Papers

Classical PET glycolysis struggles with industrial scale-up due to a research paradigm defect: uncoupled reaction optimization that ignores system-level engineering entropy. By establishing a governing reference frame, Scientific Reasoning Units (SRUs) and human-AI collaborative discovery in this article, we present a Three-Step Synergistic Reinforcement Paradigm addressing the triple failure modes of traditional systems-mass-transfer collapse, viscosity deadlock, and side-reaction accumulation. By integrating twin-screw controlled shearing, solid-state micro-homogenization, and deep depolymerization (2:1 EG/Oligomer ratio), we achieve an ultrafast kinetic regime. BHET yields surge to 80% to 95% within 10 minutes, eliminating multi-hour thermal bottlenecks and delivering an economically viable framework for closed-loop polyester recycling.

Gridded-dataset suitability varies by support, season, variable, and application. This study evaluated E-OBS, AgERA5, MARS-STAT/JRC Agri4Cast, ERA5, and ERA5-Land against observations from 624 European Climate Assessment & Dataset (ECA&D) and Hellenic National Meteorological Service (HNMS/EMY) stations, using 18.18 million source-station-day records containing minimum (TN) and maximum (TX) temperatures. Derived variables included mean temperature (Tmean), diurnal temperature range (DTR), and growing degree days (GDD). Identical-support rankings and station bootstraps assessed daily and GDD performance; Fisher's z transformation summarized seasonal correlations; temporal and network tests screened trends. E-OBS minimized daily and monthly-climatology-removed anomaly root mean square error (RMSE) for TN, TX, Tmean, and DTR in all 16 country-variable comparisons. With at least 10 matched years, E-OBS led 42 of 64 seasonal RMSE comparisons and 59 of 64 correlation comparisons; MARS-STAT/JRC Agri4Cast led the remaining 22 and five, respectively. E-OBS minimized GDD RMSE in Spain and Italy, whereas MARS-STAT/JRC Agri4Cast led in Greece and narrowly led in France. GDD RMSE equaled 3.3-7.2% of observed mean GDD. Robustness tests supported increases in Spanish annual DTR, French annual TX, and French growing-season GDD. The lapse-rate correction improved 22 of 40 comparisons but usually worsened TN. Dataset choice should reflect support, variable, season, terrain, and application.

Despite the remarkable observational success of the $\Lambda$CDM model, the physical origin of dark energy and the cosmological constant remain unresolved, motivating the exploration of dynamical vacuum scenarios within modified theories of gravity. Inspired by the quantum field theoretical description of running vacuum energy, we investigate a running vacuum cosmology in the framework of linear $f(R,T)=R+\lambda T$ gravity. Unlike recent studies that establish an effective correspondence between running vacuum energy and modified gravity, our approach directly incorporates a quantum field theory motivated running vacuum scenario into the $f(R,T)$ framework and examines its cosmological implications. Exact analytical solutions of the modified field equations are obtained in a spatially flat Friedmann–Lemaître–Robertson–Walker spacetime, yielding an analytical expression for the Hubble parameter. The free model parameters are constrained using the latest Pantheon+SH0ES compilation, DESI baryon acoustic oscillation measurements, and their joint dataset through Markov Chain Monte Carlo analysis. The observationally constrained model successfully reproduces the observed expansion history, remains consistent with independent Cosmic Chronometer measurements, predicts an age of the universe within current observational bounds, and yields an effective equation of state compatible with current observations. Furthermore, the statefinder and $Om$ diagnostics indicate close agreement with the $\Lambda$CDM scenario at late times, while the generalized second law of thermodynamics remains valid throughout cosmic evolution. These results demonstrate that the proposed framework provides an observationally viable and thermodynamically consistent realization of running vacuum cosmology in $f(R,T)$ gravity, offering a theoretically motivated alternative for describing the present accelerated universe.

