Papers

Being structures characterized by discrete rotational symmetry $\mathcal{C}_\nu$ of order $\nu$, photonic quasicrystals are capable to support stable propagation of linear vortex-carrying light beams and vortex solitons. However, the impact of discrete rotational symmetry $\nu$ of quasicrystals on the properties of vortex light states was not investigated so far, as only the systems constructed using the simplest Penrose tiling or corresponding optically induced Penrose lattices were considered in this context. Here we propose a broad class of quasicrystals with global topological defects – disclinations – introduced into their structure that allows to produce new quasicrystalline structures with any desired order of discrete rotational symmetry from basic Penrose structure. Such global topological deformation substantially enriches linear spectrum of quasicrystals, allowing them to support new types of linear vortex states and bifurcating from them families of stable thresholdless vortex solitons with unusual intensity and phase distributions. We found two different classes of stable vortex solitons consisting of in-phase or out-of-phase pairs of closely located bright spots, with total intensity distribution reflecting particular discrete rotational symmetry of the quasicrystal with disclination. Remarkably, even low-charge vortex solitons can be stable in quasicrystals with disclinations, while stability intervals for them broaden with decrease of the discrete rotational symmetry $\mathcal{C}_\nu$ of quasicrystal. Our results expand the theory of localization in quasicrystals to structures with global topological deformation, highlighting new prospects for robust transmission of power or information arising in these systems.

Super-resolution machine learning methods hold promise for addressing the high computational costs associated with high-dimensional features in traditional transport simulations. This paper proposes a physics-enhanced neural network coupling algorithm for discrete ordinates (SN) transport calculations. Based on high- and low-accuracy data, we develop a predictor-corrector neural network (pcNN) to predict correction factors for coarse-grid solutions. Based on transport equations, local physical features containing subgrid-scale information are selected as network inputs. Dimensionless transformation of physical quantities reduces the input space dimensionality and narrows parameter distributions, facilitating network training. Physical constraints are incorporated into the network model, and the corrected solution retains its original conservation properties. pcNN and SN transport sweeps are internally coupled, and numerical solutions are corrected online. Numerical results demonstrate that this algorithm can effectively improve coarse-grid solutions to achieve a level of accuracy comparable to that of fine-grid results. The physics-based network design enables the model to demonstrate a certain degree of reusability under different computational conditions. Compared to mesh refinement, the pcNN-SN algorithm reduces the computation time for the three-dimensional test problem by more than 60%.

For positive exponents $\alpha_1,\ldots,\alpha_4$ we evaluate the cyclic four-electron Slater integral, the topology recorded as case V in Bonham's 1965 classification, in a finite real expression of weight at most three. The general answer is an explicit sum of eighteen blocks involving only logarithms, dilogarithms and trilogarithms of rational functions of the exponents; all logarithm arguments are positive and all nonconstant polylogarithm arguments may be placed in $(-1,1)$, so no radical, generalized polylogarithm, contour prescription or unevaluated limit is required. At equal exponents the value collapses to $I(\lambda,\lambda,\lambda,\lambda)=(4\pi)^4\lambda^{-4}[\mathrm{Li}_3(1/4)/2-7\zeta(3)/24+\pi^2\log 2/12-2\log^3 2/3]$, a weight-three period over $\mathbb{Q}$ ramified only at 2 and 3. Three independently implemented numerical routes agree at four exponent tuples to at least 32 decimal places, an exact-dyadic evaluation of the classical formula gives eight 100-place certificates, and three published four-electron calibration values are reproduced to every printed digit. We also prove the small- and large-exponent limit laws with closed-form coefficients, dihedral symmetry, and a strict rearrangement law for the three inequivalent cyclic placements of four exponents. The formula applies to the four-cycle and does not evaluate the two chorded topologies.

Classical macroscopic electrodynamics allows free and bound sources to cancel as distributions while the auxiliary equations retain nonzero free-source terms. A neutralized uniformly polarized sphere has $\mathbf{E}=\mathbf{0}$ and $\mathbf{D}=\mathbf{P}$, and a compensated uniformly magnetized sphere has $\mathbf{B}=\mathbf{0}$ and $\mathbf{H}=-\mathbf{M}$. These auxiliary fields are fixed by $\mathbf{P}$ and $\mathbf{M}$ rather than forming independent degrees of freedom. Time dependence separates charge cancellation from complete four-current cancellation. Canceling the charge alone leaves a divergence-free total current whose on-shell transverse transform controls radiation. For a neutralized polarized sphere of radius $R$ maintained by a tangential free-current sheet, the exterior field equals that of a point electric dipole whose effective moment is proportional to the spherical Bessel function $j_2(kR)$, where $k=\omega/c$. Every charge multipole and the ordinary magnetic dipole vanish, yet the source radiates at generic frequencies, and the complete exterior field vanishes at nonzero roots of $j_2$. Complete four-current cancellation removes all retarded source-generated fields. Among the compactly supported separable currents that maintain the same charge, it is the only choice that never radiates. In vacuum the auxiliary fields reduce to $\mathbf{D}=\varepsilon_0\mathbf{E}$ and $\mathbf{H}=\mathbf{B}/\mu_0$, so no radiation can be carried by $\mathbf{D}$ and $\mathbf{H}$ alone.

