Papers

This study examines how the chemical-reaction timescale alters hypersonic wall-model predictions of heat and momentum transfer. A one-dimensional finite-rate-chemistry wall model for high-enthalpy air is evaluated over 3 <= Ma_e <= 25, 2 <= p_e <= 100 kPa, and a broad range of edge and wall temperatures. All forward and reverse reaction rates are multiplied by a common factor Gamma, which changes the chemical timescale while preserving the equilibrium constants and thereby connects the frozen and fast-chemistry limits within the same governing equations. Taking a 1 percent difference in wall heat flux between the frozen and finite-rate solutions as the threshold for retaining finite-rate chemistry, two quantities obtained from the frozen solution provide practical selection criteria: Da_fr = t_shear / t_ch >= 1.93 x 10^3 and T_max >= 3760 K. The wall heat flux does not generally vary monotonically with reaction rate; instead, it can decrease below both the frozen and fast-chemistry predictions before recovering at higher rates. The two limiting chemistry solutions therefore do not generally bound the finite-rate heat-flux response, and interpolation between them can be misleading. At Ma_e >= 20, the median wall-heat-flux errors of the frozen and fast-chemistry limits are 7.1 percent and 27.6 percent, respectively. Chemistry also produces substantial changes in wall heat flux and shear stress, whereas the Reynolds analogy factor responds much less because the momentum- and enthalpy-side wall-law shifts nearly cancel. These results show that finite-rate wall transfer cannot in general be inferred from its frozen and fast-chemistry limits and provide frozen-state indicators for determining when finite-rate chemistry should be retained in hypersonic wall modelling.

In seismic exploration, the propagation of seismic waves naturally gives rise to long-range dependencies in seismic data. Capturing such global correlations can significantly improve the accuracies of seismic signal processing, inversion, and interpretation. Global modeling (GM) methods have therefore emerged as an effective paradigm for seismic exploration, offering a powerful means of exploiting the intrinsic global relationships within seismic data. However, the mainstream GM approaches, particularly Transformers, incur prohibitively high computational costs for their inherent quadratic complexity, which restricts the scalability of such methods to higher-dimensional data. To achieve efficient GM for three-dimensional (3D) seismic exploration, we propose a general framework, named lightweight Mamba-based global modeling (LMGM), to establish long-range dependencies at a relatively low computational cost. Specifically, LMGM fully leverages the linear complexity of Mamba. A three-directional scanning Mamba block is designed to exploit spatial correlations along the three dimensions of 3D seismic data. Meanwhile, a dual-domain-aware block is further developed to integrate time- and frequency-domain features for enhanced feature representation. In addition, a two-stage plug-and-play nonlinear normalization strategy is proposed to enhance the adaptability of LMGM to diverse seismic records while preserving signal characteristics. We employ 3D seismic data interpolation as a representative task to comprehensively evaluate LMGM. The results demonstrate that LMGM achieves superior processing performance while substantially reducing computational cost compared with U-Net-based and Transformer-based methods. Furthermore, experiments on random noise removal demonstrate the applicability of LMGM beyond interpolation, supporting its potential as a general GM framework.

We explore a class of Lorentz-violating scalar-field models where the effects of the scalar field on antimatter can be strongly enhanced compared to matter. Non-trivial differences in phenomenology arise for spectroscopy measurements depending on whether the scalar field changes slowly or rapidly on the atomic scale. We highlight opportunities for precision tests in the laboratory including dark matter searches, in particular with spectroscopy and interferometry measurements, as well as certain types of antimatter experiments.

In order to ascertain the precise atomic characteristics, it is important to incorporate a wide range of electron correlations in the computational analysis. However, it is important to note that undertaking such studies will result in substantial increases of the CSF expansions. The present publication, as well as the series of articles that have been published by G. Gaigalas, P. Rynkun, and L. Kitovienė, are dedicated to the analysis and resolution of the problem in question. In this series, a method was developed based on second-order perturbation theory to identify the most important core-valence, core, core-core, and valence-valence correlations. The method under discussion is based on a combination of the relativistic configuration interaction method and the stationary second-order Rayleigh-Schrödinger many-body perturbation theory in an irreducible tensorial form. In this study, the method is further expanded to encompass additional core-valence electron correlations of the third and fourth types. Conversely, the correlations that are not encompassed by perturbation theory are addressed in a conventional manner. This method can be used to calculate the properties of an atom or ion with any number of valence and core electrons. As an example of its application, the atomic calculations of the energy structure and lifetime for Ne II are presented.