There is a longstanding dogma in the physics of ultracold neutrons (UCN): The optimal temperature of ortho-deuterium based UCN-sources must not exceed $5\,\mathrm{K}$. This value has been established by the pioneering work of Golub et al. decades ago, utilizing previously established cross sections for solid ortho-deuterium. But recently, an improved calculation of the crucial 1-phonon up-scattering cross section - using the so-called corrected Incoherent Approximation - leads to a new upper limit for the optimal operation temperature of ortho-deuterium based UCN sources. After a discussion of the relevant cross sections of the \(\mathrm{D}_2\)-molecule we conclude that the optimal temperature lies between $10\,\mathrm{K}$ and $12\,\mathrm{K}$, a factor 2 higher than estimated earlier. This offers significantly relaxed cryogenic conditions for UCN experiments with solid ortho-deuterium and especially for the operation of ortho-deuterium based ultracold neutron sources.

We describe two open-source, 3D-printable, flexure-based tools for the quantitative measurement of extensional properties of viscoelastic fluids. These low-cost, portable, and scalable devices (which we have termed “PocketCaBER” and “PocketDoS”) are particularly applicable for use in the field and in graduate-level teaching environments due to their low cost, printability on hobby 3D printers, compatibility with cell phone cameras, portability and user-friendly operation. We characterize and benchmark each device's performance against its lab-equivalent counterpart and provide downloadable STL files for rapid fabrication. We discuss experimental limitations of these devices compared with their bench-top counterparts. Finally, we illustrate the use of such tools in facilitating student engagement in polymer science and complex fluids classes—specifically, how progress in learning goals can be uniquely and effectively accelerated by providing the necessary rheological instruments directly to each individual student (especially for advanced modules such as nonlinear extensional rheology). By giving students personal, indefinite access to laboratory-level instrumentation through these open-source frugal science tools, we discuss our efforts to expand participation and engagement within the field of nonlinear rheology.

Following the molecular discovery and synthesis revolutions, scalable automated structure elucidation from routine spectroscopic data remains an outstanding challenge. Despite decades of computational efforts, no existing system achieved reliable reasoning over unseen spectra. Here, we propose MACROS, a multi-agent system automating structure elucidation by emulating expert iterative hypothesis-testing. Trained on 100M simulated and 1.6M experimental spectra-molecule pairs, it natively supports arbitrary combinations of routine spectroscopic techniques. It achieves unprecedented zero-shot generalization to diverse real-world samples, correctly identifying synthetic compounds, natural products and metabolites above 500 Da with 1D NMR. Remarkably, MACROS spontaneously recovers textbook spectroscopic correlations from unassigned data and exhibits emergent chemical intuition such as a ring-first parsing preference, learning fundamental chemical principles rather than memorizing database patterns. MACROS augments chemists via collaboration to deliver sixfold faster, 40% more accurate elucidation. MACROS establishes a scalable foundation for fully automated structure elucidation, and catalyzes accelerated molecular discovery toward autonomous laboratories.

The Hadley circulation is Earth's dominant tropical overturning circulation, regulating atmospheric energy transport, tropical rainfall, and subtropical aridity. Although its variability has been extensively studied on seasonal to decadal climate-change timescales, its subseasonal behaviour remains poorly understood. Here we show that the Madden-Julian Oscillation (MJO), the leading mode of tropical intraseasonal variability, systematically modulates the Hadley circulation and influences global hydroclimate variability. Using reanalysis data and dynamical diagnostics, we identify a robust hemispherically asymmetric Hadley circulation anomaly associated with active MJO events, with amplitudes comparable to the climatological intraseasonal variability of the Hadley circulation. Breaking down the dynamical components reveals that the response is primarily maintained by latent heating associated with moist convection, while the overturning strength is driven by interhemispheric moisture gradients. Lead-lag analyses further show that the Hadley circulation lags the MJO by 4-15 days, indicating that MJO convection may drive overturning adjustments on subseasonal timescales. This coupled MJO-Hadley circulation state reveals a previously overlooked pathway linking tropical intraseasonal oscillations, meridional circulation and the hydrological cycle.

