Papers

Quantum key distribution is unbreakable in theory but may be hacked via imperfections in its hardware implementations. While many imperfections have been mitigated by countermeasures and advanced security proofs, several remain unsolved. One of these is a superlinear behaviour in single-photon detectors, when the click probability rises faster with the photon number of an incoming light pulse than expected from individual independent photon detections. Here we test an avalanche single-photon detector sinusoidally-gated at 312.5 MHz for superlinearity. Its click probability is moderately superlinear. However, we notice that the click timing depends strongly on the incoming pulse energy. The click occurs progressively earlier, shifting more than 2 ns as the energy rises over a wide 50-dB range. An attacker might use this energy-time effect to conditionally toggle the click between adjacent key bit slots, violating an implicit assumption in the security proofs and rendering them inapplicable. We propose two attacks that exploit this flaw.

The stabilization mechanisms of fully premixed NH3/H2/air flames anchored behind a bluff body are investigated using combined experiments and direct numerical simulations. Particular attention is given to the interplay between preferential diffusion, heat release, flow recirculation, and turbulence-flame interaction. Comparison between non-reactive and reactive cases shows that thermal expansion strongly alters the flow field, increasing the recirculation zone length by about 40% and the shear layer width by roughly 50% near the end of the recirculation region. Excellent agreement between measurements and simulations for mean and fluctuating axial velocities validates the numerical approach. Analysis of the flame structure reveals a distinctive stabilization mechanism at the flame root: preferential hydrogen diffusion generates a localized diffusion flame branch that enhances radical production and promotes robust anchoring. Combustion proceeds sequentially, with hydrogen mainly consumed in the shear layer, followed by ammonia cracking and the main heat-release region. Near the bluff body, heat release is concentrated within the recirculation zone, while downstream regions are increasingly influenced by turbulence and velocity fluctuations. The roles of curvature and strain are quantified to assess stretch effects along the flame front. Convex curvature near the flame root enhances hydrogen enrichment and locally increases burning rates, reinforcing stabilization. In contrast, concave curvature and higher stretch near the end of the recirculation zone weaken the flame and mark a transition toward a turbulence-dominated regime. Overall, stabilization results from a coupled feedback between recirculation-driven heat exchange and rapid hydrogen oxidation, sustaining an intermediate ammonia reaction zone and enabling robust anchoring of carbon-free NH3/H2 flames.

While generating ultracold plasma (UCP) by photoinization of laser-cooled atoms, only a small fraction of atoms are ionized, and the remaining neutrals interact with the electrons present therein. These interactions, in addition to the Coulomb interactions between ions and electrons, cause phenomena such as ionization of Rydberg atoms and three body recombination, all of which affect the overall behaviour of the ultracold plasma. We had earlier developed a quantum treatment to analyze these interactions and investigated the ionization of Rydberg atoms in Cesium, which showed good agreement with measured results. We now extend it to other atomic species to investigate Rydberg ionization, and other effects such as three-body recombination and a resulting additional `quantum pressure' which causes a faster expansion of the UCP. Our results successfully explain experimental observations which were hitherto deemed as `anomalies'.

The rapid advancement of LLMs has generated growing interest in their potential role in physics education and assessment, yet a focused evaluation of their performance on multi-faceted, free-response physics problems remains underexplored. In this study, we systematically evaluate the performance of four widely accessible AI systems-ChatGPT 4.1 mini, Gemini 2.5 Flash, Claude 4.0 Sonnet, and DeepSeek R1-on AP Physics 1 and 2 free-response questions administered between 2015 and 2025. Model-generated solutions were produced under standardized exam-style prompting and evaluated by three independent physics experts using official College Board scoring guidelines. All models achieved relatively high mean scores (82-92%), indicating strong capability in structured algebraic problem solving. However, substantial year-to-year variability was observed, particularly for AP Physics 1, where statistical testing revealed no consistent performance hierarchy among models. In contrast, AP Physics 2 results showed statistically significant differences, with Gemini and DeepSeek demonstrating more consistent performance than Claude. A qualitative analysis revealed recurring error patterns across all models, including misinterpretation of diagrams and graphs, incorrect graph construction, incorrect reasoning about vector direction, circuit topology errors, partial and misleading qualitative explanations, and difficulties applying three-dimensional concepts such as the right-hand rule. These findings suggest that while contemporary AI systems can effectively support routine physics problem solving, they remain limited in tasks requiring spatial reasoning, visual interpretation, and conceptual integration. The results highlight both the instructional potential and current pedagogical limitations of AI-assisted learning tools in physics education.