The method based on the second-order perturbation theory to identify the most important configuration state functions of the various correlations is extended to include valence-valence correlations, which are described by the three-particle Feynman diagram. The extension presented in this work complements the core-valence, core, core-core, and valence-valence correlations which were developed in a series of previous papers by G. Gaigalas, P. Rynkun and L. Kitovienė. Whereas these valence-valence correlations are described by the three-particle Feynman diagram, additional developments to calculate the spin-angular parts of this diagram have been made to the program library librang of the Grasp. As an example of the application of the developed method, the atomic calculations of the energy structure for the Se III are presented. In the present work, this method was also used to select the most significant configuration state functions and to use this basis to solve the self-consistent field equations.

Traditional electoral analyses often rely on aggregate sociopolitical indicators or on macroscopic models grounded in statistical physics that treat election data at national level; however, these models have rarely been applied to local-level voting data, where the analytical complexity is substantially higher. In this study, we propose a framework rooted in statistical physics and complex network theory to investigate the fine-grained architecture of voting behaviour at the local scale. Leveraging a granular dataset from the 2024 municipal and 2025 regional elections in Bari, Italy, we represent the urban electoral landscape as a complex network, in which polling sections are modelled as nodes connected by links that encode statistically significant correlations based on vote expression. For each coalition participating in the elections, community detection reveals geographically proximal clusters that transcend administrative boundaries. Furthermore, in order to quantify whether a coalition exhibits territorially homogeneous voting behaviour or, instead, is fragmented into distinct voting blocs, we adapt latent-ideology estimation and the information-theoretic Shannon entropy $H$ to the intra-coalition scale. Our results indicate that winning coalitions exhibit significantly higher territorial fragmentation, and these findings suggest that, at the local level, electoral success is not driven by the maintenance of geographically uniform consensus rather than by the capacity to aggregate diverse and non-homogeneous voting blocs. This scalable, data-driven framework moves beyond simple geographic accounting, providing a robust tool for uncovering the structural dynamics of political idea clustering and coalition fragmentation in complex urban environments.

The properties of water under high pressure and high temperature (HP-HT) are critical in multiple geochemical processes in deep Earth. Ab initio molecular dynamics (AIMD) is a promising approach to study water under extreme conditions without any empirical parameters. However, the accuracy of AIMD simulations is determined by the exchange-correlation (XC) functional including the dispersion correction used in density functional theory calculations. While van der Waals (vdW) interactions are well known to be critically important for water under ambient conditions, the influence of the dispersion correction on the properties of HP-HT water as found in Earth's mantle remains largely unexplored. To address this, we carried out AIMD simulations for supercritical water at 1, 5, and 10 GPa, and 1000 K. We compared PBE, PBE-D3, RPBE-D3, and SCAN functionals, where D3 means Grimme's D3 dispersion correction. We compared the structural, diffusion, and vibrational properties of water as computed with these XC functionals. Overall, the discrepancies between the functionals are reduced under extreme P-T conditions relative to ambient conditions. PBE and PBE-D3 exhibit higher proton-transfer rates than RPBE-D3 and SCAN, suggesting that while vdW interactions do not significantly affect the water structure under extreme conditions, the oxygen-hydrogen bond strength does influence proton transfer. Our results provide molecular-level insight into water in Earth's mantle and offer valuable guidance for selecting appropriate XC functionals in AIMD simulations of aqueous solutions under extreme conditions.

Diffractive optical processors provide a promising platform for high-throughput, low-latency analog computing by exploiting engineered wave propagation to transform optical fields. However, implementing nonlinear mappings in optical hardware remains challenging. Here, we introduce a wavelength-multiplexed encoding-and-decoding (E+D) diffractive processor that exploits multiple illumination wavelengths to enhance the nonlinear function-approximation capability of a compact single-layer diffractive architecture, without cascading multiple diffractive layers or increasing the trainable degrees of freedom. Under sequential wavelength scanning, each wavelength-detector channel implements a distinct nonlinear function at the output of the diffractive processor. Numerical simulations using 100 wavelength channels and 10,000 spatial detector regions at the output of the diffractive processor demonstrate the implementation of one million distinct nonlinear functions through wavelength multiplexing. We also demonstrate a simultaneous multiwavelength illumination configuration in which the E+D diffractive processor performs wavelength-division multiplexing and nonlinear function approximation concurrently. This multiwavelength illumination configuration is experimentally validated by optically implementing 64 distinct nonlinear functions under simultaneous illumination at eight wavelengths from 495 to 635 nm. Through numerical simulations and visible-light experimental demonstrations, our results indicate that this wavelength-multiplexed nonlinear function approximation framework provides a scalable route toward compact, high-capacity diffractive processors for large-scale analog optical computing and optical information processing.