Beam abort by shutting off the rf cavities is a widely used machine-protection scheme in modern diffraction-limited synchrotron light source storage rings. In this process, the stored beam loses energy turn by turn until it is intercepted by a dedicated absorber. A key parameter in this process is the number of turns after the rf shutdown until the subsequent beam loss, especially for the beam-size blow-up abort scheme. Despite its importance for designing an abort protection system, this quantity has not been characterized at high stored currents. We report measurements of abort turns over a broad current range from 3 to 400 mA in the 3-GeV NanoTerasu storage ring. The results show a clear current dependence: the beam is lost significantly faster at higher stored currents, with the number of turns until beam loss reduced from 435 at 3 mA to 187 at 400 mA. Our results indicate that transient beam loading induced by the aborting beam in empty rf cavities is the primary mechanism responsible for the enhanced energy loss. The number-of-turns behavior is not simply proportional to the beam loading at high current but saturates. Time-resolved cavity pickup signals, together with tracking simulations and analytical modeling, quantitatively reproduce the observed trend. Our experimental results and theoretical modeling demonstrate that transient beam loading significantly influences abort dynamics in high-current fourth-generation storage rings, emphasizing the need to incorporate this effect into machine-protection system design. Our approximate extension of the steady-state cavity-beam response to beam-abort transients reproduces experimental results well, indicating that the number of turns during a beam abort can be predicted purely numerically.

Purpose: In conventional coplanar radiotherapy for cervical cancer, nearby pelvic bones receive high radiation doses, increasing the risk of acute hematologic toxicity (HT). This study aims to estimate the HT risk reduction achievable with non-coplanar (4$\pi$) radiotherapy. Methods: We retrospectively analyzed 114 cervical cancer patients treated with coplanar volumetric modulated arc therapy (VMAT) between 2021 and 2023. Radiomic features from planning CTs were extracted and combined with clinical and dosimetric data to train predictive machine learning models. Patients were then replanned using integrated non-coplanar beam orientation and fluence map optimization (4$\pi$ planning). The top performing model was applied to these new plans to evaluate potential HT reduction. Results: Models combined radiomics, clinical, and dosimetric features achieved the highest predictive performance (AUC = 0.79). Feature importance analysis highlighted CT radiomic and dosimetric variables as the strongest predictors. Compared to VMAT, 4$\pi$ non-coplanar planning significantly reduced doses to the bone marrow (V10Gy, V20Gy, V30Gy, and V40Gy by 28%, 52%, 47%, and 33%, respectively) while significantly reducing dose to other pelvic organs at risk (OAR). When evaluated by the model, this improved dosimetry translated into a 23% reduction in predicted HT risk (risk ratio: 0.77; 95% CI: 0.75-0.79 and odds ratio: 0.68; 95% CI:0.65-0.71). Conclusions: Non-coplanar 4$\pi$ radiotherapy significantly lowers radiation doses to major pelvic bones during cervical cancer treatment without compromising target coverage or sparing of other OARs. Based on our predictive model, this superior dosimetry should translate to a marked reduction in acute hematologic toxicity.

Free-space quantum communication provides a flexible complement to fiber-based quantum networks, but its point-to-point capacity is fundamentally limited by diffraction, atmospheric extinction and beam wandering induced by turbulence. In this work, we study the end-to-end performance of large-scale free-space quantum networks connecting randomly distributed fixed or mobile users, modelled as Waxman random graphs. We derive the mean network capacity, edge consumption and connectivity phase transitions for both single-path and multi-path (flooding) routing. We also study router-centered star networks, deriving the full distribution of end-to-end capacities as a function of the router's coverage radius. We then consider how performance may be improved by reinforcing free-space networks with a small number of optimally placed fiber-based backbone nodes. We prove that any optimal backbone configuration must correspond to a capacity-maximizing Voronoi tessellation of the network region, and show that this can be efficiently approximated by a centroidal Voronoi tessellation via Lloyd's algorithm, with backbone nodes connected according to a Delaunay triangulation. Numerical results show that even a modest number of backbone nodes substantially improves end-to-end capacity and reduces edge consumption for both mobile and fixed users.

