Papers

A companion paper \cite{ItkinDF2026} introduced the Diagonal Frog (DF) positivity-preserving schemes for anisotropic Fokker–Planck equations, advancing each directional substep by a Krylov-computed matrix exponential, which dominates the cost. Replacing that exponential by a rational map $r(\gamma L)$ reduces the substep to a banded solve, but the positivity argument no longer applies. We prove that for eventually exponentially positive generators the entrywise sign of $r(\gamma L)$ at large steps is decided by a single number, the value $r(\infty)$ taken on infinitely stiff modes. Nonnegativity holds above a computable threshold when $0\le r(\infty)<1$, and at most on a bounded interval, empty or vanishingly narrow in all our tests, when $r(\infty)<0$. The criterion rejects the Crank–Nicolson (trapezoidal) method, where $r(\infty)=-1$, and selects the subdiagonal Padé$(0,2)$ method, which is second order, L-stable and provably positive above an explicit threshold. The resulting DF-ADI scheme costs $O(N)$ per step, keeps the implicit factorized mixed derivative unchanged, is second order in space and time, and conserves discrete mass exactly. In the strong cross-diffusion regime, however, the directional factors demand a step larger than the mixed derivative permits, so the composite second-order scheme is only empirically positive there, and the criterion serves to discriminate the well-behaved multiplicative factors from the stabilizing-correction schemes rather than to guarantee positivity. Against the Krylov exponential it runs ten to thirty-two times faster at matched accuracy in our tests with the gain growing with the mesh. We extend the construction to the backward Kolmogorov equation and to jump-diffusion models.

In this work, we propose a model-based feedback controller that regulates the vertical instability growth rate ($\gamma_{gr}$) of a high-elongation, double-null tokamak directly, using only out-vessel poloidal field (PF) coils. High elongation raises the achievable plasma current and fusion performance but makes the plasma vertically unstable, and in a fusion power plant the in-vessel coils that present devices rely on for stabilization may be absent, leaving only distant out-vessel circuits. The controller couples a machine learning surrogate of non-rigid, profile agnostic vertical instability metric to a constrained quadratic program: the surrogate supplies real-time $\gamma_{gr}$ estimates and, via automatic differentiation, the actuator sensitivities, while the program allocates coil voltages to track a target growth rate, maintain double-null divertor balance, and respect electromechanical limits. We tested this method on the ARC~V3A power plant design configuration across 24 closed-loop simulations spanning equilibrium variations, actuator degradations, and transient disturbances. We achieved full or marginal success in 83% of these cases (full in 50%, marginal in a further 33%) and lose control in the remaining 17%; the failures map the boundary of out-vessel controllability (occurring at the highest growth rates) and under actuator limits. The controller does not regulate boundary shape explicitly: separatrix geometry follows indirectly from growth rate and flux balance control and would require a separate shape control layer for sustained scenario evolution.

We harness two established ingredients for collective demixing: differential speed and curvature to create a self-sorting device. In binary mixtures, motility differences drive spontaneous spatial segregation, while confinement geometry determines how rapidly and strongly this demixing develops. Using particle based simulations, we systematically identify the geometrical conditions that promote efficient segregation and use these results to guide the design of a finite sorting architecture. We then translate these physical mechanisms into a sequence of curved microfluidic units that progressively amplify the separation of the two species and direct them toward distinct collection regions. Experiments with binary Quincke-roller mixtures confirm that an initially mixed suspension progressively demixes as it propagates through the device, leading to strong enrichment downstream. Our results demonstrate how collective active demixing can be converted into a functional continuous sorting strategy, providing a route toward autonomous microfluidic separation based on particle motility and confinement geometry.