Collective discovery often requires the integration of partially overlapping knowledge, yet the beneficial level of overlap and the role of background topology remain unclear. We model individual knowledge as connected subnetworks of a common knowledge space and distinguish internal synergy, in which integration shortens paths between concepts, from external synergy, in which an unknown concept connects to the shared and all individual-specific regions. Internal synergy peaked at intermediate overlap across random, scale-free, modular, and small-world backgrounds, reflecting a balance between complementarity and common ground. External synergy was strongly topology dependent and was most pronounced when small-world structure maintained overlapping local boundaries. Rewiring separated the total supply of synergistic nodes from their concentration at the accessible boundary; moderate rewiring could increase structural opportunities while dispersing them among more external alternatives and reducing their discovery. Group-size effects on internal synergy depended on topology, whereas strict higher-order external synergy declined as its all-region criterion became more restrictive. Collective discovery therefore depends jointly on knowledge overlap and on how complementary knowledge regions are organized within the surrounding knowledge space.

Simultaneously achieving high quantum efficiency (QE), ultralow mean transverse energy (MTE), and robust long-term operation under conditions relevant to continuous-wave (CW) X-ray free-electron lasers (XFELs) remains a central challenge for semiconductor photocathodes. This challenge arises from the trade-off between QE and MTE, as well as the difficulty of maintaining stable operation in high-field CW electron guns. Here we demonstrate a cryogenic K2CsSb photocathode that simultaneously achieves high QE, ultralow MTE, and robust long-term operation in a CW gun under XFEL-relevant operating conditions. Under cryogenic operation, the photocathode achieves an MTE of 50 meV while sustaining a QE of 5.4%. Milliampere-level CW current, including operation at 5 mA, was demonstrated together with an approximately 20-day operational history. The observations are consistent with improved carrier survival and/or surface escape in photocathodes prepared using the optimized recipe. These results show that the practical QE-MTE trade-off can be substantially mitigated in cryogenic bialkali photocathodes and provide a practical pathway toward high-brightness electron sources for CW XFELs and energy-recovery linacs.

Medical images of soft tissues typically depict loaded configurations rather than true unloaded (stress-free) states, which can bias biomechanical simulations and inverse material identification. A gradient-based inverse finite element framework is presented that jointly reconstructs an effective unloaded reference geometry and estimates hyperelastic material parameters from two or more observed deformed configurations. The formulation is fully differentiable and leverages exact end-to-end gradients to enable unified, simultaneous optimization of geometry and constitutive parameters. The objective function combines a nodal-position misfit with a deformation-gradient-based mismatch term, improving robustness under large deformations. Benchmark studies quantify the influence of loading diversity and observation count and demonstrate reduced sensitivity to poor material initialization. Finally, application to an MRI-derived breast model shows accurate recovery of the unloaded configuration and constitutive parameters from multiple gravity-loaded states. The framework provides a unified and scalable tool for inverse biomechanics with potential applications in personalized modeling, elastography, and surgical planning.

In the simulation of conjugate magnetohydrodynamic (MHD) flows, where a moving conducting fluid dynamically interacts with bounding conducting walls under the influence of an externally imposed magnetic field, resolving the electrical conductivity transition across the fluid-solid interface introduces significant numerical challenges. In problems with high magnetic Reynolds numbers, it is required to solve the full magnetic induction formulation that has a curl-of-curl term for the magnetic diffusion, which is simplified to a divergence term in the original vector-valued lattice Boltzmann method (LBM). The resulting LBM scheme is valid for simulating fluid domains with a constant conductivity where its bounding solid walls are modelled using appropriate boundary conditions. The current study shows that this scheme is also valid for two-dimensional conjugate MHD simulations where the magnetic field component with non-zero gradients is perpendicular to the conductivity gradient. For general conjugate MHD simulations, we improve the LBM scheme by considering the difference between the curl-of-curl term and the simplified divergence form, which is treated as a source term in the LBM evolution algorithm and computed using an efficient LBM discretisation scheme. The improved LBM scheme automatically satisfies the required conjugate constraints at the fluid-solid interface as a volume integral inside two adjacent boundary layers. The accuracy of the improved LBM scheme is verified by piecewise analytical solutions in benchmark problems with an abrupt conductivity jump across the fluid-solid interface.

