Papers

A direct quantitative comparison of Scrape-Off Layer (SOL) filament properties from fluid turbulence simulations using the GBS code and from experiments on the TCV tokamak is performed within the TCV-X21 validation case. This comparison is made possible by extending the open TCV-X21 dataset with 2D turbulence measurements obtained with Gas Puff Imaging (GPI), providing critical information on the size, velocity, and other key characteristics of turbulent filaments at the outboard midplane and in the divertor region. For the comparison, GBS simulations of TCV-X21 are analyzed using a dedicated synthetic GPI diagnostic that models the neutral helium-plasma interaction and emission processes and accounts for line-integration effects. Poloidal and radial filament velocities are found to be in good agreement between simulations and experiments, while the simulations overestimate the filament radial and poloidal sizes and underestimate the relative fluctuation levels. The simulations further indicate that filaments in the SOL are predominantly represented by density perturbations rather than temperature perturbations, consistent with previous assumptions in experimental analyses of cross-field turbulent transport from GPI data. The poloidal velocity direction of the filaments agrees with the time-averaged $\boldsymbol{E}\times\boldsymbol{B}$ direction at the outboard midplane and X-point region, but not in the divertor leg. Possible explanations are proposed and discussed, highlighting the influence of the instantaneous $\boldsymbol{E}\times\boldsymbol{B}$ velocity components in both poloidal and radial directions. This study provides new insights into turbulent filament behavior and contributes to guiding future efforts to improve first-principles simulations of the boundary plasma.

Microwave (MW) field sensing is foundational to modern technology, yet its evolution, reliant on classical antennas, is constrained by fundamental physical limits on field, temporal, and spatial resolutions. Here, we demonstrate an MW electrometry that simultaneously surpasses these constraints by using individual Rydberg atoms in an optical tweezer array as coherent sensors. This approach achieves a field sensitivity within 13% of the standard quantum limit, a response time that exceeds the Chu limit by more than 11 orders of magnitude, and in-situ near-field mapping with {\lambda}/3000 spatial resolution. This work establishes Rydberg-atom arrays as a powerful platform that unites quantum-limited sensitivity, nanosecond-scale response time, and sub-micrometer resolution, opening new avenues in quantum metrology and precision electromagnetic field imaging.

Maintaining comfortable temperatures for buildings, humans, and devices consumes a substantial portion of global energy, underscoring the urgent need for energy-efficient thermoregulation technologies. Dynamic radiative thermal emitters that can switch between passive cooling and heating modes offer a promising solution, but most existing devices exhibit broadband optical responses, resulting in unwanted parasitic heat exchange and limited performance. Here, we introduce an elegant strategy that uses a dielectric cap to transform broadband metal-insulator transition (MIT) materials into spectrally selective dynamic emitters. This design creates a highly tunable Fabry-Perot cavity, enabling a tailored thermal emission spectrum by engineering the reflected-wave phase profile. Our Fresnel-formalism-based phasor diagram analysis reveals two key routes for realizing high spectral selectivity: a high-index dielectric cap and a low-loss metallic MIT state, which are further validated by Bayesian optimization. Following this principle, we demonstrated a wide-angle spectrally-selective thermoregulator operating in the atmospheric transparency window (8-13 um), where the thermal emittance can be electrically tuned from about 0.2 to 0.9 through reversible copper electrodeposition on a germanium cavity. Furthermore, this strategy can be extended to multispectral electrochromic windows, enabling switching between solar heating and spectrally-selective radiative cooling. Our work establishes a versatile and generalizable paradigm for spectral engineering of dynamic thermal emitters, opening opportunities in energy-efficient buildings, wearable thermal comfort, spacecraft thermoregulation, and multispectral camouflage.

