Papers

Dense-liquid relaxation evolves from local particle collisions to cooperative structural rearrangements. While hard-sphere kinetics determines an early $t^{3/2}$ fractional decay in density correlation functions, collective theories describe the subsequent structural relaxation. A central open question has been how short-time contact physics supplies an exact starting point for the memory kernel governing later times without being modified by subsequent many-body rearrangements. Here we resolve this problem for a broad class of reversible Brownian systems. We prove that hard particle contacts act as reflecting boundaries in configuration space, uniquely dictating the amplitude of the leading $t^{3/2}$ density relaxation. Mechanistically, diffusion samples a contact boundary layer of thickness $O(\sqrt{t})$, which combines with the local density response to produce the fractional signal. We derive an explicit surface formula expressing this amplitude in terms of equilibrium contact probability, normal mobility, and density sensitivity. For monodisperse hard spheres, this yields an exact, fit-free prediction determined entirely by static structure $S(k)$, radial contact value $g(\sigma^+)$, and short-time diffusion $D_0$. Extending the construction, we determine the corresponding normalization for soft interfaces and prove via a Gram–Schur projection hierarchy that regular collective variables leave the leading contact amplitude strictly invariant. The resulting formulation connects microscopic collision kinetics directly to caging and glass-like structural relaxation, providing an exact microscopic boundary condition for scattering experiments, molecular simulations, and memory-kernel reconstructions.

Agentic artificial intelligence (AI) systems that are capable of planning and executing multi-step analytical tasks are increasingly available to environmental health researchers, but their reliability in real-world practice has not been fully explored. This paper describes a human-in-the-loop agentic framework for environmental health research, involving the review, verification, and correction of AI-generated data analysis code and results at each step - a process that mirrors the mentorship structure of traditional research teams. This approach offers a path toward rigorous, reproducible use of agentic AI in routine data-rich environmental health research. We illustrate this framework through a case study analyzing nitrogenous organic contaminants at U.S. Superfund sites, a chemical family linked to the emerging tire-derived contaminants 6PPD and 6PPD-quinone. Using an agentic large language model to generate R code for data filtering, spatial mapping, and cluster analysis, we document instances where initial agentic AI outputs benefited from a human-in-the-loop process to produce more rigorous and reproducible results. We conclude that effective use of agentic AI requires both domain expertise to frame questions and evaluate outputs, and coding literacy to guide the AI's approach, while outlining future opportunities for agentic AI workflow to advance the environmental health sciences.

A new gyrokinetic code, TEK, was benchmarked in simulating energetic particle (EP) driven toroidal Alfven eigenmodes (TAEs) in the simple tokamak configuration chosen by the ITPA-EP group for code benchmarking purpose. Linear benchmark has been well established by other codes, whereas nonlinear benchmark for this case is lacking. This paper presents, besides the linear benchmark, nonlinear results for both single-n and multiple-n simulations (n is the toroidal mode number). The nonlinear results are in good agreement with an analytical theory on zonal field beat-driven by Alfven eigenmodes, partially verifying correctness of the nonlinear simulations. The saturation level and the resulting EP transport are examined. This provides data for future inter-code nonlinear benchmarking. In TEK, all species (electrons, thermal ions, EPs) are treated on the same footing using the gyrokinetic model (with electrons in the zero Larmor radius limit). The electromagnetic cancellation problem is mitigated by using the mixed-variable pullback method. Numerical details related to electromagnetic gyrokinetic simulation are discussed.

Objective. Existing commercial time-of-flight positron emission tomography (TOF-PET) systems yield a coincidence time resolution (CTR) of ~200 ps or less full width at half maximum (FWHM). Recently, there has been a challenge to achieve a CTR of 100 ps FWHM at the system level. However, current silicon photomultipliers (SiPMs) and 20-mm-thick scintillators in conventional single-ended readout scheme is difficult to achieve 100 ps CTR; the photon transport time spread (PTS) within the scintillator crystal is a major barrier. Differences in the interaction position result in variations in PTS on the order of several tens of ps, thereby degrading the CTR. A shorter scintillator can improve CTR; however, this can degrade detection efficiency. Approach. To overcome this trade-off between the CTR and detection efficiency, we previously proposed xDetector, an orthogonally stacked configuration along the longitudinal axis of scintillator crystals. We investigated the CTR potential of the xDetector by improving the scintillator, photodetector, and readout electronics, and by applying CTR correction based on a three-dimensional interaction within the scintillator. Main results. Based on error propagation, the CTR of the paired xDetector was calculated as 113.5 $\pm$ 2.7 ps FWHM. Furthermore, the CTR of the xDetector was measured at four positions along the longitudinal axis by manually sliding the xDetector, and the corrected achieved CTR was 108.6 $\pm$ 1.9 ps FWHM. Moreover, compared with the conventional single detector using a 20.0 mm scintillator, CTR improved by an average of 10.3%. Significance. The xDetector offers potential as a PET detector concept to achieve a CTR of 100 ps FWHM. Such timing performance is expected to improve TOF-PET image quality and quantitative accuracy, contributing to more reliable disease detection and diagnosis than current PET detectors.

