Papers

Optical frequency standards have progressed rapidly over the past two decades, leading to the anticipated redefinition of the SI second by an optical frequency. However, time scales have not yet significantly improved despite this development because they are still fully reliant on rf flywheel oscillators, mostly hydrogen masers, which impose a performance limit related to incompletely sampled noise known as the Dick effect. To best benefit from the exceptional stability and accuracy of optical frequency standards, time scales must employ optical flywheels with orders-of-magnitude better short-term (<$10^4$ s) stability than masers. Here, we introduce three optical flywheel oscillators (two cryogenic silicon cavities and one iodine optical clock) with superior short-term stability than hydrogen masers and long-term stability on par with masers. Steering each optical flywheel with a high-uptime Sr optical frequency standard generates three parallel all-optical time scales with continuous operation over >20 days. When compared with each other, these all-optical time scales achieve <$10^{-16}$ relative instability after just a few days of averaging. During typical steering gaps of ~6 hours, the accumulated time difference is ~20 ps, leading to the total time difference of <100 ps over the full measurement period. With the proliferation of long-distance optical fiber links and commercialization of optical flywheels and frequency standards, we anticipate all-optical time scales to be the future of timekeeping.

Leaf Area Index (LAI) is a fundamental biophysical variable governing land-atmosphere interactions; however, LAI forecasting at high spatial resolution remains an unsolved challenge. While recent machine learning approaches have demonstrated LAI estimation at point or regional scales, none provides a gridded, meteorology-driven prognostic forecast suitable for subseasonal land surface and climate modeling applications. Here we present a sequence-to-sequence Convolutional LSTM (ConvLSTM) framework that generates daily 1-km LAI forecasts up to 30 days ahead, driven by historical LAI sequences and daily meteorological forcing including temperature and precipitation. Trained and evaluated over the South-Central United States – a region of strong climate gradients and diverse vegetation – the model achieves a domain-averaged RMSE of 0.36 at a 30-day lead time, more than a third lower than the persistence baseline. Forecast skill remains robust across seasons, geographic distributions, and plant functional types, including forests, grasslands, shrublands, and croplands. To our knowledge, this is the first demonstration of skillful LAI forecasting at a 30-day horizon at 1-km resolution.

The dispersion of reactive solutes in shear flows is governed by the interplay between advective stretching, transverse diffusion, and boundary exchange kinetics. While classical analytical methods and grid-based numerical solvers have extensively characterised these transport mechanisms, accurately resolving the spatiotemporal evolution of solute plumes in asymmetric reactive environments remains computationally demanding. In this study, we introduce a physics-informed neural network (PINN) framework to simulate two-dimensional wall-reactive solute dispersion in canonical shear flows (Couette, Poiseuille, and Couette-Poiseuille) bounded by absorbing walls. By embedding the governing convection-diffusion equation and Robin boundary conditions into a unified loss function, the mesh-free PINN reconstructs the spatiotemporal concentration field. The network predictions are validated against an alternating-direction implicit (ADI) finite-difference benchmark, showing close agreement across non-reactive, symmetric, and asymmetric reactive regimes. The computations are carried out at $\mathrm{Pe}=10$ for impermeable walls, symmetric absorption $(\beta_1,\beta_2)=(1,1)$, and tenfold asymmetric wall-reactivity contrasts $(\beta_1,\beta_2)=(0.2,2)$ and (2,0.2). Leveraging the differentiable nature of the trained PINN, we extract wall-resolved transport diagnostics, including the apparent axial dispersion coefficient, cumulative wall-removal dynamics, and localised uptake fluxes. The results show that the imposed shear profile governs the streamwise organisation of reactive uptake, while unequal wall reactivities induce transverse asymmetry that modifies the macroscopic spreading rate. Overall, this framework establishes PINNs as an interpretable mesh-free tool for analysing boundary-coupled reactive transport in shear flows.

