Papers

We study by means of direct numerical simulations the influence of the dimensionality of convection on flow properties. We call attention to a few general principles from considering in totality the results from one dimension, two dimensions, three dimensions and four dimensions. In particular, we explore two practical aspects: (1) the transient time, or the amount of time it takes for the flow to reach the steady state; and (2) possible implications for the so-called ultimate state.

High-performance breast magnetic resonance imaging (MRI) remains limited by the availability, geometry, and cost of dedicated receive coils. In contrast, spine coils are widely installed in MR scanners globally but provide insufficient sensitivity for anterior breast tissue. Here, we report a wireless volumetric metamaterial resonator (VMR) that converts a standard built-in spine coil into a scalable breast MRI receiver platform without modifying the scanner hardware. The VMR consists of axially stacked coaxial resonant rings that support a co-rotating collective mode, producing a centrally concentrated RF magnetic field within the enclosed imaging volume. Leveraging distributed capacitance in the coaxial cable, gap-engineered geometric tuning, and PIN-diode-based self-detuning, the VMR enables size scalability, electric-field confinement, and transmit-receive compatibility, as validated through bench measurements and 3.0 T MRI experiments. Three VMR prototypes spanning small, medium, and large breast-equivalent volumes were designed and evaluated using electromagnetic simulations, bench measurements, and phantom MRI experiments. Compared with a commercial 16-channel breast coil, the VMR-augmented spine coil achieved more than 4-fold higher central SNR and reduced spatial non-uniformity from approximately 70% to 20% within the target imaging region. Under SENSE parallel imaging at acceleration factors of 2-4, the VMR-augmented spine coil maintained an average 4-fold higher central SNR than the commercial coil for three sizes. These results establish the VMR as a passive, scalable hardware strategy for improving breast MRI performance using existing clinical scanner infrastructure.

We establish a universal mapping between the maximum spreading of a drop under wetting and Leidenfrost impact conditions from an energy-dissipation perspective, with the latter featuring a stable vapor film between the drop and the substrate. Experiments and direct numerical simulations demonstrate that the mapping remains valid over two to three orders of magnitude in the Ohnesorge and Weber numbers. This framework provides a simple route for converting established predictions for wetting impacts into their Leidenfrost counterparts, with implications for predicting and controlling droplet impact.

We present an extension of the single-population Human and Nature Dynamics (HANDY) model that explicitly incorporates non-renewable resources and societal effects. The model is formulated as a system of four differential equations for which we provide a faithful discrete integrator. In the model, resource extraction and conversion is described by realistic Holling-type functional responses that capture saturation effects for both renewable and non-renewable resources, while efficiency losses are introduced to describe inherent waste. We derive analytic conditions on the model parameters, needed to ensure stable (and sustainable) equilibria, despite population growth and depletion of resources. The different dynamical scenarios that appear in this analysis and the parametric conditions that lead to them, are illustrated by extensive numerical simulations. The societal effects we investigate include adjustable extraction and consumption rates of the resources, alongside a population-driven degradation of Nature and of its regenerative capacity. One immediate conclusion is that permanent environmental damage inevitably precipitates population collapse. In contrast, we also show that the possibility of recoverable capacities, even delayed, may lead to long-term survival and that timely late-stage interventions often succeed in averting extinction entirely.