Enhanced supercontinuum generation (SCG) is experimentally demonstrated in integrated silicon nitride (Si3N4) waveguides incorporating highly nonlinear graphene oxide (GO) in the form of two dimensional (2D) films. Onchip integration of the 2D GO films with precise control of their thickness is realized by using a transfer free and layer by layer coating method. The control of the film length and coating position is achieved via window opening in the upper silica cladding of the photonic integrated chips. Detailed SCG measurements are performed using the fabricated devices with different waveguide geometries and GO film thicknesses, and the results are compared with devices without GO. Significantly improved spectral broadening of ultrashort optical pulses with ultrahigh peaks powers exceeding 1000 W is observed for the hybrid devices, achieving up to 2.4 times improvement in the spectral bandwidth relative to devices without GO. Theoretical analyses for the influence of GO film thickness, coating length, coating position, and waveguide geometry are also provided by fitting the experimental results with theory, showing that there is still significant room for further improvement. This work opens up a promising new avenue towards improving the SCG performance of photonic integrated devices by incorporating functional 2D materials.

Monolayer semiconductors hold great potential for nanoscale electronics, optoelectronics, and photonics. Excitons dominate their optical properties. As their electric fields extend outside the monolayer, they are sensitive to their surroundings. Thus, disorder can cause exciton instability, which is detrimental to device performance and critical for scalability and reproducibility. Here, we adapt a super-resolution fluorescence fluctuation microscopy technique to image localized exciton fluctuations in monolayer semiconductors, allowing us to identify unstable spots in an otherwise continuous monolayer with constant fluorescence. These spots correspond to interfacial disorder measured by atomic force microscopy. We examine how different material interfaces influence the fluctuations by comparing several substrates and provide additional insight into the disorder behind the fluctuations using hyperspectral imaging. We also assess the reduction of disorder upon thermal annealing, evidenced by a decrease in fluctuations. Our results show that fluorescence fluctuation imaging can detect disorder features similar to those of atomic force microscopy and hyperspectral imaging, while being faster and easier to implement. Therefore, it is a promising method for evaluating the quality of monolayer semiconductors, particularly when integrated with nanostructures and heterostructures found in nano-optoelectronic devices.

On-chip integration of highly anisotropic two-dimensional (2D) materials offers new opportunities for realizing high performance polarization selective devices. Obtaining optimized designs for such devices requires extensively sweeping large parameter spaces, which in conventional approaches relies on massive mode simulations that demand considerable computational resources. Here, we address this limitation by developing a machine learning (ML) model based on fully connected neural networks (FCNNs). Trained by using mode simulation results for low resolution structural parameters, the FCNN model can accurately predict polarizer figures of merits (FOMs) for high resolution parameters and rapidly map the global variation trend across the entire parameter space. We test the performance of the FCNN model using two types of polarizers with 2D graphene oxide (GO) and molybdenum disulfide (MoS2). Results show that, compared to conventional mode simulation approach, our approach can not only reduce the overall computing time by about 4 orders of magnitude, but also achieve highly accurate FOM predictions with an average deviation of less than 0.04. In addition, the measured FOM values for the fabricated devices show good agreement with the predicted ones, with discrepancies remaining below 0.2. These results validate artificial intelligence (AI) as an effective approach for designing and optimizing 2D-material based optical polarizers with high efficiency.

