Papers

In 1054 AD a daytime star appeared in the constellation of Taurus, for three weeks, and it was reported in various sources from Europe to China/Japan: it was one of the few documented galactic supernovae of the last two millenia. This paradigm has been established about sixty years ago, as the comprehension of the physics of supernovae progressed with enough observational data. The Gamma-ray bursts were discovered in the same period, but only in the past few years have their observations become daily and their distances have been fully understood as cosmological. After the explosion, the exponential decay of the luminosity in gamma-rays and X-rays has been followed with telescopes onboard dedicated satellites. Also the exponential decay of the afterglow's optical and radio frequencies have been observed with the largest optical and radio telescopes. Within the binary-driven hypernova framework, successful in explaining all the observed phases of the Gamma-ray bursts, the universal exponential decay can be extended to 1000 years after the burst, to account for the present values of Gamma and X-rays as well as optical and radio frequencies of the Crab Nebula. Both the daytime visibility of the burst, and the simultaneous radiation plagues appeared in Constantinople and Cairo is a strong evidence of the presence of Gamma-rays in the lower atmosphere, coming from the same source originating the Crab nebula. The association to the daytime visibility of that star and the following plague meets exactly the etymology of the word dis-aster, bad star.

This study evaluates viscosity-dependent limitations in refined potential flow theory (RPT) applied to incompressible flow over an impulsively started circular cylinder. RPT adapts classical potential flow theory to account for viscous effects, flow fluctuations, and three-dimensional variables. By utilizing a quasi-irrotational viscous stream function, the framework solves the continuity and Navier-Stokes equations while satisfying both free-stream and no-slip boundary conditions.

Natural load-bearing patterns such as leaf venation, trabecular bone, and spider webs achieve high stiffness per unit mass, yet classical topology optimizers rarely reach such geometries, and few let engineers express structural design intent through natural language. This work treats a frozen text-to-image diffusion model as a training-free source of design knowledge and distills it into the physics loop of density-based topology optimization via score distillation sampling, so that a text prompt becomes an explicit, machine-interpretable representation of engineer intent. The prompt-induced generative gradient and the finite element sensitivity are combined at every iteration, letting physics decide which prompt-induced features survive. In 245 primary SDS runs spanning four geometric domains and two physics regimes, 38 of 49 prompt–domain combinations achieved statistically significant compliance reductions (up to $-31.5\%$ mechanical and $-23.0\%$ thermoelastic), outperforming gradient-based baselines. Cross-domain morphological analysis identifies a recurring structural signature of improvement: in most domains the generative prior suppresses dead-end branches in the rib skeleton, with endpoint–compliance correlation $r = +0.56$ to $+0.99$. A Heaviside projection with $\beta$-continuation resolves a pronounced intermediate-density tendency in this diffusion–physics coupling ($42.6\%$ to $<3\%$), and an automated skeleton-based pipeline converts optimized density fields into \rev{candidate geometry ready for computer-aided design. By retargeting the generative prior across domains, loading conditions, and physics objectives through a change of text prompt, with each new problem's physics setup specified separately, the framework uses a pretrained generative model as a reusable, training-free prior for engineering design.

We develop a vectorial lattice Boltzmann model with a space-time adaptive relaxation coefficient, combined with adaptive time-stepping for compressible Euler dynamics with generic equation of state. The model, due to special form of the relaxation coefficient devised here is shown to converge to the Euler limit with second-order accuracy in the absence of shocks under acoustic scaling. The solver is robust and able to properly capture compressible gas dynamics for both ideal and non-ideal equations of state, including the Bethe–Zel'dovich–Thompson regime. This is verified and demonstrated through a variety of configurations of increasing complexity.

