement to later periods characterised by contested interpretations, particularly surrounding the so-called Martian "canals" and the speculative claims advanced by personalities such as Percival Lowell in the USA. By examining how newspapers mediated between observational astronomy, engineering analogies and popular imagination, this study contributes to a broader understanding of how planetary science entered public discourse beyond metropolitan centres. In doing so, it underscores the active role of colonial newspapers in shaping scientific meaning and situates Australian Mars reporting within the wider history of nineteenth-century astronomical culture.

The central limit theorem provides the theoretical foundation for the universality of the normal distribution: under broad conditions, the asymptotic distribution of a sum of independent random variables approaches a Gaussian. Yet, physical systems described by positive random variable -- from earthquakes to microbial growth to epidemic spreading -- consistently exhibit gamma rather than Gaussian statistics -- what leads to field-specific mechanistic explanations that are non robust to small changes in the model details. We show that gamma distributions emerge naturally from large deviation theory when Padé approximants replace polynomial expansions of the derivative of the scaled cumulant generating function, respecting positivity constraints that the central limit theorem violates. Gamma universality thus emerges as the constrained analog of Gaussian universality, providing a mechanism-free explanation for its pervasive appearance across different disciplines.

Scintillation light, produced alongside ionisation charge from particle interactions, plays a critical role in liquid argon time projection chamber (LArTPC) detectors. A detailed understanding of its production and detection mechanisms is essential for robust calibration, systematic uncertainty evaluation, and physics analysis. This article describes the MicroBooNE light simulation, light-based triggering schemes, photomultiplier tube gain calibration, light response stability, and light-based systematic uncertainties over the course of five years of data collection. In addition, we present a measurement of scintillation light triggering efficiency, focusing on the lowest-light regime relevant to rare-event searches and low-energy neutrino interactions. Finally, we discuss two notable observations in MicroBooNE's data, both reported here for the first time: an approximately 50% decline in MicroBooNE's light yield over time, concentrated in the first two years of running; and a higher than expected O(200 kHz) rate of single photoelectron noise. The results presented provide an important benchmark of long-term light detection performance in LArTPC neutrino detectors.

Three-dimensional (3D) photoacoustic computed tomography (PACT) is a powerful noninvasive biomedical imaging modality that provides volumetric data for structural and functional assessment \textit{in vivo}. To maximize angular coverage and mitigate limited-view artifacts, modern 3D PACT systems frequently employ hemispherical transducer arrays. While substantial effort has been devoted to improving image quality through post-processing algorithms, the intrinsic impact of the hardware array layout on the baseline image quality remains underexplored. In this study, we systematically investigate how the spatial sampling characteristics of hemispherical array distributions affect imaging performance under fixed hardware constraints. We propose a uniform-spacing sampling criterion to generate three representative array distributions and evaluate their performance using quantitative metrics across static, noise-perturbed, reduced-element, and rotational acquisition scenarios. Across these diverse testing regimes, the Fibonacci distribution consistently demonstrates superior structural robustness and more globally balanced reconstruction quality, a result we attribute to its highly isotropic sampling properties. These findings demonstrate that an optimal hardware-level sampling strategy is critical for maintaining global reconstruction stability. Ultimately, this framework establishes a rigorous quantitative methodology for benchmarking hemispherical array distributions and provides practical design guidance for the future development of 3D PACT systems.

Using a closed quantum optical coupled-dipole model, we investigate why sub-sevenfold symmetries are likely absent in the stacked-ring scaffolds of light-harvesting 2 (LH2) complexes in purple photosynthetic bacteria.

We report several technical approaches that significantly improve the performance of a vapor-cell atomic electrometer operating in the quasi-DC frequency domain ($\ll$ 1 kHz). With a very small active volume of approximately 11 mm$^3$ inside the vapor cell, we demonstrated a noise floor for electric field (E-field) sensitivity ranging from 0.2 to 7.7 mV/m$\sqrt{\rm Hz}$ for a frequency band of 1--100 Hz. Our work utilizes only a bare vapor cell for electrometry, without any metal parts or electrodes, to ensure minimal distortion of the measured E-field and to minimize the effective sensing volume for high spatial resolution. The E-field-sensitive atomic state (Rydberg state) is excited and read out optically, maximizing the simplicity of the system design and enabling the miniaturization of quasi-DC E-field sensors for potential applications, such as diagnostics of electronics without physical contact, communications in and below the super-low frequency (SLF) band, proximity detection, remote activity surveillance, tracing charge signatures, and research in bioscience and geoscience.

We present a widefield two-dimensional electronic spectroscopy microscope (2DESM) that integrates multidimensional coherent spectroscopy with optical imaging, enabling femtosecond temporal and micrometer spatial resolution. The broadband coverage (1.4-1.8 eV) allows the direct acquisition of spatially resolved two-dimensional electronic spectroscopy (2DES) maps of relevant near infrared excitations without the need for spatial scanning. By capturing both spectral and spatial domains simultaneously, 2DESM overcomes limitations of pump-probe microscopy and scanning 2DES, providing access to decoherence dynamics, inhomogeneous broadening, and coherent coupling in heterogeneous systems. As a proof-of-concept we performed 2DESM measurements on bilayer WSe2 encapsulated in hBN, revealing distinct spatial variations in excitonic dynamics. These results validate the ability of 2DESM to link local environments with ultrafast coherent processes and establish 2DESM as a versatile platform for probing quantum coherence, many-body interactions, and non-local energy transfer in two-dimensional materials, heterostructures, and micrometer-scale optoelectronic devices.

We apply the recently introduced aperiodic defect model (ADM) to a negatively charged monovacancy in a phosphorene monolayer. In contrast to conventional supercell approaches, the ADM treats a single defect embedded in the true non-defective crystalline mean field thereby avoiding spurious defect-defect interactions and the need for charge corrections. At the same time, it effectively reduces the calculation to a fragment, enabling the use of high-level molecular electronic-structure methods. Converging the Hartree-Fock and correlation contributions to the thermodynamic limit yields a benchmark CCSD(T)/POB-TZVP-rev2 formation energy of 0.91 eV for the negatively charged monovacancy in the (5|9) configuration. The excitation energy to the lowest singlet excited state of this defect at the EOM-CCSD/POB-TZVP-rev2 level is found to be 1.95 eV. Overall, the ADM provides a highly promising route towards quantitatively accurate and systematically improvable descriptions of defects in solids and on surfaces, bridging the gap between solid-state physics and molecular quantum chemistry.

Wavefront-free behavior and the resulting quantification difficulties are intrinsic limitations of vibrothermography due to the diffusive nature of thermal fields. This work proposes a generalized virtual wave reconstruction framework to address the absence of propagation features in thermal diffusion-wave fields and its impact on quantitative defect characterization. Unlike conventional virtual wave formulations restricted to Dirac-type excitations, the proposed approach establishes a rigorous spatiotemporal mapping between diffusion-wave fields and virtual wave fields under arbitrary heat-generation conditions without simplifying assumptions on thermo-mechanical coupling. The resulting ill-posed inversion problem is solved using truncated singular value decomposition (T-SVD) and the alternating direction method of multipliers (ADMM) to enhance numerical stability and suppress noise amplification. Numerical simulations demonstrate that the reconstructed virtual wave fields recover propagation characteristics absent in temperature distributions, leading to improved defect boundary definition, contrast enhancement, and depth-resolved analysis. Experiments on CFRP laminates validate the robustness of the approach and show significantly improved signal-to-noise ratio, spatial clarity, and defect size estimation compared with conventional thermographic processing methods.

We investigate the competition between horizontal convection (HC) and Rayleigh-Bénard convection (RBC) in a fluid layer subject to a uniform destabilizing buoyancy flux at the bottom and a horizontally varying buoyancy distribution at the top. The RBC forcing imposes negative horizontal mean vertical buoyancy gradients at the top and bottom of the fluid layer. But if the HC forcing is sufficiently strong then the volume averaged vertical buoyancy gradient, $\langle b_z \rangle$, is positive i.e.~opposite in sign to destabilizing RBC buoyancy gradients at the boundaries. If $\langle b_z \rangle>0$ we say that the layer has been ''restratified''. Using scaling analysis based on power integrals together with two-dimensional direct numerical simulations at Rayleigh numbers up to $10^{10}$, we identify two cases: a neutral stratification state, in which HC first offsets RBC so that $\langle b_z \rangle = 0$, and a strong stratification regime, in which HC dominates and $\langle b_z \rangle$ is opposite in sign, and greater in magnitude, than the prescribed destabilizing vertical buoyancy gradient at the layer boundaries. For the range of parameters explored in this study, we derive scaling laws for the onset of these regimes in terms of the horizontal and vertical flux Rayleigh numbers, $\RaH$ and $\RaV$, finding $\RaHN \sim \RaV^{4/5}$ for the neutral state and $\RaHstrg \sim \RaV$ for the onset of strong stratification. The results highlight the controlling role of the top boundary layer in setting the mean stratification and clarify the conditions under which HC suppresses RBC. These findings are relevant to geophysical environments such as subglacial lakes, and the oceans of Snowball Earth and icy moons, where bottom heating and horizontal buoyancy variations jointly shape ocean stratification.

Global and local linear gyrokinetic simulations of 42 pedestal equilibria from three DIII-D discharges are used to investigate pedestal stability and its impact on pedestal structure and confinement. Microtearing modes (MTMs) and kinetic ballooning modes (KBMs) represent the main ion scale instabilities. For all three discharges, MTMs lie near a stability boundary in the mid-pedestal and exhibit threshold behavior, with growth rates increasing at and beyond pre-ELM pressure gradients. Pedestal MTMs retain conventional signatures but also show enhanced particle transport and partial density-gradient drive, indicating they can constrain pedestal {\it pressure} rather than electron temperature alone. KBMs are typically second-stable in this region due to low magnetic shear and large pressure gradients, though they can become active near the pedestal foot where magnetic shear is higher. These findings suggest MTMs play the role of inter-ELM pressure limit in the mid-pedestal when KBM is second stable. A preliminary quasilinear mixing-length transport model, with properly tuned free parameters, reproduces experimental temperature and density profiles when coupled to ASTRA. When applied to a case with doubled separatrix density, the model predicts reduced pedestal pressure consistent with ITPA H-mode confinement trends, attributable to increased MTM and ETG transport. These results clarify pedestal-limiting mechanisms and establish a physics-based link between separatrix conditions, pedestal structure, and global confinement. This work lays the foundation for new predictive modeling capabilities for core-edge integration in burning plasma regimes.

Laser-driven proton acceleration provides a powerful route for generating ultrashort, high-charge proton beams. Many applications, including secondary neutron sources and inertial fusion, benefit from tight proton beam focusing. Concave targets offer a robust solution, yet the scaling of proton focusing with laser and target parameters remains poorly understood. Here, we present a numerical study of laser-driven proton acceleration and focusing from hemispherical targets using the fully kinetic, relativistic Particle-In-Cell code EPOCH. We focus on the sub-picosecond laser-pulse regime (duration $\lesssim 10^2$ fs), centering on the laser parameters of our recent experiment at the CSU ALEPH laser facility. We investigate the proton acceleration mechanisms, characterize proton focusing, and assess how focal spot parameters scale with laser and target parameters. We identify Target Normal Sheath Acceleration as the dominant mechanism, supplemented by a secondary post-acceleration stage near the geometrical center of the hemisphere. We demonstrate that both the proton focal spot size and focal plane position scale approximately linearly with the hemisphere radius, with the focal plane consistently located downstream of the geometrical center. The opening angle of the concave target mainly affects the proton beam waist. Energy-dependent proton focusing is interpreted as a consequence of the evolving curvature of the accelerating structure, which departs from the target curvature. Evidence for self-similar proton focusing is found in the regime of nearly uniform target irradiation.

The results of numerical simulations of the Hasegawa-Wakatani equation demonstrate that, similarly to decaying turbulence in 2D fluids, at a small electron adiabaticity parameter, the resistive-drift-wave (RDW) turbulence is dominated by the vortices. Occasionally, vortices with different signs become coupled and propagate ballistically as a dipole over a large distance, entraining plasma density. Such ballistic motion of the vortex-density clumps in radial direction provides a non-local feature of plasma transport associated with the RDW turbulence. Large magnitude of plasma parameters perturbations associated with clumps can initiate other plasma instabilities and nonlinear phenomena.

Visible photonic integrated circuits underpin applications ranging from AR/VR to quantum control, yet lack a high-resolution, nondestructive diagnostic comparable to the optical frequency-domain reflectometry used in infrared silicon photonics. Here we adapt spectral-domain optical coherence tomography to measure guided-mode back-reflections in visible PICs. Broadband visible light injected into a circuit generates back-reflections that interfere with a depth-referencing local oscillator, and the resulting spectral fringes are recorded on a spectrometer. We validate the approach by resolving multiple round-trip echoes in a waveguide-coupled ring resonator using only single-port access. We then extend it to circuits integrated with diamond quantum micro-chiplets, clearly resolving input and output facets as well as PIC--QMC transition regions. The system achieves shot-noise-limited sensitivity, 50 dB dynamic range, 8 um axial resolution in silicon nitride, and a 2 mm imaging depth at 6 dB roll-off. SD-OCT therefore provides a practical, high-resolution diagnostic for visible PICs that uses a broadband probe source and requires only single-port optical access, enabling rapid characterization of propagation loss, backscattering, and dispersion.

The optimization of the dynamic aperture (DA) of a storage ring is typically a non-convex problem with multiple local optima. Recent studies showed that reducing the variation of resonance driving terms (RDTs) along the longitudinal position improves DA very effectively, as the reduction in the longitudinal variation of lower-order RDTs suppresses higher-order nonlinear terms. Therefore, minimizing the longitudinal variation of third-order RDTs is crucial for DA optimization. In this paper, we prove that the longitudinal variation of third-order RDTs, quantified using their RMS value $f_{3,\mathrm{rms}}$ at sextupole locations, is a special convex function. In the space of sextupole strengths, the iso-surfaces of $f_{3,\mathrm{rms}}$ are a series of concentric and coaxial ellipsoidal surfaces, with the central position possessing minimum $f_{3,\mathrm{rms}}$. The scanning results of a storage ring lattice show a strong consistency between the distributions of $f_{3,\mathrm{rms}}$ and DA, indicating that the optimization of DA can be regarded as a roughly approximate convex optimization problem. Based on this, a fast DA optimization method based on particle tracking is developed, where a high-quality initial population for an intelligent algorithm is generated with a Gaussian distribution based on the geometric structure of $f_{3,\mathrm{rms}}$.

This study experimentally investigates the aerodynamic drag reduction capabilities of distributed micro-roughness (DMR) coatings on a streamlined model, utilising the 1-m magnetic suspension and balance system (MSBS) at Tohoku University. Previous direct numerical simulations (DNS) indicated that DMR can mitigate turbulent-energy growth by suppressing Tollmien--Schlichting (TS) waves and influencing the breakdown of streamwise vortices. The present work provides the first experimental validation of these effects using an interference-free MSBS, which is essential for accurate measurement in the laminar and transitional regimes. A streamlined model was tested with two rows of artificial tripping tape to induce transition; the DMR height was approximately 1% of the local boundary layer thickness, significantly smaller than typical roughness elements. Direct aerodynamic drag measurements using the MSBS revealed a substantial reduction of up to 43.6% within the transitional flow regime. Crucially, integrated analysis using wall-resolved large-eddy simulations (LES) and dynamic oil-flow visualisation confirmed that this benefit does not mainly originate from the suppression of flow separation. The LES drag decomposition established that the total pressure-drag budget is subordinate to skin friction, a finding complemented by oil-flow observations, which revealed qualitatively similar flow patterns regardless of the surface condition. Consequently, the observed drag reduction is primarily ascribed to friction drag reduction achieved through the modification of the boundary layer state. These findings provide compelling experimental evidence for the efficacy of DMR and offer valuable insights for optimising surface designs for passive flow control.

We introduce the FLASH haloscope experiment and present its electronic read-out system, currently under development. FLASH searches for Dark Matter (DM) particles and High-Frequency Gravitational Waves (HFGWs) using two cryogenic resonant cavities to scan the radio frequency spectrum between 117 and 360 MHz, looking for signals as weak as $10^{-22}$ W. The signal readout uses Microstrip Superconducting Quantum Interference Amplifiers (MSAs) as low-noise amplifiers and Software-Defined Radio (SDR) techniques to acquire, preprocess and reduce the physics signal into a format suitable for permanent storage and offline analysis.

We present an improved version of the sum-of-Gaussians tensor neural network (SOG-TNN) architecture for solving many-electron Schrödinger equation for one-dimensional soft-Coulomb systems. Model reduction techniques are introduced to reduce the number of tensor-factorized bases under the SOG approximation of the kernel. The Slater determinant ansatz is employed so that the anti-symmetric property of the wave function can be strictly preserved. Numerical results show that the SOG-TNN achieves high accuracy with remarkably small basis sizes. Robust spectral convergence with respect to the basis size is also observed, consistently characterized by a mixed algebraic-exponential model for the error decay. These findings validate that the SOG-TNN architecture provides an ultra-efficient and low-rank representation of complex multi-electron wave functions, shedding light on high-fidelity quantum calculations in larger-scale many-electron systems.

A robust composite mean velocity profile is developed for turbulent boundary layers (TBLs) subjected to adverse pressure gradients (APGs), extending the composite formulation for generic pressure-gradient TBLs proposed by \citeauthor{nickels} (\textit{J.\ Fluid Mech.}, vol.\ 521, 2004). Several modifications are introduced to capture key features of APG flows. A new parameter accounts for pressure-gradient history effects in the wake region, a velocity-overshoot function is incorporated in the inner region, and the wake function is reformulated using an independent, physically motivated definition of boundary-layer thickness. A compilation of APG TBL datasets from the literature, including the new dataset presented in Part~1, is used to assess and refine the formulation. The resulting composite profile contains three physically meaningful parameters that capture pressure-gradient effects on the mean velocity profile, determined through nonlinear curve fitting. These parameters provide a framework for identifying `well-behaved' APG TBLs and quantifying the strength of pressure-gradient history effects. The profile also enables reliable estimation of the friction velocity and boundary-layer thickness in well-behaved APG TBLs, providing a practical tool for scaling analyses when these quantities are not directly measurable. Its analytical form yields improved estimates of mean velocity gradients, facilitating evaluation of the indicator function and identification of inflection points. Finally, the formulation predicts both the coefficients and spatial extent of the logarithmic region of the mean streamwise velocity profile, enabling assessment of its universality in high-Reynolds-number APG TBLs. This shows that the von K'arm'an coefficient approaches an invariant value of $\kappa \approx 0.39$ at sufficiently high Reynolds numbers, independent of pressure-gradient effects.

Optical skyrmions are structured vector fields with nontrivial polarization topology and subwavelength-scale features. One common approach to generating optical skyrmions is the superposition of a zeroth-order Bessel beam and a higher-order Bessel beam carrying orbital angular momentum, with each beam possessing an orthogonal circular polarization state. However, creating such complex beams typically requires bulky free-space optical setups; therefore, recent efforts have focused on compact optical skyrmion generators based on metasurfaces. Nevertheless, achieving the degrees of freedom required for simultaneous phase and polarization control remains challenging because of the limited design flexibility of conventional meta-atoms. Here, we address this challenge by employing an inverse-design approach and demonstrate a single-layer metasurface that generates high-fidelity optical skyrmions. We employ an adjoint-based topology-optimization method to design a silicon metasurface that converts an incident beam into an optical skyrmion without the need for additional optical components. The optimized metasurface generates an optical skyrmion with skyrmion number $(N_\mathrm{sk}) = 0.970$. This work demonstrates that inverse design can be a promising route to compact skyrmion generators, and our approach provides a basis for near-field particle manipulation and the generation of independent topological bits in dense photonic integration.

