Papers

CdSnP2-based integrated solar cell-photodetector (SC-PD) devices employing CdS and CuGaSe2 (CGS) as the window and back surface field (BSF) layers, respectively are investigated using a hybrid machine learning (ML)-assisted SCAPS-1D framework. Device optimization is performed by varying the thickness, doping concentration, and defect density of individual layers. SCAPS-generated data are used to train six ML models and one deep learning model, with ensemble-based algorithms exhibiting the highest predictive accuracy. The ML-guided optimization identifies the n-CdS/p-CdSnP2 (CTP)/p+-CGS architecture as the optimum configuration among thirteen candidate structures. Incorporation of a 200 nm CGS BSF layer significantly enhances both photovoltaic and photodetection performance, increasing the efficiency from 20.67% to 32.69%, responsivity from 0.53 AW-1 to 0.72 AW-1, and detectivity from 2.51x1014 Jones to 1.78x1016 Jones. SHapley Additive exPlanations (SHAP) analysis reveales that band-offset engineering, particularly at the window/absorber and absorber/BSF interfaces, together with absorber properties, governs device performance. These findings demonstrate the potential of CdSnP2 and the proposed data-driven SCAPS-ML framework for the accelerated design of high-efficiency multifunctional optoelectronic devices.

A new type of decision networks is suggested and its operation is analyzed. The network nodes are represented by intelligent agents who can denote either some biological beings, like humans, or neurons of the brain, or the nodes of artificial intelligence. The specifics of the network are in the following: It is probabilistic in the sense that the choice, accomplished by each agent, is characterized by the related probability. It is dynamic, with the probabilities varying in time due to the exchange of information between the agents. It is affective, because the agents choose between alternatives by taking account of utility as well as of biases and emotions. In general, it is heterogeneous, being composed of the groups of agents with different properties, for instance having long-term memory and short-term memory. The network dynamics, caused by the information exchange, results in decision error decrease. The network operation is illustrated by the example starting with the Allais paradox, its resolution, and the decision error diminution in the process of decision dynamics with information exchange. Resorting to machine-learning techniques it is possible to regulate the behavior of the network agents forcing them to choose particular alternatives.

Numerical integration with Monte Carlo methods is a central computational task in many scientific and industrial applications, including financial derivative pricing and risk management. Classical Monte Carlo algorithms are computationally demanding: achieving an accuracy $\epsilon$ typically requires a number of function evaluations scaling as $O(1/\epsilon^2)$. Quantum-accelerated Monte Carlo methods based on quantum amplitude estimation can in principle quadratically improve this dependence. However, quasi-Monte Carlo methods have not been explored in the quantum context. In this work, we introduce a quantum quasi-Monte Carlo algorithm that combines low-discrepancy nets with quantum amplitude estimation. The proposed method prepares the quasi-random point set coherently in superposition. The method does not yield an asymptotic improvement over classical quasi-Monte Carlo, since the total error separates into a discretization error, determined by the finite net, and a quantum estimation error. Instead, we explore a pre-asymptotic advantage window: for a target accuracy that would classically require $2^q$ low discrepancy points, one can prepare a higher-resolution net of size $2^Q$, with $Q>q$, in superposition and reach the same accuracy using significantly fewer function queries. This window can be controlled by tuning the circuit resolution and amplitude-estimation parameters, making the approach relevant for practical regimes where the number of queries is finite rather than asymptotically large.

For a simple model fluid, the Yukawa fluid, the condition for dynamic crossover, known as the Frenkel line, is defined. The condition is related to the fact that the roton minimum in the dispersion relation of longitudinal acoustic-like excitations exists only when the collective vibrational dynamics of particles dominates in the liquid. Based on the self-consistent relaxation theory for the Yukawa fluid, thermodynamic states are determined in which the roton minimum disappears. The obtained values of the state parameters for the Frenkel line are consistent with the results of studies in which the position of this line on the phase diagram of the Yukawa fluid was determined using molecular dynamics simulations. It is shown that the Frenkel line in this system can be determined directly from the structural characteristic – the static structure factor. A physical interpretation of the roton minimum frequency for the simple liquids near the Frenkel line is proposed.