We present a comparative cosmological analysis of the Anton-Schmidt, $\Lambda$CDM, and CPL models using baryon acoustic oscillation measurements from the Dark Energy Spectroscopic Instrument Data Release 2, combined with the Planck PR4 (NPIPE) CamSpec Cosmic Microwave Background likelihoods and three Type Ia supernova catalogues: Pantheon$+$, DES-Dovekie, and Union3. We use Markov Chain Monte Carlo analyses to constrain the parameters of the Anton-Schmidt model against each dataset combination. We find that the Anton-Schmidt model provides a good fit to current cosmological observations. The Anton-Schmidt model provides a good fit to the current cosmological observations but does not provide a significant alleviation of either the $H_0$ or the $S_8$ tensions. The Anton-Schmidt parameter is tightly constrained to $B \simeq -0.38$, indicating that the logarithmic correction becomes relevant only at late times. The Anton-Schmidt and CPL models predict quintessence-like present-day dark-energy equations of state and shows Quintom-B evolution, with their dark-energy equation-of-state parameters crossing the phantom divide at approximately $z \approx 0.5$. The characteristic density scale is constrained to be $\rho_\ast/\rho_{c,0}\sim4\text{--}5$, substantially smaller than the large density scales typically assumed in logotropic-inspired scenarios, indicating that the Anton-Schmidt correction mainly affects the late-time expansion history. Finally, Bayesian evidence shows moderate-to-strong preference for the Anton-Schmidt model over the $\Lambda$CDM model and strong-to-decisive preference over the CPL parametrization. Although the Anton-Schmidt model does not alleviate the current cosmological tensions, it emerges as a statistically favored dynamical dark-energy scenario whose cosmological implications deserve further tested with forthcoming Stage-IV large-scale structure observations.

European summer warming reflects interactions among background change, persistent ocean–land–circulation states, and same-season variability. We develop an empirical reduced-dynamics framework that decomposes regional summer indicators into inherited slow-state memory, its predictable component, and contemporaneous innovation. Projection-operator theory motivates the decomposition, implemented with finite causal filters, ridge-regularised prediction, and logistic risk models. Using ERA5-derived summer indicators for 28 IPCC AR6 European sub-regions over 1950–2024, with validation on 2006–2024, we find that Mediterranean-state memory improves mean summer-temperature prediction relative to trend-only and ARX baselines. The gain over ARX is modest, while moving-average, exponentially weighted, and tempered filters contain similar annual information, indicating that the data identify useful slow-state memory more robustly than a unique kernel shape. Predictable-state reconstruction is ridge-sensitive and therefore treated diagnostically rather than as a forecasting model. The strongest result concerns warm-tail risk. In parsimonious logistic models, high accumulated Mediterranean memory raises predicted upper-tail event probability by about 8–11 percentage points for annual maximum summer temperature, warm-day frequency, and warm-spell duration at 1-, 3-, and 5-year horizons. Regional bootstrap intervals remain positive for all targets and horizons. Circular-shift placebos yield one-sided probabilities of approximately 0.05–0.14 and do not survive strict family-wise correction across nine tests, so the evidence is moderate rather than decisive. Overall, annual projected memory is not a universal short-horizon predictor, but a physically interpretable inherited risk-loading variable identifying years and regions predisposed to warm-tail outcomes.

Europe is warming faster than the global mean, yet the spatial organisation of this acceleration remains incompletely understood. Using ERA5 reanalysis for 1950–2024 across 28 IPCC AR6 European sub-regions, we identify two connected empirical results. First, the DFA1 Hurst exponent of interannual temperature residuals is strongly and negatively associated with the 1996–2024 warming rate ($r=-0.792$, $p=5.1\times10^{-7}$). High-persistence, mainly Atlantic-proximal regions warm more slowly, whereas low-persistence continental regions warm faster. This relationship is robust to five residualisation schemes, three memory estimators, leave-one-region-out analysis, and five null-test families, including spatial block permutation. It also persists across warming windows ($r=-0.550$ for 1981–2024, $-0.792$ for 1996–2024, and $-0.875$ for 2000–2024), but vanishes under DFA2, indicating that the signal lies in low-frequency interannual-to-decadal persistence rather than trend curvature. The pattern is consistent with, but does not prove, North Atlantic cold-blob and thermohaline influence on European land temperatures. Second, under a strict 2006–2024 holdout, contemporaneous Mediterranean SST reduces mean annual temperature RMSE by 43% (from $0.787$ to $0.449,^{\circ}\mathrm{C}$). A causal lag-weight predictor based on prior-year Mediterranean and Atlantic SST also outperforms AR(2) ($0.578$ versus $0.695,^{\circ}\mathrm{C}$) and remains informative after removing NAO, AO, and PNA effects. Similar skill from five-year moving-average and exponentially weighted predictors shows that short Mediterranean SST persistence at 1–5-year lags is the key predictive ingredient. Together, the results support a two-regime interpretation: Atlantic-proximal regions exhibit stronger memory and oceanic buffering, while continental interiors show faster warming and weaker interannual persistence.