We present a novel reduced-order data assimilation framework, termed Reduced-Order Dynamical Assimilation (RODAS), for reconstructing high-resolution, time-resolved flow fields from sparse velocity measurements. The method combines low-dimensional experimental observations with a physics-based parametric reduced-order model, enabling both spatial extrapolation beyond the measurement region and temporal super-resolution. The approach first identifies the dominant dynamics from sparse measurements through Dynamic Mode Decomposition (DMD), and subsequently reconstructs the corresponding full-order flow evolution by projecting the identified dynamics onto a parametric Proper Orthogonal Decomposition (POD) manifold generated from high-fidelity numerical simulations. Unlike conventional reduced-order data assimilation methods that estimate independent snapshots or treat time as an additional parameter, RODAS reconstructs an entire dynamical trajectory in a single inference step while naturally incorporating parametric variability. We assess the proposed methodology on vortex shedding behind a circular cylinder for both Newtonian and non-Newtonian (Carreau-Yasuda) fluids. Numerical experiments demonstrate accurate reconstruction of high-resolution velocity fields from localized, low-resolution measurements, achieving sub-percent reconstruction errors with sufficiently rich reduced bases, robust performance under severe temporal undersampling, and accurate prediction of engineering quantities of interest such as the drag coefficient. These results demonstrate that RODAS provides an efficient framework for real-time, physics-informed reconstruction of unsteady flows from sparse experimental data.

This paper presents a unified invariant-domain-preserving (IDP) framework for hybrid discretizations of hyperbolic conservation laws, including active flux and PAMPA methods, in which cell averages are updated conservatively while cell-boundary point values evolve under a possibly non-conservative operator. The main challenge is to preserve admissibility for these two coupled sets of states under a single local stability condition, without adding evolved degrees of freedom or relying on post-update repairs. For the point-value update, we introduce an admissibility transform based on a barrier–Legendre map for convex admissible interiors described by concave constraints, and prove that the inverse map is globally defined and Lipschitz continuous on the relevant sets. For the conservative cell-average update, we establish a structural obstruction theorem: the single-state continuous physical flux built from admissible boundary traces alone cannot provide a conservative IDP guarantee, for any prescribed CFL number, when internal reconstruction values are uncontrolled. This identifies the missing local control that must be supplied by an additional IDP flux mechanism. To realize this mechanism explicitly, we combine cell average decompositions (CAD), geometric quasilinearization, and local a priori scaling to construct admissible, generally discontinuous trace states before flux evaluation. Under an explicit trace-based CFL condition, with constants determined by local CAD weights and trace-state wave-speed bounds, the coupled hybrid update preserves the prescribed invariant domain. Concrete third-order schemes are developed on triangular, Cartesian, convex quadrilateral, and general convex polygonal meshes. Numerical results demonstrate the designed order of accuracy for smooth solutions and the strict preservation of physical admissibility.

Urban rail transit (URT) faces increasing flood risk while relying on traction power systems whose failures can trigger cascading disruption. Existing studies on URT flood resilience often oversimplify power dependency by overlooking traction power redundancy and rarely consider flood-conditioned cascading impacts, providing limited insight into how flood-induced traction power failures propagate through operations and affect system resilience. This study marks the first quantitative assessment of flood-conditioned power dependency of URT and its effects on system resilience. The methodology integrates a double-layer URT network model, spatial flood exposure assessment, service disruption simulation, a flood-conditioned power dependency index, and a journey-based performance metric that captures resilience properties of robustness and redundancy. It is applied to the London rail transit network under surface water flood scenarios across current and RCP8.5 climate conditions, two traction power feeding mechanisms, and four substation flood-failure thresholds. Results show that traction power redundancy fundamentally shapes the severity of cascading impacts. Under double-end feeding, power-attributable performance loss remains minimal, whereas single-end feeding leads to substantially greater loss and is highly sensitive to substation failure thresholds. The proposed dependency index is strongly correlated with dependency-related performance loss, supporting its use as a proxy for dependency-induced additional consequences in strategic planning.

The detection performance of liver metastases decreases with the reduction of radiation dose, but misses are heterogeneous. Previous eye tracking work has characterized missed metastases into two categories: search errors i.e., the eyes never land on the lesion, and decision errors i.e., the lesion is seen but not recognized as malignant. We integrated three prior reader studies to answer this question. In all studies, radiologists interpreted the same set of 40 contrast enhanced abdominal CT exams containing 91 liver metastases whose locations had been previously marked. In two studies, the workstation recorded their gaze and eye movements. Using eye dwell times, metastases were classified as search-error-dominant (majority of misses had <2 sec gaze time) or decision-error-dominant (>2 sec gaze time). In the third study, exams were interpreted both at 120 and 200 quality reference mAs (QRM) by ten radiologists. The third study did not include eye tracking. Out of 91 liver metastases, we excluded 16 that were never missed in the eye tracking studies and used 75 liver metastases for the present study.