Periodic density structures (PDS) are trains of plasma enhancements carried into the heliosphere, but their repeated timing does not by itself identify where the cadence is set. We test whether PDS selected independently in the outer corona can be traced back to a low-coronal clock, and whether that organization survives through the EUVI–COR1–COR2 observing chain. We analyze 12 STEREO-A/SECCHI events on 2008 January 11–14 along a nonradial path. EUVI 171 A at 1.10–1.20 R_sun shows an unusual concentration of power in the predeclared 80–130 minute band relative to filter-matched red-noise controls, while the ensemble is most strongly organized in upper COR1 at 2.5–3.0 R_sun. These signatures identify a low-coronal modulation candidate and an intermediate-height organization domain, but they do not form a unique phase-preserving clock extending into COR2. Instead, the more persistent observable is spatial order. Events 9 and 12 retain expansion-stable outward ordering. In polar r–theta maps, intersections of oppositely inclined, expansion-aware matched-filter ridge supports form repeated X-/diamond-like patterns. The morphology is compatible with stationary or quasi-stationary shock-cell processing, although the brightness diagnostics are insufficient to establish a stationary MHD shock branch. The observations therefore favor an intermittent source–gate–transfer picture: a low-coronal cadence may modulate plasma release, while event-dependent propagation progressively destroys exact phase coherence. The outward ordering of the density structures can survive, providing a more persistent signature of the source-to-wind transfer than phase locking itself.

The ability to tailor photon-number statistics is central to the generation and control of nonclassical states of light. Here, we investigate how Fock-state filtering of squeezed coherent states can be used to engineer their photon statistics. We consider the selective removal of the $m=0$ and $m=1$ Fock components and show that this process can enhance the sub-Poissonian character of the field, depending on the coherent amplitude. We then analyze the evolution of these engineered states in a thermal environment. Although all states eventually relax toward thermal statistics, the signatures of the filtering process may persist over a significant part of the evolution, while the purity of the filtered states is rapidly degraded by incoherent population redistribution. Our results illustrate both the potential and the limitations of photon-number engineering in realistic, dissipative settings.

Reconstructing the state of a large networked system from measurements at only a few nodes is a challenge for monitoring, prediction, and intervention. Here we ask whether such reconstruction is possible and whether it can transfer across different nonlinear dynamics. We study this question across various networks and across $16$ nonlinear dynamics on networks from different domains. For each network, we observe only a vanishingly small fraction of sentinel nodes and train either a neural network or linear decoder. We find that accurate reconstruction is often possible, and that transferability is structured rather than universal. Eleven dynamics with adjacency-matrix-type coupling form a robust transferable class. By contrast, five diffusively coupled dynamics form isolated transfer components. Non-random sentinel selection is consistently important, and linear decoders often approach neural-network performance. These results show that sparse node observations can encode enough information to reconstruct full network equilibria across broad families of nonlinear dynamics, while also revealing sharp limits to universal transfer.

Thunderstorm electrical discharges, which vary in their type of initiation and development (including compact intracloud discharges and initial breakdown pulses), also manifest differently as recorded electromagnetic signals in both the radio and optical ranges. A unique ionospheric optical imprint of a discharge is an ELVES - a sub-millisecond glow at altitudes around 90 km with a characteristic spatiotemporal pattern in the form of an expanding ring, reaching a diameter of several hundred or even a thousand kilometers. The position of the elve, its expansion velocity, the azimuthal and radial distribution of the glow intensity (and their temporal dynamics) contain information about the location and orientation of the discharge, as well as its current function. This information is most fully represented in the data recorded by orbital detectors of dynamic images with a wide field of view and microsecond temporal resolution, such as the TUS and Mini-EUSO. Recovering the parameters of a discharge from orbital detector data is associated with solving a complex inverse problem, since the phenomenon of interest is mediated by several processes, including the interaction of the discharge's electromagnetic pulse with the ionosphere and the non-trivial instrument response function of the detector. These additional sources of uncertainty are most consistently accounted for within the Bayesian paradigm. The application of simulation-based inference (SBI) allows for the reconstruction of discharge parameters within a dynamic model, thereby taking a step towards creating a new method for studying the thunderstorm atmosphere – elveslocation. This work presents a dynamic model of an elve, which enables both the identification of characteristic spatiotemporal patterns of an event (generator mode) and the reconstruction of discharge parameters (SBI simulator mode).

