Papers

Accurate global medium-range weather forecasting is fundamental to Earth system science. Most existing Transformer-based forecasting models adopt vision-centric architectures that neglect the Earth's spherical geometry and zonal periodicity. In addition, conventional autoregressive training is computationally expensive and limits forecast horizons due to error accumulation. To address these challenges, we propose the Shifted Earth Transformer (Searth Transformer), a physics-informed architecture that incorporates zonal periodicity and meridional boundaries into window-based self-attention for physically consistent global information exchange. We further introduce a Relay Autoregressive (RAR) fine-tuning strategy that enables learning long-range atmospheric evolution under constrained memory and computational budgets. Based on these methods, we develop YanTian, a global medium-range weather forecasting model. YanTian achieves higher accuracy than the high-resolution forecast of the European Centre for Medium-Range Weather Forecasts and performs competitively with state-of-the-art AI models at one-degree resolution, while requiring roughly 200 times lower computational cost than standard autoregressive fine-tuning. Furthermore, YanTian attains a longer skillful forecast lead time for Z500 (10.3 days) than HRES (9 days). Beyond weather forecasting, this work establishes a robust algorithmic foundation for predictive modeling of complex global-scale geophysical circulation systems, offering new pathways for Earth system science.

Nuclear fusion promises a nearly limitless energy source, but achieving breakeven-where fusion output exceeds input-requires extreme plasma conditions and complex confinement systems. Here we propose an alternative approach based on beam-target interactions, introducing a simple energy-based criterion that compares fusion energy generation with energy loss. By creating electron-free targets, stopping power is drastically reduced, enabling conditions where fusion energy surpasses beam energy deposition under practical scenarios. This approach offers a viable alternative pathway to fusion energy without high-temperature plasma confinement and warrants further experimental investigation.

In optoacoustic imaging, recovering the absorption coefficients of tissue by inverting the light transport remains a challenging problem. Improvements in solving this problem can greatly benefit the clinical value of optoacoustic imaging. Existing variational inversion methods require an accurate and differentiable model of this light transport. As neural surrogate models allow fast and differentiable simulations of complex physical processes, they are considered promising candidates to be used in solving such inverse problems. However, there are in general no guarantees that the derivatives of these surrogate models accurately match those of the underlying physical operator. As accurate derivatives are central to solving inverse problems, errors in the model derivative can considerably hinder high fidelity reconstructions. To overcome this limitation, we present a surrogate model for light transport in tissue that uses Sobolev training to improve the accuracy of the model derivatives. Additionally, the form of Sobolev training we used is suitable for high-dimensional models in general. Our results demonstrate that Sobolev training for a light transport surrogate model not only improves derivative accuracy but also reduces generalization error for in-distribution and out-of-distribution samples. These improvements promise to considerably enhance the utility of the surrogate model in downstream tasks, especially in solving inverse problems.

Accurately determining the underlying physical parameters of individual elements in integrated photonics is increasingly difficult as device architectures become more complex. Inferring these parameters directly from spectral measurements of the system as a whole provides a practical alternative to traditional calibration, allowing characterization of photonic systems without relying on detailed device-specific models. Here, we introduce a supervised machine-learning strategy to learn the onsite losses and resonant frequency shifts of each individual ring in an array of coupled ring resonators from measured spectral power distributions of the whole array. The neural network infers these parameters with high accuracy across multiple experimental configurations. Our methodology provides a scalable and non-invasive method for extracting intrinsic parameters in coupled photonic platforms, paving the way for future development of automated calibration and control methods.

The ePIC collaboration is developing a multidetector system to explore the fundamental properties of the strong interaction at the future Electron-Ion Collider (EIC), to be built at Brookhaven National Laboratory. A key component of the ePIC detector is the Silicon Vertex Tracker (SVT), which provides high-precision tracking and microvertex reconstruction. The SVT consists of the Inner Barrel (IB), the Outer Barrel (OB), and the Forward/Backward Disks, all based on Monolithic Active Pixel Sensors (MAPS) that combine high granularity, low power consumption, and minimal material budget. This paper presents a concise overview of the SVT design and its development status.