Accurate representation of interfaces and flux exchange is vital for coupled multiphysics simulations across a broad range of applications. Currently, coupling approaches are limited by the underlying discretization or to specific physical problems, restricting their generality. To remove these constraints, a consistent interface reconstruction and coupling method has been developed to bridge multiphysical computational domains. The proposed numerical framework contains two complementary steps. The first step reconstructs a continuous bounding surface from discretized spatial data using a weighted interpolation and marching-grid approach that preserves geometric fidelity across a wide range of resolutions. The second step consists of a conservative flux mapping algorithm that projects surface quantities such as aerodynamic loads, heat fluxes, or mass transfer onto nearby discrete elements while maintaining global conservation of flux quantities. This new numerical formulation allows for integration of the various partitioned domains and ensures consistent data transfer between them. The framework is tested on various geometries, and surface containment errors below 2.5% and flux-transfer errors below 1% are obtained. Transient simulations of uniform surface recession further confirm accuracy in coupled evolution, with predicted volume loss agreeing with analytical solutions to within 1%. The proposed method establishes a general and extensible framework for consistent interface reconstruction and coupling in multiphysics environments, providing a pathway toward unified treatment of discrete-continuum interactions for a wide range of systems.

We revisit the controversial "abscopal" effect in the context of Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy (PULSAR). By allowing long interval between fractions, PULSAR may enhance systemic immune activation and increase the likelihood of abscopal responses compared with conventional daily fractionation. To quantify treatment-induced effects, we introduce an interaction-picture transformation adapted from quantum mechanics, which separates intrinsic tumor growth from radiation and immune-mediated perturbations. In this preliminary study, we tested this method to two preclinical bilateral tumor models (4T1 and MC38). Our model provides a quantitative measure of the interaction strength between primary and secondary tumors at the individual level, capturing dynamics over time rather than relying solely on cohort averages. This approach frames the abscopal effect as a continuous, stochastic phenomenon rather than a binary response. The framework is flexible for future studies, particularly in concurrent radiation and immunotherapy with PULSAR, where different radiation doses and fractionation schedules can be compared, and immune checkpoint inhibitors (ICIs) can be incorporated to further enhance systemic anti-tumor immunity. The framework can also help us make cross-study comparison of abscopal effects and standardizes the reporting of abscopal magnitude beyond simple statistical significance.

Constructing subsurface facies models that are geologically plausible and constrained by well facies is essential for analyzing sedimentary evolution, reservoir characterization, and flow simulation. Recent deep generative model-based geomodelling methods have demonstrated promising capabilities for both unconditional and conditional settings. We investigate denoising diffusion models as a generative framework for producing realistic and diverse facies realizations in both settings. Diffusion models generate samples through a fixed forward noising process and a learned reverse denoising process. For unconditional geomodelling, we use denoising diffusion probabilistic models (DDPMs) to learn geological patterns from training facies models, and adopt denoising diffusion implicit models (DDIMs) to accelerate sampling by reducing inference steps from 1500 to 50 (30x fewer steps). We assess the geological plausibility using data distribution, class proportions, variograms, and geometric features. To enable conditional generation, we encode well facies and their spatial locations as conditional indicators and apply a mask-based denoising strategy that generates facies only in between-well regions, guaranteeing hard conditioning without introducing additional loss weights. We evaluate both unconditional and conditional generation on three scenarios: two two-dimensional cases (meandering channels and point bars) and one three-dimensional point-bar case. Across these scenarios, unconditional generation reproduces geological realism, and conditional generation honors well data while producing geologically consistent between-well realizations, demonstrating practical utility for facies geomodelling applications.

Active stresses in biological cells and tissues drive many developmental processes. However, increasing experimental evidence suggests that additional mechanical interactions with surrounding material can play a crucial role in guiding these processes. We introduce a minimal model of this scenario and investigate how pattern formation in an active material can be controlled by an inhomogeneous environment. Specifically, we consider an active fluid in which a chemical species regulates local active stresses and is redistributed by the resulting flows. We show that active stress patterns within such a fluid exhibit frictiotaxis and systematically characterize how inhomogeneous external friction affects mechanochemical pattern formation instabilities. We find that hydrodynamic screening plays a crucial role in mediating the cross-talk between friction patterns and active fluid self-organization and identify a mechanochemical frustration mechanism that gives rise to pattern oscillations caused by inhomogeneous friction.