Recently, radiative coolers have been widely explored for reducing cooling loads or lowering temperatures in buildings, and at urban scales as a heat-mitigation measure. However, the potential impacts of radiative cooler deployment on the chemical composition of the atmosphere remain largely unexplored. A defining feature of recently-designed radiative coolers is their high ultraviolet (UV) reflectance, which is required for sub-ambient cooling under strong sunlight. Yet, wide adoption of such UV-reflective radiative coolers could substantially increase the UV actinic flux in the atmosphere above. This, in turn, may affect tropospheric ozone concentrations, particularly in urban atmospheres with high NOx concentrations. Here, as a case study, we use a 0-dimensional photochemical box model, constrained by field measurements of meteorological conditions and chemical concentrations in the urban environment of Houston, Texas, to explore the potential impact of the widespread use of UV-reflective radiative coolers on ozone concentrations. Our calculations show that complete deployment of radiative coolers may increase tropospheric ozone levels by as much as 30% during specific meteorological conditions in Houston. Informed by the wavelength-dependent modelling results, we propose specific designs, namely pigmented radiative coolers with different UV reflectances, and UV-absorptive visible-reemitting fluorescent radiative coolers, that could minimize negative ozone formation while retaining appreciable cooling performance. Our results motivate further study on the effects of widespread deployment of radiative cooling designs like superwhite roof paints on air quality, and materials that simultaneously minimize adverse photochemical impact and maximize cooling performance.

We present a general method for estimating entropy differences between arbitrary probability distributions using stochastic interpolants. The marginal distributions which bridge the base and target obey a continuity equation, which allows a straightforward calculation of the entropy difference in terms of an inner product of the probability flow velocity and score fields. This formulation has several advantages: (i) no computationally expensive divergence calculations of either field are required, (ii) the score field need not even be learned directly if model transferability is not required, and (iii) on-the-fly estimates are produced nearly for free during training. When tractable base distributions are chosen (e.g. Gaussian chain, ideal gas, etc.), statistical thermodynamic entropies are then immediately recovered. Notably, the analysis relies only on the definition of the Gibbs-Shannon entropy, rather than any particular statistical mechanical ensemble, so that generalized non-equilibrium entropies may be computed. The method is demonstrated on several systems of increasing complexity: (i) a 40-dimensional Gaussian mixture model, (ii) the classical XY model of N spins arranged in one dimension, (iii) the 13-atom Lennard-Jones cluster, and (iv) an active Brownian polymer.

In the present study, the consistent and conservative Phase-Field method is extended to the six-equation model for compressible multiphase flows. Based solely on the conservation laws and the second law of thermodynamics, the six-equation model with the Phase-Field mechanism is first derived. In addition to satisfying Galilean invariance and consistency of reduction, the model is general to admit an arbitrary number of phases with different formulations of the Phase-Field mechanism. The isobaric closure by the pressure relaxation and the incompressible limit of the proposed model are analyzed. The derivation and analysis identify additional terms arising from the Phase-Field mechanism, which are absent from previous studies. The consistent and conservative numerical approach is adapted to the proposed six-equation model with moderate modifications, retaining the interfacial equilibrium condition, conservation, and flexibility and robustness of incorporating different formulations of the Phase-Field mechanism with bound preservation. As the new component of solving the six-equation model, both the pressure and pressure-temperature relaxations are theoretically analyzed in a general multiphase setup, with a proof of the existence and uniqueness of a thermodynamically admissible solution for these two relaxations. Various two-phase compressible flow benchmarks are performed to demonstrate the method, and good agreement with exact solutions is achieved.