Low-frequency magnetic fields carry vital information for neuroscience, navigation, and Earth science. However, they are generally weak, making it challenging to measure them with compact, room-temperature magnetometers. To overcome this challenge, we combine an on-chip optomechanical magnetometer with a high-permeability flux concentrator. Beyond boosting sensitivity and bandwidth, exploiting the concentrator's nonlinear response converts low-frequency magnetic fluctuations into higher-frequency signals where the sensor is intrinsically most responsive. This sidesteps the technical noise that has long constrained the application of optomechanical magnetometry at low frequencies. Our measurements show order-of-magnitude improvements in sensitivity and extend performance into the sub-hertz regime, achieving below 20 nT Hz$^{-1/2}$ down to 3 Hz and less than 100 nT Hz$^{-1/2}$ at 0.1 Hz. Because this approach requires no redesign of the underlying architecture, it can be readily applied across magnetometer technologies, opening the way to practical low-frequency sensing for applications from brain activity mapping to undersea navigation and biomedical diagnostics.

Machine vision, including object recognition and image reconstruction, is a central technology in many consumer devices and scientific instruments. The design of machine-vision systems has been revolutionized by the adoption of end-to-end optimization, in which the optical front end and the post-processing back end are jointly optimized. However, while machine vision currently works extremely well in moderate-light or bright-light situations -- where a camera may detect thousands of photons per pixel and billions of photons per frame -- it is far more challenging in very low-light situations. We introduce photon-aware neuromorphic sensing (PANS), an approach for end-to-end optimization in highly photon-starved scenarios. The training incorporates knowledge of the low photon budget and the stochastic nature of light detection when the average number of photons per pixel is near or less than 1. We report a proof-of-principle experimental demonstration in which we performed low-light image classification using PANS, achieving 73% (82%) accuracy on FashionMNIST with an average of only 4.9 (17) detected photons in total per inference, and 86% (97%) on MNIST with 8.6 (29) detected photons -- orders of magnitude more photon-efficient than conventional approaches. We also report simulation studies showing how PANS could be applied to other classification, event-detection, and image-reconstruction tasks. By taking into account the statistics of measurement results for non-classical states or alternative sensing hardware, PANS could in principle be adapted to enable high-accuracy results in quantum and other photon-starved setups.

Voltage breakdown in high-voltage pulsed vacuum systems arises from nonlinear multiscale interactions among circuit dynamics, kinetic plasma evolution, and ion-induced secondary electron emission (SEE) at electrode surfaces. Although circuit-plasma co-simulation frameworks couple lumped circuits with particle-in-cell (PIC) solvers, most neglect energy-resolved SEE and its feedback to both plasma and circuit, limiting predictive capability. We present a self-consistent framework for multiscale circuit-plasma coupling that incorporates ion-energy-dependent SEE into the electrode boundary of an electrostatic PIC solver. The emitted electron flux is included in the surface charge update, leading to a modified Poisson boundary condition that couples plasma and circuit within a unified formulation. Two integration strategies are developed: (i) a fully implicit strict coupling scheme solving the plasma-circuit system monolithically, and (ii) a weak coupling scheme based on operator splitting, compatible with SPICE solvers and enabling partitioned time integration with one-step lag. The framework is applied to a Tesla-transformer-driven vacuum capacitor with ion injection. Results show that SEE alters surface charge evolution, triggering rapid voltage collapse and sustaining a near-zero-voltage plateau, while SEE-free models fail. Agreement between strict and weak coupling confirms robustness. The method provides a unified framework for predictive simulation of multiscale circuit-plasma interactions.

Finite-difference time-domain (FDTD) simulations often involve physical quantities spanning multiple orders of magnitude, such as the speed of light or electromagnetic field amplitudes. The standard practice for maintaining numerical accuracy in many FDTD implementations is to use 32-bit or 64-bit floating-point values to represent the electric and magnetic fields. However, this approach is not always optimal when recording field values, particularly during time-reversible gradient computation where electric and magnetic field values need to be saved at the boundary of the simulation domain. Since this memory bottleneck is often the limiting factor in time-reversible inverse design for nanophotonics, we present two field optimizations for enhancing memory efficiency in FDTD simulations. Using a smaller bit-width representation of field values as well as interpolation, we achieve similar accuracy at lower memory cost. This approach is particularly beneficial for GPU-accelerated computing, where reduced-precision data types are increasingly preferred due to their computational efficiency and prevalence in machine learning frameworks. We integrate our approach into FDTDX, an open-source, differentiable FDTD solver that natively supports time-reversible gradient computation. Our approach is especially important for future developments towards large-scale open-source simulations, which are critical for advancing computational nanophotonic applications.

The sensitivity of Ramsey interferometry experiments is governed by the interplay between the beam phase-space distribution and the magnetic field environment through which the spins propagate. Quantitative optimisation thus requires a consistent treatment of optics, magnetics and spin dynamics. We present a simulation framework that enables such an analysis by combining neutron optics simulations in McStas, magnetic field modelling in COMSOL and spin-dynamics simulation in the new RamseyProp program. We describe how important experimental parameters such as adiabaticity, flip angle distributions and Ramsey fringe contrast can be studied. The code is being applied to design an experiment to search for axion-like particles at the European Spallation Source (ESS). We examine how the pulsed time structure of the ESS can be exploited to perform Ramsey interferometry on a broad neutron velocity spectrum. In the absence of velocity or timing restrictions, the standard deviation of the spin flip angle at zero detuning can be reduced from 0.67 to 0.17 radians using time-dependent amplitude modulation. Similarly, the phase sensitivity can be improved by a factor of 4 for a 10 m long setup starting 15 m from the ESS moderator.

We present a system for generating arbitrary, triaxial magnetic waveforms with a spectral content spanning from DC to tens of kHz, a critical capability for quantum control and spin manipulation. To compensate for amplifier-coil dynamics, we implement a data-driven approach to identify a numerical compensation model. The method parametrizes the system response using a Finite Impulse Response (FIR) filter calibrated on the specific waveform to be generated. The application of a driving signal designed via frequency-domain inversion of the identified model enables the synthesis of complex field sequences with sharp transitions between static and single- or multi-frequency temporal segments. The work is validated with experimental results demonstrating waveform fidelity and transient performance, thereby showcasing the precision and feasibility of the method.

We derive the conserved energy-like quantity and ensemble measure for Martyna--Tobias--Klein (MTK) barostats in which only a restricted subset of the cell degrees of freedom are active. In the standard fully anisotropic MTK formulation, the number of barostat degrees of freedom is $d^{2}$, where $d$ is the spatial dimension. When only $n_c$ axes of the cell matrix are allowed to fluctuate, the conserved energy-like quantity retains the same functional form but with $d^{2}$ replaced by $n_c$ in every term that counts barostat degrees of freedom. The derivation builds on the generalized Liouville framework for non-Hamiltonian systems and the existing MTK integration machinery. We verify that this quantity is exactly conserved, show that the resulting dynamics samples the isothermal--isobaric ensemble restricted to the submanifold of cell shapes in which inactive components are held fixed, and provide a complete Liouville-operator-based integration scheme for the masked MTK variant.

In several natural and engineering systems, changes in control parameters can trigger bifurcations that lead to sustained or growing periodic oscillations, indicating the onset of oscillatory instabilities. Such emergent behaviour often results from positive feedback between interacting subsystems, resulting in large-amplitude oscillations that can be detrimental. Several precursors are available to provide early warning of an impending oscillatory instability. In reality, practical systems may exhibit different sequences of bifurcations, including a primary bifurcation to an oscillatory state that may be either continuous or abrupt, followed by an abrupt secondary bifurcation, and further transitions beyond the secondary bifurcation. Existing precursors for oscillatory instabilities typically forewarn the onset of the primary bifurcation to an oscillatory state and tend to saturate once the system enters the oscillatory regime. Notably, primary bifurcations often involve lower amplitudes compared to the more severe states after secondary bifurcation. In this study, we propose a methodology based on spectral visibility graphs to get forewarning for both primary and secondary bifurcations. The method inherently captures the evolution of the harmonic content of the signal relative to other frequency components. The approach employs tuning of a single sensitivity parameter to detect different sequences of bifurcation. We demonstrate the usefulness of our method for thermo-acoustic and aero-acoustic instabilities in multiple engineering systems involving turbulent flow and reactions. Our methodology can help systems prepare in advance or even avoid undesirable transitions. Tuning the sensitivity parameter allows adaptive, risk-based warnings, ensuring high performance without tipping into undesirable regimes.

Geomaterials often exhibit progressive creep characterized by an initial decelerating phase, frequently followed by an extended period of approximately constant deformation rate, and ultimately an accelerating regime leading to catastrophic failure. Despite extensive research, the timing of rupture and its relationship to the different creep phases, particularly in natural systems, remain poorly constrained. Here, we compile creep data from laboratory experiments on rocks, composites, papers, and glasses, together with observations from field systems including landslides, rockfalls, and glaciers. We find that the duration of the early-stage creep, marked by the transition to the minimum (or quasi-stationary) deformation rate, correlates nearly linearly with the time to rupture over five orders of magnitude. This unified scaling highlights that the early-time dynamics reflect the full evolution toward failure, providing a simple and robust framework for forecasting rupture across laboratory and natural systems.

Snell law is traditionally regarded as a hallmark of phase-propagating phenomena such as optical, acoustic, elastic, electromagnetic, and quantum waves. In contrast, purely diffusive processes, such as Fourier heat conduction and chemical diffusion, are generally considered incapable of exhibiting refractive/reflective behavior. In this letter, we demonstrate that although diffusion waves including thermal diffusion, mass diffusion, Lindblad quantum diffusion, and electromagnetic diffusion do not follow Snell law in either time or frequency-domain, nevertheless they obey a spectral form of Snell law which reveals a hidden analog of wave refraction/reflection within the mathematical structure of diffusion dynamics. Remarkably, the spectral refraction ratio is governed not by the diffusion coefficient itself but by the constitutive relations of the media across the interface, establishing a new physical paradigm for diffusion-wave fields. Importantly, while each spectral eigenmode satisfies a rigorous Snell-type refraction relation, the inverse Fourier-Laplace transformation mixes these modes and suppresses any persistent real-space refraction angle, thereby reconciling the modal-level directionality with the long-standing absence of geometric refraction in diffusive systems.

Wide-field mid-infrared photothermal (MIP) imaging offers rapid labelfree chemical contrast for biomedical and polymer analysis. However, its field of view (FOV) is limited by the pulse intensity of conventional infrared lasers. Here, we present a wide-field MIP microscope that uses a high-power free-electron laser (FEL) rather than a quantum cascade laser (QCL) as the pump source to achieve a substantially larger FOV. Both implementations use counter-propagating beam paths with a 450 nm LED as the probe source and a CMOS camera that records images using a virtual lock-in detection scheme. QCL nanojoule pulse energies enables FOV of around 45 micrometers for widefield MIP imaging with a sub-micrometer resolution for polystyrene beads, Mycobacterium tuberculosis infected fixed tissues, and laryngeal cancer cryosections. IR spectra in the range of 1000-1800 wavenumbers can be reconstructed by tuning the QCL. FEL pulse energies of up to microjoules expand the FOV by a factor of nearly 20 as demonstrated by wide-field MIP imaging of polystyrene beads, single cells, and murine brain tissue. We discuss current challenges and further improvements to implement high-power IR lasers for wide-field MIP imaging with even larger FOVs in the context of biomedical research and diagnostics.

We consider a round turbulent jet grazing a rectangular plate angled at $45^\circ$. Through sound pressure measurements, the tonal dynamics associated with jet-edge interaction are explored in a parameter space comprising jet Mach number, $M_j$, and plate radial position, $R/D$. A variety of spectral signatures are observed and classified. The classification - based on analysis of power-spectral density and bicoherence, and on the resonance model proposed by Jordan et al. (2018) - comprises: broadband spectra; tonal spectra associated with purely linear frequency-selection mechanisms; tonal spectra associated with both linear and non-linear frequency selection. The classification identifies regions in the parameter space ($M_j$, $R/D$); and clarifies mechanisms underpinning regime changes. The linear frequency selection (LFS) regime comprises multiple tones, with no evidence of triad interaction. A regime involving non-linear frequency selection emerges from this state, with the strong amplification of one LFS tone, which then generates multiple harmonics. Intermediate regimes are identified involving weaker, non-harmonic triadic interactions where two LFS tones interact to generate a third tone. In addition to these mechanisms a mode-switching mechanism is identified at $M_j$ = 0.84 and shown to result from the cut-on of a new upstream-travelling wave at that Mach number. The mode-switch is found to be remarkably robust, occurring in a repeatable manner over a Mach-number increment of 0.01 regardless of whether the Mach number is increased or decreased (no hysteresis is observed).

Why kinetically stable oil droplets in water spontaneously acquire a negative charge remains one of the most vigorously debated questions in interfacial science. Here, we combine neural-network based deep potential molecular dynamics with a data-driven and information theory approach to probe the real-space electron density at an extended decane-water interface. While decane-water clusters show nearly symmetric forward and backward charge transfer (CT) and thus negligible net CT, the extended interface displays a systematic electronic asymmetry, yielding a net CT from water to the hydrocarbon phase producing an average surface charge density of $\sim0.006~e^{-}\,\mathrm{nm}^{-2}$ on the oil phase. This imbalance is accompanied by much larger intra-phase self-polarization, particularly within the hydrocarbon phase, demonstrating that collective many-body polarization dominates the interfacial electronic response. Structural analysis reveals an asymmetry between forward C--H$\cdots$O and backward O--H$\cdots$C motifs, providing a microscopic origin for a net CT from one phase to the other. Curiously, both the water O--H and decane C--H covalent bonds incur subtle contractions which originate from a response to the charge-separation layers at the interface. These features are fully consistent with the weak improper hydrogen-bonds forming at the oil-water interface that results in blue-shifts of the C-H modes.

Italy and the surrounding seas are recognised as one of the European hotspots for tornadoes and waterspouts. In recent years, the town of Rosignano Solvay (on the Northern Tyrrhenian coast) experienced repeated waterspouts affecting the same areas, raising local concern about the possible influence of heated wastewater discharged into the sea by a nearby industrial site. We reconstruct the mesoscale meteorological conditions of four intense waterspouts near Rosignano Solvay using a limited-area weather model at high-to-very-high resolution (inner domain grid spacing 500 m; sensitivity tests at 100 m). At the reported event times, the intensity of key mesoscale precursors (low-level wind shear, 1 km storm-relative helicity, maximum updraft intensity, and lifting condensation level) is consistent with the values typically associated with EF1 (or stronger) tornadoes and waterspouts. The model systematically predicts the peak of instability indices 2-3 hours earlier than the reported event times. For one case study, we conduct two sea surface temperature sensitivity experiments to assess the potential atmospheric impact of heated wastewater discharge (temperature increases of +1.5 K and +5 K over a 10 km$^2$ area). The resulting changes in instability indices are marginal, with differences of at most 3\% relative to the control run. A simple mass-balance estimate for the modified sea patch suggests that, given the reported discharge rates, a plausible impact of the warm water released from the industrial site could lead to an increase in the local sea surface temperature of approximately +0.7 °C over two months. We conclude that synoptic and mesoscale conditions primarily govern waterspout initiation in this region, while the direct effect of the small, warm coastal plume from the industrial discharge appears to be minor.

Heatwaves are intensifying as a major climate extreme and have emerged as a growing public health threat in rapidly urbanizing regions such as India. In this study, we integrate long-term heat-related mortality records (1970-2023) with bias-corrected CMIP6 climate projections to quantify future heatwave-related mortality across 67 Indian cities under intermediate (SSP2-4.5) and high-emission (SSP5-8.5) scenarios. A time-series forecasting framework was applied using summer mean temperature as the primary climate driver to project mortality trajectories through the end of the 21st century. Results indicate a strong and sustained increase in heat-related mortality under both scenarios, with multi-fold amplification under SSP5-8.5 relative to SSP2-4.5, reflecting the high sensitivity of health outcomes to emission pathways. Spatial analysis reveals increasing regional divergence under high-emission conditions, with urban regions in the Deccan Plateau, western India, and parts of eastern and northeastern India exhibiting disproportionately higher mortality growth. Multidimensional scaling further highlights emerging clustering of state-level mortality behavior under extreme warming, indicating structurally different regional responses to future heat stress. In contrast, the intermediate mitigation pathway produces more moderate and spatially uniform mortality trends. These findings demonstrate that climate mitigation can substantially reduce both the magnitude and inequality of future urban heat-health burdens. By linking updated climate projections with long-term mortality data at national and sub-national scales, this study provides policy-relevant evidence to support heat adaptation planning and climate-resilient urban development in one of the world's most heat-vulnerable regions.

Applications for ultra-stable lasers outside controlled laboratory environments require compact and robust optical resonators with reduced sensitivity to temperature fluctuations. The low thermal expansion coefficient (CTE) and the high stiffness make cordierite-based ceramics, such as NEXCERA, attractive for vibration insensitive room-temperature resonators. We revisit the effective CTE of resonators with spacers and mirrors made of different materials and use finite element simulations to analyze the impact of a CTE mismatch in a cordierite-based resonator with mirrors made of ultra-low expansion (ULE) glass or fused silica (FS). This enabled us to determine the CTE of a cordierite spacer from the measured effective CTE of a resonator. We confirm a six-fold larger CTE slope of cordierite around the zero-crossing temperature than in ULE glass. The steep CTE slope, in combination with the large stiffness, makes cordierite-based resonators far less sensitive to CTE mismatch with FS mirrors, thereby eliminating the need for additional compensation rings. We further consider the so far neglected case, where the CTE of the spacer is larger than that of the mirror, and propose resonator designs in which the thermal length change of the spacer is fully or partially compensated by the deflection of the mirrors. This results in a cordierite-based resonator with ULE mirrors whose effective CTE can be close to zero over a temperature range of several tens of Kelvin. We are extending our concept to resonators based on crystalline materials with high stiffness and low isothermal length change, such as silicon, enabling compact and robust room-temperature resonators for terrestrial and space-born applications.

Colloidal perovskite nanocrystals (NCs) are a well-proven platform for growing anisotropic structures. Nanowires (NWs) exhibiting a quantum confinement phenomenon and microwires (MWs), which enable lasing, are of particular interest for optoelectronic devices. Synthesis of the latter is challenging. Herein, we report a straightforward access to high-quality CsPbBr3 MW lasers. We utilize a diphenyl ether (DPE) solvent for the hot-injection synthesis. DPE coordinates strongly to Pb2+ and allows to reduce an excess of oleic acid/oleylamine ligand pair well established for PbBr2 dissolution and inhibition of as-formed NCs regrowth. Therefore, a rapid injection of Cs-oleate into the PbBr2-containing solution yields lead-depleted Cs4PbBr6 NCs which slowly release perovskite precursors and produce CsPbBr3 counterparts. The latter transform into NWs through an oriented-attachment mechanism, which in turn evolve into laser MWs. To demonstrate spectrally tunable lasing in MWs we employ YCl3 for ion exchange in perovskite lattice. Resultant CsPb(Cl,Br)3 MWs show high-Q coherent emission in the 485-540 nm range. To highlight the potential of synthesized MWs for integrated optoelectronics, we assemble a device comprising a CsPb(Cl,Br)3 MW laser coupled to MoO3 lossless nanowaveguide, which delivers coherent light to a CsPbBr3 MW photodetector. The device exhibits a nonlinear optoelectronic response applicable for on-chip neuromorphic computing.