Efficient detection of harmful organic volatile compounds is a major health and environmental need in industrialized societies. Tailor-made metal-organic frameworks and other coordination polymers are emerging as promising sensing molecular materials thanks to their responsivity to a wide variety of external stimuli, and could be used to complement conventional sensors. Here, a non-porous crystalline 1D Fe(II) coordination polymer acting as a porous acetonitrile host is presented. The desorption of interstitial acetonitrile is accompanied by magneto-structural transitions easily detectable in the optical and electronic properties of the material. The structural switch and therefore its (opto)electronic readout are reversible under exposition of the crystal to acetonitrile vapor. Coordination polymers can be versatile sensors for volatile acetonitrile and potentially other organic compounds.

During Type III solar radio bursts, beam-driven upper-hybrid wave turbulence is converted into electromagnetic emissions at the fundamental plasma frequency and its harmonic, through a chain of various linear and nonlinear wave processes. In this work, we mainly investigate the relative roles and interplay of two key mechanisms: the nonlinear decay of Langmuir/$\mathcal Z$-mode waves and their linear transformations on random density fluctuations and, in particular, their mode conversion at constant frequency into electromagnetic waves. Using two-dimensional Particle-In-Cell simulations, we employ a diagnostic approach based on large ensembles of virtual satellites that record local waveforms, enabling detailed temporal and spatial characterization of wave processes in randomly inhomogeneous plasmas. This method allows robust statistical analysis and direct comparison with spacecraft observations. The study focuses on the dependence of wave dynamics on the average level of density fluctuations and the plasma magnetization. Our results quantify the occurrence rate of decay under varying physical conditions and demonstrate how developed plasma density turbulence can significantly alter the balance between nonlinear wave-wave interactions and linear wave transformations. These findings provide new insights into the mechanisms responsible for electromagnetic emissions during type III radio bursts and strengthen the connection between numerical simulations and in situ solar wind measurements, offering a valuable framework for the interpretation of future space-based waveform observations.

Large regions of giant planets are thought to possess unstable thermal gradients stabilised by gradients in heavy-element composition. The fluid can then develop semi-convection, a double-diffusive instability driven by the unequal molecular diffusivities of heat and composition. While previous studies have focus mainly on local Cartesian models, we investigate semi-convection in rotating spherical shells, the geometry relevant to planetary interiors, using direct numerical simulations In a first non-linear phase, the flow spontaneously forms concentric density staircases composed of well-mixed layers separated by thin, strongly stratified interfaces. We propose scalings for both the thickness of these layers and their survival time in terms of the rotation rate and stratification. Over longer timescales, layers merge to produce statistically steady states consisting either of a fully convective shell or of a convective layer overlain by a persistent stably stratified layer (SSL), depending on the balance between stratification and rotation. Dynamo simulations show that flow within the convective region can generate a self-sustained magnetic field, which is filtered by zonal flows in the overlying SSL. This results in a strongly dipolar and axisymmetric external field, in encouraging agreement with Saturn's magnetic field. Across the explored parameter range, both the Rossby number and the thickness of the stably stratified layer are governed by a single combination of control parameters. This enables identification of a regime favourable to planetary-like dynamos.

The identification of forces and stresses is a central task in biophysics research: Knowledge on forces is key to understanding dynamic processes in active biological systems that are able to self-organize and display emergent properties by converting energy into mechanical work. The aim of this paper is to identify forces generated by a filament-motor network of F-actin and myosin – actomyosin – and exerted on the surrounding fluid, therefore causing a fluid flow. In particular, we evaluate optical microscopy data stemming from two different physical settings, confined and non-confined active gels. As a theoretical model, we use the Stokes equation together with an incompressibility condition and suitable boundary conditions reflecting the physical settings. The problem of determining the forces from knowledge on the fluid flow is formulated as an inverse source problem. Due to experimental limitations, only incomplete data are available. We provide a rigorous analysis of the forward problems and the impact of missing data, derive the adjoints of the forward operators needed for regularization, and demonstrate our methods on both synthetic and experimentally measured data.