Physics-informed neural networks (PINNs) have emerged as a versatile approach for solving nonlinear partial differential equations (PDEs), yet achieving high accuracy efficiently using these techniques remains challenging for high-dimensional or multiscale systems. Here, we present a hybrid quantum-classical framework that enhances Quantum PINNs (QPINNs) through adaptive collocation point sampling and loss-aware attention mechanisms. By dynamically prioritizing points in regions with large PDE residuals or steep solution gradients, our method mitigates the spectral bias inherent in conventional PINNs. Current Quantum Physics-Informed Neural Networks are commonly assumed to be limited by the expressive power of quantum circuits. In our work, we observed that, across diverse differential equations, optimization - not only expressivity - can be an important bottleneck. Furthermore, a trainable loss-weighting scheme balances contributions from physics residuals, boundary conditions, and data fidelity during training. Integrating these strategies with quantum computing techniques (including variational quantum circuits and quantum gradient estimation) can yield at least a 60% improvement in solution accuracy under specific regimes for benchmark fluid flows and reaction-diffusion systems. Finally, we argue that merely increasing model expressivity is insufficient for resolving complex PDEs via QPINNs, as they remain constrained by the structural optimization limitations of classical PINNs. This framework provides a scalable pathway for quantum-enhanced scientific machine learning, bridging physics-based modeling with emerging quantum computational capabilities.

We develop a Bayesian framework for model comparison of second-order Langevin dynamics from position-only trajectories. While approximate increment likelihoods for nonlinear position-only inference have been formulated previously, a unified evidence-based framework for comparing multiple second-order models under positional observation has remained lacking. Here we address this problem by combining exact increment likelihoods for linear Gaussian models with a previously proposed approximate likelihood for nonlinear dynamics. Synthetic-data benchmarks show reliable recovery of the generating model at fine sampling intervals and progressive loss of identifiability under coarse temporal sampling. Application to Dictyostelium discoideum trajectories demonstrates that the statistically supported model depends strongly on temporal resolution. Moreover, the selected models reproduce key statistical properties of the experimental trajectories, providing additional support for the model-comparison results. Our framework therefore offers a practical approach to evidence-based comparison of partially observed stochastic dynamics.

This paper presents a mathematical derivation for three new conserved quantities in the motion of spacecraft on optimal continuous-thrust trajectories in a central gravitational field. The process presented in this paper is rooted in Noether's theorem that connects the point symmetries of a dynamic system with the associated conservation laws of the system. In the approach presented in this paper, the system's Lagrangian is modified to account for the non-conservative control force. Using this generalized Lagrangian, the action functional to be minimized is written. Then, Killing equations are formulated to find the dynamic symmetries for this system. In this paper a process is laid out for how to solve the Killing equations; Noether's theorem is applied to this solution of the Killing equations to write the conserved quantities of the system. Conserved quantities are presented for both the two-dimensional and the three-dimensional trajectories in several different coordinate frames. Numerical simulations and mathematical proofs are used to demonstrate that the computed quantities are conserved.

Fragile X Syndrome (FXS) is a neurodevelopmental disorder caused by reduced expression of fragile X mental retardation protein (FMRP), leading to disrupted synaptic plasticity, cortical hyperexcitability, and impaired network synchronization. Electroencephalography (EEG) provides a noninvasive window into these mechanisms and consistently reveals abnormalities in alpha (8 to 12 Hz) and gamma (30 to 100 Hz) oscillations that relate to inhibitory control, sensory processing, and cognition. This paper proposes a multi representation deep learning framework for automated characterization of FXS EEG phenotypes by integrating convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and recurrence plot (RP) analysis. Band limited EEG signals are decomposed into alpha and gamma components and transformed into complementary representations, including temporal feature sequences, time frequency maps, and RP images encoding the nonlinear recurrence structure. CNN modules learn discriminative spatial-spectral and dynamical textures from image based representations, while LSTM modules model temporal modulation of oscillatory activity; a hybrid CNN LSTM architecture jointly captures spatial, temporal, and nonlinear dependencies. Subject-independent evaluation demonstrates that the hybrid model outperforms single modality baselines, with gamma features providing strong discriminative power and alpha gamma integration yielding the best overall performance. These findings support deep learning with nonlinear representations as a scalable approach for EEG biomarker development in FXS, with potential utility for diagnosis, stratification, and treatment monitoring in translational settings.

