Papers

Neural recordings are often interpreted as local measurements, yet the signal at any one sensor can also reflect structured activity distributed across the broader network. This raises a basic question: to what extent does an electrode's signal reflect local versus distributed information in the underlying system? More specifically, how much of an electrode's activity is carried by its immediate neighborhood, and how much is embedded more broadly across the array? We address this with a Spatially Masked Regression (SMR) framework that reconstructs each electrode's timeseries from the remaining electrodes while excluding a configurable neighborhood around the target. By progressively increasing this mask, spatial locality becomes an experimental control for quantifying how much predictive information survives after nearby channels are withheld. We apply SMR to intracranial EEG with heterogeneous electrode coverage and to scalp EEG with standardized montages over sensorimotor cortex. Using distance correlation between original and reconstructed signals, we find strong within-subject reconstruction in both modalities, substantial residual predictability even when local neighbors are excluded, and markedly stronger cross-subject transfer in EEG than in iEEG. Masking shows that nearby electrodes contribute strongly to reconstruction but do not account for all of it, indicating that individual channels reflect both local redundancy and broader distributed structure. Surrogates that preserve selected marginal or spectral properties while disrupting phase structure or temporal ordering substantially reduce performance, supporting the conclusion that SMR depends on structured temporal and cross-channel organization rather than on marginal statistics alone. These results position SMR as an interpretable framework for quantifying the balance between local and distributed information in recordings.

Charge-exchange (CEX) collisions can affect measurements of plasma plumes and neutralized ion flows in vacuum facilities, particularly when the background gas pressure increases and the CEX mean free path becomes comparable to the characteristic plume or facility dimension. Here, we investigate that regime in the plasma plume of a gridded ion source operating with a 400 eV argon ion beam. The fast-ion flux and low-energy ion flux were measured using a retarding potential analyzer (RPA) and planar probes, while the fast-neutral flux was inferred from deposited-power measurements with a thermal flux probe using a power-balance analysis. The low-energy ion flux increases with both background gas pressure and axial distance and its detection also depends on probe geometry. After the fast-ion component is isolated, its attenuation is described more accurately by an analytical reduced semi-empirical quasi-2D model that includes charge exchange and the experimentally observed plume divergence than by a one-dimensional attenuation law. The inferred fast-neutral flux also increases with pressure; however, the model underpredicts it at small axial distance and overpredicts it at elevated pressure and larger axial distance. This discrepancy suggests additional angular and collisional effects, as well as possible fast-neutral production near or inside the ion source, that are not captured by the present model. These results show that background gas pressure affects both the plasma plume and the diagnostic response, and that complementary electrostatic, thermal, and energy-selective diagnostics are required to distinguish source behavior from facility-induced effects.

High damage threshold gratings are in demand worldwide as critical components for next generation ultrahigh intensity lasers. Here we investigate the pulse-duration dependence of ultrafast laser-induced damage thresholds (LIDT) in hybrid multilayer dielectric gratings, touted to combine superior performance properties of both metallic and multilayer dielectric (MLD) gratings, using a dynamic finite-difference time-domain model incorporated with linear and non-linear absorption models. Simulations agree with reported experimental LIDT values for three representative designs and predict scaling exponents which vary with pulse durations ranging from 10 to 500 fs. The results reveal strong dependence on both material bandgap and grating field distribution, providing guidance for designing high LIDT gratings.

The translation dynamics of two binary mixture droplets is investigated theoretically and is corroborated with experiments. The proposed model accounts for the effects of Marangoni stresses generated by evaporative cooling and concentration gradients, as well as vapour diffusion, for both components of the binary mixture. We consider thin droplets, allowing us to use the lubrication theory to derive the evolution equation for the droplet profiles. We numerically solve the evolution equations using the finite element method and examine various cases of pure and binary droplet pairs exhibiting translational behaviours like attraction, repulsion, and 'chasing'. The results show that the combined effect of solutal Marangoni, capillary effect, and thermal Marangoni determines the movement of the droplets. The non-uniform evaporation generated from 'vapour shielding' creates such effects. We observe that for droplets with the same initial composition, solutal Marangoni and capillary forces induce droplet attraction, while thermal Marangoni effects drive their repulsion. For droplets with different initial compositions, the drop with a higher concentration of the more volatile component pushes, or `chases', the drop with a lower initial concentration of this component, completely driven by the solutal Marangoni. We carried out experiments involving water-morpholine binary mixture droplets to validate the results predicted by our model.