We experimentally investigate the effects of finite-system size on the dynamics of weakly nonlinear random gravity-capillary surface waves. Experiments are conducted in rectangular tanks with varying aspect ratios, in which the fluid surface is perturbed locally and erratically by small, partially submerged magnets. Driven by an oscillating vertical electromagnetic field, these magnets generate a statistically homogeneous and isotropic random wave field. This setup enables us to probe finite-size effects without the dominant influence of global forcing present in horizontally oscillated tanks. Spatiotemporal measurements of the wave field reveal multiple branches in the wave-energy spectrum along the unconfined direction, corresponding to sloshing modes in the confined direction. We show that the spectral properties of these modes can be tuned by varying either the wave steepness or the confinement. Signatures of discrete wave turbulence in the confined direction and mesoscopic continuous wave turbulence in the unconfined direction are observed. As the confinement is gradually relaxed, we further demonstrate a smooth transition from discrete to continuous wave turbulence, consistent with the nonlinear-to-discreteness timescale ratio. Using high-order correlation analysis, we also show that finite-size effects alter wave dynamics by depleting two-dimensional three-wave resonant interactions along the confined direction.

Geological earthquake records are important for probabilistic seismic risk assessment. Such records can be obtained from studies of turbidites triggered by seismic activity in marine and lake basins. The Sea of Marmara (SoM), located on the North Anatolian Fault (NAF), serves as an important laboratory for subaqueous palaeoseismological research. This is because it has 2500 yrs. of historical earthquake records that can be correlated with radiometrically dated sedimentary earthquake records. Additionally, the relatively high sedimentation rate ($\sim$3 mm/yr) in the deep subbasins allows the recognition of individual events. Following the destructive 1912 Mw 7.4 Şarköy-Mürefte and 1999 Mw 7.4 Izmit and Mw 7.2 Düzce earthquakes, the main submerged part of the NAF in the SoM represents a seismic gap, where the long-term earthquake history is of crucial importance for earthquake risk assessment. We have studied nine cores recovered along the most active northern strand of the NAF (i.e. the Main Marmara Fault: MMF), using high resolution digital X-ray radiography, $\mu$-XRF core scanning, MSCL physical properties and grain-size analyses. The chronology was determined using accelerated mass spectrometry (AMS) radiocarbon and radionuclide methods. In the cores, coseismic turbidites commonly consist of a basal part with multiple sand-silt laminae above a sharp and often erosional base and an overlying graded mud part (homogenite). The basal parts exhibit high gamma density and MS, and are often enriched in one or more elements, such as Si, K, Fe, Ti, Zr, Ca and Sr, indicative of coarse siliciclastic and carbonate shell fragments. Radionuclide- and radiocarbon-dated coseismic turbidites in different SoM subbasins extending back to more than 5000 yrs. indicate an average earthquake recurrence time between 220 and 300 yrs. along the different segments of the MMF. These results are compatible with the GPS velocities and geological slip rates. However, the intervals between two consecutive events vary widely between 50 and 695 yrs. for the different northern NAF segments. Integration of our results with previous studies indicates that the M > 7 events recorded are the 1999, 1509, 1296 and 740 earthquakes on the __zmit Gulf segment; the 1894, 1509, 1343, 1090 and 740 earthquakes on the Prince Islands segment in the Ç{\i}narc{\i}k Basin; the 1894, 1766, 1509, 989, 869 or 862, 740 and 447 earthquakes on the Central High segment and in the Central Basin; and the 1912, 1766, 1344, 989 and 447 earthquakes on the western Marmara segment. The 1912 Şarköy-Mürefte earthquake record is absent in the Central Basin, which suggests that the earthquake rupture did not extend beyond the western end of the Western High. The distribution of the 2500 yr-long sedimentary earthquake records matched with the historical earthquakes along the MMF indicates the common occurrence of multiple-segment ruptures, and suggests that presently, the most earthquake-prone part of the MMF is the Central High segment, located SW of Istanbul.

