The Walker-Anderson half-space penetration model has been successfully used for the rapid, efficient calculation of penetration of walls by rigid and eroding rods. These models align well with detailed simulations for thick targets; however, existing extensions for finite targets struggle to accurately capture nose-tail velocity profiles in thinner targets. For stack-ups of thin-walled targets, this deficiency results in mischaracterized rod-erosion relative to hydrocode or experimental predictions. In this work, we leverage insights from detailed hydro-code simulations to propose an updated modification to the Walker-Anderson model to correctly account for wave propagation within a given target. This addition improves results for thin targets while retaining good behavior for thick targets with zero additional model parameters. Our updated model exhibits strong agreement with detailed simulations for targets with multiple thin walls.

Emperor penguins are the only penguin species that winter in Antarctica. As is known, during cold weather, birds huddle together to share body heat. We developed a microscopic model in which penguins interact with each other through an effective potential that describes the birds' intention to move along the gradient of the thermal field. The model describes the aggregation of penguins into a motionless huddle, as observed previously. More interestingly, we found that increasing the number of birds leads to a second-order phase transition, characterized by the excitation of a vortex motion in a huddle. The dynamic behavior ensures a more efficient redistribution of heat between penguins and, consequently, the survival of all birds in the flock. To study the instability mechanism, we developed a continuous model and applied both linear and weakly nonlinear analysis. Numerically, we studied the increasing complexity of vortex structures in a fluidized huddle with increasing its power. Finally, we demonstrate that the effect of sudden fluidization of a huddle, resulting in the spontaneous motion of penguins from the edge of the crowd to its center and back, is essentially thermal convection. The findings are juxtaposed against observations of emperor penguins.

We present FDTRImageEnhancer, an open-source computational framework that improves thermal conductivity mapping from Frequency Domain ThermoReflectance (FDTR) phase data by integrating a physics-based Gaussian convolution abstraction with microstructure-aware deep learning. The Gaussian kernel models the spatial averaging effects of pump and probe beams, while k-means clustering of high-resolution structural images reduces the parameter space for inverse modeling. A physics-informed neural network jointly minimizes phase-data error and deviation from analytically recovered conductivity maps, enabling the detection of grain boundary thermal conductivity drops visually obscured in conventional FDTR inversions. Demonstrated on finite element-generated synthetic data, the framework recovers bulk values within less than 0.5% error and qualitatively resolves grain boundary effects despite limited image resolution. Full Python code and datasets are provided for reproducibility, with the methodology readily adaptable to other inverse thermal transport problems.

Background: Accurately estimating volumetric water content (VWC) can greatly enhance the prediction of landslide risk. The standard approach involves using locally limited, invasive sensor measurements. Recently, however, electrical resistivity tomography (ERT) has emerged as a cost-effective, minimally invasive, real-time, indirect method of monitoring VWC. However, linking ER to VWC poses distinct challenges. Purpose: Random forest models were developed to estimate and predict VWC from ER. Spatially and temporally heterogeneous measurements were conducted to improve robustness and accuracy. Methods: The models were trained using 370-500 ER sensor measurements at depths of 10, 50, 150 and 190 cm. Precipitation and air temperature with a depth-dependent impact delay were introduced to simulate infiltration. The models were then validated at various locations, and their predictive capabilities were examined. Results: Including precipitation and temperature as parameters reduced the mean absolute error (MAE) by between 7.3% and 73.0%, depending on the depth. However, not all outliers could be eliminated. Delaying the model parameter depth-dependently reduced the maximum relative error (MaRE) by 14.7-56.0% and the mean relative error (MRE) to below 2.8%. Observed uncertainties for the top meter remained fairly constant. A 24 h forecast revealed an MRE below 5% for all depths; however, outliers occurred more frequently. Nevertheless, models trained on data from one location failed to generalize to other locations. VWC tomography based on ERT revealed significant moisture bands at depths of one to two meters. Conclusion: VWC can be reliably predicted from ERT. However, validation of VWC from ERT in different locations revealed significant deficits. It is expected that an automated ERT training of models at various locations will eliminate these differences.

The influence of initial shape imperfections on the post-buckling and translational behavior of encapsulated microbubbles is investigated subject to acoustic excitation in an unbounded flow. Bifurcation analysis reveals that imperfections lower the critical overpressure for buckling, guiding the bubble along stable branches with transitions influenced by the nature of the imperfections. The resulting branches emerge as unfoldings of bifurcation diagrams and closely resemble solution families emanating from the standard bifurcation diagram of a perfect sphere. Dynamic simulations for fixed forcing frequency demonstrate that the microbubble buckles at the defective pole and at maximum compression adopts the shape corresponding to the buckled shape that emerges in the static bifurcation diagram for the same pressure disturbance. Simulations further reveal that the deformed microbubble exhibits translational motion in the direction of concavity, driven by the balance between pressure and viscous drag, both aligned with this motion as a result of the deformed shape acquired by the microbubble. The translational speed is highly dependent on the shell stretching and bending elasticities and forcing frequency, with an optimal speed achieved when the forcing frequency equals the difference between the resonance frequencies of the initial shape and the compressed static shape corresponding to the imposed sound amplitude. Higher sound amplitude or lower bending resistance lead to more pronounced deformations that enhance translational motion. The impact of this dynamic response pattern on the recently reported translational motion of properly engineered coated microbubbles to act as microswimmers in modern drug delivery modalities under ultrasound excitation, is highlighted.

Social identity theory (SIT) and social categorization theory (SCT) are two facets of the social identity approach (SIA) to understanding social phenomena. SIT and SCT are models that describe and explain how people interact with one another socially, connecting the individual to the group through an understanding of underlying psychological mechanisms and intergroup behaviour. SIT, originally developed in the 1970s, and SCT, a later, more general offshoot, have been broadly applied to a range of social phenomena among people. The rise of increasingly social machines embedded in daily life has spurned efforts on understanding whether and how artificial agents can and do participate in SIA activities. As agents like social robots and chatbots powered by sophisticated large language models (LLMs) advance, understanding the real and potential roles of these technologies as social entities is crucial. Here, I provide a primer on SIA and extrapolate, through case studies and imagined examples, how SIT and SCT can apply to artificial social agents. I emphasize that not all human models and sub-theories will apply. I further argue that, given the emerging competence of these machines and our tendency to be taken in by them, we experts may need to don the hat of the uncanny killjoy, for our own good.

Geological parameterization procedures entail the mapping of a high-dimensional geomodel to a low-dimensional latent variable. These parameterizations can be very useful for history matching because the number of variables to be calibrated is greatly reduced, and the mapping can be constructed such that geological realism is automatically preserved. In this work, a parameterization method based on generative latent diffusion models (LDMs) is developed for 3D channel-levee-mud systems. Geomodels with variable scenario parameters, specifically mud fraction, channel orientation, and channel width, are considered. A perceptual loss term is included during training to improve geological realism. For any set of scenario parameters, an (essentially) infinite number of realizations can be generated, so our LDM parameterizes over a very wide model space. New realizations constructed using the LDM procedure are shown to closely resemble reference geomodels, both visually and in terms of one- and two-point spatial statistics. Flow response distributions, for a specified set of injection and production wells, are also shown to be in close agreement between the two sets of models. The parameterization method is applied for ensemble-based history matching, with model updates performed in the LDM latent space, for cases involving geological scenario uncertainty. For three synthetic true models corresponding to different geological scenarios, we observe clear uncertainty reduction in both production forecasts and geological scenario parameters. The overall method is additionally shown to provide posterior geomodels consistent with the synthetic true model in each case.

Thermoelectric metamaterials featuring width modulation through constrictions (constricted geometries) have emerged as a promising approach for improving heat management and thermoelectric performance. Through a combination of theoretical calculations, analytical formalism, and validation against experimental data, it is shown that thermoelectric performance in such geometries is governed by two fundamental mechanisms of pure geometrical origin: (i) a characteristic scaling behavior of resistance with Transmissivity, and (ii) the critical formation of the Constriction Thermal Resistance. Hourglass-shaped thermoelectric legs - identified as optimal in recent experiments - are found to exhibit the same underlying transport mechanisms observed in other constricted profiles, including single and multiple sharp constrictions. The commonly used Geometric Parameter is found to be insufficient for capturing the full influence of geometry on transport, whereas Transmissivity serves as a robust descriptor of constricted geometry, independent of material choice or device operating conditions. A universal scaling formalism is derived linking electrical and thermal resistances, along with key thermoelectric performance metrics, to the Transmissivity. A unified optimization framework is also developed for composite legs, incorporating both constricted material and contact electrodes. This framework indicates that previously reported performance gains may be largely attributed to contact resistance, rather than geometry alone. Transmissivity is established as a key geometric descriptor, enabling generalized design principles and global optimization criteria for enhancing thermoelectric power generation. This analysis elucidates new avenues in the design of thermoelectric metamaterials for efficient energy conversion.

A new approach, based on the analysis of a spectral problem for a discrete gyroscopic system, to modelling earthquakes on a rotating planet is presented in this paper. The eigenmodes of gyroscopic three-dimensional icosahedron-dodecahedron lattices are used for the discrete approximations of Earth's vibrations. Related model of the gyroscopic centred icosahedron lattice reveals new results about the motion of Earth's core. The vibrations of the tectonic plate boundaries, inducing shear and longitudinal motions of the ground, are described through the oscillations of the nodal elements within the discrete gyroscopic models. The theoretical analysis is complemented with the illustrative numerical examples linked to natural resonant vibrations observed during large earthquakes.

Geophysical flows are characterized by rapid rotation. Simulating these flows requires small timesteps to achieve stability and accuracy. Numerical stability can be greatly improved by the implicit integration of the terms that are most responsible for destabilizing the numerical scheme. We have implemented an implicit treatment of the Coriolis force in a rotating spherical shell driven by a radial thermal gradient. We modified the resulting timestepping code to carry out steady-state solving via Newton's method, which has no timestepping error. The implicit terms have the effect of preconditioning the linear systems, which can then be rapidly solved by a matrix-free Krylov method. We computed the branches of rotating waves with azimuthal wavenumbers ranging from 4 to 12. As the Ekman number (the non-dimensionalized inverse rotation rate) decreases, the flows are increasingly axially independent and localized near the inner cylinder, in keeping with well-known theoretical predictions and previous experimental and numerical results. The advantage of the implicit over the explicit treatment also increases dramatically with decreasing Ekman numbers, reducing the cost of computation by as much as a factor of 20 for Ekman numbers of order of $10^{-5}$. We carried out continuation for both the Rayleigh and Ekman numbers and obtained interesting branches in which the drift velocity remained unchanged between pairs of saddle-node bifurcations.

Geological Carbon Sequestration (GCS) has emerged as a promising strategy for mitigating global warming, yet its effectiveness heavily depends on accurately characterizing subsurface flow dynamics. The inherent geological uncertainty, stemming from limited observations and reservoir heterogeneity, poses significant challenges to predictive modeling. Existing methods for inverse modeling and uncertainty quantification are computationally intensive and lack generalizability, restricting their practical utility. Here, we introduce a Conditional Neural Field Latent Diffusion (CoNFiLD-geo) model, a generative framework for efficient and uncertainty-aware forward and inverse modeling of GCS processes. CoNFiLD-geo synergistically combines conditional neural field encoding with Bayesian conditional latent-space diffusion models, enabling zero-shot conditional generation of geomodels and reservoir responses across complex geometries and grid structures. The model is pretrained unconditionally in a self-supervised manner, followed by a Bayesian posterior sampling process, allowing for data assimilation for unseen/unobserved states without task-specific retraining. Comprehensive validation across synthetic and real-world GCS scenarios demonstrates CoNFiLD-geo's superior efficiency, generalization, scalability, and robustness. By enabling effective data assimilation, uncertainty quantification, and reliable forward modeling, CoNFiLD-geo significantly advances intelligent decision-making in geo-energy systems, supporting the transition toward a sustainable, net-zero carbon future.

Numerical simulations of geological CO2 storage in deep saline aquifers have demonstrated that vertical equilibrium (VE) models offer a robust and computationally efficient framework for reservoir optimization and upscaling. These studies emphasize the influence of fines migration and partial miscibility between water and CO2 on the evolving storage behaviour. Specifically, capillary-driven mobilization of clay and silica fines can impair injectivity, while water evaporation into the CO2 phase leads to near-wellbore drying, salt precipitation, and additional injectivity loss. However, conventional VE models do not account for these coupled physical and geochemical mechanisms. This work introduces an extended VE model that incorporates water vaporization into CO2, CO2 dissolution into the displaced brine, and fines migration leading to permeability reduction. For layer-cake aquifers, the model admits an exact analytical solution, providing closed-form expressions for both injectivity decline and sweep efficiency. The analysis identifies two distinct fronts during displacement: a leading displacement-dissolution front and a trailing full-evaporation front. Vertical model downscaling further reveals that gas saturation varies with the depth-dependent permeability profile. In formations where permeability decreases with depth, saturation declines monotonically; in contrast, in systems with increasing permeability, the competition between viscous and gravitational forces can generate non-monotonic saturation profiles. The results also indicate that while fines migration reduces injectivity, it may simultaneously enhance sweep efficiency; highlighting a complex trade-off that must be considered in the design and optimization of CO2 storage operations.

In a previous work we have described the classical structure and analyzed the interaction of the classical Dirac particle with uniform and oscillating electric and magnetic fields. In the present paper we consider the interaction of the Dirac particle at rest with an electromagnetic plane wave packet. The integration of the dynamical equations is done numerically with a Mathematica notebook that is available to the reader. Independently of the electric charge of the Dirac particle the interaction produces a linear momentum transfer such that the center of mass of the particle is moving with a component of its CM velocity in the direction of the propagation of the wave. The electromagnetic plane wave transfers energy, linear momentum and angular momentum to the particle. The CM spin is also modified and its change depends on the torque of the external Lorentz force defined at the center of charge position with respect to the center of mass and on the work of the external force along the center of mass trajectory of the particle. We analyze how the different physical parameters of the wave and the spin orientation determine the outcome of the numerical experiment.

Micro- and nanoscale fabrication, which enables precise construction of intricate three-dimensional structures, is of foundational importance for advancing innovation in plasmonics, nanophotonics, and biomedical applications. However, scaling fabrication to industrially relevant levels remains a significant challenge. We demonstrate that triplet-triplet annihilation upconversion (TTA-UC) offers a unique opportunity to increase fabrication speeds and scalability of micro- and nanoscale 3D structures. Due to its nonlinearity and low power requirements, TTA-UC enables localized polymerization with nanoscale resolutions while simultaneously printing millions of voxels per second through optical parallelization using off-the-shelf light-emitting diodes and digital micromirror devices. Our system design and component integration empower fabrication with a minimum lateral feature size down to 230 nm and speeds up to 112 million voxels per second at a power of 7.0 nW per voxel. This combination of high resolution and fast print speed demonstrates that TTA-UC is a significant advancement in nanofabrication technique, evidenced by the fabrication of hydrophobic nanostructures on a square-centimeter scale, paving the way for industrial nanomanufacturing.

The paper ``Magnetic moments in the Poynting theorem, Maxwell equations, Dirac equation, and QED", arXiv:2501.02022, by Peter J. Mohr, purports to show that Maxwell's equation, $\mathbf{\nabla}\cdot{\bf B}=0$, and Poynting's theorem require significant modications for the $\bf B$ field of a magnetic dipole. We show here that, because of critical errors in the paper, these claims are false, and that Maxwell's equation and the Poynting theorem are the same for a magnetic moment as for any other charge-current distribution.

The stability analysis of elastic rings subjected to various loading conditions is examined, focusing on stable and unstable configurations. The harmonic balance method is employed to investigate the stability range under different loading conditions. This method provides improved accuracy in results and yields a smooth, stable range of values for all modes of symmetry. Additionally, the bifurcation analysis technique of linearization is utilized to identify unstable regions, and it demonstrates good agreement with the results obtained using the harmonic balance method.

The vertical velocity in convective clouds ($w_c$) mediates convective anvil development and global moisture transport, influencing Earth's energy budget, but has yet to be estimated globally over long periods due to the absence of spaceborne retrievals. Here, a method for estimating $w_c$ given vertical profiles of in-cloud temperature, pressure, and latent heating rate is presented and assessed. The method relies on analytical models for the approximately linear relationship between $w_c$ and condensation rate ($\dot{q}_{vc}$) in convective clouds, which we derive from steady-state and non-steady-state plume models. We include in our analysis a version of $\dot{q}_{vc}/w_c$ derived from the supersaturation rate in convective clouds, recently presented in Kukulies et al. (2024). We assess the accuracy of the analytical models against convective cloud simulations run with different model cores and spatial resolutions in both tropical and mid-latitude environments. Differences in accuracies between tropical and mid-latitude environments suggest that this approach for estimating $w_c$ may lead to higher uncertainties in the mid-latitudes, although the estimated velocities are accurate. Despite assumptions in the analytical expressions that theoretically restrict them to liquid water clouds, $w_c$ is estimated to within $\approx2$ m/s of the simulated values at temperatures as low as 240 K. Potential applications, validation against future satellite mission observables, and future approaches for improving the estimation are discussed.