Grain boundaries are pervasive in polycrystalline olivine, yet their structure and trace-element chemistry remain poorly constrained. We characterize boundaries in undeformed olivine aggregates from Piton de la Fournaise (La Reunion) and Mauna Loa (Hawaii) by correlating electron backscatter diffraction, transmission electron microscopy, and atom probe tomography. The boundaries are crystalline, with a structural width of about 1 nm and no continuous glassy film resolved. Ca, Al, P, Na, and Ti are selectively enriched, whereas Mg, Fe, Ni, and Mn remain homogeneous. Several enriched mass windows coincide with nominal rare-earth-element ion positions, but unresolved isobars and the lack of a diagnostic isotope envelope preclude secure assignments. Divalent-cation segregation follows an ionic-size-misfit trend, while aliovalent departures require unconstrained contributions. The Ca interfacial excess is 2.94-2.97 atoms per square nanometer, equivalent to 0.327-0.330 monolayers, and lies near the upper published 1523 K equilibrium segregation isotherm of Hiraga et al. (2004), within the range of basaltic olivine crystallization temperatures. This supports boundary formation during crystal growth, although synneusis followed by high-temperature residence could produce a similar signature. Ca chemical widths are about 6-9 nm, and those of the most strongly enriched elements reach about 10-15 nm, demonstrating decoupling between the narrow structural core and broader chemical segregation. Compared with published atom-probe data on deformed olivine, these boundaries preserve broader halos, although analytical differences make the contrast tentative. Olivine grain boundaries therefore constitute a distinct nanoscale reservoir that should be included alongside crystal interiors and residual melt in trace-element mass balances of olivine-rich aggregates.

This work presents the demonstration of a superconducting niobium nitride (NbN) nanowire surface acoustic wave transducer, enabling a cryogenic acoustic delay line in scandium aluminum nitride (ScAlN) on silicon carbide (SiC). The superconducting nanowire slows the effective electromagnetic phase velocity along the acoustic wave propagation axis to match that of the surface acoustic wave, such that the co-propagating electrical signal continuously and coherently drives the acoustic wave along the length of the wire. The operating principle is validated through coupled full-wave electromagnetic and piezoelectric finite element method simulations and then experimentally confirmed through demonstration of a 500 {\mu}m delay line exhibiting a 125 ns time delay, with broadband transduction characterized across 0.5-20 GHz at 0.9 K, establishing superconducting nanowires as an emerging class of acoustic transducers with direct implications for cryogenic signal processing and integration with superconducting quantum circuits.

Orbital localization methods have a long history in quantum chemistry, as they provide a convenient way to describe individual chemical bonds and local properties. However, only a handful of localization functionals are known up to date, and most localization methods do not separate $\sigma$- and $\pi$-orbitals, which is important to describe chemical bonding patterns and reactivity. In this work, we present a new class of localization functionals, based on nucleus-electron physical observables, that preserve $\sigma$- and $\pi$-orbitals separation in molecular systems. We also present a general mechanism behind $\sigma/\pi$-separating variational localization which can be used for constructing a broader class of $\sigma/\pi$-separating localization functionals from other local physical quantities.

As organisms navigate the environment to locate critical resources, their behavioral actions must be tightly coupled to their sensory inputs. Here, we introduce an information-theoretic framework that quantifies this coupling using transfer entropy, which measures information flow between sensory inputs and behavioral outputs. Information flow from sensory inputs to behavior defines a "reactive" component of a navigational strategy, whereas information flow from behavior to sensory inputs defines an "active" component, whereby actions shape subsequent sensory experiences. Analyzing these bidirectional information flows enables us to both predict navigational performance and dissect navigation strategies from trajectories. Using a minimal model that captures the active and reactive components, we connect macroscopic performance to microscopic information flows. We then apply the framework to experimentally measured trajectories of bacteria, worms, and flies, as well as to machine learning agents navigating sensory landscapes. Across systems, bidirectional information flow reliably predicts navigation efficiency, revealing a common behavioral-environment feedback loop. Decomposing active and reactive information flows further exposes distinct strategies underlying bacterial chemotaxis, the spatial dependency of the navigation strategy in fly olfactory navigation, and the learned policies of a reinforcement-trained agent. Together, these results establish bidirectional information flow as a unifying principle for understanding navigation in biological and artificial systems.