We study the self-assembly of colloidal microgel particles at a quasi-two-dimensional air-water interface of a drying droplet. Using bright-field microscopy, we demonstrate that increasing particle elasticity drives interfacial organization from repulsion-stabilized crystallization to attraction-dominated gelation, via diverse metastable structures including clusters, voids and anisotropic aggregates. Molecular dynamics simulations using an effective potential that captures the interplay between hydrophobic, capillary, steric and dipolar interactions, reproduce the overall phenomenology of the observed colloidal morphogenesis. Our findings establish particle elasticity as a key parameter governing non-equilibrium structural organization of colloids at an interface.

The beating flagella of the green alga Chlamydomonas reinhardtii play a prominent role in cellular mechanics, enabling cells to both displace and sense surrounding fluid. Specifically, flagellum-induced fluid transport enables microalgae to swim through fluid media and interact with other microorganisms. Minimal models, such as the three-sphere model with one sphere representing the cell body and two orbiting spheres mimicking the flagella, have been widely adopted to study various aspects of algal motility, including the synchronization of flagellar beating, run-and-tumble swimming, responses to shear flow, cell-body rolling, and helical navigation. However, detailed investigation of the algal flow fields generated by this minimal model remains limited. In this study, we systematically examine the time-averaged and time-resolved fluid flows generated by the three-sphere algae model and compare the numerical predictions with experimental data. Our findings reveal that the standard three-sphere model fails to produce key flow characteristics observed experimentally. To address this discrepancy, we explore a modified three-sphere model with refined flagellar beating dynamics and identify that differential drag acting on the flagellar spheres is the dominant factor influencing the fidelity of the modeled flow fields. These results advance the fundamental understanding of the flagellum-fluid interactions and algal flow and enhance our ability to accurately simulate microswimmer dynamics.

An efficient method of solving the time-dependent Schrödinger equation for pure states is described: At each timestep, a restricted subspace of the total Hilbert space is systematically and naturally constructed via the image of repeated applications of the Hamiltonian operator, and the time evolution is computed exactly within said subspace. The subspace is dynamically recomputed such that it co-evolves with the state vector. The method is built from the ground up as a parallel algorithm for graphics processing units and suited to Hamiltonians that are sparse in a given basis. We benchmark the method by comparing its results for a 1D Holstein model to previously published multiset-MPS results, and then apply the method to compute optical spectra and non-equilibrium dynamics of one-, two- and three-dimensional model chromophore nanoaggregates.

One of the most interesting questions that astronomy can hope to answer is: are we alone in our Milky Way galaxy? A detection of an electromagnetic (EM) signal generated by an extraterrestrial technological intelligence, or the presence in our solar system of an alien probe, would answer this question in the negative. Purposeful interstellar communication is a 2-way street - the transmitting and receiving technological intelligences (TIs) both need to do their parts. As the receiving TI, our EM search programs should incorporate a model of what a transmitting TI is likely to be doing. Published searches for extraterrestrial technological intelligence (SETI) have generally not done so and thus have often been suboptimally designed. We propose an improved search technique that more closely corresponds to astronomical surveys that have been undertaken for reasons that have nothing to do with SETI. Published non-SETI radio and optical surveys are sufficiently extensive that they already supply meaningful constraints on the prevalence of nearby purposely communicative alien civilizations. Purposeful communication can also include the sending of spaceships (probes). The absence of evidence for alien probes in the solar system suggests that no alien civilization has passed within ~100 lt-yr of Earth during the past few billion years.