Photonic integrated circuits (PICs) promise order-of-magnitude reductions in the size, weight and power (SWaP) of optical subsystems for astronomy, planetary and Earth-observation missions, yet no PIC-specific space-qualification standard exists. This paper consolidates the principal NASA and ESA qualification documents that apply, or can be tailored, to astrophotonic PICs — arrayed waveguide gratings, photonic lanterns, fibre Bragg gratings, and integrated beam combiners (ABCD, discrete beam combiners, nullers) for spectrographs and stellar interferometers. A master qualification table lists 19 standard test steps with applicable standards and EU/USA test facilities. Two reference mission profiles — a LEO smallsat demonstrator and an HWO-class Lagrange-2 flagship — yield a tailoring matrix, while a TRL-versus-test-coverage roadmap maps each activity onto the NASA/ESA readiness levels and review gates. A survey of UV/visible/near-infrared platforms relates spectral coverage, maturity and flight heritage, and a radiation-effects summary shows passive silica, Si3N4 and laser-written cores are essentially radiation-tolerant while active III–V and Ge devices carry the hardness burden. The central outcome is a seven-phase qualification template (PIC-SQT) with explicit TRL gates, exact test procedures and mission-class tailoring; we further identify qualification processes relevant to PICs that current standards do not cover, and document 40+ years of optical-fibre flight heritage.

Efficient fiber-to-chip coupling remains challenging for thin-film lithium niobate (TFLN) devices operating at 1064 nm. Here, we fabricate and characterize an on-chip optical mode converter based on a bilayer inverse taper. The proposed structure demonstrated a coupling loss of 1.9 dB per facet for the TE mode, with a 1 dB bandwidth spanning 1055–1085 nm. The polarization-dependent alignment tolerance was measured along the lateral and vertical directions. Theoretical and simulation analyses indicate that, when using a lensed fiber with a mode field diameter of 2.5 $\mu$m, the coupling loss can be reduced to as low as 0.48 dB per facet. These results demonstrate the feasibility of providing a high-efficiency solution for on-chip optical coupling at 1064 nm.

Head-and-neck (HN) proton therapy is highly sensitive to anatomical change over a 4-to-6-week course, as tumor shrinkage, weight loss, and setup variation can misposition the Bragg peak near critical organs such as the parotids, oral cavity, brainstem, and spinal cord, leading to target underdosing or organ-at-risk overdosing. Online adaptive proton therapy replans on the anatomy of the day, yet standard workflows rely on offline replanning that requires repeated CT acquisition and roughly a week of preparation, adding burden, cost, and delay. We investigate whether a patient's treatment-day anatomy can be predicted before image acquisition by transferring longitudinal change from a population database. We propose a digital-twin framework built on a pretrained foundation-model deformable registration network used without patient-specific training. A first registration aligns a prior patient's planning CT to the target and carries the prior's during-treatment quality assurance CT (QACT) into the target frame; a second registration estimates the prior's planning-to-QACT change, which is then applied to the target's own planning CT to synthesize predicted CTs (pdCTs) with propagated contours. Using 88 HN patients, each with a planning CT and three QACTs, we show that pdCTs better match treatment-day anatomy than the static planning CT. Compared with the planning CT alone, normalized cross-correlation improves by 22.8%, Dice for organs-at-risk by 20.2%, and CT-number error decreases by 23.4%. Gains are largest for patients with major anatomical change and negligible when anatomy is stable. This cross-patient motion transfer leverages the digital-twin concept to anticipate treatment-day anatomy, enabling personalized online adaptive proton therapy without repeated imaging.

Conceptual surveys of multiple choice format have been developed to test the effect of pedagogical interventions on students understanding of physics knowledge. Predominantly, they are administered in pre and posttest settings and analyzed to obtain a performance gain. However, focusing on the correct answers to each question alone ignores the incorrect options which could inform us the stability and coherency of the knowledge structure of students. According to the resource model framework, each specific answer from students could reflect a specific cognitive resource being activated and implemented in the context at hand. Consequently, conceptual surveys could demonstrate how instruction could affect the activation of different cognitive resources of students in a variety of contexts when administered before and after instruction. Guided by the resources framework, we propose a transition matrix analysis to analyze data collected through conceptual surveys to investigate how instruction affects the consistency of cognitive resources activation by students. To provide proof of concept, we demonstrated how to utilize this method by analyzing students responses to a subset of questions from the DIRECT survey in a classroom study.