The topology of a bounded three-dimensional domain can strongly affect electromagnetic fields. In a topologically complex domain, a curl-free field may not have a globally single-valued scalar potential and a divergence-free field may also fail to have a global vector potential. These topological effects lead naturally to harmonic vector fields in the Helmholtz decomposition. This paper gives a geometric and constructive study of such fields using vector analysis, circulation integrals, cutting surfaces, scalar Laplace problems, Stokes' theorem, and Green's identity. The first Betti number is interpreted through independent handle-type circulations, whereas the second Betti number counts enclosed voids. Direct proofs are given for the dimensions of the Neumann and Dirichlet harmonic-field spaces. The analysis is also extended to anisotropic lossless media. The results provide a simple topological interpretation of harmonic fields and physical DC modes in bounded electromagnetic resonators.

Since Taylor's seminal work on the dispersion of soluble matter, the modeling and control of solute dispersion have received considerable attention. This study investigates the solute dispersion in polymer solutions driven by unsteady magnetohydrodynamic-oscillatory electroosmotic flow within a microtube, aiming to reveal the transport mechanisms in complex rheological media through microscale flow. Firstly, based on the Debye-Hückel approximation, analytical solutions for the electric potential and velocity distributions of mixed magnetohydrodynamic-electrohydrodynamic flows are rigorously derived. Subsequently, using Sankarasubramanian & Gill's generalized dispersion model, mathematical models are developed to systematically investigate the temporal evolution of hydrodynamic dispersion. By investigating the synergistic modulation of electrokinetic and magnetic fields, we identify three distinct flow regimes, namely the electric-field-dominant, competitive transition, and magnetic-field-limited regimes, and quantitatively define their boundaries and evolution with oscillatory Reynolds number $Re$ and Deborah number $De$, thus allowing accurate flow regime classification. Moreover, a non-monotonic critical response governing solute dispersion is observed over a wide range of oscillatory Reynolds numbers, which provides a novel control paradigm for microfluidic mixing and separation. Additionally, through a comparative analysis of fractional Maxwell, classical Maxwell, and Newtonian fluids, we show that microscopic structural relaxation plays a crucial role in macroscopic solute transport under multi-field coupling. Our findings help to reveal flow mechanisms for precise mass transport control in electro-magnetically coupled systems, advancing the development of microfluidic devices and biomedical detection technologies.

Several propagation-based and analyser-based X-ray phase-contrast imaging experiments with over 100 m between the imaged objects and the detector were carried out at the Imaging and Medical beamline (IMBL) of the Australian Synchrotron. These experiments were aimed at characterization of possible phase-contrast imaging setups at IMBL. Effects of the beam divergence, polychromaticity and X-ray source size on the contrast and spatial resolution of images were evaluated quantitatively in different imaging scenarios. The results of this work will be used for improving the existing and developing new imaging capabilities at the beamline. Applications to X-ray phase-contrast tomography of biomedical samples, particularly for medical breast cancer imaging, are briefly discussed.

We address a long-standing paradox in plate tectonics concerning the thermodynamics of its very origin: What supplies the thermal energy for the intense island-arc volcanism at the subduction zones, the supposedly cool end of the mantle convection? We surmise a first-principle physics model tracking the gravitational energy (Eg)-heat conversion within the mantle convection. The scenario points to a great amount of Eg release of ~10 TW induced by the thrust-faulting earthquakes that occur predominantly in the upper mantle at the subduction zones. Generally ~1000 times greater than the seismic energy release or nearly a quarter of the total terrestrial heat flow, this Eg release, upon conversion into heat in the irreversible convection process, supplies amply for the island-arc volcanism. Meanwhile similar but opposite and less vigorous Eg gains at ~2 TW occur at the diverging plate boundaries via the normal-faulting earthquakes. This model resolves the said paradox, and more importantly provides a new paradigm pointing to an active role of seismicity in the plate tectonic energetics and evolution of the convecting mantle.

MLegS is an open-source Modern Fortran code package for solving linear and nonlinear time-dependent partial differential equations on radially unbounded domains. It builds on an earlier in-house prototype, recasting it as a modular solver with a unified interface for fields, transforms, and spectral operators. Within this framework, the numerical formulation maps $0\leq r<\infty$ onto a finite interval, where associated Legendre functions provide the radial basis, while Fourier series represent the periodic azimuthal and axial directions. The mapped radial basis represents radial infinity without imposing an artificial boundary condition at finite radius while incorporating the regularity required at $r=0$ according to azimuthal order. The implementation distributes the corresponding field layouts using the Message-Passing Interface (MPI), with strong- and weak-scaling measurements extending to 512 cores across four compute nodes. Beyond parallel performance, three examples assess the solver across diffusion, reaction-diffusion, and vortex dynamics: closed-form diffusion solutions establish spectral spatial convergence and the design orders of both semi-implicit integrators; a Fisher-Kolmogorov-Petrovsky-Piskunov front matches the linearized solution and propagates at the classical speed within its logarithmic correction; and vortex-pair simulations based on toroidal-poloidal decomposition yield a measured Crow growth rate $2.7\%$ above the theoretical prediction and demonstrate the reconnection of the vortex pair.