Indonesia's nickel ore export ban has driven rapid expansion of smelting and hydrometallurgical processing capacity at the Indonesia Morowali Industrial Park (IMIP), now the world's largest integrated nickel processing complex, on the coast of Central Sulawesi. Whether this industrialization has degraded the adjacent marine environment remains unquantified. We apply Bayesian structural time-series (BSTS) causal inference to a multi-decadal, multi-sensor satellite ocean color record of the diffuse attenuation coefficient at 490 nm, $K_d(490)$, to test for a causal link between IMIP expansion and nearshore turbidity change. A consensus structural breakpoint, a significant posterior causal effect estimated against a Banda Sea counterfactual, and a distribution-free placebo rank test collectively establish that coastal water clarity deteriorated after the transition from initial nickel pig iron production to hyper-expansion of high-pressure acid leaching facilities for battery-grade nickel. Satellite-derived land cover analysis independently corroborates this timing, showing substantial built-area growth and concurrent tree cover loss within the IMIP footprint. The resulting euphotic zone shoaling occurs in oligotrophic waters supporting high marine biodiversity, where even moderate optical degradation may impair coral photosynthesis and compress depth-dependent reef habitat. These findings quantify a marine environmental cost absent from Indonesia's mineral downstreaming policy discourse and demonstrate a transferable, satellite-based quasi-experimental framework for causal impact assessment at coastal industrial sites in data-limited tropical settings.

Depth-of-interaction (DOI) encoding is an effective strategy for reducing parallax error and preserving spatial resolution in positron emission tomography (PET), particularly in compact small-animal scanners. To enable efficient simulation-driven design of DOI-capable systems, we extend the GPU-accelerated Monte Carlo toolkit gPET to support flexible multi-layer detector geometries. The original three-level hierarchical detector model in gPET (panel-module-crystal) was expanded by introducing an intermediate "layer" level, enabling parameterized modeling of stacked scintillator architectures. The photon transport algorithm was correspondingly updated to sample interactions across multiple layers and detector panels while preserving GPU-efficient memory usage. The framework was validated using three scanner configurations: a conventional single-layer ring (H2RSPET-1CL), an aligned split-layer design (H2RSPET-1CL-split), and an offset dual-layer design (H2RSPET-2CL). System performance was evaluated following NEMA NU4-2008 protocols using sensitivity, spatial resolution, and Derenzo phantom simulations with CASToR-based maximum likelihood expectation maximization reconstruction. The H2RSPET-1CL and H2RSPET-1CL-split configurations produced statistically identical hit distributions, while H2RSPET-2CL exhibited the expected offset interaction patterns. Sensitivity of H2RSPET-2CL remained comparable to H2RSPET-1CL, generally within about 2-5 percent, while radial spatial resolution improved substantially (0.8-1.6 mm vs. 1.0-4.2 mm from the center to a 50 mm radial offset). Runtime performance remained essentially unchanged between configurations. The extended gPET framework therefore enables fast and flexible simulation of multi-layer PET detectors and supports efficient optimization of DOI-enabled PET system designs.

Dimensional reduction is a generic consequence of dissipation in nonlinear evolution equations, often leading to attractor collapse and the loss of dynamical richness. To counteract this, we introduce a geometric framework for Covariant Multi-Scale Negative Coupling Systems (C-MNCS), formulated intrinsically on smooth Riemannian manifolds for a broad class of semilinear dissipative PDEs. The proposed coupling redistributes energy across dynamically separated spectral bands, inducing a scale-balanced feedback that prevents finite-dimensional degeneration. We establish the short-time well-posedness of the coupled state-metric evolution system in Sobolev spaces and derive a priori estimates for phase-space contraction rates. Furthermore, under a global boundedness hypothesis, we prove that the global attractor possesses a strictly finite Hausdorff and Kaplan-Yorke dimension. To bridge abstract topological bounds with physical realizability, we isolate the core Adaptive Spectral Negative Coupling (ASNC) mechanism for numerical validation. High-resolution experiments, utilizing a fully coupled ETDRK4 scheme and continuous QR-based Lyapunov exponent computation on a conformally flat 2D dynamic scalar manifold, corroborate the theoretical predictions. These computations explicitly demonstrate the stabilization of high-dimensional attractors against severe macroscopic dissipation. This geometrically consistent mechanism offers a new paradigm for maintaining structural complexity and multiscale control in infinite-dimensional dynamical systems.