The current study presents a three-dimensional linear stability analysis of particle-laden Couette-Poiseuille flow suspended in a Newtonian fluid between two parallel plates, with the lower plate coated by a porous medium. The influence of suspended particles is examined using a two-domain formulation in which particles are confined to the fluid layer and do not penetrate the porous substrate. The particle-laden suspension is modeled using the dusty-gas framework, while the flow within the porous layer is described by the volume-averaged Navier-Stokes (VANS) equations. In particle-laden flows over impermeable walls, particle inertia may either stabilize or destabilize the flow depending on the governing parameters. In contrast, the presence of a porous layer introduces an additional permeability-dependent destabilizing mechanism that fundamentally modifies these classical trends. Consequently, particle loading can reduce the critical Reynolds number at sufficiently high permeability, even in parameter regimes where particles stabilize the corresponding rigid-wall flow. The coupled formulation also introduces additional disturbance branches associated with fluid-particle coupling near the permeable interface. Although these modes remain stable across the parameter space investigated, they modify the eigenspectrum and influence the dominant instability by altering coupling pathways. Furthermore, unlike impermeable-wall Couette-Poiseuille flow, where increasing the Couette component generally stabilizes the flow, the porous-wall configuration exhibits a monotonic decrease in the critical Reynolds number over the range examined. These results demonstrate that porous boundaries can fundamentally alter established stability behavior in particle-laden shear flows through permeability-dependent coupling between the suspension and the porous substrate.

Large language models (LLMs) can translate and modify source code, and have been shown to do so for codes of different complexity. Whether they can port a complete, production geophysical model to a different language without degrading its physics has not been established. We demonstrate that LLM-assisted code translation can preserve the physics of a complete production ocean model while moving it into a modern performance-portable form. We report our experience using an agentic LLM coding assistant, directed by domain experts, to port the FESOM2 unstructured mesh ocean–sea-ice model (about 74000 lines of core Fortran) first to C and then to C++/Kokkos for performance portability across CPUs and GPUs. We describe the practices that proved necessary, what worked and what did not, and the failure modes that we encountered. Three practices mattered most: translating in two stages that separate reproducing the numerics (Fortran to a clean C reference) from introducing parallelism (C to Kokkos); requiring a strictly literal translation in which the assistant was not permitted to “improve” the source; and validating each stage against an acceptance criterion suited to it. The C port reproduces the original Fortran at the level of long-term simulation statistics over five years. The Kokkos port is bit-for-bit identical to the C reference on CPU and statistically close on GPU over multi-year runs. On eddy-rich meshes up to 7.4 million surface vertices a single A100 GPU node runs 1.6–3.7 times faster than a CPU node, reaching the 1-2 simulated-years-per-day required for production integrations. The result is more than a single GPU port: by following a clear validation procedure, an LLM moved a full Fortran ocean model into another language and onto accelerators while preserving its physics in a matter of weeks.

We present a time-dependent wave-packet study of dissociative recombination (DR) and resonant ion-pair (RIP) formation in $\mathrm{HeH^+}$ isotopologues. Nuclear dynamics are treated on a manifold of 23 coupled electronic states of $^2\Sigma$, $^2\Pi$, and $^2\Delta$ symmetries, including rotational couplings between different symmetries. The results reveal that inclusion of a large manifold of resonant states and rotational couplings significantly enhances the DR cross section relative to earlier theoretical studies. In the diabatic representation, $^2\Sigma$ states dominate the recombination dynamics, while in the adiabatic representation, $^2\Pi$ and $^2\Delta$ states contribute significantly at low collision energies. For RIP formation, two different diabatization schemes yield systematically larger cross sections than previous models, highlighting the sensitivity of ion-pair production to electronic coupling structure. Isotopic effects are examined, showing a clear inverse dependence of cross section magnitude on reduced mass. Thermal rate coefficients are computed over $10^{2}$ to $2\times 10^4$ K thermal electron temperatures. Isotopic effects are examined, showing a clear inverse dependence of cross section magnitude on reduced mass. The results are compared with rotational-state-resolved experimental and theoretical results. The present results highlight the importance of multistate coupling and rotational interactions in electron-driven fragmentation processes relevant to primordial and astrophysical plasmas.