Multi-detector programs such as Earth 2.0 (ET) require detector characterization across different technologies, operating conditions, and production batches, together with consistent procedures for operation, characterization, and device selection. Traditional laboratory workflows often distribute camera control, illumination, image analysis, and report generation across detector-specific programs and manual steps, slowing iterative development and hindering standardized characterization. We address this gap with PixelXVision, a layered software platform that combines a common camera SDK, preset-based configuration, and a runtime plugin system. Detector-specific behavior is configured through declarative presets, allowing the same host interface to operate CCD, CMOS, and infrared detectors without model-specific core changes. Characterization procedures are implemented as plugins that use a shared object manager to access camera, illumination, storage, and database services. Each run links raw frames, measured operating conditions, configuration, analysis products, and reports into a traceable archive with a queryable index. The platform supports lightweight deployment for iterative single-detector development and structured workflows for multi-detector campaigns. The platform has been applied in characterization testing across CMOS, CCD, and HgCdTe cameras, and its configurable architecture can accommodate additional camera models for subsequent CCD testing for WFST without model-specific changes to the core host software. For operational troubleshooting, the platform integrates ChatPixel, a knowledge-base assistant that provides answers with cited sources when it has relevant information.

Marine lakes offer a unique opportunity to study how physical processes, such as turbulent mixing, biomixing, and tidal exchanges, regulate the vertical transport of heat, oxygen, and nutrients in relatively isolated ecosystems. The effective diffusion coefficient characterizes the overall mixing and is critical in models of marine lakes, but it is difficult to measure directly. This paper addresses the problem of inferring the depth-dependent diffusion coefficient from synthetic and real measurements of vertical temperature and salinity profiles. To do so, an inverse problem governed by a marine lake model described by the screened-Poisson equation (similar to Helmholtz) is formulated. The inverse problem is formulated as a nonlinear least-squares optimization problem, where the cost functional quantifies the misfit between the observed and recovered profiles. A Tikhonov regularization term is added to improve the conditioning of the discretized problem, with a regularization parameter chosen using the L-curve method. We solve this problem using an adjoint-based inexact Gauss-Newton method. To leverage the complementary information in these profiles, the formulation optimizes the fit across both datasets simultaneously. The accuracy of the reconstructed diffusion coefficient and the method's robustness to noise are investigated through comprehensive synthetic studies. Additionally, we contrast this framework with physics-informed neural networks, highlighting the advantages and limitations of each. When applied to real marine lake data, the adjoint method reconstructed a smaller diffusion coefficient in regions of strong density stratification, in line with physical expectations. The results indicate that the proposed approach provides a foundation for accurate modeling of marine lake dynamics.

In this study, we investigate the role of bulk viscosity in the evolution of a spatially flat Friedmann-Lemaitre-Robertson-Walker (FLRW) universe dominated by dust. We parameterize the bulk viscosity as p_tilde = -3[l + m(H'(t) + H^2) + nH^2]H, derive the modified Einstein field equations, and reformulate them in terms of redshift z. Using 46 Hubble parameter measurements and the Pantheon+ compilation of 1701 supernovae, we estimate the model parameters (H0, l, m, n) through chi2 minimization and refine them using Markov chain Monte Carlo (MCMC) simulations. The analysis gives a transition redshift z_t = 0.585 and a present deceleration parameter q0 = -0.705, consistent with Lambda CDM. The present age of the universe is estimated as t0 = 14.5734 Gyr. We obtain H0 = 68 km/s/Mpc from the Hubble datasets and approximately 73 km/s/Mpc from Pantheon+. These results illustrate the Hubble tension between late- and early-universe determinations of H0. We further investigate the evolution of the equation-of-state parameter omega(z), energy density rho(z), and pressure p(z). Statefinder diagnostics (r,s) demonstrate deviations from and convergence toward Lambda CDM at different epochs. The results highlight the potential role of bulk viscosity in describing the accelerated expansion of the universe and provide a useful framework for studying cosmological models.