Background: Early prediction of distant metastasis (DM) risk in head and neck cancer (HNC) can enable timely interventions that may improve treatment outcomes. Many current machine learning methods rely on prior knowledge of the region of interest such as tumor segmentations, which require expert knowledge, is time-consuming and introduces user-dependent variability. Medical image-based foundation models have recently been developed for specific imaging modalities to streamline down-stream prediction tasks by extracting modality-relevant features. Purpose: In this study, we evaluate the effectiveness of using a foundation model as the feature extractor to predict DM risk in HNC patients and compare its performance with traditional approaches that require prior knowledge on the regions of interest. Methods: Preoperative CT images of 2327 patients from the RADCURE dataset were used. Three features-sets were created including radiomics, deep-learning based features, and CT Foundation derived features. The feature-sets were used individually in a multi-layer perceptron (MLP) to predict DM risk. Results: The model using CT Foundation embeddings outperformed the radiomics and deep learning-based models, achieving a Receiver Operating Characteristic Area Under the Curve (AUC) of 0.791, compared to AUC values of 0.772 and 0.753 for the radiomics and deep learning-based models, respectively. The CT Foundation based model had similar performance to a model that combined the use of radiomics and deep learning-based features that achieved an AUC of 0.794. Conclusions: Features based on foundation models offer a promising alternative to traditional radiomics while reducing the need for domain expertise and extensively annotated datasets. Their minimal preprocessing requirements also make them a more accessible and scalable option.

In reciprocating flapping-like motions, wing-wake interaction plays a crucial role in fluid force generation. While this effect's existence has been acknowledged, particularly in explaining discrepancies between measured forces and quasi-steady approximations, fundamental research on the mechanism underlying this interaction and its scaling remains limited. To address this, we investigate the excess drag force, relative to quasi-steady estimates, acting on a flat plate during the reversal phase of a forward and back translational motion. The flow produced by this motion, studied at insect-flight-relevant Reynolds numbers, serves as a simplified analogue to biological flapping. We demonstrate that interaction with pre-existing wake flow indeed generates excess drag. The main parameter governing this interaction is the distance travelled before reversal, which influences both magnitude and temporal dynamics of the peak drag. We link our observations to optimal vortex formation, as the time trace of the additional drag during reversal is qualitatively altered by the detachment of the starting vortex ring: vortex detachment and re-formation lead to two distinct wake-force peaks. Furthermore, as the pre-reversal distance traversed increases, the wake interaction force post-reversal decays more slowly. Flow observations reveal a similar spatial decay of the streamwise velocity in the wake at the moment of reversal, suggesting a direct link. Representing the starting vortex as a point vortex indicates that the wake's spatial scaling, and commensurately the temporal scaling of the wing-wake interaction effect, is primarily governed by the vortex ring position, shape, and circulation. This simplification reveals a dependence on the pre-reversal translation distance that can be described by a combined fourth-root and linear scaling.

Parasitic interference is a common limitation in laser interferometers, arising from unwanted beams that corrupt the phase measurement and degrade displacement sensitivity. In this work, we present a unified framework for the characterization and mitigation of parasitic interference in heterodyne interferometers. Parasitic beams are classified into two types based on the orientation of their corresponding phase vectors, and their noise contribution is modeled as a function of polarization, relative amplitude, and phase of the parasitic beam. Central to this framework is the concept of coupling coefficients, which quantify the interferometer's susceptibility to parasitic interference and can be readily computed using Jones calculus for any optical configuration. The two types of parasitic interference motivate distinct mitigation strategies: differential interferometry and balanced detection, complemented by polarization control and high-quality beam splitters. The models and mitigation strategies are validated experimentally in both a simplified Mach-Zehnder interferometer and a differential interferometer used for optomechanical inertial sensing, demonstrating reductions in parasitic phase noise by up to four orders of magnitude and achieving sub-picometer displacement sensitivity at frequencies as low as 9 mHz under ambient atmospheric pressure.