In compressible turbulent boundary layers (TBLs), roughness drag is typically characterised by first applying a velocity transformation to account for compressibility, after which the momentum deficit $\Delta U^+$ (Hama, 1954) and the equivalent sand-grain roughness $k_s$ are inferred. In practice, $k_s$ is often obtained from measurements at a single Mach number $M$ and Reynolds number $Re$, effectively forcing the roughness into the $\Delta U^+$--$\log(k_s)$ relation of Nikuradse (1933). This raises a key question: if a rough surface has a known $k_s$ in incompressible flow, under what conditions can this value be used in compressible flows? This question is explored using data obtained through a series of experiments of TBLs on rough walls (P60- and P24-grit sandpapers) over $0.3 \leq M \leq 2.9$ and $7427 \leq Re_{\tau} \leq 30292$, including independent variation of $Re_{\tau}$ at $M=2$. Results show that $\Delta U^+$ is largely insensitive to the velocity transformation, but the fully rough regime exhibits a Mach-number-dependent shift in the logarithmic relation. Three empirical scalings are examined: an equivalent incompressible $k_s$, a viscosity-scaled roughness $k_{*} = k/\nu_\infty^+$ with $\nu_\infty^+ = \nu_\infty/\nu_w$, and a correction factor $\sqrt{1/F_c}$ where $F_c$ depends on $T_\infty/T_w$. The last provides the most consistent improvement across datasets, although all corrections remain empirical and rely on smooth-wall compressibility transformations. This paves the way for future work to develop custom transformation for a rough-wall TBL that can account for roughness properties and other parameters including wall conditions.

We study two-dimensional periodic metamaterials in which idealised cylindrical inclusions are modelled by boundary conditions. In the scalar time-harmonic setting, the background field satisfies the Helmholtz equation, and high-contrast inclusion limits reduce to Dirichlet or Neumann conditions, with direct analogues in dielectric and acoustic media. By switching the condition assigned to selected inclusions, we break point-group symmetries of the primitive cell and thereby lift symmetry-induced degeneracies in the Floquet--Bloch spectrum of hexagonal and square lattices, opening valley-type band gaps with Berry curvature localised near opposite valleys. To analyse infinite and finite structures within a unified framework, we derive matched-asymptotic point-scatterer approximations for mixed Dirichlet--Neumann arrays. For doubly periodic systems, this yields a finite-dimensional generalised eigenvalue problem for the Floquet--Bloch spectrum; for finite arrays, it yields a generalised Foldy multiple-scattering system. In both hexagonal and square lattices, geometrically identical crystals can realise distinct valley-Hall phases solely through boundary-condition assignment while retaining an overlapping bulk gap. Spatially varying this assignment therefore creates and relocates internal interfaces without altering the underlying geometry, enabling the associated valley-Hall interfacial modes to be repositioned within the same crystal.

Anisotropic two-dimensional diffraction signals contain more information than the conventional isotropic signals for both gas phase ultrafast electron and X-ray diffraction experiments and are common in typical time-resolved diffraction experiments due to the use of linearly polarized lasers to excite the sample that imprints spatial anisotropy on the molecules. We report an iterative algorithm to restore the missing data at low scattering angles in a two-dimensional diffraction signal, which is essential to obtain real-space representation. The iterative algorithm transforms two-dimensional signals back and forth between the momentum transfer domain and the real space domain through Fourier and Abel transforms and apply real space constraints to retrieve missing signal at low scattering angles. The algorithm only requires an approximate a-priori knowledge of the shortest and longest internuclear distances in the molecule. We demonstrated successful retrieval of the missing signal in simulated patterns and in experimentally measured diffraction patterns from laser-induced alignment of trifluoroiodomethane molecules.

In the present study we determine from the available experimental data the cross section of muon transfer to molecular oxygen at low energies with account of the oxygen molecule structure. Building on an earlier work, the results highlight the role of the molecular structure effects and signifcantly improve the agreement with theoretical calculations of the muon transfer rate. An effcient computational model of the kinetics of processes involving muonic hydrogen atoms in gaseous mixture of H2 and O2 is developed and analyzed. The model is applied in the description of the FAMU experiment for the measurement of the hyperfine splitting in muonic hydrogen and the Zemach radius of the proton.

A rigid connection between the optical fiber and the rock makes amplitudes of 'fiber strain' measured with Distributed Acoustic Sensing (DAS) equal to 'rock strain'. We demonstrate this by running four interrogator units (IU) on a DAS testbed with single-fiber patch cables being cemented into a groove in the concrete floor of Black Forest Observatory (BFO). The recorded signals are compared with the recordings of a calibrated Invar wire strain meter array that has been continuously in operation for the last decades. This way we measure 'strain transfer rate' (ratio of 'fiber strain' over 'rock strain') at frequencies below 0.2 Hz. Waveform similarity for strong earthquake signals is high with typical values of the normalized correlation coefficient greater than 0.95. The 'strain transfer rate' is close to 1 for all four IUs, while it was significantly less in a previous study with DAS cables unreeled on the floor and loaded down by sand and sandbags, only. At frequencies up to 14 Hz we make an intercomparison of IUs, showing no significant variation with frequency. The scatter of 'strain transfer rate' in between channels which are spatially near to each other in the same fiber route is about $\pm$10 % in most cases. The variation of median values in between different IUs and earthquakes is less than 5 %. By subtracting the common mode laser noise, which is coherent along the fiber route, we lower the background signal level to an rms-amplitude of 100 pstrain at 0.1 Hz and 5 pstrain at 1 Hz in a bandwidth of 1/6 decade for the best cases. This allows the detection of the marine microseisms during times of moderate amplitude level.

Solidification governs the microstructure and, therefore, the mechanical response of metal components, yet the atomistic details of nucleation and defect formation are often difficult to determine experimentally. Molecular dynamics can bridge this gap, but only if the interatomic model is both accurate and computationally efficient. Here, we develop a Machine Learning Potential (MLP) for aluminum and demonstrate its near ab initio fidelity when trained with the sequential-refinement workflow that fine-tunes the model on low-energy structures. The favorable scaling of the model enables nanosecond simulations involving millions of atoms, thereby overcoming finite-size effects in simulations of polycrystalline solidification and subsequent mechanical testing. Comparison with classical potentials and recent MLP models, including a general-purpose model, shows that inaccuracies in stacking-fault energetics and diffusion can lead to qualitatively incorrect solidified grain structures and post-solidification mechanical behavior. Since our framework is based on an equivariant graph neural network, it allows for straightforward extensions to multi-component systems, providing valuable guidance for the future design and fine-tuning of both specialized and universal MLPs in computational mechanics simulations.

In the field of computational fluid dynamics, direct numerical simulations generate highly detailed data for the analysis of turbulent flows by resolving all relevant physical scales. Yet their large size, complexity, and heterogeneity make systematic post-processing and data reuse increasingly challenging. Despite the growing availability of high-fidelity simulations through public repositories, extracting meaningful physical insight often requires substantial technical effort, specialized workflows, and access to high-performance computing resources. In this article we introduce \texttt{aPriori}, an open-source Python package developed to address these limitations by providing a dedicated, memory-efficient, and user-oriented framework for the analysis of direct numerical simulation data. The software enables streamlined handling of three-dimensional fields, including filtering, scale separation, gradient evaluation, thermochemical analysis, and visualization, using concise and reproducible scripts. Its pointer-based data management strategy allows very large datasets to be processed on standard workstations without excessive memory usage, significantly lowering the barrier to advanced analysis. Beyond basic post-processing, \texttt{aPriori} supports workflows central to modern turbulence and combustion research, such as a priori model assessment, data-driven closure development, and detailed chemical analyses that include computational singular perturbation. By unifying these capabilities within a coherent and extensible software architecture, \texttt{aPriori} enhances productivity, promotes reproducibility, and facilitates broader and more effective use of high-fidelity simulation data within the computational fluid dynamics community.

The present study examines evaporative cooling and the resulting deposition patterns of a sessile $Al_2O_3$-based nanofluid droplet on a hydrophobic glass substrate at different temperatures. Evaporation predominantly occurs in the pinned contact line mode for both heated and non-heated cases, with only slight recession observed without heating. The droplet height and contact angle decrease linearly with time, and scaling relations are proposed to describe the evolution of droplet geometry and volume. A non-dimensional parameter, $\Pi_{rel}$, is introduced to characterize transitions in deposition patterns. For $\Pi_{rel} \leq 1$ ($T_s \leq 26^\circ$C), interconnected irregular polygonal network structures form at the periphery, which are rarely reported in evaporating droplets. With increasing substrate temperature, this structure is suppressed, giving rise to a classical coffee-ring pattern for $1 < \Pi_{rel} \leq 10$. At higher temperatures ($T_s > 40^\circ$C), dual-ring formation along with central particle deposition is observed for $\Pi_{rel} > 10$. The interfacial temperature is higher near the contact line and decreases toward the apex, and a universal scaling for the temperature profile is proposed. Internal flow velocity increases with substrate temperature, exhibiting asymmetric multi-vortex structures. Evaporative cooling intensifies with heating, enhancing evaporation flux and capillary flow. Appropriate scaling relations for evaporation flux and capillary velocity are established. Overall, the dynamics are governed by thermocapillary (Marangoni) flow induced by evaporative cooling, which enhances internal circulation and governs nanoparticle deposition morphology.

We present the first systematic comparison of three TiO2 metamaterial solid immersion lens geometries - sub-hemispherical, super-hemispherical, and full-spherical - for label-free super-resolution imaging. Using SEM, we characterised both the cap profiles and the nanoparticle-fluid immersion at the lens-sample interface, revealing that super-hemispherical lenses achieve the deepest immersion and closest contact with sample features. Imaging experiments under wide-field and laser confocal microscopes show that this enhanced immersion drives superior resolution and contrast. In addition, we introduce a deep learning approach based on a SinCUT image translation model to establish a cross-modal mapping between SEM morphology and optical imaging response, enabling virtual optical predictions and providing a first step toward a digital twin representation of mSIL imaging behaviour. Electromagnetic simulations further confirm a direct correlation between immersion depth and far-field main lobe intensity. Our findings demonstrate that careful control of lens shape and nanoparticle-fluid penetration, together with data-driven modelling, is essential to maximise super-resolution performance in TiO2 mSILs.

Hourly rainfall extremes cause some of the most destructive weather disasters, yet numerical weather prediction models still struggle to forecast them, and a physical basis for their predictability remains unclear. Here, we identify a trivariate clustering of hourly rainfall extremes with surface temperature, phases of the Monsoon Intraseasonal Oscillation (MISO), and precipitable water vapor, establishing a physical foundation for the medium range predictability of these events. This clustering arises from multiscale interactions in which extremes organize into storm systems embedded within mesoscale convective clusters and synoptic low-pressure systems during active MISO phases. We develop an algorithm to identify, track, and monitor these storm systems. Although rapid error growth limits the prediction of isolated hourly extremes, our results provide basis for a physics informed training of deep learning, data driven models to forecast organized clusters of hourly rainfall extremes more than a week in advance, offering substantial potential to reduce losses from extreme rainfall.

Leadership in social groups is often a dynamic characteristic that emerges from interactions and opinion exchange. Empirical evidence suggests that individuals with strong opinions tend to gain influence, at the same time maintaining alignment with the social context is crucial for sustained leadership. Motivated by the social psychology literature that supports these empirical observations, we propose a novel dynamical system in which opinions and leadership co-evolve within a social network. Our model extends the Friedkin-Johnsen framework by making susceptibility to peer influence time-dependent, turning it into the leadership variable. Leadership strengthens when an agent holds strong yet socially aligned opinions, and declines when such alignment is lost, capturing the trade-off between conviction and social acceptance. After illustrating the emergent behavior of this complex system, we formally analyze the coupled dynamics, establishing sufficient conditions for convergence to a non-trivial equilibrium, and examining two time-scale separation regimes reflecting scenarios where opinion and leadership evolve at different speeds.

Raman phonons arise from the inelastic scattering of light and represent quantized molecular motions that mediate a wide range of spectroscopic and nonlinear optical phenomena. In this work, we clarify the physical role of Raman phonons within a previously-developed time-domain framework based on the Raman-induced index modulation, and show that phonons correspond to the oscillatory component of the Raman-induced index modulation. The analysis further reveals a linear phonon-mediated interaction embedded within Raman scattering, in which optical fields couple through wave-vector matching with existing phonons. This mechanism underlies what has long been described as coherent Stokes and anti-Stokes scattering, as well as molecular modulation. Building on this insight, we introduce a phonon-controlled approach that enables efficient conversion into a selected Stokes order by tuning the wave-vector-matching relation between the driven phonons and the targeted Raman process. These results provide a clearer physical interpretation of Raman phonons and its corresponding Raman dynamics and offer new strategies for controlling Raman interactions.

Vaccination campaigns play a pivotal role in controlling infectious diseases. Their success, however, depends not only on vaccine efficacy and availability but also significantly on public opinion and the willingness of individuals to vaccinate. This paper investigates a coupled opinion-epidemic model on heterogeneous networks, where individual opinions influence vaccination probability, and opinions themselves evolve through a combination of peer interaction and local risk perception derived from observed infection rates. Embedding the coupled dynamics in scale-free networks, particularly barabasi-Albert structures, allows us to examine the role of network heterogeneity beyond homogeneous-mixing assumptions. Using Monte Carlo simulations and a semi-analytical microscopic Markov-chain approach, we derive and numerically validate analytical expressions for the critical infection threshold and stable vaccinated population where risk perception dominated peer influence. Our results show that stronger local risk perception enhances pro-vaccination opinions and suppresses infection, while dominant peer influence can increase long-term infection levels. These findings underscore the importance of accounting for social behavior and network structure when designing effective vaccination and epidemic control strategies.

Two criteria for the spectra of relativistic waves are proposed. Zero-point radiation provides the identity representation of the conformal group in Minkowski spacetime. Thermal radiation provides the irreducible representation of the conformal group in Minkowski spacetime which involves exactly one scaling parameter (the temperature) which is also time-stationary in a Rindler frame. Zero-point radiation is the limit of thermal radiation as the temperature goes to zero. Crucially, both zero-point radiation and thermal radiation take basically the same functional form in a Rindler frame. For relativistic scalar waves, a full derivation of the Planck spectrum including zero-point radiation is obtained with the classical theory.

This article provides a gentle, visual introduction to the basic concepts of differential geometry appropriate for students familiar with special relativity. Visual methods are used to explain basics of differential geometry and build intuition for all types of Riemannian and Lorentzian manifolds of constant curvature. A visual derivation of the Thomas precession is given, showcasing the utility of differential geometry while also pointing a spotlight at certain intricacies of Minkowski space crucial from a pedagogical perspective. In addition, a straightforward method to generate some Carter-Penrose diagrams -- suitable for students with no differential geometry knowledge -- is presented, and a new method of indicating distortion on spacetime diagrams is shown.

Accurate modeling of ultrasound wave propagation is essential for high-fidelity simulation and imaging in ultrasonic testing. A primary challenge lies in characterizing the excitation source, particularly for transducers with large apertures relative to the acoustic wavelengths. In such cases, non-uniform excitation and spatial interference significantly affect the resulting radiation patterns. This paper proposes a distributed source inversion strategy to reconstruct an effective spatio-temporal transducer model that reproduces experimentally measured wavefields. The reconstructed source model captures aperture-dependent phase and amplitude variations without the need for detailed knowledge of the transducer structure. The approach is validated using directivity measurements on an aluminum half-cylinder, where simulations incorporating the reconstructed source model show close agreement with experimental directivity patterns and waveform shapes. Finally, synthetic studies on reverse time migration and full-waveform inversion demonstrate that accurate transducer modeling is critical for the success of simulation-based imaging and inversion workflows and significantly improves reconstruction quality.

Irreversible transport is generally attributed to vorticity, nonlinear forcing, or explicit symmetry breaking. We show that it can arise even in strictly time-periodic and locally irrotational flows through a purely geometric mechanism. By reconstructing the velocity gradient through causal self-transport over a finite memory time, deformation acquires the structure of a geometric connection whose holonomy generates a finite Lagrangian drift over one forcing cycle. The resulting contribution admits a closed-form, parameter-free expression. A quantitative consistency analysis using independently published experimental measurements shows that the predicted scaling and magnitude agree with observations without fitting or normalization. These results identify geometric memory as a minimal and generic source of irreversible transport.

Motivated by evidence for core-edge coupling in the form of double-peaked fishbone-like low-frequency modes ($\lesssim 20\,{\rm kHz}$) in KSTAR, which exhibit synchronized Alfvénic activity both in the central core and near the plasma edge [1], we study the nonlocal response of a tokamak plasma in a visco-resistive full MHD simulation model using the code MEGA. The waves are driven by an internal "antenna" that is localized both radially and azimuthally in the poloidal $(R,z)$ plane and has a sinusoidal form $\exp(in\zeta - i\omega t)$ with Fourier mode number $n=\pm 1$ in the toroidal angle $\zeta$ and fixed angular frequency $\omega$ in time $t$. By flattening the safety factor profile $q(r)$ at suitable locations in the minor radius $r$, we created plateaus in the low-frequency Alfvén continua that act as wave "receivers". First, we confirm that such continuum plateaus respond with a coherent quasi-mode even when the driving antenna is located at a distant radius. Second, by varying the antenna location, we confirm the expectation of inward drive being more efficient than outward drive, which we attribute to volumetric focusing. Third, we find that the central core also responds well at frequencies below the central Alfvénic continuum plateau, which could facilitate chirping. Our results show that a core-localized low-frequency response does not necessarily require core-localized drive nor an exactly matching continuum, but may be driven from the edge and sub-resonantly. It remains to be seen to what extent the examined effects play a role in double-peaked fishbone-like activity. Other possible contributing mechanisms are discussed to motivate further study. Our analyses also elucidate the mode structure formation process, from transients to quasi- or eigenmodes, here in the realm of MHD, and to be followed by a verification study against kinetic models.

Clinical imaging is routinely used for cochlear implant surgical planning yet lacks the resolution and contrast necessary to visualize the fine intracochlear structures critical for individualized intervention. To address this limitation, an ensemble deep learning model was developed to automatically segment cochlear micro-anatomy from standard clinical scans. The model was trained and validated using an independent internal dataset comprised of paired synchrotron and clinical scans of the same cochlea across various acquisition protocols. Performance was evaluated quantitatively on an unseen internal test dataset and a multi-institutional external test dataset. The deep learning model achieved accurate segmentation of intracochlear anatomy across all tested modalities, outperformed all previously published models, and demonstrated strong viability on the multi-institutional external dataset. Furthermore, anatomical measurements on the automatic segmentations closely matched those obtained from high-resolution ground truth segmentations, confirming reliable estimation of clinically relevant metrics. By bridging the gap between high-resolution imaging and routine clinical imaging, this work provides a practical solution for patient-specific cochlear implant surgical planning and postoperative assessment, advancing the goals of atraumatic insertions and more effective hearing restoration.