Large-amplitude entrywise corruption can distort flow-field data and contaminate extracted modes. Robust principal component analysis (RPCA) separates low-rank flow content from a sparse corruption component, but recovery deteriorates when corruption occupies a large fraction of measurements. We introduce library-learning-assisted robust principal component analysis (LLA-RPCA), which restricts the recovered spatial basis to combinations of functions from a fixed candidate library and prescribes the available modal capacity. Here, the library contains standard trigonometric functions and graph-Laplacian eigenfunctions. The decomposition is solved via augmented-Lagrangian alternating minimization. The method is evaluated on a post-stall NACA0012 wake, oscillating-cylinder wake video data, and particle image velocimetry (PIV) measurements of a flat plate undergoing a transverse gust encounter. For the first two cases, synthetic large-amplitude entrywise corruption is imposed over fractions from 0% to 90%. LLA-RPCA retains coherent wake structures and recovers dominant linear modes at corruption levels where standard RPCA retains residual corruption, attenuates the reconstructed field, or collapses to a one-dimensional reconstruction. For the experimental gust-encounter case, standard RPCA exhibits a trade-off between attenuating coherent flow content and retaining naturally occurring PIV artifacts as its tuning factor increases. Conversely, LLA-RPCA suppresses artifacts while consistently preserving coherent velocity and derived-vorticity structures. These results indicate that a library-constrained reconstruction with prescribed modal capacity improves denoising and modal recovery when the prescribed representation adequately captures the relevant spatial content.

Contemporary memory technologies are increasingly constrained by the fundamental trilemma of storage capacity, access latency, and power consumption. Among the emerging technologies, spin-orbit torque magnetic random-access memory (SOT-MRAM) shows promise to circumvent these challenges, owing to its fast switching dynamics and high endurance. However, the application of SOT-MRAM is hindered by the relatively low write and read efficiencies, resulting in a large bitcell area and an insufficient sensing margin. Meanwhile, the involvement of an ultrathin spin-source channel, typically within a few nanometers, imposes technological challenges for mass production. Here, we resolve these issues on a 300-mm wafer platform by exploiting the emerging orbital degree of freedom and the resultant orbital torque (OT) from the relatively thick Ti/W bilayer. In particular, OT memory nanodevices exhibit a giant tunnel magnetoresistance (TMR) of 182%, nanosecond-scale response, 1012 endurance, together with an enhanced switching efficiency (E_b/I_c), which consequently enables an ultra-low write energy of less than 0.1 pJ/bit. Our findings demonstrate that orbital angular momentum can be implemented for building energy-efficient MRAM devices, offering a practical pathway towards low-latency memory that is demanded for high-performance computing and AI applications.

Can the terminal macroscopic outcome of a many-body system be inferred from its microscopic initial data without simulating the full trajectory? Rather than construct such a shortcut, we address a more fundamental question for Gaussian microscopic inputs: can any fixed Wiener-Hermite degree retain a nonvanishing fraction of the variance of the terminal outcome as the system grows? We consider homogeneous systems with independent Gaussian disorder in which all microscopic coordinates are symmetry-equivalent, the terminal event is monotone in each disorder variable, and a uniform disorder shift is exactly equivalent to a control-field shift with a size-independent conversion factor. Using forward and inverse Gaussian influence bounds together with a Gaussian Russo formula, we derive a directly measurable criterion that is both necessary and sufficient for noise sensitivity. Specifically, the correlation between the original and coordinate-perturbed terminal outcomes vanishes asymptotically for every fixed nonzero level of coordinatewise noise if and only if the slope of the outcome probability with respect to the control field at the balanced threshold grows more slowly than the square root of the system volume. Event-driven simulations of the three-dimensional driven random-field Ising model up to linear size 192 find that both the normalized response and the correlations between perturbed samples decrease overall, consistent with the noise-sensitive regime at finite size. For spatial Stag-Hunt dynamics with prescribed seeds, the criterion generalizes through an effective number of influential coordinates. Separately, simulations of a path-dependent best-response game show, over the sizes studied, that the terminal equilibrium can depend on the update schedule while still carrying substantial finite-order predictive information.