Ion stopping in partially ionized plasmas often receives roughly equal contributions from bound and free electrons, yet bound electron contributions are usually treated at a very coarse level, with the exception of costly time-dependent density functional theory (TD-DFT) simulations. At energies above the Bragg peak, where linear response methods are a good approximation, more accurate treatment is both feasible and critical for predictive modeling. We develop a channel-mixed random phase approximation (cmRPA) dielectric response function that combines Lindhard free response with explicit average-atom bound state transitions. Validating against ambient condition experiments and TD-DFT simulations, we demonstrate excellent agreement for proton stopping near and above the Bragg peak. The computational efficiency of our approach enables systematic exploration of extreme conditions across a wide range of densities and temperatures. We find non-trivial stopping effects from channel mixing between bound and free transitions via RPA, bound-bound stopping contributions at low velocity, and predict significant bound electron contributions to stopping in tungsten at peak compression conditions relevant to inertial confinement fusion experiments. Applying our model to the warm dense matter stopping experiment of Malko et al. (2022), we evaluate whether improved bound-state modeling could resolve the reported theory-experiment discrepancy, finding that within the constraints of linear response theory, bound stopping mismodeling is unlikely to be the source of the observed deficit.

Traditional reaction yield prediction is constrained by 1D quantum descriptors that lack explicit spatial information. To address this gap, a dual-modal Vision Cross-Attention architecture is proposed, fusing tabular physical-organic data with 2D molecular topologies. Notably, it is demonstrated that a generic computer vision backbone processing simple 2D skeletal structures independently outperforms purely quantum-based baselines. By synergizing both modalities, superior predictive accuracy compared to traditional methodologies is achieved by the optimal cross-attention framework (Test RMSE = 5.27%). Through mechanistic probing, active, descriptor-guided spatial querying is observed, effectively offloading macroscopic steric identification to the visual pathway. Furthermore, a dynamic chemical hierarchy is learned by the network to heavily prioritize critical steric bottlenecks, such as the aryl halide. Concurrently, residual skip connections are utilized to protect non-spatial electronic parameters from destructive attenuation during fusion. Collectively, a scalable and highly interpretable blueprint is provided for augmenting physical chemistry with deep visual learning.

Gradient-based Bayesian inference methods require efficient access to Jacobian and adjoint-Jacobian operators of high-dimensional forward models. While multigrid solvers provide near-optimal complexity for elliptic partial differential equations, they are rarely available in forms compatible with automatic differentiation (AD). We develop a differentiable multigrid solver for steady-state diffusion-absorption problems and derive its adjoint operations analytically through the full multigrid hierarchy. The resulting solver, DMGS, is implemented in C++ and interfaced with JAX to provide efficient Jacobian-vector and vector-Jacobian products for variational inference in the NIFTy framework. We validate the approach on a 3D inverse problem involving diffuse radiative transfer in tissue, reconstructing an effective radiative source from Monte Carlo-simulated data. The reconstruction reproduces the data at a reduced chi-squared of 1.1 and generalizes to 32 independent validation datasets. Benchmarks against a JAX-native multigrid implementation show comparable runtimes and consistently lower peak memory for the hand-derived adjoint, with modest reverse-mode overhead. These results establish differentiable multigrid solvers as practical building blocks for variational inference in PDE-constrained problems.

The conducting bar sliding on rails through a uniform magnetic field is a standard textbook illustration of Faraday's law, almost always solved assuming the magnetic field produced by the induced current is negligible. We extend this classic problem by retaining the self-induced field: modelling the circuit as a rectangular loop of round wire of radius $d$, we compute in closed form its geometry-dependent self-inductance $L(x,l)$ and its gradient $dL/dx$ from the Biot–Savart law, including the flux inside the wire and at the corners. The bar then obeys coupled mechanical–electrical equations of motion containing, besides the familiar braking force $-B_0lI$, the inductance-gradient force $\tfrac{1}{2}I^2\,dL/dx$ familiar from electromagnetic launchers. In the absence of resistance the total energy $\tfrac12Mv^2+\tfrac12LI^2$ is exactly conserved; with resistance the system becomes an electromechanical damped oscillator that, in an appropriate regime, maps onto a series resistor–inductor–capacitor (RLC) circuit with equivalent capacitance $C_{eq}=M/(l^2B_0^2)$, the bar's momentum playing the role of the capacitor charge. Numerical integration of the full equations confirms these analytic approximations in their respective regimes and locates the crossover between over-damped and under-damped behaviour.