The Pattern Effect describes the dependence of top-of-atmosphere radiation anomalies on changes in the pattern of sea surface temperatures. The emerging consensus in the field explains the impact of Pacific warm pool temperature on radiation using Convective Quasi-Equilibrium Weak Temperature Gradient (QE-WTG) theory: warm pool warming leads to increase in free-tropospheric temperatures across the tropics, a strengthening of inversion, increased cloud cover in the East Pacific low cloud decks, and negative radiative anomalies. Here we call on overlooked past results and new simulations from the Energy Exascale Earth System model to show that Rossby waves dominate the low-cloud response over the subtropical East Pacific low cloud decks, leading to decrease cloud cover in the low cloud decks. While the global radiative response is negative and consistent with QE-WTG, it is dominated by the response of the deep tropics, rather than the subtropical low cloud decks.

This study investigates the impact of educational comics as an active learning strategy in physics workshops for undergraduate students in Chemistry and Pharmacy and Biochemistry during the second semester of 2025. Conceptual understanding was assessed using the Force Concept Inventory (FCI), and student motivation and attitudes toward physics were evaluated through a Likert-type survey administered in pre- and post-test formats. The results show an average normalized gain of g = 0.21 on the FCI, corresponding to a low-to-medium range according to physics education research. A higher gain is observed in items directly related to the intervened content (g = 0.23) compared to non-intervened items (g = 0.19), suggesting that instructional design influences domain-specific conceptual development. At the motivational level, improvements are observed in student interest, self-efficacy, and perceived usefulness of physics, along with a reduction in negative emotional responses toward the subject. These findings indicate that educational comics can serve as an effective pedagogical scaffold, promoting positive learning dispositions and supporting targeted conceptual development in non-physics undergraduate contexts.

Particle-in-Cell (PIC) Monte Carlo (MC) simulations are central to plasma physics but face increasing challenges on heterogeneous HPC systems due to excessive data movement, synchronization overheads, and inefficient utilization of multiple accelerators. In this work, we present a portable, multi-GPU hybrid MPI+OpenMP implementation of BIT1 that enables scalable execution on both Nvidia and AMD accelerators through OpenMP target tasks with explicit dependencies to overlap computation and communication across devices. Portability is achieved through persistent device-resident memory, an optimized contiguous one-dimensional data layout, and a transition from unified to pinned host memory to improve large data-transfer efficiency, together with GPU Direct Memory Access (DMA) and runtime interoperability for direct device-pointer access. Standardized and scalable I/O is provided using openPMD and ADIOS2, supporting high-performance file I/O, in-memory data streaming, and in-situ analysis and visualization. Performance results on pre-exascale and exascale systems, including Frontier (OLCF-5) for up to 16,000 GPUs, demonstrate significant improvements in run time, scalability, and resource utilization for large-scale PIC MC simulations.

Spin-polarized beams are important for some nuclear and high-energy physics experiments, such as those planned for the future Electron-Ion Collider (EIC). However, maintaining polarization during the acceleration of a charged-particle beam is difficult because the periodic nature of circular accelerators leads to spin-orbit resonances where the spin-precession frequency is a sum of integer multiples of the orbital frequencies. Usually, the dominant depolarization mechanisms are first-order spin-orbit resonances and the depolarization associated with crossing such a resonance can be computed using the Froissart-Stora formula. However, accelerating polarized hadron beams to high energy requires special magnet structures called Siberian snakes. When these are implemented to maintain a spin-precession frequency of one-half the revolution frequency, there will be no first-order spin-orbit resonance crossings. The dominant depolarization mechanisms are then higher-order spin-orbit resonances. The Froissart-Stora formula can be applied to higher-order resonances when the slope of the amplitude-dependent spin tune is constant. However, the slope of the amplitude-dependent spin tune often changes at the moment of resonance crossing. This work introduces a generalization of the Froissart-Stora formula which is applicable when the slope changes in this manner. The applicability of this formula is demonstrated through tracking simulations of a higher-order resonance crossing in both a toy model and the Relativistic Heavy Ion Collider (RHIC). It is additionally shown that the Froissart-Stora formula is mathematically equivalent to the Landau-Zener formula for the diabatic transition probability in two-level systems with a linearly increasing energy gap and constant coupling. This work therefore also extends the Landau-Zener formula to the case of changing slope.

Experimental inferences of cross-field particle flux at the separatrix, $\Gamma_{\perp}^\mathrm{sep}$, show rapid growth near H-mode and L-mode density limits at high magnetic field on Alcator C-Mod. Increases in $\Gamma_{\perp}^\mathrm{sep}$ correlate well with proximity to high density operational boundaries as proposed by the separatrix operational space model. $\Gamma_{\perp}^\mathrm{sep}$ grows as the L-mode density limit and the H-L-mode back transition boundaries are approached, consistent with expectations of plasma instability-driven turbulence suggested by theory, confirming the power dependence of density limits. $\Gamma_{\perp}^\mathrm{sep}$ is well-organized by the characteristic wavenumber for resistive ballooning mode turbulence, $k_\mathrm{RBM}$, from interchange-drift-Alfvén fluid turbulence theory, with additional dependence on the cylindrical safety factor, $\hat{q}_\mathrm{cyl}$, yielding an empirical limit to plasma operation of $k_\mathrm{RBM}^{2}\hat{q}_\mathrm{cyl} = 1$. This limit corresponds to the point where the perpendicular heat flux, $Q_{\perp}$, reaches the level of the parallel heat flux, $Q_{\parallel}$, i.e. $Q_{\perp} \approx Q_{\parallel}$, beyond which point thermal equilibrium is not satisfied, resulting in a fold catastrophe.

Nonlinear spectroscopy provides a unique perspective to understand time-resolved molecular dynamics under vibrational strong coupling (VSC). Herein, equilibrium-nonequilibrium cavity molecular dynamics simulations are performed to compute the two-dimensional (2D) infrared-infrared-Raman (IIR) spectroscopy of liquid water under VSC. In conventional computational chemistry practices, accurate molecular spectra are often constructed by using an advanced molecular dipole or polarizability model to post-process molecular dynamics trajectories evolved under a computationally efficient potential. By contrast, this work highlights the necessity of employing a consistent dipole surface model in both CavMD simulations and spectroscopic post-processing. While utilizing inconsistent dipole models only mildly influences the linear polariton spectrum, it severely distorts 2D spectra in wide frequency regions. With a consistent dipole-induced-dipole model, compared to the outside-cavity molecular 2D-IIR spectrum, the cavity 2D-IIR spectrum splits the OH stretch band to a pair of polariton branches along only the IR (not Raman) axis, while fading molecular signals at other frequency regions. This work provides the foundation for employing direct CavMD simulations to construct 2D spectra of realistic molecules under VSC.

Double-peaked fishbone events across multiple KSTAR discharges are investigated. The normalized beta $\beta_{\mathrm{N}}$ and the edge safety factor $q_{\mathrm{95}}$ under which the fishbones appear vary depending on the presence and form of external magnetic perturbations. The fishbone strength is closely related to $\beta_{\mathrm{N}}$ and $q_{\mathrm{95}}$: as $\beta_{\mathrm{N}}$ increases and $q_{\mathrm{95}}$ decreases, the fishbone strength increases. Measured fishbone-relevant signals are decomposed into amplitude envelope and phase components in the temporal domain, which are analyzed separately. In terms of the amplitude envelope component, the edge electron temperature fluctuation $\tilde T_\mathrm{e}^{\mathrm{Edge}}$ becomes more correlated with the poloidal magnetic fluctuation $\dot{B}_\mathrm{\theta}$ compared to the core electron temperature fluctuation $\tilde T_\mathrm{e}^{\mathrm{Core}}$ as fishbone strength increases. In terms of the phase component, the phase of $\tilde T_\mathrm{e}^{\mathrm{Edge}}$ precedes the phase of $\tilde T_\mathrm{e}^{\mathrm{Core}}$ except in the case of very weak fishbones where the phase relations are inconclusive due to weak fishbone activity at the edge plasma, which is comparable to background fluctuations. The investigation suggests the possibility that the edge activity is not a mere side effect of the core activity, but could play an active role.

The world's power systems are undergoing a rapid transformation, shifting away from carbon-intensive power generation to renewable sources. As a result, electricity is being transported over ever longer distances, while the intrinsic system inertia provided by thermal power plants decreases. Together, these developments raise the probability of cascading line failures and reduce the stability of the system after a system split. In this article, we assess the risk of cascading failures and system splits in the European power grid for different carbon reduction scenarios. We analyze the most likely and most dangerous splits, and identify critical transmission infrastructures and we discuss potential countermeasures that can address the problem of cascades. Our results show that while the risks of splits causing power failures rises with decarbonization, it can be mitigated cost efficiently.

We solve the orientation recovery of a tumbling protein in the gas phase from single-event measurements of the spatial positions of its ions after an X-ray laser induced explosion. We simulate diffracted X-ray signal and ion dynamics under experimental conditions and compare our method to conventional orientation recovery in single-particle imaging with X-ray free-electron lasers using only diffraction data. We reconstruct 3D diffraction intensities using orientations recovered from the ion signatures and retrieve the electron density with established phase-retrieval algorithms. We test our orientation recovery procedure on 56 proteins ranging from 14 to 52 kDa (1800 to 6500 atoms), achieving roughly an angular error of around 5°. The resulting 3D electron-density reconstructions are compared to ground-truth volumes simulated at the same nominal resolution, and achieve the resolution at the edge of the detector in conditions similar to current single-particle imaging setups. We investigate the reconstruction quality and demonstrate that ion data can be used for reliable orientation recovery of particles in single-particle imaging, achieving orientation on par or better than currently used recovery techniques. This work shows the potential of ion detection for retrieving additional information from the sample fragmentation, and boost single particle imaging with X-ray lasers in the cases where the diffraction signal is a limiting factor.

We present a theoretical investigation on the electronic structure and properties of radium monochalcogenides, with chalcogens O, S, and Se, as well as the ionic species RaO +/-. Our approach combines fully relativistic and partially relativistic quantum-chemistry methods. Electronic properties are obtained using the exact two-component Hamiltonian-based coupled-cluster approach with single, double, and perturbative triple excitations [CCSD(T)+ X2C], while potential energy curves are computed using an internally contracted multireference configuration interaction method, including relativistic effects through small-core pseudopotentials and Pauli-Breit operator diagonalization (MRCI+Q+ECP+SO). The dimers exhibit very large permanent dipole moments and sizable dipolar polarizabilities, while the Franck-Condon factors among the lowest electronic states are highly non-diagonal. These features are discussed in terms of the divalent character of the chemical bonding in the neutral species.

An intercontinental metrological clock comparison between Italy and the Republic of Korea was performed by means of geodetic K-band VLBI observations. The comparison involved the hydrogen masers (H-masers) used at Medicina and Sejong radio telescopes. The same clocks were simultaneously compared by a satellite link and by high-precision optical clocks maintained at the National Metrology Institutes, KRISS in Korea and INRIM in Italy, and delivered to VLBI antennas via optical fiber. The H-masers frequency difference was estimated by extrapolating the clock rate from VLBI data using two geodetic VLBI software. This was subsequently compared with clock differences derived by satellite link and by local optical clocks. Results obtained with different approaches were in agreement at the level of $10^{-15}$ s/s. This pilot study demonstrates that standard high-frequency (K-band) geodetic VLBI campaigns could be a viable approach to conduct intercontinental clock comparisons, now only possible via satellite links. This uncertainty can be reduced thanks to the planned installation of new-generation, broadband, high-frequency receivers on the involved telescopes. K/Q/W-band geodetic observations will allow an improvement of the accuracy of the resulting group delays through broad bandwidth synthesis from 20 to 100 GHz. Furthermore, the Frequency Phase Transfer (FPT) method will also be explored together with the use of PCAL systems installed at the radio telescopes to improve phase stability and thus allow a better estimation of the station clock parameters.

Efficient thermal management and precise field prediction are critical for the design of advanced energy systems, including electrohydrodynamic transport, microfluidic energy harvesters, and electrically driven thermal regulators. However, the steady-state simulation of these electrothermal coupled multiphysics systems remains challenging for physics-informed neural computation due to strong nonlinear field coupling, temperature-dependent coefficient variability, and complex interface dynamics. This study proposes a Residual Attention Physics-Informed Neural Network (RA-PINN) framework for the unified solution of coupled velocity, pressure, electric-potential, and temperature fields. By integrating a unified five-field operator formulation with residual-connected feature propagation and attention-guided channel modulation, the proposed architecture effectively captures localized coupling structures and steep gradients. We evaluate RA-PINN across four representative energy-relevant benchmarks: constant-coefficient coupling, indirect pressure-gauge constraints, temperature-dependent transport, and oblique-interface consistency. Comparative analysis against Pure-MLP, LSTM-PINN, and pLSTM-PINN demonstrates that RA-PINN achieves superior accuracy, yielding the lowest MSE, RMSE, and relative $L_2$ errors across all scenarios. Notably, RA-PINN maintains high structural fidelity in interface-dominated and variable-coefficient settings where conventional PINN backbones often fail. These results establish RA-PINN as a robust and accurate computational framework for the high-fidelity modeling and optimization of complex electrothermal multiphysics in sustainable energy applications.

Foundation models have the potential to extend the discovery reach for anomaly detection searches. When studying the large OmniLearned foundation model on data from the CMS experiment, unexpected behavior was observed in a mass sideband. The purpose of this paper is to perform a full analysis, including a complete background estimate, on the phase space picked out by the large model. We find that the background estimation describes the data well in validation regions, but is unable to accurately model the signal region. We invite further scrutiny of these events and our methods.

Origami-inspired robots offer rapid, accessible design and manufacture with diverse functionalities. In particular, origami robots without conventional electronics have the unique advantage of functioning in extreme environments such as ones with high radiation or large magnetic fields. However, the absence of sophisticated control systems limits these robots to simple autonomous behaviors. In our previous studies, we developed a printable, electronics-free, and self-sustained oscillator that generates simple complementary square-wave signals. Our study presents a quadrature oscillation system capable of generating four square-wave signals a quarter-cycle out of phase, enabling four distinct states. Such control signals are important in various engineering and robotics applications, such as orchestrating limb movements in bio-inspired robots. We demonstrate the practicality and value of this oscillation system by designing and constructing an origami crawling robot that utilizes the quadrature oscillator to achieve coordinated locomotion. Together, the oscillator and robot illustrate the potential for more complex control and functions in origami robotics, paving the way for more electronics-free, rapid-design origami robots with advanced autonomous behaviors.

Underwater observatories have recently emerged as an efficient mean of marine biodiversity monitoring. In order to conduct data muling from the underwater sensors in an efficient and cost-effective way, we consider the use of optical wireless communications to transmit the data from the underwater sensors to an aerial node close to the water surface, such as an unmanned aerial vehicle (UAV). More specifically, we utilize a direct water-to-air (W2A) optical communication link between the sensor node equipped with an LED emitter and the UAV equipped with an ultra-sensitive receiver, i.e., a silicon photomultiplier (SiPM). To characterize this particularly complex communication channel, we introduce a ray-tracing algorithm based on the Monte Carlo method, incorporating the impact of bubbles modeled through the Mie scattering theory and a realistic sea surface representation derived from the JONSWAP spectrum. Additionally, we incorporate into this model the channel losses resulting from UAV instability under windy weather conditions. Furthermore, we conduct a comprehensive analysis of the wireless channel, examining the influence of key parameters such as wind speed, transmitter configurations, and receiver characteristics. Finally, we evaluate the end-to-end performance of the system by analyzing the average bit-error rate at varying depths and data rates, providing valuable insights into the feasibility and efficiency of the proposed approach.

Mixed quantum-classical models are widely used to reduce the computational cost of fully quantum simulations. However, their general applicability across different classes of problems remains an open question. Here, we address this issue for systems featuring spin-orbit coupling. In particular, we study the interaction dynamics of quantum spin-1/2 and classical orbital momentum in one-dimensional models of Rashba nanowires. We tackle this problem by resorting to a new quantum-classical Hamiltonian model that, unlike conventional approaches, retains the Heisenberg principle and captures correlation effects beyond the common Ehrenfest approach. Based on Koopman wavefunctions in classical mechanics, the new model was recently implemented numerically via a particle scheme -- the koopmon method -- which is extended here to treat spin-orbit coupling. We apply the koopmon method to study the quantum-classical dynamics of nanowire models, with and without the presence of a harmonic potential and in both Rashba-dominated (strong coupling) and Zeeman-dominated (weak coupling) regimes. Considering realistic semiconductor parameters, the results are contrasted with both fully quantum and quantum-classical Ehrenfest dynamics. In the absence of external potential, the koopmon method qualitatively reproduces the features of the fully quantum evolution for all coupling regimes. While it exhibits a slight loss in spin accuracy compared to Ehrenfest simulations, the latter fail to capture the orbital dynamics. In the presence of a harmonic potential, the koopmon scheme reproduces the full quantum results with accuracy levels that are unachievable by the Ehrenfest model in both quantum and classical sectors. We conclude by presenting a test case that exhibits the formation of cat-like states.

Accurate and timely crop yield estimation is critical for global food security, agricultural policy, and farm management. The Copernicus Sentinel-2 satellite constellation, with high spatial, temporal, and spectral resolution, has transformed agricultural monitoring by enabling field- and sub-field-scale analysis. This review synthesizes recent advances in Sentinel-2-based crop yield estimation. A key trend is the shift from regional models to high-resolution field-level assessments driven by three main approaches: (i) empirical models using vegetation indices combined with machine and deep learning methods such as Random Forest and Convolutional Neural Networks; (ii) integration of process-based crop growth models (e.g., WOFOST, SAFY) via data assimilation of Sentinel-2-derived variables like Leaf Area Index (LAI); and (iii) data fusion techniques combining Sentinel-2 optical data with Sentinel-1 SAR to mitigate cloud-related limitations. The review shows that machine learning, deep learning, and hybrid modeling frameworks can explain substantial within-field yield variability across crops and regions. However, performance remains constrained by limited ground-truth data, cloud-induced gaps, and challenges in model transferability across years and locations. Future directions include tighter integration of multi-modal data and improved in-season observations to support robust, operational decision-making in precision agriculture and sustainable intensification.

We model the spatiotemporal dynamics of cellular protein concentrations near membranes composed of different lipids using a three-variable continuum model for membrane-bound protein, cytosolic protein, and the local composition of a binary lipid membrane. The model contains two globally conserved quantities: the total protein content and the average fractions of the two lipid species. It combines a conserved reaction-diffusion model for protein dynamics with a Cahn-Hilliard equation for lipid demixing. Linear stability analysis of the homogeneous steady state and direct numerical simulations show that the lipid dynamics undergoes classical phase separation, whereas the protein dynamics exhibits oscillatory phase separation for intermediate total protein contents, associated with a long-wavelength instability and traveling domains. In parameter regions where both instabilities are present, we find multiscale patterns with larger-scale traveling and rotating protein domains coexisting with smaller-scale stationary lipid domains. In this regime, traveling protein domains coexist with arrested coarsening of stationary lipid domains above a critical coupling. We further show that the main instabilities and phase diagram are well captured by an extension of a recently proposed conserved FitzHugh-Nagumo model for non-reciprocal pattern formation. The extended model consists of two non-reciprocally coupled Cahn-Hilliard equations with different interface tensions, reflecting the distinct physical properties of lipids and proteins. This also explains the observed asymmetry between static lipid patterns and traveling protein patterns.

