Papers

Velocity space discretization is a primary source of computational cost in deterministic methods for multiscale gas flows. A fixed velocity grid often retains redundant kinetic degrees of freedom (DOFs), particularly under near equilibrium conditions. To address this issue, this work develops a nonequilibrium-capturing formulation based on the unified gas kinetic scheme (UGKS), in which the number of kinetic DOFs adapts locally to the degree of nonequilibrium. A discrete orthogonal basis is constructed for numerical implementation, and the velocity distribution function is decomposed into an analytical local equilibrium and a basis expanded nonequilibrium component. The retained, locally adapted DOFs are evolved directly, substantially lowering computational overhead while preserving the discrete conservation of mass, momentum, and energy, as well as accurately capturing nonequilibrium states. The formulation retains the core multiscale coupling of molecular transport and collision inherent to UGKS, providing explicit and implicit schemes for unsteady and steady flows, respectively. Numerical tests across a wide range of flow speeds and rarefaction regimes (from continuum to highly rarefied) demonstrate close agreement with benchmark solutions. For unsteady flows, the explicit scheme achieves speedups of 12.64 to 58.50 over the standard UGKS. In steady state tests, only about $2\%$ of the original kinetic DOFs are required in continuum regions, accelerating each implicit iteration by a factor of 33.87. Even in the most rarefied cases, the scheme captures nonequilibrium behavior using only about $27\%$ of the original DOFs.

Response-spectrum matching is an underdetermined waveform inverse problem in which spectral compatibility must be achieved without uncontrolled changes in other engineering-relevant ground-motion characteristics. This study formulates the problem as differentiable waveform inverse design using a fixed SDOF dynamics engine and configurable optimization modules. An envelope-guided parameterization defines the correction waveform, while a hard projection enforces zero correction-induced terminal velocity and displacement. Differentiable SDOF responses then provide gradients for waveform optimization. Spectral fidelity and Arias-intensity accumulation are treated as adaptive objectives. Spectral feasibility, PGA, total Arias intensity, and displacement-path requirements are incorporated as constraints. The differentiable spectrum operator agrees with an exact solution of the equation of motion to within 0.002%. The complete five-damping configuration achieves a maximum spectral error of 5.00% over 375 ordinates. On a common benchmark, maximum errors of 2.61% and 8.47% are obtained for single- and multi-damping matching, respectively, while retaining the additional waveform constraints. Three-component applications further show that different engineering acceptance requirements can be accommodated through module reconfiguration without changing the forward dynamics.

Quantifying the biological effects of radiation is essential for radiotherapy and radiation protection. While research has centered on statistical analyses of experimental data, this approach often fails to elucidate underlying mechanisms, limiting the development of generalizable models. Describing time dependent phenomena, such as sub-lethal damage (SLD) recovery during fractionated irradiation, presents a particularly significant challenge for existing static models. To address this, we developed a mechanistic mathematical model that extends the classical multi-target, single-hit model by introducing explicit recovery transitions among damage states, allowing the simultaneous description of radiation-induced damage accumulation and biological recovery. We applied this model to Elkind and Sutton's classical split-dose experimental data to determine its parameters. The model reproduced the major features of split-dose survival curves for treatments with 0-, 2.5-, and 23-hour recovery intervals. We visualized the temporal changes in damage state distributions within the cell populations. These simulations provided a process-level interpretation of the characteristic shoulder-like response appearing after an inter-fraction recovery interval. Furthermore, the estimated recovery half-life was about 120 minutes, which is broadly consistent with reported timescales of DNA double-strand break repair in mammalian cells, supporting the biological interpretability of the model parameters. The proposed framework provides a simple, interpretable description of radiation-induced cell death dynamics, including SLD recovery, and may serve as a basis for the improved mechanistic modeling of radiobiological effects under different irradiation protocols.

The optical Kerr effect in high-refractive-index membrane metasurfaces patterned with a square lattice of five-pointed star-shaped pores is investigated numerically using gallium phosphide as a model material. The star geometry supports resonances with pronounced electromagnetic energy confinement, including an anapole state. The sharp tips of the star-shaped pores concentrate the electric energy density in air, exceeding that of Mie voids by a factor of seven. The effective second-order refractive index is enhanced by up to three orders of magnitude relative to bulk GaP, with the largest values obtained near the anapole resonances. The nonlinear response remains stable under polarization rotation and random misalignment of the nanoholes, indicating tolerance to fabrication imperfections. These results establish star-shaped pore lattices in freestanding membranes as a promising platform for nonlinear optics exploiting strong field confinement.