Structure-preserving machine learning embeds physical structure directly into model architectures, yet uncertainty quantification (UQ) for such hard-constrained models remains limited because standard UQ methods may violate the encoded admissibility conditions, require architectural modifications, or impose substantial computational costs. In this work, we propose Structure-Preserving Epistemic Neural Networks (S-PENNs), a general framework for UQ in scientific machine learning models with hard architectural constraints, and instantiate it for GENERIC (General Equation for Non-Equilibrium Reversible-Irreversible Coupling) dynamics. S-PENNs preserve the structural constraints of a pretrained model by attaching lightweight epinets to its constrained components, ensuring that every sampled realization remains physically admissible by construction. When applied to GENERIC dynamics, such a proposed framework yields thermodynamically consistent rollouts that preserve the first and second laws. Furthermore, we combine S-PENNs with split conformal prediction as a post-hoc calibration method to produce prediction intervals with finite-sample marginal coverage guarantees. We validate S-PENNs on three numerical examples: a harmonic oscillator coupled to a heat bath and an idealized chemical motor, both governed by ODEs, and a one-dimensional viscoplastic model governed by PDEs. Across all three examples, S-PENNs produce thermodynamically consistent stochastic realizations and well-calibrated prediction intervals while reducing the computational cost by about one to three orders of magnitude compared to deep ensembles. Although the present study focuses on GENERIC dynamics, S-PENNs can be extended more broadly to scientific machine learning models in computational mechanics with either hard or soft constraints.

Diffusive shock acceleration, at shocks from coronal mass ejections to supernova-remnant blast waves, presupposes a scattering wave field that the accelerated particles themselves maintain. This self-regulation has not been resolved in situ. We report Parker Solar Probe observations of a fast (~2800 km/s), near-parallel interplanetary shock at 0.24 AU on 2023 March 13 and separate its upstream wave field into four families, a classification not made before at a fast shock near the Sun. Right-hand and left-hand circularly polarized families over a common wavenumber band, with a field-aligned linearly polarized family, are cyclotron-resonant with the suprathermal-to-MeV protons streaming from the shock: the beam drives the field that scatters it, and the measured mean free path, half the precursor scale, leaves the beam anisotropic enough to sustain the drive. Outside this loop lies a weak, oblique, linearly polarized component, a few per cent of the wave power, resolved here for the first time at an in situ foreshock. Its in-phase density and field-magnitude fluctuations identify the compressive part as fast magnetosonic and shift the cyclotron-resonance energies of the resonant families by up to 13 % along the precursor. Acceleration at shocks inside 0.3 AU is governed upstream, in a foreshock the shock builds for itself.

Calibrating computational models to experimental data is a core task in applied statistics, especially in scientific domains, where physical experiments are costly and simulations play a central role in design and inference. Motivated by uncertainty quantification challenges in particle accelerator experiments, we develop and evaluate a Hierarchical Bayesian Calibration framework. In contrast to standard Bayesian calibration, certain inputs - such as beam injection amplitude - must be estimated separately for each experiment. We adopt the Kennedy-O'Hagan formulation and extend it with a hierarchical prior structure to model the distribution of experiment-specific calibration parameters, thus borrowing strength and improving generalisation across repeated experiments. A key methodological challenge arises from the need to evaluate a large number of forward simulations, which renders conventional Markov chain Monte Carlo approaches computationally prohibitive. To address this, we leverage the Bayesian Committee Machine as a scalable modelling strategy for Gaussian Process emulators. The BCM provides a principled divide-and-conquer approach, enabling parallel inference and reducing computational cost without requiring problem-specific tuning of the emulator approximation. Posterior sampling is performed using the No-U-Turn Sampler, supported by automatic differentiation in Julia, which removes the need for analytic gradient derivation and facilitates flexible model specification. We assess the proposed framework using established benchmark problems and simulated data from the Argonne Wakefield Accelerator. The results demonstrate substantial computational savings and robust calibration performance, highlighting the applicability of the method to large-scale scientific modelling problems.

The probe position of a microstrip element is normally chosen from the isolated-element input resistance. This paper replaces that procedure with a decision available before any array optimisation. A single Floquet unit-cell solution at one arbitrary probe position is decomposed into a feed inductance, a transformer ratio carrying the probe position, and an array-loaded resonator. Three closed-form results follow. The set of input impedances reachable by probe position and resonant length is a disk in the impedance plane. An exact match to a real reference Z0 exists if and only if Rp >= Z0 + Xp^2/Z0. When this fails, the best attainable reflection follows from the image of that disk under the bilinear map. Admitting one series reactance as a second variable, the worst-case reflection over a scan sector is shown to be quasiconvex and globally solvable by bisection. The results are applied to a sixteen-element X-band array and the corresponding 16x24 lattice. For the array considered, the isolated interior element is matched to -16.94 dB while the broadside active reflection is -6.6 dB. For the two-dimensional lattice the criterion is violated at every scan angle, and the minimax design improves the worst-case sector reflection from -2.6 to -11.1 dB. Both matching designs are predictions of the extracted circuit model and have not been re-solved in the full-wave model.