Recently, experimental Raman spectra of the isotopologues $^{13}$C$^{16}$O$_2$ and $^{16}$O$^{13}$C$^{18}$O at high temperature have been reported. Separately, a reliable approach for obtaining vibrational wave functions for most isotopologues has been proposed. These descriptions, based on the $SU_1(2)\times U(3)\times SU_2(2)$ dynamical group, provide spectroscopic-quality fits to vibrational term values with root-mean-square deviations of $0.06$ and $0.07~\mathrm{cm}^{-1}$, respectively. Using the resulting wave functions, we simulate the Raman spectra of both species by evaluating transition moments of the mean polarizability, represented as an expansion in curvilinear coordinates up to cubic terms within the same algebraic framework. The difference of two independent experimental Raman spectra under comparable temperature conditions is analyzed with the help of the simulations provided by our model. The resulting simulations show excellent agreement with experiment, reinforcing the reliability of the model, while the estimated transition moments are also consistent with experimental values.

Electricity demand from data centers is expected to grow from roughly 5% of U.S. consumption in 2025 to between 9% and 17% by 2030, and corporate artificial intelligence (AI) use is following a similar trajectory, spanning employee productivity assistants, direct access to large language models (LLMs), and AI features embedded in enterprise software. AI emissions today are a small share of footprints for many enterprises, but that share is unlikely to remain small for long. Without reasonable estimates, companies cannot set reduction targets or identify effective decarbonization levers as emissions grow. Companies, regulators, and auditors are asking for emissions estimates that withstand scrutiny, but no widely accepted methodology exists today. Published per-query estimates can differ by several orders of magnitude depending on what is counted, which provider is measured, and what assumptions are made about electricity use and the grid mix. This white paper proposes a standardized framework for corporate-level AI emissions accounting. The framework is designed to be defensible with current data constraints, tiered to meet companies where their data are, transparent about its assumptions, updatable as provider disclosure matures, and built for action rather than disclosure alone. Since AI emissions accounting is still nascent, it has the opportunity to design for actionability from the outset, so that measurement incentivizes responsible choices during AI's rapid buildout.

The viscosity and density of organic mixtures are essential properties for designing lubricants, solvents, and heat transfer fluids. In engineering practice, formulating a functional fluid requires understanding how these properties change with composition and temperature. However, exhaustive experimental characterization across the full parameter space is impractical due to the vast number of possible species and combinations. Here we introduce a mixture-aware 3D molecular representation learning strategy, built upon a pre-trained molecular encoder, that jointly encodes component structures, mole fractions, and temperature to achieve accurate predictions for organic mixtures. Fine-tuning on publicly available datasets covering a wide range of binary organic mixtures yields test-set R2 values of 0.973 for dynamic viscosity and 0.996 for density, significantly outperforming traditional machine learning baselines. Beyond this overall accuracy, the model captures non-monotonic viscosity changes upon mixing, surpassing simple linear or logarithmic mixing rules. The architecture is extendable to ternary and multicomponent mixtures, as verified via preliminary experiments. Using this model, we quantitatively analyze how molecular structure-branching, cycloalkane, and aromatic rings-affects viscosity-temperature behavior, which benefits the design of lubricants with superior viscosity-temperature performance. Altogether, this work provides a practical, data-driven tool for mixture property prediction, accelerating the rational formulation of functional fluids in chemical engineering.