Species Distribution Modelling (SDM) is essential for understanding how environmental conditions shape biodiversity, particularly for destructive pests such as the Desert Locust (Schistocerca gregaria), whose breeding dynamics are tightly coupled to rapidly evolving environmental conditions. Maxent has become the dominant method for presence-only data, but its reliance on a linear combination of hand chosen feature transforms limits its ability to capture the nonlinear, temporal relationships common in ecological monitoring, where covariates such as precipitation, soil moisture, and vegetation indices evolve meaningfully over time. Standard implementations flatten time-series covariates into independent features, discarding sequential structure that carries critical signal. We introduce RNN Maxent, an extension of the Maxent framework that replaces the fixed feature dictionary with a neural network, specifically a Gated Recurrent Unit (GRU), trained end to end via backpropagation. The approach preserves Maxent's presence only statistical foundations, background normalization, and probability calibration, differing only in that the nonlinearity is learned from data rather than fixed in advance. We apply RNN Maxent to map suitable habitat for the Desert Locust using 50 day environmental time series derived from ERA5 Land, MODIS, and Sentinel 3, maintaining a 7 day gap between covariates and presence records to yield forecasting behavior. Compared against standard Maxent, RNN Maxent improves performance across metrics (ROC AUC 0.862 std 0.036 vs. 0.792; F1 0.671 std 0.056 vs. 0.590).

Reusable collider representations can be evaluated through downstream discrimination and probes of retained information, but neither quantity directly tests the behaviour of score templates in a profiled likelihood. We test a specific prediction in a controlled two-channel routing protocol: if reduced physics-label readability in a nuisance branch indicates a more inference-robust representation, it should accompany a smaller profiled signal-strength bias under fixed unmodelled shifts. In a public Compact Muon Solenoid $H\rightarrow ZZ\rightarrow4\ell$ workflow, a downstream split of fixed EveNet embeddings preserves signal/background area under the receiver operating characteristic curve ($0.9894\pm0.0004$) while reducing nuisance-branch physics readability from $0.961\pm0.013$ to $0.593\pm0.030$. Probe-sensitivity and effective-rank controls exclude a failed readout and branch collapse. In a separate top quark jet-tagging workflow, the leakage reduction recurs with preserved task performance. Across two development event shards, however, its Spearman association with maximum absolute profiled bias is $0.036$, and three of six material leakage-improving transitions do not reduce that bias. A one-shot preregistered confirmation on an independently accessed shard produces material leakage reductions in all three paired seeds, while the maximum absolute bias increases in two. Thus, within the tested protocol, latent readability is a useful routing diagnostic but not a likelihood-robustness certificate. The result supports a practical validation rule: claims about inference robustness require a prespecified likelihood-facing stress test and held-out confirmation.

When fitting a localized signal whose position or shape is not known in advance, one typically allows these parameters to vary together with the signal amplitude and chooses the values that maximize the likelihood. This freedom has two related statistical consequences. If a genuine signal is present, its fitted amplitude will be affected by a positive bias; otherwise, the same freedom increases the chance of finding an unusually signal-like background fluctuation, giving rise to the look-elsewhere effect. We show that these two effects can be understood as consequences of the same local geometry of the family of signal templates. We study this connection in a Gaussian matched-filter model, where a smooth D-dimensional family of normalized templates describes the unknown signal location or shape. In the normalized matched-filter problem, the curvature of a genuine signal peak and the fluctuations that determine the curvature of a high background peak are governed by the same template metric. This allows us to derive an explicit asymptotic relation. We then follow the problem away from the strong-signal and high-threshold limits. Separating the signal-associated maximum from the best competing maximum gives an exact decomposition of the global bias into a local profiling contribution and a contribution from remote-peak competition. In a one-dimensional Gaussian example, the second factorial cumulant accounts for most of this correction, while the third brings the prediction into close agreement with simulation. A two-point Kac–Rice calculation reproduces the second cumulant and reveals a quartic short-distance suppression of nearby maxima. The resulting picture separates the roles of local dimension, model-dependent curvature, and global extremal competition within a common framework.