Protecting confidential information requires not only preventing unauthorized access to encrypted data but also concealing the very existence of the protected information. In this work, we propose a computational dual-layer optical security framework that integrates optical encryption and steganographic camouflage into a unified strategy. The proposed method is based on a 4F optical architecture and employs two two-dimensional private keys: a phase-only key represented through Circular Harmonic Components (CHC) and a periodic amplitude mask acting as a second secret key. Their combined action generates visually diverse steganograms from encrypted RGB images while preserving the correct recovery of the original information by authorized users. Unlike conventional optical encryption methods that produce easily recognizable cryptograms, the proposed approach disguises the encrypted information within camouflage patterns, providing an additional layer of protection before any decryption process is attempted. Numerical simulations demonstrate successful encryption, camouflage, and image recovery while showing that different steganographic appearances can be generated by modifying the private key and the periodic-mask parameters. Furthermore, a prospective optical implementation based on a Mach–Zehnder interferometer and digital holographic recording is presented, providing a feasible path toward future experimental realization. The proposed methodology is introduced as a proof of concept of the dual-layer optical security framework; a comprehensive cryptanalytic evaluation is beyond the scope of this first study and is left for future work.

We introduce a universal bound on the separable powers of a wave field after passing through arbitrary passive optical or wave systems. Any wave field can be expressed as a combination of mutually incoherent and mutually orthogonal components, each with some power or "brightness". We prove that, in passing through a lossless or lossy optical system, writing the components in order of power, the power in each such component at the output cannot exceed the power in each such component at the input, even though each resulting output may be an arbitrary mixture of the inputs. This result encompasses previous brightness theorems, has several immediate consequences, and gives a simple limit to the concentration of light, radio-frequency or other waves into single-mode outputs.

We present the fabrication and characterization of the first silicon-platelet feedhorn arrays produced using laser micromachining. First, we present a demonstration of the technology for the millimeter-wave band of 80~GHz to 170~GHz, i.e. covering the 90/150~GHz bands typical of CMB experiments. Next, we expand the technology to large-scale production on 150~mm wafers and demonstrate operation at submillimeter wavelengths. This feedhorn array is optimized for operation in a band centered at 350~GHz (330~GHz to 370~GHz) and is being deployed as one of the focal plane elements of the CCAT 350~GHz module of Prime-Cam. We present the design and fabrication processes for these feedhorn arrays and compare the optical performance directly to simulation and to feedhorns of identical design but produced using traditional deep reactive-ion etching (DRIE). We conclude with a discussion of future expansions of this technology, including the potential of sidewall control and using thicker (and thus fewer) wafers, which could significantly reduce production costs and labor.

Electronic excitation induced by nuclear motion is a key energy dissipation channel in chemical dynamics at metal surfaces. Here, nonadiabatic effects can be treated via molecular dynamics with electronic friction, where they act as frictional drag and fluctuation force contributions. Commonly, the Markov approximation is imposed, so memory effects are ignored. A theoretical formalism is presented to evaluate tensorial and configuration-dependent electronic friction memory kernels from first principles. We evaluate friction kernels for Newns–Anderson Hamiltonian models as well as within Kohn–Sham density functional theory and analyse their mathematical properties and configuration dependence. For hyperthermal atomic and diatomic scattering, memory effects arising from frequency and configuration dependence of electronic friction affect energy exchange between adsorbate and metal electrons. Memory effects lead to an increase of vibrational and a reduction of translational energy loss in the case of nitric oxide scattering on Au(111), leading to an increase of directional anisotropy of friction. Importantly, memory-dependent evaluation of electronic friction removes the need to define a single effective Markovian friction coefficient from the structured frequency-dependent electronic response.