Quantifying the coherence properties of complex optical fields is essential for applications ranging from high power laser arrays to quantum coherent systems. Here, we present a new paradigm for coherence retrieval inspired by the Gerchberg Saxton framework, enabling reconstruction of the first order spatial coherence function (mutual intensity) of partially coherent light from intensity measurements in the near and far fields. We introduce two complementary approaches: a four dimensional Tensor GS algorithm that directly reconstructs the mutual intensity with high accuracy, and a Monte Carlo GS variant that significantly reduces computational cost at the expense of controlled approximation. We validated both methods by reconstructing partially coherent fields in simulated linear and ring arrays of up to 600 beams with prescribed Gaussian decaying coherence. Experimentally, we applied the Tensor GS method to a triangular array of 130 coupled lasers with inhomogeneous spatial coherence, achieving good agreement with theory and a root mean square phase error as low as 2pi over 250.

We introduce SPADE (SPlit And Delay Embeddings), an autoregressive transformer for sequences whose tokens carry multiple features. Rather than embedding these features jointly, SPADE embeds them independently. Delaying each feature stream relative to the previous one allows intra-token correlations to be learned by the standard self-attention mechanism. Applied to point-cloud calorimeter shower generation in the highly granular ILD detector, SPADE is competitive with the state of the art AllShowers model on photon showers, and substantially outperforms its VQ-VAE-based predecessor OmniJet-$\alpha_C$. The mechanism is applicable to any generative task with multi-feature tokens, enabling LLM-style pretraining workflows for higher-dimensional data.

Reliable extrapolation remains a central challenge for generative models in computational physics, because models trained over finite ranges of time, parameters, or geometries may produce physically inconsistent predictions outside the training distribution. We introduce a least-action-principle-guided diffusion, LAPG, a framework that promotes physical consistency during inference rather than relying solely on constraints imposed during training. The method combines a conditional score-based diffusion model with an action-derived physical guidance score. In the first stage, the learned score model generates an in-distribution proposal; in the second, an action-based variational prior refines this proposal toward the target out-of-distribution condition. This formulation turns the principle of least action into a differentiable inference-time correction mechanism and provides an alternative to pointwise residual penalties that often require empirical loss balancing. We evaluate LAPG on representative ordinary- and partial-differential-equation systems, including free fall, conservative and dissipative spring-mass dynamics, interacting point vortices, and potential flow over parameterized airfoils. In temporal, parameter, and geometric extrapolation tests, LAPG reduces phase drift, preserves dissipative decay, captures vortex motion, and improves the lift response of airfoil flows compared with training-time physics-informed baselines.

Rendezvous is a critical task for multi-agent systems, requiring agents to coordinate to meet at an unspecified location. However, achieving this in fluid environments presents a challenge, as it remains unclear how agents can exploit underlying fluid kinematics to facilitate convergence. In this study, we adopt a multi-agent reinforcement learning (MARL) approach to develop physics-informed rendezvous strategies in vortical flows. Compared to a naive strategy, where agents navigate toward their counterparts, MARL strategies significantly improve the rendezvous rate. MARL strategies also show transferability across varying vortex intensities, vortex scales, and swarm sizes. By breaking the symmetry of the state-action map, MARL strategy leverages a non-intuitive mechanism that prevents agents from becoming trapped in separate vortices, thereby enhancing rendezvous success. Additionally, a heuristic strategy is extracted from the learned strategy and also outperforms the naive strategy. Furthermore, a theoretical analysis demonstrates that fluid deformation impedes the rendezvous process. Large finite-time Lyapunov exponents identify where fluid effects separate adjacent agents, suggesting that targets should be planned in weak-deformation regions. Our findings reveal the important role that agent-fluid interactions play in multi-agent tasks and highlight the MARL capability to explore swarm intelligence in complex flow environments.