Urban rail transit systems (URTSs) are increasingly exposed to extreme floods following heavy precipitation, yet passenger travel impacts are often assessed through delay-based indicators that overlook infeasible journeys under large-scale disruptions. This study develops a passenger-oriented, resilience-informed framework for assessing flood impacts on URTS journeys from disruption onset to recovery completion. The framework presents a novel six-category classification of journey impacts, explicitly considering rerouting, alternative station use, and a delay threshold. It is demonstrated through hourly dynamic simulations of 15 London URTS lines under 30-year, 100-year, and 1,000-year flood risk scenarios. Results indicate that severe flood disruptions lead to substantial unsatisfied demand, driven primarily by unavailable routes rather than unacceptable delays. Compared with finer behaviour adjustments, rerouting dominates travel impacts. These findings highlight the significance of moving beyond delay-based assessment and provide valuable evidence on essential behavioural mechanisms for strategic-level stress testing intended to inform URTS flood resilience intervention planning.

The thermal stability and carrier recombination kinetics of an as-received InGaN/GaN multiple quantum well (MQW) structure, capped with a protective AlN thin film, are studied following a series of thermal annealings at temperatures between 500 deg C and 1100 deg C. Under 325 nm focused laser excitation at room temperature, the sample's photoluminescence (PL) spectrum exhibits three emission bands: an ultraviolet peak at 363 nm, a blue peak at 455 nm, and a yellow peak at 565 nm. We find that the MQW's 455 nm emission is preserved after annealing at 1100 deg C. Moreover, room-temperature PL excitation (PLE) and time-resolved PL (TRPL) have been investigated to shed light on the sample's energy-transfer mechanisms and emission decay characteristics. Power-dependent PL spectra analysis shows that the carrier recombination mechanism of the MQW's emission has not been affected by thermal treatment. Rate equation modelling and chromaticity coordinate analysis also show limited thermal impact on the calculated equivalent emission lifetime and emissive colour. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) analysis has been performed, further evidencing the preservation of the MQW structure in the annealed sample.

We propose and numerically demonstrate a deterministic protocol for preparing and tailoring topological defects in two-dimensional single-species Coulomb crystals confined in radio-frequency traps. In conventional finite-rate quenches through the linear-to-zigzag transition, defects form stochastically from independently chosen broken-symmetry domains, and the quench rate controls the resulting domain structure. We instead use an initial three-dimensional helix structure and planarize it into a deterministic defected state. We demonstrate that the number and approximate axial positions of kinks are encoded by the projected nodes of the initial helix. We find that selected helices can remain on metastable finite-winding branches during planarization and yield reproducible single- or multi-kink states, even in the adiabatic limit. Using the quench rate as an additional control parameter substantially enlarges the set of precursor helices that result in defected states, as a fast planarization can prevent reordering during the quench and freeze in the pre-existing domain pattern. After planarization, the survival and mutual interaction of the defects are governed by their effective Peierls–Nabarro potential landscape. By numerically isolating the interaction energy for the two defect types, we find that the interaction of extended kinks is attractive over the investigated parameter range, while odd kinks exhibit a short-range repulsive barrier tunable by the anisotropy of the trapping potential $\alpha$. Finally, we introduce an experimentally motivated post-selection scheme in which $\alpha$ is used to selectively destabilize, annihilate, or convert targeted defects. Our results provide a route to deterministic single- and multi-kink preparation in single-species Coulomb crystals, enabling controlled studies of defect interactions, transport, and nanofriction.