The adoption of generative AI across commercial and legal professions offers dramatic efficiency gains -- yet for law in particular, it introduces a perilous failure mode in which the AI fabricates fictitious case law, statutes, and judicial holdings that appear entirely authentic. Attorneys who unknowingly file such fabrications face professional sanctions, malpractice exposure, and reputational harm, while courts confront a novel threat to the integrity of the adversarial process. This failure mode is commonly dismissed as random `hallucination', but recent physics-based analysis of the Transformer's core mechanism reveals a deterministic component: the AI's internal state can cross a calculable threshold, causing its output to flip from reliable legal reasoning to authoritative-sounding fabrication. Here we present this science in a legal-industry setting, walking through a simulated brief-drafting scenario. Our analysis suggests that fabrication risk is not an anomalous glitch but a foreseeable consequence of the technology's design, with direct implications for the evolving duty of technological competence. We propose that legal professionals, courts, and regulators replace the outdated `black box' mental model with verification protocols based on how these systems actually fail.

We present a unified method to derive both solar wind velocities and coronal electron densities in the near-Sun corona using Doppler spectral broadening of spacecraft radio signals. The method is generalized to be frequency independent under the assumption that electron density fluctuations follow a Kolmogorov spectrum. We validate the approach using S-band data from India's Mars Orbiter Mission during the October 2021 superior conjunction at 5-8 R$_\odot$, and X-band data from Japan's Akatsuki during June 2016 and October 2022 conjunctions spanning 1.4-10 R$_\odot$. From S-band we obtained wind speeds of 100-150 km s$^{-1}$ and electron densities of order $10^{10}$ m$^{-3}$. X-band results show speeds ranging from $\sim$150 km s$^{-1}$ near the equator to $\sim$400 km s$^{-1}$ in coronal-hole regions, with consistent radial trends in density. We provide a compact, frequency-scaled relation that maps Doppler spectral width to both $v$ and $N_e$. The formulation enables consistent application across telecommunication bands and complements in-situ probes for coronal plasma studies.

Reconstruction in limited-angle digital breast tomosynthesis (DBT) suffers from slow convergence of low spatial-frequency components when using weighted data-fidelity terms within primal-dual optimization. We introduce a two-channel fidelity strategy that decomposes the sinogram residual into complementary low-pass and high-pass bands using square-root Hanning (Hann^{1/2}) filter families, each driven by an independent \ell_2-ball constraint and dual update in the PDHG (Chambolle-Pock) algorithm with He-Yuan predictor-corrector relaxation. By assigning a larger dual step size and slightly looser tolerance to the low-frequency channel, the method delivers stronger per-iteration correction to the near-DC band without violating global PDHG stability. Experiments on a 2D digital breast phantom across multiple resolutions demonstrate that the two-channel approach yields 19%--61% RMSE improvement over the single-channel baseline, with larger gains at coarser discretizations where problem conditioning is more favorable, supporting more balanced spectral convergence in clinically realistic limited-angle regimes.

In recent years, a number of neural-network (NN) methods have exhibited good performance in seismic data processing, such as denoising, interpolation, and frequency-band extension. However, these methods rely on stacked perceptrons and standard activation functions, which imposes a bottleneck on the representational capacity of deep-learning models, making it difficult to capture the complex and non-stationary dynamics of seismic wavefields. Different from the classical perceptron-stacked NNs which are fundamentally confined to real-valued Euclidean spaces, the quantum NNs leverage the exponential state space of quantum mechanics to map the features into high-dimensional Hilbert spaces, transcending the representational boundary of classical NNs. Based on this insight, we propose a quantum-classical synergistic generative adversarial network (QC-GAN) for seismic data processing, serving as the first application of quantum NNs in seismic exploration. In QC-GAN, a quantum pathway is used to exploit the high-order feature correlations, while the convolutional pathway specializes in extracting the waveform structures of seismic wavefields. Furthermore, we design a QC feature complementarity loss to enforce the feature orthogonality in the proposed QC-GAN. This novel loss function can ensure that the two pathways encode non-overlapping information to enrich the capacity of feature representation. On the whole, by synergistically integrating the quantum and convolutional pathways, the proposed QC-GAN breaks the representational bottleneck inherent in classical GAN. Experimental results on denoising and interpolation tasks demonstrate that QC-GAN preserves wavefield continuity and amplitude-phase information under complex noise conditions.

Spatial patterning and synchronization are pervasive features of plankton communities, yet the mechanisms that allow such patterns to persist coherently under environmental noise remain unresolved. In vertically structured aquatic ecosystems, plankton populations are often organized into distinct layers, raising the question of how interactions between layers shape both spatial self-organization and robustness. Here, we develop a spatiotemporal ecosystem model of a two-layer plankton community to examine the role of passive diffusive coupling under stochastic environmental fluctuations. We show that interlayer diffusion induces a sharp transition from independent, layer-specific Turing patterns to fully synchronized spatial patterns once the coupling strength exceeds a critical threshold. Importantly, the same coupling mechanism markedly enhances the stability of spatial patterns against environmental noise, extending their persistence far beyond that of non-coupled layers. Moreover, we uncover a trophic hierarchy in noise sensitivity, with zooplankton exhibiting substantially greater vulnerability than phytoplankton. Together, these results identify passive diffusive coupling as a unifying mechanism that simultaneously promotes spatial synchronization and robustness, providing a mechanistic explanation for the persistence of coherent plankton patterns in fluctuating aquatic environments.

Quantum probes that enable enhanced exploration and characterization of complex systems are central to modern science, spanning applications from biology to astrophysics and chemical design. In large many-body quantum systems, interactions delocalize phase information across many degrees of freedom, dispersing it away from accessible measurements and limiting the scalability of quantum metrology. Here we show that experimentally accessible Clifford operations acting jointly on quantum states and observables can refocus this distributed information. These operations implement what we term {\it Clifford lensing}--transformations that coherently localize phase information onto a reduced set of degrees of freedom, mapping optimal measurements onto observables of reduced Pauli weight. We establish a correspondence between quantum error-correcting codes and interferometric constructions that enforce deterministic phase kickback, and generalize this to circuits that concentrate many-body phase information onto a controllable subset of qubits. We further develop partial shadow tomography protocols for estimating subsystem-supported phases. We experimentally demonstrate these principles in liquid-state nuclear magnetic resonance systems of up to fifteen qubits, achieving optimal sensing with constrained resources. Our results establish a scalable route to coherent control of information flow in interacting quantum systems, enabling many-body quantum sensing and multimode interferometry across complex architectures.

Active filaments driven by tangential forces can become trapped in a spinning state when attached to a heavy head, where activity and inertia drive persistent rotation rather than directed transport. Using three-dimensional Langevin dynamics of tangentially driven bead-spring chains anchored to a common heavy head, we demonstrate that increasing the filament number $\Nf$ systematically \emph{rescues} directed transport by sterically preventing the coiled conformations that underlie spinning. The rescue is established through three independent diagnostics: (i)~the mean-square displacement recovers monotonic growth (transport rescue), (ii)~the spatial tangent autocorrelation loses its negative dip signaling helical coiling (conformational rescue), and (iii)~the tangent time autocorrelation ceases crossing zero (orientational rescue). At high bending stiffness ($\kb = 1000$), coiling is fully eliminated at a critical filament number $\Nf^* \approx 3$. At moderate stiffness ($\kb = 100$), residual coiling persists ($\min C_s \approx -0.13$) yet transport is still rescued -- demonstrating that the destruction of spinning \emph{coherence}, not coiling elimination, is the essential mechanism. The multi-filament architecture achieves up to five orders of magnitude transport enhancement. Two physically distinct rescue pathways emerge: at high stiffness, steric constraints force filaments into a coordinated bundle sustaining directed propulsion; at low stiffness, steric interactions destroy orientational coherence, producing enhanced active diffusion. These results demonstrate a purely mechanical, density-independent route to overcome inertia-induced motility arrest, with implications for synthetic microswimmer design, motor-driven filament assays, and multi-filament organization in biological systems.

We investigate the quantum evolution of the pump field in second-harmonic generation under strong pump depletion. Starting from a coherent state, the pump develops a nonclassical phase-space structure resembling a Schrödinger cat state. This behavior originates from phase instability induced by vacuum fluctuations of the harmonic mode. A rigorous quantum analysis has been performed for mean photon numbers up to $\langle \hat n \rangle = 100$ in pump mode. For larger photon numbers, up to $\langle \hat n \rangle = 10^{7}$, the dynamics have been analyzed using a classical trajectory method with sampled initial conditions that reproduces the main features of the quantum evolution. The results indicate that nonlinear frequency conversion can generate macroscopic superposition-like states of the pump field. Although the resulting state is not pure due to correlations with the second-harmonic wave, it remains non-classical with negative zones of Wigner function. These results indicate that strongly nonlinear frequency conversion can provide a scalable route toward macroscopic nonclassical states of light.

The polarization of soft and tender X-rays serves as a widely utilized probe for investigating diverse physical properties, such as magnetic order in materials. However, experimental methods for determining the polarization of tender X-rays (1.5-3.0 keV) have remained limited. In this work, we propose a polarization measurement method for this energy range based on the photoelectron angular distribution. The angular distribution of photoelectrons emitted from carbon targets was measured using linearly polarized synchrotron radiation. The results showed a clear dependence on the incident photon polarization across the energy range of 0.4 to 3.0 keV. This demonstrates that the photoelectron angular distribution can serve as a reliable tool for determining the linear polarization of soft and tender X-ray photons, facilitating the development of polarization-dependent measurements across this broad energy range.

We present the Ising noise filter, a highly portable, graph-based pre-filtering algorithm for early-stage background suppression in particle accelerators and astrophysical detectors. Standard noise rejection methods relying on track fitting suffer from severe combinatorial explosion. Our method bypasses this by mapping individual detector hits to a network of binary spins and minimizing an energy functional. The interaction kernels are physics-informed, tailored to the underlying physics and geometry of the experiment. We demonstrate the efficacy of this approach in two distinct experimental regimes. Applied to the Baikal-GVD neutrino telescope the filter yields fast, standard-quality noise rejection with 96.8\% recall for astrophysical neutrinos. For the SPD detector at the NICA collider the filter attains recall of 97\% on a toy Monte Carlo sample. Furthermore, when combined with a Peterson--Hopfield network for track finding, our physics-informed coupling improves the TrackML score from 0.5 to 0.95.

Quantum-electrodynamical density-functional theory (QEDFT) provides a first-principles framework for describing materials coupled to quantized electromagnetic fields. While QEDFT has successfully captured cavity-induced modifications of electronic structures in atoms and molecules, a fully self-consistent and accurate framework to simulate and predict the structural, phonon-related, polarization and optical response of periodic solids in optical cavities has remained elusive. Here, we introduce a unified QEDFT approach that incorporates collective light-matter coupling in the electronic ground state, density functional perturbation theory for phonons, and real-time time-dependent QEDFT for optical excitations. This framework enables \textit{ab initio} calculations of cavity-modified electronic and phononic dispersions, Born effective charges, dielectric tensors, and both resonant and non-resonant optical absorption spectra. Using wurtzite \ac{GaN} in an optical cavity as a case study, we demonstrate that the quantized vacuum field reshapes electronic, phononic and polarization properties, producing experimentally accessible signatures in the transmission and absorption spectra. These results establish QEDFT as a general first-principles platform for predicting and exploring cavity-modified quantum materials.

Multi-Conjugate Adaptive Optics (MCAO) is essential for increasing the corrected Field-of-View (FoV) in astronomical imaging and potentially for free-space optical communications, particularly for small-aperture, transportable systems. We demonstrate the viability and performance of a Refractive-MCAO system utilizing a novel multi-actuator Deformable Lens (DL) as the wavefront correction element. Unlike conventional Deformable Mirrors (DMs), the transmissive nature of the DL simplifies the optical train, making it ideal for compact setups. Using a Shack-Hartmann Wavefront Sensor (SH-WFS) in conjunction with two DLs conjugated to different atmospheric layers, we achieved an extension of the isoplanatic patch up to three times the uncorrected atmospheric isoplanatic angle under moderate turbulence D/r0 = 2. We tested the MCAO system in a setup that emulates a free space optical communication for compact transportable system. In a double-channel, single-mode fiber coupling experiment we demonstrated the efficiency of this method.

Thermodynamic computing harnesses the relaxation dynamics of physical systems to perform matrix operations. A key limitation of such approaches is the often long thermalization time required for the system to approach equilibrium with sufficient accuracy. Here, we introduce a hybrid digital-thermodynamic algorithm that substantially accelerates relaxation through optimized initializations inspired by the Mpemba effect. In the proposed scheme, a classical digital processor efficiently computes an initialization that suppresses slow relaxation modes, after which the physical system performs the remaining computation through its intrinsic relaxation dynamics. We focus on overdamped Langevin dynamics for quadratic energy landscapes, analyzing the spectral structure of the associated Fokker-Planck operator and identifying the corresponding optimal initial covariances. This yields a predictable reduction in thermalization time, determined by the spectrum of the encoded matrix. We derive analytic expressions for the resulting speedups and numerically analyze thermodynamic implementations of matrix inversion and determinant computation as concrete examples. Our results show that optimized initialization protocols provide a simple and broadly applicable route to accelerating thermodynamic computations.

We study the nonlinear chaotic dynamics in a system of linear oscillators coupled by social network links with an additional stratification of oscillator energies, or frequencies, and supplementary nonlinear interactions. It is argued that this system can be viewed as a model of social stratification in a society with nonlinear interacting agents with energies playing a role of wealth states of society. The Hamiltonian evolution is characterized by two integrals of motion being energy and probability norm. Above a certain chaos border the chaotic dynamics leads to dynamical thermalization with the Rayleigh-Jeans (RJ) distribution over states with given energy or wealth. At low energies, this distribution has RJ condensation of norm at low energy modes. We point out a similarity of this condensation with the wealth inequality in the world countries where about a half of population owns only a couple of percent of the total wealth. In the presence of energy pumping and absorption, the system reveals features of the Kolmogorov-Zakharov turbulence of nonlinear waves.

In scientific computing, the formulation of numerical discretisations of partial differential equations (PDEs) as untrained convolutional layers within Convolutional Neural Networks (CNNs), referred to by some as Neural Physics, has demonstrated good efficiency for executing physics-based solvers on GPUs. However, classical grid-based methods still face computational bottlenecks when solving problems involving billions of degrees of freedom. To address this challenge, this paper proposes a novel framework called 'Quantum Neural Physics' and develops a Hybrid Quantum-Classical CNN Multigrid Solver (HQC-CNNMG). This approach maps analytically-determined stencils of discretised differential operators into parameter-free or untrained quantum convolutional kernels. By leveraging amplitude encoding, the Linear Combination of Unitaries technique and the Quantum Fourier Transform, the resulting quantum convolutional operators can be implemented using quantum circuits with a circuit depth that scales as O(log K), where K denotes the size of the encoded input block. These quantum operators are embedded into a classical W-Cycle multigrid using a U-Net. This design enables seamless integration of quantum operators within a hierarchical solver whilst retaining the robustness and convergence properties of classical multigrid methods. The proposed Quantum Neural Physics solver is validated on a quantum simulator for the Poisson equation, diffusion equation, convection-diffusion equation and incompressible Navier-Stokes equations. The solutions of the HQC-CNNMG are in close agreement with those from traditional solution methods. This work establishes a mapping from discretised physical equations to logarithmic-scale quantum circuits, providing a new and exploratory path to exponential memory compression and computational acceleration for PDE solvers on future fault-tolerant quantum computers.

Control over crystallographic orientation in metal-organic framework (MOF) thin films is essential, as many of their functional properties critically depend on exact alignment along a defined crystallographic direction. Spin-assisted layer-by-layer liquid-phase epitaxy (LbL-LPE) offers significant advantages over conventional synthesis approaches, including reduced chemical consumption, shorter processing times, and operation under ambient conditions. In this work, this LbL-LPE spin-coating is established as a robust, high-throughput fabrication protocol suitable for application-ready materials. The flexible pillar-layered framework Zn2BDC2DABCO serves as a proof-of-concept framework for the development of an automated spin-assisted LbL-LPE workflow enabling reproducible fabrication of homogeneous and highly oriented MOF thin films with integrated monitoring of critical processing steps. The protocol incorporates correlative characterization combining grazing-incidence wide-angle X-ray scattering (GIWAXS), infrared and UV-Vis spectroscopy, scanning electron microscopy (SEM), and time-of-flight secondary ion mass spectrometry (ToF-SIMS) to ensure control over surface chemistry, reactant delivery, and film growth. Determination of the degree of orientation and the Hermans orientation parameters provides a key quality metrics for assessing crystal alignment and reproducibility. The automated experimental workflow significantly accelerates the fabrication of thin films whose properties depend on crystal orientation, providing processing optimizations and control that can be readily extended to increasingly complex MOF architectures.

Spontaneous parametric down-conversion (SPDC) in subwavelength nonlinear nanostructures is emerging as a promising source of quantum light, owing to its intrinsic multifunctionality and ability to generate versatile and complex quantum states. Despite this growing interest, the physical mechanisms governing photon-pair generation in nanostructures remain only partially understood. In particular, experimental investigations of key emission properties in individual resonators, such as spatial directionality and spectral characteristics, are still lacking, and predictive theoretical frameworks with direct experimental validation have not yet been established. Here we measure, for the first time, the spatial and spectral properties of photon pairs generated via SPDC in a lithium-niobate bullseye nanostructured resonator. Both spatial and spectral properties show a resonant behavior, which we describe within an extended quasi-normal-mode theoretical framework. This comparison with the theory is enabled by photon-pair count rates reaching up to 0.45 Hz/mW, to our knowledge, the highest reported to date for a nanostructured resonator. Our results provide new physical insight into SPDC in nanostructures and represent an important step toward predictive design strategies for efficient nanoscale sources of quantum light.

This paper introduces soliton_solver, an open-source GPU-accelerated software package for the simulation and real-time visualization of topological solitons in two-dimensional non-linear field theories. The software is structured around a theory-agnostic numerical core implemented using Numba CUDA kernels, while individual physical models are introduced through modular theory components. This separation enables a single computational framework to be applied across a broad class of systems, from nanoscale magnetic spin textures in condensed matter physics to cosmic strings spanning galaxies in high energy physics. The numerical backend provides finite-difference discretization, energy minimization, and GPU-resident evaluation of observables. A CUDA--PyOpenGL rendering pipeline allows direct visualization of evolving field configurations without staging full arrays through host memory. The package is distributed in Python via PyPI and supports both reproducible batch simulations and interactive exploration of metastable configurations, soliton interactions, and model-dependent initial states. We describe the software architecture, numerical workflow, and extensibility model, and we present representative example applications. We also outline how additional theories can be incorporated with minimal modification of the shared numerical infrastructure.