We tackle two key difficulties in the simulation of the viscous hydrodynamics of a large dense collection of rigid particles: (i) the poor convergence rate of an iterative solution of the discretized linear system as particle gaps shrink, and (ii) the large number of unknowns needed to accurately discretize the resulting lubrication-driven flows. Our focus is the 2D Stokes resistance and mobility boundary value problems for nearly-touching disks. To address both challenges, we introduce a general two-body preconditioning strategy, and implement it with the method of fundamental solutions. For each close particle pair, the hard-to-resolve interaction is represented in a basis precomputed by solving a local boundary value problem on a fine grid. In an iterative solve, the resulting flow field corrects that obtained from a coarse representation of all particles. The local fine-grid correction can even be compressed so that all particles except the pair itself are affected by an equivalent set of coarse sources. Numerical experiments demonstrate rapid GMRES convergence in challenging multi-particle settings, with iteration counts remaining low even in densely packed suspensions. For example, the mobility problem is solved for a random close packing with area fraction $\varphi = 0.65$, $P = 10000$ monodisperse disks, and minimum separation $10^{-3}$, in just 47 GMRES iterations, achieving five digits of accuracy with 72 vector unknowns per body.

Gradient-based inversion of reaction-diffusion systems is typically approached via surrogate models or physics-informed neural networks (PINNs), while the most direct route, backpropagation through the PDE's structure itself, has largely been avoided. We pursue this direct route as a diagnostic probe, backpropagating a steady-state loss through unrolled Gray-Scott simulation to recover its parameters, with no surrogate or neural-network augmentation. Optimization fails to converge, and plotting the landscape directly locates the failure in its geometry – flat plateaus with no gradient signal, bounded by sharp cliffs that align with bifurcation boundaries – a structure that recurs across loss functions and is inherited however the gradients are routed to parameters. Reading this minimal setup as an ablation of PINN, we disentangle each component's role: with the neural network fixed, the residual loss is quadratic in the PDE parameters and yields a smooth landscape, so it alone already avoids the pathology, by implicitly encoding the full PDE dynamics across all initial conditions. The neural network, for its part, cannot repair an ill-posed parameter subspace, and so serves only to complete the observed data – a division of labor not previously made explicit. These findings carry concrete design implications for PINN-type methods and a broader heuristic on when added dimensions actually help.

Equilibrium quantum many-body methods differ not only in approximation, but in which information they represent explicitly. We formulate this distinction by fixing an equilibrium specification and viewing every representation as an encoder from admissible states to represented variables. The identity encoder gives a full-state representation, whereas a non-injective encoder gives a reduced representation whose value labels a fiber of compatible states. For a specified task, an exact decoder exists on a state class if and only if the task is constant on the encoder fibers within that class. Variational principles, reconstruction correspondences, functionals, kernels, and closures are different realizations of additional structure used to select, restrict, or approximate the task-relevant content of a fiber when the retained variable alone is insufficient. Static moments and imaginary-time correlation functions are unified as restrictions of a complete equilibrium readout functional to different probe families. Within the same principle, quantum embedding can be viewed as consistency or replacement between global and local descriptions through reduced interface encoders and their conjugate fields.

We present DarkAgents: a multi-agent system that leverages the reasoning and code-generation capabilities of large language models (LLMs), together with deterministic tested human-written code, to build orchestrated pipelines for theoretical astroparticle physics research. While related approaches have been proposed in collider physics and cosmology, DarkAgents targets the specific challenges of this domain, such as model building, complex pipeline computations, multiple constraints and assumption auditing. The framework can be powered by different agentic command-line tools, including Mistral's, Anthropic's, OpenAI's and local LLMs via Ollama. As first implementation, we apply DarkAgents to the study of cosmological first order transitions, starting from a classically scale-invariant particle-physics model and ending with the fit to the NANOGrav nanohertz gravitational-waves spectrum. DarkAgent-PT provides as output i) the best-fit values of model parameters, ii) their existing experimental and observational constraints, iii) an audit report of the assumptions and priors entering both i) and ii), of particular relevance for astroparticle physics. Our test runs identify inconsistencies in some fits in the literature and produce novel ones based on the dissipative bulk-flow GW template. The code is publicly available at https://github.com/PhysicsZandi/DarkAgents.