The coupled dynamics of ions and water molecules in their first hydration shell impact a variety of processes including ion diffusion, selective ion transport in water-filled nanopores, and the kinetics of ion-pairing, ion adsorption, and metal-ligand binding reactions. In this work, we study these coupled dynamics for alkali metals (Li, Na, K, Rb, Cs), alkaline Earth metals (Mg, Ca, Sr, Ba), and chloride through the lens of their dependence on ion isotopic mass. Results are validated against previous measurements of the isotopic mass-dependence of ion diffusion coefficients in water and previous ab initio calculations of ion high-frequency dynamics in water. We find that the vibrational power spectra of ions in water consistently exhibit either two or three peaks, i.e., ions have several rattling frequencies within their solvations shells as previously reported for a subset of the species examined here. These frequencies have different sensititivies to isotopic mass that may serve as signatures of ion solvation processes (such as the tendency of ions to orient their first-shell water molecules) and that also may relate to Hofmeister-like effects including the relative affinity of different metals for ribonucleic acid (RNA).

Motivated by the application of spray combustion in aviation industry, this work investigates the dispersion of non-spherical droplets in turbulence. The most common strategy for modeling sprays relies on LPT method, which represents the spray as a discrete collection of spherical particles. One limitation of LPT is that it neglects the influence of droplet deformation on spray dynamics. Prior studies have highlighted the importance of non-sphericity in droplet vaporization, combustion and drag coefficient. However, these works are restricted to idealized configurations such as an isolated droplet in a uniform flow. To study droplet deformation in a more realistic configuration, we adopt homogeneous isotropic turbulence (HIT) as the framework to investigate its effect on droplet dispersion. Droplets of various Stokes number are studied to investigate the interplay between deformation and inertia. Analysis of droplet statistics reveals that the impact of droplet deformation on both dispersion and clustering is dependent on the inertia regime. For weakly-inertial droplets, deformation weakens both dispersion and preferential concentration, whereas for strongly-inertial droplets, deformation tends to enhance preferential concentration while weakening dispersion. The results also suggest that to achieve the same level of clustering, deformed droplets require a higher Stokes number. Interestingly, for non-inertial droplets, the deformation seems to induce an effective inertia. This is verified by a comparison between the full unsteady TAB model and its steady-state limit, which suggests that unsteady shape dynamics affect temporal correlation statistics, but leave the mean clustering pattern unchanged. These findings demonstrate that accounting for droplet deformation and its unsteady shape oscillation is essential for accurately predicting droplet dispersion and clustering in turbulence.

Compact heavy ion accelerators have numerous applications, ranging from heavy ion fusion to carbon ion radiotherapy, and testing radiation-hardened electronics. The demand could be met by developing high-gradient traveling wave plasma accelerators of high-charge ($\sim\mu\mathrm{C}$) relativistic ion beams. We will discuss a novel ion acceleration regime – Counter-propagating ionization Front Acceleration (CFA) – utilizing counter-propagating Ionization Front (IF) and high-current Relativistic Electron Beam (REB). Theoretical modeling and 3D PIC simulations demonstrate the possibility of using typical REBs produced by induction voltage adders propagating through a gas-filled tube undergoing laser ionization to achieve acceleration gradients in excess of $\sim 250 {\rm MeV/m}$ while accelerating micro-Coulombs of ions over meters distance. A unique energy conversion mechanism – from the REB to electromagnetic fields to the ions is discussed, as well as the limits on the accelerated ions charge and the degree of its neutralization, acceleration gradient, and ion energy spread.

In our previous work, we proposed and experimentally demonstrated a physics-based simulator for combinatorial optimization problems (SSBM), which exploits a distinctive spontaneous symmetry breaking (SSB) phenomenon identified in a dissipative system exhibiting robust causality described by an iterative equation. We further conducted numerical simulations in which initial fluctuations of distinct patterns were applied to large-scale benchmark problems, and reported that the SSBM exhibits a unique behavior absent in conventional Ising machines: under specific conditions, pseudo-spin patterns progressively converge during the solution-search process and ultimately coalesce into a single pattern, with the cut value of the converged solution reaching 99.7% of the best-known cut value. In the present paper, we elucidate the conditions under which the SSBM can be configured to emulate the behavior of a continuous-variable Ising machine. We further demonstrate that by additionally incorporating an effect analogous to physical annealing into these conditions, the SSBM becomes capable of identifying the best-known cut value for the K_2000 benchmark instance.