Ion pumps used in cold-atom experiments generate stray magnetic fields that can perturb magneto-optical trapping, shift Zeeman sensitive energy levels, and introduce systematic errors in precision spectroscopy. We numerically investigate a compact low-stray-field ion pump configuration designed to reduce magnetic flux leakage through the ultrahigh-vacuum port without increasing the overall pump size. Starting from a simplified magnetic model of a standard ion pump, we study two complementary modifications: auxiliary permanent magnets surrounding the original pump magnets with opposite magnetisation, and a soft-magnetic honeycomb field stopper integrated at the pump port. The resulting combined configuration strongly reduces the magnetic flux density outside the pump. In the geometry considered here, the field at 10 mm from the UHV port is reduced by 97.9%, while the calculated reduction in effective pumping speed due to the honeycomb stopper is 17.9%. These results indicate that engineered magnetic compensation and field-guiding structures can support more compact cold-atom vacuum systems while preserving pumping performance. Further work should validate the design with nonlinear magnetostatic modelling, molecular-flow simulations, and measurements on a complete pump geometry.

We develop a quasi-cylindrical direction-splitting (QDS) dispersionless Maxwell solver for the open-source EPOCH particle-in-cell framework. The method preserves the exact axial phase velocity for every retained azimuthal mode by advecting transverse transport variables with the $c\Delta t=\Delta x$ shift. Building on the same transport viewpoint, we formulate an axion solver that advances the Klein–Gordon equation and the axion-regenerated electromagnetic fields within the QDS solver, so that axion–photon coupling is handled self-consistently. Benchmarks demonstrate that the cylindrical QDS solver reproduces the standard Yee wakefield structure while removing the axial group-velocity error and reducing computational cost by orders of magnitude. In the resonant axion generation test via two-color laser mixing, the QDS solver eliminates the spurious vacuum source term and maintains phase matching, whereas the Yee solver suffers from rapid dephasing due to numerical dispersion. The proposed algorithm therefore enables quasi-3D simulations of axion–photon physics in laser-plasma interaction with dispersion-free laser transport.

The multi-state Bennett acceptance ratio (MBAR) equations combine the data collected under different thermodynamic conditions in a statistically optimal way. Due to their practical importance, several solution strategies have been devised to optimize the convergence of the resulting set of coupled equations. However, even with graphics processing unit (GPU) acceleration, the convergence of these methods can still be either unstable or slow. We propose a new approach where the equations are interpreted as the limit of infinitesimal bin width of the respective weighted histogram analysis method (WHAM). In the proposed binned-to-unbinned MBAR (BU-MBAR) method, the bin width is adapted dynamically to ensure a stable and efficient convergence to the asymptotic solution.

The nonlinear evolution and mutual interaction of two-dimensional electron acoustic (EA) nonlinear structures in superthermal plasma environment are studied. The plasma model consists of inertial cold electrons, superthermal hot electrons described by kappa ($\kappa$) distribution, and stationary ions providing overall charge neutrality. Using the extended Poincaré-Lighthill-Kuo (PLK) reductive perturbation technique, a pair of two-sided Kadomtsev-Petviashvili (KP) equations governing right- and left-propagating EA solitary waves (EASWs) is derived. Exact analytical solutions of the KP equations, including single soliton, multisoliton, breather, and lump structures, are obtained via the Hirota bilinear method. The effects of key plasma parameters such as hot electron concentration, temperature ratio, and superthermality index on the characteristics of these nonlinear excitations are examined. Particular attention is devoted to the head-on collision dynamics between solitons, breather-soliton, and breather-breather interactions. The results reveal quasi-elastic collisions accompanied by phase shifts, transient amplitude modulation, and localized energy concentration, with clear distinctions between oscillatory and non-oscillatory mode interactions. The present study provides new insights into multidimensional electron acoustic wave (EAW) dynamics and energy redistribution mechanisms in superthermal space plasmas, with direct relevance to planetary magnetospheric environments such as Saturn's ring region.

In this paper, a conservative sharp-interface and diffuse-interface coupling method is developed for compressible two-phase multi-species flows with phase change and chemical reactions. The liquid–gas interface is represented by a sharp-interface model, whereas a diffuse-interface model treats the transport and chemical reactions of gas-phase species. Conservation is enforced by coupling the two phases through interfacial fluxes obtained from a multi-species phase-change Riemann problem. The original single-species four-wave Riemann solver is extended to multi-species gas mixtures by modifying the interfacial energy jump condition. Interfacial mass transfer is restricted to the condensable vapor species. Accordingly, both the interfacial energy jump condition and the gas-mixture energy-exchange flux are constructed using the internal energy of the phase-changing vapor species rather than the mixture internal energy. With this species-selective energy coupling, an approximate multi-species Riemann solver is constructed that retains the four-wave structure while avoiding the multidimensional nonlinear root-finding required by the exact solution. A series of numerical tests, including impulsive evaporation and condensation, reacting aluminum vaporization, shock-droplet interaction, and detonation-droplet interaction, are performed to assess the accuracy and robustness of the method. The numerical results agree well with reference solutions and benchmark data, demonstrating that the present method resolves the effects of phase change and chemical reactions in compressible multi-species multiphase flows while preserving conservative interfacial coupling.