Reticular chemistry allows metal-organic frameworks (MOFs) to be designed from their building units, yet their synthesis remains constrained by sparse experimental data, noisy crystallization outcomes, and the absence of benchmarks for comparing active-learning strategies. While data-driven approaches such as Bayesian optimization (BO) can improve synthesis success rates, selecting appropriate optimizers beforehand for mapping MOF crystallization landscapes remains largely empirical. Here we present ActMOF, an experiment-grounded benchmark for comparing search strategies in aluminum-carboxylate MOF synthesis. ActMOF is emulated from experimentally measured microwave syntheses with each encoded condition assigned a crystallinity index calculated based on diffraction peak intensity and full width at half-maximum. By benchmarking policies guided by Gaussian processes (GP), tree ensembles and large language models (LLM), ActMOF placed the GP family among the leading policies for active learning in sparse synthesis landscapes. It then demonstrated transferability by guiding the microwave synthesis of six reticularly related aluminum MOFs. All six campaigns gave crystalline products within a fixed budget of 12 experiments each, and established a reusable testbed for AI-driven MOF synthesis.

The iodine $4d$ giant resonance provides an unusually strong and predominantly iodine-centered photoionization channel in iodoalkanes. Recent attosecond measurements by Schröder et al. [Phys. Rev. A 112, L051104 (2025)] reported pronounced, nonmonotonic, and isomer-dependent variations of the streaking-derived $4d$ photoemission delay across the molecular series. We calculate field-free molecular Eisenbud–Wigner–Smith (EWS) delays from photoionization amplitudes represented in a multicenter B-spline basis of the time-dependent density-functional theory (TDDFT). A tensor formulation is developed in which the delay for randomly oriented molecules is evaluated from the same rank-0 and rank-2 contractions that determine the cross section and anisotropy parameter. The resulting TDDFT delays of CH$_3$I, C$_2$H$_5$I, 2-iodopropane, and both calculated conformers of 1-iodopropane form a narrow bundle throughout the 90–118~eV region and closely follow atomic-I and I$^-$ reference calculations. This robustness is consistent with the similarly weak molecular dependence of the giant resonance cross section and angular distribution, but it is in marked contrast with the much larger molecular variation found by streaking experiments. The asymptotic Coulomb–laser-coupling/continuum–continuum correction is controlled mainly by the photoelectron energy and residual charge and is therefore expected to be nearly common to the iodoalkane series. This does not exclude target-specific molecular IR effects beyond the universal Coulomb term.

Under anthropogenic greenhouse forcing, climate models project a decrease in year-to-year temperature variability in the mid-to-high latitudes and an increase in the tropics and subtropics. Here, we examine year-to-year temperature variability in the CESM Large Ensemble in concert with an idealized stochastic moist energy balance model (SMEBM) with effective surface heat capacity and local feedback parameter specified based on CESM. We find that the zonal-mean CESM variability change pattern largely persists after filtering out the impact of El Niño. The SMEBM largely captures this change pattern. In the SMEBM, changes in the mid-to-high latitudes are driven by changes in the effective heat capacity due to sea ice retreat, combined with changes in anomalous moist static energy convergence modulated by the polar-amplified time-mean temperature change. The CESM local feedback parameter evolves to be less stabilizing in the low latitudes, which causes tropical and subtropical variability to increase in the SMEBM.

While magnetic flux ropes (MFRs) are widely accepted to exist prior to many solar eruptions, the physical mechanism responsible for triggering and subsequently driving their eruptions remains debated. Classical models commonly attribute eruption onset to ideal magnetohydrodynamic instabilities, particularly the torus instability, while magnetic reconnection is often regarded as a secondary process. Using high-resolution three-dimensional MHD simulations, we investigate the eruption of an MFR that forms self-consistently through flux cancellation driven by converging motions and photospheric diffusion following an initial shearing phase. The pre-eruption MFR rises quasi-statically and reaches a height where the decay index exceeds the classical torus instability threshold, yet the system continues to evolve quasi-statically without eruption. Instead, eruption begins only when the current sheet beneath the MFR becomes sufficiently thin for magnetic reconnection to set in. Increasing numerical resolution systematically delays the eruption onset, whereas the decay index at eruption onset remains nearly unchanged, demonstrating that the onset is governed by reconnection rather than torus instability. During the eruption, magnetic reconnection continuously converts surrounding sheared arcades into newly reconnected twisted flux that merges with the pre-eruption MFR and continuously to build the erupting MFR. A component-resolved analysis of the Lorentz force work further shows that the newly reconnection flux contributes predominantly to the main acceleration phase. These results demonstrate that magnetic reconnection plays a central role throughout the entire lifecycle of the eruptive MFR: it builds the MFR, directly triggers its eruption, and provides the dominant driving of the eruption.