This report describes six demonstration cases of two numerical ocean wave models in high latitude regions where waves interact with sea ice. The two wave models are SWAN (Simulating WAves Nearshore) and WW3 (WAVEWATCH III), run in uncoupled mode within the Navy's coupled regional modeling system, COAMPS(R) (Coupled Ocean Atmosphere Mesoscale Prediction System). The COAMPS software handles a large majority of the tasks associated with the setup, running, and post-processing of the wave models. All six cases are cycling runs with 12-hour increments, each providing a continuous hindcast of four to 26 days duration. SWAN is applied in a Bering Strait case and two Gulf of Bothnia cases. WW3 is applied in a Sea of Okhotsk case and two Barents Sea cases. Verification is performed by visual inspection of model output fields, and by comparing model runs with alternative settings. In the standard configuration, forcing comes from archived global model output, including information on surface wind vectors, sea ice concentration and thickness, and surface current vectors, and the wave model uses a new empirical formula for dissipation of wave energy by sea ice that is dependent on ice thickness. The Barents Sea cases are compared to spectral wave data from satellite (SWIM instrument on CFOSAT) and from motion sensors deployed on the ice by the Norwegian Meteorological Institute. Experiments are performed with non-standard settings, 1) disabling the dissipation by sea ice, 2) using an older formula for dissipation by sea ice which does not depend on ice thickness, 3) using higher resolution wind and sea ice concentration forcing fields, and 4) omitting surface currents. The impact of these settings on model skill is quantified by comparison to the observations. The skill is also compared to that from a global (thus, lower resolution) ocean wave model.

The impact of femtosecond (fs) laser radiation on semiconductors with direct (ZnSe, GaAs, CdZnTe) band gap, with the structurally induced direct-to-indirect band gap transition (PbI2, GaSe) and indirect band gap (Si) has been studied. The fs-laser treatment of semiconductors has been performed in the multi-pulse regime in air environment. The influence of fs-laser radiation parameters on surface morphology of the semiconductors has been analysed by scanning electron microscopy (SEM) and 2D Fourier transform of SEM images, optical photoluminescence spectroscopy. Under the treatment with the fundamental fs-laser radiation (800 nm, about 130-150 fs) both low spatial frequency LIPSS and high spatial frequency LIPSS have been observed. Specific features of LIPSS of two types (low spatial frequency LIPSS and high spatial frequency LIPSS) and other structure peculiarities (grooves, grains, different defects in periodic structure, i.e. loops, strip breaks, and ablation products) have been also analysed. The formation mechanisms of LIPSS are also considered within the scope of two approaches,namely an electromagnetic approach and matter reorganization processes.

Obtaining predictive low-order models is a central challenge in fluid dynamics. Data-driven frameworks have been widely used to obtain low-order models of aerodynamic systems; yet, resulting models tend to yield predictions that grow unbounded with time. Recently introduced stability-promoting methods can facilitate the identification of bounded models, but tend to require extensive brute-force tuning even in the context of simple academic systems. Here, we show how recent theoretical advances in the long-term boundedness of dynamical systems can be integrated into data-driven modeling frameworks to ensure that resulting models will yield bounded predictions of incompressible flows. Specifically, we propose to solve a specific set of convex semi-definite programming problems to (i) certify whether a system admits a globally attracting bounded set for the chosen modeling parameters, and (ii) compute a model with the optimal (tightest) bound on this globally attracting set. We demonstrate the approach via integration within the sparse identification of nonlinear dynamics (SINDy) modeling framework. Application on two low-order benchmark problems establishes the merits of the approach. We then apply our approach to obtain a low-order (6-mode) model of unsteady separation over a NACA-65(1)-412 airfoil at Re = 20, 0000, a flow that has been notoriously difficult to model using data-driven methods. The resulting model is found to accurately predict the dynamics of unsteady separation, with model predictions remaining bounded indefinitely. We anticipate this work will benefit future efforts in modeling strongly nonlinear flows, especially in settings where physically-viable long-term forecasts are paramount.

A multispecies diffuse interface model is formulated in a fluctuating hydrodynamics framework for the purpose of simulating surfactant interfaces at the nanoscale. The model generalizes previous work to ternary mixtures, employing a Cahn-Hilliard free energy density combined with incompressible, isothermal fluctuating hydrodynamics where dissipative fluxes include both deterministic and stochastic terms. The intermolecular parameters in the free energy are chosen such that one species acts as a partially miscible surfactant. From Laplace pressure measurements we show that in this model the surface tension decreases linearly with surfactant concentration, leading to Marangoni convection for interfaces with concentration gradients. In the capillary wave spectrum for interfaces with and without surfactant we find that for the former the spectrum deviates significantly from classical capillary wave theory, presumably due to Gibbs elasticity. In non-equilibrium simulations of the Rayleigh-Plateau instability, deterministic simulations showed that the surfactant delays pinching of a fluid cylinder into droplets. However, stochastic simulations indicate that thermal fluctuations disrupt the surfactant's stabilizing effect. Similarly, the spreading of a patch of surfactant, driven by Marangoni convection, was found to be partially suppressed by thermal fluctuations.

Second order stochastic optimization methods, such as the linear method, couple the updates of different parameters and, in so doing, allow statistical uncertainty in one parameter to affect the update of other parameters. In simple tests, we demonstrate that the presence of unimportant orbital optimization parameters, even when initialized to zero, seriously degrade the statistical quality of the linear method's update for important orbital parameters. To counteract this issue, we develop an expand-and-prune selective linear combination of atomic orbitals algorithm that removes unimportant parameters from the variational set on the fly. In variational Monte Carlo orbital optimizations in propene, butene, and pentadiene, we find that large fractions of the parameters can be safely removed, and that doing so can increase the efficacy of the overall optimization.

The advent of high-performance graph libraries, such as the GraphBLAS, has enabled the analysis of massive network data sets and revealed new models for their behavior. Physical analogies for complicated network behavior can be a useful aid to understanding these newly discovered network phenomena. Prior work leveraged the canonical Gull's Lighthouse problem and developed a computational heuristic for modeling large scale network traffic using this model. A general solution using this approach requires overcoming the essential mathematical singularities in the resulting differential equations. Further investigation reveals a simpler physical interpretation that alleviates the need for solving challenging differential equations. Specifically, that the probability of observing a source at a temporal ``distance'' $r(t)$ at time $t$ is $p(t) \propto 1/r(t)^2$. This analogy aligns with many physical phenomena and can be a rich source of intuition. Applying this physical analogy to the observed source correlations in the Anonymized Network Sensing Graph Challenge data leads to an elegant cyber orbit analogy that may assist with the understanding network behavior.

To address the systematic underestimation of typhoon intensity in artificial intelligence weather prediction (AIWP) models, we propose the Intelligent Shanghai Typhoon Model (ISTM): a unified regional-to-typhoon generative probabilistic forecasting system based on a two-stage UNet-Diffusion framework. ISTM learns a downscaling mapping from 4 years of 25 km ERA5 reanalysis to a 9 km high resolution typhoon reanalysis dataset, enabling the generation of kilometer-scale near-surface variables and maximum radar reflectivity from coarse resolution fields. The evaluation results show that the two-stage UNet-Diffusion model significantly outperforms both ERA5 and the baseline UNet regression in capturing the structure and intensity of surface winds and precipitation. After fine-tuning, ISTM can effectively map AIFS forecasts, an advanced AIWP model, to high-resolution forecasts from AI-physics hybrid Shanghai Typhoon Model, substantially enhancing typhoon intensity predictions while preserving track accuracy. This positions ISTM as an efficient AI emulator of hybrid modeling system, achieving fast and physically consistent downscaling. The proposed framework establishes a unified pathway for the co-evolution of AIWP and physics-based numerical models, advancing next-generation typhoon forecasting capabilities.

Curable hydrogels have tunable properties that make them well-suited for applications in drug delivery, cell therapies, and 3D bioprinting. Advances in microfluidic droplet generation enable rapid fabrication of polymer-filled droplets. UV-curable polymers offer a clear path toward using fluidic generation to produce monodisperse microgels with uniform properties. In flow, polymer concentration and UV exposure both control the degree of crosslinking. High UV intensity is often used to ensure complete gelation and avoid complications that may arise from partial curing. Optical microscopy can assess droplet and particle sizes in flow. However, optimizing formulations for mechanical properties usually requires removal of generated material and external measurement outside of flow. In this study, we couple droplet generation, microgel fabrication, and mechanics assessment within a single fluidic device. We make and measure soft polyethylene glycol diacrylate (PEGDA) microgels by curing polymer-filled water drops in mineral oil. Crosslinking is tuned by varying UV dosage, allowing us to study how gelation degree influences microgel properties. Within the device, we use shape deformation in flow to measure the restoring stress of both droplets and particles. Our results suggest that PEGDA droplets gel from the inside out. If gelation is incomplete, a particle resides within a fluid drop. Independent measurements outside of flow corroborate this observation. Crosslinking PEGDA-filled droplets in a pendant drop geometry, with dye, suggests the persistence of an aqueous shell around the gel. Similarly, microparticles in PEGDA-filled drops undergoing gelation exhibit diffusive arrest near the drop center, while maintaining mobility in an outer region. Together, these results suggest the importance of considering the extent of gelation when fabricating microgels using fluidics.

We present a novel method for aligning a laser ionized plasma source to a pair of ultra-relativistic electron beams that comprise a plasma wakefield accelerator (PWFA). We achieve alignment by analyzing the plasma afterglow light observed at two longitudinal locations as the plasma column is scanned across the electron beam. By analyzing the relative plasma light intensity at the two locations, we aligned an 85-cm plasma source to a 10 GeV, 1.6 nC electron beam to within 10 um offset, 10 urad tilt. The alignment is verified by analyzing the drive beam energy loss, energy transfer efficiency, and the witness beam energy gain as a function of the misalignment between the beams and the plasma. From this measurement, we extract the alignment tolerance required between the laser-ionized plasma source and electron beams, an important metric necessary for collider design studies or light source applications based on a laser-ionized plasma source.

Particle methods play an important role in computational fluid dynamics, but they are among the most difficult to implement and solve. The most common method is smoothed particle hydrodynamics, which is suitable for problem settings that involve large deformations, such as tsunamis and dam breaking. However, the calculation can become unstable depending on the distribution of particles. In contrast, the neural particle method has high computational stability for various particle distributions is a machine learning method that approximates velocity and pressure in a spatial domain using neural networks. The neural particle method has been extended to viscous flows, but until now it has been limited to incompressible flows. In this paper, we propose the compressible neural particle method, which is a new feed-forward neural network-based method that extends the original neural particle method to model compressible viscous fluid flows. The proposed method uses neural networks to calculate the velocity and pressure of fluid particles at the next time step, and the Tait equation to calculate the density to handle the compressibility. The loss function is composed of the governing equations of compressible flow and the boundary conditions, which are free surface and solid boundary conditions. We demonstrate that the proposed method can accurately solve the compressible viscous fluid flow, a problem that was difficult to solve with the smoothed particle hydrodynamics method, by applying it to a dam breaking problem.

In Navier-Stokes (NS) turbulence, large-scale turbulent flows inevitably determine small-scale flows. Previous studies using data assimilation with the three-dimensional NS equations indicate that employing observational data resolved down to a specific length scale, $\ell^{3D}_{\ast}$, enables the successful reconstruction of small-scale flows. Such a length scale of `essential resolution of observation' for reconstruction $\ell^{3D}_{\ast}$ is close to the dissipation scale in three-dimensional NS turbulence. Here we study the equivalent length scale in two-dimensional NS turbulence, $\ell^{2D}_{\ast}$, and compare with the three-dimensional case. Our numerical studies using data assimilation and conditional Lyapunov exponents reveal that, for Kolmogorov flows with Ekman drag, the length scale $\ell^{2D}_{\ast}$ is actually close to the forcing scale, substantially larger than the dissipation scale. Furthermore, we discuss the origin of the significant relative difference between the length scales, $\ell^{2D}_{\ast}$ and $\ell^{3D}_{\ast}$, based on inter-scale interactions and orbital instabilities in turbulence dynamics.

Optimizing the stability and control performance of complex networks often hinges on effectively identifying critical nodes for targeted intervention. Due to their inherent complexity and high dimensionality, large-scale energy flow networks, prevalent in domains like power grids, transportation, and financial systems, present unique challenges in selecting optimal nodes for resource allocation. While numerous centrality measurements, such as Katz centrality, eigenvector centrality, closeness centrality, betweenness centrality, and PageRank, have been proposed to evaluate node importance, the impact of different centrality metrics on stability outcomes remains inadequately understood. Moreover, networks manifest diverse structural characteristics-including small-world, scale-free, and random graph properties-which further complicates the optimization problem. This paper systematically investigates how various node centrality measurements influence control stability across representative complex network structures. A unified energy-flow dynamical model is developed, and performance metrics such as the L1 norm are employed to quantify the network stability implications of employing different centrality metrics. Extensive numerical simulations over statistically generated network ensembles reveal significant variances in stability outcomes, highlighting the crucial role of centrality selection. The findings underscore the sensitivity of energy-flow stability to seemingly minor changes in topological node rankings, providing practical insights for enhancing control efficiency and robustness in real-world networked systems.

In conventional x-ray ptychography, diffraction data is collected by scanning a sample through a monochromatic, and spatially coherent, x-ray beam. A high-resolution image is then retrieved using an iterative algorithm. Combined with a scan of the incident photon energy, it is also possible to access chemical and elemental information. Although powerful, the high brilliance required currently constrains the method to 3rd and 4th generation synchrotron sources and long scanning times. An alternative approach is to use a broadband illumination in combination with an energy resolving detector. These detectors record the data in a series of energy channels simultaneously, creating a stack of coherent data suitable for a ptychographic reconstruction. This approach promises to unlock the full power of the radiation source and provide spectral imaging at a higher rate and in a single acquisition. However, these detectors currently saturate well below reaching the flux rates produced at synchrotrons, which is preventing the uptake of this approach. Furthermore, current monochromatic synchrotron setups typically employ Fresnel zone plates for pre-sample focusing due to their stability, flexibility, and affordability, but these diffractive optics limit the spectral bandwidth that the setup can accept. In this article, we analyze the problem and consider alternative optics that can both maximize the total photon detection rates and broaden the tolerable bandwidth. Broadband x-ray ptychography has the potential to dramatically reduce data collection times at synchrotron sources, but also to harness the full power of lower brilliance sources and transition x-ray ptychography into a laboratory technique.

The high Weissenberg number problem has been a persistent challenge in the numerical simulation of viscoelastic fluid flows. This paper presents an improved lattice Boltzmann method for solving viscoelastic flow problems at high Weissenberg numbers. The proposed approach employs two independent two-relaxation-time regularized lattice Boltzmann models to solve the hydrodynamic field and conformation tensor field of viscoelastic fluid flows, respectively. The viscoelastic stress computed from the conformation tensor is directly embedded into the hydrodynamic field using a newly proposed local velocity discretization scheme, thereby avoiding spatial gradient calculations. The constitutive equations are treated as convection-diffusion equations and solved using an improved convection-diffusion model specifically designed for this purpose, incorporating a novel auxiliary source term that eliminates the need for spatial and temporal derivative computations. Additionally, a conservative non-equilibrium bounce-back (CNEBB) scheme is proposed for implementing solid wall boundary conditions in the constitutive equations. The robustness of the present algorithm is validated through a series of benchmark problems. The simplified four-roll mill problem demonstrates that the method effectively improves numerical accuracy and stability in bulk regions containing stress singularities. The Poiseuille flow problem validates the accuracy of the current algorithm with the CNEBB boundary scheme at extremely high Weissenberg numbers (tested up to Wi = 10,000). The flow past a circular cylinder problem confirms the superior stability and applicability of the algorithm for complex curved boundary problems compared to other existing common schemes.

In this article, we derive and discuss the properties of the symplectic group Sp(2), which arises in Hamiltonian dynamics and ray optics. We show that a symplectic matrix can be written as the product of a symmetric dilation matrix and a rotation matrix, in either order. A symplectic matrix can be written as the exponential of a generating matrix, and there is a one-to-one relation between the coefficients of the symplectic and generating matrices. We also discuss the adjoint and Schmidt decompositions of a symplectic matrix, and the product of two symplectic matrices. The results of this article have applications in many subfields of physics.

In this article, matrix and vector formalisms for Lorentz transformations in time ($t$) and two space dimensions ($x$ and $y$) are developed and discussed. Lorentz transformations conserve the squared interval $t^2 - x^2 - y^2$. Examples of Lorentz transformations include boosts in arbitrary directions, which mix time ansd space, and rotations in space, which do not. Lorentz transformations can be described by matrices and coordinate vectors. Lorentz matrices comprise the special unitary group SO(1,2). The general form of a Lorentz matrix is derived, in terms of both components and block matrices. Each Lorentz matrix $L$ has the Schmidt decomposition $QDP^t$, where $D$ is a diagonal matrix, and $P$ and $Q$ are orthogonal matrices. It also has the Schmidt-like decomposition $R_2BR_1^t$, where $B$ is a boost matrix, and $R_1$ and $R_2$ are rotation matrices. Hence, a Lorentz matrix is specified by three parameters, namely the boost energy $\gamma$, and the rotation angles $\theta_1$ and $\theta_2$. Each Lorentz matrix has a pair of reciprocal Schmidt coefficients (which are real), and a unit coefficient, which is its own reciprocal. It also has a pair of reciprocal eigenvalues (which are real or complex), and a unit eigenvalue. The physical significances of the input and output Schmidt vectors, and the eigenvectors, are discussed. Every Lorentz matrix can be written as the exponential of a generating matrix. There are three basic generators, which produce boosts along the $x$ and $y$ axes, and a rotation about the $t$ axis (in the $xy$ plane). These generators satisfy certain commutation relations, which show that SO(1,2) is isomorphic to Sp(2) and SU(1,1). Simple formulas are derived for the energy and angle parameters of a composite transformation, in terms of the parameters of its constituent transformations.