High-quality InAs quantum wells grown on InP are a promising platform for topological quantum information processing due to their large g-factor, strong Rashba spin-orbit interaction, and their compatibility with in-situ-deposited superconductors. In this work, we investigate InAs/InGaAs quantum wells grown on InP (001) wafers, focusing on how the layer structure and strain influence the electronic properties and surface morphology. By combining quantum transport measurements with atomic force microscopy, we show that the layer design predominantly affects the mobility anisotropy, which aligns well with the surface morphology. Surface characterization further reveals the mechanism of quantum well collapse when the layer thickness exceeds the strain limit. In addition, transport measurements demonstrate that quantum confinement has a clear impact on band nonparabolicity.

Dynamical-systems approaches to spatiotemporal chaos have been developed primarily for single-phase flows, where the system state is defined by bulk velocity fields. Extending these ideas to two-phase flows remains challenging because the dynamics are intrinsically coupled to the evolution of a deforming interface. Here, we address this challenge for a two-dimensional vertical falling film by formulating the dynamics in terms of the interface evolution. Starting from the Navier–Stokes equations, we recover a classical long-wave interface evolution equation, originally derived by Topper & Kawahara (1978). Using this formulation, we perform an extensive parametric study to construct a regime map in the space of domain size and dispersion parameter. The resulting map reveals a rich range of interfacial behaviors, including travelling waves, bursting travelling waves, and fully chaotic regimes. In the chaotic falling film regime, we exploit the dissipative nature of the governing equation, which suggests that the long-time dynamics evolve onto an inertial manifold. Using a data-driven approach, we parameterize this inertial manifold and estimate its intrinsic dimension, suggesting approximately linear growth with domain size. We then construct low-dimensional models in manifold coordinates to facilitate the search for exact coherent states of the full system. Using this approach, we identify travelling waves, relative periodic orbits and equilibria embedded within the chaotic attractor. Chaotic trajectories repeatedly approach the neighbourhoods of these invariant solutions, indicating that the recurrent interfacial patterns observed in the dynamics correspond to visits to these coherent states. To the best of our knowledge, this constitutes the first identification of exact coherent structures embedded in chaotic falling-film dynamics.

In the work [1], within the framework of a dipole approximation for the radiation transition operator, of the second-order nonrelativistic quantum perturbation theory and the Hartree-Fock single-configuration approximation, the first theoretical study of the generalized cross-section of the direct two-photon sweeping out of the K-shell of a light neon atom was carried out. In this Letter we supplement the Preprint [2] with the results of taking into account non-dipole effects when constructing the amplitude of the probability of the radiation transition between continuum-spectrum states. As the main result, it was found that the non-dipole effects reduce the generalized cross-sections of two-photon sweeping out of the K-shell of an atomic ion (Fe16+) the calculated in the dipole approximation by several orders of magnitude (giant non-dipole effect).

Chaotic time series are notoriously difficult to forecast. Small uncertainties in initial conditions amplify rapidly, while strong nonlinearities and regime dependent variability constrain predictability. Although modern deep learning often delivers strong short horizon accuracy, its black box nature limits scientific insight and practical trust in settings where understanding the underlying dynamics matters. To address this gap, we propose two complementary symbolic forecasters that learn explicit, interpretable algebraic equations from chaotic time series data. Symbolic Neural Forecaster (SyNF) adapts a neural network based equation learning architecture to the forecasting setting, enabling fully differentiable discovery of compact and interpretable algebraic relations. The Symbolic Tree Forecaster (SyTF) builds on evolutionary symbolic regression to search directly over equation structures under a principled accuracy complexity trade off. We evaluate both approaches in a rolling window nowcasting setting with one step ahead forecasting using several accuracy metrics and compare against a broad suite of baselines spanning classical statistical models, tree ensembles, and modern deep learning architectures. Numerical experiments cover a benchmark of 132 low dimensional chaotic attractors and two real world chaotic time series, namely weekly dengue incidence in San Juan and the Nino 3.4 sea surface temperature index. Across datasets, symbolic forecasters achieve competitive one step ahead accuracy while providing transparent equations that reveal salient aspects of the underlying dynamics.