Ion irradiation creates optically active defects in wide-bandgap van der Waals materials, yet most approaches tune defect formation by varying the ion species, energy, or fluence while leaving the incidence geometry fixed. The ion-incidence angle is established here as a geometric control parameter for engineering visible emitters in hexagonal boron nitride (hBN). The angle and ion fluence of a plasma-focused heavy-ion (Xe+) beam are varied across hBN flakes of different thickness, and the resulting photoluminescence is quantified. In thick flakes, oblique irradiation shifts the fluence for maximum emission by nearly two orders of magnitude relative to normal incidence, whereas thin flakes exhibit an angle-independent optimum. Ion-trajectory simulations attribute this thickness dependence to lateral redistribution of the collision cascade and enhanced oblique sputtering. Atomic force microscopy identifies distinct processing regimes that delineate the useful defect-creation window. Post-irradiation annealing quenches the emission and shifts the spectral weight toward the green-yellow band while preserving the angle-dependent activation trends. Spectrally resolved lifetime measurements show comparable biexponential dynamics for normal and oblique incidence, consistent with emission from related defect families rather than a geometry-specific emitter species. These results establish ion-incidence geometry as a materials-level knob for programming optical defect activation and spatial defect distributions in van der Waals photonic materials.

Artificial intelligence (AI) is transforming materials discovery, yet conventional data-driven approaches often suffer from limited interpretability, poor extrapolation, and inconsistency with physical laws. Since materials behavior is fundamentally governed by thermodynamics, kinetics, electronic structure, transport processes, and operating environments, the next generation of materials intelligence must move beyond correlation-based prediction toward physics-grounded reasoning. In this Perspective, we systematically discuss Physics-Grounded Materials AI (PhysMat AI) as a unifying perspective for integrating physical knowledge into materials intelligence through five complementary roles: physics as prior knowledge, descriptors, constraints, verifiers, and infrastructure. Using representative examples from catalysis, solid-state electrolytes in solid-state battery, and hydrogen-storage materials, we illustrate how physical principles guide data representation, model reasoning, validation workflows, and knowledge management. We further present how AI agents can leverage these physics-aware components to perform mechanism-guided discovery within physically feasible search spaces. Finally, we outline a developmental roadmap from physics-aware AI to physics-reasoning AI and ultimately physics-autonomous AI. Looking forward, materials intelligence should evolve from predictive models toward autonomous scientific systems capable of integrating physical reasoning, multiscale simulations, experimental validation, and continuous knowledge updating for reliable materials discovery.

The coupled-cluster (CC) equations are most frequently solved via fixed-point (FP) iterations. However, when formulated in a non-canonical gauge, as in local correlation CC, the FP iteration may converge slowly or even diverge. Practical fixes, such as level-shifting and a direct inversion of iterative subspace (DIIS), often improve the convergence, but remain fundamentally heuristic and gauge dependent. Yang et al.~demonstrated that preconditioned Newton–Krylov (PNK) methods provide substantial wall-time advantage for canonical CC. In this work, we generalize the preconditioner to arbitrary gauges by replacing the energy denominator with a gauge-invariant formulation. Combined with Krylov-based approximate Jacobian inversion, the resulting framework removes the need for level-shifting and yields robust and efficient convergence across various gauges and challenging chemical systems. Our numerical results indicate that PNK consistently outperforms carefully optimized FP-based approaches across a range of molecular systems, positioning the proposed PNK method as a promising new standard for solving the CC equations.

Foundation machine-learning interatomic potentials (MLIPs) enable atomistic simulations at substantially lower computational cost than first-principles methods, but their reliability across structural geometries remains insufficiently understood. Here, we construct a density-functional-theory dataset of ZrO2 configurations spanning bulk, slab, particle, neck, and atomically thin wire environments motivated by an experimentally observed ZrO2 desintering process involving neck thinning and atomic wire formation. We first benchmark 26 pretrained MLIPs and observe pronounced geometry-dependent degradation in zero-shot predictions. Without any training, after only reference-energy alignment, the best zero-shot model (ORB-V3) reaches energy and force root-mean-square errors of 6 meV/atom and 197.3 meV/Å, respectively, with the largest force errors in neck and wire configurations. We then compare zero-shot inference, fine-tuning, and training from scratch strategies. Fine-tuning yields lower energy and force errors than training from scratch, while both require comparable wall-clock time. Geometry-specific fine-tuning improves in-domain accuracy but frequently produces negative transfer to other structural classes, whereas mixed-geometry fine-tuning reduces cross-geometry errors. Evaluations of elastic and vibrational properties, surface energies, and neck dynamics further show that rankings based on average energy and force errors do not universally predict property-level behavior. These results demonstrate that geometry-diverse target data and independent physical validations are necessary when adapting foundation MLIPs to low-coordination (ionic) nanostructures.