A downward fin thrust near a cohesive seabed lofts sediment. Whether this cloud settles or organizes into a self-sustaining down-slope current determines if a diver loses visibility briefly or catastrophically. We reduce the layer-averaged balances of fluid mass, sediment mass, and momentum to a temporal slab of fixed thickness in the weakly entraining limit. This yields an autonomous planar vector field in dimensionless near-bed speed and suspended load, governed by three dimensionless groups: drag against settling, erosion strength, and near-bed concentration. The field possesses a quiescent equilibrium at the origin and, above a critical bed slope obtained in closed form as the ratio of near-bed concentration times drag-settling number to erosion-strength number, an interior saddle equilibrium. The saddle's stable manifold partitions the state space into basins of decay and unbounded growth, establishing the exact threshold between self-limiting and igniting disturbances. On a flat bed, the origin attracts globally for all admissible erosion laws. For representative silt, the ignition threshold is near a 17-degree bed angle. The closed-form structure is confirmed numerically to machine precision. A closure study demonstrates the critical slope is robust in existence but closure-dependent in value, identifying the linear erosion law as the conservative choice. The model thus isolates bed slope as the controlling parameter for ignition.

We investigate the cosmological implications of the bulk viscosity within the framework of $f(R,T)$ gravity by considering the linear model $f(R,T)=R+2\lambda T$, where $\lambda$ characterizes the matter–geometry coupling. Assuming a homogeneous and isotropic Friedmann–Lemaître–Robertson–Walker Universe, we derive analytical expressions for the Hubble parameter in two different scenarios: model A, corresponding to a pressureless viscous fluid ($\omega=0$), and model B, where the equation of state (EoS) parameter is treated as a free parameter. The cosmological parameters are constrained through a Markov Chain Monte Carlo analysis. The results show that both viscous $f(R,T)$ models provide an excellent description of the current expansion history and remain compatible with the combined observational data. Model A is slightly favored over the standard cosmological model ($\Lambda$CDM) according to AICc, reflecting the improved fit achieved with only one additional free parameter. In contrast, the BIC continues to favor the simpler $\Lambda$CDM model owing to its stronger penalty on model complexity. Although model B yields the lowest minimum $\chi^2$, the improvement is too small to justify the introduction of an additional free parameter, resulting in a statistical performance comparable to $\Lambda$CDM according to the AICc but less favorable according to the BIC.

We propose a relational noncommutative framework in which quantum kinematics, semiclassical geometry, internal gauge structure, and ultraviolet fixed-point behaviour arise as sectors of a common $*$-algebra equipped with a positive state and its Gelfand–Naimark–Segal (GNS) representation. The central construction is a projected fusion product on normalized quadratic current operators. Within the closed current sector, $P_{\rm rel}(O_A^2)=2O_A$ up to irrelevant corrections, and ultraviolet self-similarity expressed as $X_A\star X_A=X_A$ with $X_A=r_AO_A$ yields the interacting normalized fixed point $r_A^*=1/2$. With the retained operator basis fixed by the GNS Gram metric, a reduced per-generation fermionic current convention for the gauge sectors and canonical graviton normalization give a common algebraic value for $(10/3)g_Y^2$, $2g_2^2$, $2g_3^2$, and $32\pi G\mu^2$. We discuss conditional emergence of Lorentzian geometry and Einstein gravity, a Standard-Model-like internal algebra, three generation channels from a minimal quartic pairing sector, minimal selection of a $3+1$ dimensional branch, and relational time with a branch-dependent arrow of records. A Standard Model–Pati–Salam renormalization-group analysis provides a phenomenological consistency test of the proposed ultraviolet basin.