Oscillations are ubiquitous wave phenomena in physical systems ranging from electromagnetic and acoustic to gravitational waves. The behavior of finite-size systems is traditionally understood to be governed by fundamental oscillatory modes arising from bulk physics and boundary conditions. A paradigmatic example is the particle-in-a-box model introduced with the advent of quantum mechanics, in which confinement leads to discrete resonances and quantized energy levels. Such quantization underpins phenomena including semiconductor quantum dots, where electronic waves are confined in all three spatial dimensions, producing standing-wave modes analogous to the vibrational states of a guitar string. These modes are characterized by integer quantum numbers corresponding to the number of envelope oscillations fitting within the cavity. Recently, counter-intuitive modes have been observed in finite systems with Dirac dispersion, including states that do not oscillate spatially, yet a general theoretical framework for modes in such cavities has been lacking. Here, we discover the phenomenon of arbitrary fractional quantization in wave physics and show that, in finite-size boxes with linear dispersion, the quantum number need not be an integer but can take any real value, including zero. Using Bloch theory in a Dirac-cone photonic crystal with a controllable fractional number of unit cells at its boundaries, we demonstrate continuous control of the cavity-mode envelope wavenumber. We introduce a unified nomenclature for these unconventional modes and derive their corresponding wavefunctions. These counter-intuitive states in open Dirac potentials challenge conventional notions of quantization and open new avenues across wave physics.

The viscous force should be shear force, the difference between the strain-rate viscosity and the slip viscosity is that the former has conjugate shear force, while the latter does not. The study in this paper verifies the physical authenticity of two viscosity models through Taylor Couette laminar flow experiments with inner and outer cylinders rotating at the same angular velocity, and numerically investigate the influence of relative cylinder spacing and rotational speed on the circumferential velocity under the slip model. The experimental results of LDV measurement with a relative cylinder spacing of 0.3 indicate that the maximum deviation from rigid-body rotation is about 0.86%, which is consistent with the theoretical prediction of slip viscosity model. The numerical simulations show that the end-walls have no effect under the strain-rate viscosity model; but when the slip viscosity model is introduced, the end-walls inevitably bring about the circumferential velocity profile changing along the axial direction, and result in a three-dimensional (3D) spiral streamline pattern influenced by the relative cylinder spacing and angular speed of cylinders.

Developing Reynolds-averaged Navier-Stokes (RANS) turbulence models that remain accurate across diverse flow regimes is a long-standing challenge. In this work, we propose a novel framework, termed the progressive mixture-of-experts (PMoE), designed to enable continual learning for RANS turbulence modelling. The framework employs a modular autoencoder-based router to associate each flow scenario with a specialised turbulence model, referred to as an expert. When a new flow regime cannot be adequately represented by the existing router and expert set, a new expert together with its routing component can be introduced at low cost, without modifying or degrading previously trained ones, thereby naturally avoiding catastrophic forgetting. The framework is applied to a range of flows with distinct physical characteristics, including airfoil wake, channel, periodic hill, and square duct flows. The resulting PMoE model effectively integrates multiple experts and achieves improved predictive accuracy across both seen and unseen test cases that differ in operating conditions or configurations. Owing to sparse activation, model expansion does not incur additional computational cost during inference. The proposed framework therefore provides a scalable pathway towards lifelong-learning turbulence models for industrial computational fluid dynamics.

Efficient Monte Carlo (MC) sampling of many-body systems with long-range electrostatics is often limited by the cost of per-move energy-difference evaluation under periodic boundary conditions. We present DMK-MC, an accelerated MC method that adapts the dual-space multilevel kernel-splitting (DMK) framework to single-particle Metropolis updates. DMK-MC computes the energy change and, upon acceptance, updates the stored incoming plane-wave fields with $O(1)$ work per tree level, yielding an overall $O(\log N)$ expected work per trial move for fixed accuracy. The method decomposes the Coulomb kernel into three components: a global, periodized smooth part; a multilevel sequence of smooth difference kernels whose interactions are restricted to same-level colleague boxes; and a singular residual kernel whose short-range interactions are evaluated directly. Benchmarks on uniform, highly nonuniform, and implicit-solvent electrolyte and colloidal configurations show that DMK-MC consistently outperforms a recent FMM-based $O(\log N)$ Monte Carlo method, delivering several-fold speedups at comparable tolerances.