We present the design, development, and first calibration results of a compact beta electron detector for the Quantum Invisible Particle Sensor (QuIPS) experiment. The QuIPS electron detector is designed to reconstruct the full momentum vector of $\beta$ particles emitted from radioisotope-doped optically levitated nanospheres in ultra-high vacuum (UHV), enabling a measurement of the neutrino momentum and a search for heavy sterile neutrinos. The detector comprises two thinned CMOS detectors for directional tracking and a plastic scintillator read out by silicon photomultipliers (SiPMs) for calorimetry. The entire assembly must operate inside an existing optical trapping vacuum chamber at pressures below $10^{-7}$ mbar, imposing stringent constraints on material selection, power dissipation, outgassing, and compactness. We demonstrate sensitivity to $\beta$-decay electrons with energies as low as 100 keV and a detection efficiency of 35% above 500 keV, the primary window of interest for a heavy sterile neutrino search. The CMOS tracker resolves the momentum direction at the mrad scale and reconstructs the $\beta$ emission vertex with sub-mm precision, while the scintillator-SiPM system achieves an energy resolution of 5% at 1 MeV.

We describe the development, validation and implementation of a Quantum Interactive Learning Tutorial (QuILT) on quantum key distribution (QKD) using entanglement, a context which involves a practical application of quantum concepts relevant for the second quantum revolution. The QuILT helps students learn quantum concepts relevant for quantum cryptography using a simple two-particle system. The protocol uses two entangled particles and two Stern-Gerlach Apparati to generate a random shared key over a public channel for encrypting and decrypting information. It actively engages students in the learning process and helps them build links between the concepts learned in class and their real world applications. The QuILT was implemented as a homework in a traditional quantum mechanics course and a quantum computing and quantum information course. The evaluation suggests that the QuILT is helpful in improving students' understanding of the concepts related to QKD in both courses. Also, the evaluation of the QuILT in both types of courses with or without lecture-based instruction in relevant QKD concepts suggests that the QuILT can be given as a homework after lecture-based instruction on entanglement without in-class discussion on QKD. Therefore, entanglement can be covered without taking up much in-class time, while also providing students with an understanding of the QKD method and how it protects from eavesdroppers.

Erosion-induced landslide mobility is a prominent phenomenon in nature. However, this topic remains poorly understood, even though erosion greatly alters nearly every aspect of landslide mechanics, dynamics, run-out, mobility, deposition structure and the enormous destructive power carried by such catastrophic events. Here, I present a simple, elegant, and exact analytical model describing the kinematics of erosion-induced landslide mobility in terms of a newly constructed mobility controller containing essential forcing mechanisms in erosion. In contrast to the classical perspective, I formally prove that, irrespective of how large the erosion rate is, the erosion rate alone is not sufficient to determine enhanced or reduced mobility; mobility is also greatly regulated by the volume bulking rate, erosion drag or thrust, and flow depth. With this mechanical mobility controller, it is now possible to explain precisely when the mobility of an erodible landslide is enhanced or reduced. I demonstrate that, a mechanically valid, physically founded erosion model must be able to explain both the enhanced and reduced mobility of erosive landslides.

The law of conservation of energy can be stated: Energy can neither be created or destroyed but only converted to from one form to another. Laboratory experiments demonstrating that no energy is lost or gained during a conversion have been rare. Interestingly, there is a close connection between energy conservation, Lorentz violation and the possibility of a second dimension of time. We explore this connection within a model of Lorentz invariance that incorporates a second dimension of time. The model's Lorentz transformation matrix reduces to the well-known one-dimension of time version in the limit of a parameter that defines the relative size of the extra dimension. If the parameter is finite, one might misinterpret the energy equivalence of mass or observe a deviation from the accepted time dilation formalism. We explore different experimental limits on this new parameter including Lorentz violating parameters from the Standard-Model Extension.

This study applies Duan's topological current $\phi$-mapping theory to investigate a newly proposed charged modified AdS black hole using the Rényi entropy formalism, examining its thermodynamic properties across the canonical, mixed, and grand canonical ensembles.} Electric and magnetic charges are fixed in the canonical ensemble, whereas the mixed ensemble incorporates electric and magnetic potentials. The grand canonical ensemble, meanwhile, ensures consistency by employing these potentials exclusively. Initially, we compute topological charges by identifying critical points in each ensemble. In both the canonical and mixed ensembles, we identify a conventional critical point with a topological charge of $-1$. We then model the black hole's domain in the AdS regime as a topological defect in thermodynamic space and analyze its local and global topology by calculating the winding numbers at these defect locations. {Interestingly, the canonical and mixed ensembles exhibit identical black hole topologies, each possessing a total topological charge of $1$ within the framework of the Rényi entropy.} Finally, we characterize the canonical, mixed, and grand canonical ensembles based on the presence of generation/annihilation points: the canonical ensemble has one such point, the mixed ensemble has a negative one, and the grand canonical ensemble has none.