Quantification of energy transport and dissipation in weakly collisional heliospheric plasmas that are far from local thermodynamic equilibrium is an outstanding scientific problem. A central challenge is determining how non-Maxwellian velocity-space structure affects damping and emission of coherent ion-scale waves, especially compared to simplified analytical models for background plasma velocity distributions. In this work, we study the damping and emission of parallel-propagating proton cyclotron waves for two models of proton velocity distributions measured by the SPAN-I instrument on board Parker Solar Probe during an extended storm of waves with left-hand polarization in the solar wind at a heliocentric distance of 30.1 solar radii. Using the measured velocity distribution rather than a two-component bi-Maxwellian model predicts instabilities consistent with the observed coherent waves. For intervals in which both models predict net damping, the observed VDF model yields weaker damping in 90% of cases, with a reduction in the integrated heating rate of 0.44 relative to the bi-Maxwellian model. These results suggest that simplified analytical velocity distribution models may overestimate cyclotron damping and underestimate wave emission in the near-Sun solar wind.

We present a physical framework for Alfvénic solar wind turbulence in which the plasma is modeled as discrete domains with local rotational symmetry about the domain-mean magnetic field. Using this symmetry, we construct minimalist cascade models governed by two characteristic time scales, nonlinear and Alfvénic, associated, respectively, with the perpendicular and parallel directions relative to the domain-mean magnetic field. Within this partial symmetry, we also characterize the anisotropy of each domain by a single additional geometrical parameter, the alignment angle between the domain-mean velocity and magnetic fields. We introduce a stochastic renewal process with a bimodal waiting-time distribution based on these two time scales, yielding a two-branch renormalization solution for the total energy cascade: a statistically robust branch with an Iroshnikov-Kraichnan-like $k^{-3/2}$ spectrum, and a statistically marginal branch with a Kolmogorov-like $k^{-5/3}$ spectrum. Utilizing principles of causality and cascade stability, we show that the system selects the faster cascade rate between the two available whenever energy-flux fluctuations become supercritical, preventing intermittent flux accumulation. Consequently, during solar wind expansion, balanced domains (with low cross-helicity) undergo a first-order phase transition from the slow $k^{-3/2}$ cascade to the fast $k^{-5/3}$ cascade. The transition is accelerated by heterogeneous nucleation at switchbacks. Incorporating a forward magnetic helicity cascade slaved to the energy cascade, we show that the large-scale spectra decouple into a flat $k^{-3/4}$ magnetic spectrum and a $k^{-3/2}$ kinetic spectrum. Data from Voyager, Ulysses, Helios, Wind, and PSP confirm these spectral signatures across diverse heliospheric regions.

Temporal-network models attribute memory in contact data to either node self-excitation (branching ratio n_node) or tie reinforcement (kappa), carrying major consequences for epidemic spreading. We prove that when event initiators are observed, the two mechanisms are orthogonal: the Fisher information is block-diagonal and neither trades off against the other. In undirected proximity data, where initiators are unobserved, marginalising over them couples the mechanisms into a structural confound that survives posterior smoothing. On empirical proximity, messaging, and email records, however, a cruder failure dominates: fitted node memory is pinned to the inter-event marginal law and remains virtually invariant across latent label posterior samples (coefficient of variation below 1%). An inter-event-order shuffle test and burstiness-memory diagnostics reveal that exponential-Hawkes node memory is recovered from none, while tie reinforcement remains identifiable throughout. This near-unidentifiability is intrinsic, not an artefact of the exponential kernel: refitting flexible scale-free (sum-of-exponentials) kernels on synthetic power-law self-exciting processes fails to distinguish genuine node memory from memoryless renewal controls, with identical collapses recurring on algorithmic networks (edit bots, cloud microservices) and cortical spiking. Downstream epidemic consequences are quantitative: simulations fitted to empirical contact records under-predict outbreak sizes by up to a factor of 2.5 and shift the epidemic threshold. We conclude that observational temporal networks face a two-fold identifiability boundary: contact directionality is essential to decouple tie reinforcement, whereas heavy-tailed node self-excitation is intrinsically unidentifiable from contact timings alone.