Laser wakefield acceleration (LWFA) is an advanced method of high gradient acceleration of charged particles with wakefield excited in plasma by laser pulse. A distinctive feature of this method is the ability to create and accelerate so-called self-injected bunches with unique parameters - charge, energy, emittance, and geometric dimensions - without the need for an external injector of the required bunches for their subsequent acceleration to higher energies. Self-injected bunches emerge due to the plasma electrons trapping by the excited wakefield (self-injection phenomenon). For many applications, self-injected bunches can be used directly in relevant experiments. In this paper the dependence of the self-injected bunch parameters on the laser pulse amplitude and longitudinal gradient of plasma density in tapered plasma channel is investigated using numerical simulation with the WarpX code. At the laser amplitude $a_0=E_0(m_{e0}c\omega/e)^{-1}=3.6$ in a conical channel with the radius decreasing from $4.0\,c/\omega_{pe}$ to $2.16\,c/\omega_{pe}$ and with the plasma density increasing linearly from $n_e=2.61\cdot10^{19}$ cm$^{-3}$ at the channel entrance to $3n_e$ at the exit, a self-injected bunch is obtained with the charge $32.1\,\mu$C/m, the mean longitudinal momentum $111.9\,m_ec$, the length $5.10\,\mu$m, the area $5.3\,\mu$m$^2$, the transverse emittance $1.6\cdot10^{-2}$ mm$\cdot$mrad.

How does particle shape affect driven transport through obstacle fields? We simulate a dissipative rod falling under gravity through randomly placed fixed obstacles. For sufficiently slender rods, the mean descent speed before trapping decreases, increases, and then decreases again as obstacle density rises, opening a window of densities in which rods fall faster in denser fields. Scaling arguments based on collision rates and rod geometry explain the three regimes and their crossovers. Particle shape can thus reverse the expectation that crowding slows driven transport.

The critical behavior of a system near a continuous phase transition is commonly characterized by power laws and a set of universal critical exponents. Yet universality at phase transitions has even more fundamental manifestations. It dictates that all observables follow universal scaling functions, which may extend beyond simple power laws. Furthermore, microscopic details are captured entirely by only two system-specific scale factors, that rescale any measured quantity onto its universal form. In this work, we report the experimental validation of this prediction in ultracold lattice Bose gases undergoing the superfluid transition. We extract the scale factors by collapsing the order-parameter cumulants measured at varying entropies. We show that these factors specify scaling functions governing both the order-parameter distribution and two-point correlations. By measuring these scaling functions, we also determine three critical exponents $\beta$, $\gamma$ and $\nu$. Our results provide an experimental demonstration that universal scaling is simultaneously obeyed by multiple observables in a quantum many-body system.

In this paper, we do not try to solve the measurement problem, but rather to properly formulate it. One of the reasons why it still lacks a precise, agreed definition is that the problem may take very different aspects depending on the interpretation of quantum mechanics embraced. Inspired by the methodology of theory-independent results like Bell's theorem, we propose to identify the common root of the puzzle in an interpretation-independent way i.e. as a property of the empirical statistics only, before deriving its philosophical consequences. The key observation is that quantum matter can not be described by a Kolmogorovian probabilistic theory. Arguing that the Kolmogorov axioms of probability theory are the postulates of epistemic uncertainty leads us to reformulate the measurement problem as the impossibility to build an ontology independent of epistemology for quantum matter. Said differently, there exists no God's-eye view on quantum systems. Although it is meaningless to solve the measurement problem as defined in this way, insofar as it is a feature of the universe's statistics, such a formulation may on the other hand bring side benefits. In particular, we argue that it allows to: (i) shed a new light on the variety of interpretations; (ii) propose a fundamental reason why quantum mechanics and general relativity are so incompatible, not relying on purely mathematical or technical arguments; (iii) guide the quest for quantum gravity.