In this tutorial, exponentiation and factorization (decomposition) formulas are derived and discussed for common matrix operators that arise in studies of classical dynamics, linear and nonlinear optics, and special relativity. To understand the physical properties of systems of common interest, one first needs to understand the mathematical properties of the symplectic group Sp(2), the special unitary groups SU(2) and SU(1,1), and the special orthogonal groups SO(3) and SO(1,2). For these groups, every matrix can be written as the exponential of a generating matrix, which is a linear combination of three fundamental matrices (generators). For Sp(2), SU(1,1) and SO(1,2), every matrix also has a Schmidt decomposition, in which it is written as the product of three simpler matrices. The relations between the entries of the matrix, the generator coefficients and, where appropriate, the Schmidt-decomposition parameters are described in detail. It is shown that Sp(2) is isomorphic to (has the same structure as) SU(1,1) and SO(1,2), and SU(2) is isomorphic to SO(3). Several examples of these isomorphisms (relations between Schmidt decompositions and product rules) are described, which illustrate their usefulness (complicated results can be anticipated or derived easily). This tutorial is written at a level that is suitable for senior undergraduate students and junior graduate students.

There hardly is a fluid mechanics phenomenon attracting more attention than the impact of a droplet, due to its undeniable beauty, many applications and the numerous challenges it poses. One of the crucial factors turns out to be the cushioning effect of the gas surrounding the droplet. This fact, together with the observation that almost all of the relevant literature was done in air, triggers the question what would happen when the liquid was a boiling liquid, i.e., a liquid in thermal equilibrium with its own vapor, as is the case during transport of cryogenic liquids such as liquid hydrogen. To investigate precisely this question, we experimentally generate droplets in thermodynamical equilibrium with their own vapor, even before impact, such that minute energy exchanges of the droplet with its surroundings can trigger phase change. Using a frustrated total internal reflection (TIR) setup, we make the exciting observation that depending on the impact speed and vapor conditions, the entrapment of vapor can be completely suppressed under boiling liquid conditions. We create a simplified model based on scaling arguments and perform numerical simulations considering both the compressible and condensable properties of the vapor layer that are in very good agreement with our experimental findings. Our results can be of great consequence to the pressures exerted during droplet impact and on an industrial scale may help better understand the loads experienced during sloshing wave impact inside cryogenic liquid containers.

The Carnot theory is unique among the theories of heat developed before the emergence of thermodynamics because it considers the relationship between heat and work. The theory is contained in Carnot's book published in 1824, which includes the basic ideas on how thermal machines work, including the need for a temperature difference. The fundamental principle of the theory is stated with the help of a cyclic process invented by Carnot, involving two isotherms and two adiabatics. The ratio between the mechanical work produced during the cycle and the heat involved depends only on the temperatures. We make a critical analysis of Carnot theory and show how the fundamental principle was used by Clausius to define entropy in terms of which he enunciated the second law of thermodynamics.

Urban segregation research has long relied on residential patterns, yet growing evidence suggests that racial/ethnic segregation also manifests systematically in mobility behaviors. Leveraging anonymized mobile device data from New York City before and during the COVID-19 pandemic, we develop a network-analytic framework to dissect mobility segregation in racialized flow networks. We examine citywide racial mixing patterns through mixing matrices and assortativity indices, revealing persistent diagonal dominance where intra-group flows constituted 69.27% of total movements. Crucially, we develop a novel dual-metric framework that reconceptualizes mobility segregation as two interlocking dimensions: structural segregation-passive exposure patterns driven by residential clustering, and preferential segregation-active homophily in mobility choices beyond spatial constraints. Our gravity-adjusted indices reveal that racial divisions transcend residential clustering-all racial groups exhibit active homophily after spatial adjustments, with particularly severe isolation during the pandemic. Findings highlight how pandemic restrictions disproportionately amplified asymmetric isolation, with minority communities experiencing steeper declines in access to White-dominated spaces. Finally, we propose a Homophily Gravity Model with racial similarity parameter, which significantly improves the prediction accuracy of race-specific mobility flows and more accurately reproduces intra-group mobility preferences and directional exposure this http URL, this study redefines mobility segregation as a multidimensional phenomenon, where structural constraints, active preferences, and crisis responses compound to reshape urban racial inequality.

This paper reports two versions of terahertz (THz)-driven nanotip field-emission electron guns: single-layer reflective guns (SLRGs) and double-layer reflective guns (DLRGs). Both guns use nanotip emitters and accelerate electrons through the electric field of the THz wave. SLRGs employ a reflective structure to superimpose the initial and subsequent half-cycles of the THz electric field, enhancing the field amplitude and acceleration efficiency. Experiments have demonstrated that SLRGs achieve higher acceleration efficiency than single-layer nonreflective guns (SLNRGs) for identical THz input energies. This constitutes direct experimental verification of the efficacy of the reflective structure. Theoretically, SLRGs operating in single-feed mode can match the acceleration efficiency of dual-feed SLNRGs while reducing operational complexity. DLRGs demonstrate THz-driven cascaded electron acceleration through precise scanning of the delay between two incident THz beams. This represents a direct experimental demonstration of cascaded acceleration in THz-driven electron sources. The experimental results of DLRGs align closely with the results of electron dynamics predicted by simulations. This establishes the foundation for developing multilayer high-acceleration-efficiency THz-driven high-energy electron guns. The ability to manipulate the THz for each layer individually holds promising potential for improving the beam quality of THz electron guns.

The aim of the paper is to provide information on a newly developed design methodology for the evaluation of bridge expansion joints with respect to their noise emission and overall technical condition. The methodology also gives recommendations for operational non-destructive measurements of the condition of bridge gates using a crossing laser sensors, CPX sensors or the necessary technical equipment and the method of collecting, processing and evaluating the measured data. The method can easily scan and evaluate geometry and/or noise emission of extensive number of joints by passing them without traffic interruption. The aim is to establish a methodology for comparing the noise and condition of bridge expansion joints in the road network, both over time (monitoring long-term trends in noise emission and degradation) and comparing different states and different types of bridge closures between each other.

The paper thoroughly analyses the significant cracking on the newly refurbished masonry bridge pier in Lovosice. The cracks of width up to 15 mm appeared shortly after the reconstruction of the bridge and were the subject of a detailed assessment. First, a monitoring system was mounted to analyse the behaviour during the year and the dependency on the temperature. Next, a dive inspection was done in order to evaluate the conditions under the water. The next step was to make a diagnostics survey, based on the drilling, endoscopic tests and water pressure tests. Based on the results, several detailed numerical models were created in order to analyse the pier behaviour and conduct a parametric study to investigate various factors, leading to the defects. The numerical models covered the complex geometry of the pier, consisting of the inner poor concrete core and outer shell from sandstone masonry blocks. The above-described procedure revealed the causes of the defects and also recommended the proper strengthening of the pier.

We present an open-source background-oriented schlieren dataset with 70 views of high-speed flow over a flight body. Sample analyses are performed using a neural-implicit reconstruction technique (NIRT) with total variation regularization as well as data assimilation via the 3D compressible Euler equations. Limited-data reconstructions based on nine views resolve sharp shocks consistent with the geometry, reproduce validation deflections with high fidelity, and exhibit minimal artifacts. Data assimilation recovers unmeasured fields, marking the first experimental demonstration of 3D state estimation directly from experimental schlieren measurements. The NIRT also enables efficient uncertainty quantification, providing insight into well-resolved flow features and guiding design-of-experiments efforts. Public access to the data and code repositories is detailed at the end of this correspondence.

Coded wavefront sensing (Coded-WFS) is a snapshot quantitative phase imaging (QPI) technique that has been shown to successfully leverage the memory effect to retrieve the phase of biological specimens. In this paper, we perform QPI on static silica beads and dynamic HEK cells using Coded-WFS. The accuracy of the retrieved phase map is validated using digital holographic microscopy (DHM) for the same specimens. We report comparisons of simultaneous bright-field intensity and optical path delay.

A fully automated and continuous calibration framework for All-Sky Imagers (ASIs) that significantly enhances the spatial accuracy and reliability of geo-referenced ASI data is presented. The technique addresses a critical bottleneck in ASI image data reliability and usability for real time space weather via automated geo-referencing under real-world field conditions. The system corrects the lens distortion in ASIs using a well-established fisheye lens model and automatically estimates camera orientation in terms of roll, pitch, and yaw angles relative to True North and the horizontal plane perpendicular to the zenith using star tracking. Unlike traditional methods that require manual intervention and periodic recalibration, Auto-Cal performs nightly unattended recalibrations using observed stellar motion, adapting to mechanical shifts or environmental changes. Each calibration step includes formal error estimates, allowing end users to assess the confidence of geo-located data in real-time. This capability enables dependable ASI operations in remote or unmanned settings and supports higher-fidelity integration with other geophysical instruments. Auto-Cal provides a scalable foundation for maintaining a large array of ASIs, thus enabling long-term atmospheric monitoring and real-time space weather alerts.

Ionization is one of the basic physical processes, occurring when charged particles penetrate atomic matter. When atoms are bombarded by very dense and compact beams of extreme relativistic electrons, two qualitatively new -- and very efficient -- ionization mechanisms arise: the tunnel or over-barrier ionization and the coherent impact ionization, which are driven by the low- and high-frequency parts, respectively, of the beam field. In these mechanisms significant fractions of the beam electrons act coherently, strongly enhancing the ionization process. They are also very sensitive to the spatiotemporal structure of the beam that can be used for analysing the beam properties.

Purpose To evaluate the reasoning capabilities of large language models (LLMs) in performing root cause analysis (RCA) of radiation oncology incidents using narrative reports from the Radiation Oncology Incident Learning System (RO-ILS), and to assess their potential utility in supporting patient safety efforts. Methods and Materials Four LLMs, Gemini 2.5 Pro, GPT-4o, o3, and Grok 3, were prompted with the 'Background and Incident Overview' sections of 19 public RO-ILS cases. Using a standardized prompt based on AAPM RCA guidelines, each model was instructed to identify root causes, lessons learned, and suggested actions. Outputs were assessed using semantic similarity metrics (cosine similarity via Sentence Transformers), semi-subjective evaluations (precision, recall, F1-score, accuracy, hallucination rate, and four performance criteria: relevance, comprehensiveness, justification, and solution quality), and subjective expert ratings (reasoning quality and overall performance) from five board-certified medical physicists. Results LLMs showed promising performance. GPT-4o had the highest cosine similarity (0.831), while Gemini 2.5 Pro had the highest recall (0.762) and accuracy (0.882). Hallucination rates ranged from 11% to 51%. Gemini 2.5 Pro outperformed others across performance criteria and received the highest expert rating (4.8/5). Statistically significant differences in accuracy, hallucination, and subjective scores were observed (p < 0.05). Conclusion LLMs show emerging promise as tools for RCA in radiation oncology. They can generate relevant, accurate analyses aligned with expert judgment and may support incident analysis and quality improvement efforts to enhance patient safety in clinical practice.

Neural operators have recently emerged as a powerful tool for solving partial differential equations (PDEs), including Maxwell's equations in computational electromagnetics. Prior machine learning models often fail to capture both temporal field evolution and generalization to irregular geometries. Here, we introduce a Data-Aware Fourier Neural Operator (DA-FNO) as a surrogate solver. Applied autoregressively, it predicts the temporal evolution of all field components while monitoring energy dynamics, terminating automatically once energy converges. The model generalizes to complex geometries and the optical C-band without retraining, achieving a 7.5* speedup with nearly 92.5% accuracy. This approach offers a potentially efficient and accurate alternative to conventional iterative solvers for C-band photonic simulations.

Energy-efficient computing is becoming increasingly important in the information era. However, electronic computers with von Neumann architecture can hardly meet the challenge due to the inevitable energy-intensive data movement, especially when tackling computationally hard problems or complicated tasks. Here, we experimentally demonstrate an energy-efficient photonic computer that solves intractable subset sum problem (SSP) by making use of the extremely low energy level of photons (~10^(-19) J) and a time-of-flight storage technique. We show that the energy consumption of the photonic computer maintains no larger than 10^(-15) J per operation at a reasonably large problem size N=33, and it consumes 10^(8) times less energy than the most energy-efficient supercomputer for a medium-scale problem. In addition, when the photonic computer is applied to deal with real-life problems that involves iterative computation of the SSP, the photonic advantage in energy consumption is further enhanced and massive energy can be saved. Our results indicate the superior competitiveness of the photonic computer in the energy costs of complex computation, opening a possible path to green computing.

Distortions in the electron distribution function driven by intense temperature gradients critically influence the generation and evolution of heat flux and magnetic fields in plasmas under the condition of inertial confinement fusion. Describing such kinetic behaviors at large spatiotemporal scales typically requires multigroup models based on simplified Vlasov-Fokker-Planck equations. However, the accuracy of existing multigroup models remains uncertain, without a well-defined methodology for implementing nonlocal magnetic field corrections. This paper develops an improved nonlocal multigroup model for magnetized plasmas. The advancements comprise: (i) a revised source term in the diffusion equations, (ii) a Biermann-producing electric field equation incorporating the density perturbation, and (iii) a nonlocal correction method for the Nernst velocity. The numerical method for the anisotropic heat conduction equation is analyzed, and three test cases demonstrate that the model accurately predicts the key phenomena arising from nonlocal effects in magnetized plasmas.

Modeling the wave nature of light and the propagation and diffraction of electromagnetic fields is crucial for the accurate simulation of many phenomena, yet wave simulations are significantly more computationally complex than classical ray-based models. In this work, we start by analyzing the classical path integral formulation of light transport and rigorously study which wave-optical phenomena can be reproduced by it. We then introduce a bilinear path integral generalization for wave-optical light transport that models the wave interference between paths. This formulation subsumes many existing methods that rely on shooting-bouncing rays or UTD-based diffractions, and serves to give insight into the challenges of such approaches and the difficulty of sampling good paths in a bilinear setting. With this foundation, we develop a weakly-local path integral based on region-to-region transport using elliptical cones that allows sampling individual paths that still model wave effects accurately. As with the classic path integral form of the light transport equation, our path integral makes it possible to derive a variety of practical transport algorithms. We present a complete system for wave tracing with elliptical cones, with applications in light transport for rendering and efficient simulation of long-wavelength radiation propagation and diffraction in complex environments.

In the last few years several ``universal'' interatomic potentials have appeared, using machine-learning approaches to predict energy and forces of atomic configurations with arbitrary composition and structure, with an accuracy often comparable with that of the electronic-structure calculations they are trained on. Here we demonstrate that these generally-applicable models can also be built to predict explicitly the electronic structure of materials and molecules. We focus on the electronic density of states (DOS), and develop PET-MAD-DOS, a rotationally unconstrained transformer model built on the Point Edge Transformer (PET) architecture, and trained on the Massive Atomistic Diversity (MAD) dataset. We demonstrate our model's predictive abilities on samples from diverse external datasets, showing also that the DOS can be further manipulated to obtain accurate band gap predictions. A fast evaluation of the DOS is especially useful in combination with molecular simulations probing matter in finite-temperature thermodynamic conditions. To assess the accuracy of PET-MAD-DOS in this context, we evaluate the ensemble-averaged DOS and the electronic heat capacity of three technologically relevant systems: lithium thiophosphate (LPS), gallium arsenide (GaAs), and a high entropy alloy (HEA). By comparing with bespoke models, trained exclusively on system-specific datasets, we show that our universal model achieves semi-quantitative agreement for all these tasks. Furthermore, we demonstrate that fine-tuning can be performed using a small fraction of the bespoke data, yielding models that are comparable to, and sometimes better than, fully-trained bespoke models.

We unify k-local Ising optimization and optical KAN function learning on a single photonic platform, establishing a critical convergence point in optical computing that enables interleaved discrete-continuous workflows. We introduce a single spacial light modulator (SLM)-centric primitive that realizes, in one stroke, all-optical k-local Ising interactions and fully optical Kolmogorov-Arnold network (KAN) layers. The central idea is to convert structural nonlinearity of a nominally linear photonic scatterer into a per-window computational resource by adding one relay pass through the same spatial light modulator. A folded 4f relay reimages the first Fourier plane onto the SLM so that each chosen spin clique or ridge channel occupies a disjoint window with its own second-pass phase patch. Propagation remains linear in the optical field, yet the measured intensity in each window becomes a freely programmable polynomial of the clique sum or projection amplitude. This yields native, per-clique k-local couplings without nonlinear media and, in parallel, the many independent univariate nonlinearities required by KAN layers, all with in-situ physical gradients for training using two-frame (forward and adjoint) physical gradients. We outline implementation on spatial photonic Ising machines, injection-locked VCSEL arrays, and the Microsoft analog optical computers. In all cases the hardware change is one extra lens and a fold (or an on-chip 4f loop), enabling a minimal overhead, massively parallel route to high-order optical Ising optimization and trainable, all-optical KAN processing.