In the present study, we propose a consistent and bound-preserving finite-volume WENO scheme that satisfies the requirements of consistency, conservation, equilibrium, and bound preservation for compressible multiphase flows with the Phase-Field mechanism. The proposed WENO scheme is developed based on a new calculation of WENO weights determined by relative smoothness between stencils and on a coupled reconstruction of the masses and volume fractions. Consistency of reduction and volume fraction summation to unity are considered during the development so that fictitious phases, local voids, or overfilling are not produced numerically when there are $N$ ($N \geqslant 1$) different immiscible phases. The proposed WENO scheme is applied to the consistent and conservative Phase-Field method with adaptive mesh refinement enabled. Various benchmark compressible two- and $N$-phase flows are performed to verify the properties of the proposed WENO scheme as well as its variant with the consistent limiter. We finally demonstrate the capability of the proposed WENO scheme in shock-induced cavity collapse and shock-vessel-bubble interaction problems, with discussion of the necessity of bound preservation for high-order schemes and comparison of different compressible multiphase flow models.

Reliable estimates of the melt rate of tidewater glaciers require accurate knowledge of salt and heat fluxes at the near-vertical ice-ocean interface and such knowledge is currently lacking. In this paper, we present a theory for salt and heat fluxes in the idealized system of turbulent double-diffusive convection in an infinite vertical channel and show how this theory can be extended to give theoretical estimate of the buoyancy-driven melt rate of a vertical ice surface in seawater. Our theoretical results are shown to be in good agreement with direct numerical simulation data and measurements from laboratory experiments.

This minireview provides a brief summary and a discussion of directions for further development of theoretical and, chiefly, experimental studies of bright solitons in optical couplers, i.e., dual-core waveguides which combine the linear inter-core coupling (tunneling of light between the parallel cores with the intra-core group-velocity dispersion and self-focusing Kerr (cubic) nonlinearity. Following a short introduction to the field, the article focuses on a brief review of relatively recent experimental results for the switching of solitons in dual-core nonlinear optical fibers and the spontaneous emergence of stable asymmetric two-core solitons in the couplers with the symmetric dual-core structure.

Autoregressive models accumulate error over long rollouts, yet at deployment there is no ground truth to measure it against. We train a single conditional latent diffusion model that steps a dynamical system forward or backward in time via a direction flag, and show that this bidirectionality supplies a measurement-free test-time error signal: rolling forward $i$ steps and then backward $i$ steps must return the model to its start, so the round-trip discrepancy $\mathcal{C}_i$ is a self-supervised proxy for the unobservable rollout error: no ensembles, no held-out data, no governing equations, for one extra rollout. We validate on compressible magnetohydrodynamics (MHD), an astrophysical turbulent radiative mixing layer, and natural face videos (CelebV-HQ). On held-out MHD trajectories, $\mathcal{C}_i$ ranks rollout error (Spearman $0.91$-$0.98$ at fixed depth; $0.69 \pm 0.16$ within trajectories), and a simple calibrator fit on training rollouts predicts its magnitude to within $1.14\times$ ($68\%$) and $1.29\times$ ($95\%$) with near-nominal coverage - one nat beyond a depth-only predictor, transferring to all six decoded physical fields. The same signal flags the out-of-distribution Orszag-Tang vortex (AUROC $0.98$; $1.0$ by depth $10$) exactly where sampling-dispersion baselines invert, and it cuts incurred error by $15\%$ at $80\%$ coverage - three times the depth-only baseline. Bidirectional training comes at negative cost, beating direction specialists in both directions, and the backward direction doubles as a fast inverse solver. On LE-PDE-UQ's turbulent Navier-Stokes benchmark, a single bidirectional model reaches accuracy within $1.3\times$ of their ten-model ensemble at a tenth of the training cost, with the best training-free pixel-level calibration. Round-trip consistency turns reversibility into a practical trust signal for generative models.