Accurate prediction of vapor–liquid equilibrium (VLE) for hydrocarbon-nitrogen mixtures remains challenging for cubic equations of state, particularly across broad ranges of composition and hydrocarbon chain length. While deep learning models can provide accurate predictions, they often lack interpretability and explicit analytical expressions. In this work, we propose a symbolic machine learning approach to discover interpretable symbolic corrections to Peng-Robinson equation-of-state (PR-EOS) predictions from experimental data. The proposed approach adopts a two-level strategy: symbolic expressions are first identified for individual hydrocarbon systems, after which their coefficients are represented as functions of carbon number to enable accurate prediction across different hydrocarbon systems. The results demonstrate significantly improved prediction accuracy over the original PR-EOS across all hydrocarbon-nitrogen systems. Overall, the proposed approach provides an interpretable symbolic correction framework for improving PR-EOS predictions of hydrocarbon-nitrogen VLE.

Accurate solar irradiance forecasting is essential for the reliable integration of photovoltaic power into modern electricity grids. All-sky imagers (ASI) provide high-resolution observations of clouds, making them well suited for intra-hour forecasting. Recent deep learning approaches have substantially improved forecast accuracy but are often limited by deterministic predictions and a reduced capability to anticipate ramp events. This work proposes FarSky, a generative forecasting framework that leverages latent-space coupling to learn task-aware representations of sky images. A multi-task autoencoder first learns a shared latent representation for image reconstruction and irradiance estimation. A latent diffusion model then generates future latent states conditioned on recent observations, from which irradiance forecasts are directly decoded. Probabilistic forecasts are inherently obtained through stochastic sampling. The framework is developed using a multi-year ASI dataset acquired at the Plataforma Solar de Almería, Spain, and evaluated on two independent test datasets against persistence, state-of-the-art end-to-end, and generative forecasting approaches. FarSky achieves the best overall deterministic and probabilistic forecasting performance, improving forecast skill by up to 11 percentage points. Furthermore, it substantially improves ramp event detection over existing methods, achieving F1-scores above 60%. These results demonstrate the potential of combining generative models with task-aware latent-space coupling for solar forecasting.

The in-Situ Thunderstorm Observer for Radiation Mechanisms (iSTORM) is a gamma-ray spectrometer to study gamma-ray transients originating from thunderstorms, such as glows and terrestrial gamma-ray flashes (TGFs). It is designed and built by the U.S. Naval Research Laboratory for deployment aboard a NASA ER-2 aircraft. Using an array of 32 one-inch-diameter $\mathrm{CeBr}_3$ scintillators read out with silicon photomultipliers (SiPMs), the instrument achieves an energy range of $\sim 250 \ \mathrm{keV}$ to $5 \ \mathrm{MeV}$ under flight conditions, with a total geometrical area of $157 \ \mathrm{cm^2}$. One of two gamma-ray instruments in the ALOFT campaign, iSTORM recorded glows, terrestrial gamma-ray flashes (TGFs), and the newly discovered flickering gamma-ray flashes (FGFs).

The transition to automated transportation introduces mixed-autonomy traffic where human drivers may strategically exploit the risk-averse behavior of Connected and Automated Vehicles (CAVs). While microscopic models capture these dyadic interactions, their aggregate impact on network-level stability remains unquantified due to the scale gap between agent-based and continuum models. This study bridges this analytical divide by integrating a game-theoretic friction term directly into the macroscopic kinematic wave framework. We model the cut-in maneuver as a Stackelberg game, identifying a distinct "exploitation window" where human drivers leverage CAV defensiveness to execute aggressive merges. By deriving a closed-form micro-macro bridge, we translate these discrete strategic outcomes into a continuous friction parameter that endogenously modifies the traffic conservation law. Theoretical analysis and numerical simulations confirm that this behavioral asymmetry functions as a deterministic destabilizer, generating perturbation source terms that trigger phantom jams and strictly reduce road capacity. Crucially, we reveal a convex relationship between CAV penetration and system efficiency, identifying a critical instability regime at intermediate penetration rates (approximately 45 percent) where the frequency of exploitable interactions is maximized. These findings demonstrate that without socially aware control policies, the defensive nature of early-deployment CAVs may paradoxically degrade traffic flow stability.