We employ statistical physics and information-theoretic methods to quantify the dependencies between key atmospheric pollutants and meteorological variables across multiple Indian cities. To capture both linear and nonlinear relationships, we introduce a Composite Correlation Index (CCI) that combines the Pearson correlation coefficient with entropy-based measures, including mutual information and conditional entropy. Based on the CCI values, cities are clustered into distinct groups, uncovering regional similarities in pollutant-meteorology interactions that may reflect shared climatic or environmental conditions. To explore temporal structure and causal dynamics, we analyze the relationship between particulate matter (PM2.5) and relative humidity (RH) using transfer entropy, which reveals a bidirectional flow of information in most locations. Further time-domain analysis via time-delayed mutual information shows that, in many cities, the dependence between PM2.5 and RH peaks at zero lag and decays exponentially thereafter, indicating predominantly contemporaneous interactions with limited memory. This integrative framework provides a robust approach to characterizing atmospheric interaction regimes, bridging statistical physics with environmental complexity and revealing new insights into the pollutant-meteorology dynamics.

This paper describes the new Simple Ocean Data Assimilation version 4 (SODA4) global eddy-resolving ocean/sea ice reanalysis that spans the 45-year period 1980-2024. The reanalysis is constructed using GFDL MOM5/SIS1 numerics and ECMWF ERA5 forcings with surface and subsurface temperature and salinity observations as constraints within an optimal interpolation data assimilation algorithm. The method of construction and resulting output files are described. Comparison of the SODA4 temperature and salinity fields to observations and to the UK Met Office EN4 temperature and salinity analyses in the upper ocean shows SODA4 has marginal bias and exhibits more regional variability, with less of an imprint of the sparse and inhomogeneous distribution of observations. Comparison of transports across major ocean sections and passages are generally consistent with independent moored observations.

A design strategy for achieving broadband optical gain in GaSb-based semiconductor amplifiers operating beyond 2 \mu m is presented. By employing asymmetric GaInSb/AlGaAsSb quantum wells (QWs) of varying thicknesses, a flat and wide gain spectrum is demonstrated. The approach leverages carrier density and transition energy tuning across QWs to access various energy levels at specific current densities. Simulations using "Harold" self-consistent environment predict a full-width at half-maximum (FWHM) gain bandwidth exceeding 340 nm for a structure comprising one 7 nm and three 13 nm-thick QWs. The modelling parameters were validated against experimental data, ensuring a robust framework for designing broadband amplifiers and superluminescent diodes for mid-infrared applications.

Rydberg atom radio frequency sensors are unique in a number of ways, including possessing extraordinary carrier bandwidth, self-calibration and accuracy. In this paper, we examine the impact of thermal radiation on Rydberg atom sensors. Antennas are limited by their thermal background, while Rydberg atom sensors are coherent sensors. Incoherent thermal radiation does not limit Rydberg atom sensors in the same way as an antenna. The primary consequence of a thermal radiation field on Rydberg atom sensors is to decrease their coherence, as the decay rates of the Rydberg states used for sensing the radio frequency field are increased due to the thermal field, i.e. blackbody, modification of the atomic decay rates. Thermal and coherent field excitation are fundamentally different in that thermal fields produce statistically independent excitations with well-defined frequency, polarization, and propagation direction, while coherent states are coherent superpositions of photon number states. Consequently, thermal fields do not contribute to the coherences of the density matrix that are used for Rydberg atom sensing, except for damping them.

Transient electromagnetic logging (TEL) is of great significance for detecting deep formation structures outside the wellbore. Forward modeling using analytical solutions requires frequency-domain calculations followed by time-domain conversion. For electromagnetic field computations in horizontally stratified media, mature Hankel transform methods already enable precise solutions. However, for cylindrically stratified media, oscillatory integrals involving cosine functions must be evaluated. This study employs the sine transform to achieve frequency-to-time domain conversion for TEL, proposing a new set of 125-point sine transform filter coefficients with enhanced computational accuracy. For frequency-domain calculations in cylindrically stratified media, the Gauss-Legendre quadrature rule combined with Levin-Sidi extrapolation is adopted to accelerate the convergence of partial sum sequences toward the true integral value. The accuracy of the proposed computational method is verified through numerical experiments in both horizontally layered and cylindrically stratified media.

The numerical simulation of rarefied gas mixture dynamics with disparate masses using the direct simulation Monte Carlo (DSMC) method is slow, primarily because the time step is constrained by that of the lighter species, necessitating an enormous number of evolution steps to reach a steady state. Here, we address this issue by developing a general synthetic iterative scheme, in which the traditional DSMC simulation is intermittently enhanced using a macroscopic synthetic equation. Specifically, after running the DSMC for a certain number of time steps, the high-order constitutive relations for stress and heat flux, as well as the momentum and energy exchange terms from inter-species collisions, are extracted from the DSMC and incorporated into the macroscopic synthetic equations. These equations are solved to obtain the steady state, and the solution is then used to update the particle distribution in DSMC, thereby skipping unnecessary intermediate evolutions. This two-way coupling not only accelerates convergence to the steady state but also asymptotically preserves the Navier-Stokes limit in the continuum flow regime, allowing the spatial cell size to be much larger than the molecular mean free path. The accuracy of our method is validated for one-dimensional force-driven Poiseuille flow and two-dimensional hypersonic flow past cylinders, considering Maxwell and hard-sphere gases with mass ratios of 10 and 100. Although our in-house DSMC method is approximately an order of magnitude slower than the open-source DSMC code SPARTA, intermittently augmenting it with the synthetic equation makes it roughly 30 times faster at a Knudsen number of 0.01, with even greater computational gains anticipated at smaller Knudsen numbers. This work represents a critical step toward developing fast-converging and asymptotic-preserving schemes for hypersonic chemical reactions.

Plasmon-driven ultrafast nonlinearities hold promise for advanced photonics but remain challenging to harness in two-dimensional materials at telecommunication wavelengths. Here, we demonstrate few-layer V2C MXene as a high-performance saturable absorber by leveraging its tailored surface plasmon resonance. Combining transient absorption spectroscopy and first-principles calculations, we unveil a plasmon-driven relaxation mechanism dominated by interfacial high-energy hot electron generation (~100 fs), enabling giant ultrafast nonlinearities. Crucially, at the communication band (1550 nm), V2C exhibits a high saturable absorption coefficient of -1.35 cm/GW. Integrating this into an erbium-doped fiber laser, we generate mode-locked pulses with a duration of 486 fs at 1569 nm, a 39.51 MHz repetition rate, and exceptional stability (92 dB SNR). This work establishes plasmonic MXenes as a paradigm for tailored ultrafast photonic devices.

The wettability of monolayer and multilayer graphene remains a topic of longstanding debate. Here, we combined first-principles molecular dynamics simulations accelerated with the atomic cluster expansion machine learning interatomic potential to investigate how substrate, graphene layer number, and intercalated water molecules influence graphene's wettability. Simulated vibrational sum-frequency generation (vSFG) spectra revealed that the experimentally observed hydrophilic behavior of monolayer graphene on hydrophilic substrates arose not from wetting transparency, but from signal cancellation induced by intercalated water. Energetic analyses further showed that intercalated water molecules were thermodynamically favorable for monolayer graphene on hydrophilic substrates, but not for multilayer systems, leading to changes in the vSFG response in line with experimental observations. These results offer a mechanistic understanding of graphene-water interactions and have broad implications for the design of graphene-based interfaces and devices.

OpenStar Technologies is a private fusion company exploring the levitated dipole concept for commercial fusion energy production. OpenStar has manufactured a new generation of levitated dipole experiment, called "Junior", leveraging recent advances made in high-temperature superconducting magnet technologies. Junior houses a ~5.6 T REBCO high-temperature superconducting magnet in a 5.2 m vacuum chamber, with plasma heating achieved via < 50 kW of electron cyclotron resonance heating power. Importantly, this experiment integrates novel high temperature superconductor power supply technology on board the dipole magnet. Recently OpenStar has completed first experimental campaigns with the Junior experiment, achieving first plasmas in late 2024. Experiments conducted with the full levitated system are planned for 2025. This article provides an overview of the main results from these experiments and details improvements planned for future campaigns.

Atom interferometer (AI)-based dynamic gravimeter enable high-precision absolute gravity measurements, crucial for applications in geophysics, navigation, resource exploration, and metrology. Understanding their underlying mechanisms and minimizing measurement noise are essential for enhancing performance. This work investigates the gravity measurement noise in AI-based systems induced by the under-sampling time of the classical accelerometer. Using actual dynamic gravity measurement data, we demonstrate that an under-sampling time of 0.12 s introduces significant gravity measurement noise of 8 mGal. To elucidate the mechanism of this noise, we derive a formula for this noise in frequency domain, identifying high-frequency aliasing as its source. Analysis of the derived expressions indicates that reducing the under-sampling time duration and suppressing the high-frequency noise of the acceleration are effective strategies for mitigating this noise. This work provides significant insights for noise analysis and future scheme design of AI-based dynamic gravimeters.

We study the process of magnetic reversals in the presence of a stably-stratified layer below the core-mantle boundary using direct numerical simulations of the incompressible magnetohydrodynamics equations under the Boussinesq approximation in a spherical shell. We show that the dipolar-multipolar transition shifts to larger Rayleigh numbers in the presence of a stably-stratified layer, and that the dipolar strength of the magnetic field at the core-mantle boundary increases due to the skin effect. By imposing an heterogeneous heat flux at the outer boundary, we break the equatorial symmetry of the flow, and show that different heat flux patterns can trigger different dynamo solutions, such as hemispheric dynamos and polarity reversals. Using kinematic dynamo simulations, we show that the stably-stratified layer leads to similar growth rates of the dipole and quadrupole components of the magnetic field, playing the role of a conducting boundary layer, favouring magnetic reversals, and a dynamics predicted by low-dimensional models.

In this study, we report intermittent bursting of cyclonic columnar structures in direct numerical simulations of forced rotating turbulence in a three-dimensional (3D) periodic box. Columnar structure formation is associated with dominant inverse cascade of energy in addition to the typical forward cascade at large wavenumbers. The forcing is random and isotropic, applied in two distinct wavenumber bands (kf = [3, 4] and [6, 7]). Notably, bursting is observed only for small forcing wavenumber, kf = [3, 4] and not at kf = [6, 7], for the same rotation rate and initial conditions. These bursting events are associated with forward cascade of energy in contrast to the inverse cascade of rotating turbulence, as confirmed from the ring spectrum and time-series of modal kinetic energy of modes with kz = 2, 3. Identifying some of the triads involved in the energy transfer from modal analysis, we also calculate the mode-to-mode energy transfer. We find that, before the event, the circular cyclonic vortex becomes elliptical due to cyclone-anticyclone interactions leading to elliptical instability. We argue that energy transfer preceding the bursting events to modes with higher kz triggers the Crow instability. These instabilities ultimately lead to bursting of the columnar vortex, which subsequently forms again due to the external rotation. Our results portray a tug-of-war between the destabilizing effect of elliptical instability and the stabilizing effect of external rotation.

Monolayer transition metal dichalcogenides (TMDs) support robust excitons in the visible to near-infrared spectral range. Their reduced dielectric screening results in large binding energies, and combined with a direct bandgap in monolayer form, these excitons dominate the optical response of TMDs. In this work, we present a comprehensive investigation of the temperature-dependent optical and polaritonic properties of high-quality, flux-grown $\mathrm{WS_2}$, $\mathrm{MoS_2}$, $\mathrm{WSe_2}$, and $\mathrm{MoSe_2}$ monolayers, encapsulated by hBN, in the full temperature range between $5-300 \mathrm{K}$. Using reflection spectroscopy measurements, we evaluate and compare the optical and polaritonic constituents of the TMD excitons in terms of oscillator strength, linewidth and negative permittivity. We find that it is $\mathrm{MoSe_2}$ that exhibits the most pronounced optical and polaritonic response, stemming from its rapid linewidth narrowing at low temperatures, as compared to the temperature-dependent response of the other TMDs. In addition, we find that all four TMDs exhibits a temperature-dependent negative real part permittivity, thus supporting surface-exciton-polaritons. We derive their dispersion relation, confinement factors and losses, similarly revealing that $\mathrm{MoSe_2}$ exhibit enhanced polaritonic properties. These findings establish a comparative framework for understanding the optical and polaritonic properties of monolayer TMDs, with implications on their utilization in optoelectronic devices based on 2D semiconductors.

The on-chip integration of nonreciprocal optical devices remains a critical challenge for modern optoelectronics, as conventional magneto-optic approaches suffer from material incompatibility and excessive optical losses. Nonlinear photonic diodes have emerged as a promising magnet-free alternative, yet their widespread adoption has been constrained by inherent limitations in reconfigurability. Here, we present an all-silicon, electrically tunable photonic diode leveraging engineered chiral resonances in an ultra-compact microring architecture. The pronounced asymmetric modal coupling enables nonreciprocal transmission with two distinct operation modes at threshold powers down to -5 dBm. The chirality further enables unprecedented control over self-pulsation dynamics, manifesting in propagation-direction-dependent oscillation thresholds and temporal signatures. Crucially, post-fabrication electrical reconfigurability allows dynamic switching between forward, backward, and disabled states. This work represents a significant advancement in integrated nonreciprocal photonics, offering a CMOS-compatible solution with transformative potential for optical interconnects, photonic neural networks, and signal processing systems.

The modulator pocket is a cryptic site discovered in the TREK1 K2P channel that accommodates agonists capable of increasing the channel's activity. Since its discovery, equivalent sites in other K2P channels have been shown to bind various ligands, both endogenous and exogenous. In this review, we attempt to elucidate how the modulator pocket contributes to K2P channel activation. To this end, we first describe the gating mechanisms reported in the literature and rationalize their modes of action. We then highlight previous experimental and computational evidence for agonists that bind to the modulator pocket, together with mutations at this site that affect gating. Finally, we elaborate how the activation signal arising from the modulator pocket is transduced to the gates in K2P channels. In doing so, we outline a potential common modulator pocket architecture across K2P channels: a largely amphipathic structure -consistent with the expected properties of a pocket exposed at the interface between a hydrophobic membrane and the aqueous solvent- but still with some important channel-sequence-variations. This architecture and its key differences can be leveraged for the design of new selective and potent modulators.

Resolving how defects emerge and interact within the hierarchical structure of polycrystalline materials remains a core challenge in materials science. Grain-mapping methods such as three-dimensional X-ray diffraction (3DXRD) and diffraction contrast tomography (DCT) provide essential mesoscale context but lack the resolution to image lattice defects. Conversely, high-resolution methods like Dark Field X-ray Microscopy (DFXM) capture lattice distortions but not the surrounding microstructure. Here, we introduce a transferable framework that unifies these complementary approaches into a single, non-destructive workflow. Enabled by open-source software, the method translates grain orientation and position data into precise goniometer settings for DFXM imaging without dismounting or reorienting the sample. Applied to an iron polycrystal containing 1100 grains, DFXM motor positions were calculated for all grains within seconds, enabling on-the-fly targeting of specific grains. This allows reproducible zooming from the millimetre-scale aggregate to individual dislocations. We resolve three-dimensional misorientation fields across grain boundaries with 36 nm pixel size, directly capturing grain-grain interactions within their microstructural context. Finally, we show transferability from LabDCT to synchrotron and XFEL platforms, enabling new ways of studying defect interactions across scales.

Numerical Weather Prediction (NWP) models that integrate coupled physical equations forward in time are the traditional tools for simulating atmospheric processes and forecasting weather. With recent advancements in deep learning, Neural Weather Models (NeWMs) have emerged as competent medium-range NWP emulators, with performances that compare favorably to state-of-the-art NWP models. However, they are commonly trained on reanalyses with limited spatial resolution (e.g., 0.25° horizontal grid spacing), which smooths out key features of weather systems. For example, tropical cyclones (TCs)-among the most impactful weather events due to their devastating effects on human activities-are challenging to forecast, as extrema like wind gusts, used as proxies for TC intensity, are smoothed in deterministic forecasts at 0.25° resolution. To address this, we use our best observational estimates of wind gusts and minimum sea level pressure to train a hierarchy of post-processing models on NeWM outputs. Applied to Pangu-Weather and FourCastNet v2, the post-processing models produce accurate and reliable forecasts of TC intensity up to five days ahead. Our post-processing algorithm is tracking-independent, preventing full misses, and we demonstrate that even linear models extract predictive information from NeWM outputs beyond what is encoded in their initial conditions. While spatial masking improves probabilistic forecast consistency, we do not find clear advantages of convolutional architectures over simple multilayer perceptrons for our NeWM post-processing purposes. Overall, by combining the efficiency of NeWMs with a lightweight, tracking-independent post-processing framework, our approach improves the accessibility of global TC intensity forecasts, marking a step toward their democratization.