A suite of tunable diode laser absorption spectroscopy (TDLAS) diagnostics targeting rovibrational states of nitric oxide (NO) in the mid-infrared (MIR) and electronic states of atomic oxygen (O) and nitrogen (N) in the near-IR (NIR) was combined with optical emission spectroscopy (OES) for overlapping wavelengths at the NASA Electric Arc Shock Tube (EAST) facility for three selected high-temperature air experiments. Absorption features were probed at rates up to 1 MHz to provide quantitative, time-resolved measurements of temperature and species number density profiles behind incident shocks, enabling comparisons to spatially-resolved emission measurements, with the two typically agreeing within their respective uncertainties. Inferred time-histories are further compared to incident shock simulations from two one-dimensional models - one leveraging the Park two-temperature rates and the other more recent rate values - as well as the Data Parallel Line Relaxation (DPLR) code. For the two low-velocity cases (3.81 and 3.98 km/s) in which NO is probed, inferred temperatures are typically well-captured by all three models, although the NO number densities show stronger variation, agreeing better for the model with updated NO formation and dissociation rates. Finally, one high-velocity case (7.03 km/s) demonstrates the simultaneous resolution of three O-atom and six N-atom electronic states, with all states showing a steady increase in number density over time - possibly due to shock trajectory effects. Together, this combined absorption and emission study demonstrates resolution of many states of nonequilibrium air species, while directly comparing ground- and excited-state populations via the combined diagnostic strategy.

This dissertation discusses the distribution of city sizes using censuses of the Brazilian Institute for Geography and Statistics (IBGE) spanning 150 years, from 1872 to 2022, as well as the allometric scaling relation with the 2022 census data. The Gompertz distribution is fitted to the population size of less populated cities, while the Zipf power law is used to represent the cities having larger populations, with threshold varying between 20,216 and 102,857 inhabitants. The data analysis shows that the Gompertz-Zipf distribution can describe the data across all censuses, with the Gompertz parameter $B$ oscillating between $-2.39\times10^{-5}$ and $-1.49\times10^{-5}$, indicating periods with faster decreases and periods with slower decreases in the number of cities within the less populated segment as population increases, while the Zipf exponent ranges from $a=3.61\pm0.09$ in 1872 to $a=2.28\pm0.02$ in 2022. The Gini coefficient $G$ and the Zanardi index $Z$ are used to quantify the inequality in the city population distribution, whose obtained values are $G=0.439$ and $Z=-0.002$ for the 1872 census and $G=0.733$ and $Z=0.018$ for the 2022 dataset, while the Pietra index $p$ and the Kolkata index $k$, associated with the most populated cities, are $p=31.893\%$ and $k=65.894\%$ for 1872, and $p=57.395\%$ and $k=78.342\%$ for 2022. These results reveal a high concentration of population in larger cities, with $G$ increasing from 1872 to 2022 and $Z$ becoming positive after 1900, indicating a greater concentration of population in the group of more populated cities than in the group of less populated cities. Finally, urban allometry is also examined, generating an exponent $b=0.835$ for the 2022 data, showing that the scaling relation between area and population holds for urbanized area but not for total administrative area, indicating that scaling is associated with spatial occupation.