Motivated by the fact that quantum effects leave an imprint on the vacuum equation of state, making it depart from the standard cosmological constant relation, $P_{\rm vac}=-\rho_{\rm vac}$, this work investigates generalized running vacuum models (RVMs) by considering a dynamical vacuum equation of state in which the vacuum energy density, $\rho_{\text{vac}}$, evolves as a function of the Hubble parameter, $H$, and its time derivative, $\dot{H}$. This formulation extends previous running vacuum approaches by incorporating a dependence of the vacuum energy density on both, $H^2$ and $\dot{H}$. The corresponding Friedmann equations are derived and analyzed to study their impact on cosmic expansion. The model parameters are constrained through a joint statistical analysis combining the cosmic microwave background shift parameters, DESI-DR2 observations, PantheonPlus type Ia supernovae compilation, and Hubble rate $H(z)$ measurements. Model comparison is performed using information criteria, the Akaike Information Criterion (AIC) and Deviance Information Criterion (DIC), in order to assess the statistical performance of the RVMs relative to the standard $\Lambda$CDM scenario. The results show that the generalized RVMs provide a good fit to current observations and represent a statistically competitive alternative to the standard $\Lambda$CDM model. Notably, all three running vacuum formulations yield lower AIC and DIC values than $\Lambda$CDM, indicating that dynamical running vacuum energy remains a viable cosmological scenario.

The construction of a grid-based potential energy surface (PES) can be prohibitively expensive as the number of grid points grows exponentially with molecular size. Molecular symmetry can reduce this cost by eliminating symmetry-equivalent points. We present an algebraic symmetry-based grid reduction method, ASyBGR, that is capable of handling non-Abelian and higher-order cyclic symmetry groups. Non-coordinate-mixing operations are encoded into a vector space bit-string, where Gaussian elimination and closure can identify all symmetry-valid sign change patterns and the coordinate axes whose grids can be halved about the origin. In the presence of degenerate subspaces, we optimize the coordinate basis to maximize reduction. The method was validated by comparing vibrational configuration interaction (VCI) energies calculated using the full and symmetry-reduced fourth-order HDMR PESs for molecules spanning a broad range of symmetries. The number of grid points required to construct the PES was reduced by as much as 81%, while VCI energies deviated below 1 1/cm for all test cases on average. The relative root-mean-square deviation (RRMSD) between the full and symmetry-reduced potential energy and dipole moment surfaces were at most on the order of 10$^{-3}$ and 10$^{-2}$, respectively.

Laser-induced plasmas are primarily characterized by their temporal density decay. Using time-resolved transverse optical diffractometry, we reveal their concurrent spatial evolution, demonstrating pronounced broadening and flattening during the first 200 ps following ionization. This non-self-similar evolution arises from the local density dependence of electron-ion recombination, without particle transport. Exploiting the continuum of initial densities within a single plasma disc, we experimentally reconstruct an effective, time-dependent local recombination law over a broad density range. Applied locally to independently measured initial plasma profiles, this kinetic map quantitatively predicts their subsequent evolution. These results are relevant to transient diffractive optics, plasma-based optical elements and waveguides, and the development of laser-induced plasmas as platforms for gas-phase THz plasmonics.

The hydrogen molecular ion and the hydrogen molecule are treated in an approximate calculation based on classical electrodynamics which includes classical zero-point radiation. It is found within the classical theory that a molecular ion is less-well bound than a hydrogen atom plus a distant proton. The smaller binding energy is explained due to the repulsive nature of the force between the proton and the atom when considering the most natural resonant orbit of the electron. The approximate classical electromagnetic calculation gives a binding energy of $2.1eV$ and a proton separation of $0.916A^{o}$ for the hydrogen molecular ion. The hydrogen molecule is formed by adding a single additional electron to the ion. The electrostatic attraction of the electron to the ion gives a binding energy of $4.6eV$ and a inter-proton separation of $0.6A^{o}$ for the hydrogen molecule in this classical electromagnetic approximation.

Reliable early disruption prediction is critical for the safe operation and real-time control of tokamaks. However, machine learning based prediction frameworks have predominantly targeted medium and long pulse devices, with comparatively limited attention given to short pulse tokamaks where available warning time is inherently constrained. In this work, an interpretable machine learning framework is developed for feature engineering and early prediction of disruptions in the ADITYA using the initial plasma evolution information, prior to the activation of the negative converter of the ohmic transformer power supply. Statistical descriptors comprising the mean, variance, skewness, kurtosis and wavelet energy entropy are extracted from routinely available plasma diagnostics over different operation time windows. Decision tree based feature selection is employed to identify physically meaningful disruption precursors and to reduce feature dimensionality. These selected features are used to train a random forest classifier. The proposed framework achieves stable predictive performance across different analysis windows, with a maximum ROC-AUC of 0.87 for 0-35 ms and 0-40 ms windows. Comparable and in some cases improved, performance is obtained using the reduced feature set, demonstrating that the selected statistical descriptors retain the essential information required for disruption prediction. The proposed methodology provides an interpretable and computationally efficient framework for real time disruption prediction in short pulse tokamaks and establishes that carefully engineered statistical descriptors can effectively replace raw time series inputs for early disruption prediction, thereby offering a practical pathway toward real time plasma control in short pulse tokamaks similar to ADITYA and ADITYA-U.