The effects on the bistatic echo width of an elliptical cylinder due to a perfect magnetic material are reported in this article. The configuration is analyzed using the separation of variables method and Mathieu functions. In this approach, the structural geometry is illuminated by an electromagnetic field. Radial and angular Mathieu functions have been used in the formulation. Notably, the maxima of the scattered elliptic transfer electric mode ($\theta = 180^{\circ}$) are much higher than those of the scattered transfer magnetic mode, comparable to terms $\theta = 120^{\circ}$ and $\theta = 240^{\circ}$, respectively. It can be observed that an increase in the in-plane radial component leads to the linearity principle for the transfer electric mode, while non-linear behavior is investigated for the elliptic transfer magnetic mode. Therefore, the unidirectional bistatic echo width is subject to non-directional behavior. These analogous results may have applications in the fields of optics, meteorology, acoustics, radio astronomy, collision physics, and other disciplines where wave scattering phenomena play a crucial role. Furthermore, the findings of this study contribute to the fundamental understanding of electromagnetic interactions with complex geometries and materials

A fundamental challenge in numerical weather prediction is to efficiently produce high-resolution forecasts. A common solution is applying downscaling methods, which include dynamical downscaling and statistical downscaling, to the outputs of global models. This work focuses on statistical downscaling, which establishes statistical relationships between low-resolution and high-resolution historical data using statistical models. Deep learning has emerged as a powerful tool for this task, giving rise to various high-performance super-resolution models, which can be directly applied for downscaling, such as diffusion models and Generative Adversarial Networks. This work relies on a diffusion-based downscaling framework named CorrDiff. In contrast to the original work of CorrDiff, the region considered in this work is nearly 40 times larger, and we not only consider surface variables as in the original work, but also encounter high-level variables (six pressure levels) as target downscaling variables. In addition, a global residual connection is added to improve accuracy. In order to generate the 3km forecasts for the China region, we apply our trained models to the 25km global grid forecasts of CMA-GFS, an operational global model of the China Meteorological Administration (CMA), and SFF, a data-driven deep learning-based weather model developed from Spherical Fourier Neural Operators (SFNO). CMA-MESO, a high-resolution regional model, is chosen as the baseline model. The experimental results demonstrate that the forecasts downscaled by our method generally outperform the direct forecasts of CMA-MESO in terms of MAE for the target variables. Our forecasts of radar composite reflectivity show that CorrDiff, as a generative model, can generate fine-scale details that lead to more realistic predictions compared to the corresponding deterministic regression models.

We introduce FieldSeer I, a geometry-aware world model that forecasts electromagnetic field dynamics from partial observations in 2-D TE waveguides. The model assimilates a short prefix of observed fields, conditions on a scalar source action and structure/material map, and generates closed-loop rollouts in the physical domain. Training in a symmetric-log domain ensures numerical stability. Evaluated on a reproducible FDTD benchmark (200 unique simulations, structure-wise split), FieldSeer I achieves higher suffix fidelity than GRU and deterministic baselines across three practical settings: (i) software-in-the-loop filtering (64x64, P=80->Q=80), (ii) offline single-file rollouts (80x140, P=240->Q=40), and (iii) offline multi-structure rollouts (80x140, P=180->Q=100). Crucially, it enables edit-after-prefix geometry modifications without re-assimilation. Results demonstrate that geometry-conditioned world models provide a practical path toward interactive digital twins for photonic design.

To study the geometry of Gerstner's water wave model, we analyse the velocity of his fluid particles in a reference frame that moves with the wave. Gerstner wave profiles are cycloidal, curtate (flattened) trochoids, or prolate (extended) trochoids. We derive both the height of each profile's characterising point (cusp, inflection, or self-intersection), as well as a condition under which the arc lengths of prolate and curtate profiles coincide over a single wave cycle. We conclude with a discussion of how Galilean transformations affect particle acceleration and the geometry of their trajectories.