Generalized trace dynamics (GTD) is a deterministic dynamics of aikyons (atoms of space-time-matter) whose near-equilibrium statistical mechanics yields quantum dynamics, while large anti-self-adjoint fluctuations drive spontaneous localisation and emergent classical spacetime. We separate two independent large limits: long-Connes-time coarse-graining of a finite system, which conditionally yields the quantum Ward identities, and the extensive many-aikyon/large-matrix limit required for a collective singularity. We turn the condensed-matter analogy of the E8 x omega E8 programme into a quantitative, falsifiable effective framework. Four coupled composite channels (bifermionic localisation, geometric soldering/Lorentz-Higgs, gauge-invariant electroweak, and matrix-valued flavour) define an exact susceptibility matrix and Legendre effective action, giving zero-mode and free-energy criteria for a common continuous or first-order transition. We pose the nonequilibrium problem in Schwinger-Keldysh form, define a projected fluctuation-dissipation defect and the martingale condition for Born probabilities, identify Newton's constant with the stiffness of the ordered geometric phase, and give conditions under which the Sakharov, constrained-BF, Jacobson, Padmanabhan and Verlinde descriptions of gravity are limits of one substrate. A regulated two-sector surrogate ensemble (a Myers-deformed geometric triple coupled to a matter matrix, with a Grassmann-regulated fermionic extension) shows that selected diagnostics are calculable: exact Adler-Millard charge conservation, a multi-channel first-order jump, a verified fluctuation-dissipation relation and its quench defect, and a fermionic spectral dimension d_s = 1.96 on the emergent two-sphere. The result is a calculational programme, not a completed derivation; the next step is the many-aikyon composite two-point matrix for GTD itself.

We develop a covariant Hamiltonian formalism in which evolution is measured along the proper-time flow of an observer congruence, replacing the external coordinate-time derivative with the Lie derivative. Our formalism keeps the Hamiltonian split between evolution and configuration variables and expresses it through spacetime tensors projected relative to the chosen congruence. After quantization, the resulting observer-congruent brackets lead to relativistic Heisenberg and Schrödinger equations whose evolution parameter is proper time. We then apply this framework to gravity by taking the projected spatial metric and its conjugate momentum as canonical variables, obtaining a complete model of canonical quantum gravity. Among the predictions of this theory we find a resolution to the singularity problems in cosmology, black holes and black hole thermodynamics.

The existence of three fermion generations remains one of the unexplained structural features of the Standard Model. This article reviews an algebraic framework in which an intrinsic $S_3$ family symmetry relates three gauge-equivalent fermion sectors. The framework is motivated by division- and Clifford-algebra constructions in which the complex octonions and $\mathbb{C}\ell(6)$ organise the colour and electromagnetic quantum numbers of one generation. Continuing the Cayley–Dickson sequence, the sedenions provide an intrinsic $S_3$ automorphism structure, while their complexified left-multiplication operators generate an associative algebra isomorphic to $\mathbb{C}\ell(8)$. In the $\mathbb{C}\ell(8)$ formulation, the order-three family action generates three linearly independent fermion sectors while leaving a single $\mathfrak{su}(3)_C\oplus\mathfrak{u}(1)_{\mathrm{em}}$ gauge algebra. Extension to $\mathbb{C}\ell(10)$ incorporates a single $\mathfrak{su}(3)_C\oplus\mathfrak{su}(2)_L\oplus\mathfrak{u}(1)_Y$ gauge algebra, with each generation including a right-handed neutrino sterile under the SM gauge interactions. We place the construction in context by comparing it with representative algebraic and family-symmetry approaches to three generations, including triality-based proposals, and clarify why the family symmetry used here is distinct from standard $\operatorname{Spin}(8)$ triality despite the common appearance of $\mathbb{C}\ell(8)$ and $S_3$. The framework remains algebraic and representation theoretic rather than a complete dynamical theory; family-symmetry breaking, realistic fermion masses and mixing, and a dynamical account of the gauge and matter sectors remain open problems.

We formalize the gauge structure of the Navier–Stokes equation for incompressible fluids, interpreting $\nabla\cdot\mathbf{v}=0$ as a gauge fixing analogous to the Coulomb gauge in electromagnetism. We construct the Martin–Siggia–Rose action and its full BRST symmetry, introducing BRST doublets for the Gribov parameter $\gamma$ and the monodromy phase $\theta$. Through a Higgs mechanism, $\gamma$ acquires a vacuum expectation value $\gamma_{0}$, generating a mass scale for the vorticity and the ghosts; we compute the one-loop effective potential and analyze vacuum stability. Intermittency — measured by the exponents $\zeta_{n}$ — is described by fluctuations of the Higgs field $\sigma$ around the condensate, via a Gribov-inspired log-Poisson hierarchy that satisfies exactly $\zeta_{3}=1$ (Kolmogorov's four-fifths law). Fits to DNS data favor filamentary vorticity structures, with $D_{f}\approx1$ (one parameter) or $D_{f}\approx2.1$, $\Delta\approx0.44$ (two parameters). The one-loop anomalous dimension in $d=3$, $\Delta_{\sigma}\approx0.50$, supports the identification $D_{f}=2\Delta_{\sigma}$. The formalism unifies classical hydrodynamics with gauge theory and spontaneous symmetry breaking, opening the study of turbulence and intermittency to quantum field theory methods.