Symmetries are ubiquitous in physics and play a pivotal role in light-matter interactions, where they determine the selection rules governing allowed atomic transitions and define the associated conserved quantities. For the up-conversion process of high harmonic generation, the symmetries of the driving field determine the allowed frequencies and the polarization properties of the resulting harmonics. As a consequence, it is possible to establish classical selection rules when the process is driven by coherent radiation. In this work, we show that fluctuation-induced symmetry breaking in the driving field leads to the appearance of otherwise forbidden harmonics. This is achieved by considering bicircular quantum light, and demonstrate that the enhanced quantum fluctuations due to squeezing in the driving field break the classical selection rules. To this end, we develop a quantum optical description of the dynamical symmetries in the process of high harmonic generation, revealing corrections to the classical selection rules. Moreover, we show that the new harmonics show squeezing-like signatures in their photon statistics, allowing them to be clearly distinguished from classical thermal fluctuations.

Dopamine levels are linked to neurological illnesses like Parkinson's and Alzheimer's. Thus, reliable and sensitive detection of dopamine is crucial for early diagnosis and surveillance of neurodegenerative diseases. Non-noble-metal-based nanomaterials are ideal for light-mediated sensing of organic molecules. Among these, 2D quasicrystal structures consisting of five elements, namely Al70Co10Fe5Ni10Cu5, provide active sites due to their high surface-to-volume ratio, making them excellent for organic chemical sensing. Here, we propose a simple, label-free, spatial self-phase-modulation (SSPM)-based sensing method in liquid form. SSPM-based time evolution of the diffraction pattern for varied mixing levels of a 1100 ppb dopamine solution shows a shift in the active 2D Al QC solution. The 1100 ppb solution shows a distinct value, indicating a change in the nonlinear refractive index. Time-evolution analysis is used to calculate sensitivities to changes in the nonlinear refractive index and time constant. The SPR-activated 2D Al QC nanostructure is used to demonstrate dopamine sensing and to perform qualitative and quantitative evaluations. The SSPM-based sensing has been further compared with other optical-based sensing methods such as Raman spectroscopy, UV-Vis spectroscopy, and FTIR spectroscopy. The experimental observations are also explained using DFT-based simulations. The current SSPM method can be used for rapid, large-scale medical diagnostics.

Recent laser altimeters are able to not only measure the ranging distance between the spacecraft and the surface but also the full time-of-flight of the photons or pulse shape. This new capabilities allows to measure the intra-footprint properties: surface slope distribution and surface microtexture. Here we simulate and discuss for the first time the effect of surface microtexture, especially for ice covered surface with longer penetration depth. Using the WARPE simulation software, two kind of microtextures are simulated: compact slab and granular. Laser pulse shape for an ideal instrument is simulated using physical properties such as the grain size, material composition, thickness, compacity (filling factor, porosity) rather than radiative properties. The effects of these parameters on the pulse shape are discussed as well in the range that could be possibly be observed with actual BELA measurement. Finally, examples of WARPE's simulated pulse shapes are used as input in the precise simulation chain of the BELA measurement output, to further assess the capability to detect variation in surface microtexture.

Parametric roll is a rare but high-consequence instability that can trigger abrupt regime changes in ship response, including pronounced shifts in roll statistics and tail risk. This paper develops a data-driven surrogate that learns the nonlinear, causal functional mapping from incident wave--motion time series to vessel motions, and demonstrates that the surrogate reproduces both (i) parametric roll episodes and (ii) the associated statistical shifts in the response. Crucially, the learning framework is data-source agnostic: the paired wave--motion time series can be obtained from controlled experiments (e.g., towing-tank or basin tests with wave probes and motion tracking) when a hull exists, or from high-fidelity simulations during design when experiments are not yet available. To provide a controlled severe-sea demonstration, we generate training data with a URANS numerical wave tank, using long-crested irregular seas synthesized from a modified Pierson--Moskowitz spectrum. The demonstration dataset comprises 49 random-phase realizations for each of three sea states, simulated at a fixed forward speed selected to yield encounter conditions under which parametric-roll episodes can occur. A stacked LSTM surrogate is trained on wave-elevation time series and evaluated on held-out realizations using time-domain accuracy and distributional fidelity metrics. In the most severe case, the model tracks the onset and growth of large-amplitude roll consistent with parametric excitation, and captures the corresponding changes in roll probability density functions (PDFs). We further compare loss-function choices (MSE, relative-entropy-based objectives, and amplitude-weighted variants) and show how they trade average error for improved tail fidelity relevant to operability and risk assessment.

Optical turbulence, driven by fluctuations of the atmospheric refractive index, poses a significant challenge to ground-based optical systems, as it distorts the propagation of light. This degradation affects both astronomical observations and free-space optical communications. While adaptive optics systems correct turbulence effects in real-time, their reactive nature limits their effectiveness under rapidly changing conditions, underscoring the need for predictive solutions. In this study, we address the problem of short-term turbulence forecasting by leveraging machine learning models to predict the atmospheric seeing parameter up to two hours in advance. We compare statistical and deep learning approaches, with a particular focus on probabilistic models that not only produce accurate forecasts but also quantify predictive uncertainty, crucial for robust decision-making in dynamic environments. Our evaluation includes Gaussian processes (GP) for statistical modeling, recurrent neural networks (RNNs) and long short-term memory networks (LSTMs) as deterministic baselines, and our novel implementation of a normalizing flow for time series (FloTS) as a flexible probabilistic deep learning method. All models are trained exclusively on historical seeing data, allowing for a fair performance comparison. We show that FloTS achieves the best overall balance between predictive accuracy and well-calibrated uncertainty.

Learning-based models for fluid dynamics often operate in unconstrained function spaces, leading to physically inadmissible, unstable simulations. While penalty-based methods offer soft regularization, they provide no structural guarantees, resulting in spurious divergence and long-term collapse. In this work, we introduce a unified framework that enforces the incompressible continuity equation as a hard, intrinsic constraint for both deterministic and generative modeling. First, to project deterministic models onto the divergence-free subspace, we integrate a differentiable spectral Leray projection grounded in the Helmholtz-Hodge decomposition, which restricts the regression hypothesis space to physically admissible velocity fields. Second, to generate physically consistent distributions, we show that simply projecting model outputs is insufficient when the prior is incompatible. To address this, we construct a divergence-free Gaussian reference measure via a curl-based pushforward, ensuring the entire probability flow remains subspace-consistent by construction. Experiments on 2D Navier-Stokes equations demonstrate exact incompressibility up to discretization error and substantially improved stability and physical consistency.

The controlled propagation of spin textures at bifurcations is a critical challenge for racetrack-based logic devices. Here, we investigate the effect of longitudinal and transverse magnetic fields on the propagation of magnetic antivortices at bifurcations within the stripe domain pattern of a reconfigurable NdCo/NiFe racetrack in order to control the preferred antivortex trajectory. Magnetic Transmission X-ray Microscopy experiments were employed to correlate the observed propagation path with the local magnetic configuration. We demonstrate that Zeeman coupling to the magnetization components at the bifurcation core enables switching of the preferred propagation branch using low-amplitude transverse magnetic fields, without modifying the global stripe domain configuration that defines the guiding racetrack landscape. In-plane magnetic anisotropy provides an additional mechanism to break the symmetry between the upper and lower bifurcation branches by tuning the relative orientation between the stripe domain pattern and the longitudinal magnetic fields.

Density functional theory at finite temperatures often relies on the zero-temperature approximation, which uses a ground-state exchange-correlation functional with thermalized densities. This approach, however, neglects the explicit temperature dependence of the exchange-correlation free energy -- a key factor in regimes such as warm dense matter, where both electronic and thermal effects are significant. In this work, we introduce the entropy-corrected zero-temperature approach, in which the exchange-correlation entropy is extracted using the generalized thermal adiabatic connection formula to construct a thermal correction to the standard zero-temperature approximation. Using a uniform electron gas parametrization, we compare this approach to the finite-temperature adiabatic connection and demonstrate that it performs best at lower densities. This provides a useful complement to zero-temperature density functional approximations, which generally perform better at moderate-to-large densities. We further identify a density-dependent intersection between the adiabatic connection curves, revealing a dependence on the ground state correlation energy and correlation potential. Additionally, extension of the entropy corrected approach applied as a local density approximation--like temperature correction to the zero temperature approximation is discussed.

A statistical model for the calculation of the ionisation-cluster size distribution in nanodosimetry is proposed. It is based on a canonical ensemble and derives from the well-known nuclear droplet model. The model especially can be applied to the scenario 'low energy primaries (smaller than 100 eV) moving in nanovolumes (in the order of a few nanometers)'; a scenario to which trajectory models should not be used. The focus of this work is on presenting the model and demonstrating its feasibility.

The single sideband (SSB) framework of analytical electron ptychography can account for the presence of residual geometrical aberrations induced by the probe-forming lens. However, the accuracy of this aberration correction method is highly sensitive to noise, in part due to the necessity of phase unwrapping. In this work, we thus propose two strategies to improve aberration correction performance in low-dose conditions: confining phase unwrapping within the sidebands and selecting only well-unwrapped sidebands for calculating aberration coefficients. These strategies are validated through SSB reconstructions of both simulated and experimental 4D-STEM datasets of monolayer tungsten diselenide (WSe2). A comparison of results demonstrates significant improvements in Poisson noise tolerance, making aberration correction more robust and reliable for low-dose imaging.

We present a methodology for the characterization of deep-ultraviolet (UV) photonic integrated circuits (UV-PICs) based on polycrystalline Al2O3, operating at a wavelength of 261 nm. The platform enables low-loss propagation in the deep UV, and we demonstrate an image-based analysis pipeline for estimating waveguide attenuation using free-space coupling and scattered-light imaging. The characterization approach combines spatial calibration of the imaging system, background analysis, and controlled exposure conditions to extract the exponential decay of scattered light along the propagation direction. Preliminary measurements suggest propagation losses on the order of 4.6 dB/cm for 600 nm wide waveguides, while narrower waveguides exhibit higher attenuation due to increased scattering and reduced mode confinement. This work primarily documents the experimental setup and analysis methodology used for deep-UV characterization, providing a foundation for further validation and refinement of propagation-loss measurements in integrated photonic devices operating in the deep-ultraviolet regime.

We demonstrate an ultra-low dissipation, one-dimensional mechanical oscillator formed by levitating a millimeter-scale composite graphite rod in a room-temperature magneto-gravitational trap. The trap's magnetic field geometry, based on a linear quadrupole, eliminates first-order field gradients in the axial direction, yielding a low oscillation frequency with ultra-low eddy-current losses. Direct ring-down measurements under vacuum compare the damping of the vertical and axial motion; while the vertical motion damps in seconds, the axial motion damps with a time constant of over 5 days. Analysis reveals that this dramatic difference in damping is a result of the symmetry of the magnetic field and the anisotropy of the trap strength. The results are remarkably robust, demonstrating a potential platform for inertial and gravitational sensing.

Head impacts can cause concussion, but the precise biomechanical conditions that produce injury remain uncertain. Rotational acceleration has long been posited as the primary cause and has guided concussion prevention strategies. Using instrumented mouthguards to record head kinematics of diagnosed concussions, we directly tested this hypothesis and found that linear acceleration predicted injury with greater precision than rotational acceleration, while rotational velocity provided additional predictive value. Injury risk functions derived from these measurements indicated substantial predicted concussion risk during typical impacts to an American football helmet. Introducing a liquid-filled helmet pad designed to attenuate linear acceleration reduced predicted risk by up to 52%. These results indicate that effective concussion prevention requires targeting linear acceleration.

To meet the demands of the Beam Energy Scan phase-II (BES-II) program, the STAR experiment at RHIC developed a dual real-time framework consisting of a High Level Trigger (HLT) and an Express Data Production system (xProduction). The HLT operates online within the Data Acquisition (DAQ) chain on a multicore CPU cluster, with optional acceleration using Xeon Phi coprocessors. It employs parallelized algorithms, such as the Cellular Automaton track finder, for fast tracking, vertexing, and event filtering, enabling real-time event selection and detector monitoring. In parallel, xProduction runs independently of the DAQ loop and performs near offline-quality calibration and reconstruction within hours. Using the express data stream, enhanced by HLT selections, and the STAR calibration framework, it enables early physics analysis and provides collaboration-wide access to analysis-ready datasets. Together, HLT and xProduction form a complementary system combining real-time selection with rapid high-quality reconstruction. This framework has enabled prompt reconstruction of the ${}^5_{\Lambda}\mathrm{He}$ hypernucleus and efficient processing of large datasets, demonstrating scalability for future high-luminosity experiments.

This study presents a controlled examination of the universality of the von Karman and additive coefficients in the logarithmic law of the mean streamwise velocity profile for high-Reynolds-number turbulent boundary layers under low-to-moderate adverse pressure gradients. The experiments use a method for prescribing pressure gradients along Melbourne's high-Reynolds-number boundary layer wind tunnel, combined with direct friction velocity measurements from oil-film interferometry. This allows systematic variation of upstream pressure-gradient history while maintaining locally matched Reynolds number and Clauser parameter at the measurement location. The configuration therefore separates the effects of Reynolds number, local adverse pressure gradient, and pressure-gradient history on turbulence statistics and energy spectra across the boundary layer. Owing to the high Reynolds number and moderate pressure-gradient conditions, the overlap region is sufficiently extended to assess the logarithmic law. The von Karman coefficient remains invariant within experimental uncertainty, whereas the additive coefficient varies systematically with both local pressure gradient and pressure-gradient history. Local adverse pressure gradients energize both large- and small-scale motions in the wake region around 0.4 delta, while pressure-gradient history also affects large-scale motions down to about 0.25 delta, just above the overlap region. In contrast to lower-Reynolds-number studies, neither effect extends into the inner region. These measurements provide a high-fidelity dataset for improving physical understanding and developing composite mean velocity profile formulations for adverse-pressure-gradient turbulent boundary layers.

We numerically investigate the feasibility and limits of jointly estimating flow fields and unknown particle properties (e.g., position, size, and density) from Lagrangian particle tracking (LPT) data. LPT offers time-resolved, volumetric measurements of particle trajectories, which are markers of the carrier fluid motion. However, experimental tracks are spatially sparse and potentially noisy, and the problem of reconstructing flow fields may be further complicated by inertial particle transport, such that particle slip velocities must be determined to access the velocity field of the carrier fluid. To address this problem, we develop a data assimilation framework that couples an Eulerian representation of the flow with Lagrangian particle models, enabling the simultaneous inference of carrier fields and particle properties under the governing equations of disperse multiphase flow. We show that flow fields and particle properties can be jointly estimated in three representative regimes: (1) In a turbulent boundary layer with noisy tracer tracks (St to 0), flow fields and true particle positions are jointly estimated, which amounts to a physics-informed particle tracking problem; (2) in homogeneous isotropic turbulence seeded with inertial particles (St ~ 1-5), we demonstrate simultaneous recovery of flow states and particle diameters, showing the feasibility of implicit particle characterization; and (3) in a compressible, shock-dominated flow, we report the first joint reconstructions of velocity, pressure, density, and inertial particle properties (diameter and density), highlighting both the potential and certain limits of joint estimation in supersonic regimes. A systematic sensitivity study reveals how the seeding density, noise level, and Stokes number govern reconstruction accuracy for our method.

We investigate the development of tearing-mode instability using the highest-resolution two-dimensional magnetohydrodynamic simulations of reconnecting current sheets performed on a uniform grid, for Lundquist numbers of $10^3 \le S \le 5 \times 10^5$ , reaching up to $65,536^2$ grid cells. We demonstrate a Sweet--Parker scaling of the reconnection rate $V_{\text{rec}} \sim S^{-1/2}$ up to Lundquist numbers $S \sim 10^4$. For larger values of Lundquist number, between $2\times 10^4\le S \le 2 \times 10^5$, plasmoid formation sets in, leading to a slight enhancement of the reconnection rate, $V_{\text{rec}} \sim S^{-1/3}$, consistent with the prediction from linear tearing mode induced reconnection, indicating that reconnection remains resistivity-dependent and therefore slow. In this range of $S$-values, the plasmoids do not undergo a merger cascade, as they are rapidly advected out of the reconnection layer. Only for $S > 2 \times 10^5$, we observe the nonlinear development of the tearing-mode instability, with plasmoid coalescence and a saturation of the reconnection rate at $V_\text{rec} / V_A \sim 0.01$. At such high $S$, however, the corresponding Reynolds number is large, reaching $\text{Re} > 2000$ even on scales comparable to the current-sheet thickness. We therefore conclude that, in astrophysical systems, it is essential to account for the dominant influence of turbulence and three-dimensional effects in the reconnection process.

Classical actuator disk theory, developed more than a century ago, provides an idealised description of turbine rotor performance. It treats a rotor as an infinitesimally-thin permeable disk and applies the governing flow equations over a streamtube encompassing the disk. A well-known limitation of the theory is its assumption of ideal flow downstream of the disk, which restricts its applicability to short downwind distances before turbulence and mixing processes governing the wake evolution take hold. The classical theory also leads to unphysical predictions of thrust and power coefficients for highly-loaded rotors. Turbulent axisymmetric wakes, by contrast, represent an extensively-studied canonical free shear flow with much of the progress and its applications to wind turbines limited to the far-wake dynamics. In this work, we introduce a generalised actuator disk theory based on a hybrid stream-tube and wake control volume, that seamlessly integrates classical actuator disk analysis with wake turbulence modelling at arbitrary distances from the rotor. The resulting model, while still idealised, can be used to predict variations in velocity, pressure, and cross-sectional flow area as function of position, both upstream and downstream of the rotor disk. Furthermore, by accounting for turbulent entrainment in the wake development, it provides more realistic predictions of thrust and power coefficients for highly-loaded disks.

We present CaloClouds3, a model for the fast simulation of photon showers in the barrel of a high granularity detector. This iteration demonstrates for the first time how a pointcloud model can employ angular conditioning to replicate photons at all incident angles. Showers produced by this model can be used across the whole detector barrel, due to specially produced position agnostic training data. With this flexibility, the model is usable in a full simulation and reconstruction chain, which offers a further handle for evaluating physics performance of the model. As inference time is a crucial consideration for a generative model, the pre-processing and hyperparameters are aggressively optimised, achieving a speed up factor of two orders of magnitude over Geant4 at inference.

The lambda phi4 scalar field model can be applied to interpret pion-pion scattering and properties of hadrons. In this work, the mathematical basis, phase transitions and singularities of a (3+1)-dimensional (i.e., (3+1)D) phi4 scalar field model are investigated. It is found that as a specific example of topological quantum field theories, the (3+1)D phi4 scalar field model must be set up on the Jordan-von Neumann-Wigner framework and dealt with the parameter space of complex time (or complex temperature). The use of the time average and the topologic Lorentz transformation representing Reidemeister moves ensure the integrability, which takes into account for the contributions of nontrivial topological structures to physical properties of the many-body interacting system. The ergodic hypothesis is violated at finite temperatures in the (3+1)D phi4 scalar field model. Because the quantum field theories with ultraviolet cutoff can be mapped to the models in statistical mechanics, the (3+1)D phi4 scalar field model with ultraviolet cutoff is studied by inspecting its relation with the three-dimensional (3D) Ising model. Furthermore, the direct relation between the coupling K in the 3D Ising model and the bare coupling lambda0 in the (3+1)D phi4 scalar field model is determined in the strong coupling limit. The results obtained in the present work can be utilized to investigate thermodynamic physical properties and critical phenomena of quantum (scalar) field theories.