Achieving significant energy gain in laser-driven relativistic electron beams remains challenging due to dephasing between the accelerating laser field and the electrons. We show that transverse electric fields, when aligned with the plane of laser polarization, can mitigate dephasing and enable substantial energy gain without compromising beam directionality. As a practical realization, we propose a two-laser scheme in which one laser generates the transverse field while the other drives electron acceleration. By tailoring the interaction geometry, this configuration sustains phase locking, enhances energy transfer, and opens a pathway toward compact, high-efficiency electron accelerators.

The imposition of boundary velocities in finite element models of port-Hamiltonian elastodynamics typically relies on Lagrange multipliers, yielding Differential-Algebraic Equations (DAEs). Alternatively, weak imposition methods that maintain an Ordinary Differential Equation (ODE) structure often exhibit poor accuracy at Dirichlet boundaries. To address these limitations, this paper introduces an additive kinematic decomposition at the continuous level, splitting the displacement and velocity fields into a relative dynamic component that vanishes on the boundary and a prescribed lifting function extending into the interior domain. This decomposition induces a distributed port that maps the effects of the boundary actuation inside the domain. By incorporating this mapping into suitable virtual power principles, we derive lifted port-Hamiltonian system (PHS) models that, upon finite element discretization, reduce to ODE systems in which Dirichlet boundary velocities are strongly imposed. The framework is applied to derive 2-field and 4-field formulations suited to distinct PHS geometric representations. Furthermore, we show that under specific shape functions, standard FEM schemes are recovered, demonstrating that the lifting framework in the discrete models is equivalent to the classic algebraic matrix partitioning in computational mechanics practice. The energy-balance properties and computational performance of the proposed methodology are verified through numerical simulations.

Human mobility plays a central role in shaping contact patterns that drive infectious disease transmission, yet mobility is often simplified in agent-based models (ABMs) due to data and computational constraints. The effects of these simplifications on model outputs are poorly understood. In this study, we systematically examined how alternative mobility assumptions influence emergent contact networks and epidemic dynamics within a large-scale ABM. Using a synthetic population of one million agents representing an urban environment, we implemented five mobility models varying along two dimensions: activity patterns (empirically derived vs. randomized) and destination choice mechanisms (empirical popularity, distance-based, or random). Holding disease parameters constant, we found that mobility assumptions alone produced substantially different contact network structures and epidemic trajectories, including differences in peak incidence and shifts in outbreak timing. Importantly, these differences could not be attributed to agents simply moving more or less overall since aggregate movement volumes were broadly comparable across models. Instead, the contrasting dynamics arose from how mobility generates contact opportunities: specifically, who meets whom, where, and how often. These results suggest that in models where mobility has not been carefully calibrated, simulated epidemic outcomes and evaluations of interventions may reflect mobility assumptions as much as underlying disease parameters. Our findings underscore the importance of mobility model calibration and validation, particularly in policy-facing applications.

Computational ghost imaging (CGI) reconstructs objects from known illumination patterns and bucket-detector measurements, but quality deteriorates at low sampling ratios (SRs). We present a deep-learning framework that optimizes grayscale diffuser patterns before reconstruction. In simulations using CIFAR-10 and MNIST images with Split Bregman reconstruction, the learned patterns outperform random patterns in peak signal-to-noise ratio and structural similarity, including at SRs below 5%. Patterns trained on CIFAR-10 also transfer to MNIST and remain effective under moderate perturbations of the sensing matrix. These results support learned pattern design as a route to fewer CGI measurements.