Digital assays represent a shift from traditional diagnostics and enable the precise detection of low-abundance analytes, critical for early disease diagnosis and personalized medicine, through discrete counting of biomolecular reporters. Within this paradigm, we present a particle counting algorithm for nanoparticle based imaging assays, formulated as a multiple-hypothesis statistical test under an explicit image-formation model and evaluated using a penalized likelihood rule. In contrast to thresholding or machine learning methods, this approach requires no training data or empirical parameter tuning, and its outputs remain interpretable through direct links to imaging physics and statistical decision theory. Through numerical simulations we demonstrate robust count accuracy across weak signals, variable backgrounds, magnification changes and moderate PSF mismatch. Particle resolvability tests further reveal characteristic error modes, including under-counting at very small separations and localized over-counting near the resolution limit. Practically, we also confirm the algorithm's utility, through application to experimental dark-field images comprising a nanoparticle-based assay for detection of DNA biomarkers derived from SARS-CoV-2. Statistically significant differences in particle count distributions are observed between control and positive samples. Full count statistics obtained further exhibit consistent over-dispersion, and provide insight into non-specific and target-induced particle aggregation. These results establish our method as a reliable framework for nanoparticle-based detection assays in digital molecular diagnostics.

Accurate segmentation of thalamic nuclei from magnetic resonance images is important due to the distinct roles of these nuclei in overall brain function and to their differential involvement in neurological and psychiatric disorders. However, segmentation remains challenging given the small size of many nuclei, limited intrathalamic contrast and image resolution, and inter-subject anatomical variability. In this work, we present CATNUS (Coordinate-Aware Thalamic Nuclei Segmentation), segmenting 13 thalamic nuclei (or nuclear groups) using a 3D U-Net architecture enhanced with coordinate convolution layers, which provide more precise localization of both large and small nuclei. To support broad clinical applicability, we provide pre-trained model variants that can operate on quantitative T1 maps as well as on widely used magnetization-prepared rapid gradient echo (MPRAGE) and fast gray matter acquisition T1 inversion recovery (FGATIR) sequences. We benchmarked CATNUS against established methods, including FreeSurfer, THOMAS and HIPS-THOMAS, demonstrating improved segmentation accuracy and robust test-retest reliability across multiple nuclei. Furthermore, CATNUS demonstrated strong out-of-distribution generalization on traveling-subject datasets spanning multiple scanners, field strengths, and vendors, producing reliable and anatomically coherent segmentations across diverse acquisition conditions. Overall, CATNUS provides an accurate and generalizable solution for thalamic nuclei segmentation, with strong potential to facilitate large-scale neuroimaging studies and support real-world clinical assessment.

This paper addresses theoretical issues associated with probabilistic partial least squares (PLS) regression. As in the case of factor analysis, the probabilistic PLS regression with unique variance suffers from the issues of improper solutions and lack of identifiability, both of which causes difficulties in interpreting latent variables and model parameters. Using the fact that the probabilistic PLS regression can be viewed as a special case of factor analysis, we apply a norm constraint prescription on the factor loading matrix in the probabilistic PLS regression, which was recently proposed in the context of factor analysis to avoid improper solutions. Then, we prove that the probabilistic PLS regression with this norm constraint is identifiable. We apply the probabilistic PLS regression to data on amino acid mutations in Human Immunodeficiency Virus (HIV) protease to demonstrate the validity of the norm constraint and to confirm the identifiability numerically. Utilizing the proposed constraint enables the visualization of latent variables via a biplot. We also investigate the sampling distribution of the maximum likelihood estimates (MLE) using synthetically generated data. We numerically observe that MLE is consistent and asymptotically normally distributed.

We report on imaging magnetic flux distributions in a multilayered superconductor integrated circuit which emerge during magnetization and after field cooling of the circuit in the DC magnetic field. The obtained complicated field maps expose the flux propagation across the patterned superconducting ground planes sandwiching layers with Josephson junction-based logic cells, fine wire grid around the functional units, and multiple superconducting fill structures located in different inner layers. The observed intricate flux distributions are explained by specific patterns of Meissner screening currents and superconducting critical currents in different mutually interacting parts of the integrated circuit. Our results provide important insights into possible ways of improving the protection of superconductor integrated circuits from magnetic fields and their resilience against flux trapping.