Spectral gaps are widely interpreted as signatures of collective organization, yet it is rarely clear whether the resulting modes correspond to physical variables or merely to convenient mathematical coordinates. Here we show that, in resistor-coupled LC networks, the graph Laplacian defines an experimentally accessible hierarchy of electrical descriptions. Its eigenvectors become measurable voltage patterns, while its eigenvalues quantify the coupling-induced resistive damping of those patterns. When coupling is strong within regions and weak between them, microscopic voltages collapse into nested collective variables. A network of 1002 resonators reduces first to 42 regional voltages and then to four geometry-controlled global modes. Summing the microscopic Kirchhoff equations produces a directly constructible 42-node RLC circuit, without parameter fitting, that reproduces regional dynamics with 1.4 to 4.6% global RMS error. Finer modal coordinates improve accuracy but no longer correspond generally to simple resistor graphs. Robustness tests show that the regional level survives substantial conductance disorder and internal rewiring, but weakens when perturbations impair mixing within a region; changes in interregional connectivity selectively disrupt the global level. Thus the Laplacian spectrum becomes a physical design principle: it identifies which collective electrical variables emerge, how they can be constructed, and when they provide an adequate reduced description.

The potential of crossing climate tipping points (TP) has reached the attention of many researchers and the general public. On the one hand, the basis for this concern is strengthening, with simulations showing that abrupt transitions might occur even for moderate emission scenarios. On the other hand, our understanding of what constitutes such transitions mathematically is becoming more nuanced. This leads to challenges for the fidelity of early-warning signals (EWS), which accompany bifurcations in systems that closely track a slowly changing steady state. Different kinds of out-of-equilibrium dynamics in a rapidly changing climate, as well as chaos, blur the critical tipping threshold and imply limits in predictability. Using EWS to detect loss of local stability from data requires careful evaluation in the high-dimensional system, and may have limited predictive power in a highly multistable climate, where there is more than a single, well-known alternative state. This calls for new methods using non-equilibrium statistical mechanics and dynamical systems to efficiently probe the global stability properties of climate models and observations.

The magnetic fields that make up the internal structure of coronal mass ejections (CMEs) are thought to be organised in a flux rope configuration, consisting of twisted magnetic fields that wind about a central axis. Remote-sensing observations of CMEs show a wide range of morphologies, dynamics, and evolution, including rotation, non-uniform expansion, deflection, and interaction with the ambient solar wind. In-situ measurements, however, typically consist of a single 1D spacecraft trajectory through a large 3D structure (or few at best), restricting our ability to determine how CME magnetic structure varies in space and time. In this work, we analyse the magnetic configuration of two idealised CMEs during their early evolution in the range 1-30 $R_{\odot}$ using the Magnetohydrodynamic Algorithm Outside a Sphere code. The initial flux ropes differ only in their orientation with respect to the global dipole and erupt from a bipolar active region below the streamer belt in a simplified coronal environment. We deploy a fleet of synthetic spacecraft throughout the CMEs' paths at different combinations of latitudes, longitudes, and heliocentric distances. We identify and examine flux rope signatures in the synthetic in-situ profiles to characterise radial variations as well as latitudinal/longitudinal ones. We find that, even in a simplified CME erupting under solar minimum-like conditions, the sampling location significantly affects the global structure that would be deduced from common flux rope reconstruction and analysis techniques used for in-situ measurements. Additionally, we consider possible in-situ signatures that may serve as indicators of the source region's topology.

Physics-based lithium-ion battery models provide access to physically meaningful internal electrochemical states and processes, but cell-specific parameter inference from terminal current-voltage data is computationally expensive and limited by identifiability. We present a surrogate-accelerated inverse framework based on a single-particle model with electrolyte dynamics (SPMe). Its forward map uses our Artiphy surrogate framework for rapid, differentiable evaluation of voltage and selected internal states. After rescaling to remove exact structural redundancies, we infer non-redundant transport, kinetic and capacity parameter groups, including concentration-dependent solid and electrolyte diffusivities. Synthetic voltage data from a Doyle-Fuller-Newman (DFN) model under a WLTP-like current protocol provide a benchmark with known reference parameters and controlled model discrepancy. The inferred SPMe reproduces the benchmark voltage with an error of order 1 mV and recovers electrode capacities well. Positive-electrode diffusivity is recovered accurately over much of the probed stoichiometric range. Local sensitivity and Fisher-information analysis identifies correlated kinetic-Ohmic and electrolyte-transport directions, and shows how localised information and the global diffusivity parameterisation can yield narrow Fisher-curvature envelopes despite weak voltage sensitivity to negative-electrode diffusion over much of the drive cycle. These results represent a step towards rapid physics-based in-silico parameterisation and reduced reliance on destructive cell characterisation.