We present a global-in-time spectral Duhamel-Picard method with polynomial expansions for post-exposure bake (PEB) simulation. The coupled acid-quencher reaction-diffusion equations are reformulated as nonlinear Volterra integral equations using the Duhamel principle. Full trajectories are represented by finite polynomial expansions, with temporal coefficient fields determined over the bake interval by Anderson-accelerated Picard iteration, thereby avoiding sequential time marching. In 3-D PEB simulations, we employ Chebyshev and Legendre temporal bases while evaluating the Duhamel integrals using the corresponding Gaussian quadrature rules. We demonstrate that the proposed method simulates a large domain of $1200\times1200\times100$ voxels on the order of one minute. The representation is further incorporated into a physics-informed neural operator (PINO) with a mixed-transform variant of a factorized Fourier neural operator, which is trained without precomputed solution trajectories. Together, the integral formulation and its PINO extension enable accelerated, time-marching-free prediction of concentration fields at arbitrary times within the bake interval.

Chern insulators have optical properties that are strikingly different than ordinary chiral media. Unlike the natural optical activity of the latter, which is a finite-wavelength effect arising from spatial dispersion, the optical activity of a Chern insulator is already present in the long-wavelength limit, and exhibits a striking directional asymmetry in the propagation of circularly polarized light. We study this unnatural optical activity using a recently developed field theory for Chern insulators. For bulk Chern insulators, we derive the normal-incidence transmission and reflection coefficients of multilayer structures, identify the associated Faraday and Kerr angles, and show that these optical rotations provide a direct optical signature of topology at low frequencies. We also show that an interface between bulk Chern insulators can act as a kind of mirror, completely reflecting light at normal incidence when the Chern invariants have opposite signs. Moving to a more realistic model of a two-dimensional current sheet, we study the Faraday and Kerr effects in a thin film of $\mathrm{Mn}\mathrm{Bi}_2\mathrm{Te}_4$ in its Chern insulator phase. The predicted rotations are many orders of magnitude larger than those of optically active media of comparable thickness that are topologically trivial.

Phononic integrated circuits enable the development of compact systems to manipulate radio frequency signals through interconnected networks of acoustic waveguides. Introducing topologically protected robustness in these circuits to counter fabrication imperfections and local perturbations, remains a challenge. This work presents a validated modeling framework for topologically protected edge states using surface acoustic waves in etch-defined waveguides consisting of scandium aluminum nitride on silicon carbide, directly comparing analytical, full-wave numerical simulation, and laser Doppler vibrometer measured experimental approaches and their agreement. This work represents the first experimental demonstration of topological edge states on a ScAlN on SiC platform, validated through direct quantitative comparison with analytical and numerical analyses, with future implications for topological protection in phononic and optomechanical integrated circuit platforms.

High-resolution optical imaging of the human retina is essential for diagnosing ophthalmic and neurodegenerative diseases. While adaptive optics combined with coherence gating can resolve retinal structures at the cellular scale, it requires complex, costly, and cumbersome hardware to physically correct for ocular aberrations and motion. Matrix imaging offers a powerful digital alternative by leveraging wave linearity to undo aberrations and multiple scattering; however, its in vivo deployment has been hindered by continuous, involuntary eye movements that destroy phase coherence during acquisition. Here, we overcome this fundamental barrier and demonstrate the first in vivo reflection matrix imaging of the human retina using an ultra-simplified optical setup without physical correctors. We introduce a fully computational framework that tracks and compensates for multi-axial ocular motion directly from raw interferometric data, thereby restoring the phase coherence of a multi-spectral distortion matrix. This enables local, high-order aberration correction to achieve diffraction-limited, volumetric imaging of retinal tissues. By shifting the hardware burden to physics-guided numerical computation, this paradigm opens the way for accessible, non-invasive, and high-resolution digital ophthalmology.

We present the RemoTAP-Hybrid algorithm for global aerosol products retrieved from Multi-Angle Polarimeters (MAP), and apply it to one-year aerosol retrievals from the SPEXone MAP instrument. For the first time, Multiple Collaborated Neural Networks (MCNN) are used to perform global aerosol MAP retrievals over land and ocean. The combined use of MCNN with a full physics inversion in RemoTAP-Hybrid, significantly improves accuracy and computational speed of SPEXone aerosol retrievals. We validate the retrieved Aerosol Optical Depth (AOD), Angstrom Exponent (AE), Single Scattering Albedo (SSA), Absorption AOD (AAOD), Fine mode AOD (FAOD), Coarse mode AOD (CAOD), and fine mode effective radius (reff_fine) with AERONET data. The validation suggests that aerosol retrievals can be further improved when extending the aerosol description to include a fine non-spherical mode. For AOD, the Root-Mean-Square Error (RMSE) is 0.051 (0.037) over land (ocean), while 78.3% (80.8%) of the retrievals are within the requirement formulated by the Global Climate Observing System (GCOS). The RMSE is 0.186 (0.207) for AE, 0.038 (0.044) for SSA, 0.023 (0.014) for AAOD, 0.047 (0.031) for FAOD, 0.043 (0.027) for CAOD, and 0.021 (0.026) for reff_fine over land (ocean). Compared to the current RemoTAP algorithm, RemoTAP-Hybrid shows improved agreement with AERONET for AOD, AE, and FAOD over land and ocean while the number of valid retrievals is increased by 4% (3%) over land (ocean) and the computational time is reduced by 43% (32%). Finally, we present the first global seasonal maps of aerosol optical and microphysical properties and chemical composition from SPEXone.