Modern atomic spectroscopy has reached a level of precision at which nuclear-structure effects can no longer be neglected and must be quantified reliably. In particular, hyperfine splittings depend on the Zemach radius, which encodes the convolution of the nuclear charge and magnetization distributions. The third electric Zemach moment provides a related finite-size measure and enters the elastic two-photon-exchange contribution to the Lamb shift in muonic atoms. Here, we compute Zemach radii and other electromagnetic moments for light nuclei using quantum Monte Carlo techniques within modern ab initio nuclear theory. Using Norfolk two- and three-body interactions derived within chiral effective field theory, we assess the model dependence and study the role of two-body currents. For $^6$Li, we obtain a Zemach radius larger than that extracted from atomic measurements, consistent with recent calculations, confirming that the discrepancy is not an artifact of the nuclear model. For $^9$Be, our results agree with experiment; the discrepancy of previous phenomenological evaluations is traced to a model-dependent input for the magnetic radius.

Data assimilation (DA) in subsurface flow entails calibrating model parameters to match observed data, typically at wells, while preserving geological realism. Latent diffusion models (LDMs) provide efficient mappings from high-dimensional geological model space to a low-dimensional latent variable, reducing the dimensionality of the inverse problem while maintaining plausibility in posterior geomodels. However, the high nonlinearity in the LDM mapping may degrade the performance of Kalman-gain-based ensemble updates. We present a systematic comparison of DA algorithms applied to large-scale 3D channelized geomodels with hierarchical geological uncertainty. We compare model-space and latent-space DA using the ensemble smoother with multiple data assimilation (ESMDA), and demonstrate a key trade-off: model-space updates achieve significant uncertainty reduction but produce geologically unrealistic posterior models, while latent-space updates preserve realism but exhibit limited uncertainty reduction. Motivated by this, we explore rigorous Markov chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) algorithms in the 3D-LDM latent space. To accommodate their high computational demands, we develop a fast surrogate flow model that approximates well-rate responses. MCMC and SMC are evaluated against ESMDA across three synthetic test cases, with DA performed in the LDM latent space. All models maintain geological realism due to the LDM parameterization. MCMC and SMC are consistent with one another and achieve lower data mismatch and more uncertainty reduction than latent-space ESMDA. Our overall results demonstrate that ensemble Kalman methods may provide overestimated posterior uncertainty with highly nonlinear parameterizations, while rigorous Monte Carlo sampling, enabled by fast surrogate models, can provide a more reliable alternative.

Oscillating-foil-based energy harvesters have demonstrated strong potential for low-speed hydrokinetic energy extraction; however, the actuator-level mechanical energy balance associated with prescribed pitching motion remains poorly understood. The present work experimentally characterizes how foil mass ratio, pitching-axis location, and reduced frequency jointly govern the hydrodynamic and mechanical efficiencies of a semi-passive oscillating hydrofoil. Results show that rotational inertia redistributes actuator demand through phase-dependent torque exchange, while heave-pitch coupling can partially cancel this demand when favorably phased. Pitching-axis location modifies the phase and direction of the fluid torque through changes in the effective hydrodynamic moment arm. Reduced frequency governs the balance between enhanced unsteady loading and inertia-amplified actuator demand. Optimal performance is achieved within reduced frequency region of 0.125-0.16 using quarter-chord to one-third-chord pitching axes and relatively low foil mass ratios from about 0.5 to 2.0, yielding a peak mechanical efficiency of 33.96% – which can diverge from the hydrodynamic efficiency by approximately 38.16% depending on configuration. Torque-loop analysis and PIV measurements show that this synchronization is a key mechanism governing the observed efficiency trends.