The expansion of commercial launch capabilities has significantly increased opportunities for interplanetary small satellite (SmallSat) missions. As researchers plan for more missions beyond Earth, there is a demand for accessible tools that help better predict and understand the thermal effects on their spacecraft in orbital environments around Earth and other bodies. While commercial thermal analysis tools offer high-fidelity modeling capabilities and results, they are often expensive and require extensive training to be used effectively. This paper details a framework for a user-friendly Satellite Thermal Model (SATMO) to support the early stages of space mission planning for CubeSats orbiting Earth and other Solar System bodies. SATMO is an open-source, MATLAB-based, six-node thermal analysis program designed for satellites in low-altitude circular orbits. Although SATMO requires a MATLAB license – typically inexpensive or institutionally provided in academic settings – it remains substantially more accessible than professional thermal analysis software. SATMO requires an internet connection for some features but does not rely on additional MATLAB toolboxes. The SATMO modeling approach is validated with the space industry standard Thermal Desktop software, with temperatures comparable to within 1.17$^\circ$C for a 10 cm $\times$ 10 cm $\times$ 10 cm CubeSat in various configurations, orbiting around primary bodies including Earth, Venus, and Mars. An example use case of SATMO is presented with a Mars-orbiting CubeSat to demonstrate its functionalities and the outputs available to users. SATMO offers increased accessibility to satellite thermal modeling for the research community, enabling quick thermal trade studies and interplanetary mission plans.

The propagation properties of ion-acoustic solitary and shock waves in the magnetized viscous plasma with nonthermal trapped electrons are investigated. The Cairns-Gurevich distribution as the electron distribution is considered to describe the plasma nonthermality and particle trapping. By adopting the reductive perturbation technique, we derived the nonlinear Schamel-Korteweg-de Vries-Burgers (SKdVB) equation, and then obtained the ion-acoustic shock and solitary wave solutions of the SKdVB equation for different limiting cases. It is found that the impact of nonthermal parameter {\alpha}, external magnetic field {\Omega}, obliqueness lz, wave speed U0, and the ion kinematic viscosity {\eta}0 can significantly change the characteristics of the shock and solitary waves. These results may be useful for better understanding the propagation of nonlinear structures in space (i.e. Earth's magnetosphere and ionosphere, auroral regions) and laboratory plasma with nonthermal trapped electrons.

We study the electron firehose mode propagating parallel to the ambient magnetic field in the space plasma with anisotropic Cairns-distribution electrons. The dispersion relation, the wave frequency and the growth rate of electron firehose mode are derived, and the condition for onset of the firehose instability is obtained. We show that the wave frequency and the growth rate both depend significantly on the parameters, such as the parallel electron beta , the nonthermal parameter {\Lambda} and the electron temperature anisotropy Ae , and the anisotropic Cairns-distribution electrons change the instability condition. The numerical analyses show that the wave frequency and the growth rate of the electron firehose mode increase with increase of the parameters. The results may be helpful for understanding the firehose instability in space plasma environments.

The dispersion and damping rate of kinetic Alfvén waves are studied in temperature anisotropic space plasma with kappa-Maxwellian distribution. Employing a kinetic approach, the wave frequency and damping rate of kinetic Alfvén waves and the modified ion acoustic waves are derived in a low e̱ta} plasma, which both depend on the parameters ąppa} and . The numerical analyses show that the wave frequency of kinetic Alfvén waves is larger in kappa-Maxwellian plasma than that in Maxwellian case. The wave frequency of the modified ion acoustic waves in kappa-Maxwellian plasma is larger in the short-wave region but smaller in the long-wave region than that in Maxwellian case. Again, we found that the damping rate of kinetic Alfvén waves in kappa-Maxwellian plasma is stronger than that in Maxwellian case. The damping rate of modified ion acoustic waves in kappa-Maxwellian plasma is stronger in the short-wave region but weaker in the long-wave region than that in Maxwellian case. The impact of the parameter on the two modes is relatively small because we consider the low e̱ta} case. These results are helpful for us to understand better the characteristics of kinetic Alfvén waves in space plasma.