Surface active agents (surfactants) are common contaminants found on most air-liquid interfaces. Surfactants lower the surface tension, and hence surfactant concentration gradients generate surface tension gradients, which leads to flow. A canonical surfactant-induced flow is the outward spreading due to the localised deposition of surfactants onto an otherwise clean liquid interface. Eshima et al. (Phys. Rev. Lett., accepted, 2026) demonstrated experimentally and theoretically, through a late-time similarity solution, that surfactant solubility enhances surfactant-induced flows of air-liquid-air sheets and that soluble surfactant solutions can be mapped onto equivalent insoluble surfactant solutions. Here we extend the work theoretically and numerically, developing a systematic framework for the derivation and analysis of such solutions. The relevant nondimensional parameters are quantitatively defined, and the physically accessible parameter space is wide, which our theory and numerical simulations are able to span. We link the solutions for finitely soluble surfactants to the solutions for the limiting regimes of insoluble and infinitely soluble surfactants, where the latter link only holds transiently: the solution of the infinitely soluble limit appears as an intermediate solution that persists ever longer as solubility increases, before the ultimate late-time solution, which maps onto insoluble surfactants, is obtained.

Identifying ground-state and low-energy atomic configurations is a central problem in alloy design. The cluster-expansion (CE) method represents configurational energetics on a fixed lattice as an effective Hamiltonian; for binary alloys, higher-order CE models become polynomial Ising Hamiltonians. Using Au-Cu as a model binary alloy, we formulate cubic and quartic cluster-expansion Hamiltonians as penalty-augmented polynomial unconstrained binary optimization (PUBO) problems under fixed-composition constraints. We optimize these PUBO problems using SQBM+, a simulated-bifurcation-based solver that can treat higher-order polynomial binary objectives directly. This direct PUBO treatment avoids the need to construct an explicit quadratic reformulation with auxiliary variables. Composition constraints are imposed through quadratic penalty terms, whose weights are estimated from derivative coefficients of the continuous relaxation of the CE objective. Benchmark calculations for systems up to 2048 atoms show that SQBM+ robustly obtains low-energy feasible configurations for cubic CE models and remains effective for many quartic instances. Formation-energy convex hulls constructed from the optimized configurations recover the CuAu and Cu3Au ordering trends and reveal finite-size effects at off-stoichiometric compositions. These results demonstrate simulated bifurcation as a practical route to constrained higher-order CE optimization for alloy configuration search.

With the rapid spread of news on social media, understanding the propagation of misinformation is becoming increasingly important. One factor that affects individuals' vulnerability to false information is their ideological predisposition. Despite the large number of agent-based models that focus on social influence as a driver of the spread of false claims, they often fail to explicitly integrate personal ideological biases into belief formation. In this work, we explore how misinformation spreads through the interaction between individuals' ideological biases and social influence. Our model accounts for both the strength of individuals' ideological biases and the extent to which a false claim aligns with their ideology. Social influence modifies the effects of ideological intensity and false claim alignment through network interactions. Notably, the influence of neighbours' ideological intensity on belief is strongly affected by how well those neighbours are connected to one another. These results highlight the importance of considering both network structure and personal ideological biases when modelling misinformation propagation.

The KAPAE Phase II detector was developed to search for invisible decays of positronium as a probe of physics beyond the Standard Model. The detector geometry was optimized with Geant4 simulations, and the scintillation and readout performance were evaluated experimentally. The temperature dependence of the BGO scintillator and several readout configurations were studied to find the operating condition giving the best energy resolution. The background level was then measured both at the surface and underground. At the surface, the positron-gamma coincidence background was of order 1 Hz, with occasional bursts up to about 65 Hz. Underground, it decreased to below about 3 \times 10^{-4} Hz, more than three orders of magnitude lower.