The interfacial resistance to evaporation, in particular for the case of water, is a longstanding issue. The previous data on its main characterizing parameter, the accommodation coefficient, manifest a dispersion over three orders of magnitude. We study the evaporation of a thin liquid layer (microlayer) under the growing single bubble in saturated pool boiling of ultra-pure water at atmospheric pressure. However, the water contamination during the experiment cannot be excluded. The local and instantaneous data on interfacial thermal resistance are recovered from the synchronous local measurements of the microlayer thickness and the heater temperature. At each particular interfacial point, the resistance shows a linear increase in time suggesting the effect of accumulation of impurities at the interface. We compute the water mass evaporated at a given point of microlayer that should be proportional to the interfacial concentration of accumulated impurities. A clear increase of the interfacial thermal resistance with the evaporated mass is observed. This demonstrates a link of the temporal increase of the interfacial resistance (i.e. the evaporation deterioration) with the accumulation of impurities at the interface, which can explain the dispersion of the published data on the accommodation coefficient.

We report on the formation of moir{é} patterns when observing the diffraction of surface plasmons by periodic gratings of finite extent with an imaging spectrometer that maps the light emission as a function of the wavelength and the propagation distance. This phenomenon results from the formation of standing waves upon the partial reflection of surface plasmons at the far end of the grating and their scattering by the periodic corrugations. The moir{é} fringes are created as a result of the incommensurability between the sampling pitch of the grating and the wavelengthdependent standing waves. We introduce a scalar model that supports these observations and provides physical insight into these results, including a method to estimate the complex Fresnel reflection coefficient of a surface plasmon at the edge of a grating.

The small magnitude and long-range character of non-covalent interactions pose a significant challenge for computational quantum chemical and electronic-structure methods alike. State-of-the-art coupled cluster (CC) theory and benchmark-grade diffusion Monte Carlo (DMC) are ideally positioned to tackle these problems, but concerning differences between both methods have been reported in numerous studies of the interaction energy of non-covalently bound dimers. Given that the basic theoretical frameworks underpinning both methods are exact in principle, the error must arise from one or several of the approximations required to make the calculations computationally tractable. Here we carry out a rigorous and systematic case study of the effect of each of these approximations using the acetic acid dimer as a convenient testing ground. Thanks to the use of stringently optimized backflow wave functions we are able to find that the $\sim 0.8$ kcal mol$^{-1}$ discrepancy is dominated by a >0.5 kcal mol-1 fixed-node error incurred by the Slater-Jastrow DMC result, while errors in the CC calculations only account for up to ~0.2 kcal mol-1. This finding, likely applicable to other non-covalent systems, helps establish that CC should be regarded as the benchmark for these systems, and can potentially guide the search for pragmatic solutions to the fixed-node problem in the future.

The Stochastic Backscatter Model involves the generation of a set of random variables characterised by prescribed correlations in space and time. These variables are obtained by smoothing an initially uncorrelated random field, which produces an exponentially decaying spatial correlation. The smoothing is applied implicitly and sequentially along the three coordinate directions of the computational domain, making the approach suitable only for structured CFD solvers. To extend the method to unstructured solvers, implicit Laplace smoothing can be employed instead. However, the spatial correlation resulting from this alternative approach must be derived analytically. This constitutes the main objective of the present dissertation.

We investigate terahertz (THz) radiation generation at the vacuum-plasma interface driven by the oblique incidence of s-polarized Gaussian laser pulse(s) on a semi-infinite underdense plasma.~Extending beyond the conventional single-frequency bipolar pulse (B-pulse), this work focuses on leveraging two-color mixed-frequency pulse-- (M-pulse) excitation to enhance THz performance in terms of strength and broadening that can be tuned through controlled amplitude and phase of the constituent pulses of the M-pulse. A new expression for the ponderomotive force (PF) -- which acts as the main driver of THz radiation at the vacuum-plasma boundary -- is derived to capture the hitherto unexplored effects of phase asymmetry intrinsic to the M-pulse, in contrast to a single B-pulse where its phase is irrelevant. This PF formulation captures the underlying cycle-to-cycle symmetry-breaking for the M-pulse field, responsible for efficient THz emission. We demonstrate analytically that such M-pulses of the same total energy as a B-pulse may generate significantly enhanced PF, leading to THz yields several orders of magnitude higher. With a judicious choice of low-frequency to high-frequency ratio, the M-pulse configuration is shown to emerge as a highly efficient, phase-controllable driver of THz radiation and offers a promising route for optimizing THz source design via tailored two-color laser-plasma interactions.

Understanding how biomechanical reorganization governs key biological processes, such as morphogenesis and development, requires predictive insights into stress distributions and cellular behavior. While traditional approaches focused on cell motion as a response to stress, we demonstrate that Lagrangian coherent structures (LCSs) -- robust attractors and repellers in cellular flows -- precede and drive long-term intercellular stress reorganization, physically governed by the mechanical properties of intercellular junctions. We show that this hidden flow skeleton correlates strongly with biomechanical metrics, bridging microscopic cell motion with mesoscopic biomechanics. Specifically, attractors and repellers mark hotspots of compressive and tensile stress enrichment (exceeding tenfold), alongside heterogeneities in cell packing. Notably, these connections remain robust across varying strengths of cell-cell and cell-substrate force transmission. Finally, by linking the attracting regions in the flow skeleton to future cell extrusion spots, we establish a direct link between cell motion and biologically significant outcomes. Together, these findings establish a framework for using cell motion to independently infer biomechanical metrics and bridge the scale mismatch between cell motion and biomechanics, potentially offering a new route to interpret mechanosensitive biological processes directly from cell trajectories.

The dispersion of a passive scalar in an axially strained flow in a slender tube is studied, with particular focus on large-time asymptotics following the approach of~\cite{rajamanickam2020dispersion}. For times exceeding the cross-sectional diffusion timescale, the scalar field forms an axial (Ornstein--Uhlenbeck) Gaussian profile whose variance increases exponentially with the local axial strain, which itself varies with radial location, while radial diffusion only slowly modulates the overall amplitude. In other words, the scalar is dominated by strong axial stretching, completely overwhelming radial diffusion. In striking contrast to classical Taylor dispersion, where radial diffusion rapidly homogenizes the profile and axial convection appears only as a small correction, here axial stretching governs the dominant dynamics, producing a fundamentally different transport mechanism.

We study the electronic spin Zeeman effect for an effective spin-1/2-system subject to both strong coupling to a low-frequency optical cavity and an external static magnetic field. Specifically, we address the interplay between the cavity magnetic field component in a cavity Zeeman interaction and the canonical spin Zeeman interaction from the perspective of an effective spin-polariton Hamiltonian. The latter is derived from the minimal coupling Pauli-Fierz Hamiltonian beyond the common dipole approximation via first-order quasi-degenerate perturbation theory. We find the spin Zeeman effect to be modified in the presence of the cavity field due to the formation of spin-polariton states, which result from an intricate interplay of cavity and external magnetic fields in our model. Spin-polariton signatures are discussed in the context of electron paramagnetic resonance (EPR) spectroscopy along with cavity-induced modifications of the electronic g-factor.

Non-equilibrium molecular-scale dynamics, where fast electron transport couples with slow chemical state evolution, underpins the complex behaviors of molecular memristors, yet a general model linking these dynamics to neuromorphic computing remains elusive. We introduce a dynamic memristor model that integrates Landauer and Marcus electron transport theories with the kinetics of slow processes, such as proton/ion migration or conformational changes. This framework reproduces experimental conductance hysteresis and emulates synaptic functions like short-term plasticity (STP) and spike-timing-dependent plasticity (STDP). By incorporating the model into a reservoir computing (RC) architecture, we show that computational performance optimizes when input frequency and bias mapping range align with the molecular system's intrinsic kinetics. This chemistry-centric, bottom-up approach provides a theoretical foundation for molecular-scale neuromorphic computing, demonstrating how non-equilibrium molecular-scale dynamics can drive information processing in the post-Moore era.

At the conclusion of Run 3 of the Large Hadron Collider (LHC) at CERN, the accelerator complex will be upgraded to the High-Luminosity LHC (HL-LHC), allowing it to increase the dataset sizes of LHC experiments by about a factor of 20. This significant increase in dataset size will improve the sensitivity and precision of all physics analyses from LHC experiments, but will come with a more challenging data-taking environment. In order to handle this, the ATLAS detector will undergo a substantial upgrade, including an upgrade of its inner tracker to an all-silicon tracker called the Inner Tracker (ITk), made of pixel and strip sub-detectors. During the pre-production phase of the ITk strips, it was discovered that thermally cycling modules loaded onto local support structures led to physical fractures in silicon sensors due to the induced thermal stress. This is understood to be the result of several factors, including the difference in coefficients of thermal expansion between the different module layers, and the close proximity of the module electrical components. Several mitigation strategies were tested to reduce the rate of module fracturing. This paper describes the assembly setup, testing setups, and electrical testing results of ITk strips barrel modules loaded onto local support structures. 69 of 70 modules with an in-built additional kapton layer were found to survive testing down to -70°C.

Through a series of experiments, we provide evidence that the GraphDOP model - trained solely on meteorological observations, using no prior knowledge - develops internal representations of the Earth System state, structure and dynamics as well as the characteristics of different observing systems. Firstly, we demonstrate that the network constructs a unified latent representation of the Earth System state which is common across different observation types. For example, cloud structures maintain physical consistency whether viewed in predictions for satellite radiances from different sensors, or for direct in-situ measurements of the cloud fraction. Secondly, we show examples that suggest that the network learns to emulate viewing effects - learned observation operators that map from the unified state representation to observed properties. Microwave sounder limb effects and geometric viewing effects, such as sunglint in visible imagery, are both well captured. Finally, we demonstrate that the model develops rich internal representations of the structure of meteorological systems and their dynamics. For instance, when the network is only provided with observations from a single infrared instrument, it is able to infer unobserved, non-local structures such as jet streams, surface pressure patterns and warm and cold air masses associated with synoptic systems. This work provides insights into how neural networks trained solely on observations of the Earth System spontaneously develop coherent internal representations of the physical world in order to meet the training objective - enhancing our understanding and guiding future development of these models.

The transmission of photons through a pair of coupled single-mode waveguides is studied in detail. It is assumed that one of the waveguides is coupled to a non-Markovian reservoir, described by a Lorentzian spectrum distribution. We identified four distinct regimes for the transmission coefficient and found that, in some conditions, it is more efficient to launch the photons in the lossy waveguide to achieve high transmission. We also report on a loss-induced transparency effect that is induced by the distribution of the frequencies present in the reservoir.

A fluid described by an Abelian Chern-Simons action principle in 4+1 dimensions is considered. Letting 3+1 dimensions correspond to the usual space and time, and assuming the fields to be independent of the fifth coordinate, the free theory provides an interpretation as a system of advection equations, where the advecting velocity field is defined as the null vector of the field strength tensor (curvature). The free theory possesses a number of conservation laws which turn out to be prototypical forms of helicity and entropy conservation. Coupling the Chern-Simons field to an external source, a new conserved charge density is obtained which has the form of the Rossby-Ertel's potential vorticity (PV). Finally, by identifying the external current with the Chern-Simons field in a gauge-invariant setting, based on non-relativistic ideas, a self-interacting action principle is obtained whose Euler-Lagrange equations correspond precisely to a classical dissipationless compressible (3+1)-dimensional fluid endowed with thermodynamics, with only one extra condition: a constraint on the initial profile of the PV. After analysing this constraint of the "Chern-Simons fluid formulation", we investigate the helicity conservation of general fluids, going beyond classical analyses of barotropic fluids and no-cross boundary conditions for vorticity (Moffatt 1969). A new fluid helicity invariant for barotropic fluids under generic boundary conditions is obtained and the role of baroclinity in the helicity production is clarified. Inside a region bounded by an isentropic surface, the theory's constraint on the PV gives an integral formula for the mass, and for the evolution of fluid helicity in the baroclinic case. Finally, for an ideal gas exact, steady solutions of the equations of motion are found in a rotating scenario, showing that Ferrel-cell like patterns are produced in a rotating planet.

We demonstrate dual-frequency absorption spectroscopy (DFAS) using laser-cooled 87Rb and 85Rb atoms. Doppler-free resonances with high-contrast are produced, which suggest the suitability of using dual-frequency absorption spectroscopy (DFAS) for laser stabilization in a cold-atom-based coherent population trapping (CPT) clock, and for developing a compact, high-performance optical frequency standard using an integrated magneto-optical trap (MOT). We developed a model using density-matrix equations to accurately simulate DFAS in the atomic medium without applying any simplifying approximations. Comprehensive simulations are performed using our multi-level system model to analyze dual-frequency spectra produced in cold atom ensembles under different experimental conditions, including the effect of magnetic field, and two-photon detuning. The simulations accurately yield amplitudes, linewidths, frequency shifts, and lineshapes of DFAS resonances under these experimental conditions. We also demonstrate a simple mechanism for performing CPT spectroscopy by implementing a DFAS laser lock using trapped atoms in the MOT. Additionally, we have extended our model to accurately model the dual-frequency spectrum produced in rubidium cell, which is a medium of practical interest for vapor-cell-based quantum sensing applications.

An ideal electrometer should measure electric fields accurately while causing minimal disturbance to the field itself. Rydberg atomic electrometers are promising candidates for ideal electrometry due to their SI traceability and non-invasive nature. However, in practice, the atomic vapor cell shell can distort the electric field, limiting the device's performance. In this work, we overcome this challenge by fabricating a chip-scale vapor cell using a novel combination of femtosecond laser writing and optical contact. This method enables the development of a non-invasive atomic electrometer with a radar cross-section (RCS) 20 dB lower than that of commercial atomic cell-based electrometers. Furthermore, we observe a new sub-Doppler spectral narrowing phenomenon in these chip-scale cells. The effect originates from an incoherent, collision-driven mechanism--hereafter referred to as incoherent Dicke narrowing (ICDN). This advancement supports future revisions to the international system of units and broadens applications in metrology and quantum measurement.

The Madelung equations offer a hydrodynamic description of quantum systems, from single particles to quantum fluids. In this formulation, the probability density is mapped onto the fluid density and the phase is treated as a scalar potential generating the velocity field. As examples of potential flows, quantum fluids described in this way are inherently irrotational, but quantum vortices may arise at discrete points where the phase is undefined. In this paper, starting from this irrotational description of a quantum fluid, a coarse-graining procedure is applied to arrive at a macroscopic description of the quantum fluid in which the role of velocity is played by a Favre average of the microscopic velocity field, allowing for finite vorticity at any point in the fluid. It is shown that this vorticity obeys a similar equation to the vorticity equation in classical hydrodynamics and includes a vortex-stretching term. This coarse-graining procedure also gives rise to novel stress terms in the fluid equations, which in the appropriate limit appear analogous to artificial viscous stresses from computational fluid dynamics.

Accurate prediction of urban wind flow is essential for urban planning, pedestrian safety, and environmental management. Yet, it remains challenging due to uncertain boundary conditions and the high cost of conventional CFD simulations. This paper presents the use of the modular and efficient uncertainty quantification (UQ) framework OpenLB-UQ for urban wind flow simulations. We specifically use the lattice Boltzmann method (LBM) coupled with a stochastic collocation (SC) approach based on generalized polynomial chaos (gPC). The framework introduces a relative-error noise model for inflow wind speeds based on real measurements. The model is propagated through a non-intrusive SC LBM pipeline using sparse-grid quadrature. Key quantities of interest, including mean flow fields, standard deviations, and vertical profiles with confidence intervals, are efficiently computed without altering the underlying deterministic solver. We demonstrate this on a real urban scenario, highlighting how uncertainty localizes in complex flow regions such as wakes and shear layers. The results show that the SC LBM approach provides accurate, uncertainty-aware predictions with significant computational efficiency, making OpenLB-UQ a practical tool for real-time urban wind analysis.

The finite difference time domain method is one of the most popular methods in computational electromagnetics. This work considers two possible ways of generalising it to a meshless setting by employing local radial basis function interpolation. The resulting methods turn out to be unstable, but can be stabilised by adding properly chosen hyperviscosity terms to the update equations. We demonstrate that the proposed meshless methods are convergent and can enjoy a decreased dispersion anisotropy compared to the finite difference time domain method.

Optical frequency combs have revolutionised time and frequency metrology [1, 2]. The advent of microresonator-based frequency combs ('microcombs' [3-5]) is set to lead to the miniaturisation of devices that are ideally suited to a wide range of applications, including microwave generation [6, 7], ranging [8-10], the precise calibration of astronomical spectrographs [11], neuromorphic computing [12, 13], high-bandwidth data communications[14], and quantum-optics [15, 16] platforms. Here, we introduce a new microcomb application for three-dimensional imaging. Our method can simultaneously determine the chemical identity and full three-dimensional geometry, including size, shape, depth, and spatial coordinates, of particulate matter ranging from micrometres to millimetres in size across nearly $10^5$ distinct image pixels. We demonstrate our technique using millimetre-sized plastic specimens (i.e. microplastics measuring less than 5 mm). We combine amplitude and phase analysis and achieve a throughput exceeding $1.2~10^6$ pixels per second with micrometre-scale precision. Our method leverages the defining feature of microcombs - their large line spacing - to enable precise spectral diagnostics using microcombs with a repetition frequency of 1 THz. Our results suggest scalable operation over several million pixels and nanometre-scale axial resolution. Coupled with its high-speed, label-free and multiplexed capabilities, our approach provides a promising basis for environmental sensing, particularly for the real-time detection and characterisation of microplastic pollutants in aquatic ecosystems [17].