Signed networks provide a principled framework for representing systems in which interactions are not merely present or absent but qualitatively distinct: friendly or antagonistic, supportive or conflicting, excitatory or inhibitory. This polarity reshapes how we think about structure and dynamics in complex systems: a negative tie is not simply a missing positive one but a constraint that generates tension, and possibly asymmetry. Across disciplines, from sociology to neuroscience and machine learning, signed networks provide a shared language to formalise duality, balance, and opposition as integral components of system behaviour. This review provides a comprehensive and foundational summary of signed network theory. It formalises the mathematical principles of signed graphs and surveys signed-network-specific measures, including signed degree distributions, clustering, centralities, motifs, and Laplacians. It revisits balance theory, tracing its cognitive and structural formulations and their connections to frustration. Structural aspects of signed networks are examined, analysing key topics such as null models, node embeddings, sign prediction, and community detection. Subsequent sections address dynamical processes on and of signed networks, such as opinion dynamics, contagion models, and data-driven approaches for studying evolving networks. Practical challenges in constructing, inferring and validating signed data from real-world systems are also highlighted, and we offer an overview of currently available datasets. We also address common pitfalls and challenges that arise when modelling or analysing signed data. Overall, this review integrates theoretical foundations, methodological approaches, and cross-domain examples, providing a structured entry point and a reference framework for researchers interested in the study of signed networks in complex systems.

The ability to switch and program molecular transitions via deterministically located plasmonic nanoantennae presents opportunities for wide spectrum of applications from biosensors to quantum computing. Due to its topology, toroidal nanoantenna (TNA) focuses immense amount of three-dimensional (3D) local electric field by toroidal moment while allowing pre and post positioning around quantum emitters (QEs). Here, we report complete switching of molecular transition energies of quantum objects (QOs) with modulation depth of 99.9% over 2840-fold radiative enhancement. At optimized TNA geometries, Fano interference between the broadband plasmonic continuum and narrow quantum transitions of QOs suppresses both radiative and non-radiative decay channels near 850 nm, yielding an observable full switching that traps energy within the hybrid mode instead of re-emitting it. To show the promises of the concept, we further demonstrate systems with multiple QOs where spectral degeneracy enhances the transparency bandwidth, while detuning generates distinct minima, enabling individually addressable spectral responses. These results establish plasmonic TNA as a promising architecture for spectral detection of single or multi-molecule configurations with high sensitivity and empowers the user for the implementation of quantum mode switches to be used in photonic processing.

Halbach spheres provide a theoretically elegant means of generating highly homogeneous magnetic fields, but practical implementation is hindered by challenging fabrication and restricted interior access. This study examines discrete spherical Halbach configurations assembled from permanent magnets placed at the vertices of Platonic and Archimedean solids. Analytical calculations, numerical field simulations, and experimental measurements indicate that polyhedra with icosahedral symmetry achieve the most favorable balance among field strength, homogeneity, and interior accessibility. They produce exceptionally flat fourth-order central saddle points, resulting in a usable homogeneous field volume up to a factor of 260 larger than that of traditional Halbach disk or cylindrical arrays. Several magnet assemblies composed of cubical NdFeB magnets are fabricated and their three dimensional field distributions characterized, demonstrating homogeneous regions of up to several cubic centimeters with deviations below 1%. The findings establish discrete icosahedrally symmetric magnet arrays as practical, scalable building blocks for compact, highly homogeneous magnetic field sources suited to mobile magnetic resonance, and magnetophoretic applications.

Models of faults incorporating slip rate- and state-dependent friction have reproduced phenomena from spontaneous slow, aseismic slip to earthquake-generating dynamic rupture. Numerical explorations of model parameter space regularly show sudden transitions in behavior. However these boundaries are poorly constrained analytically, with commonly used scalings derived assuming unrepresentative conditions of uniform sliding on an infinite, homogeneous fault. In this work, we demonstrate that an analysis of linear stability can reflect model conditions. We examine two scenarios that move beyond the classical case: an asperity driven by the steady creep of its surroundings, and a finite fault experiencing a constant rate of shear loading. We identify the critical fault dimension $L_c$ at which point linear stability is lost. Beyond this linear regime, the non-linear nature of the friction law implies the loss of memory of loading conditions as instability progresses and the existence of universal solutions describing this process. We refine prior analyses of this non-linear instability and find the minimum fault size that can support self-sustaining, unstable acceleration towards dynamic rupture. We examine the role of the state evolution law and delineate conditions under which faults may be linearly stable but non-linearly unstable, requiring finitely large perturbations to trigger instability. On the basis of numerical solutions, approximate but accurate algebraic expressions for the transition boundaries are presented. These results provide means for careful model design and to delimit plausible regions of parameter space when considering physical observations of stable creep, aseismic (slow slip) or seismic transients.

MulAtoLEG (Multi-Atom Liouville Equation Generator) is an open-source Mathematica package for generating Liouville superoperators and Liouville equations, specialized for multilevel atomic systems comprising an arbitrary number of atoms. This scheme is based on an extension to multilevel atomic systems, originally developed by Lehmberg [R. H. Lehmberg, Phys. Rev. A 2, 883 (1970)] as an adjoint master equation for ensembles of two-level emitters and later reformulated by Genes [M. Reitz, C. Sommer and C. Genes, PRX Quantum 3, 010201 (2022)] as a master equation. The package facilitates the generation of equations for complex transition configurations in alkali atoms. Although primarily designed for atomic systems, it can also generate the master and adjoint master equations for general Hamiltonians and Lindbladians. In addition, it includes functionalities to construct the differential equations in the dressed-state basis, where, in many cases, the non-unitary evolution operator can be determined explicitly. To maximize computational efficiency, the package leverages Mathematica's vectorization and sparse linear algebra capabilities. Since MulAtoLEG produces exact equations without approximations, the feasible system size is naturally limited by the available computational resources.

Implicit solvent models (ISMs) promise to deliver the accuracy of explicit solvent simulations at a fraction of the computational cost. However, despite decades of development, their accuracy has remained insufficient for many critical applications, particularly for simulating protein folding and the behavior of intrinsically disordered proteins. Developing a transferable, data-driven ISM that overcomes the limitations of traditional analytical formulas remains a central challenge in computational chemistry. Here we address this challenge by introducing a novel strategy that distills the evolutionary information learned by a protein language model, ESM3, into a computationally efficient graph neural network (GNN). We show that this GNN potential, trained on effective energies from ESM3, is robust enough to drive stable, long-timescale molecular dynamics simulations. When combined with a standard electrostatics term, our hybrid model accurately reproduces protein folding free-energy landscapes and predicts the structural ensembles of intrinsically disordered proteins. This approach yields a single, unified model that is transferable across both folded and disordered protein states, resolving a long-standing limitation of conventional ISMs. By successfully distilling evolutionary knowledge into a physical potential, our work delivers a foundational implicit solvent model poised to accelerate the development of predictive, large-scale simulation tools.

We present numerical evidence that Kelvin waves (KWs) on quantized vortices in superfluid helium can be directly observed in the normal fluid component at finite temperatures. Using the Fully cOUpled loCAl model of sUperfLuid Turbulence (FOUCAULT) model, we analyze the propagation and temperature dependence of KWs by simultaneously measuring the dispersion of waves on the vortex displacement and the normal fluid velocity. The results demonstrate that the normal fluid supports a coherent KW-like response, with a dispersion relation matching that of the vortex filament (VF). Unlike the Schwarz model where there is almost no temperature dependence, in FOUCAULT KWs frequency and damping both depend on temperature, highlighting the role of mutual friction in mediating the coupling between the two fluids. These findings open a pathway for experimental observation of KWs in the normal phase using tracer based visualization.

The collision phenomenon of polyatomic gases is described by the collision operator of extended Boltzmann equation or the energy-exchange model in particle direct simulations, for example, the Borgnakke-Larsen model. However, as a collision kernel, it dose not guarantee the entrinsic detailed balance and is not integrable. In this work, the Pullin equation, which possesses an integrable collision kernel and satisfies the detailed balance constraint, is adopted as an extended Boltzmann equation for the theoretical analysis of near-continuum relaxation mechanisms. For clarity, only the translational and rotational degrees are considered in this work. Explicit analytical expressions for the temporal relaxation of macroscopic variables, including the stress force, (translational/rotational) temperature and heat flux, are obtained at the first time. This is achieved by approximating the distribution function in mixed Hermite and Laguerre for rotation and computing the collision operator moments, enabling a direct description of macroscopic non-equilibrium evolution. Base on the same elementary moment (integral) of collision operator, the macroscopic transport coefficients is found in Chapman-Enskog framework. The long-standing speculation, that thermal conduction coefficient should be depended on the degrees of thermal non-equilibrium, is rigorously confirmed and evaluated. When thermal equilibrium is enforced, the present thermal conduction coefficients can be degenerated to the famous results of Mason and Monchick. Given the correct relaxation rate, a Rykov-type novel relaxation model for Pullin equation is proposed. It can recover the interaction of transaltional and rotatioanl heat fluxes in relaxation process, which is ignored in the widely used Rykov equation. Finally, the precision of this new Rykov-type equation is examined using a series of benchmark test cases.

Inverse design, particularly geometric shape optimization, provides a systematic approach for developing high-performance nanophotonic devices. While numerous optimization algorithms exist, previous global approaches exhibit slow convergence and conversely local search strategies frequently become trapped in local optima. To address the limitations inherent to both local and global approaches, we introduce BONNI: Bayesian optimization through neural network ensemble surrogates with interior point optimization. It augments global optimization with an efficient incorporation of gradient information to determine optimal sampling points. This capability allows BONNI to circumvent the local optima found in many nanophotonic applications, while capitalizing on the efficiency of gradient-based optimization. We demonstrate BONNI's capabilities in the design of a distributed Bragg reflector as well as a dual-layer grating coupler through an exhaustive comparison against other optimization algorithms commonly used in literature. Using BONNI, we were able to design a 10-layer distributed Bragg reflector with only 4.5% mean spectral error, compared to the previously reported results of 7.8% error with 16 layers. Further designs of a broadband waveguide taper and photonic crystal waveguide transition validate the capabilities of BONNI.

The superposition principle is fundamental to linear wave systems, ensuring that their physical behaviour is independent of the chosen basis representation. While this principle underpins many analytical techniques, including modal decompositions and scattering formulations, we show that superposition expansion can fail in multilayered media when fields are expressed as infinite series of evanescent and inhomogeneous waves. Using the Airy formula and the scattering-matrix formalism, we identify conditions under which the superposition of partial waves diverges, particularly in systems with three or more interfaces. This divergence occurs because evanescent wave components cannot be normalised within the conventional basis and is not a numerical artefact. To address this, we introduce power flux modes corresponding to orthonormal basis wave solutions that preserve energy conservation in scattering events and consequently restore convergence. We prove that in the flux-orthonormal basis, interface scattering is unitary and propagation eigenvalues are bounded guaranteeing convergence. Our approach generalises to scalar, electromagnetic, and elastic wave systems, providing a robust framework for eliminating evanescent mode divergence without regularisation or renormalisation.

Brasília offers a rare test of how urban form shapes experienced segregation. Built almost at once around modernist neighbourhood units, then expanded through planned satellites and informal peripheries, it lets us ask whether urban form turns mobility into mixing or into a more efficient engine of separation. We combine data on human mobility with urban morphometrics, amenities, road networks, along with enclosures and tessellations that capture segregation at the scales where access is structured: districts, neighbourhoods, blocks, and street-and-building cells. We find that segregation intensifies as resolution sharpens, from 0.282 at the district scale to 0.545 at the block scale, indicating that Brasília looks most integrated at coarse units and most segregated where everyday encounters are actually organised. Mobility softens home segregation for most users, but not symmetrically: poorer groups travel farther, while affluent groups remain the most selectively exposed. civic cores and mid-rise, mixed-use areas are the least segregated morphotypes, yet they occupy only a sliver of the metropolis. Elsewhere, rich lakefront suburbs and dense poor settlements reach similarly high segregation through opposite spatial logics. Amenities predict lower segregation, while barriers and enclosed residential interiors predict higher segregation. Built form explains more of this pattern than visit volume alone in the segregation models: integration is less a property of residential design than of shared destinations and porous connections. Planned capitals can build order without building isolation if they distribute mixing space rather than sequestering it.

The rapid growth of large-scale AI models has intensified energy consumption and data-movement challenges in modern datacenters. Photonic accelerators offer a promising path by executing the linear matrix multiplications of transformer inference at high throughput and low energy. However, the softmax attention layer, which requires element-wise exponentiation followed by normalization, still relies on electronic post-processing, creating an electro-optic conversion bottleneck that negates much of the potential photonic advantage. We present a cascaded micro-ring resonator (MRR) architecture that synthesizes the per-channel exponential function required by softmax, e^{x_n - max(x)}, over a finite interval with tunable worst-case relative error. A control signal detunes each ring via an electro-optic mechanism; a weak probe at fixed frequency experiences Lorentzian transmission, and cascading N identical stages yields a multiplicative transfer function whose logarithm is approximately linear. We derive mapping rules, depth-scaling estimates, and a minimax fitting formulation, and validate the framework with three-dimensional FDTD simulations of X-cut thin-film lithium niobate (TFLN) add-drop micro-ring resonators. Direct multi-ring FDTD validation extends to a five-ring cascade and confirms agreement with theory primarily over the upper operating range; deeper cascades and higher quality factors are assessed analytically. The cascade implements the per-channel exponential block, the key missing nonlinearity for photonic softmax. We further present a WDM-parallel chip architecture with closed-loop PI feedback that completes the full softmax-exponentiation, summation, and normalization-on a single photonic chip without per-channel normalization circuitry.

Reliable, high-intensity operation of the Fermilab Accelerator Complex is critical to the success of the Long-Baseline Neutrino Facility and Deep Underground Neutrino Experiment. We describe the requirements and infrastructure necessary to support routine use of artificial intelligence and machine learning (AI/ML) in the accelerator control system. Three capabilities are identified: a machine learning operations (MLOps) framework standardizing the lifecycle of AI/ML automation from data management through deployment and monitoring; a data quality framework defining and enforcing standards required to build trustworthy AI/ML applications; and workflow integration with large language models to assist physicists, engineers, and operators with information retrieval, code development, and routine analysis. Use cases spanning beam diagnostics, beam control, and support system automation illustrate the technical requirements across the complex.

Quantitative high-resolution transmission electron microscopy (HRTEM) provides an indispensable means to understand the structure-property relationships of a material in atomic dimensions. Successful quantification requires reliable retrieval of essential atomic structural information despite artifacts arising from unwanted but practically unavoidable imaging imperfections. Experimental observation carried out in tandem with model-based iterative image simulation shows vast applications in quantitative structural and chemical determination of objects spanning zero to three dimensions [Prog. Mater. Sci. 133, 101037, 2023]. However, the large number of parameters involved in the simulations make the current multi-step, user-guided iterative approach highly time consuming, thereby restricting its application primarily to small sample areas and to experienced users. In this work, we implement and apply a physics-informed Bayesian optimization (BO) framework to advance HRTEM quantification towards full automation and large-field-of-view analysis. Unlike conventional optimization approaches, our method adopts a stepwise strategy that fully leverages the strength of BO in handling high-dimensional parameters, while its probabilistic engine rigorously and efficiently refines the parameter space to enable rapid quantification. Using a BaTiO3 single crystal that contains heavy, medium and light elements as a model system, we demonstrate that the three-dimensional crystal structure can be determined from a single HRTEM image with a three to four order-of-magnitude improvement in time efficiency. This approach thus opens new avenues for fast and automated image quantification over larger sample volumes and, potentially, in the time domain.

Long-context LLM inference is bottlenecked not by compute but by the O(n) memory bandwidth cost of scanning the KV cache at every decode step -- a wall that no amount of arithmetic scaling can break. Recent photonic accelerators have demonstrated impressive throughput for dense attention computation; however, these approaches inherit the same O(n) memory scaling as electronic attention when applied to long contexts. We observe that the real leverage point is the coarse block-selection step: a memory-bound similarity search that determines which KV blocks to fetch. We identify, for the first time, that this task is structurally matched to the photonic broadcast-and-weight paradigm -- the query fans out to all candidates via passive splitting, signatures are quasi-static (matching electro-optic MRR programming), and only rank order matters (relaxing precision to 4-6 bits). Crucially, the photonic advantage grows with context length: as N increases, the electronic scan cost rises linearly while the photonic evaluation remains O(1). We instantiate this insight in PRISM (Photonic Ranking via Inner-product Similarity with Microring weights), a thin-film lithium niobate (TFLN) similarity engine. Hardware-impaired needle-in-a-haystack evaluation on Qwen2.5-7B confirms 100% accuracy from 4K through 64K tokens at k=32, with 16x traffic reduction at 64K context. PRISM achieves a four-order-of-magnitude energy advantage over GPU baselines at practical context lengths (n >= 4K).

Marathons are now common ways of seeing cities, yet little is known about how representative their routes are. Using 311 marathon routes across five continents, we compare landmarks and amenities along the course with those elsewhere in the same city, finding that museums are 15.7 times denser near the route and that the median city has about 8.5 times more luxury brands near the route than elsewhere in the city. These patterns persist under perturbed routes with the same start and finish lines: monuments and landmarks, in particular, are more prevalent on the race course than on similar alternative routes, suggesting that marathons function as intentionally selective urban portraits.

Trapped ions are one of the most advanced platforms for quantum technologies, with applications ranging from quantum computing to precision timekeeping. A crucial step towards more compact and scalable systems involves integrating photonic integrated circuits (PICs) into surface ion traps to enable on-chip light delivery and optical addressing of individual ions. Currently, most implementations rely solely on transverse-electric (TE) mode grating couplers, where the emitted light is polarized in the plane of the chip. In this work, we design, fabricate and characterize silicon nitride (Si\(_3\)N\(_4\)) PIC components, including incoupling structures, splitters, and grating couplers that support both TE and transverse-magnetic (TM) modes with comparable optical losses. We benchmark the PIC at 760\,nm, which is a typical wavelength for Yb$^{+}$-applications. The fabricated grating couplers enable the outcoupling of collimated free-space beams for both polarizations, exhibiting distinct emission angles. This dual-polarization capability gives more flexibility in polarization control and expands the accessible optical design space for trapped-ion quantum technologies.

Surface roughness plays a substantial role in many flows for which Reynolds averaged prediction is needed. The transformation used in the k-omega0 model is extended to rough surfaces by adding an effective origin. The log-layer offset is computed as a function of this effective origin, thereby creating a correspondence between effective origin and equivalent sandgrain roughness. A formula is derived for the virtual origin of the fully rough log law. It is shown how the present model is consistent with the fully rough limit.