Agitated filter dryers (AFDs) are a crucial unit operation in small molecule manufacturing that enables simultaneous filtration, washing, and drying of active pharmaceutical ingredients. One of the key challenges in AFDs is associated with undesired agglomeration, where the presence of hard agglomerates results in off-spec products, equipment damage, and additional downstream processing. This article presents a novel mechanistic model that describes the formation of soft and hard agglomerates during agitated filter drying. By integrating population balance and multiphysics modeling, the model can accurately predict the evolution of the product temperature, moisture content, and particle size distribution, and hence quantify the extend and impact of undesired agglomeration across various operating conditions. Our proposed model-based framework enables the rational design and operation of AFDs for improving the product quality and process reliability.

A narrowband emitter aligning with the bandgap of the underlying solar cell is essential for improving the spectral efficiency and thermal stability of thermophotovoltaic (TPV) systems. Emitters based on oxide materials present a promising solution to the optical and mechanical performance degradation of traditional emitters, which employ metal-dielectric structures that experience metal oxidation and structural deterioration at high temperatures. Here, we presented a surrogate-assisted inverse-design framework for a narrowband 1D grating emitter comprising ITO and Al2O3 layers on a sapphire substrate. We performed Bayesian optimization over the trained ExtraTrees surrogates on a penalty-augmented objective containing a peak-emission constraint (E_peak > 0.90) while minimizing the full width at half maximum (FWHM) and maximizing the fraction of emission concentrated within the selected peak-centered spectral band to acquire a high, narrowband peak emission. The surrogate model was trained using a dataset of emission spectra obtained from the finite-difference time-domain (FDTD) by systematically varying the layers' thicknesses and the structure's period as the input features, and the narrowband emission's figure of merit (FOM) ((E_peak), wavelength of peak emission (_peak), FWHM, in-band fraction (f_in) for determining the amount of emission outside the peak band, and concentration of peak emission near the peak) was used as the prediction target. The resulting set of predictions for the optimized structure was further validated using the FDTD method.

The paper is devoted to the phenomenological theory of aftershocks occurring in the source of a tectonic earthquake following the main shock. The theory was developed by the author jointly with A.D. Zavyalov and O.D. Zotov during the course of a long-term study of aftershocks. The theory is based on the concepts of source deactivation and the source's proper time. The holistic law governing the decay of aftershock activity over proper time follows from the theory. The damping decrement is equal to the source deactivation coefficient. The main focus of this paper is the analysis of the logical structure of the theory. The paper also contains a brief description of the experimental results obtained using the theory. Keywords: earthquake source, aftershocks, Omori's law, Utsu's law, deactivation coefficient, proper time, underground clock, foreshock convergence, aftershock divergence.

Directly self-starting implicit methods are attractive for transient analysis because they avoid auxiliary starting procedures while retaining the original first- or second-order governing equations. However, most existing formulations usually fix the last sub-step at the end of each time interval, which restricts the attainable order. This study develops a generalized $s$-sub-step implicit framework by releasing this constraint and treating all sub-step locations as design variables. The resulting methods admit a unified Runge–Kutta representation for both first- and second-order transient systems and preserve identical effective matrices over all sub-steps. Accuracy conditions are derived by simultaneously matching the numerical amplification factor and load operator, thereby accounting for both homogeneous and forced responses. For $s=1,~\cdots,~6$, two complementary families are obtained: $s$th-order members with user-controllable high-frequency numerical dissipation and adjustable sub-step locations, and $(s+1)$th-order members obtained by selecting the sub-step locations, with fixed dissipation. The latter reach up to seventh-order accuracy without increasing the number of sub-steps, although some high-order members are $A(\alpha)$-stable with stability angles extremely close to $90^\circ$. Analytical amplitude and phase errors further reveal parity-dependent superconvergence in undamped systems, and appropriate parameter selections can substantially increase either phase or amplitude accuracy beyond the formal order. Numerical benchmarks confirm the predicted convergence orders and the controllable suppression of spurious high-frequency responses.