Complex systems are often organised into hierarchies whose internal interactions are stronger or faster than interactions across levels. When markets are treated as genuinely multilevel systems it becomes natural to represent them as systems with hierarchical causality. Here we show that reflexivity can be formulated within such a discrete hierarchical causal system; but one in which a higher-level actor state restricts the lower-level transition kernels that remain admissible. Then event dynamics can be separated from calendar embeddings: a set-valued actor-conditioned correspondence can be used to define the admissible family of event kernels, while joint state and waiting-time laws can be used to determine compatible timing to then natural demonstrate reflexivity. A selected event-state law need not determine a unique calendar embedding. Locally, uniqueness of the joint event or timing specification requires uniqueness of both the admissible event kernel and its compatible timing law. Reflexivity is thus the endogenous closure of a hierarchical constraint loop, while timing and projection can generate calendar-time memory or causal ambiguity even for Markov event dynamics.

Speed of sound (SoS) is fundamental to ultrasound beamforming and a promising quantitative biomarker for tissue characterization. Although transmission- and reflector-based methods have demonstrated the diagnostic value of SoS, their need for dedicated hardware or constrained acquisition geometries limits clinical usability. Pulse-echo SoS imaging addresses these limitations by estimating SoS with conventional ultrasound probes and data acquisition paradigms. This review provides a structured overview of pulse-echo SoS estimation methods, categorized by the source of information used, the estimated SoS representation, and the reconstruction strategy. We review methods by their locality, e.g., global, layer-wise, and spatially-resolved predictions, as well as in terms of prediction strategies including gradient-free search, optimization-based solutions, analytical closed-form approximations, and deep learning-based methods. Finally, we summarize emerging diagnostic applications in the breast, liver, and muscle, while discussing challenges and possibilities with SoS imaging.

Plasma wakefield accelerators (PWFAs) offer accelerating gradients of 10-100~GV/m and relativistically propagating plasma bubbles capable of confining charged particles. We study the stationary states of a vortex electron at the bubble center by solving the corresponding quasi-relativistic Schrödinger equation. Analytical solutions are obtained with Laguerre-Gaussian transverse modes and Hermite-Gaussian longitudinal envelopes. Comparing the resulting beam parameters with experimentally accessible vortex-electron bundles, we find that the transverse beam waist supported by the plasma bubble is comparable to that achieved by current electron-optical techniques. The longitudinal confinement further provides a favorable parameter regime for stable injection. Our results indicate the feasibility of maintaining localized vortex-electron states in a plasma-bubble wakefield and provide an analytical starting point for investigating their subsequent acceleration and stability.

Low Gain Avalanche Diodes or LGADs are silicon sensors capable of achieving excellent timing resolution due to their characteristic internal gain. Detectors based on LGAD technology play a crucial role in High Energy and Nuclear Physics experiments, among other applications. However, their performance is affected by environmental factors such as temperature, humidity, and storage conditions. A systematic evaluation of the response of LGAD sensors as a function of these environmental parameters is therefore of essential importance for any application. LGAD sensors fabricated at the Brookhaven National Laboratory are characterized and stress-tested against various operating conditions, such as rapid temperature and humidity changes. Dedicated and detailed simulations are used to interpret the experimental results.