Objective. The soft tissue attenuation laws have a magnitude and frequency dependence that varies across tissue types and generally follow power laws. An accurate model of ultrasound propagation in the human body thus may require spatially heterogeneous power-law attenuation alpha(x,f) = alpha_0(x) f^(y(x)). However, a spatially heterogeneous representation of frequency-dependent attenuation is technically challenging, so existing methods introduce simplifying assumptions. For example, prior approaches such as Fullwave 2 achieved <5% error for individual tissue types but required manual parameter tuning for each (alpha_0, y) pair, limiting the construction of realistic tissue libraries. Approach. We introduce a calibration framework that uses derivative-free optimization to systematically fit relaxation parameters across diverse tissue combinations spanning alpha_0 = 0.0022-1.0 dB/(MHz^y cm) and y = 0.4-2.0. The Nelder-Mead algorithm minimizes complex-wavenumber mismatch. The attenuation is extended to a convolutional perfectly matched layer, where the same relaxation formulation is used in the boundaries. Main results. The method achieves mean errors below 3% over 1-20 MHz with dispersion error of 1.1 +/- 0.8 m/s across the clinically relevant core region (y = 0.7-1.4). Boundary reflections remain below -50 dB for clinically relevant tissue exponents (y <= 1.5). We validated the method with two-layer muscle/fat/liver models and confirmed per-layer accuracy (<2.5% normalized error). A 3D abdominal simulation using the Visible Human dataset demonstrates stable propagation with voxel-level heterogeneity in both alpha_0(x) and y(x). Significance. The open-source multi-GPU implementation (Fullwave 2.5) provides a practical tool for patient-specific therapy planning, training data generation, estimation of acoustic radiation force, quantitative imaging, and inverse problem applications.

The Ohio State University Radio Observatory (OSURO), known as the Big Ear, played a pivotal role in both radio astronomy and the Search for Extraterrestrial Intelligence (SETI). Following the completion of the Ohio Sky Survey, the facility was repurposed in 1973 as the world's first full-time dedicated SETI observatory and operated continuously until its decommissioning in 1998. During this period, the Ohio SETI Program evolved from an 8-channel hydrogen-line receiver into increasingly sophisticated survey systems. Over three decades, these surveys covered approximately 70% of the radio sky using a largely consistent instrumental configuration, creating one of the most extensive long-term radio astronomy archives ever assembled. The program is best known for the detection of the Wow! Signal in 1977, but it also accumulated an archive of over 40,000 transient narrowband events, revealed unusual concentrations of radio bursts near the Galactic Center, and established one of the longest continuous radio monitoring records in astronomy. Following the closure of the Big Ear, its scientific legacy continued through Project Argus and, more recently, the Arecibo Wow! project. This paper provides an overview of the final decades of the Ohio SETI Program, including its instrumentation, survey strategies, scientific discoveries, and enduring impact on SETI, time-domain radio astronomy, and the preservation of historical astronomical data. Despite its scientific significance, most of the data collected by the Ohio SETI Program remains unexplored, leaving a unique archive available for future research.

The Fuel Optimizer is an inverse design framework for sustainable aviation fuels that starts from a user-defined merit function and identifies the optimal combination of chemical species or hydrocarbon families that optimize a combination of targets. As a case study, a database of fuel blends meeting selected property standards was simulated in a reactor model to obtain pollutant emissions at cruise conditions. A surrogate model was developed to reduce the computational cost of evaluating candidate blends, taking fuel composition as input and predicting emissions as output. A genetic algorithm was used to optimize fuel formulation according to two merit functions designed to break the nitrogen oxides - CO trade-off, and minimize pollutant emissions over the landing-and-take-off cycle. Constraints included selected property standards (including seal swelling) and composition limits. Fuel candidates from the framework outperformed the training database across all merit functions, and the optimal candidates were validated through reactor simulations.

How can a growing biological filament remain straight despite stochastic fluctuations in growth? Motivated by filamentary structures that develop reproducibly across biological systems, we study the stability of a noisy, growing elastic filament regulated by feedback. We formulate a minimal model in which growth responds to the filament's strain, curvature, and orientation through local or nonlocal spatiotemporal feedback laws. Linear stability analysis identifies the conditions under which these feedback mechanisms stabilize a straight configuration. In the presence of noise, we show that purely local feedback requires orientation sensing to suppress long-wavelength instabilities, whereas nonlocal feedback allows stabilization through proprioceptive (curvature) sensing alone. Coupling to an elastic substrate further suppresses large-scale fluctuations. Our results establish minimal control strategies that ensure robust straight growth and suggest experimental signatures for identifying the feedback mechanisms underlying morphogenesis.