Cryptoassets launched by political figures, e.g., political finance (PoliFi) tokens, have recently attracted attention. Chief among them are the eponymous tokens backed by the 47th president and first lady of the United States, TRUMPandMELANIA. We empirically analyze both, and study their impact on the broad decentralized finance (DeFi) ecosystem. Via a comparative longitudinal study, we uncover a "Trump Effect": the behavior of these tokens correlates positively with presidential approval ratings, whereas the same tight coupling does not extend to other cryptoassets and administrations. We additionally quantify the ecosystemic impact, finding that the fervor surrounding the two assets was accompanied by capital flows towards associated platforms like the Solana blockchain, which also enjoyed record volumes and fee expenditure.

This is a re-editing, which takes quantum mechanics into account, of Wittgenstein's famous Tractatus. The operation has a playful side in the form, but is a serious attempt to capture possible philosophical implications of the Relational Interpretation of Quantum Mechanics, and formalize the naturalistic third-way between realism and instrumentalism explored by this interpretation.

We present MRPARCv2, Multi-resolution Physics-Aware Recurrent Convolutional Neural Network, designed to model complex flows by embedding the structure of advection-diffusion-reaction equations and leveraging a multi-resolution architecture. MRPARCv2 introduces hierarchical discretization and cross-resolution feature communication to improve the accuracy and efficiency of flow simulations. We evaluate the model on a challenging 2D turbulent radiative layer dataset from The Well multi-physics benchmark repository and demonstrate significant improvements when compared to the single resolution baseline model, in both Variance Scaled Root Mean Squared Error and physics-driven metrics, including turbulent kinetic energy spectra and mass-temperature distributions. Despite having 30% fewer trainable parameters, MRPARCv2 outperforms its predecessor by up to 50% in roll-out prediction error and 86% in spectral error. A preliminary study on uncertainty quantification was performed, and we also analyzed the model's performance under different levels of abstractions of the flow, specifically on sampling subsets of field variables. We find that the absence of physical constraints on the equation of state (EOS) in the network architecture leads to degraded accuracy. A variable substitution experiment confirms that this issue persists regardless of which physical quantity is predicted directly. Our findings highlight the advantages of multi-resolution inductive bias for capturing multi-scale flow dynamics and suggest the need for future PIML models to embed EOS knowledge to enhance physical fidelity.

The program Simourg (Simulator of Usually Requested Geometries) is based on the Geant4 toolkit and created for Monte Carlo simulation of gamma-ray spectrometric nuclear detectors with a simple axial symmetric geometry, which is typical for many tasks of studying the decay of long-lived nuclei and measuring the radioactivity of natural objects. The program is designed for quick estimation of the effectiveness and the response function of the detector to monoenergetic gamma quanta in the energy range from keV to several MeV.

Thermally Activated Delayed Fluorescence (TADF) emitters must satisfy two competing requirements: small singlet-triplet energy gaps for thermal upconversion and sufficient spin-orbit coupling for fast reverse intersystem crossing. Predicting these properties accurately demands expensive calculations. We address this using a validated semi-empirical protocol (GFN2-xTB geometries, sTDA/sTD-DFT-xTB excited states) on 747 molecules, combined with charge-transfer descriptors from Natural Transition Orbital analysis. The hole-electron spatial overlap She emerges as a key predictor, accounting for 21% of feature importance for the triplet state alone. Our best model (Support Vector Regression) reaches MAE = 0.024 eV and R2 = 0.96 for $\Delta E_{ST}$. Active learning reduces the data needed to reach target accuracy by approximately 25% compared to random sampling. Three application domains are explored: NIR-emitting probes for bioimaging, photocatalytic sensitizers, and fast-response materials for photodetection.