Boiling heat transfer underpins many applications where large heat fluxes must be dissipated. Boiling is characterized by a wide range of coupled multiscale transport phenomena, making its accurate numerical prediction a long-standing challenge. In this context, interface-resolved simulation approaches - where the liquid-vapor interface is either explicitly or implicitly described - have undergone significant advancements, driven by improved numerical formulations, more complete thermodynamic and compressibility modeling, and the growing availability of computational resources. Here, we provide a comprehensive overview of the state of the art in interface-resolved simulations of boiling flows. We examine the different formulations governing the flow and temperature field, strategies for coupling heat and mass transfer at the interface, and current modeling approaches for small-scale physics such as nucleation, microlayer evaporation, and contact-line dynamics. By analyzing insights from recent high-fidelity simulations, a central observation is that interface-resolved simulations of boiling are hydrodynamically resolved but microscopically modeled: their predictive accuracy depends on the sub-grid closures for nucleation, microlayer, and contact-line physics no less than on the fidelity of the hydrodynamic solver. We discuss the strengths and limitations of available methods on this basis, together with the open challenges in the accurate description of boiling heat transfer.

Bubble detachment from solid surfaces governs heat, mass, and charge transport across technologies vital to clean energy, including high-current-density water electrolysis and boiling thermal management. At high gas fluxes, however, bubbles remain trapped at active surfaces despite immense buoyancy, severely restricting mass transfer and increasing energy losses. Here, we show that this unexpected surface retention originates from cascade coalescence between unequal-sized bubbles. High-speed observations around microelectrodes demonstrate that when a rising bubble merges with a smaller surface-attached successor, its trajectory abruptly reverses, accelerating toward the substrate at nearly two orders of magnitude above its rising speed. Direct numerical simulations and scaling analysis reveal that asymmetric interfacial retraction during merging generates non-canceling viscous stresses, producing a net downward impulse toward the smaller bubble. Repeated cascade coalescence events accumulate these transient impulses into a steady, time-averaged retaining force capable of opposing buoyancy three to four orders of magnitude beyond quasistatic limits. Our findings establish bubble coalescence as a previously unrecognized mechanism that dynamically sustains bubble retention under high gas flux.

This paper proposes a Physical-Structure-Driven Waveform Inversion Imaging framework (PSDWII), grounded in the recognition that surface-recorded seismic data are directly related to subsurface virtual sources rather than to the model parameters themselves. Based on this recognition, we decompose seismic wave propagation into a three-step physical process: source excitation and incident wave propagation; interaction of the incident wave with heterogeneities, which generates virtual sources with distinct radiation patterns and excites secondary waves; and propagation and reception of the secondary waves. The adjoint of the propagation operator (reverse-time extrapolation) is then used to formulate a linear inversion projection for the virtual sources. By distinguishing different virtual-source expressions and their underlying physical mechanisms, we establish a unified mathematical representation for three inversion tasks: Full Waveform Inversion(FWI), stratigraphic physical properties imaging, and stratigraphic structures imaging. Deconvolution and linear inversion are respectively adopted to achieve multi-parameter decoupling in FWI and in angle-domain common-image gather inversion for stratigraphic physical properties imaging. The PSDWII framework shifts the conventional “mathematical optimization driven” paradigm of waveform inversion to a “physical-structure-driven” one. It neither constructs objective functions nor computes their gradients or Hessian inverses. Within this framework, we develop three specific methods: physical-structure-driven FWI (PSDFWI), physical-structure-driven stratigraphic physical properties imaging with angle-domain common-image gather inversion (PSDSI), and physical-structure-driven stratigraphic structures imaging (PSDMig).

Improving the sub-seasonal forecast of tropical cyclones (TCs) remains a significant challenge for climate models. This study evaluated the characteristics of tropical cyclones (TCs) in sub-seasonal forecasts using the Global Seasonal Forecast System 6 (GloSea6), an operational seasonal-to-sub-seasonal forecasting model operated by the Korea Meteorological Administration (KMA) during June-September (JJAS) from 1993 to 2016 over the western North Pacific (WNP). GloSea6 was found to underestimate TC frequency, tracks, particularly in mid-latitudes, lifetime, and intensity across all months, with the most significant errors occurring in August. To address these deficiencies, we examined whether applying dynamical downscaling to GloSea6 during August 2016, a period characterized by the lowest TC forecast skill in GloSea6, could improve sub-seasonal TC forecasts. The application of dynamical downscaling demonstrated added value by directly improving the simulation of TCs in terms of frequency, structure, intensity, and lifetime, although slight overestimations were observed. Furthermore, improved reproductions of the Indian monsoon and circumglobal teleconnection (CGT), both of which strongly influence the western North Pacific subtropical high (WNPSH), contributed to more accurate forecasts of WNPSH variability and, consequently, better predictions of TC activity in the mid-latitudes and East Asia. Therefore, dynamical downscaling can significantly advance sub-seasonal TC forecasts by not only directly improving TC characteristics but also indirectly enhancing the environmental fields (e.g., WNPSH, Indian monsoon, and CGT) that are critical to TC activity.