This study examines Artificial Intelligence (AI)-generated physics solutions from two connected perspectives: how prompt design shapes these solutions and how students can be prepared to critique them. Using a rotational-mechanics problem, we adapted a problem-classification framework to examine prompt variations, evaluating OpenAI's o4-mini responses with the Minnesota Assessment of Problem Solving (MAPS) rubric. Well-specified prompts improved solution completeness; underspecified and multimodal prompts exposed weaknesses in physics reasoning and correctness. In the student-evaluation phase, 24 introductory physics lab groups evaluated an o4-mini solution to this problem after either independently solving a related problem or critiquing its AI-generated solution with MAPS-based reflection questions. Problem-solving-only groups exhibited uncritical or misconception-based critiques; MAPS-guided groups identified more expert-aligned issues, including skipped numerical procedures and undefined notation. Together, our findings contribute to physics education research by showing how AI-generated solutions can ground both model-reasoning benchmarks and improved student critique of that reasoning through MAPS-based reflection.

At every scale they occupy, magnetic fields affect various phenomena, including star formation, cosmic ray transport, charged particle acceleration, space weather, transport in planetary atmospheres, and laboratory plasmas. These fields are often generated and sustained by turbulent flows in a process called the dynamo. In 1955, E. N. Parker parameterized the effects of small-scale turbulence to propose a mean-field dynamo theory. The widely used theory reproduces observed large-scale fields but suffers from difficulty in tuning parameters as they are not justified from first principles: Studies of turbulent flows show tangled magnetic fields, which are folded and fragmented into small-scale structures due to shear-flow straining. Here, considering a shear flow that is unstable and driven, we develop analytic theory and perform three-dimensional (3D), advanced computer simulations of turbulence with up to 4096 x 4096 x 8192 grid points, showing ab initio generation of quasi-periodic, large-scale magnetic fields. The generation occurs via the mean-vorticity effect—an additional mean-field dynamo process postulated in 1990. Crucial to this dynamo is the prior generation of large-scale 3D jets, robustly produced as topologically protected and exact nonlinear solutions of the magnetohydrodynamic equations. The jet-driven dynamo applies to shear-driven laboratory and astrophysical systems. These include binary neutron star mergers, where the reported dynamo likely operates on microsecond timescales to produce in milliseconds some of the strongest magnetic fields in the Universe, providing signals for multimessenger astronomy.

Problem posing, a pedagogical practice that asks students to generate novel problems or meaningful variations to problems encountered, supports transfer of learning and strengthens problem-solving skills in physics. However, generating physics problems can be challenging, particularly for novice learners. This study investigates students' perceptions of an approach to facilitate their use of Generative AI in ways that maximize their benefits and limit risks. Students were introduced to Generative AI-assisted problem posing as a self-study technique. This study utilizes a phenomenological approach to investigate students' perceptions on how training shaped AI interactions, attitudes towards problem posing with Generative AI, and how students view its incorporation into personal study practices. Results of this study suggest that students perceived a positive change in their interactions with Generative AI after receiving training on prompt engineering techniques. This study also reveals that students hold generally positive views towards the problem-posing technique, with a smaller subset of students showing hesitations towards using Generative AI. These results lay the foundation to introduce and employ training methods for Generative AI more widely and to continue to incorporate Generative AI into structured study techniques, like problem posing.

Accurate numerical simulation of rarefied hypersonic plasmas is increasingly important for optimization of re-entry spacecraft design and the development of advanced aerospace technologies. For kinetic simulation methods, it is convention to enforce ions and electrons to diffuse at the same rate, known as the ambipolar diffusion approximation. This approach circumvents costly resolution of fast electron motion, but neglects the complex plasma dynamics of ions and electrons. Almost all studies that investigated the efficacy of the ambipolar diffusion approximation in hypersonics report noticeable differences in flowfield properties when electrostatic modeling is used, including increases in vehicle surface heat flux and decreases in electron temperature. However, it is unknown whether these reported differences originate directly from acceleration and deceleration of charged species through the electric fields and momentum-exchange collisions between charged and neutral species, defined as first-order effects, or from subsequent interactions with particles experiencing first-order effects, defined as second-order effects. Kinetic hypersonic flow simulations with electrostatic modeling are performed with argon to quantify the validity of the ambipolar diffusion approximation in terms of capturing first-order plasma effects along a one-dimensional stagnation streamline. Three different plasma diffusion regimes are studied under two sets of rarefied freestream flow conditions. The approximation is evaluated in terms of predicting plasma density distributions, electron temperature, and stagnation point heat flux. New criteria are proposed for identification of plasma diffusion regimes in hypersonic flows and use of the ambipolar diffusion approximation.