Physics-informed neural networks (PINNs) provide a mesh-free framework for solving governing equations, but their application to granular avalanche dynamics over curved terrain remains largely unexplored. This study extends a depth-averaged PINN formulation based on the Savage-Hutter equations to an exponentially curved chute with spatially varying inclination and a strain-rate-dependent Mohr-Coulomb earth-pressure closure. The model is validated against measured front- and rear-edge trajectories from a laboratory granular-avalanche experiment, with selected observations withheld from training. A staged temporal curriculum proved essential for accurate prediction, reducing the held-out trajectory error by approximately two orders of magnitude compared with training over the full time domain from the outset. Sparse-data experiments further showed that observation placement was more influential than observation number within the configurations tested. Four observations bracketing the transition from acceleration to deceleration achieved nearly the same accuracy as the eight-observation reference configuration, whereas observations clustered at early or late times performed poorly. The results demonstrate the importance of both training strategy and informative data placement when applying PINNs to granular flows over curved topography.

Biological molecular machines are mesoscopic systems that act repeatedly and periodically to perform important cellular tasks while contending with strong fluctuations and operating in an overdamped regime. Optimal control theory is a tool that can be used to understand the design principles behind efficient operation of these machines; however, most studies on optimal control of classical mesoscopic systems have focused on control problems that do not repeat periodically. Here, we automatically differentiate Fokker-Planck simulations to design efficient nonequilibrium control strategies for simple models of periodic molecular machines with and without explicit changes in the machine's chemical state. The designed protocols and theoretical analysis provide insight into the design principles governing efficient driving in these nonequilibrium systems. Designed control protocols should seek to reduce mechanical heat by rotating the entire angular probability distribution at a constant speed without changing its shape, and should reduce chemical heat by reducing the proportion of chemical transitions with large heat.

Precipitation forecasts shape decision-making across the global economy, particularly in sectors such as agriculture. However, unlike variables such as temperature, precipitation is highly intermittent and localized, making it difficult to forecast. While recent advances in AI weather prediction systems have enabled them to surpass physical models on globally averaged metrics, improvements in mean error rarely translate to actionable forecasts of severe flooding or dry crop fields. Furthermore, most of these models are trained and evaluated against a reanalysis data product, ERA5, which has well-known biases. Here we retrain AIFS, ECMWF's widely-used, open-source operational 0.25{\deg} probabilistic graph-transformer weather model, on satellite-based precipitation observations. Our model, Laxmi, improves global probabilistic accuracy by 19% and resolves systematic distributional biases in ERA5. Specifically, Laxmi reduces drizzle overprediction by 33% for amounts less than 3 mm per day. It also mitigates extreme rainfall underprediction, improving the global 95th percentile Brier skill score by 57%. Across a case study of 10 Indian tropical storms, Laxmi delivered the most accurate forecast of 150 mm event-total precipitation in 7 events, compared to 1 for AIFS and 2 for the leading physical model, IFS. Our results demonstrate that incorporating observation-based precipitation data directly into training can substantially improve forecasts.

Reconfigurable integrated photonic spectrometers can generate exponentially increasing measurement states from a small number of switching elements, making them a promising design class for high-performance portable spectroscopy. However, selecting a circuit architecture remains largely intuition driven. We introduce a grammar-based topology optimization framework that composes standard photonic building blocks, canonicalizes and physically prunes candidate circuits, and jointly searches topology and component parameters using a coherent scattering-matrix model. Designs are screened by a decoder-independent architecture objective that combines absolute singular-value noise gain with multi-line conditioning, thereby accounting for response diversity and optical throughput. Direct held-out spectrum reconstruction confirms that this surrogate is a strong predictor of reconstruction quality at high incident signal-to-noise ratio (SNR). Across the nonresonant and resonator-augmented design spaces examined, dFT and its variants offer the best balanced performance; increased component loss favors a shallower dFT variant in which one differential-delay stage is replaced by an analog optical phase shifter. Passive and active rings provide no robust advantage attributable to resonant amplitude coding. The results identify balanced complementary interferometric responses, efficient terminal collection, and limited lossy circuit depth as central design rules, and establish a systematic route for selecting task-optimized photonic circuit topologies under technology-specific constraints.