Oscillator Ising machines (OIMs) and dynamical Ising machines (DIMs) encode binary spins in phase states stabilized by second-harmonic injection (SHI). In a coupled network, the competition between SHI and the instantaneous local network field reshapes each oscillator's conditional energy landscape. We show that this competition drives a transition between monostable and bistable regimes through an astroid spinodal boundary. Near this boundary, the barrier scales as $\Delta E_i\propto\mu_i^{3/2}$ at generic smooth points and as $\Delta E_i\propto\mu_i^{2}$ at the longitudinal cusp. OIMs and DIMs obey the same spinodal geometry, with their conditional landscapes related by a reversal of the transverse field. Finally, the first-harmonic conditional landscape is mathematically equivalent, up to an additive constant, to the Stoner–Wohlfarth energy of a uniaxial magnetic particle.

Sideband-resolved cavity optomechanical resonators provide a powerful platform for coherent photon–phonon interactions, enabling applications ranging from quantum state transduction and optomechanically induced transparency to precision sensing and microwave photonics. Achieving this regime in integrated microresonators, however, requires simultaneously realizing a narrow optical cavity linewidth, a high-frequency mechanical mode, and low mechanical dissipation. Here, we report the first sideband-resolved optomechanical resonators based on the 4H silicon carbide (4H-SiC) platform. By combining compact microdisk geometries with interference-engineered anchor-loss suppression, we simultaneously achieve intrinsic optical quality factors exceeding $1\times10^6$, room-temperature mechanical quality factors up to $1.51\times10^4$, and a sideband-resolution factor greater than seven. Systematic numerical and experimental studies reveal that a local minimum in anchor loss enables high mechanical quality factors without requiring aggressive undercutting, substantially improving fabrication yield and device robustness. We further demonstrate the first observation of optomechanically induced transparency in integrated 4H-SiC resonators, confirming coherent cavity optomechanical interactions in this material platform. These results establish 4H-SiC as a promising platform for integrated cavity optomechanics and provide a practical route toward scalable photon–phonon devices for classical and quantum photonic technologies.

Interparticle Coulombic Electron Capture (ICEC) provides an environment-assisted pathway for electron attachment that can compete with photorecombination. Here, we present the first comprehensive survey of ICEC in atom-atom and atom-molecule systems, screening 2442 combinations in search of promising candidates for future experimental investigation. We employ the efficient asymptotic approximation to predict ICEC cross sections and electron spectra. Intramolecular nuclear motion is included, while interparticle nuclear dynamics is neglected to permit an extensive survey. We identify several classes of atom-atom and atom-molecule systems with favourable ICEC cross sections and ICEC-to-photorecombination ratios, including halogen-halide systems and systems involving a proton and diatomic molecules such as N$_2$, O$_2$, CO, and NO. These findings point to atmospheric and astrochemical environments in which ICEC may be relevant.

Why do some teams consistently mobilize collective effort and achieve superior performance while others struggle to coordinate action? We introduce Attention-Directing Ability (ADA), a latent team capability capturing how effectively members' interaction signals elicit engagement and coordinated responses from others. Extending the attention-based view, we conceptualize attention direction as an emergent coordination capability embedded in patterns of interaction rather than as a cognitive state or an outcome. Teams differ in the extent to which attention-directing signals trigger collective responses, and these differences shape how teams mobilize effort and perform. We examine ADA in a distributed innovation effort involving 2,233 participants collaborating asynchronously in 79 self-organized teams across 165 public Slack channels, generating over 30,000 messages. We model the causal responsiveness among interaction signals and derive a latent measure of ADA from teams' attention dynamics. We find that ADA strongly predicts both teams' likelihood of mobilizing engagement to sustain collective work and their performance conditional on participation. Correcting for self-selection into project submission, teams with higher ADA are more likely to submit proposals and achieve higher expert-evaluated outcomes. Causal mediation analyses show that these effects operate primarily through collective effort, indicating that attention direction functions as an upstream coordination capability. By conceptualizing attention as an emergent, measurable team capability, this study advances theories of collective attention and team coordination.