Solid-state triplet-triplet annihilation upconversion offers a compelling route to harvest sub-bandgap photons to increase the efficiency of photovoltaics, but low sensitizer doping required to suppress non-radiative aggregation quenching severely restricts light absorption. Here, we present a dual-resonator planar silver microcavity housing a thermally co-evaporated rubrene:palladium phthalocyanine (PdPC) active layer designed to simultaneously enhance triplet-sensitizer absorption and out-couple upconverted emission. By aligning a dispersive cavity mode with the PdPC absorption peak at $32^\circ$, we achieve a 12-fold enhancement in absorptance at 665 nm. This absorption boost, combined with directional outcoupling through a second cavity mode, outweighs cavity-induced reabsorption losses to yield a 22-fold peak and 4.5-fold integrated enhancement in detected upconversion emission. Overall, this work highlights the potential of microcavity integration to substantially enhance solid-state upconversion performance across low-intensity optoelectronic applications.

Planck-mean absorption coefficients of pure gases are calculated from line-by-line integrations of HITEMP and ExoMol spectra up to 5000 K for use in high-enthalpy radiative heat transfer simulations. The dataset extends beyond conventional combustion products, including high-temperature radicals, nitrogen-bearing species, soot precursors, metal oxides, metal hydrides, and halides: AlF, AlH, AlO, BH, C2, C2H2, CH, CH4, CN, CO, CO2, H2O, HBO, HCN, HF, MgH, MgO, N2O, NO, NO2, OH, SiO, TiH, TiO. New calculations are validated against historical correlations for overlapping species, demonstrating good agreement over their applicable temperature ranges. Strong visible opacities result in large Planck-mean absorption coefficients for many of the considered species, highlighting the need to account for their radiative contribution in extreme environments. A brute-force search over several candidate functional forms is performed to provide a common set of coefficients, establishing a standardized framework that can be easily integrated into production solvers and extended as additional high-temperature line lists become available.

Full waveform inversion (FWI) is a powerful tool for high-resolution subsurface model reconstruction, but it remains highly nonlinear and ill-posed, especially when the initial model is inaccurate or the acquired data are noisy and incomplete. Annealed Stein variational gradient descent (ASVGD) provides a particle-based Bayesian framework for FWI by approximating the posterior distribution with an ensemble of velocity models, thereby enabling uncertainty quantification beyond a single deterministic solution. However, conventional ASVGD-FWI commonly relies on simple handcrafted priors, such as Gaussian or smoothing assumptions, which are often insufficient to represent complex geological structures. To address this limitation, we introduce a learned diffusion model (DM) as an implicit regularization mechanism within the ASVGD-FWI framework. Instead of explicitly evaluating the diffusion prior density, the trained diffusion model acts as a learned geological projection operator that guides updated particles toward the distribution of plausible velocity models. In this way, the proposed method combines data consistency from FWI, posterior diversity from ASVGD, and structural realism from the diffusion prior. Numerical experiments demonstrate that the diffusion-regularized ASVGD-FWI produces more geologically consistent mean models, suppresses nonphysical artifacts, and yields uncertainty estimates that are more concentrated around structurally complex and poorly constrained regions. These results suggest that learned diffusion priors can effectively enhance Bayesian FWI by improving both inversion stability and the interpretability of posterior uncertainty.