In EEG source imaging, the precision of the imaging of the brain activity depends on the accuracy of the forward head model which is, in turn, affected by the representation of tissue conductivity, including anisotropic compartments such as the skull and white matter. We introduce an EEG source imaging framework based on a hybrid volume-surface-wire integral formulation, enabling tract-aware modeling of anisotropic white matter contributions without a full volumetric head discretization. The proposed framework is assessed on a realistic MRI-derived head anatomy, showing close agreement with FEM reference solutions while avoiding full-volume meshing and providing a natural representation of white matter fiber tracts. We then integrate this model into a real-time pipeline that couples EEG processing with immersive visualization, enabling synchronized inspection of scalp potentials, cortical sources, and white matter fiber tracts-related quantities. The results support the proposed formulation as a tract-aware alternative to canonical formulations, combining robust localization performance with interactive visualization capabilities.

The Bandt-Pompe permutation entropy framework, alongside the complexity-entropy causality plane, has become a standard tool for characterizing the dynamical properties of time series. However, observational noise distorts ordinal pattern probability distributions in ways that can systematically misplace time series within the causality plane, compromising dynamical classification. This effect is particularly relevant for geophysical signals, which are typically poorly and irregularly sampled, and have a low signal-to-noise level. In this work, we characterize the distortions on ordinal pattern statistics using the formalism of majorization. We provide theoretical results and propose corrective strategies that restore discriminability under realistic measurement conditions. To achieve this, we introduce methodology that allows the characterization of noisy dynamical series and further allows the quantification of observational noise without the need of a fitting procedure. Finally, we illustrate our methodology by analyzing paleomagnetic records to determine if the geological evolution of the Earth dipole is better described by a stochastic or chaotic system.

Current particle flow filters rely on Gaussian prior assumptions that fail to capture the non-Gaussian attractor structure of chaotic systems. This study proposes a Score-based Particle Flow Filter (Score-PFF) that replaces the parametric prior gradient with a neural network-learned score function via denoising score matching. This enables flexible characterization of multimodal, skewed, and complex prior distributions in chaotic dynamics. Pure prior adjustment experiments demonstrate correct gradient directions toward the attractor (26.9%-29.0% error reduction over Gaussian priors). Under linear observations, Score-PFF significantly outperforms both Gaussian PFF and EAKF (Cohen's d = 1.03 and 1.40), preserving non-Gaussian structure that EAKF progressively Gaussianizes. Under nonlinear observation operators, Score-PFF maintains robust performance with up to 60% RMSE reduction in strongly non-Gaussian regimes. On the 1000-dimensional Lorenz-96 system, Score-PFF achieves 49.5% RMSE reduction over PFF while reducing per-assimilation cost by replacing SVD-based covariance inversion with neural network inference. Score-PFF establishes a computationally tractable, non-Gaussian data assimilation framework suitable for high-dimensional geophysical systems.

For nearly two centuries, conventional self-imaging has obeyed a single rule: a periodic wave reconstructs itself at a fixed distance, independent of its bandwidth. Here we show this law is programmable. Sampling the angular spectrum in inverse powers of the mode index makes each mode rephase with its own integer period, deferring exact revival to the least common multiple of all periods: $z_{\mathrm{rev}}=z_{\mathrm{T}}\,e^{r\psi(M)}$, with $\psi$ the second Chebyshev function. We observe these revivals optically as a prime-power staircase in propagation distance, turning free-space diffraction into a readout of the multiplicative structure of the integers.

Structured coherence – partially coherent light spanned by a finite number of modes – is emerging as a powerful tool in optical communications, computation, cryptography, and spectroscopy. Key to these prospects is the recent development of on-chip processing of structured coherence, in which large meshes of interferometers implement unitary and non-unitary transformations on the Hermitian coherence matrix representing multimode partially coherent light. Two related critical tasks for the applications of structured coherence are the reconstruction of an unknown coherence matrix and its diagonalization. Stokes tomography has been utilized in reconstructing the coherence matrix, whereas variational processing has been employed in its diagonalization. We show here that Stokes tomography can also be exploited in the on-chip diagonalization of an unknown coherence matrix, which we verify for two-mode and four-mode structured coherence in an integrated hexagonal mesh of Mach-Zehnder interferometers. This photonic circuit implements a predetermined sequence of configurations to estimate the generalized Stokes parameters, which – in a final step – inform a reconfiguration of the photonic circuit that diagonalizes the coherence matrix. The field is thus left in a coherent-mode representation comprising uncorrelated, orthogonal modes whose weights correspond to the eigenvalues of the original coherence matrix. Moreover, the integrated photonic circuit can be configured to provide the original field alongside its diagonalized counterpart at the circuit output. We verify the diagonalization procedure for coherence matrices of different coherence rank, entropy, and structure. Finally, we dispel the common notion that O(N^2) steps are required for reconstructing an N x N coherence matrix and show that only O(N) steps are needed.