The equations of reduced magnetohydrodynamics (RMHD) isolate the Alfvénic energy transfer and turbulence dynamics from MHD in a computationally tractable way. In this study, we derive the equations of RMHD for a low $\beta$ plasma in a dipole coordinate system aligned with the background magnetic field using a multiscale analysis. The Kreiss theorem is used to derive the equilibrium conditions that the background conditions must satisfy on the time scale of MHD turbulence. From these, we find a connection between plasma flow along flux tubes and changes in the Alfvén speed that may drive nonlinear wave effects. The final equations demonstrate an intimate coupling between field aligned currents and plasma vorticity including the effects of nonuniform flux tube area and realistic plasma density profiles. This work has immediate application to the magnetosphere-ionosphere coupling problem in the Earth's magnetosphere as it provides a way to link dynamically evolving field aligned currents from the magnetosphere, especially important during geomagnetic storms and substorms, with the development of magnetohydrodynamic turbulence at low altitudes in a self-consistent manner. This also clarifies the role of Alfvén waves as a transfer mechanism of energy under these inhomogeneous circumstances while remaining simple enough to make predictions. Furthermore, this study makes use of a novel methodology, a computer algebra system, in performing the brunt of algebraic work under complicated coordinate systems. We hope this approach serves as a template for performing reproducible and verifiable multiscale perturbation analysis under arbitrarily complex geometries.

We present an experimental and numerical investigation of the quality factor degradation ($\Delta Q$) due to pump phase modulation and pump relative intensity noise (RIN) in single- and double-pumped fiber optical parametric amplifiers (FOPAs). These penalties are investigated in FOPAs constructed with fibers having constant and randomly varying zero-dispersion wavelength ($\lambda_0$) along the fiber. We show that pump phase modulation in uniform fibers (constant $\lambda_0$) produces large $Q$ penalties, while in fibers with variations of $\lambda_0$, these penalties can be strongly reduced in both single- and double-pumped FOPAs. We also show that $Q$ penalties due to pump phase modulation tend to disappear in FOPAs made with very short fibers ($L<0.1~\mathrm{km}$) and very low dispersion slope $ S_0 < 0.002~ \mathrm{ps}\, \mathrm{nm}^{-2}\, \mathrm{km}^{-1}. $ The $Q$ penalties due to pump RIN, which for typical FOPA parameters are much smaller than those due to pump phase modulation, are also reduced in non-uniform fibers (varying $\lambda_0$), but do not depend on fiber length nor on fiber dispersion slope.

Anticipating tipping points in complex systems is difficult because many early warning signals require long time series, which are often unavailable in practice. Spatial early warning signals offer an alternative by using a single snapshot across many interacting elements, or nodes. However, their performance in heterogeneous systems is often inconsistent because raw node states reflect both dynamical changes associated with an approaching transition and static heterogeneity induced by network structure. Here, we propose a baseline-referenced framework for spatial early warning signals. The method compares each node's state with its own baseline far from the tipping point before computing a spatial statistic, thus reducing network-structure-induced variation. We evaluate baseline-referenced variants of five classical spatial early warning signals across diverse tipping scenarios and networks, and find that baseline referencing markedly improves variance-based spatial signals. The best variants increase consistently and progressively toward tipping points across different scenarios, outperform a single-node temporal variance that requires long time series, and retain high performance even when up to 80% of nodes are omitted from observation. These results provide a practical route for using spatial early warning signals in heterogeneous networked systems when dense temporal monitoring or complete network-wide observation is infeasible, as is often the case in real applications.