Whereas there is growing interest in exploring longer-term climate with overshoot, including tipping elements, beyond 2100, most Integrated Assessment Models (IAMs) generate emissions scenarios only till 2100. Here we propose a framework to extend scenarios until 2150 using an emulator of IAMs. Our framework offers a potential interim solution for developing very long-term scenarios, such as the Scenario Model Intercomparison Project (ScenarioMIP), circumventing the challenges of fully simulating IAMs beyond 2100.

Precise three-dimensional (3D) reconstruction of wave free surfaces and associated velocity fields is essential for developing a comprehensive understanding of ocean physics. To address the high computational cost of dense visual reconstruction in long-term ocean wave observation tasks and the challenges introduced by persistent visual occlusions, we propose an wave free surface visual reconstruction neural network, which is designed as an attention-augmented pyramid architecture tailored to the multi-scale and temporally continuous characteristics of wave motions. Using physics-based constraints, we perform time-resolved reconstruction of nonlinear 3D velocity fields from the evolving free-surface boundary. Experiments under real-sea conditions demonstrate millimetre-level wave elevation prediction in the central region, dominant-frequency errors below 0.01 Hz, precise estimation of high-frequency spectral power laws, and high-fidelity 3D reconstruction of nonlinear velocity fields, while enabling dense reconstruction of two million points in only 1.35 s. Built on a stereo-vision dataset, the model outperforms conventional visual reconstruction approaches and maintains strong generalization in occluded conditions, owing to its global multi-scale attention and its learned encoding of wave propagation dynamics.

In this paper, we consider an adaptive optimal control problem for an SIR/V epidemic model with human behavioral effects.We develop a model where effective management of infectious diseases are monitored by the means of non pharmaceutical interventions.This study develops an adaptive optimal control function within an SIR/V framework embedding a non cooperative game theoretic mechanism to capture the dynamic interplay between individual vaccination behavior and population level transmission. We derive analytical expression for the optimal control trajectory under resource constrain and heterogeneous susceptibility and we validate our model using numerical simulations,calibrated with the real world epidemic parameters. We find that for the adaptive optimal policy for a generally known SIR/V model depending on the game theoretic epidemic state leads to substantial reduction in expenses compared to non adaptive policies. Moreover, our results demonstrate that, adaptive strategies significantly outperform the static policies by achieving lower peak infections and faster epidemic extinctions while evolutionary game dynamics identify critical behavioral thresholds that drive strategy evolution and inform timely policy adaptation

Based on Jaffer's (2023) heat engine analysis of natural convection, this investigation mathematically derives a novel, comprehensive formula predicting the natural convective heat transfer from an inclined cylinder given its length, diameter, angle, and Rayleigh number, and the fluid's Prandtl number and thermal conductivity. The present formula was tested with 116 inclined cylinder measurements having length-to-diameter ratios between 1.48 and 12500 in ten data-sets from four peer-reviewed studies, yielding (data-set) root-mean-squared relative error values between 1.0% and 4.7%.

Interferometric contrast is a key factor limiting the sensitivity of precision optical measurements, including the laser interferometers used in gravitational-wave detection. While standard formulas describe the interference of circular Gaussian beams, many real systems use beams with elliptical cross sections, where differing waists and radii of curvature can reduce fringe visibility. This paper derives an analytic expression for the maximum contrast achievable between two aligned elliptical Gaussian beams, written entirely in terms of their geometric and power parameters. We then test the formula using a free-space Michelson interferometer in which all beam parameters are independently measured through beam profiling and nonlinear fitting. In our experiment, the predicted maximum contrast was 0.968 while the experimentally optimized value was 0.950. The small discrepancy is consistent with expected imperfections such as beam rotation, mode mismatch, and non-Gaussian aberrations. This work provides a practical tool for modeling and optimizing elliptical-beam interferometers.