Deep atmospheric convection governs South Asian monsoon variability, yet attempting to learn its latent world model directly from zero-inflated, heavy-tailed precipitation yields suboptimal predictive representations. Continuous atmospheric proxies, such as outgoing longwave radiation (OLR), express this convective organization far more coherently. We address this mismatch with cross-variable proxy learning: M-JEPA, a multiscale Monsoon Joint-Embedding Predictive Architecture, is pretrained on five continuous proxy fields over Lagrangian patches tracking moving convective systems—without rainfall supervision at any point. The resulting frozen representation is transferred to daily precipitation forecasts through a shared decoder trunk featuring parallel probabilistic and deterministic branches. Because rainfall is strictly unobserved during pretraining, downstream skill directly measures the predictive information captured in the latent rollout. A frozen-backbone probing framework with two controls (an identical architecture trained on rainfall alone, and a randomly initialized backbone) attributes the transfer specifically to proxy pretraining: direct rainfall training exhibits $36\%$ higher CRPS error ($7.52$ vs.\ $5.54$\,mm/day). Against the 51-member operational ECMWF ensemble, the transferred model attains a statistically resolved CRPS advantage ($6.81$ vs.\ $6.89$\,mm/day) and higher Brier skill ($+0.05$ vs.\ $-0.04$) using $15.4$M parameters on a single consumer GPU, concentrated at heavy-rain thresholds and fine spatial scales, while the ensemble retains an advantage in neighborhood skill and deterministic references on point metrics. The result provides a competitive monsoon precipitation forecast grounded in intraseasonal dynamics and a diagnostic framework for evaluating transferred atmospheric representations.

Low-field MRI scanners based on permanent magnet arrays require accurate B0 shimming to achieve sufficient field homogeneity for imaging. Here, we present Shimmer, an open-source methodology for passive shimming of low-field MRI magnets using additional NdFeB magnets placed outside the main magnet array. The end-to-end methodology combines magnetic field mapping, magnetic field simulations, numerical optimization of shim magnet positions and orientations, and automated generation of 3D-printable shim holders. Continuous magnet rotations and several optimization strategies are considered and evaluated. The method was validated and applied to three different permanent magnet arrays ~50 mT in 200 mm diameter spherical volumes, reducing initial field inhomogeneities of 9361ppm, 31721 ppm and 34143 ppm to 888 ppm, 1812 ppm and 1602 ppm respectively. The improved homogeneity enabled undistorted MR imaging of a phantom. Shimmer provides a reproducible and adaptable approach for improving the performance of low-field MRI magnets.

Recent technical advances in atomic force microscopy (AFM) have led to the development of high-speed AFM (HS-AFM), which enables video-rate imaging and direct visualization of the nanoscale dynamics of biomolecules functioning in solution. Because HS-AFM rapidly generates large datasets, dedicated software is essential for efficient processing, organization, and analysis. Recent improvements in imaging speed have further increased data throughput, underscoring the need for faster analysis software with improved usability. To address this need, we developed Ultrafast Microscopy Exploration (UMEX) Viewer, a software suite for HS-AFM data analysis. UMEX Viewer provides an integrated environment for HS-AFM data handling, including image processing, drift correction, image and movie export, data management, and quantitative analysis. These capabilities have enabled the software to be applied to a wide range of recent studies. In this article, we describe the core functions of UMEX Viewer and demonstrate its practical applications for HS-AFM data analysis aimed at elucidating biological functions.