Collective motion in animals and cells often exhibits rapid reorientations and scale-free velocity correlations. This allows information to spread rapidly through the group, allowing an adequate collective response to environmental changes and threats such as predators. To explain this phenomenon, we introduce a simple, biologically plausible mechanism: a minority-triggered reorientation rule. When local order is high, agents sometimes follow a strongly deviating neighbor instead of the majority. This rule qualitatively changes the macroscopic system behavior compared to traditional flocking models, as it generates heavy-tailed cascades of reorientations over broad parameter ranges. Our mechanism preserves cohesion while markedly enhancing collective responsiveness because localized directional cues elicit amplified group-level reorientation. Our results provide a parsimonious, biologically interpretable route to critical-like fluctuations and high responsiveness during flocking.

The immersed interface method (IIM) for fluid-structure interaction imposes discontinuities in the fluid stress along immersed boundaries that are generated by forces concentrated along those boundaries. For a viscous incompressible fluid, imposing these discontinuities requires decomposing the boundary force into its normal and tangential components, which determine jump conditions for the pressure and velocity gradient. Previously, we developed an IIM for C0 triangulated surfaces, with a focus on piecewise linear surface representations. In this setting, the normal and tangent vectors of the discrete surface are constant on each element, and that method uses those piecewise constant vectors to determine the normal and tangential force components and, ultimately, the jump condition. We demonstrated that this is substantially more accurate than immersed boundary methods that use regularized delta functions at corresponding grid resolutions for situations in which shear stresses dominate. However, this IIM formulation struggles to accurately capture pressure loads. Here, we identify that the primary cause of this limitation is the discontinuous surface normal inherent in C0 triangulated surfaces. We propose a procedure that uses approximations of the surface normals that more accurately account for the curvature and avoid discontinuities in the reconstructed normal vectors. We investigate two ways to construct the continuous surface normal approximation: a standard L2 projection of the discontinuous surface normal field into a continuous finite element space, and constructing vertex normal vectors using inverse centroid-distance weighting and applying linear interpolation across each element. Numerical experiments show that the use of jump conditions computed with reconstructed continuous normal vector fields reduce leakage by up to six orders of magnitude across a range of pressures.

Recent theoretical studies show that nanoscale contact on dielectric substrates can induce flexoelectric polarization large enough to drive electron transfer. This has been supported by experimental evidence, indicating that contact electrification is inherently a coupled electromechanical phenomenon. In this work, we develop a computational model for flexoelectricity-driven contact electrification that integrates finite-deformation flexoelectricity with contact mechanics and physically motivated charge transfer. A tunneling transparency function is introduced to regulate the interfacial channel based on the WKB approximation, capturing the irreversible charge trapping during unloading. Three contact scenarios are investigated with specific hypotheses for charge transfer: unbiased metal-dielectric contact driven by surface polarization, biased contact restricted to carriers of a single polarity, and dielectric-dielectric contact where surface states with finite capacity limit the transferable charge. The model is compared with atomic force microscopy measurements on PMMA and PDAP substrates under both biased and unbiased conditions.For contact between identical dielectric materials, we show that geometric asymmetry in surface curvature is sufficient to induce charge separation, with polarity reversal occurring at a critical surface wavenumber. Three-dimensional simulations on random rough surfaces reproduce the mosaic charge distributions observed experimentally, confirming that contact-induced local strain gradient heterogeneity can generate spatially non-uniform charge patterns without introducing any material inhomogeneity.