We report the environmental qualification and performance of CombSat, a dual optical frequency comb payload designed for deployment on a 12U CubeSat in low Earth orbit. The payload integrates two self-referenced, 200 MHz hybrid fiber-waveguide frequency combs, a narrow-linewidth optical reference, a heterodyne beat detection unit, and FPGA-based control electronics in a total mass of 8.3 kg and volume of 5.7 L, and nominal power consumption of 21W. We describe the mechanical, thermal, and radiation-tolerant design features of the system and present the results of random vibration, storage temperature cycling, and thermal-vacuum testing conducted in accordance with NASA's General Environmental Verification Standard. We show that key comb performance metrics including oscillator and amplifier output power, frep, fCEO, and fopt signal-to-noise ratios, and integrated phase noise, remain within specification after environmental exposure, and we discuss observed failure modes and design modifications that improved robustness. These results demonstrate that compact, low power, fully stabilized, autonomous dual-comb systems can be engineered and qualified for spaceflight on CubeSat platforms, providing a path forward for future low-cost comb-based space applications.

In this paper, we study Renyi and Tsallis information entropies for a Hamiltonian system with position-dependent mass confined in harmonic oscillator potential. Gegenbauer polynomials are used to obtain the position eigenfunction of such a system, and the modified Bessel function of the second kind is used to determine the equivalent momentum eigenfunction. By means of probability densities of both representations, we analytically and numerically evaluate the Heisenberg-like uncertainty of this system. Because the Renyi and Tsallis information entropies in position representation are described by integral functionals of the Gegenbauer polynomials, these quantities are much more difficult to calculate. To get around this problem, we evaluate these information entropies at the system s asymptotical limit, which corresponds to the behavior of an undeformed harmonic oscillator. Nevertheless, no approximation method is used to obtain the Tsallis and Renyi information entropies in momentum representation. In both representations, we find that these information entropies approach the Shannon entropy when the entropic parameter alpha-> 1 and are closed to the results of similar models of the literature. Finally, we evaluate the related entropic uncertainty relations numerically to validate the latter obersevations.

Probabilistic downscaling must represent kilometer-scale structure left unresolved by a deterministic regional prediction. We introduce an analog-initialized latent transport method that uses historical regional residuals as a meteorologically informed empirical source. A frozen graph neural network predicts the deterministic state from the Integrated Forecasting System (IFS). For each input, residuals from 20 training dates with similar IFS conditions initialize the ensemble. A conditional flow-matching model transports these fields in a carefully designed 65-factor, 100,000-node latent space, and a grid decoder reconstructs 81 atmospheric channels including precipitation at 2.5km spacing. On a season- and cycle-balanced 2025 test, ensemble CRPS improves over deterministic mean absolute error for all 81 variables. Matched analogs improve point and spatial errors over Gaussian initialization and reduce spectral error compared with covariance matching. Comparisons with released CorrDiff, a local CorrDiff-mini model and full grid EDM provide context on the 2023 validation sample. Total training of the downscaling model takes approximately 12h on one NVIDIA H200, and inference takes about 12s per 81-field member. The frozen networks also generate fields over France when supplied with local static data and a high-resolution IFS-derived analog bank. The method combines empirical initialization with whole-domain latent transport for computationally practical regional ensembles.