Nested sampling (NS) resolves the thermodynamics of an atomistic system from a single simulation, but its practical reach is limited by the Markov-chain updates needed to decorrelate walkers within each likelihood-constrained ensemble. Flow-based NS has reduced this bottleneck for gravitational-wave (GW) inference, yet its transfer to atomistic systems is not merely a change of application. Comparing a GW150914-like binary-black-hole likelihood with an eight-particle two-dimensional Lennard-Jones (LJ) system of comparable dimensionality, we show that the two landscapes differ fundamentally: atomistic multimodality is discrete and combinatorial, generated by particle permutations separated by hard collision walls, and its coordinate coupling is dense and collective, whereas the GW posterior exhibits smooth degeneracies and localized parameter coupling. Guided by this diagnosis, we introduce NS-Flows: a single conditional normalizing flow, conditioned on the NS energy bound and trained on a sliding window of recent live sets, that replaces MCMC by direct parallel draws corrected by importance-weighted rejection resampling. Live sets supply the data self-consistently, allowing flow training without structured priors or a pre-existing dataset. For LJ disks under periodic boundary conditions, the algorithm can reduce energy evaluations by two orders of magnitude and wall-clock time by about 30%, an advantage that becomes more favorable as the cost of the potential grows. The flow's generative efficiency further acts as a physical diagnostic: it varies non-monotonically along the annealing trajectory, is lowest in the dense disordered regime, and is quantitatively captured by the constrained ensemble's internal mode complexity together with target drift across the training window, identifying liquid-like ensembles, not prior-target separation, as the hard case for current flow architectures.

Training deep learning-based medical image segmentation models is challenging with limited curated datasets. For AGITG TOPGEAR, a gastric cancer trial, the Clinical Target Volume (CTV) is complex and defined by multiple anatomical landmarks, making upfront training data preparation difficult for an automated contour QA segmentation model. We investigate anatomical priors, derived from surrounding organ segmentations, to provide spatial context and improve TOPGEAR CTV segmentation accuracy. We also evaluate active learning, iteratively expanding the training dataset by selecting cases expected to improve performance. One hundred TOPGEAR CT scans were retrospectively analyzed. An initial set of 10 expert-contoured cases was used to train an nnU-Net model. TotalSegmentator generated a voxel-wise anatomical prior map from surrounding structures as an additional input channel. Active learning was simulated over four iterations, selecting cases by model uncertainty and segmentation performance. All models used five-fold cross-validation for an ensemble uncertainty measure. Evaluation used a hold-out testing set of 50 cases. The anatomical prior improved CTV segmentation accuracy, increasing mean Dice Similarity Coefficient (DSC) from 0.84 to 0.86. Active learning similarly improved performance to 0.86, with greatest benefit in the final round. Combining the anatomical prior with active learning achieved the highest accuracy, with a DSC of 0.87. Model uncertainty correlated with DSC, supporting its use in identifying suboptimal predictions and guiding active learning. Anatomical priors and active learning each improved CTV segmentation accuracy and generalizability, with their combination achieving the best performance, supporting integration into segmentation model development for automated contour QA in radiotherapy clinical trials.

The origin of magnetic fields in turbulent astrophysical systems has long been a central problem in plasma astrophysics. Fluctuation dynamos are a class of field amplification mechanisms that occur in turbulent magnetohydrodynamics whereby stochastically forced motions of plasma at sufficiently high magnetic Reynolds numbers exponentially amplify magnetic energy. For steady forcing, such dynamos saturate with magnetic energies at a sizable fraction of the turbulent kinetic energy. Although fluctuation dynamo is widely studied within the framework of resistive magnetohydrodynamics (MHD) and driven-turbulence numerical simulations, this work explores the difference when the Hall term is included in the magneto-fluid's generalized Ohm's law. The inclusion is motivated in part by recent high energy-density plasma experiments studying fluctuation dynamo that are governed by an extended magnetohydrodynamics (xMHD) ansatz, which includes the Hall term. We first discuss the details of the Hall-MHD implementation in the FLASH code, the tool we use to model xMHD fluctuation dynamo. We then investigate the influence of the Hall term on the fluctuation dynamo in a three-dimensional periodic box, driven with stochastic forcing at the box scale. We compare cases with a Hall term of varying magnitude to no-Hall cases with respect to the magnetic field growth rate, saturation level, and magnetic field structure. The Hall-MHD fluctuation dynamo is found to saturate at lower magnetic energies and with fewer small-scale magnetic structures than the no-Hall cases. Both findings are consistent with the interpretation that the Hall term acts as an additional, non-linear transport term, akin to an enhanced turbulent diffusivity.

Second-order Moller–Plesset perturbation theory (MP2) is one of the simplest correlated wave-function methods, but its conventional $O(N^5)$ cost limits its application to large systems. Stochastic tensor contraction (STC) has recently appeared as a general technique to evaluate high-order tensor contractions in quantum chemistry. Here, we apply STC to the exchange contribution of Laplace-transformed MP2, which is the source of $O(N^5)$ scaling in the formulation. The resulting STC exchange algorithm has an $O(N^3)$ deterministic setup cost and an $O(N^2)$ stochastic cost at fixed absolute error. The estimator is unbiased and provides a way to specify the target stochastic error by estimating the number of required samples before the full calculation. We implement the algorithm using a hybrid deterministic–stochastic evaluation strategy, with grouped index sampling, to reduce the computational prefactor. Over a range of benchmark molecules containing up to $\sim 7000$ basis functions, the stochastic exchange evaluation takes as little as $1/270$ of the time of a complete DF-MP2 calculation on the same system. Within the Laplace-transformed formulation, the $O(N^4)$ scaling direct contribution is thus the only significant cost. Our results further substantiate the power of STC to serve as a general tensor-contraction engine for quantum chemistry.