Diurnal rhythms in the gut microbiome are commonly read as evidence of host-driven entrainment or of microbial oscillators that synchronise to a common clock. We reanalyse hourly genome-resolved (MAG-level) mouse-gut time series with diagnostics tailored to test that interpretation. At this resolution and for both animals in the dataset, the time-frequency representation carries no persistent ridge; the time-averaged spectrum is enhanced at low frequencies and depleted at intermediate frequencies; the lagged covariance is markedly time-asymmetric, with a global imbalance peak near tens of hours; and an amplitude-adjusted Fourier surrogate test identifies a weak time-averaged construction in the candidate circadian band, never as a fixed time-frequency ridge. The two functional guilds that carry the inferred non-normal amplification are identified independently by the rankings of two inferred dynamical modes (the reaction mode, into which fluctuations are transiently amplified, and the non-normal mode, which injects them), and recover the primary polysaccharide degraders of Bacteroidota and the secondary butyrate and propionate fermenters of Bacillota A without invoking any phase information. The conjunction of these signatures matches a stable but strongly non-normal stochastic regime, that is, pseudo-coherence: geometric amplification reshapes stochastic fluctuations onto a low-dimensional reaction subspace, producing intermittent synchronisation-like episodes, broken time-reversal symmetry, and emergent time-averaged characteristic scales without an underlying oscillator. We propose a falsifiable test via high-resolution clock-gene-knockout cohorts.

The correct description of ion traffic during an Action Potential in neurons has fundamental importance from its theoretical description to practical clinical applications. The classical Hodgkin-Huxley theory hypothesized that a delayed K+ current causes the hyperpolarization of the Action Potential. The paper shows that the membrane's capacitive current was misinterpreted as K+ current, and that the direct evidence for that current is an experimental artifact, both arising from an incomplete understanding of the electrical model. After introducing the Axon Initial Segment (discovered decades after constructing the classical model) and using the correct cross-disciplinary physical laws for living nature (instead of using laws valid for inanimate nature), one can construct the correct model for neuronal operation, which provides the perfect description of neuronal operation, including the Action Potential, in full accordance with the laws of science.

An event is usually regarded as a zero-dimensional coordinate label in spacetime, rather than as an object with spatial extent, internal structure or topology. Here we show that in nonlinear photonic spacetime crystals, a complete energy-momentum gap ($\omega k$-gap) suppresses extended radiation channels, while Kerr self-trapping localizes an optical occurrence in the physical $(x,t)$ plane. Imposing and relaxing symmetry constraints then yields multipole and vortex events with distinct internal structure. Their spacetime action of these states establishes a hierarchy of structural cost, Bogoliubov-de Gennes spectra distinguish phase-winding vortices from real-valued multipoles, and initial-value reconstructions provide an independent robustness check together with a finite preparation window. Using representative optical parameters, we predict that these structured events span transverse scales of 30-60 $\mu$m and temporal duration of 85-170 fs. These results suggest that an event can be treated not only as a point label, but also as a localized wave object with controllable internal degrees of freedom, offering a route to wave control in time-varying nonlinear media.

We report the first measurement of the total hyperfine interval in the manifold of the ($v=0,L=3$) rovibrational state of HD$^+$ using microwave spectroscopy of HD$^+$ ions in a linear Paul trap. To overcome the low (0.2%) occupancy of the quantum states involved, we employ a novel technique that combines intermittent Majorana depolarization of the HD$^+$ ensemble with redistribution of rotational-state population by blackbody radiation to effectively amplify the occupancy by a factor of 25. This enables the observation of a single field-insensitive magnetic subcomponent of the hyperfine transition, leading to a measured total hyperfine interval of ${1\,050\,804.511}\pm 0.011\,$kHz with an unprecedented relative uncertainty of 10 parts per billion. This differs from the theoretically predicted value ${1\,050\,802.78}\pm 0.89\,$kHz by 1.9$\sigma$. We show that our experimental value is consistent with the recently measured hyperfine structure of the ($v=0,L=0$) rovibrational state of HD$^+$ [C.M. König et al., High-precision Penning trap spectroscopy of the ground state spin structure of HD$^+$, Phys. Rev. Lett. $\mathbf{136}$,143002 (2026)], for which a similar deviation from theory was found. Our work may help resolve puzzling discrepancies between the theoretical and experimental hyperfine structure observed previously in optical rovibrational spectra of HD$^+$.