Choosing a representative element volume (REV) from finite cylindrical Computed Tomography (CT) scans becomes ambiguous when a key field variable exhibits a slow axial trend, reflecting either geological variability or CT acquisition/reconstruction artifacts. In such cases, estimated statistics may vary systematically with subvolume size and position rather than converging by simple averaging. We present a practical workflow for sizing an REV under nonstationary conditions by first suppressing axial drift/trend to obtain a residual field suitable for second-order analysis, and then selecting the smallest analysis diameter for which the low-wavenumber spectral content stabilizes within a prescribed tolerance. The method is demonstrated on Thalassinoides-bearing rocks, where branching burrow networks introduce heterogeneity at length scales comparable to laboratory core diameters, making imaging-based microstructural statistics and digital-rock estimates sensitive to subvolume choice. From segmented data, we define a scalar “burrowsity” field capturing burrow-related pore spaces and infills. Axial detrending, with optional normalization, mitigates acquisition drift and nonstationary trends, while covariance/spectral convergence is evaluated on nested cylinders consistent with the core geometry. Representativeness is posed as diameter convergence on nested inscribed cylinders: the two-point covariance and isotropic spectrum $\widehat{C}$ are estimated, and the smallest diameter at which the low-wavenumber plateau becomes stable is selected. Applied to a segmented Thalassinoides core, the method gives $D_{\mathrm{REV}}\simeq 93~\mathrm{mm}$ and $H_{\mathrm{REV}}\simeq 83~\mathrm{mm}$, enabling reproducible correlation-scale reporting and connectivity-sensitive property estimation.

We investigate the feasibility of using high-harmonic generation (HHG) as a complementary probe of tunneling delay in strong-field ionization. By combining time–frequency analysis of HHG spectra obtained from full time-dependent Schrödinger equation (TDSE) simulations with classical three-step-model (TSM) trajectories, we extract an effective tunneling delay associated with electron motion through the laser-suppressed Coulomb barrier. The analysis is carried out for Hydrogen, Helium, and Argon atoms over a range of laser wavelengths and peak intensities within the tunneling regime. The extracted delay exhibits a systematic dependence on the instantaneous field strength and barrier width at the ionization time, and follows the expected $\tau_d \propto 1/\sqrt{I_0}$ scaling consistent with Keldysh–Rutherford-type models and attoclock observations. When recast in terms of the Keldysh parameter, the tunneling delay collapses onto a near-universal trend across different atomic species. While HHG does not provide a direct measurement of tunneling time, the present results demonstrate that it can serve as a robust, internally consistent diagnostic of tunneling dynamics, offering an independent and complementary perspective to established attoclock techniques.

Personalized computed tomography (CT) dosimetry has great potential in assessing patient-specific radiation exposure, supporting risk assessment, and optimizing clinical protocols. The aim of this study is to evaluate the potential of synthetic anatomical data for improving machine learning-based personalized computed tomography (CT) dosimetry. It is investigated whether the combination of synthetic human body geometries with real patient data can improve model accuracy and generalization for CT organ dose estimation while maintaining the uncertainty requirements outlined in IAEA TRS-457. Deep learning models for organ dose prediction are trained using datasets with varying proportions of real and synthetic data. Synthetic datasets are generated from computational human phantoms with controlled distributions of organ volumes and body. A dedicated model uncertainty evaluation method is implemented to quantify prediction reliability and verify compliance with TRS-457 accuracy limits. Model performance and uncertainty are compared across different training data compositions, including a model trained solely on real patient data. As baseline validated Monte Carlo simulation is used. Models trained solely on synthetic data show limited predictive accuracy, particularly for small or peripheral organs. Incorporating as little as 10 % real patient data significantly improves both statistical accuracy and uncertainty estimates, achieving a performance comparable to that of real-only models. The hybrid training approach improves robustness across different anatomies while maintaining TRS-457-compliant uncertainty levels (k=2 uncertainty < 20% for adults). The results indicate that the combination of real and synthetic data in combination with a systematic uncertainty assessment supports the development of CT dosimetry models and at the same time reduces the amount of real data required.