Global gyrokinetic particle simulations remain computationally expensive, as they demand both adequate marker statistics and three-dimensional field solvers. In this work, we present a hybrid spectral method within the particle-in-Fourier (PIF) framework and implement it in the electrostatic model of GTC. Charge scatter and field gather are performed between particles and fields on a two-dimensional poloidal mesh, while the corresponding Poisson solver is discretized using radial finite differences and poloidal $m$-harmonics. Truncated spectral transforms are employed to connect multiple representations for fields, avoiding costly particle-grid operations for each individual $m$-harmonic within the particle loop. Benchmarks against conventional particle-in-cell (PIC) simulations successfully reproduce single-$n$ ion temperature gradient (ITG) mode structures and dispersion relations, as well as multi-$n$ nonlinear ITG transport and its regulation by zonal flows. Compared to conventional PIC, the proposed method reduces the effective problem size by more than a factor of 48 and achieves a speedup of over two orders of magnitude for single-$n$ cases. A 2000-step single-$n$ simulation with approximately 2 million markers completes in 78.2 seconds on a laptop GPU, while multi-$n$ turbulence simulation also completes within minutes. Furthermore, the elimination of toroidal particle-shift communication yields promising preliminary scaling performance on multiple NVIDIA A100 GPUs. The numerical scheme is broadly applicable for accelerating particle simulations on platforms ranging from laptops to supercomputers.

Large-language models (LLMs) are rapidly becoming part of human culture, reshaping how information is produced, transmitted, and used. Here we propose that their diffusion can be understood through a viral analogy, with LLM use spreading through populations, becoming embedded in cognitive and cultural practices. We model transitions among uncoupled, coupled, and persistently dependent users, and show that the interplay between social transmission, recovery, and collective reinforcement can generate tipping points and technological lock-in. A central consequence is the possibility of runaway dynamics: once a critical threshold is crossed, small increases in adoption can trigger rapid population-level shifts toward persistent dependence, with abrupt losses in cognitive competence. The same framework, however, identifies conditions for cognitive immunization, based on reducing transmission and facilitating reversibility. Our results highlight how LLM adoption may involve nonlinear collective transitions with important consequences for cognitive autonomy.

Long-duration gravitational-wave modelling must resolve fast orbital motion together with slow dissipative evolution while preventing small numerical errors from accumulating into secular phase drift. Here we ask whether the finite-time evolution map itself can be learned as an explicit, differentiable, structure-preserving object and then repeatedly composed through a complete inspiral. We construct three neural-flow architectures: a symplectic and slimplectic flow on Galley's doubled phase space, [SINFONIA-J0]; a Taylor-anchored flow, [SINFONIA-J1]; and a Magnusian flow that learns the finite-time dissipative correction in the interaction picture, [SINFONIA-J2]. Applied to a 2.5PN neutron-star inspiral, all three expose the same controlling mechanism: long-time accuracy is governed not by pointwise map error alone, but by its signed projection onto a single secular channel fixed by energy–angular-momentum balance. Encoding this structure allows the learned maps to remain accurate through $10^{2}$–$10^{5}$ window compositions to coalescence at timesteps of a full orbital period and beyond, reaching chained phase errors orders of magnitude below a benchmark slimplectic integrator at lower cost. The same secular structure can also be exploited for physics inference: when the channel is left unconstrained, the accumulated phase retains enough information to recover an un-modelled dynamical-friction-like force, both parametrically and as a learned function of separation. Network-off controls isolate the contribution of learning from the analytic structure already built into each map. These results establish a proof of concept for structure-preserving learned evolution maps as tools for fast long-duration integration and physics inference in gravitational-wave source modelling.