The equations of fluid motion are known; retaining their physics after removing most degrees of freedom remains a fundamental problem. Closure must preserve the geometry of the dynamics it replaces. We introduce Symplectic Geometric Closure (SGC), a prognostic stochastic field theory carrying Euler's Hamiltonian, area-preserving transport into two-dimensional and $\beta$-plane turbulence. SGC couples resolved vorticity to a hidden stochastic reservoir. The coupling conserves augmented enstrophy while permitting bidirectional energy transfer. This yields a compact random attractor in the hyperviscous Navier–Stokes–$\beta$ realization. Numerically, SGC sustains jets, vortices and filaments in high-Reynolds-number turbulence with high fidelity to filtered DNS at an inertial-range cutoff, preserving geometric identities to machine precision. The induced transport folds, stretches and rearranges vorticity: cross-gradient coupling selects interactions through resolved–reservoir gradient misalignment, retaining geometric selectivity and memory. The same geometry addresses spurious sweeping decorrelation, a longstanding obstacle to Eulerian closure. The interaction vertex excludes uniform translation and is Random Galilean compatible. Eliminating the reservoir generates finite memory, stochastic backscatter and a Dyson–Volterra equation for the dressed propagator. Its one-loop, line-renormalized self-energy reproduces Kraichnan-type Direct-Interaction Approximation architecture with fourth-order infrared suppression of sweeping. Deformation-controlled memory and conservative triad constraints yield the $k^{-5/3}$ inverse-energy and $k^{-3}$ forward-enstrophy cascades under standard assumptions. SGC thus realizes Kraichnan's program through Eulerian field dynamics, opening a route to data-driven closures that learn admissible geometric interactions rather than unconstrained forces.

Internal electromagnetic fields determine the behaviour of dielectric nano-resonators, yet they remain exceptionally difficult to measure directly because subwavelength scales place them beyond conventional optical measurement, and any probe capable of local sensing perturbs the field it seeks to measure. We circumvent this limitation using water's high refractive index at microwave frequencies, which lets centimetre-scale aqueous spheres—grapes—serve as directly measurable, scale-invariant analogs of high-index nanophotonic resonators. Using polarization-controlled free-space excitation and infrared thermography, we show that internal heating patterns in grapes closely track simulated electric-, but not magnetic-, field distributions for both the fundamental magnetic- and electric-dipole Mie resonances. Extending this approach to grape dimers, we demonstrate that the massively sub-wavelength inter-object hotspot associated with microwave-induced sparking forms only under axial polarization, thus providing both a clue to its origin and a means for its control.

The bulk elemental abundances of Jupiter provide critical insights into its formation history and interior structure. Recent observations by the Juno Microwave Radiometer (MWR) reveal a deep Jovian atmosphere significantly depleted in electrons, implying an alkali metal (Na, K) abundance of 10^-1 - 10^-5 times solar. This depletion stands in sharp contrast to the supersolar volatile enrichments measured by the Galileo probe. We propose that this apparent depletion arises from mineral cloud-induced processes deep in the atmosphere. We explore two physical mechanisms using thermochemical and microphysical modeling. In the "chemical sequestration" scenario, vigorous vertical mixing lofts deep refractory condensates (e.g., spinel) into the 1000-2000 bar region, where they react to form alkali feldspars (albite) and feldspathoids (leucite), efficiently sequestering gaseous Na and K. In the "dust-catalyzed recombination" scenario, the bulk alkali inventory remains gaseous, but the free electron density is suppressed by dust-plasma interactions. Thermally emitted alkali ions from the surfaces of micron-sized iron and silicate grains significantly increase the cation density, driving rapid recombination of free electrons. Both mechanisms allow for a bulk solar or even supersolar alkali inventory while suppressing the electron density to match Juno observations. Analyzing an extended dataset of MWR observations with 61 perijoves, we detect spatial variability in the deep atmosphere that suggests modulation by mineral clouds. Our findings challenge the traditional rainout framework, unveiling a deep "mineralogical zone" in Jupiter shaped by dynamics and heterogeneous chemistry, resembling the photospheres of hot exoplanets and brown dwarfs.

The longest-wavelength channel of the Juno Microwave Radiometer (MWR) probes Jupiter's kilobar atmosphere through free electrons produced by sodium and potassium ionization. Under equilibrium chemistry the electron abundance is the small residual of the charge balance between alkali cations and the anions Cl- and HS-. Chlorine is not directly measurable in Jupiter's deep atmosphere because gaseous HCl is removed from the observable atmosphere by NH4Cl condensation, whereas sulfur has been measured by the Galileo probe. The MWR-derived electron measurement therefore constrains the alkali-to-chlorine ratio rather than the alkali abundance alone. We combine the MWR observations with equilibrium chemistry and microwave radiative transfer in a Bayesian framework, finding that the deep gas-phase elemental alkali-to-chlorine abundance ratio is (Na+K)/Cl = 0.05 over 0.3-5 times solar in chlorine, about 180 times below the protosolar ratio of 8.7. At 3 times solar chlorine, the inferred alkali metallicity is 1.6 x 10^-2 times solar (1 sigma: 1.2 x 10^-2 - 2.7 x 10^-2 times solar), while at low chlorine abundance HS- sets an alkali floor near 10^-3 times solar. The inferred gas-phase alkali abundance exceeds the ~10^-5 times solar threshold by more than two orders of magnitude and rules out the long-proposed global kilobar radiative zone. Because sodium and potassium are refractory whereas chlorine is volatile, the inferred ratio provides a new diagnostic of the rock-to-ice balance in the solids accreted by Jupiter. This compositional interpretation assumes equilibrium chemistry; if lofted mineral clouds instead control the electron abundance under disequilibrium conditions, the inferred alkali-chlorine relationship need not hold.