Breast cancer is the most common cancer among women, and mammography remains the primary modality for early detection. Because mammography uses ionizing radiation, accurate estimation of normalized glandular dose (DgN) is important for risk assessment. Recent breast dosimetry models, including TG-282, incorporate population-based glandular tissue distributions; however, patient-specific glandular distributions remain unknown from conventional mammographic projections. In previous work (Smith, Dey et al., 2025), glandular fraction (GF) maps were estimated from a single mammographic projection. While these maps determine glandular path length along each projection ray, they do not uniquely define glandular tissue depth. In this work, we propose a framework for estimating a patient-specific range of DgN from a projection-derived GF map. Using simulated data, glandular tissue was distributed to the top, center, and bottom of the breast volume using Siddon ray-tracing. These configurations preserved the GF map while producing maximum, intermediate, and minimum DgN values. Monte Carlo simulations were performed, and DgN was normalized to entrance air kerma. DgN varied by up to a factor of three solely due to differences in glandular tissue depth, despite identical GF maps and visually indistinguishable projection images. Using randomized realizations derived from TG-282 glandular distributions for Cranio-Caudal (CC) and Medio-Lateral Oblique (MLO) views, dose ratios were calculated relative to central glandular placement. Central placement overestimated DgN by less than 5 and 15 percent on average for MLO and CC distributions respectively, whereas centroid-based placement underestimated dose by up to 25 percent. These results indicate that patient-specific bounds on DgN can be estimated from limited mammographic information and that central placement provides a conservative dose estimate.

Reliable density-functional simulations require numerical settings whose residual errors are smaller than the chemical and materials trends being interpreted. In CP2K/Quickstep, this requirement is complicated by the joint use of atom-centered Gaussian basis sets and norm-conserving pseudopotentials: a code-to-code discrepancy usually contains both contributions. We present the UZH protocol, a closed-loop CP2K workflow that calibrates molecularly optimized Gaussian basis sets on small molecules, validates the resulting settings in unary-crystal equation-of-state benchmarks, identifies whether the limiting approximation is the Gaussian basis or the pseudopotential. The diagnosis is then used to revise the parameter files. The central diagnostic is a three-way comparison between production CP2K-GTH-UZH calculations, SIRIUS calculations using the same Goedecker–Teter–Hutter pseudopotential in a systematic plane-wave representation, and all-electron full-potential linearized augmented-plane-wave SIRIUS references. This construction decomposes the practical CP2K error into a Gaussian-basis component and a pseudopotential component. The protocol distinguishes basis-limited noble-gas and heavy-element cases from pseudopotential-limited transition-metal cases, guides targeted revisions with the CP2K basis and pseudopotential optimizers, and produces improved MOLOPT basis sets and GTH pseudopotentials as explicit outputs of the workflow. The UZH protocol is therefore constructive: it does not merely measure or reduce errors a posteriori, but allows turning verification outliers into validated CP2K parameter files for simulations across molecules and condensed phases.

A substantial body of recent work has debated whether large language models (LLMs) can serve as substitutes for human participants in behavioural research. This debate, however, captures only one direction of a rapidly changing relationship. The more consequential question is not simply whether researchers should use LLMs as human surrogates, but whether - and under what conditions - humans are beginning to use LLMs as surrogates for their own deliberation. Across domains including health, law, finance, education, and personal guidance, increasing numbers of people consult generative AI systems before, alongside, or instead of human experts, peers, or independent judgment. Although evidence for actual delegation remains uneven, this uncertainty makes the phenomenon an urgent social-scientific object of study. We argue for a research programme that treats LLMs as consequential social actors in a functional sense: systems whose outputs shape human decisions, social norms, and collective dynamics.

We exhibit a family of smooth incompressible velocity fields on the two-dimensional unit sphere such that the time evolution of any mean-free initial data passively advected by any of them is mixed exponentially fast. In the presence of molecular diffusivity, we show that the solution to the associated advection-diffusion equation experiences enhanced dissipation with optimal decay rates. Each member of this family is an alternating combination of two Rossby-Haurwitz flows with random amplitudes and constitutes a spherical analogue to the sine shear-alternating example of Pierrehumbert.