Atmospheric surface layer flows are computationally challenging, predominantly due to surface roughness and the high Reynolds number, both of which demand exceptionally high spatial resolution in the near-surface region. We have developed a 2-D stochastic-based model for the generation and the streamwise concatenation of instantaneous, step-like velocity profiles featuring key elements of wall turbulent flows, i.e., uniform momentum zones (UMZ) and shear layers (Ehsani et al. 2024a,b), and vortices (Ehsani et al. 2026). The model is extended herein to the top of the logarithmic layer to reproduce the high-Reynolds-number, rough-wall, turbulent boundary layer measured by Saddoughi and Veeravalli(2000), without the support of a UMZ dataset, using only a handful of critical flow parameters: the Taylor microscale {\lambda}T, the boundary layer height {\delta}, the friction velocity u{\tau}, and the aerodynamic roughness length z0. The primary challenge lies in the integration of the stochastic model with the scaled distributions of the UMZ and vortex characteristics. The resulting statistical moments, energy spectra, and structure functions are compared against the experimental results. The validated code is made available in a GitHub repository.

We introduce an original optical cavity design intended to efficiently output-couple extreme ultraviolet (XUV) light produced via cavity-enhanced high harmonic generation (CE-HHG). It supports the amplification of oppositely circularly polarized modes, crossing with a small angle in the high finesse cavity, where circularly polarized HHG scheme (NCP-HHG) will be implemented. We present an analytical model, numerical simulations, and experimental results obtained with a low-power continuous (CW) laser, demonstrating the strong potential of the cavity as an efficient XUV output coupling method. In particular, it is well suited for producing the 7th harmonic (H7) from an Ytterbium frequency comb at 8.4 eV, which is of particular interest for precision nuclear spectroscopy of Thorium.

We present a comprehensive description of the network of Italian Business-to-Business commercial relationships, based on the universe of electronic invoices collected by the Italian Tax Office. The firm-to-firm detail of the data is exploited to describe the distribution of the numbers of buyers (customers) and sellers (suppliers) per firm, the centrality of each firm in the production network, and the average distance between firms. We characterize for the first time the firm-to-firm network describing the Italian production system, and present its geographic and sectoral breakdown. The analysis reveals heavy tailed distributions for the numbers of buyers and sellers per firm, implying a scale-free structure of the network. The distributions of centrality values display heavy tails as well, indicating a strong concentration of importance in a relatively small number of firms. We estimate the tail exponents for all these distributions, finding in all cases lower exponents for downstream (buyer-side) distributions than for the upstream (seller-side) ones.

Our central premise is that quantum mechanics may be the statistical limit of a more fundamental discrete theory: any such theory equips a physical system with a finite information capacity, and its departure from quantum statistics is controlled by how much of that capacity the system uses. We show that commercial cloud photonic quantum processors have reached the precision required to bound this capacity from below, using the binary-sequence model of Powers et al. as the concrete test theory: outcome probabilities arise from counting discrete sequences of length $n$, quantum mechanics is recovered as $n \to \infty$, and $n$ measures the information capacity of the register behind a prepared state. Photon Fock states $|1\rangle$, $|1,1\rangle$, heralded $|2\rangle$, and cascaded beam-splitter pairs are measured on programmable interferometers with dominant systematics determined in situ. The model's composition-consistent parametrization, singled out by requiring that rotations compose, recovers quantum mechanics with deviations $1.24/n$; a random-effects likelihood analysis calibrated by parametric bootstrap excludes all $n \le 100$: the information capacity of the register carrying the two-photon state, if finite, exceeds $10^2$. Cascaded beam splitters test the composition law directly: the data are split-invariant, excluding naive count composition at $8\sigma$ and confirming the interference-sign rule. Model-independently, curve-averaged deviations from the quantum partition law larger than $2.3\times10^{-2}$ are excluded at 95% CL, and the originally published linear parametrization is excluded outright. Because the compilation offset is frozen per circuit it is calibratable, opening the $10^{-3}$ floor ($n \sim 10^3$) to current hardware: cloud photonic processors are quantitative instruments for quantum foundations, and information capacity an experimentally boundable quantity.