Loudspeakers inherit their directivity from their geometry and dimensions. Enclosed loudspeakers are omnidirectional in the low frequency range, but their directivity depends on frequency for wavelengths smaller than the radiator size, precluding the directional control over the whole bandwidth. Loudspeakers pairs allow achieving simultaneously monopolar (in-phase) and dipolar (out-of-phase) sources, thus allowing directivity control. However, they are limited by their bulkiness, preventing extending controllable directivities over high frequencies. The Corona Discharge transducer (CDT) concept relies on ionizing an ultra-thin layer of air and oscillating it through an alternating electric field, generating sound without resorting to a mechanical membrane. This transducer combines a monopolar source linked to heat exchanges, and a dipolar linked to electrostatic forces, although these two sources strengths are interconnected, yielding a given unidirectional directivity. In this paper, we propose to leverage the combination of monopole and dipole at the heart of the CDT concept to achieve controllable directivities by stacking two independent CDTs. The very thin dimensions of the CDT allows achieving coincident controllable monopolar and dipolar sound sources making the control of directivity over the whole operating frequency range. An analytical model of the dual CDTs concept is first compared to full-wave simulations, and an experimental prototype is finally assessed in anechoic conditions. Our findings opens the way to a new range of broadband directionally-controllable transducers that have application to sound generation, active noise reduction, or even non-reciprocal active acoustic metamaterials.

We study flows induced in a two-dimensional corner by the chemical activity of the confining boundaries. A catalytic reaction on the surfaces drives the diffusiophoretic flow of viscous fluid in the whole domain. We show that phoretically active sectors can generate eddies in the corner, similar to Moffatt eddies, induced mechanically in a similar confinement. We analyse the exact solution of the diffusion problem in the wedge geometry, coupled to diffusiophoretic flow generation in the slip-velocity formulation, to ultimately arrive at the stream function of the flow. We discuss cases in which simple exact solutions for the flow are available. Our work can inspire the design of microscale mixing devices involving dead-end pores in microfluidic devices and can serve as benchmarks for numerical solutions for complex geometries in (diffusio)phoretic systems.

Membrane filtration is widely used in wastewater treatment to remove foulants from contaminated water. Foulant build-up on the membrane occludes the area open for fluid flow, which impairs the efficiency of the filtration operation by decreasing the flux through the membrane. This necessitates the introduction of cleaning interventions to regenerate the membrane after fouling. Backwashing is a strategy to restore the membrane, wherein clean water is processed backward through the membrane to dislodge attached foulants. We develop a Monte Carlo model based on physical parameters to simulate constant-pressure forward filtration and backwashing through dead-end, flat-sheet membranes, with membrane fouling dominated by intermediate blocking. We validate our model against real-world experiments and show good agreement between experimental results and numerical simulations. The newly developed model can be used to further investigate the impact of varying backwashing duration, frequency, and/or pressure on the rate of flux recovery.

We report a detailed inelastic neutron scattering (INS) and muon spin relaxation (muSR) investigation of a trimer Ruthenate Ba5Ru3O12 system, which undergoes long-range antiferromagnetic ordering at TN = 60 K. The INS reveals two distinct spin wave excitations below TN: one at 5.6 meV and the other at 10-15 meV. By accompanying the INS spectra based on a linear spin wave theory using SpinW software and machine learning force fields (MLFFs), we show that Ba5Ru3O12 exhibits spin frustration due to competing exchange interactions between neighboring and next-neighboring Ru-moments, exchange anisotropy, and strong spin-orbit coupling, which yields a non-collinear spin structure, in contrast to other ruthenate trimers in this series. Interestingly, these magnetic excitations do not completely vanish even at high temperatures above TN, evidencing short-range magnetic correlations in this trimer system. This is further supported by muSR spectroscopy, which exhibits a gradual drop in the initial asymmetry around the magnetic phase transition and is further verified through maximum entropy analysis. The results of muSR spectroscopy indicate a dynamic nature of magnetic order, attributed to local magnetic anisotropy within the trimer as a result of local structural distortion and different hybridization, consistent with canted spin-structure. We predict the ground state of Ru3O12-isolated trimer through theoretical calculations, which agree with the experimentally observed spin excitation

We develop and implement numerically a phase field model for the growth and detachment of a gas bubble resting on an electrode and being filled with hydrogen produced by water electrolysis. The bubble is surrounded by a viscous liquid, has a prescribed static contact angle and is also subject to gravitational forces. We compute, as a function of the static contact angle, the time at which the bubble detaches from the substrate and what volume it has at that time. We also investigate de dependence of the detachment time on other parameters such as the applied voltage and the hydrogen ion concentration at the fluid bulk.

Acquiring temporally high-frequency and spatially high-resolution turbulent wake flow fields in particle image velocimetry (PIV) experiments remains a significant challenge due to hardware limitations and measurement noise. In contrast, temporal high-frequency measurements of spatially sparse wall pressure are more readily accessible in wind tunnel experiments. In this study, we propose a novel cross-modal temporal upscaling framework, LatentFlow, which reconstructs high-frequency (512 Hz) turbulent wake flow fields by fusing synchronized low-frequency (15 Hz) flow field and pressure data during training, and high-frequency wall pressure signals during inference. The first stage involves training a pressure-conditioned $\beta$-variation autoencoder ($p$C-$\beta$-VAE) to learn a compact latent representation that captures the intrinsic dynamics of the wake flow. A secondary network maps synchronized low-frequency wall pressure signals into the latent space, enabling reconstruction of the wake flow field solely from sparse wall pressure. Once trained, the model utilizes high-frequency, spatially sparse wall pressure inputs to generate corresponding high-frequency flow fields via the $p$C-$\beta$-VAE decoder. By decoupling the spatial encoding of flow dynamics from temporal pressure measurements, LatentFlow provides a scalable and robust solution for reconstructing high-frequency turbulent wake flows in data-constrained experimental settings.

We present a novel approach to solve the advection-diffusion equation under arbitrary transporting fields using a quantum-inspired 'Schrodingerisation' technique for Hamiltonian simulation. Although numerous methods exist for solving partial differential equations (PDEs), Hamiltonian simulation remains a relatively underexplored yet promising direction-particularly in the context of long-term, fault-tolerant quantum computing. Building on this potential, our quantum algorithm is designed to accommodate non-trivial, spatially varying transport fields and is applicable to both 2D and 3D advection-diffusion problems. To ensure numerical stability and accuracy, the algorithm combines an upwinding discretization scheme for the advective component and the central differencing for diffusion, adapted for quantum implementation through a tailored mix of approximation and optimization techniques. We demonstrate the algorithm's effectiveness on benchmark scenarios involving coupled rotational, shear, and diffusive transport in two and three dimensions. Additionally, we implement the 2D advection-diffusion equation using 16 qubits on IBM Quantum hardware, validating our method and highlighting its practical applicability and robustness.

Thin metal film on Germanium, in the presence of water, results in a remarkable pattern forming system. Here we present an analysis of spirals spontaneously etched on the Ge surface. We obtain measurements of the growth dynamics of the spirals and measurements of the local strain field in the metal film. Both indicate that the near geometric order of the pattern originates from the unique far field of a singularity - a crystal defect. The measured engraving profile is found in quantitative agreement with a model of metal catalyzed corrosion of the Ge surface. Specifically, local etch depth is inversely proportional to the normal velocity of the Ge-metal contact line. The growth mechanism combines crack propagation, reaction diffusion dynamics, and thin film mechanical instabilities, and illustrates how a defect's long range field can impose geometric order in a non-equilibrium growth process. General features relevant to other pattern forming systems are the coupling of chemistry and mechanics and the singularity driven order.

Interplanetary coronal mass ejections (ICMEs), the large-scale eruptive phenomena capable of shedding a huge amount of solar magnetic helicity and energy are potential in driving strong geomagnetic storms. They complexly evolve while preceded and followed by other large-scale structures e.g. ICMEs. Magnetic interaction among multiple ICMEs may result intense and long-lived geomagnetic storms. Our aim is to understand the reason of substantial changes in the geoeffectivity of two meso-scale separated counterparts of a complex solar wind structure through investigating their magnetic content e.g. helicity, energy and magnetic interaction among multiple ICMEs. We utilized the insitu observations of solar wind from Wind and Solar Terrestrial Relations Observatory-A (STA) spacecraft during the strongest geomagnetic storm period in past two decades on May 10-11, 2024 and heliospheric imagers onboard STA. Our investigation confirms complex interactions among five ICMEs resulting in distinct counterparts within a coalescing large-scale structure. These counterparts possess substantially different magnetic contents. We conclude that the complex counterpart resulted from the interaction among common-origin ICMEs observed by STA, favorably orientated for magnetic reconnection, had 1.6 and 2.8 times higher total magnetic energy and helicity, respectively, than the counterpart containing a left-handed filament-origin ICME observed by Wind. The left-handed ICME non-favorably oriented for magnetic reconnection with the surrounding right-handed, common-origin ICMEs. Therefore, two medium-separated counterparts despite belonging to a common solar wind structure, were potential to lead different geoeffectivity. This ultimately challenges space weather predictions based on early observations.

Shear cell tests have been conducted on twenty different lactose powders, most of which commercially available for oral or inhalation purposes, spanning a wide range of particle sizes, particle morphologies, production processes. The aims of the investigation were: i) to verify the reliability of the technique in evaluating and classifying the flowability of powders; ii) to understand the connection between the flowability of a powder and the morphological properties of its particles; iii) to find a general mathematical relationship able to predict the yield locus shape given the particle size, shape and consolidation state of a lactose powder. These aspects and their limitations are detailed in the manuscript together with other interesting findings on the stick-slip behavior observed in most of the lactose powders examined.

Scanning probe microscopy (SPM) investigations of on-surface chemistry on passivated silicon have only shown in-plane chemical reactions, and studies on bare silicon are limited in facilitating additional reactions post-molecular-attachment. Here, we enable subsequent reactions on Si(100) through selectively adsorbing 3D, silicon-specific "molecular tools". Following an activation step, the molecules present an out-of-plane radical that can function both to donate or accept molecular fragments, thereby enabling applications across multiple scales, e.g., macroscale customizable silicon-carbon coatings or nanoscale tip-mediated mechanosynthesis. Creation of many such molecular tools is enabled by broad molecular design criteria that facilitate reproducibility, surface specificity, and experimental verifiability. These criteria are demonstrated using a model molecular tool tetrakis(iodomethyl)germane ($Ge(CH_{2}I)_{4}$; TIMe-Ge), with experimental validation by SPM and X-ray photoelectron spectroscopy (XPS), and theoretical support by density functional theory (DFT) investigations. With this framework, a broad and diverse range of new molecular engineering capabilities are enabled on silicon.

Representing vector fields by scalar or vector potentials can be a challenging task in domains with cavities or tunnels, due to the presence of harmonic fields which are both irrotational and solenoidal but may have no scalar or vector potentials. For harmonic fields normal to the boundary, which can exist in domains with cavities, it is possible to define scalar potentials with Dirichlet boundary conditions fitted to the domain's cavities. For harmonic fields tangent to the boundary, which can exist in domains with tunnels, a similar construction was lacking. In this article we present a construction of vector potentials that yield a basis for the tangent harmonic fields. Our vector potentials are obtained by solving curl-curl problems with inhomogeneous tangent boundary conditions that are fitted to closed curves looping around the tunnels. Applied to structure-preserving finite elements, our method also provides an exact geometric parametrization of the discrete harmonic fields.

Neural-Network (NN) based turbulence closures have been developed for being used as pre-trained surrogates for traditional turbulence closures, with the aim to increase computational efficiency and prediction accuracy of CFD simulations. The bottleneck to the widespread adaptation of these ML-based closures is the relative lack of uncertainty quantification (UQ) for these models. Especially, quantifying uncertainties associated with out-of-training inputs, that is when the ML-based turbulence closures are queried on inputs outside their training data regime. In the current paper, a published algebraic turbulence closure1 has been utilized to compare the quality of epistemic UQ between three NN-based methods and Gaussian Process (GP). The three NN-based methods explored are Deep Ensembles (DE), Monte-Carlo Dropout (MCD), and Stochastic Variational Inference (SVI). In the in-training results, we find the exact GP performs the best in accuracy with a Root Mean Squared Error (RMSE) of $2.14 \cdot 10^{-5}$ followed by the DE with an RMSE of $4.59 \cdot 10^{-4}$. Next, the paper discusses the performance of the four methods for quantifying out-of-training uncertainties. For performance, the Exact GP yet again is the best in performance, but has similar performance to the DE in the out-of-training regions. In UQ accuracy for the out-of-training case, SVI and DE hold the best miscalibration error for one of the cases. However, the DE performs the best in Negative Log-Likelihood for both out-of-training cases. We observe that for the current problem, in terms of accuracy GP > DE > SV I > MCD. The DE results are relatively robust and provide intuitive UQ estimates, despite performing naive ensembling. In terms of computational cost, the GP is significantly higher than the NN-based methods with a $O(n^3)$ computational complexity for each training step

Contrast-enhanced computed tomography (CT) imaging is essential for diagnosing and monitoring thoracic diseases, including aortic pathologies. However, contrast agents pose risks such as nephrotoxicity and allergic-like reactions. The ability to generate high-fidelity synthetic contrast-enhanced CT angiography (CTA) images without contrast administration would be transformative, enhancing patient safety and accessibility while reducing healthcare costs. In this study, we propose the first bridge diffusion-based solution for synthesizing contrast-enhanced CTA images from non-contrast CT scans. Our approach builds on the Slice-Consistent Brownian Bridge Diffusion Model (SC-BBDM), leveraging its ability to model complex mappings while maintaining consistency across slices. Unlike conventional slice-wise synthesis methods, our framework preserves full 3D anatomical integrity while operating in a high-resolution 2D fashion, allowing seamless volumetric interpretation under a low memory budget. To ensure robust spatial alignment, we implement a comprehensive preprocessing pipeline that includes resampling, registration using the Symmetric Normalization method, and a sophisticated dilated segmentation mask to extract the aorta and surrounding structures. We create two datasets from the Coltea-Lung dataset: one containing only the aorta and another including both the aorta and heart, enabling a detailed analysis of anatomical context. We compare our approach against baseline methods on both datasets, demonstrating its effectiveness in preserving vascular structures while enhancing contrast fidelity.

Integrating reconfigurable intelligent surfaces (RIS) into wireless communication systems is a promising approach for enhancing coverage and data rates by intelligently redirecting signals, through a process known as beamforming. However, the process of RIS beamforming (or passive beamforming) control is associated with multiple latency-inducing factors. As a result, by the time the beamforming is effectively updated, the channel conditions may have already changed. For example, the low update rate of localization systems becomes a critical limitation, as a mobile UE's position may change significantly between two consecutive measurements. To address this issue, this work proposes a practical and scalable physics-based solution that is effective across a wide range of UE movement models. Specifically, we propose a kinematic observer and predictor to enable proactive RIS control. From low-rate position estimates provided by a localizer, the kinematic observer infers the UE's speed and acceleration. These motion parameters are then used by a predictor to estimate the UE's future positions at a higher rate, allowing the RIS to adjust promptly and compensate for inherent delays in both the RIS control and localization systems. Numerical results validate the effectiveness of the proposed approach, demonstrating real-time RIS adjustments with low computational complexity, even in scenarios involving rapid UE movement.

Stationary potential scattering admits a formulation in terms of the quantum dynamics generated by a non-Hermitian effective Hamiltonian. We use this formulation to give a proof of the reciprocity theorem in two and three dimensions that does not rely on the properties of the scattering operator, Green's functions, or Green's identities. In particular, we identify reciprocity with an operator identity satisfied by an integral operator $\widehat{\mathbf{M}}$, called the fundamental transfer matrix. This is a multi-dimensional generalization of the transfer matrix $\mathbf{M}$ of potential scattering in one dimension that stores the information about the scattering amplitude of the potential. We use the property of $\widehat{\mathbf{M}}$ that is responsible for reciprocity to identify the analog of the relation, $\det{\mathbf{M}}=1$, in two and three dimensions, and establish a generic anti-pseudo-Hermiticity of the scattering operator. Our results apply for both real and complex potentials.

We present a conditional variational autoencoder (CVAE) that generates stellar spectra covering 4000 $\le$ $T_{\mathrm{eff}$ $\le$ 11,000 K, $2.0 \le \log g \le 5.0$ dex, $-1.5 \le [\mathrm{M}/\mathrm{H}] \le +1.5$ dex, $v\sin i \le 300$ km/s, $\xi_t$ between 0 and 4 km/s, and for any instrumental resolving powers less than 115,000. The spectra can be calculated in the wavelength range 4450-5400 Å. Trained on a grid of \textsc{SYNSPEC} spectra, the network synthesizes a spectrum in around two orders of magnitude faster than line-by-line radiative transfer. We validate the CVAE on $10^4$ test spectra unseen during training. Pixel-wise statistics yield a median absolute residual of <$1.8\times10^{-3}$ flux units with no wavelength-dependent bias. A residual error map across the parameters plane shows $\langle|\Delta F|\rangle<2\times10^{-3}$ everywhere, and marginal diagnostics versus $T_{\mathrm{eff}}$, $\log g$, $v\sin i$, $\xi_t$, and $[Fe/H]$\ reveal no relevant trends. These results demonstrate that the CVAE can serve as a drop-in, physics-aware surrogate for radiative transfer codes, enabling real-time forward modeling in stellar parameter inference and offering promising tools for spectra synthesis for large astrophysical data analysis.