Metasurfaces are artificially engineered ultrathin nanostructured surfaces, capable of flexibly manipulating light-matter interactions on compact platforms, and thereby of great significance for a wide range of applications within modern optics and photonics, including communications, computing, sensing, and quantum technologies. However, the inherently static nature of conventional metasurfaces severely limits their functionalities and thus range of possible applications. Benefiting from integration of the metasurface platform for shaping optical wavefronts with ultrafast electro-optic (EO) materials, active EO metasurfaces have emerged as a frontier research direction targeting advanced photonic devices. This paper systematically reviews the latest progress in this field, featuring a comprehensive comparison of performances and application scenarios of mainstream EO materials such as lithium niobate, barium titanate and organic EO polymers. Modulation mechanisms based on the Pockels and Kerr effects along with the corresponding active metasurface implementations are summarized. Furthermore, improvements in modulation efficiency enabled by advantageously exploiting resonant structural designs and associated phenomena, including Fabry-Perot resonances, Mie resonances, surface plasmon polaritons, quasi-bound states in the continuum, surface lattice resonances, and guided-mode resonances, are presented and summerized in detail. Current challenges related to metasurface design, nanofabrication, performance and heterogeneous integration are also discussed. Finally, future research directions are outlined, highlighting interdisciplinary developments, novel material engineering, and AI-assisted design as key pathways to enable practical use of active EO metasurfaces in modern optics and photonics, including quantum information technologies.

Disordered traffic flow is characterized by weak or non-existent lane discipline in the presence of strong vehicle heterogeneity and continuous lateral interactions, challenging traditional lane-based modeling assumptions. This study presents an empirical study of macroscopic and microscopic aspects of disordered traffic using high-resolution UAV trajectory data collected on an urban arterial. A two-dimensional extension of Edie's framework is applied to quantify aggregate traffic variables and produce a two-dimensional fundamental diagram, revealing that traffic states cannot be adequately represented using one-dimensional formulations and highlighting the persistent role of lateral redistribution. The propagation of congestion is estimated directly from the spatiotemporal speed fields, demonstrating the emergence of coherent stop-and-go waves and showing a similar dynamics as conventional lane-based flow, in spite of the heterogeneous vehicle interactions. At the microscopic level, steady-state follower-leader identification is used to examine desired time gaps and minimum lateral spacing, vehicle dimension distributions, and kinematic characteristics, revealing pronounced inter-class heterogeneity that explains disordered traffic behavior. The study provides an empirical framework linking vehicle-level interactions and aggregate traffic dynamics and establishes a data-driven basis for the calibration and validation of traffic models for disordered mixed traffic systems.

We report a previously overlooked general mechanism to obtain highly efficient spin selectivity in conventional time-reversal symmetric one-dimensional systems without invoking phase decoherence. We reveal that Rashba quantum wires featuring non-collinear spin states at the Fermi level inherently possess spin-selective transport properties. We show that this spin noncollinearity can be systematically designed and engineered by introducing an additional pseudospin degree of freedom - such as valley, sublattice, or orbital angular momentum - into spin-orbit coupled systems. By applying this framework to multi-subband semiconducting quantum wires and oxide nanowires, we establish a generalized route toward quantum-coherent spin selectivity up to 10 %. Our findings offer practical design principles for spin-selective transport devices.

Solid-state single organic molecules, such as dibenzoterrylene (DBT) in organic matrices, are prominent deterministic single-photon sources, usually modeled as effective two-level systems (TLS). We show that single DBT molecules in anthracene nanocrystals, under strong continuous-wave driving, depart from this picture: instead of the expected saturation, fluorescence is strongly suppressed at high resonant pump power, while the linewidth broadens beyond the TLS prediction – an anomalous saturation regime. Comparing coherent and incoherent excitation and independently calibrating temperature via phonon-induced dephasing rules out laser-induced heating and intersystem crossing. Instead, a model of intensity-dependent excited-state absorption (ESA) toward a short-lived dark state quantitatively reproduces both the fluorescence suppression and the linewidth broadening. We further show that matrix quality mitigates this quenching, and that pulsed excitation schemes are strategic toward full population inversion, with direct implications for quantum nanophotonics and molecular optomechanics.