Establishing a lasting human settlement beyond Earth requires a habitable destination at arrival and sustained renewal of its population/infrastructure. We compare Earth delivery, robotic predeployment, local resource processing, localized manufacture under coupled protection, power, recovery, health requirements. In a 1,000-resident case, specific retention times and 1% annual growth require ~1.8 tonnes of equipment replacement/expansion per resident-year. Local manufacture raises mean site electricity from 11.8 to 19.5 MW; the declared retention range gives 17-26 MW at fixed processing intensity. A separate 100-ton/year pilot reduces 10-year deliveries from 1,000 to 419 tonnes after a 3-year commissioning ramp, while retaining imported inputs. Local production thus reduces freight by adding maintained capacity at the destination. Receiving capacity is bounded by replacement-adjusted supply, not passenger throughput. Published Martian transport gives ~63,000 tonnes of planar cover per hectare for the selected 10-mSv/year GCR allocation; Earth-like acceleration at 2~rpm requires a 224-m rotation radius. These scales favor local shielding and modular rotating habitats. Repeated 10-day wastewater outages every 35 days require capacity at least 1.4 times inflow; storage cannot correct a persistent deficit. Existing evidence leaves lifelong partial-gravity, mixed-field radiation responses, gestation, development and subsequent fertility unresolved. With sustained investment, recoverable habitats, resource pilots, controlled-gravity investigations are credible 10-20-year objectives; broad industrial renewal requires multidecade development. Earth-supported habitation is a credible development path. Independent population continuity requires demonstrated health, recovery, demographic resilience, essential-product renewal within one exposure/resource history; its completion cannot yet be dated.

Young's law on a rigid solid and the Neumann balance at a three-fluid junction describe different admissible motions. We connect them through an ordered continuum construction. An affine viscoelastic third phase first acquires permanent deformation memory in the no-relaxation limit at fixed modulus. Independently specified material-surface laws distinguish surface free energy from mechanical surface stress. On the deformable support, displacement of solid material and migration of the wet-dry partition give separate equilibrium conditions. Stiffening an anchored support suppresses displacement at a fixed macroscopic observation distance but leaves reversible migration possible. The surviving variation gives Young's law; the constrained mechanics remains as a reaction carrying normal and, in general, tangential capillary loads. Two complementary results substantiate this reduction. A clamped finite-strain model with circular liquid interfaces yields uniform convergence of reduced energies and recovery of the spatial Young angle for global almost minimisers. A separate quadratic half-space model gives convergence of stationary migration derivatives and a nonzero limiting traction distribution. The results explain how a local Neumann-like ridge can coexist with a Young angle measured against the distant wall, while distinguishing geometric convergence from the control of migration and reaction forces.

Classical electrodynamics is one of the most well-tested and understood theories in physics. After more than a century of history, it may be surprising that such an established theory still makes new predictions that have not yet been experimentally verified. A noteworthy example is the memory effect—a prediction that an electromagnetic wave can leave a lasting influence long after it has passed. This influence is manifested in a velocity “kick” on a test charge. This simple remark lies at the heart of modern investigations of the low-energy behavior of gravity, electrodynamics, and gauge theories and awaits confirmation (or refutation) through experiments. From a pedagogical perspective, this current research topic beautifully epitomizes how standard concepts from undergraduate electrodynamics can still lead to new physics. In this work, we use the Liénard–Wiechert solutions for the electromagnetic fields of moving charges to understand what the memory effect is, where it comes from, and how it could be experimentally probed in the near future. We also discuss the connections between the memory effect and other important topics in fundamental physics, as well as the search for memory in modern gravitational wave observatories.

Transfer and multi-nucleon transfer reactions are essential tools for probing nuclear structure and reaction dynamics, requiring precise determination of the identity, energy, and emission angles of reaction products. The increasing granularity of modern silicon telescope arrays enhances experimental capabilities but challenges detector calibration, as conventional channel-by-channel approaches become inefficient and difficult to scale. In this work, we present a fully automated, physics-informed calibration framework based on neural networks, specifically designed for highly segmented silicon detector arrays. The method formulates calibration as a global optimization problem, in which detector gains and geometrical corrections are determined simultaneously by minimizing the width of the reconstructed excitation energy under two-body kinematics constraints. The approach relies exclusively on experimental data and well-established physical principles, without requiring explicit modeling of detector response. A distinctive feature is the use of multiple neural network sub-models sharing a common loss function with embedded physics constraints, enabling coherent and self-consistent calibration across all detector channels. This strategy ensures scalability, robustness, and reproducibility, making it particularly suitable for next-generation detector systems with increasing complexity. The performance of the method is demonstrated using experimental data from the Particle-Identification Silicon-Telescope Array (PISTA) in high-resolution fission studies in inverse kinematics. The results show excellent agreement with theoretical kinematics, high-quality particle identification, and a significant improvement in calibration efficiency. The proposed framework provides a general and adaptable solution for the calibration of complex detector systems in modern nuclear physics experiments.