We introduce a data-informed quantum-classical dynamics (DIQCD) approach for predicting the evolution of an open quantum system. The equation of motion in DIQCD is a Lindblad equation with a flexible, time-dependent Hamiltonian that can be optimized to fit sparse and noisy data from local observations of an extensive open quantum system. We demonstrate the accuracy and efficiency of DIQCD for both experimental and simulated quantum devices. We show that DIQCD can predict entanglement dynamics of ultracold molecules (Calcium Fluoride) in optical tweezer arrays. DIQCD also successfully predicts carrier mobility in organic semiconductors (Rubrene) with accuracy comparable to nearly exact numerical methods.

In many viruses, hundreds of proteins assemble an outer shell (capsid) around the viral nucleic acid to form an infectious virion. How the assembly process selects the viral genome amidst a vast excess of diverse cellular nucleic acids is poorly understood. It has recently been discovered that many viruses perform assembly and genome packaging within liquid-liquid phase separated biomolecular condensates inside the host cell. However, the role of condensates in genome packaging is poorly understood. Here, we construct equilibrium and dynamical rate equation models for condensate-coupled assembly and genome packaging. We show that when the viral genome and capsid proteins favorably partition into the condensate, assembly rates, yields, and packaging efficiencies can increase by orders of magnitude. Selectivity is further enhanced by the condensate when capsid proteins are translated during assembly and packaging. Our results suggest that viral condensates provide a mechanism to ensure robust and highly selective assembly of virions around viral genomes. More broadly, our results may apply to other types of selective co-assembly processes that occur within biomolecular condensates, and suggest that liquid-liquid phase-separated condensates could be exploited for selective encapsulation of microscopic cargo in human-engineered systems.

Semiconductor nanoplatelets present reduced Auger recombination, giving rise to enhanced multiexciton emission. This virtue makes them good candidates to investigate higher-order carrier dynamics, allowing to extract important excitonic properties, such as biexciton and triexciton binding energies that highly influence applications involving high excitation fluxes. Here, we explore triexciton emission, emanating from single core/shell CdSe/CdS nanoplatelets. We apply heralded post-selection of photon triplets using an advanced home-built single-photon spectrometer in order to resolve the triexciton$-$biexciton$-$exciton$-$ground state cascaded relaxation both in time and spectrum, and unambiguously determine the triexciton relaxation route and interaction nature. The results show a characteristic blue shift of the biexciton and triexciton, pointing to repulsive multiexciton interaction in the nanoplatelets under study. The relatively small measured energy shift of the triexciton (5.9 $\pm$ 0.7 meV) indicates that it recombines through the 1S bands rather than the 1P bands, in agreement with findings on other colloidal quantum dot systems. Most importantly, the strong correlation between the biexciton and triexciton binding energies, and the ability to tune them via control of the particle dimensions and composition, paves the way for developing emitters of nearly degenerate photon triplets.

We propose a novel design principle for achieving ultralow thermal conductivity in crystalline materials via a "heavy-light and soft-stiff" structural motif. By combining heavy and light atomic species with soft and stiff bonding networks, both particle-like ($\kappa_p$) and wave-like ($\kappa_c$) phonon transport channels are concurrently suppressed. First-principles calculations show that this architecture induces a hierarchical phonon spectrum: soft-bonded heavy atoms generate dense low-frequency modes that enhance scattering and reduce $\kappa_p$, while stiff-bonded light atoms produce sparse high-frequency optical branches that disrupt coherence and lower $\kappa_c$. High-throughput screening identifies Tl$_4$SiS$_4$ ($\kappa_p$ = 0.10, $\kappa_c$ = 0.06 W/mK) and Tl$_4$GeS$_4$ ($\kappa_p$ = 0.09, $\kappa_c$ = 0.06 W/mK) as representative candidates with strongly suppressed transport in both channels. A minimal 1D triatomic chain model further demonstrates the generality of this mechanism, offering a new paradigm for phonon engineering beyond the conventional $\kappa_p$-$\kappa_c$ trade-off.

In this paper, we study the Vlasov-Poisson-Landau Equations on $\mathbb{T}^3\times \mathbb{R}^3$ with small collision frequency $\nu\ll 1$. We prove that for $\nu$-independent perturbations of the global Maxwellians in Gevrey-$2_-$, solutions display uniform-in-$\nu$ Landau damping and enhanced dissipation. Moreover, the collisionless limit holds, that is, as $\nu\to 0_+$ for $0<t\ll \nu^{-\frac{1}{3}}$, the $\nu > 0$ solutions converge uniformly (and in much stronger norms) to the solution of the Vlasov-Poisson equation with the same initial data. To our knowledge, this work is hence the first justification that the collisionless prediction matches those of collisional plasmas in the nonlinear equations. The interaction between Landau damping and collisions requires several new ideas: (1) an infinite-regularity commuting vector field method, merged with Guo's weighted energy methods for the Landau operator and hypocoercivity to extract the enhanced dissipation; (2) A novel nearly-physical side treatment of the collisionless Vlasov echoes; (3) A new set of decomposition methods to treat the effects of the nonlinear collisions in the Volterra equation for the density (i.e., the ``collisional echoes'') (4) A new quasi-linearization method for treating the effect of the slowly evolving homogeneous modes over long times. As a side result, we also prove Landau damping and enhanced dissipation of $O(\epsilon\nu^{1/3})$ Sobolev-space perturbations of homogeneous distributions that are only $O(\epsilon)$ perturbations of global Maxwellians, generalizing the recent results of Chaturvedi, Luk, and Nguyen. As another side result, our methods also provide a nearly-completely physical-side proof of Mouhot and Villani's theorem in the full range of Gevrey-$3_-$.

We identify a new family of XB$_{12}$, boron-rich compounds formed by interconnected B$_{12}$ icosahedra and electropositive guest atoms ($X$). These structures emerged from first-principles crystal structure prediction at 50 GPa, as part of a pressure-quenching strategy to discover superconductors that could be synthesized under pressure and retained at ambient conditions. The resulting structures are thermodynamically competitive, dynamically stable at zero pressure, and - when $X$ is a mono- or trivalent element - metallic and superconducting. Predicted critical temperatures reach up to 42 K for CsB$_{12}$, rivaling MgB$_2$, the highest-$T_c$ ambient-pressure conventional superconductor. We interpret the XB$_{12}$ phase as a superatomic crystal: the B$_{12}$ units retain their molecular identity while forming extended crystalline networks. Their delocalized orbitals support doping without structural destabilization, while their covalent bonding promotes strong electron-phonon coupling. Unlike MgB$_2$, where superconductivity is driven by a narrow subset of phonon modes, the XB$_{12}$ compounds exhibit broad, mode- and momentum-distributed coupling through both intra- and inter-superatomic vibrations. Our results highlight the XB$_{12}$ family as a promising platform for metastable superconductivity and demonstrate the potential of superatoms as functional building blocks in solid-state materials design.

Mesoporous films of the metal chalcogenide $\beta$-FeSe were grown on MgO substrates by KrF pulsed laser deposition (PLD) in an argon background. At 100 mTorr, gated intensified charge-coupled device imaging and ion probe measurements showed that the plasma plume responsible for crystal growth initially comprised three components, with distinct expansion velocities. Plume interactions with the substrate heater and ablation target gave rise to complex dynamics, including collisions between the charged leading edge -- rebounding between the substrate and the target -- and slower-moving species in the plume interior. Film growth was dominated by species with kinetic energies $\le$0.5 eV/atom. X-ray reflectivity and atomic force microscopy revealed that films grown in this environment -- with a substrate temperature of 350$^\circ$C, a laser fluence of 1.0 J cm$^{-2}$, and a 7.5 mm$^2$ spot area -- formed a porous framework with 15% porosity and pore sizes below 100 nm. X-ray diffraction indicated that the porous films were epitaxial with respect to the substrate and likely grew by oriented-attachment of gas-phase molecular clusters or very small nanoparticles, in contrast to the conventional epitaxy of vacuum films from atomic constituents. The in-plane orientation of the mesoporous films was $\beta$-FeSe[100]$\parallel$[110]MgO, attributed to the soft landing of pre-formed crystallites on the MgO substrates, where protruding Se rows of $\beta$-FeSe aligned with corrugations of the MgO surface. This work implies that growth of candidate electrocatalyst materials by PLD in inert gas background may allow mesoporous frameworks with a single crystallographic orientation that expose specific crystal facets for electrochemical reactions and active site engineering.

Holographic near-eye displays offer ultra-compact form factors for virtual and augmented reality systems, but rely on advanced computer-generated holography (CGH) algorithms to convert 3D scenes into interference patterns that can be displayed on spatial light modulators (SLMs). Gaussian Wave Splatting (GWS) has recently emerged as a powerful CGH paradigm that allows for the conversion of Gaussians, a state-of-the-art neural 3D representation, into holograms. However, GWS assumes smooth-phase distributions over the Gaussian primitives, limiting their ability to model view-dependent effects and reconstruct accurate defocus blur, and severely under-utilizing the space-bandwidth product of the SLM. In this work, we propose random-phase GWS (GWS-RP) to improve bandwidth utilization, which has the effect of increasing eyebox size, reconstructing accurate defocus blur and parallax, and supporting time-multiplexed rendering to suppress speckle artifacts. At the core of GWS-RP are (1) a fundamentally new wavefront compositing procedure and (2) an alpha-blending scheme specifically designed for random-phase Gaussian primitives, ensuring physically correct color reconstruction and robust occlusion handling. Additionally, we present the first formally derived algorithm for applying random phase to Gaussian primitives, grounded in rigorous statistical optics analysis and validated through practical near-eye display applications. Through extensive simulations and experimental validations, we demonstrate that these advancements, collectively with time-multiplexing, uniquely enables full-bandwith light field CGH that supports accurate accurate parallax and defocus, yielding state-of-the-art image quality and perceptually faithful 3D holograms for next-generation near-eye displays.

We introduce a compact simulation framework for modeling open quantum systems coupled to structured, memory-retaining baths using QuTiP. Our method models the bath as a finite set of layered qubits with adjustable connections, interpreted either as a physical realization or as a conceptual representation, rather than as a continuum. This explicit modeling enables direct control over non-Markovian dynamics and allows spectral diagnostics via Fast Fourier Transform (FFT) of system observables. Using a triangle-based bath motif and its extension to a six-qubit anisotropic fractal-like architecture, we demonstrate how spectral fingerprints encode bath topology and memory depth. Standard machine learning tools such as Principal Component Analysis (PCA) and gradient boosting can then be employed to infer bath parameters and estimate proximity to exceptional points (EPs). The results suggest that spectral analysis can serve as a unifying, quantum-platform agnostic tool across theory, simulation, and experiment, offering both a student-accessible and experimentally relevant approach to understanding coherence loss and memory flow in quantum hardware. Rather than treating noise as an adversary to be eliminated, our approach views structured baths as collaborative partners, enabling controlled memory and delocalized memory and information flow for engineered quantum dynamics. In addition to its diagnostic power, the framework offers a modular and reproducible platform for teaching open quantum systems. Ultimately, we frame this as a pedagogical tool: students can pair FFT-based spectral features with lightweight ML (e.g., PCA and gradient boosting) to extract data-rich, interpretable signatures of open-system and non-Hermitian dynamics.

Methods for solving hyperbolic systems typically depend on unknown ordering (e.g., Gauss-Seidel, or sweep/wavefront/marching methods) to achieve good convergence. For many discretisations, mesh types or decompositions these methods do not scale well in parallel. In this work we demonstrate that the combination of AIRG (a reduction multigrid which uses GMRES polynomials) and PMISR DDC (a CF splitting algorithm which gives diagonally dominant submatrices) can be used to solve linear advection equations in parallel on GPUs with good weak scaling. We find that GMRES polynomials are well suited to GPUs when applied matrix-free, either as smoothers (at low order) or as an approximate coarse grid solver (at high order). To improve the parallel performance we automatically truncate the multigrid hierarchy given the quality of the polynomials as coarse grid solvers. Solving time-independent advection equations in 2D on structured grids, we find 66-101% weak scaling efficiency in the solve and 47-63% in the setup with AIRG, across the majority of Lumi-G, a pre-exascale GPU machine.

The $^{45}$Sc nuclear transition from the ground to the isomeric state at 12.389~keV, with a lifetime of 0.46~s, exhibits an extraordinarily narrow natural width of 1.4~feV and a quality factor $\simeq 10^{19}$ -- surpassing those of the most precise atomic clocks -- making $^{45}$Sc a compelling platform for advanced metrology and nuclear clocks. Here we investigate how closely the spectral width and quality factor of the solid-state $^{45}$Sc resonance can approach these natural limits. Using the European X-ray Free-Electron Laser, we confirm the isomer's lifetime via time-delayed incoherent $K_{\alpha,\beta}$ fluorescence and observe previously unreported elastic fluorescence, yielding a partial internal conversion coefficient of 390(60). The absence of a clear nuclear forward scattering signal beyond a 2-ms delay implies environmental broadening of at least $500~\Gamma_{0}$ under experimental conditions, placing bounds on solid-state decoherence mechanisms. These findings set new experimental benchmarks for solid-state nuclear clock development.

The BUQEYE model for correlated effective field theory (EFT) truncation errors assumes a regular pattern of dimensionless coefficients extracted from order-by-order observable calculations. This enables results from lower orders to inform statistical predictions for error estimates of omitted higher orders. We test the model for multiple chiral EFT ($\chi$EFT) nucleon-nucleon (NN) potentials using a suite of six common NN scattering observables represented as functions of relative momentum and scattering angle. First, we flag irregularity in the convergence patterns of potentials with so-called "soft" regulator scales, namely that the sizes of the coefficients in the observables' expansions are mismatched between the even and odd orders. Second, we test the BUQEYE model's assumption of Gaussian process (GP) stationarity against the data and find that the GP's correlation structure as encoded in its length scale is nonstationary: The GP length scale in the angular dimension $\ell_{\theta}$ is approximately inversely proportional to the relative momentum of the scattering process. After we remediate this issue by allowing $\ell_{\theta}$ to vary with momentum, diagnostics show significant improvement, validating the application of the BUQEYE model after this modification. Third, we find that good performance of the BUQEYE model relies on properly choosing the $\chi$EFT breakdown scale $\Lambda_{b}$. With $m_{\text{eff}}$ held fixed at the physical pion mass we find $\Lambda_{b}=600$--$750$ MeV for various N$^{3}$LO interactions. Statistically consistent distributions for $\Lambda_{b}$ across orders are only found for the SMS potential at a regulator scale of 450 or 500 MeV. All our results can be reproduced using a publicly available Jupyter notebook, which can be straightforwardly modified to analyze other $\chi$EFT NN potentials.

Coronal mass ejections (CMEs) are major drivers of space weather disturbances, and their deflection from the radial direction critically affects their potential impact on Earth. While the influence of the surrounding magnetic field in guiding CME trajectories is well established, accurately predicting non-radial propagation remains a challenge. In this work, we introduce and compare recently developed techniques for analyzing the early deflection of eruptive events. We revisit a largely deflected prominence-CME event of 2010 December 16 using an improved tracking framework and a new application of the topological path method. Our results suggest the deflection of the eruption is dominated by the channeling of the magnetic field lines. This study offers new physical insight into CME guidance mechanisms and validates the predictive capability of the topological path, highlighting its potential as a diagnostic tool for estimating the propagation direction of strongly deflected events.

We investigate the low-energy configurations of N mutually repelling charges confined to a spherical cap and interacting via the Coulomb potential. In the continuum limit, this problem was solved by Lord Kelvin, who found a non-uniform charge distribution with an integrable singularity at the boundary. To explore the discrete analogue, we developed an efficient numerical method that enables energy minimization while maintaining the number of charges at the cap's edge fixed. Using this approach we have obtained numerical results for various values of N and cap angular widths. Based on these results, we analyze the emergence and behavior of topological defects as functions of both N and the cap's curvature.

Nonlinear dynamical systems with continuous variables can be used for solving combinatorial optimization problems with discrete this http URL particular, numerical simulations of them can be used as heuristic algorithms with a desirable property, namely, parallelizability, which allows us to execute them in a massively parallel manner using cutting-edge many-core processors, leading to ultrafast performance. However, the dynamical-system approaches with continuous variables are usually less accurate than conventional approaches with discrete variables such as simulated annealing. To improve the solution accuracy of a representative dynamical system-based algorithm called simulated bifurcation (SB), which was found from classical simulation of a quantum nonlinear oscillator network exhibiting quantum bifurcation, here we generalize it by introducing nonlinear control of individual bifurcation parameters and show that the generalized SB (GSB) can achieve almost 100% success probabilities for some large-scale problems. As a result, the time to solution for a 2,000-variable problem is shortened to 10 ms by a GSB-based machine, which is two orders of magnitude shorter than the best known value, 1.3 s, previously obtained by an SB-based machine. To examine the reason for the ultrahigh performance, we investigated chaos in the GSB changing the nonlinear-control strength and found that the dramatic increase of success probabilities happens near the edge of chaos. That is, the GSB can find a solution with high probability by harnessing the edge of chaos. This finding suggests that dynamical-system approaches to combinatorial optimization will be enhanced by harnessing the edge of chaos, opening a broad possibility to tackle intractable combinatorial optimization problems by nature-inspired approaches.