Speckle fundamentally limits coherent imaging by introducing multiplicative, spatially correlated noise that obscures scene structure. Removing speckle noise from dynamic scenes–that do not benefit from conventional speckle averaging–is particularly challenging. We introduce a training-free framework that combines a spatiotemporal implicit neural representation with an aperture-aware maximum-likelihood formulation to recover dynamic, speckle-free imagery directly from noisy observations. The coherent likelihood explicitly models the aperture-dependent spatial covariance of speckle, enabling adaptation to arbitrary pupil geometries without retraining. A matrix-free implementation based on FFT-accelerated operators, stochastic approximations, and conjugate gradients makes optimization practical for realistic image sizes. Meanwhile, a blind holdout criterion provides automatic early stopping without clean reference data. Simulated and laboratory results demonstrate improved spatial fidelity, temporal consistency, and robustness to varying speckle statistics relative to classical, unsupervised, and supervised baselines.

The quantum lattice Boltzmann scheme of Succi and Dellar advances a four-component Dirac spinor on a lattice by a fixed sequence of local, exactly norm-preserving operations: a basis rotation, a collision, a streaming shift, and the inverse rotation. While this correspondence has been noted qualitatively, it has remained unclear whether it can be realized exactly and unitarily as a gate-model quantum circuit. This unitarity is a structural property of the scheme, which should, in principle, enable an operator-by-operator port onto quantum hardware. We give a gate-level construction of every operation of the three-dimensional Dirac QLB scheme: the fixed rotation gates, the collision gate, the streaming shift as a controlled increment on a position register, the position-dependent potential as a phase oracle, and periodic and reflecting boundary conditions as unitary circuits. Together, these constructions provide the first complete demonstration that the scheme is exactly representable in the gate model without any approximation beyond the classical method itself. We then compose them into single-axis, two- and three-dimensional time steps. On a state-vector emulator the resulting circuits reproduce the classical QLB solver to machine precision, thus verifying our quantum implementation. Our aim is representability and correctness rather than computational speedup; questions of state preparation, asymptotic cost, and hardware noise are identified as open avenues for future work. Appendices restore physical units, derive the scheme's Zitterbewegung in closed form and compare it with a trapped-ion simulation, and execute a reduced two-site instance on a Rigetti superconducting processor, where choosing the qubits from the live calibration lowers the state-preparation error. All operators, circuits, and figures are reproducible from the open-source quantumKineticMethods library.

This study investigates the influence of the spatial distribution of flexibility along a propulsor on thrust generation and propulsive efficiency in wave-assisted flapping foils. While flexibility is known to enhance propulsive performance, the role of its chordwise placement remains poorly understood. Here, the effective flexible length is systematically varied by shifting the flexure location along the tail while maintaining constant flexural rigidity and total chord length. Experiments are conducted in quiescent flow at heave frequencies of 0.8 Hz and 1.25 Hz, and non-dimensional heave amplitudes of h* = 0.13 and 0.22. Simultaneous measurements of hydrodynamic forces, flow fields, and tail kinematics are used to quantify performance and elucidate the underlying fluid-structure and fluid-particle interactions. The fully flexible configuration consistently achieves higher propulsive efficiency (up to approximately 164%) across all conditions, which is attributed to enhanced jet persistence and increased streamwise vortex spacing, indicative of a more coherent and sustained momentum jet. In contrast, the mid-flexible configuration yields substantially higher thrust (up to approximately 66%) at the largest heave frequency and amplitude, driven by a pronounced increase in near-wake jet velocity and momentum flux. These results demonstrate that the chordwise distribution of flexibility governs the trade-off between thrust and propulsive efficiency by modulating wake coherence and momentum transfer. The findings establish flexibility placement as a key design parameter in flapping propulsion and provide physics-based guidelines for enhancing the performance and endurance of wave-driven unmanned surface vehicles.