Surface-related multiple elimination (SRME) is an indispensable processing step in marine seismic exploration, comprising two main components: multiple prediction and adaptive subtraction. While the multiple prediction is theoretically data-driven and relies on the data itself, the adaptive subtraction step requires a fairly subjective manual control. Our approach introduces a purely data-driven and physics-based deep learning (PDL) algorithm for improving the adaptive multiple subtraction to significantly enhanced performance. There are two main innovations in the proposed deep-learning (DL) multiple subtraction approaches: first, being data-driven and self-supervised, which removes the dependence on inflexible label preparation and model training processes; secondly, being governed by the adaptive matching filtering (AMF) equation to guide the training process. The DL module serves as a strong regression filter analogous to the traditionally known AMF. We segment a moderately sized seismic profile and an initial multiple model into a large number of patches using a patching scheme for self-contained training. The number of patches is controlled by the patch size and the shift size to enable a sufficient number of training samples required by mainstream DL platforms. The patching-based self-training and regression approach, PDL, is inherently a non-stationary matching approach that aims to obtain a globally optimal matching from the initial multiple models to the input full wavefield data through iterative patching-based training. It is also closely connected with the non-stationary regression approach, which was developed almost two decades ago. However, the PDL approach achieves even stronger regression performance due to the backpropagation-based optimization mechanism in DL, and has shown significantly improved multiple subtraction performance over a number of examples.

A group does not usually consult all its members before every decision. This paper develops a quantitative framework for measuring how widely members are consulted in decision making within a group. Two kinds of records are considered: the number of members consulted for each decision and the number of decisions in which each member is consulted. From these records, two participation indices are defined, and their equality is proved analytically. A third index measures the fraction of decisions for which at least a specified percentage of members are consulted. The distribution of consultation levels is described using a discrete maximum-entropy model from statistical physics. Tail probabilities are used to obtain bounds for the main participation index. This study examines aggregate survey data from two groups. Their observed characteristics are broadly consistent with the patterns found across the full survey. Using binomial and Poisson calculations, the future probability that all members will be consulted for a decision is examined. Inequality in consultation among members is examined through Lorenz curves, the Gini coefficient and a normalised Shannon participation measure. The member-decision structure is represented as a bipartite network. Controlled simulations show that the same overall level of consultation can have different distributions among the members. The framework measures both the overall breadth of consultation and how consultation is distributed among the members. It therefore provides a quantitative sociophysical description of one measurable aspect of democracy in decision making within a group.

We apply large $N$ expansion techniques to derive kinetic equations for strongly coupled, far-from-equilibrium field theories with $O(N)$ symmetry. For large $N$ vector models, the quasi-classical approximation holds. Incorporating arbitrary momentum-dependent interactions, we show the dynamics obey the standard kinetic equation, with the scattering amplitude given by the retarded propagator of the auxiliary Hubbard-Stratonovich field. This has broad applications to systems like spin wave and Langmuir ion-acoustic turbulence. For large $N$ random coupling (SYK-like) models in $d$ dimensions, leading-order melon diagrams induce order-one propagator renormalization and finite spectral width. The corresponding kinetic equation, unlike those for standard weakly interacting systems, does not have a local-in-time collision integral. We find evidence for a stationary, scale-invariant infrared solution, revealing a potentially novel strong turbulence scaling exponent.

Generating novel molecules with desired properties is a central challenge in drug discovery. Although foundational work focused on de novo generation, practical use cases require a more complex setting: conditioning on an initial seed molecule, optimizing multiple properties, and operating in data-sparse regimes. We introduce PairIT, a flexible training framework for autoregressive transformers over SMILES sequences that enables seeded generation by learning from pairs of structurally similar molecules. By leveraging matched pairs, the model learns to generate local, chemically meaningful edits that improve a property of the seed molecule. Prompt tokens extend this framework by specifying the target property, desired direction, and approximate magnitude of change. Multiple prompt tokens can be concatenated to enable multi-property optimization at inference time. For properties where pair supervision alone is insufficient, reinforcement learning post-training can further improve optimization, providing a tunable trade-off between proximity to the seed and the optimization objective. PairIT naturally supports pretraining on a large dataset and fine-tuning on a smaller dataset, which is critical for performance in data-sparse regimes. Surprisingly, we observe diminishing returns to the scale of the pretraining dataset. We demonstrate empirically on MoleculeNet and Therapeutics Data Commons datasets that, with appropriate paired-data curation, a standard transformer model of modest size (68M parameters) can achieve robust seeded molecular optimization in low-data regimes.