We present a high-order multiphysics solver for the simulation of microwave-heated flows. The solver couples a discontinuous Petrov-Galerkin (DPG) finite element method for the time-harmonic Maxwell equations with continuous Galerkin finite element methods for the heat equation and the incompressible Navier-Stokes equations. We validate the electromagnetic solver against multiple benchmark problems: wave propagation in a rectangular waveguide, a cavity problem with a singular solution, and a microwave-heated obstacle problem, comparing our results against numerical and experimental data from the literature. The results confirm the validity of the implementation and demonstrate its ability to perform adaptive mesh refinement using the DPG method's built-in error estimator. The final part of the study showcases the capabilities of the multiphysics framework through simulations of microwave-heated flow around obstacles with singular geometric features. These results highlight the potential of the proposed framework for the simulation and optimization of microwave-assisted chemical processes. Finally, the developed high-order multiphysics solver has a low memory footprint, since the electromagnetic solver relies on a Conjugate Gradient (CG) iterative solver and the fluid solver is implemented in a matrix-free fashion, making the overall approach scalable and well-suited for large-scale parallel simulations.

Electrochemical reaction rates are controlled by both interfacial charge-transfer kinetics and reactive-ion transport. In charged porous media, fixed charges enrich reactive counterions near pore walls, enabling transport beyond the classical diffusion limit via surface conduction (SC). Under overlimiting conditions, classical Butler-Volmer kinetics predict indefinitely increasing current with overpotential, contrasting with microscopic electron-transfer theories, which impose a finite reaction-limited current. Here, we couple the one-dimensional leaky membrane model to coupled ion-electron transfer (CIET) kinetics to examine the interplay between transport and reaction limitations. The limiting behavior is governed by the scaled surface charge $(\tilde{\rho}_s)$ and a Damköhler number $(Da)$ comparing reaction-limited and diffusion-limited currents. We derive analytical limiting-current expressions for neutral, positive, and negatively charged porous media, mapping underlimiting-to-overlimiting transitions in the $(Da,\tilde{\rho}_s)$ plane. By preventing reactive-ion depletion, SC restores polarization-curve sensitivity to charge-transfer kinetics that would otherwise be obscured by diffusion limitation. Fitting to published Cu electrodeposition data in charged AAO membranes yields $Da\approx24$. CIET parameters fitted to AAO($-$) also describe AAO($+$) and predict a finite AAO($-$) reaction limit beyond the applied voltage range. These results provide a framework for distinguishing transport-limited and reaction-limited responses in electrochemical systems and establish charged porous media as platforms to reveal electrochemical reaction-kinetic descriptors.

Rocky planets evolve through the exchange of volatiles between their interiors and atmospheres, an interplay still poorly constrained by observations. Remarkably, highly irradiated ultrashort-period (USP) exoplanets may offer a window into this exchange – some retain low bulk densities compatible with volatile-rich envelopes surrounding rocky interiors, indicating possible secondary atmospheres. TOI-561 b is a prime example, with a bulk density of $4.3\pm0.4$ g cm$^{-3}$ and recent JWST observations favoring a thick volatile atmosphere overlying a dayside magma ocean. Here, we investigate the evolutionary pathways allowing TOI-561 b to retain a substantial atmosphere over gigayears using the PROTEUS coupled interior–atmosphere framework. We explore different core radius fractions, Bond albedos, atmospheric escape efficiencies, mantle redox states, and initial C–H–O–N–S volatile inventories, under in situ evolution and late inward migration. Over half of our simulations leave a bare interior too dense to match observations. Successful cases favor a volatile-rich origin ($\lesssim200$ Earth oceans of hydrogen, S/H $\le10$, and N/H $\le1$), an oxidized mantle ($f$O$_2 \gtrsim \mathrm{IW}+4$), a small iron core ($\le 0.40$ for the core radius fraction), and low escape efficiency ($\epsilon \lesssim 10^{-3}$) in the hydrodynamic escape regime. At present, TOI-561 b is consistent with a global magma ocean beneath a thick (surface pressure $\approx 10^{3}$–$10^{4}$ bar), high mean molecular weight atmosphere ($38$–$60$ g mol$^{-1}$). Two archetypes emerge, differentiated by bulk sulfur content: a CO$_2$-dominated and an SO$_2$-dominated atmosphere. Migration is viable but not required to reproduce the observations. Our study illustrates how interior–atmosphere coupling governs atmospheric retention on irradiated rocky planets.