The modeling of photonuclear reactions is increasingly important for applications involving high-energy photon fields, including accelerator-driven neutron sources, radiation shielding, medical physics, and fusion technologies. Although the Monte Carlo code OpenMC provides well-verified photoatomic transport capabilities, its photonuclear physics is currently available only in an unofficial development branch and requires independent verification before broader scientific use or possible integration into the official code distribution. This work presents a systematic verification of the OpenMC photonuclear implementation using six single-collision broomstick benchmarks based on 2H, 9Be, and 238U targets irradiated by monoenergetic 5 MeV and 15 MeV photons and by a continuous 1–20 MeV linear accelerator (LINAC)-representative spectrum. OpenMC was compared with MCNPX using common ENDF7u photonuclear data, with FLUKA using its native photonuclear models, and with a first-collision analytical solution for the integrated neutron yield. Calculations using the IAEA/PD-2019 library were also performed to quantify nuclear-data sensitivity. OpenMC and MCNPX agreed within 0.7% in integrated neutron yield for all benchmark cases when the same ENDF7u data were used. FLUKA, which relies on its own native photonuclear models rather than ENDF7u, differed from the analytical solution by approximately 6-9% for the monoenergetic cases and by no more than approximately 4% for the continuous-source cases. Changing the OpenMC library to IAEA/PD-2019 produced deviations of up to 11.3% from the ENDF7u-based analytical solution, with the sensitivity varying strongly by nuclide and source spectrum.

Earthquake triggering is conventionally characterised by a scalar ETAS branching ratio. We develop a three-type Hawkes-ETAS model that resolves strike-slip, normal, and reverse/thrust earthquakes through a directed branching matrix $N$. While the spectral radius $\rho(N)<1$ controls asymptotic stability, its eigenvector geometry controls finite-generation dynamics. Asymmetric cross-mechanism pathways can render $N$ non-normal, producing large transient and cumulative cascade responses in a strictly subcritical process. We motivate this geometry from receiver-fault availability, Coulomb stress projection, mechanism-dependent magnitude distributions, tectonic loading, and near-degenerate self-triggering. A physically reduced parametrisation separates diagonal self-triggering, a dominant tectonic driver column, and weaker secondary couplings. Numerical examples show that cascade amplification can increase strongly while the eigenvalues remain fixed. Five tectonically informed scenario matrices illustrate plausible geometries. The theory produces six falsifiable predictions for mechanism-resolved catalogues and identifies how scalar ETAS fits may absorb multitype amplification into an apparently elevated branching ratio.

In perovskite solar cells, the interplay between mobile ions and photo-excited charge carriers is complex,with recent studies suggesting mobile ions can be either beneficial or detrimental to cell efficiency depending on the cell properties. In this study we use drift diffusion modelling and factorial analysis to simulate 601 pairs of perovskite solar cells (1202 total cells) across a broad range of physically relevant materials parameters. In each pair of devices, one cell contains mobile ions that can move freely. The second paired cell is identical but has no mobile ions. This approach allows us to systematically investigate the impact of mobile ions on cell performance. We deconvolve the contributions of key cell parameters including ion density, recombination rate and band offsets, for n-i-p and p-i-n devices with both organic and inorganic contact layers. Importantly, we find that in efficient devices, mobile ions have only a small impact on steady-state performance.

Reliable estimation of spatial correspondences between overlapping side-scan sonar (SSS) measurements is essential for mapping, but acoustic appearance variations, weak seabed texture, repetitive patterns, and shadows make such correspondences sparse, unstable, and context-dependent. Scarce point-level annotations further limit sonar-specific training or fine-tuning of deep matching models. To this end, we propose SC-Match, a training-free scale-space matching framework with context-consistent correspondence refinement that adapts pretrained feature extraction and matching components to SSS observations without sonar-specific retraining. The framework improves correspondence reliability through scale-space representation and context-consistent refinement. For feature representation, a frozen extractor is applied to multiple observation scales, detector responses are calibrated according to the structure–texture tendency of the input SSS image, and score-aware cross-scale fusion is used to retain compact feature candidates. For correspondence refinement, adjacent local matching cases are used as neighboring contexts to verify stable fixed–moving relations and preserve non-conflicting complementary matches for alignment. Experiments on datasets acquired in different environments using different sonar platforms show that SC-Match provides more accurate correspondences and more consistent geometric alignment than representative pretrained methods, while maintaining stable behavior under unseen cross-platform acquisition conditions.