The Th-229 nuclear isomeric state has the lowest energy of all known nuclear excited states, placing it within the reach of current table-top laser technology. This extraordinary property has made this nuclear isomer an attractive candidate for a nuclear optical clock of incredibly high precision and accuracy, both as isolated trapped Th-229 ions and embedded into solid-state platforms. Activity around Th-229 has surged in recent years, driven by breakthroughs in its direct laser excitation. The underlying nuclear physics that gives rise to this unique isomer will be elucidated, as well as the nearly half-century of efforts that led to its direct excitation. The design and systematics of a Th-229 nuclear clock will be discussed, both in ion traps and in the solid-state. These systematics, such as frequency shifts and quenching channels, can be leveraged both to probe the local chemical environment, and as a control knob during clock operation. Finally, the nuclear clock's high sensitivity to the variations of fundamental constants will be discussed.

Nuclear magnetic resonance (NMR) is a powerful probe of atomic structure, but accurate quantum-mechanical predictions of tensorial NMR parameters are computationally demanding. This creates a bottleneck both for direct quantum-mechanical studies and for collecting high-quality training data for machine-learning (ML) models. Here, we introduce a synthetic pre-training and fine-tuning protocol for graph-based ML models of solid-state NMR parameters. We first pre-train models on synthetic tensorial data, as obtained using an existing ML model, and subsequently fine-tune those models on new ground-truth data. We observe a pronounced improvement in data efficiency when pre-training and fine-tuning span the same compositional and configurational space, and we carry out initial experiments regarding chemical transferability. Our work outlines a route toward future data-efficient training workflows for tensorial ML models for solid-state NMR, combining inexpensive synthetic supervision with targeted first-principles refinement.

The sustainable electrogeneration of hydrogen peroxide (H2O2) via the two-electron oxygen reduction reaction (2e$^-$ ORR) represents a promising alternative to conventional production methods. In this study, CeO2 and CeO2MnOx nanoparticles were synthesized and supported on Vulcan XC-72 carbon at varying loadings (1, 3, and 5%), aiming to assess the lowest metal loading and high H2O2 electrosynthesis. Physicochemical characterizations confirmed the successful formation of CeO2 nanowires and the effectiveness of the MnOx surface modification. XRD, TEM, XPS, EPR, and contact angle analyses revealed that CeO2 loading increased surface hydrophilicity through the presence of oxygenated functional groups, thereby favoring electrochemical activity. On the other hand, all CeO2MnOx loadings were statistically equivalent to Vulcan XC-72 in terms of contact angle. Electrochemical evaluations using a rotating ring-disk electrode (RRDE) demonstrated enhanced ORR activity and high H2O2 selectivity for the 1% CeO2MnOx/C and 3% CeO2/C catalysts, achieving up to 90% selectivity and elevated ring currents. The results suggest that low metal loading and surface modification via MnOx improve the balance between active site exposure, oxygen adsorption, and intermediate stabilization, thus favoring the selective 2e$^-$ pathway. These findings support the development of cost-effective, non-noble-metal catalysts for green H2O2 production via electrosynthesis.

The widespread presence of synthetic progestins, such as levonorgestrel (LNG) and gestodene (GES), in aquatic environments poses significant ecotoxicological risks due to their endocrine-disrupting properties. In this study, nano-octahedral magnetite (Fe3O4-NO) was synthesized via a hydrothermal route and incorporated into gas diffusion electrodes (GDEs) supported on Vulcan XC72 to enhance the in-situ electrogeneration of hydrogen peroxide (H2O2). High-resolution transmission electron microscopy, X-ray diffraction, SEM, X-ray photoelectron spectroscopy, and contact angle measurements thoroughly characterized the physicochemical and morphological properties of the materials. The 3% Fe3O4-NO/C catalyst provided a two-fold increase in H2O2 selectivity compared with Vulcan XC72. Electrochemical performance was optimized using a 2^3 factorial design and principal component analysis (PCA), with current density, pH, and Na2SO4 concentration as variables. The optimized GDE (3% Fe3O4-NO/C) achieved a maximum H2O2 production of 0.44 +/- 0.02 g L-1 with a current efficiency of 43.1 +/- 0.23% and a specific energy consumption of 0.012 +/- 0.009 kWh g-1. The electrode was further applied to the degradation of LNG and GES using solar and anodic-assisted electro-Fenton processes. Under optimal conditions, over 70% removal of both progestins was achieved, with stable performance across three operational cycles. These findings demonstrate the potential of 3% Fe3O4-NO/C-GDEs as efficient, reusable cathodes for sustainable electrochemical advanced oxidation processes (EAOPs) in water treatment.