Purpose: Free-breathing abdominal MRI is limited by respiratory motion, which introduces image artifacts and k-space inconsistencies. Existing reconstruction approaches often depend on pre-estimated motion fields or external respiratory surrogate signals. We propose a coupled reconstruction-motion estimation algorithm for self-navigated free-breathing acquisition, in which motion is represented by compact Gaussian primitives and jointly refined with the image estimates during iterative reconstruction. Methods: An iterative reconstruction that alternates between data consistency updates and motion modelling was proposed. Respiratory motion was represented using Gaussian primitives. A Gaussian splatting was integrated into the reconstruction algorithm. The method was evaluated on self-navigated free-breathing Cartesian gradient-echo (GRE) acquisition in four healthy volunteers, including retrospective scan shortening experiments, and was compared with regularized compressed sensing (CS) reconstruction. Results: Respiratory motion states were reconstructed in approximately 1.5 min. Compared with CS, the proposed method improved visual image quality, particularly in coronal liver views, and better preserved anatomical structures in retrospectively shortened acquisitions. These findings provide proof of concept for the proposed method. Conclusion: The proposed coupled reconstruction-motion estimation algorithm improved visual image quality compared with CS alone. Gaussian primitives provided a compact and sufficiently flexible representation of nonrigid respiratory motion with practical runtime, supporting further scanner integration and validation in larger cohorts.

Circuit depth and measurement count are the main limitations for chemical applications of the leading variational quantum algorithms on quantum computers. We show that both resource requirements can be reduced by augmenting adaptive variational quantum algorithms with post-optimization corrective techniques. We focus on two such techniques—Hamiltonian-moment Krylov subspace diagonalization and the identity-augmented quantum self-consistent equation of motion (q-sc-EOM)—applied to optimized ADAPT-VQE states without further ansatz growth or nonlinear optimization. Across molecular systems studied with up to 20 qubits, first-order Krylov correction reduces the optimized iterations (and the corresponding number of adaptive layers) needed to reach the $1.6~\mathrm{m}E_h$ threshold by up to approximately sixfold, with a general trend toward larger reductions at higher determinant Rényi entropy. We also find measurement-cost reductions of up to approximately fortyfold using a suitable resource-cost model. Identity-augmented q-sc-EOM improves molecular potential-energy curves obtained from truncated ADAPT-VQE ansätze with limited expressivity and recovers accuracy from states produced by sampled operator ranking without additional gradient-based operator selection, providing an advantage when small pool gradients make selection measurement intensive. Together, these post-optimization corrections offer routes to improve energy accuracy and reduce adaptive circuit growth in quantum algorithms as quantum hardware advances.

Purpose: The $^{225}$Ac decay chain includes $^{209}$Tl, whose prominent 1567 keV gamma line can create electron-positron pairs. We investigated whether a long axial field-of-view (LAFOV) PET/CT can image $^{225}$Ac using standard clinical acquisition and reconstruction methods. Methods: A syringe placed next to a vial (5.89 and 10.29 MBq of $^{225}$Ac activity) and a NEMA IEC body phantom (sphere activity concentration 4.56 kBq mL$^{-1}$) were scanned for 45 and 60 min, respectively, on a Biograph Vision Quadra (Siemens Healthineers). To support our findings, FLUKA simulations scored the annihilation locations around a $^{225}$Ac point source. Results: The contours of the syringe and vial were visible despite high noise, but the NEMA spheres could not be distinguished from the background. As illustrated by the FLUKA simulations, the number of annihilation events decreases exponentially according to the mean free path of the 1567 keV photon in the surrounding medium. Conclusion: A LAFOV PET/CT can detect positrons created by pair production of the main $^{209}$Tl gamma line. However, these positrons are created where the photons interact with the surrounding medium rather than at the location of the $^{209}$Tl decays, which rules out standard PET imaging of $^{225}$Ac.