In stream finishing, the wear experienced by a workpiece depends strongly on its orientation within the rotating abrasive media. Determining suitable orientations to achieve uniform wear requires evaluating the wear-rate field over all feasible orientations. Although the discrete element method (DEM) accurately resolves particle interactions, simulating hundreds of feasible orientations for a new geometry is computationally expensive. We present an uncertainty-guided surrogate framework that predicts, directly from geometry, the three fields governing erosion: per-triangle normal impact velocity, tangential impact velocity, and particle impact flux. These fields are combined through the Finnie wear model to reconstruct the wear-rate distribution. The surrogate employs a deep ensemble whose disagreement estimates epistemic uncertainty, enabling an active-learning strategy that selectively performs DEM simulations for the most uncertain orientations. Trained using only $13\%$ of the $696$ feasible orientations, the surrogate achieves Spearman rank correlations of $0.93$, $0.89$, and $0.93$ for the normal impact velocity, tangential impact velocity, and particle impact flux, respectively. Moreover, the predicted uncertainty is well calibrated, reliably anticipating prediction error and the fidelity of the reconstructed wear field, which matches DEM with a Spearman rank correlation of up to $0.97$ for low-uncertainty orientations and degrades in a controlled manner as uncertainty increases.

A new semi-implicit, two-phase, double-point formulation of the Material Point Method (MPM) for soil-water interaction with seepage and free-surface flows under large deformation is presented in this paper. The approach advances the water phase implicitly while keeping the soil phase explicit, enabling stable, efficient time integration in problems that involve rapid seepage and strong free-surface motion. The proposed framework models high-Reynolds-number interphase drag through a non-linear Darcy's law implemented for the first time within an incremental fractional step MPM formulation without enlarging the implicit solve. This methodology also enhances the numerical stability for fast flows and wave breaking via a hyperelastic constitutive treatment of slightly compressible viscous water, and mitigates spurious oscillations through a new stabilisation approach for the velocity. Robustness of soil-water interface is achieved by combining nodal-based, free-surface detection, suited for higher-order spline functions with smooth porosity-permeability transitions that avoid constitutive divergence at sharp material boundaries. Validation against laboratory benchmark cases reported in the literature, including pure-water dam break, dam-break seepage through a porous barrier, two granular-collapse tsunami experiments, and a dam-break wave over a movable granular bed, shows accurate and stable free-surface evolution, pressure time histories, seepage fronts, and wave-gauge records. Using an advanced critical-state soil model (NorSand) further improves the reproduction of granular flow kinematics. The results demonstrate that the proposed formulation is a reliable and computationally efficient tool for geotechnical hazards involving intense soil-water coupling, seepage, sediment transport and free water.

Electronic-structure and embedding workflows terminate in effective many-body Hamiltonians, whereas quantum eigensolver studies often start from hand-built qubit models. We present the Adaptive Clifford-Algebra Subspace Eigensolver (A-CASE), a single-reference, operator-generated Rayleigh–Ritz method. Overlap, Hamiltonian, observable, and response matrices are reconstructed from one shared Pauli-expectation bank, while adaptive growth scores overlap-aware local pencils and rejects symmetry leakage or near-linear dependence. A strict FCIDUMP adapter supplies the active-space boundary. For linear H$_4$ in STO-3G with CAS(4e,4o), the mapped sector agrees with independent determinant FCI to $3.1\times10^{-15}$ Ha. At a nine-vector budget A-CASE has a $3.019$ mHa error; replacing the determinant reference by a two-operator ADAPT-VQE state reduces it to $0.342$ mHa, without implying a matched total-cost advantage. Under a matched contract, the fixed-reference route uses one state preparation versus ADAPT-VQE's ninety but measures roughly an order of magnitude more Pauli words. Exact fixed-angle ADAPT-GCIM gives $13.364$ mHa at the nearest size match and $10.674$ mHa at the iteration match, with its transition-pair burden reported separately. Across a broader benchmark ladder, fixed Krylov bases are generally more accurate and often narrower but substantially less well conditioned. A grouped bootstrap propagates finite-shot variability through thresholding, diagonalization, root matching, spectral weights, susceptibility, and broadening; its bands are explicitly heuristic and conditional, not finite-sample confidence certificates. The work establishes an executable path from an interchange Hamiltonian to energies, correlations, and response, without claiming materials accuracy, favorable scaling, or quantum advantage.