This paper presents a scalable Bayesian framework for the inference of modern-day sea-level change and surface mass redistribution. To motivate this approach, we first review and quantify the performance of some standard methods for the analysis of satellite gravity and ocean altimetry observations. We find that these methods substantially underestimate uncertainties and are subject to systematic biases, deficiencies that stem from their incomplete treatment of sea-level physics and from uncertainty estimates based solely on the propagation of observational noise. To address these limitations, our approach combines three advances. First, we use recent developments in adjoint sea-level theory to embed the full physics into the forward and inverse modelling. Second, we formulate the Bayesian inverse problem in an infinite-dimensional setting, thereby avoiding discretisation artefacts and the underestimation of uncertainties inherent in truncated model spaces. Finally, our computational methods render such inversions tractable at full observational resolution while supporting joint model spaces and multiple data types. By employing a matrix-free approach with iterative solvers and randomised low-rank decompositions – implemented in the linked open-source libraries pygeoinf and pyslfp – basic calculations are possible on a single laptop, with the most intensive tasks parallelising trivially across available cores. We demonstrate the methodology through a series of synthetic experiments, culminating in a joint inversion of satellite gravity and ocean altimetry data that decomposes regionally averaged sea-level change into steric and manometric components with quantified uncertainties, including the degeneracies that remain.

Urban wildfire resilience depends on interactions among built form, vegetation condition, and extreme fire weather, yet city-scale risk models often overlook whether predictive skill transfers across neighborhoods. We developed a spatially validated GeoAI workflow for the January 2025 Palisades Fire, linking 12,081 CAL FIRE damage inspections to pre-fire Sentinel-2 vegetation indices, Landsat surface temperature, LANDFIRE fuels, terrain, and OpenStreetMap buildings and roads. Among 9,883 inspected residential structures, 5,566 were destroyed. Random cross-validation yielded ROC-AUC 0.92 for the integrated XGBoost model, but 1 km spatial block validation reduced performance to 0.75; logistic regression performed similarly and was better calibrated. Building count within 100 m was the strongest predictor, with destruction odds increasing 4.12-fold per standard deviation. Vegetation moisture and greenness showed opposing conditional associations: NDMI at 100-300 m was protective (OR 0.52), whereas NDVI at 30-100 m was positively associated with destruction after accounting for moisture (OR 1.74). Predictive information was concentrated at the 100-300 m neighborhood scale. A separate post-fire track mapped burn severity and vegetation recovery without leakage. The results support neighborhood-scale susceptibility screening, moisture-aware vegetation management, and spatial block validation as a minimum standard for single-event urban wildfire modeling.

Travelling-wave-like wall blowing and suction is an effective approach for enhancing heat transfer with a minimal pressure drag penalty. However, achieving such a dissimilar heat transfer enhancement effect in a passive manner remains a challenge. In the present study, we propose introducing parallel perforated plates to induce travelling-wave-like disturbances passively. Pore-resolving simulations of a spatially developing laminar flow between parallel perforated plates are performed across a wide range of Reynolds numbers of $Re = 500-1500$ and pore-to-solid length ratios of $L_\mathrm{p}/L_\mathrm{s} = 0-10$. Dissimilar heat transfer enhancement is confirmed for $9 \leq L_\mathrm{p}/L_\mathrm{s} \leq 10$ at $Re = 1000$ and $4 \leq L_\mathrm{p}/L_\mathrm{s} \leq 7$ at $Re = 1500$. The highest analogy factor, i.e., the ratio of the Stanton number to the friction coefficient is obtained at $Re = 1500$ and $L_\mathrm{p}/L_\mathrm{s} = 6$, yielding an increase of more than $30$% compared to that of an impermeable solid plate. Analysis of the fluctuating fields shows that, in the travelling-wave flow regime, a pressure-induced wall-normal velocity fluctuation transports temperature fluctuations away from the perforated plate, while breaking the correlation between the streamwise and wall-normal velocity fluctuations. This enhances the turbulent heat flux relative to the Reynolds shear stress near the perforated plate. The present results indicate that introducing a perforated plate with a suitable porosity induces travelling-wave velocity disturbances and also achieves a considerable dissimilar heat transfer effect even at low Reynolds numbers where a standard impermeable flat wall yields a steady laminar flow.