Nonlinear pulse propagation in gas-filled waveguides has attracted substantial attention over the past decade, and a variety of capabilities have been reported. However, there is no prior report of spectral compression in gas-filled waveguides or cavities, which would offer a natural route for scaling to much higher pulse energies than have been reached in solid structures. Here we report a high-energy spectral-compression technique based on nonlinear propagation in gas-filled capillaries. With 0.1- to 1-mJ pulses, compression of the spectral width by a factor up to 12 (from 60 nm to 5 nm) is demonstrated. Key to this advance is recognition that the process plays out differently in gases than in solids. In a noble gas (Ar), we find that even small structure in the spectrum, which is mapped to the time profile, of the input pulse can degrade the compression process. We identify the delayed Raman response of molecular gases (N2O and N2) as a mechanism that smooths and symmetrizes the nonlinear index modulation, which reduces the impact of spectral asymmetry and fine structure and enhances the fidelity of the compressed peak. The technique can be implemented with a capillary filled with ambient air, for sub-millijoule operation without a dedicated gas system. These results initiate a new direction in the optics of gas-filled waveguides and establish Raman-enhanced spectral compression as a robust route to high-energy narrowband optical sources, with potential impact in a broad range of applications.

Adiabatic invariants are fundamental to plasma physics but are often treated as purely theoretical concepts in undergraduate courses due to the difficulty of experimentally demonstrating them. This paper presents a pedagogical experiment to visualize and quantitatively verify the conservation of the magnetic moment ($\mu$) and the longitudinal invariant ($J$) using a standard educational electron charge-to-mass ratio apparatus configured as a magnetic bottle. By analyzing long-exposure photographs of the electron beam trajectory, we reconstructed the helical motion and calculated the invariants under different magnetic field configurations. Our results verify the conservation of the longitudinal invariant ($J$) with a ratio of 0.98 between configurations. The magnetic moment ($\mu$) exhibited a coefficient of variation of approximately 7%, a deviation consistent with the presence of collisional effects in the tube. These findings demonstrate that complex plasma dynamics can be effectively studied using accessible laboratory equipment, providing a valuable bridge between theory and experiment for physics students.

We propose the integral-equation formalism of population dynamics (IEPDYN) to describe the population dynamics of distinct configurational states. According to classical reaction dynamics theory, the probability density associated with a given state obeys the Liouville equation, including influx from and efflux to neighboring states. By introducing a Markov approximation for the crossing of boundaries separating the states, tractable integral equations governing the state populations are derived. Once the time-dependent quantities appearing in these equations are evaluated, the population dynamics on long timescales can be obtained. Because these quantities depend only on a few states in the local neighborhood of a given state, they can be computed using a set of short-timescale molecular dynamics (MD) simulations. The IEPDYN method is formulated in continuous time and therefore does not rely on a coarse-grained timescale (lag time). Consequently, kinetic quantities obtained from IEPDYN are free from lag-time dependence, which has been discussed as a limitation in other approaches. We apply the IEPDYN method to the binding and unbinding kinetics of CH$_4$/CH$_4$, Na$^+$/Cl$^-$, and 18-crown-6-ether (crown ether)/K$^+$ in water. For both kinetics, the time constants estimated from the IEPDYN method are almost comparable to those obtained from brute-force MD simulations. The required timescale of each MD trajectory in the IEPDYN method is approximately two orders of magnitude shorter than that in the brute-force MD approach in the crown ether/K$^+$ system. This reduction in the trajectory timescale enables applications to complex binding and unbinding systems whose characteristic timescales are far beyond those directly accessible by brute-force MD simulations.

We investigate vapor-liquid phase separation of an active near critical Lennard-Jones fluid confined within a cylindrical pore using molecular dynamics simulations. Activity is introduced via Vicsek-type alignment interactions, enabling a systematic study of how self-propulsion modifies domain morphology and coarsening kinetics under quasi-one-dimensional confinement. In the passive limit, the system undergoes early-time spinodal decomposition (diffusive growth characterized by the Lifshitz-Slyozov exponent $\alpha = 1/3$), followed by the formation of periodically modulated, plug-like liquid domains along the pore axis. At late times, coarsening becomes kinetically arrested, and the system remains trapped in a metastable striped state. Introducing activity destabilizes this arrested morphology by enhancing collective domain transport, leading to frequent domain mergers and complete phase separation at sufficiently high activity. The late-stage coarsening then exhibits a crossover to faster, ballistic growth with an effective exponent $\alpha = 2/3$, consistent with a cluster-coalescence mechanism. Analysis of two-point correlation functions and structure factors confirms dynamic scaling across all activity regimes. Our results demonstrate that alignment-induced activity can overcome confinement-driven kinetic arrest, providing new insight into phase separation in confined active fluids. The relevant growth laws are analyzed and interpreted using appropriate theoretical frameworks.