Nematic order of confined cell populations plays an important role in determining cell alignment and stable configurations of topological defects, which are related to various biomechanical phenomena. Topological charges (or winding numbers) of topological defects strictly depend on the Euler characteristic of the confining domain, which has typically been non-negative in studies focused on domains without internal obstacles. However, biological tissues often surround two or more internal obstacles or holes, which inherently generate defects with negative charges. To understand the mechanical interaction between cellular tissue and obstacles, it is necessary to elucidate the geometrical effects of obstacles on cell alignment and defects with negative charges. Here, we investigate how cell populations achieve stable defect configurations of two -1/2 defects in a triply connected domain. First, we present experimental observations of C2C12 myoblasts confined by two circular obstacles of varying diameter, demonstrating that two $-1/2$ defects are the most frequent configuration when the obstacles are sufficiently large. Second, to theoretically validate these experimental observations, we perform systematic stability analyses of defect configurations using an explicit expression of cell alignment and numerical minimization of the Frank elastic energy. Our numerical calculations reveal that the most stable configuration shifts continuously from a horizontal, through off-axis, to a vertical configuration as the obstacle size increases. In addition, the experimentally observed defect positions agreed with these theoretical predictions to within 60 $\mu$m. These findings suggest that obstacle sizes control the symmetry of cell alignment, providing insights into how geometric and topological constraints can generate complex force patterns during morphogenesis or organ movements.

Recent development of the differential phase contrast (DPC) scanning transmission electron microscopy (STEM) technique has significantly advanced materials science supported by high-sensitivity detectors and powerful data processing. Especially the 4D-STEM technique, employing a pixelated camera as a STEM detector, enables extensive information acquisition through post-processing of large datasets. However, magnetic or electric field imaging using DPC is often disturbed by diffraction contrasts intrinsic to the crystalline material. In this paper, we demonstrate elimination of the diffraction contrasts for magnetic field imaging by using angularly distributed multiple electron beams generated by a multi-hole aperture within the 4D-STEM framework. This multi-angle beam configuration suppresses irrelevant crystalline contrast and yields direct magnetic information with high sensitivity from the specimen. Furthermore, we performed various image analysis to extract and interpret magnetic domain structures and domain walls.

Infragravity waves generated in the surf zone contribute substantially to nearshore water-level variability, with group-scale variations in wave breaking generating free long waves through breakpoint forcing. Existing interpretations of this mechanism rely largely on an idealised moving-breakpoint picture in which breaking is tied to a local saturation relation, leaving unresolved how the finite extent and evolution of a realistic breaking region control the radiated waves. We first derive a local decomposition of the wave-induced forcing into breakpoint and bound wave forcing, separating the two contributions within the same evolving short-wave field. We then describe the breakpoint-forcing field as a finite, evolving source through its integrated strength, centroid, cross-shore width, and shape. This representation shows that finite source extent produces spatial interference, as phase differences between contributions emitted from different positions attenuate the high-frequency part of the infragravity response and shift this attenuation toward lower frequencies as the source widens. Source migration produces a directional Doppler shift by modifying the arrival times of successive emissions, with shoreward motion compressing arrivals at a shoreward observer and stretching them at a seaward observer. Breaking depth controls both the conversion of this forcing into surface elevation and the celerities governing these propagation-time effects. Controlled numerical experiments across a range of plane-beach slopes reproduce the predicted signatures: broader source footprints strengthen upper-band attenuation on both branches. The diagnosed migration history, with breaking strongest during shoreward motion, partly offsets that attenuation shoreward and reinforces it seaward.

The heliospheric current sheet (HCS) is the boundary between open magnetic fields of opposite polarity in the solar wind that connect back to their respective open flux regions in the corona. The HCS is often described as a disc-like sheet warped by the combined effects of the inclination of the magnetic equator on the Sun, solar rotation, and solar wind expansion. The HCS is an extension of the helmet streamer belt-the closed-flux region resulting from the global dipole component of the observed photospheric magnetic field. In the absence of a strong dipole, a dominant quadrupole can result in two separate HCSs. Here we report on a set of idealized, axisymmetric solar wind simulations designed to study the transition of the single-HCS corona and inner heliosphere into a multiple-HCS configuration, run with the WindPredict-AW PLUTO MHD code. We systematically vary the relative strengths of the dipole/quadrupole contributions to the global field and show this results in the formation of a large-scale, closed-flux pseudostreamer that interacts with the ambient solar wind. We find that when the dipole and quadrupole magnitudes are approximately equal, our model corona can oscillate between a one- and two-HCS configuration on the timescale of days-to-weeks. We characterize the plasma dynamics of the transition of a global-scale pseudostreamer into a localized, secondary helmet-streamer belt with its own HCS surrounding a newly-opened, opposite-polarity coronal hole. We discuss the implications of our results for solar cycle evolution and the dynamic coupling of the highly structured corona and inner heliosphere.