Expensive fusion simulations are commonly preserved in code-specific formats that limit discovery, comparison, and reuse. We present the Multiscale GyroKinetic DataBase (MGKDB), an open-source software framework and curated archive that converts heterogeneous simulation campaigns into traceable scientific records. Each record links code-native inputs and outputs to provenance and quality metadata, an IMAS-aligned physics representation, and derived diagnostics, preserving model-specific evidence while enabling common-field queries. Production pathways support linear and nonlinear GENE and CGYRO calculations and reduced quasilinear TGLF evaluations. At the September 1, 2026 snapshot, MGKDB contained 1,068,089 records, nearly all of which included a populated gyrokinetics IMAS branch. The software is openly available, while access to the NERSC-hosted production records is managed. Three demonstrations show how these linked representations support scientific reuse. Standardized quantities stored in the Diagnostics branch enable population-scale analysis of archived linear modes; common input coordinates reveal coverage, redundancy, and campaign-driven sampling structure across a multicode collection; and record-level retrieval of native CGYRO inputs drives matched TGLF calculations and produces a traceable dataset for exploratory surrogate modeling. Together, these examples demonstrate how MGKDB supports archive characterization, candidate cross-code and cross-fidelity comparisons, campaign planning, and reproducible data-driven modeling without treating different models as automatically equivalent.

Boron neutron capture therapy (BNCT) produces high-linear-energy-transfer, short-range particles through the $^{10}$B(n,$\alpha$)$^{7}$Li reaction. Conventional descriptors, including absorbed dose, compound biological effectiveness (CBE)-weighted dose, and photon-isoeffective dose ($D_{\mathrm{isoE}}$), remain clinically useful but compress cellular heterogeneity, compartmental boron localization, and stochastic reaction occurrence into macroscopic summaries. We propose a hierarchical framework that places cell-associated $^{10}$B-capture reaction statistics upstream of microdosimetric effect modeling and links clinical observations to cellular-scale determinants of BNCT response. We synthesize published work on BNCT microdosimetry, CBE and $D_{\mathrm{isoE}}$, stochastic microdosimetric kinetic modeling, PHITS-based cellular simulations, boron imaging, tissue microstructure, and boron-agent characterization, reorganizing these elements into observational, latent-cellular, reaction-count, and biological-effect layers. Imaging, pathology, agent-specific data, and neutron-field information constrain latent variables such as cellular boron burden, compartmental localization, and geometry; these variables determine expected reaction burdens and distributions of realized reaction counts, which are then translated into cell survival, tumor control, or normal-tissue injury by microdosimetric models. Cell-associated reaction-count statistics thus provide an interpretable inferential bridge between macroscopic measurements and established BNCT effect models, supporting spatially resolved and uncertainty-aware effect prediction without replacing existing approaches based on CBE, $D_{\mathrm{isoE}}$, SMK, or PHITS.

Gas sensors based on layered TMDCs (transition-metal dichalcogenides) such as MoS2 have attracted considerable attention recently and are considered as a promising alternative to the conventional metal-oxide-based gas sensing devices. Recent studies on TMDC gas sensors have mainly addressed either the theoretical description of adsorption mechanisms or the fabrication of test devices and the investigation of their gas sensing performance. However, a suitable approach for modeling the gas sensing behavior of TMDC devices and for establishing a reliable connection between theoretical results and the response of experimental devices when exposed to gases remains absent. To bridge this gap, in the present work a new Langmuir-based adsorption model, capable of calculating both the sensor sensitivity and selectivity, is developed and applied to MoS2-based gas sensors. It is shown that by using appropriate published data for the adsorption energy and the charge transfer as input data, the reported experimental sensitivities of MoS2 gas sensors can be well reproduced by the model.

Geostationary atmospheric motion vectors (AMVs) provide the dense horizontal wind vectors (u,v) and heights ingested into data assimilation systems. Traditional AMVs track features using window-based cross-correlation and estimate heights via infrared brightness temperatures paired with numerical weather prediction (NWP) background states, creating a circular dependency that yields inaccurate heights, high computational cost, and sparse retrievals. Stereo winds from GEO-GEO and GEO-LEO geometrically resolve heights from parallax shifts across different poses, eliminating NWP dependence and improving accuracy, but they remain computationally heavy with limited coverage. In this work, we replace window-based tracking in stereo matching with deep optical flow for efficient, improved retrieval. Fine-tuning balances a self-supervised geometric residual loss with supervised radiosonde reconstruction. To eliminate multi-satellite overlap requirements, we distill the stereo teacher into a single-satellite student model. Chi-square and height uncertainties from the teacher are emulated by the student for quality assurance. The student generates winds across full-disk GEO imagery globally. Validation compares stereo and student models against radiosondes, operational AMVs, ERA5 reanalysis, and EarthCARE cloud profiles. Results through triple collocation show that stereo winds improve performance beyond operational AMVs for water vapor bands (6.2, 6.9, and 7.3 {\mu}m), wit degradation in the long-wave infrared (11.2 {\mu}m) band.