When a thin film is deposited on an incompatible elastic substrate, the film develops an elastic mismatch strain, causing the film-substrate system to bend. Stoney's equation relates the curvature of the bent film-substrate system with the residual stress developed in the film, and can be used to infer film properties from curvature measurements. Certain materials exhibit electromechanical coupling, such as piezoelectricity and flexoelectricity, which can alter the curvature and strains. In this work, we generalize Stoney's equation to include flexoelectric and piezoelectric effects in the film. Considering both open and closed circuit configurations, as well as uniform and non-uniform film properties, we compare different cases of electromechanical coupling and discuss their influence on curvature, strains, and electric polarization in the film.

This study aims to conduct a bibliometric analysis of scientific publications discussing technology integration in the Project-Based Learning (PjBL) model during the 2015-2024 period. With the evolving 21st century educational paradigm, PjBL has emerged as one of the most promising pedagogical approaches. In this context, the integration of technology in PjBL becomes an important catalyst that expands the scope and depth of learning experiences while preparing students to face challenges in the digital era. This research applies a bibliometric analysis methodology and systematic literature review, utilizing the Publish or Perish (POP) application for data collection and VOSviewer for data analysis and visualization. The results reveal emerging research trends, collaboration patterns, and focus areas in the scientific literature related to technology integration in PjBL. The bibliometric analysis uncovers research dynamics and knowledge gaps that need to be addressed. These findings provide valuable insights for researchers, practitioners, and policymakers in optimizing the implementation of technology-based PjBL and formulating educational policies responsive to technological developments and 21st-century learning needs. The results of this study are expected to contribute significantly to the academic community and educational practitioners in developing more effective technology based PjBL implementation strategies and fostering interdisciplinary collaboration to advance education in the digital age.

The rapid advancement of technology and science education demands innovative approaches to develop students' critical and creative thinking skills. However, there is limited systematic evidence about the effectiveness of different physics teaching materials in fostering these essential 21st century skills. This study aims to investigate the effect of physics teaching materials on critical thinking skills and creative thinking skills of high school students through a comprehensive meta-analysis of recent research. Using purposive sampling, 20 articles from national journals published between 2019-2023 were analyzed. The results revealed three key findings: (1) physics teaching materials showed varying degrees of effectiveness across different formats, with modules and student worksheets demonstrating the highest effect sizes (3.93 and 3.97 respectively); (2) physics teaching materials significantly enhanced students' critical thinking skills with a high effect size category (3.97); and (3) these materials effectively improved creative thinking skills with a high effect size category (2.97). These findings provide valuable insights for educators and curriculum developers in designing and implementing effective physics teaching materials.

Neural operator learning accelerates PDE solution by approximating operators as mappings between continuous function spaces. Yet in many engineering settings, varying geometry induces discrete structural changes, including topological changes, abrupt changes in boundary conditions or boundary types, and changes in the computational domain, which break the smooth-variation premise. Here we introduce Discrete Solution Operator Learning (DiSOL), a complementary paradigm that learns discrete solution procedures rather than continuous function-space operators. DiSOL factorizes the solver into learnable stages that mirror classical discretizations: local contribution encoding, multiscale assembly, and implicit solution reconstruction on an embedded grid, thereby preserving procedure-level consistency while adapting to geometry-dependent discrete structures. Across geometry-dependent Poisson, advection-diffusion, linear elasticity, as well as spatiotemporal heat conduction problems, DiSOL produces stable and accurate predictions under both in-distribution and strongly out-of-distribution geometries, including discontinuous boundaries and topological changes. These results highlight the need for procedural operator representations in geometry-dominated problems and position discrete solution operator learning as a distinct, complementary direction in scientific machine learning.