Estimating changes in extremes quantiles from environmental processes non-stationary in time, from small samples is challenging since it is difficult to characterise tail non-stationarity adequately. Using annual maxima and minima of near-surface temperature (tas) from CMIP6 output for some Earth desert regions, we use generalised extreme value (GEV) regression to model changes in extreme quantiles in time. We consider candidate models with different parametric forms for the variation of GEV parameters with time, estimating parameters using Bayesian inference. We select optimal candidate models using model selection criteria, including the Akaike, Bayesian, divergence and widely-applicable information criteria. In an extreme value setting, the performance of different criteria is unreliable. We therefore undertake a simulation study using ground truth models generating data similar to our extrema, to assess performance of the criteria to minimise error in prediction of change DeltaQ in the 100-year return value of tas over (2015,2125). The Bayesian information criterion (BIC) provides best performance, out-performing the divergence and widely-applicable Information criteria. We compare BIC model selection with a stacked Bayesian model average. Using BIC-selected GEV regression, we estimate joint posterior distributions of DeltaQ, coupled over three climate scenarios, for different combinations of desert region, global climate model and climate ensemble. We find significant increases in DeltaQ for regional annual maxima under stronger forcing scenarios for all desert regions. Similar but weaker trends are observed for regional annual minima. There is evidence that extreme minima are warming more rapidly than extreme maxima in Antarctica, whereas most hot desert regions exhibit the opposite effect. An ancillary file of supplementary material is provided.

We investigate the ripeness of kiwifruit nondestructively with near-infrared pulse illumination. With this measurements in time domain, only one frequency (the wavelength 800 nm is required. Measurements were performed on three golden kiwifruits over a period of ten days. To quantify changes in temporal profiles of the detected light, we introduce two indices: the relative ripeness $r(n)$ and the first Wasserstein distance $W_1(n)$. Both indices exhibit nonmonotonic behavior.

We predict and analyze boundary-driven exceptional points in semi-infinite Hermitian photonic waveguide lattices with a side-coupled defect. The exceptional points arise from coherent reflections at the lattice termination, which induce strong memory effects in the defect dynamics. Using an exact analytic approach, we derive the defect's non-Markovian memory kernel, revealing the trajectories and coalescence conditions of the resonances, which can be precisely tuned by the defect position and the coupling strength. Our results provide a simple and experimentally accessible platform for exploring memory-enabled non-Hermitian physics in Hermitian photonic lattices.

Collective temporal organization in complex systems is commonly attributed to synchronization, resonance, or proximity to dynamical instabilities. Here we identify a distinct mechanism by which coherent, synchronization-like behavior can emerge in stochastic systems that are linearly stable and contain no intrinsic oscillators. The mechanism arises from non-normal pseudospectral amplification and leads to what we term pseudo-coherence: an intermittent form of collective organization characterized by transient phase alignment, broken time-reversal symmetry, positive entropy production, and drifting spectral peaks. Using a minimal overdamped stochastic model, we show that increasing non-normality drives a sharp pseudo-critical transition. Beyond a well-defined threshold, fluctuations concentrate along a dominant reaction mode, generating intermittent growth of Kuramoto-like order parameters and irreversible probability currents without eigenvalue crossings or Hopf bifurcations. Analytically, we demonstrate that pseudo-critical non-normal dynamics reshapes the imaginary pseudospectrum, amplifying slow fluctuations and producing coherent frequency bands under finite-time observation. These results identify pseudo-coherence as a new route to collective temporal organization in non-equilibrium systems, suggesting that apparent rhythms and synchronization in natural systems may arise from non-normal stochastic amplification rather than intrinsic oscillators.

Classical molecular dynamics and electro-diffusion theories have achieved profound success in elucidating ion selectivity and gating mechanisms. However, reconciling strict selectivity with high flux permeation in Angstrom-scaled biological ion channels poses a universal challenge in nanoscale physics, as classical models consistently underestimate single-channel conductance. Using a non perturbative quantum transport framework, we calculate the ion permeation dynamics through the selectivity filter within a transfer matrix formalism. We demonstrate that quantum tunneling allows ions to bypass classical Arrhenius suppression, quantitatively recovering the experimental conductance of Na+ and K+ channels. Crucially, our findings reveal that the exploitation of quantum mechanics is a fundamental prerequisite for achieving macroscopic physiological efficiency. By reframing ion channels as mesoscopic quantum conductors, this work establishes a transformative paradigm in quantum biology and predicts distinct transport resonances in the terahertz regime.