Infrared spectroscopy is a widely used technique for molecular identification, yet its widespread deployment remains constrained by bulky instrumentation, cryogenic cooling requirements, and limited portability. Here we demonstrate broadband near- to short-wave infrared spectroscopy enabled by a silicon-compatible GeSn photodetector integrated into a compact digital micromirror device-based single-pixel architecture. By combining detector design with targeted optical reconfiguration and wavelength recalibration, we extend the operational range up to 2.4 micrometers while maintaining room-temperature, zero-bias photovoltaic operation. System-level benchmarking quantifies the intrinsic trade-off between spectral extension and detectivity in narrow-bandgap photodetection, yielding a specific detectivity of $1.12 \times 10^{10}$ cm Hz$^{1/2}$ W$^{-1}$ at 1.55 micrometers with an extended wavelength cut-off. We validate the capabilities of the obtained spectrometer through selective identification of representative commodity plastics, demonstrating a lightweight, low-power, and mechanically robust platform for portable and autonomous deployment. Despite the expected increase in generation-limited noise, access to strong polymer combination bands beyond 1.7 micrometers allows enhanced material discrimination. This work establishes a pathway toward scalable distributed molecular sensing and broadens access to infrared spectroscopy for environmental monitoring, industrial process control, and community-level chemical detection.

Accurate patient-specific mean glandular dose (MGD) estimation in breast computed tomography (BCT) requires anatomically realistic models, but high-resolution patient-derived phantoms impose high computational demands on Monte Carlo (MC) dosimetry. We previously developed and validated an EGSnrc-based framework for synchrotron propagation-based phase-contrast BCT (PB-PCT) for the first-in-human study at the Australian Synchrotron. With participant imaging commencing, this study aimed to optimize phantom spatial resolution and MC efficiency while maintaining MGD accuracy, for a rapid patient-specific dosimetry. Four patient-specific heterogeneous breast phantoms with varying volume and glandularity were generated from PB-PCT images of mastectomy specimens acquired at 35 keV. The highest-resolution phantom for each specimen served as the reference. Phantoms were downsampled by increasing in-plane voxel size and slice thickness, and MGD was recalculated using EGSnrc/DOSXYZnrc. A 4.5% difference from reference MGD was adopted as the accuracy criterion. Primary photon histories were then reduced to determine the minimum required for acceptable MC precision. A common voxel size of 0.30 x 0.30 x 3.00 mm3 maintained MGD within 4.5% of reference for all four phantoms while reducing voxel number by approximately 20-31-fold. MGD was more sensitive to in-plane voxel size than slice thickness, supporting anisotropic downsampling. Reduced-resolution phantoms enabled a 15-fold reduction in photon histories to 2 x 10^8 while maintaining acceptable statistical uncertainty. Combined optimization reduced computation time from approximately 1600 to 72 min, a 22-fold acceleration. Patient-specific phantoms can be substantially downsampled while maintaining MGD accuracy and markedly reducing computational burden. This optimization provides a foundation for future real-time patient-specific BCT dosimetry.

The propagation of narrowband wave packets is a classical problem with broad relevance across nonlinear wave physics. Within the framework of the nonlinear Schrödinger equation, solitons are known to propagate without changing shape, whereas Gaussian envelopes may undergo substantial reshaping depending on the balance between dispersion and nonlinearity. Here, we combine laboratory experiments with numerical simulations to systematically investigate the evolution of hydrodynamic Gaussian wave packets over a wide range of amplitudes and spectral bandwidths. We show that when the envelope amplitude and width match those of the stationary NLSE soliton solution, the wave packet propagates steadily in the wave flume. More generally, by varying the amplitude of the Gaussian envelope, the wave packet may undergo either dispersive broadening or nonlinear focusing, resembling the dynamics of Satsuma-Yajima breathers. These findings may facilitate the controlled generation and manipulation of localized wave packets in a variety of nonlinear dispersive systems, including optics, plasmas, and Bose-Einstein condensates.