Storms and severe weather are often associated with atmospheric low-pressure systems. A moving low atmospheric pressure system produces storm surge that is dependent on the wave Froude number (F) or the ratio between the speed of the storm and the shallow water wave propagation speed. Proudman's resonance for such a problem goes to infinity at F=1. This work revisits the problem by presenting a nonlinear exact solution. The first order approximation goes back to Proudman's solution. The second order approximation provides a finite solution including at resonance. The solution shows that the nonlinear advection can significantly suppress the resonance. At resonance, although the solution is finite, it bifurcates into two branches, a condition linked to chaos in theoretical fluid dynamics and many nonlinear systems. Although Proudman's solution does not provide any limit to the atmospheric pressure system, it is not without any condition. The solution exists for all low-pressure systems but has only limited validity for a moving atmospheric high-pressure system induced forced waves: at low moving speed, the solution under a high-pressure system can exist for a range of high-pressure variability but quickly become invalid as the system's speed increases. The closer F is to 1 (resonance), the easier for the solution to be invalidated for a high-pressure system. This study also found that Proudman's solution is most accurate only under subcritical conditions with small low-pressure variability and has much larger error under supercritical conditions.

The variational approach based on the Bogoliubov inequality using the fluid of hard spheres as a reference system is implemented to evaluate instantaneous shear, bulk and longitudinal elastic moduli, as well as related sound velocities of Yukawa fluids. The remarkable accuracy of this method is documented. In addition, we evaluate the adiabatic sound velocity from an appropriate equation of state and discuss its relation to the longitudinal and bulk sound velocities obtained from the corresponding instantaneous elastic moduli. The transition between weakly coupled and strongly coupled regimes is analyzed in detail.

The excess energy, entropy, and pressure of a strongly coupled Yukawa fluid are calculated from the variational approach using the fluid of hard spheres as a reference system. As in the case of the one-component plasma, the Percus-Yevick virial entropy is appropriate for such calculations and delivers remarkable agreement with available results from molecular dynamics simulations. The agreement with the molecular-dynamics results is particularly impressive in the strongly coupled regime, making this approach a useful predictive tool when numerical data are scarce or not yet available. As an application of the variational approach, we estimate the location of the melting curve in the regime of sufficiently strong screening.

The high-pressure structural behavior of $\beta^\prime$-Mn$_3$(PO$_4$)$_2$ was investigated using synchrotron X-ray diffraction up to 20 GPa combined with density-functional theory calculations. At ambient conditions, $\beta^\prime$-Mn$_3$(PO$_4$)$_2$ crystallizes in a monoclinic structure that exhibits strongly anisotropic compression. The pressure dependence of the unit-cell volume was described using a third-order Birch–Murnaghan equation of state, and the principal axes of compressibility were determined. Above 14.1 GPa, significant broadening and weakening of the diffraction peaks are attributed to the onset of irreversible pressure-induced structural disorder associated with the loss of long-range crystallographic order. The disordered state persists after decompression to ambient pressure, demonstrating the irreversible nature of the transformation. The calculations accurately reproduce the experimental compressional behavior and provide insights into the microscopic structural evolution under pressure. Compression is mainly accommodated through distortions of the Mn–O polyhedra, whereas the PO$_4$ tetrahedra behave as comparatively rigid units. Several initially penta-coordinated Mn sites progressively evolve toward octahedral coordination under compression, while selected MnO$_6$ polyhedra exhibit anomalous distortions and elastic softening preceding the onset of disorder. Elastic constant calculations further reveal that the crystalline phase becomes mechanically unstable near the experimentally observed transition pressure. The combined experimental and computational results suggest that the HP response of $\beta^\prime$-Mn$_3$(PO$_4$)$_2$ is influenced by the interplay between framework complexity, anisotropic polyhedral compressibility, and elastic instability, providing new insight into pressure-induced structural degradation in structurally complex phosphate frameworks.