Wave-assisted propulsion (WAP) systems harvest ocean wave energy to generate propulsive thrust, offering a promising approach for improving endurance and energy efficiency of marine vehicles. Previous studies have focused primarily on monochromatic or unimodal wave conditions, leaving WAP performance in realistic ocean environments largely unexplored. This study investigates the hydrodynamic and hydroelastic response of a submerged flapping hydrofoil operating in bimodal sea states generated by the coexistence of swell and wind-sea wave systems. High-fidelity fluid–structure interaction simulations are performed for representative calm, transitional, and storm conditions, with passive pitching provided through a torsional spring. Simulations show that, despite increased complexity of bimodal wave forcing, propulsion performance follows the same effective peak frequency scaling previously established for monochromatic and unimodal waves, demonstrating the robustness of this scaling framework across a broad range of sea states. The findings further reveal that while the optimal normalized tuning ratio remains within a narrow range, dimensional torsional spring stiffness varies with sea state characteristics, highlighting the need for adaptive hydroelastic tuning to maximize thrust. Overall, the results demonstrate that WAP systems provide a robust means of generating wave-powered thrust under realistic ocean conditions while providing practical guidance for improved design of wave-powered marine propulsion systems.

We describe turbulent hydrodynamics of superfluids in two spatial dimensions via the dynamics of point-like vortices coupled to an emergent 2+1 dimensional $U(1)$ gauge field. The cascade of superfluid kinetic energy is equivalently described by a cascade of dual electric field energies. We study superfluid turbulence using the equations of motion of the dual gauge theory in the presence of a drive and dissipation. In the limit that the vortices are point-like, the dual equations of motion directly yield the hydrodynamical equations of the superfluid. We obtain a turbulent cascade consistent with Kolmogorov's scaling law for two dimensional fluid turbulence. We observe clustering of like-signed vortices and compute the kinetic energy flux to show that the turbulent regime exhibits an inverse energy cascade.

Is it possible to find an objective classification criterion that organizes the complexity of gust-induced loads across many flight conditions? And one that remains as interpretable as a labelling based on coarse parameters, such as the flight attitude? Our approach encodes a large number of experimental observations through a machine-learned representation and applies a summarization procedure to select a minimal subset of highly significant exemplars. The exemplars provide a similarity-based objective classification criterion of all the observations, they can be more conveniently inspected by experts and can become subject of more refined experiments. We demonstrate the approach on a database of 3480 pressure-load measurements induced by random gusts on a flying-wing model across six flight attitudes. We find nine fundamental response types that recur across multiple attitudes; analysis of a type's transient response enables physical intuition into the underlying fluid mechanics.

This paper analyzes the 112 sensor videos released under the PURSUE (Presidential Unsealing and Reporting System for UAP Encounters) initiative. Redactions to on-screen displays limit information available for identifying the reported unidentified anomalous phenomena (UAP). Converting the rate at which any object transits across part or all of a frame from pixels per frame to a physical velocity requires (1) the range to the object, (2) the velocity of the observing aircraft, (3) the aspect angle between the object's path and observer, and (4) the field angle of the camera. No PURSUE video provides this set of information. In one clip (DOW-UAP-PR113), the object crosses depression-angle markings, which allows reconstruction of the camera's field of view. Even this clip does not provide sufficient information to distinguish between a bird flying at close range and a large craft traveling at Mach 5 a kilometer away. A second clip (DOW-UAP-PR149) includes a vessel of known size in the frame, which can be used as a reference to calculate an upper bound of Mach 0.4 on the object's relative velocity. Without further information, the PURSUE sensor videos in their present form cannot fully resolve unidentified cases or conclusively indicate anomalous velocities.