This work extends the Deep Energy Method to phase-field modelling of ductile fracture in elasto-plastic solids. Its key contribution is an incremental DEM-based formulation that couples history-dependent J2 plasticity and plasticity-dependent fracture toughness with separate energy-optimised displacement and phase-field representations. The formulation combines the second-order Ambrosio-Tortorelli phase-field fracture model with small-strain J2 plasticity and linear isotropic hardening. A volumetric-deviatoric split defines the elastic crack-driving energy, while accumulated equivalent plastic strain governs the degradation of local fracture toughness. Plastic internal variables are updated locally through radial return. The reduced energy is minimised alternately with respect to the displacement and phase fields, represented by a ReZero residual neural network and spatially localised Gaussian radial basis functions with trainable centres, respectively. This split addresses the diffuse phase-field profiles observed in preliminary calculations with a shared global network. Alternating optimisation is combined with transfer learning to reuse trainable parameters and optimiser states across loading increments, while the converged constitutive history is carried forward separately at quadrature points. The numerical results demonstrate close agreement with finite-element reference solutions in predicting force-displacement responses, energy evolution, plastic deformation and crack propagation. The framework captures the main features of ductile fracture, although accuracy becomes more sensitive to loading increments near abrupt fracture events.

When a long liquid drop is confined in a Hele-Shaw cell, the pull of surface tension produces a rapid retraction of its tip, causing it to bulge out as it draws up the excess liquid. Using numerical simulations, we demonstrate that this retraction is self-similar at long times, with the speed of retraction slowing like $t^{-1/2}$, where $t$ is the duration of retraction. After showing how this scaling can be understood using force and mass balance, we perform an asymptotic analysis of the Hele-Shaw equations to derive both the shape of the retracting drop, as well as prefactors of the scaling laws. Excellent agreement is found between our simulations and theory, and the experimental consequences are pointed out.

Core level atomic photoionization is influenced by the molecular environment of the emitting atom. To probe this influence we used extreme ultraviolet (XUV) radiation to measure the I$\left(4d\to\varepsilon f \right)$ photoemission of four iodine substituted alkanes in the range from 60 eV to 160 eV photon energy. Using two detection angles relative to light polarization allowed us to extract and compare the asymmetry parameter of the investigated compounds. These results are in agreement with theory by Pi and Landsman (L.-W. Pi and A. Landsman, Attosecond time delay in photoionization of noble-gas and halogen atoms, Applied Sciences 8, 322 (2018)) and previous experiments by Olney et al. (T. N. Olney, G. Cooper, and C. Brion, Quantitative studies of the photoabsorption (4.5-488 ev) and photoionization (9-59.5 ev) of methyl iodide using dipole electron impact techniques, Chemical Physics 232, 211 (1998)) for iodomethane. 2-iodopropane behaves nearly identical to iodomethane, while for iodoethane and 1-iodopropane we find a distinct and consistent, energy dependant difference most prominent at around 130 eV photon energy. This further proves the influence of the chemical environment on core level photoionization. Due to the sensitivity on the exact positions of the carbon atoms and the energy dependence we hypothesise this difference is caused by intramolecular scattering of the outgoing photoelectron wave packet, causing changes in the angular distribution.

The exact local exchange-energy density, obtained from the Slater potential of the Hartree-Fock exchange hole, is a central ingredient of Becke's B05 and B13 functionals and of many other local hybrids. It is usually evaluated in a Gaussian basis, so the local quantity retains a pointwise basis-set incompleteness error. We describe here a basis-set-free numerical code that solves the Kohn-Sham problem on real-space grids for atoms, diatomics, and polyatomics, and returns the exact local exchange-energy density together with the density, its derivatives, the kinetic-energy density, and the reverse Becke-Roussel hole quantities. Reconstructed from the published record of Becke's non-public NUMOL code, AXEL reproduces numerical atomic references through krypton, scalar-relativistic uranium orbital energies, and Becke's diatomic spectroscopic constants. We then extend the original local-density numerical framework to generalized-gradient and hybrid functionals. Comparisons with the independent adaptive-multiwavelet code MRChem give sub-millihartree agreement for Hartree-Fock and PBE0. The same calculations agree with large Gaussian-basis references to a similar level of accuracy. Atom-centred eXact Electronic Limit (AXEL) is intended as both a basis-set-free numerical reference and a platform for exchange-hole-based density-functional development.

Properties of the Coulomb collision operator that have been found useful in plasma kinetic theory and plasma transport theory are gathered and reviewed, with derivations. The use of velocity-space coordinates appropriate to a magnetized (drift-kinetic) plasma is emphasized. Little of the material is new, although the derivations may be more detailed than is customary.