Approximately 1.4 Ga after life first appeared, atmospheric oxygen suddenly jumped by more than an order of magnitude over a 20-50 Ma period. The contrast between these two timescales does not seem to be due to any sudden, large amplitude change in external forcing. However, it could be due to processes intrinsic to the geobiological system itself, namely, positive feedback between atmospheric oxygen and photosynthetic bacteria: More oxygen leads to more photosynthesis, which leads to more oxygen, and so on. Already-published feedbacks include buildup of an ozone shield and nutrient production by oxidative weathering. The feedback proposed here is the 15-fold greater efficiency of aerobic vs anaerobic respiration and the tight coupling of respiration and photosynthesis inside the cell. As in the climate system, feedback leads to tipping points, where a rapid, large amplitude change in the state of the system occurs. For the geobiological system, the GOE is the tipping point, and the long buildup before the GOE is the gradual oxidation of the crust and ocean, due either to burial of organic matter, oxidation of volcanic gases, or escape of hydrogen to space. The feedback hypothesis is a framework for interpreting observations leading to the GOE.

We study the role of correlation in matching markets, where multiple decision-makers simultaneously face selection problems from the same pool of candidates. We propose a model in which a candidate's priority scores across different decision-makers exhibit varying levels of correlation dependent on the candidate's sociodemographic group. Such differential correlation can arise in school choice due to the varying prevalence of selection criteria, in college admissions due to test-optional policies, or due to algorithmic monoculture, that is, when decision-makers rely on the same algorithms and data sets to evaluate candidates. We show that higher correlation for one of the groups generally improves the outcome for all groups, leading to higher efficiency. However, students from a given group are more likely to remain unmatched as their own correlation level increases. This implies that it is advantageous to belong to a low-correlation group. Finally, we extend the tie-breaking literature to multiple priority classes and intermediate levels of correlation. Overall, our results point to differential correlation as a previously overlooked systemic source of group inequalities in school, university, and job admissions.

A novel, conservative discontinuous Galerkin algorithm is presented for particle kinetics on manifolds. The motion of particles on the manifold is represented using using both canonical and non-canonical Hamiltonian formulations. Our schemes apply to either formulations, but the canonical formulation results in a particularly efficient scheme that also conserves particle density and energy exactly. The collisionless update is coupled to a Bhatnagar-Gross-Krook (BGK) collision operator that provides a simplified model for relaxation to local thermodynamic equilibrium. An iterative scheme is constructed to ensure collisional invariants (density, momentum and energy) are preserved numerically. Rotation of the manifold is incorporated by modifying the Hamiltonian while ensuring a canonical formulation. Several test problems, including a kinetic version of the classical Sod-shock problem, Kelvin-Helmholtz instability on the surfaces of a sphere and a paraboloid, with and without rotations, is presented. A prospectus for further development of this approach to simulation of kinetic theory in general relativity is presented.

All materials have defects and many contain nanostructures, both of which disrupt chemical bonding - the basis of materials properties. No experimental measurements of bonding electron distributions associated with defects and nanostructures have ever been possible. We present a method enabling such measurements and interrogate nanovoids surrounded by vacancies - the most fundamental of nanostructures and defects - in aluminium. We measure the volume of a vacancy with 3% uncertainty and map vacancy concentrations surrounding nanovoids with nanometre resolution in three dimensions where previously only two-dimensional mapping was possible. We discover that radiation-damaged voids can "heal". Our bonding measurements are depth-resolved, vacancy-sensitive, and agree with density functional theory. This work opens bonding electron density measurements to inhomogeneous nanostructured multi-phased materials so that the electronic origins of phenomena such as strengthening, weakening, interface functionality, solute diffusion and phase transformations within them may be revealed.

The purpose of this note is to introduce the CUBE, a virtual reality immersion that was developed to help visualize electromagnetic fields, particularly the less familiar radiation fields students typically encounter in upper level physics courses. We discuss the pedagogical motivation for different features found in the software, and provide a brief overview of its use.