The thickness of a reconnecting solar wind current sheet is normally inferred from a single spacecraft as the product of the boundary-normal speed and the crossing duration. Multipoint measurements have tested this method for generic solar wind current sheets and constrained the geometry of reconnection exhausts, but a direct comparison with single-spacecraft thickness estimates for the same reconnection-associated crossings is still lacking. We analyze four current-sheet crossings observed by Magnetospheric Multiscale in the pristine solar wind on 2017 November 10 the two boundaries of a confirmed reconnection exhaust and two nearby current sheets. The tetrahedron separation was $\approx 16$ km. Cross-correlation of the magnetic-field ramps gives millisecond-level lag uncertainties, boundary normals to $4^\circ-8^\circ$ and speeds to $5\%-8\%$, while single-threshold timing at these separations is noise dominated. The exhaust edges are 29-31 ion inertial lengths ($d_i$) thick. The two edge normals differ by $22^\circ$ (68% interval [$19^\circ,26^\circ$]), resolving a nonparallel exhaust geometry. At the exhaust edges, the measured boundary speeds agree with three standard single-spacecraft estimates to 4%-15% and remain within about one Alfvén speed of the plasma motion. Estimating the normal from one spacecraft is the larger source of error, though the thickness is still recovered to within tens of percent when a degenerate minimum-variance solution is rejected. At the actively reconnecting sheet, the boundary speed exceeds the local plasma speed along the normal by $29\% \pm 10\%$.

Calcium carbonate formation from aqueous solution is central to biomineralization and to carbon sequestration through mineral carbonation. At near-neutral pH, the process is highly reactive, with proton transfer mediating the interconversion between carbonate species. Most atomistic simulations to date either treat carbonate speciation as fixed or consider proton transfer only in small clusters. Here, we combine an ab initio trained atomic cluster expansion (ACE) machine-learning potential for molecular dynamics with enhanced sampling to enable reactive simulations of the early stages of calcium carbonate precipitation at previously inaccessible length and time scales. We study proton transfer and carbonate speciation in ion pairs and triplets, as well as in the collective aggregation of many ions. Our simulations with few ions show that ion association provides a favorable pathway for proton transfer, facilitating interconversion between carbonate, bicarbonate, and carbonic acid. In many-ion systems, proton transfer occurs spontaneously alongside aggregation, and we observe significant changes in the coordination environments as species evolve during the simulations. These results show that ion aggregation and chemical reactivity can be strongly coupled during the early stages of nucleation from solution.

Learning solution operators across broad parameter ranges can require substantial coverage of both input functions and physical parameters, particularly for purely data-driven parametric models. In addition, the resulting models may fail silently outside the training distribution. We introduce equation recast, which reformulates parametric operator learning as the learning of a single canonical operator. Parameter-induced operator variations are derived analytically from the governing equation and absorbed into effective sources, enabling zero-shot prediction across new parameter regimes. Across multi-parameter, nonlinear, and singular PDE settings, equation recast supports extrapolation, integrates sparse heterogeneous datasets in a shared canonical representation, and uses loss of convergence as an internal warning signal for failure of the recast iteration. In high-fidelity tokamak simulations for nuclear fusion, the framework unifies electron-temperature data across four device geometries through canonical-domain mapping within one jointly trained operator. Equation recast provides a route toward reusable neural PDE solvers combining equation-guided transfer, data efficiency, and monitorable inference.

We formulate a finite-temperature residual test in IDMate that bounds window-resolved electronic errors without a spectral-gap assumption. For a fixed Hamiltonian and exact electron number, strong convexity of the matrix Fermi entropy bounds the density-matrix distance and free-energy error within a selected window. The bound remains finite at spectral crossings and extends to weighted k points with a shared chemical potential. We also derive a distance correction for particle-number mismatch. Across $3{,}586$ stress trials in $70$ seeded perturbation ladders, $1{,}942$ proposals satisfy the screen with no observed violation of the $0.05$ window-distance criterion plus its numerical allowance. This criterion differs from the uncorrected exact-trace bound, which six of ten historical in-loop candidates exceed at the numerical-error scale. An accept-or-recover loop replaces ten reference-map evaluations while meeting terminal comparison criteria in three configurations that include oracle-subspace controls. Additional candidates built only from preceding-iteration orbitals yield two acceptances and one abstention. The silicon candidate has a window distance of $1.891\times10^{-13}$ but a normalized real-space density error of $3.754\%$. Analytic examples separate errors from complement occupations and interblock coupling. Window-level accuracy therefore does not imply full-state accuracy; the screen tests compressed proposal quality, independently of nonlinear SCF convergence or net acceleration.