Recent breakthroughs in hyperbolic lattices have expanded the study of topological phases of matter from Euclidean to non-Euclidean spaces. However, prior work has mostly focused on spatial topological states at the single outer edge of type-I hyperbolic lattices. The dynamic transfer of hyperbolic topological states across multiple edges, as well as the emergence of spatiotemporal topological phenomena, remains largely unexplored. Here, we establish both spatial and spatiotemporal topologies in a newly discovered type-II hyperbolic lattice possessing outer and inner edges. Using electric circuits, we experimentally realize a type-II hyperbolic Chern insulator and directly observe degenerate chiral edge states of opposite chirality at its outer and inner edges. Furthermore, by coupling these counter-propagating chiral edge states, we demonstrate an anti-time-parity phase transition, enabling dynamic transfer between them in arbitrary proportions. Finally, we propose a novel paradigm for constructing a (2+1)-dimensional hyperbolic space-time crystal, which hosts an intertwined topology of spatial Chern and temporal winding numbers, resulting in a unique space-time topological string state. Our work expands the frontier of hyperbolic topological physics, paving the way for the spatiotemporal dynamic manipulation of hyperbolic topological states.

An initial value algorithm is utilized to examine the time dependent evolution of the electromagnetic fields arising from oblique scattering of bounded pulses from an infinite planar dielectric interface. Since the qubit lattice algorithm (QLA) is almost fully unitary, one finds excellent conservation of electromagnetic energy. Various Gaussian envelope pulses are considered in regimes where the incident angle is below that needed for total internal reflection. While the reflected pulse retains its overall Gaussian shape, the transmitted pulse exhibits a combination of a Gaussian envelope along with Huygen-like emitted wave fronts from the collision point of the initial pulse with the infinite dielectric interface. The strength of these Huygen wavefronts depends on the width of the incident pulse.

Recently, higher-order topological phases have been extended from Euclidean lattices to non-Euclidean hyperbolic lattices. Though higher-order topological type-I hyperbolic lattices have been extensively studied, their counterpart, higher-order topological type-II hyperbolic lattices, have never been reported yet. Here, by mapping the celebrated Bernevig-Hughes-Zhang model onto a type-II hyperbolic lattice, we present a theoretical exploration of the first-order topological edge states and second-order topological corner states in a type-II hyperbolic lattice. Compared with the higher-order topological type-I hyperbolic lattices, we discover two unique topological phenomena that stem from the nontrivial geometrical topology of the type-II hyperbolic lattice. First, topological edge and corner states exist on both inner and outer boundaries of the type-II hyperbolic lattice and exhibit higher degeneracy than those in the type-I hyperbolic lattice with only an outer boundary. Second, the degeneracy of type-II hyperbolic corner states can be arbitrarily tuned by changing the characteristic (or inner) radius, in contrast to its type-I counterpart, which is determined by the number of sides of the tessellated polygons. Our work explores topological states in more complex hyperbolic lattices, significantly expanding the research scope of hyperbolic topological physics.

We present SASHMAG (Saturated Absorption Spectroscopy High-field MAGnetometer), an atomic sensor designed for precision magnetic-field measurements in the intermediate-to-high field regime ($>0.2\,\text{T}$) using Rubidium-87 ($^{87}Rb$). The sensor operates in the hyperfine Paschen-Back regime, where the hyperfine and Zeeman interactions decouple, and utilizes counter-propagating pump-probe configuration in Faraday geometry to resolve isolated, Doppler-free Zeeman transitions. To interpret the resulting spectra in this strongly field-dependent regime, we developed a comprehensive multilevel optical Bloch-equation model solved explicitly in the uncoupled $\ket{m_I, m_J}$ basis, capturing state mixing and nonlinear saturation dynamics. This model reproduces measured spectra at sub-Doppler resolution and is consistent with analytical expectations for power broadening and thermal Doppler scaling. Magnetic field estimation is performed using a physics-constrained optimization routine that infers the magnetic field by minimizing the residual between experimentally extracted line centers and calculated transition frequencies from the field-dependent Hamiltonian. We demonstrate magnetic field retrieval from $0.2\,\text{T}$ to $0.4\,\text{T}$ with a precision of $\pm 0.0017 \,\text{T}$). Furthermore, the validated simulation establishes a foundation for generating synthetic training datasets, paving the way for autonomous, Machine Learning-enhanced magnetometry in applications ranging from MRI to fusion reactors.