Wafer-to-wafer (WxW) bonding is a key enabler for three-dimensional integration, including hybrid bonding for fine-pitch Cu-Cu interconnects. During bonding, wafer deformation and the air entrapped between the wafers interact through a strongly coupled, time-dependent fluid-structure interaction (FSI) that can produce non-intuitive bonding dynamics and process sensitivities. This paper develops a mathematically consistent reduced-order model for WxW bonding by deriving a Kirchhoff-Love plate equation for wafer bending from three-dimensional linear elasticity and coupling it to a Reynolds lubrication equation for the inter-wafer air film. The resulting nonlinear plate-Reynolds system is discretized and solved monolithically in the high-performance FEniCSx framework using a $C^0$ interior-penalty formulation for the fourth-order plate operator, standard continuous Galerkin discretization for the pressure field, implicit time integration, and a Newton solver with automatic differentiation. Simulations reproduce experimentally reported probe-displacement histories for multiple initial gaps and verify force equilibrium at the bond front, where the Reynolds pressure acts as an effective contact reaction. Parametric studies reveal nonlinear, and in some cases non-monotonic, sensitivities of bonding-front kinetics to the initial gap, air viscosity, and interfacial energy, providing actionable trends for process optimization.

We present the NCS-models, a family of seismic foundation models pretrained on a large share of full-stack seismic cubes from the Norwegian Continental Shelf (NCS) available through the public DISKOS database. The model weights are open-sourced for the wider geoscience community. Foundation models trained with large-scale self-supervision are emerging as a promising basis for automatic seismic interpretation. However, most existing seismic models rely on limited or proprietary datasets, and it remains unclear how well natural-image foundation models transfer to seismic data. Our goals are to develop basin-scale seismic foundation models, provide practical recipes for scalable 3D training, and quantify the effects of basin-targeted pretraining and token dimensionality on downstream interpretation performance. Using masked autoencoders with Vision Transformer backbones, we pretrain models on a DISKOS-derived corpus of 3D time- and depth-migrated seismic volumes. The NCS-model variants use 2D, 2.5D multi-view, and 3D tokenization within a matched training setup. Transfer is evaluated on interpretation benchmarks using frozen backbones and a simple k-nearest neighbor classifier. Baselines include an ImageNet-pretrained MAE, a frontier vision foundation model, and a globally pretrained seismic model. Natural-image pretrained models do not reliably transfer, reflecting the large domain gap between natural images and seismic data. Seismic pretraining is necessary for robust transfer, and large-scale basin-targeted pretraining yields further gains over a smaller globally pretrained seismic baseline. The NCS-models achieve the best overall performance without fine-tuning, while 2.5D tokenization offers the strongest accuracy-efficiency tradeoff and the embeddings support similarity search for interactive interpretation.

This work provides a brief review of the numerical methods for modelling the optical propagation of an ultrashort, intense laser pulse in plasmas and ionizable gases. These methods are implemented in an open-source simulation toolkit Axiprop, which is now actively used for design studies in the context of laser plasma acceleration of electrons (LPA). We present two examples of such studies: optical plasma waveguide generation, and phase-locked flying-focus LPA. These tools enable the identification of complex effects impacting both energy deposition during waveguide formation, and the pulse propagation dynamics that governs wakefield structure, ultimately determining the properties of the accelerated electron bunches. The developed tools and presented simulation designs are relevant to experiments on laser plasma interactions at modern high power laser facilities.

In this note we show how to construct a number of nonconvex quadratic inequalities for a variety of physics equations appearing in physical design problems. These nonconvex quadratic inequalities can then be used to construct bounds on physical design problems where the objective is a quadratic or a ratio of quadratics. We show that the quadratic inequalities and the original physics equations are equivalent under a technical condition that holds in many practical cases which is easy to computationally (and, in some cases, manually) verify.

We study the geometric particle-in-cell methods for an electrostatic hybrid plasma model. In this model, ions are described by the fully kinetic equations, electron density is determined by the Boltzmann relation, and space-charge effects are incorporated through the Poisson equation. By discretizing the action integral or the Poisson bracket of the hybrid model, we obtain a finite dimensional Hamiltonian system, for which the Hamiltonian splitting methods or the discrete gradient methods can be used to preserve the geometric structure or energy. The global neutrality condition is conserved under suitable boundary conditions. Moreover, the results are further developed for an electromagnetic hybrid model proposed in [Vu H X. J Comput Phys, 124(2):417-430]. Numerical experiments of finite grid instability, Landau damping, and resonantly excited nonlinear ion waves illustrate the behaviour of the proposed numerical methods.

Gamma-Ray Transient Monitor (GTM) is an all-sky monitor onboard the Distant Retrograde Orbit-A (DRO-A) satellite, with the scientific objective of detecting gamma-ray bursts in the energy range of 20 keV to 1 MeV. GTM is equipped with five Gamma-Ray Transient Probes (GTPs), utilizing NaI(Tl) scintillators coupled with silicon photomultiplier (SiPM) arrays for signal readout. To test the performance of the GTP in detecting electrons, we used the IHEP Electron-Beam Facility (a continuous-energy-tunable, low-current, quasi-single-electron accelerator) for ground-based electron tests of the GTP. This paper provides a detailed description of the operating principles of the electron accelerator and presents the process and results of the GTP electron-beam tests. The test results show that the GTP has a dead time of less than 4 $\mu$s for normal signals and approximately 70 $\mu$s for overflow signals, consistent with the design specifications. The time-recording capability of the GTP was tested and found to be normal, with accurate recording of overflow events. The GTP's response to electrons in the 0.4-1.4 MeV range is also normal. Additionally, we used Geant4 to simulate the GTP's energy response and performed a comparative analysis of the simulation and experimental results. The performance tests and ground-based electron calibration validated the design of the GTP and enhanced the GTP's mass model, laying the foundation for payload development, in-orbit observation strategies, and scientific data analysis.

We present recommendations to improve reproducibility and replicability in condensed matter physics. This area of physics has consistently produced both fundamental insights into the workings of matter and transformative inventions. Our recommendations result from a collaboration that includes researchers from academia and government laboratories, scientific journalists, legal professionals, representatives of publishers, professional societies, and other experts. The group met in person in May 2024 at a conference at the University of Pittsburgh to discuss the growing challenges related to research reproducibility and replicability in condensed matter physics. In this report, we discuss best practices and policies at all stages of the scientific process to safeguard the value of condensed matter. We hope this report will lay the groundwork for a broader conversation to develop subfield-specific recommendations.

Crystal structure prediction (CSP) is crucial for identifying stable crystal structures in given systems and is a prerequisite for computational atomistic simulations. Recent advances in neural network potentials (NNPs) have reduced the computational cost of CSP. However, searching for stable crystal structures across the entire composition space in multicomponent systems remains a significant challenge. Here, we propose an improvement of genetic algorithm (GA) -based CSP method using a universal NNP. Our GA-based methods are designed to efficiently expand convex hull volumes while preserving the diversity of crystal structures. Our hull-informed filtering and elitist-selection procedures incorporate an aging mechanism that prioritizes recently improved compositions. We also employ niching to prevent convergence to a small set of stoichiometries, thereby preserving a diverse, high-quality population. Our evaluation shows that the present method outperforms the symmetry-aware random structure generation and existing CSP methods, achieving a larger convex hull with fewer trials. We demonstrated that our approach, combined with the developed universal NNP (PFP), can accurately reproduce and explore phase diagrams obtained through DFT calculations; this indicates the validity of PFP across a wide range of crystal structures and element combinations. This study, which integrates a universal NNP with a GA-based CSP method, highlights the promise of these methods in materials discovery.

Flat bands exhibit high degeneracy and intrinsic localization, offering a promising platform for enhanced light-matter interactions. Here, we investigate the resonant interaction between a two-level emitter and a chiral flat band hosted by a photonic lattice. In the weak coupling regime, the emitter undergoes Rabi oscillations with a lifted photonic mode whose spatial structure reflects the nature of compact localized states and the onset of Anderson localization. We show that weak hopping disorder induces a delocalization of the lifted mode whereas the effective emitter-field coupling strength, and the associated mode volume experienced by the emitter, remains protected against structural fluctuations. We illustrate our approach using selected flat band lattices. Our findings provide a route to flat band state preparation via quench dynamics and robust cavity-QED control.

Entangled many-body states are a key resource for quantum technologies. Yet their preparation through analog control of interacting quantum systems is often hindered by experimental imperfections. Here, we introduce the adiabatic echo protocol, a general approach to state preparation designed to suppress the effect of static perturbations. We provide an analytical understanding of its robustness in terms of dynamically engineered destructive interference. By applying quantum optimal control methods, we demonstrate that such a protocol emerges naturally in a variety of settings, without requiring assumptions on the form of the control fields. Examples include Greenberger-Horne-Zeilinger state preparation in Ising spin chains and two-dimensional Rydberg atom arrays, as well as the generation of quantum spin liquid states in frustrated Rydberg lattices. Our results highlight the broad applicability of this protocol, providing a practical framework for reliable many-body state preparation in present-day quantum platforms.

People make strategic decisions many times a day - during negotiations, when coordinating actions with others, or when choosing partners for cooperation. The resulting dynamics can be studied with learning theory and evolutionary game theory. These frameworks explore how people adapt their decisions over time, in light of how effective their strategies have been. The outcomes of such learning processes depend on how sensitive individuals are to the performance of their strategies. When they are more sensitive, they systematically favor strategies they deem more successful. When they are less sensitive, their learning process is noisier and more erratic. Traditionally, most models treat this sensitivity as a fixed parameter - like the "selection strength" parameter in evolutionary models. Instead, we study how strategies and sensitivities co-evolve. We find that the co-evolutionary endpoints depend on both the type of strategic interaction and the learning rule employed. In prisoner's dilemmas, we often observe sensitivities to increase indefinitely. But in snowdrift and stag-hunt games, sensitivities often converge to a finite value, or we observe evolutionary branching altogether. These results shed light on how evolution might shape learning mechanisms for social behavior. They suggest that noisy learning does not need to be a by-product of cognitive constraints. Instead, it can serve as a means to gain strategic advantages.

We propose a time- and wavelength-multiplexed remote atom-atom entanglement generation protocol based on cavity-assisted photon scattering (CAPS). This is designed to achieve a high rate and high fidelity with robustness to operational imperfections, parameter fluctuations, and auxiliary time costs, such as percent-level photon impurity, timing and cavity parameter jitter, and atom shuttling time costs. We benchmark this protocol using comprehensive analytical and numerical modeling of the atom-cavity dynamics, including state-dependent pulse delay effects, photon temporal impurity, atom-cavity system parameter fluctuations, and crosstalk among atoms through a shared cavity mode. With realistic atom-cavity system performance, we predict $2\times 10^{5}\,\mathrm{s}^{-1}$ successful atom-atom Bell pair generation even without in-cavity qubit reset, substantially enhanced from two-photon interference based protocols, at a predicted Bell pair fidelity of 0.999.

We present a comprehensive phenomenological analysis of the calorimetric electron capture (EC) decay spectrum of $^{163}$Ho as measured by the HOLMES experiment. Using high-statistics data, we unfold the instrumental energy resolution from the measured spectrum and model it as a sum of Breit-Wigner resonances and shake-off continua, providing a complete set of parameters for each component. Our approach enables the identification and tentative interpretation of all observed spectral features, including weak and overlapping structures, in terms of atomic de-excitation processes. We compare our phenomenological model with recent ab initio theoretical calculations, finding good agreement for both the main peaks and the spectral tails, despite the limitations of current theoretical and experimental precision. The model delivers an accurate description of the endpoint region, which is crucial for neutrino mass determination, and allows for a realistic treatment of backgrounds such as pile-up and tails of low-energy components. Furthermore, our decomposition facilitates the generation of Monte Carlo toy spectra for sensitivity studies and provides a framework for investigating systematic uncertainties related to solid-state and detector effects. This work establishes a robust foundation for future calorimetric neutrino mass experiments employing $^{163}$Ho, supporting both data analysis and experimental design.

Simulating vibronic spectra is a central task in physical chemistry, offering insight into important properties of molecules. Recently, it has been experimentally demonstrated that photonic platforms based on Gaussian boson sampling (GBS) are capable of performing these simulations. However, whether an actual GBS approach is required depends on the molecule under investigation. To develop a better understanding on the requirements for simulating vibronic spectra, we explore connections between theoretical approximations in physical chemistry and their photonic counterparts. Mapping these approximations into photonics, we show that for certain molecules the GBS approach is unnecessary. We place special emphasis on the linear coupling approximation, which in photonics corresponds to sampling from multiple coherent states. By implementing this approach in experiments, we demonstrate improved similarities over previously reported GBS results for formic acid and identify the particular attributes that a molecule must exhibit for this, and other approximations, to be valid. These results highlight the importance in forming deeper connections between traditional methods and photonic approaches.

The practice of collider physics typically involves the marginalization of multi-dimensional collider data to uni-dimensional observables relevant for some physics task. In any cases, such as classification or anomaly detection, the observable can be arbitrarily complicated, such as the output of a neural network. However, for precision measurements, the observable must correspond to something computable systematically beyond the level of current simulation tools. In this work, we demonstrate that precision-theory-compatible observable space exploration can be systematized by using neural simulation-based inference techniques from machine learning. We illustrate this approach by exploring the space of marginalizations of the energy 3-point correlator to optimize sensitivity to the the top quark mass. We first learn the energy-weighted probability density from simulation, then search in the space of marginalizations for an optimal triangle shape. Although simulations and machine learning are used in the process of observable optimization, the output is an observable definition which can be then computed to high precision and compared directly to data without any memory of the computations which produced it. We find that the optimal marginalization is isosceles triangles on the sphere with a side ratio approximately $1:1:\sqrt{2}$ (i.e. right triangles) within the set of marginalizations we consider.

Flexible and accurate noise characterization is crucial for the precise estimation of gravitational-wave parameters. We introduce a Bayesian method for estimating the power spectral density (PSD) of long, stationary time series, explicitly tailored for LISA data analysis. Our approach models the PSD as the geometric mean of a parametric and a nonparametric component, combining the knowledge from parametric models with the flexibility to capture deviations from theoretical expectations. The nonparametric component is expressed by a mixture of penalized B-splines. Adaptive, data-driven knot placement, performed once at initialization, removes the need for reversible-jump Markov chain Monte Carlo, while hierarchical roughness-penalty priors prevent overfitting. Validation on simulated autoregressive AR(4) data demonstrates estimator consistency and shows that well-matched parametric components reduce the integrated absolute error compared to an uninformative baseline, requiring fewer spline knots to achieve comparable accuracy. Applied to one year of simulated LISA X-channel (univariate) noise, our method achieves relative integrated absolute errors of $\mathcal{O}(10^{-2})$, making it suitable for iterative analysis pipelines and multi-year mission data sets.

In this paper, we adapt the Discrete Variable (DV)-Circuit Quantum-Classical Physics-Informed Neural Network (QCPINN) and apply it for the first time to four typical reservoir seepage models. These include the pressure diffusion equation for heterogeneous single-phase flow, the nonlinear Buckley-Leverett (BL) equation for simplified two-phase waterflooding, the convection-diffusion equation for compositional flow considering adsorption, and the fully coupled pressure-saturation two-phase oil-water seepage equation for heterogeneous reservoirs with exponential permeability distribution. The QCPINN integrates classical preprocessing/postprocessing networks with a DV quantum core, leveraging quantum superposition and entanglement to enhance high-dimensional feature mapping while embedding physical constraints to ensure solution consistency. We test three quantum circuit topologies (Cascade, Cross-mesh, Alternate) and demonstrate through four numerical experiments that QCPINNs achieve higher prediction accuracy than classical PINNs. Specifically, the Alternate topology outperforms others in heterogeneous single-phase flow, BL equation simulations and heterogeneous fully coupled pressure-saturation two-phase flow, while the Cascade topology excels in compositional flow with convection-dispersion-adsorption coupling. The Cross-mesh topology shows competitive early-stage convergence and accuracy across scenarios with balanced performance in coupled two-phase flow. Our work verifies the feasibility of QCPINN for reservoir engineering applications, bridging the gap between quantum computing research and industrial practice in oil and gas engineering.

Supported nanoparticle catalysts are widely used in the chemical industry. Computational modeling of supported nanoparticles based on density functional theory (DFT) often involves structural searches of stable local minimum energy configurations and molecular dynamics simulations at finite temperature. These are computationally demanding tasks that are intractable within DFT for large systems. In the last two decades, machine learning interatomic potentials (MLIPs) have been successfully used to substantially increase the size and time scales accessible to simulations approximating DFT accuracy. However, training reliable MLIPs is non-trivial as it requires many costly DFT calculations. Recently, several universal MLIPs (uMLIPs) have been developed, which are trained on large datasets that cover a wide range of molecules and materials. Here, we benchmark the accuracy and the efficiency of these uMLIPs in describing Cu nanoparticles supported on Al$_2$O$_3$ surfaces against our domain-specific DP-UniAlCu model. We find that the MACE-OMAT can reproduce reasonably well the low-energy structures found in global optimization at an energy accuracy comparable to DP-UniAlCu. Interestingly, the MatterSim-v1.0.0-1M model, which exhibits larger deviations in the binding energies, can find even more stable configurations than the other two models in some supported nanoparticle sizes, showing its capability in structure exploration. For MD simulations, MACE-OMAT and MatterSim-v1.0.0-1M can qualitatively reproduce the mean-squared displacements of Cu atoms (MSD$_\mathrm{Cu}$) predicted by DP-UniAlCu, albeit at roughly two orders of magnitude higher cost. We demonstrate that the uMLIPs can be very useful in simulating supported nanoparticles even without any fine-tuning, though their reduced efficiency remains a limiting factor for large-scale simulations.

Thermodynamics is commonly presented as a theory of macroscopic systems in stable equilibrium, built upon assumptions of extensivity and scaling with system size. In this paper, we present a universal formulation of the elementary foundations of thermodynamics, in which entropy and energy are defined and employed beyond equilibrium and without assuming extensivity. The formulation applies to all systems -- large and small, with many or few particles -- and to all states, whether equilibrium or nonequilibrium, by relying on carefully stated operational definitions and existence principles rather than macroscopic idealizations. Key thermodynamic concepts, including adiabatic availability and available energy, are developed and illustrated using the energy-entropy diagram representation of nonequilibrium states, which provides geometric insight into irreversibility and the limits of work extraction for systems of any size. A substantial part of the paper is devoted to the analysis of entropy transfer in non-work interactions, leading to precise definitions of heat interactions and heat-and-diffusion interactions of central importance in mesoscopic continuum theories of nonequilibrium behavior in simple and complex solids and fluids. As a direct consequence of this analysis, Clausius inequalities and the Clausius statement of the second law are derived in forms explicitly extended to nonequilibrium processes. The resulting framework presents thermodynamics as a universal theory whose concepts apply uniformly to all systems, large and small, and provides a coherent foundation for both teaching and modern applications.

A thermodynamic framework for asymptotic inference is developed in which sample size and parameter variance define a state space. Within this description, Shannon information plays the role of entropy, and an integrating factor organizes its variation into a first-law-type balance equation. The framework supports a cyclic inequality analogous to a reversed second law, derived for the estimation of the mean. A non-trivial third-law-type result emerges as a lower bound on entropy set by representation noise. Optimal inference paths, global bounds on information gain, and a natural Carnot-like information efficiency follow from this structure, with efficiency fundamentally limited by a noise floor. Finally, de Bruijn's identity and the I-MMSE relation in the Gaussian-limit case appear as coordinate projections of the same underlying thermodynamic structure. This framework suggests that ensemble physics and inferential physics constitute shadow processes evolving in opposite directions within a unified thermodynamic description.