An exact expression is derived for the kinetic contribution to the odd (arbitrary order) frequency moments of the dynamic structure factor via a finite summation that features averages of even (all lower orders) powers of the momentum over the exact momentum distribution. The derivation is carried out for the non-interacting Fermi gas and generalized to the interacting case based on the conjecture that averages over the Fermi distribution can be substituted with averages over the exact distribution. The expression is validated against known results (first, third frequency moments) and new explicit calculations (fifth, seventh frequency moments).

Single photon sources are fundamental for applications in quantum computing, secure communication, and sensing, as they enable the generation of individual photons and ensure strict control over photon number statistics. However, current single photon sources can be limited by a lack of robustness, difficulty of integration into existing optical or electronic devices, and high cost. In this study, we present the use of off-the-shelf \ch{C60} fullerene molecules embedded in polystyrene as room-temperature reliable single-photon emitters. As our results demonstrate, these molecules exhibit on-demand single-photon emission, with short fluorescence lifetimes and, consequently, high emission rates. The wide availability and ease of preparation and manipulation of fullerenes as single photon sources can pave the way for the development of practical, economic and scalable quantum photonic technologies.

We present a Discontinuous Galerkin (DG) solver for the compressible Navier-Stokes system, designed for applications of technological and industrial interest in the subsonic region. More precisely, this work aims to exploit the DG-discretised Navier-Stokes for two dimensional vortex-induced vibration (VIV) problems allowing for high-order of accuracy. The numerical discretisation comprises a nodal DG method on triangular grids, that includes two types of numerical fluxes: 1) the Roe approximate Riemann solver flux for non-linear advection terms, and 2) an Interior-Penalty numerical flux for non-linear diffusion terms. The nodal formulation permits the use of high order polynomial approximations without compromising computational robustness. The spatially-discrete form is integrated in time using a low-storage strong-stability preserving explicit Runge-Kutta scheme, and is coupled weakly with an implicit rigid body dynamics algorithm. The proposed algorithm successfully implements polynomial orders of $p\ge 4$ for the laminar compressible Navier-Stokes equations in massively parallel architectures. The resulting framework is firstly tested in terms of its convergence properties. Then, numerical solutions are validated with experimental and numerical data for the case of a circular cylinder at low Reynolds number, and lastly, the methodology is employed to simulate the problem of an elastically-mounted cylinder, a known configuration characterised by significant computational challenges. The above results showcase that the DG framework can be employed as an accurate and efficient, arbitrary order numerical methodology, for high fidelity fluid-structure-interaction (FSI) problems.

We optimize the synthesis conditions for SmFeAsO0.80F0.20 (Sm1111) bulks using a cubic-anvil high-pressure (CA-HP) apparatus through both ex-situ and in-situ processes, applying pressures of up to 4 GPa and heating temperatures of up to 1600°C. A comprehensive characterization has been performed, including structural, microstructural, transport, and magnetic measurements. Our findings indicate that a modest growth pressure of approximately 0.5 GPa is sufficient for the formation of the Sm1111 phase in the ex-situ process. In contrast, the in-situ process requires higher synthesis pressure (4 GPa) and temperature (1400 °C for 1 hour) to achieve the Sm1111 phase with enhanced superconducting properties. Notably, the optimized in-situ process significantly reduces the reaction time needed for the formation of the Sm1111 phase compared to conventional synthesis process at ambient pressure (CSP), leading to an increase in the transition temperature by 3 K and improvements in critical current density (Jc). Conversely, the optimized ex-situ process results in an onset transition temperature (Tc) of approximately 53 K, similar to that of CSP, though it enhances the Jc by an order of magnitude. Despite these advancements, a small amount of impurity phases, as observed during CSP, persists in all Sm1111 samples prepared through either the in-situ or ex-situ CA-HP processes. These results suggest that the in-situ process under optimized conditions (1400 °C, 4 GPa for 1 hour) can effectively improve the superconducting properties of Sm1111. Additionally, a comprehensive analysis comparing these results with high gas pressure techniques, spark plasma sintering, and CSP methods suggests that a small amount of impurity phases in Sm1111 is persistent and cannot be completely eliminated by various pressure techniques, even at the applied pressure of up to 4 GPa.

Using the recent concept of fluids projected onto Log-Lattices, we investigate the validity of the Gallavotti-Cohen Fluctuation Theorem (GCFT) in the context of fluid mechanics. The dynamics of viscous flows are inherently irreversible, which violates a fundamental assumption of the fluctuation theorem. To address this issue, Gallavotti introduced a new model, the Reversible Navier-Stokes Equation (RNS), which recovers the time-reversal symmetry of the Navier-Stokes (NS) equations while retaining the core characteristics of the latter. We show that for fluids on Log-Lattices, the GCFT holds for the RNS system. Furthermore, we show that this result can be extended, under certain assumptions, to the traditional, irreversible Navier-Stokes equations. Additionally, we show that the phase space contraction rate satisfies a large deviation relation which rate function can be estimated.

This study aims to investigate the effect of various beam geometries and dimensions of input data on the sparse-sampling streak artifact correction task with U-Nets for clinical CT scans as a means of incorporating the volumetric context into artifact reduction tasks to improve model performance. A total of 22 subjects were retrospectively selected (01.2016-12.2018) from the Technical University of Munich's research hospital, TUM Klinikum rechts der Isar. Sparsely-sampled CT volumes were simulated with the Astra toolbox for parallel, fan, and cone beam geometries. 2048 views were taken as full-view scans. 2D and 3D U-Nets were trained and validated on 14, and tested on 8 subjects, respectively. For the dimensionality study, in addition to the 512x512 2D CT images, the CT scans were further pre-processed to generate a so-called '2.5D', and 3D data: Each CT volume was divided into 64x64x64 voxel blocks. The 3D data refers to individual 64-voxel blocks. An axial, coronal, and sagittal cut through the center of each block resulted in three 64x64 2D patches that were rearranged as a single 64x64x3 image, proposed as 2.5D data. Model performance was assessed with the mean squared error (MSE) and structural similarity index measure (SSIM). For all geometries, the 2D U-Net trained on axial 2D slices results in the best MSE and SSIM values, outperforming the 2.5D and 3D input data dimensions.

Understanding phase transitions between crystalline phases of a material is crucial for both fundamental research and potential applications such as phase-change memory. In this study, we investigate the phase transition between GeSe crystalline polymorphs induced by either global annealing at moderate temperatures or localized laser-induced heating. The highly conductive gamma-GeSe transforms into semiconducting, single-crystalline alpha-GeSe while preserving a well-aligned crystal orientation. The distinct structural and electronic properties at the gamma-GeSe/alpha-GeSe interface were investigated by transmission electron microscopy analysis. We propose that the clustering of Ge vacancies in the gamma-GeSe phase at elevated temperatures is a key mechanism driving the transition, leading to the formation of alpha-GeSe through the segregation of a minor GeSe2 phase. Furthermore, we observe a high electrical resistance contrast of approximately 10^7 between gamma-GeSe and alpha-GeSe, underscoring the potential of GeSe as a model polymorphic system for electronic applications, including phase-change memory.

We study the rovibronic transitions in NaK$_2$ between its electronic ground state $1^2A'$ and its second excited state $3^2A'$, to identify possible pathways for the creation of ultracold ground-state triatomic molecules. Our methodology relies on the computation of potential energy surfaces and transition dipole moment surfaces for the relevant electronic states using ab initio methods. Rovibrational energy levels and wave functions are determined using the discrete variable representation approach. A double-well structure of the potential energy surface is identified for both states, and the related transition strengths between the rovibrational levels are derived. Our calculations show that the formation of ultracold ground-state NaK$_2$ molecules is expected when starting from an excited electronic state of NaK$_2$, which can be created by photoassociation of NaK and K observed by optical means by Cao et al. (Phys. Rev. Lett. 2024, 132, 093403).

Using molecular orbital theory, we introduce magnetism in pure-carbon, charge-neutral fullerene monolayers which are otherwise non-magnetic. By controlling either molecular or lattice symmetry, we can realise highly-tuneable magnetic fullerene monolayers. We demonstrate a general design principle based on group theory analysis and explain the origin of magnetism using two representative systems with $S_4$ and $C_3$ molecular symmetries. Moreover, for building blocks that lack appropriate molecular symmetry, we can enforce crystalline symmetry to induce magnetism as well. Finally, we discuss the experimental feasibility of realising our proposed magnetic fullerene monolayers by examining a previously synthesised C$_{60}$ system. Our work opens a new direction in introducing magnetism in non-magnetic building blocks by enforcing either molecular or lattice symmetry.

Axions are hypothetical particles that provide a compelling solution to two major mysteries in modern physics: the strong CP problem and the nature of dark matter. The plasma haloscope has been proposed as a promising approach for probing the higher-mass regime for dark matter axions by employing a periodic arrangement of conducting wires. In this study, we present a novel tuning mechanism for such wire-based structures by bundling the wires into a spiral configuration. This design enables continuous frequency tuning of approximately 25% with a single central rotation while maintaining the form factor. To validate this concept, we fabricated a prototype cavity comprising six spiral arms and experimentally demonstrated its feasibility, achieving results in good agreement with numerical simulations.

Molecular docking is a critical computational method in drug discovery used to predict the binding conformation and orientation of a ligand within a protein's binding site. Mapping this challenge onto a graph-based problem, specifically the Maximum Weighted Independent Set (MWIS) problem, allows it to be addressed by specialized hardware such as neutral-atom quantum processors. However, a significant bottleneck has been the size mismatch between biologically relevant molecular systems and the limited capacity of near-term quantum devices. In this work, we overcome this scaling limitation by the use of a novel divide-and-conquer heuristic introduced in Cazals et al. This algorithm enables the solution of large-scale MWIS problems by decomposing a single, intractable graph instance into smaller sub-problems that can be solved sequentially on a neutral-atom quantum emulator, incurring only a linear computational overhead. We demonstrate the power of this approach by solving a 540-node MWIS problem representing the docking of an inhibitor to the Tumor necrosis factor-$\alpha$ Converting Enzyme--thiol-containing Aryl Sulfonamide (TACE-AS) complex. Our work enables the application of quantum methods to more complex and physically realistic molecular systems than previously possible, paving the way for tackling large-scale docking challenges on near-term quantum hardware.

The spring network model constitutes the backbone in the representations of a host of physical systems. In this work, we report the disturbance-driven microscopic dynamics of an isolated, closed spring network of spherical topology in mechanical equilibrium. The system permits self-intersection. We first show the lowest-energy configurations of the closed spring networks as packings of regular triangles. The dynamics of the disturbed spring network is analyzed from the multiple perspectives of energetics, structural instability, and speed distribution. We reveal the crumpling transition of strongly disturbed spring networks and the rapid convergence of the speed distribution toward the Maxwell-Boltzmann distribution. This work demonstrates the rich physics arising from the interplay of flexibility and dynamics. The results may yield insights into the shape fluctuation and structural instability of deformable membranes from the dynamical perspective.

Recent advances in geometric deep learning and generative modeling have enabled the design of novel proteins with a wide range of desired properties. However, current state-of-the-art approaches are typically restricted to generating proteins with only static target properties, such as motifs and symmetries. In this work, we take a step towards overcoming this limitation by proposing a framework to condition structure generation on flexibility, which is crucial for key functionalities such as catalysis or molecular recognition. We first introduce BackFlip, an equivariant neural network for predicting per-residue flexibility from an input backbone structure. Relying on BackFlip, we propose FliPS, an SE(3)-equivariant conditional flow matching model that solves the inverse problem, that is, generating backbones that display a target flexibility profile. In our experiments, we show that FliPS is able to generate novel and diverse protein backbones with the desired flexibility, verified by Molecular Dynamics (MD) simulations. FliPS and BackFlip are available at this https URL .

The scalable computing revolution of the late '80s through mid- '00s forged a new technical and economic model for computing that delivered massive societal impact, but its economic benefit has driven scalability to sizes that are now exhausting the energy grid's capacity. Our time demands a new revolution in scalable energy, mirroring in key ways the scalable computing revolution; e.g., compelling economic forces, use of mass-market components, overcoming foibles of those components, judicious use of physical locality, and the the difficult integration into an effective system. The offgrid AI approach closely fits this mold, combining local mostly renewable generation and storage to power an AI data center, starting offgrid. Obstacles to delivering this approach are social, technical, and project, but the potential is massive. I argue that the offgrid-AI approach needs pioneers among both system developers and AI-data-center operators to move it quickly from concept to large-scale deployment.

The goal of this study is to estimate the amount of lost data in electron microscopy and to analyze the extent to which experimentally acquired images are utilized in peer-reviewed scientific publications. Analysis of the number of images taken on electron microscopes at a core user facility and the number of images subsequently included in peer-reviewed scientific journals revealed low efficiency of data utilization. More than 90% of electron microscopy data generated during routine instrument operation remain unused. Of the more than 150000 electron microscopy images evaluated in this study, only approximately 3500 (just over 2%) were made available in publications. Thus, the amount of lost data in electron microscopy can be estimated as >90% (in terms of data being recorded but not being published in peer-reviewed literature). On the one hand, these results highlight a shortcoming in the optimal use of microscopy images; on the other hand, they indicate the existence of a large pool of electron microscopy data that can facilitate research in data science and the development of AI-based projects. The considerations important to unlock the potential of lost data are discussed in the present article.

In complex financial networks, systemically important nodes usually play crucial roles. Asset price forecasting is important for describing the evolution of a financial network. Naturally, the question arises as to whether node centrality impacts the effectiveness of price forecasting. To explore this, we examine networks composed of major global assets and investigate how node centrality affects price forecasting using a hybrid random forest algorithm. Our findings reveal two counterintuitive phenomena: (i) factors with low centrality usually have better forecasting ability, and (ii) nodes with low centrality can be predicted more accurately in direction. These unexpected observations can be explained from the perspective of information theory. Moreover, our research suggests a criterion for factor selection: when predicting an asset price in a complex system, factors with low centrality should be selected rather than only factors with high centrality. Finally, we verify the robustness of our results using an alternative deep learning method.

We identify a previously overlooked invariant governing planetary surface temperatures, expressed in terms of only two observables: solar irradiance and Bond albedo. The relation has universal applicability and accurately reproduces the observed climates of rocky planets and large moons with substantial cloud cover (Venus, Earth, Titan), and predicts condensation-level temperatures in the gas giants (Jupiter, Saturn, Uranus, Neptune). Expressed in its simplest form, the relation encodes global energy conservation and highlights clouds as the primary regulators of planetary climate. The key result is an empirical proportionality between Bond albedo and the fraction of outgoing longwave radiation returned to the surface, termed the inner albedo, capturing the dual role of clouds and hazes as both solar reflectors and thermal mirrors. This proportionality arises naturally from a geometric construction in which surface emission is represented by parabolic cylindrical wavefronts, yielding a universal coefficient linked to the parabolic constant. Extending the framework to exoplanets, we show that it provides first-order predictions of equilibrium surface conditions across the habitable zone, pointing to a geometric necessity that constrains planetary climates beyond the details of atmospheric microphysics.

Surface lattice resonances (SLRs) in metasurfaces have become a transformative platform for subwavelength optical devices, leveraging their high quality (Q)-factors, pronounced local field enhancement, and extensive long-range interactions. However, current high-Q SLR implementations are fundamentally limited by their dependence on homogeneous dielectric environments. This restriction significantly hinders their applicability in emerging fields such as molecular sensing, where operation in heterogeneous dielectric media (e.g., interfaces with an aqueous or air cladding) is often indispensable. To overcome this limitation, we introduce guided surface lattice resonances (gSLRs) by integrating nanoparticle arrays within slab waveguides. This configuration facilitates efficient coupling between scattered light and Bloch modes, thereby enabling high-Q multimodal resonances even in index-discontinuous environments. Experimental validation under incoherent illumination demonstrates a Q-factor of 1489 in an index-mismatched surrounding. Furthermore, the coupling strength and resonance intensity of these multimodal gSLRs can be continuously modulated by adjusting the vertical displacement of the nanoparticle arrays within the slab layers. To augment the sensitivity to local dielectric variations, we investigate gSLRs in metasurfaces integrated with metallic substrates, demonstrating suitability for biomolecule detection. A mathematical sensing model, incorporating biochemical reaction kinetics and optical responses, is established by representing adsorbed molecules as a uniform dielectric layer and validated through bovine serum albumin (BSA) sensing experiments. This work not only advances the fundamental understanding of resonance engineering in complex media but also facilitates the development of ultrathin, ultra-compact nano-optical and optoelectronic devices.

Water diffusion gives rise to micrometer-scale sensitivity of diffusion MRI (dMR) to cellular-level tissue structure. The advent of precision medicine and quantitative imaging hinges on revealing the information content of dMR, and providing its parsimonious basis- and hardware-independent ``fingerprint". Here we focus on the geometry of a multi-dimensional dMR signal, derive a complete set of 21 diffusion and covariance tensor invariants in terms of irreducible representations of the group of rotations, and relate them to tissue properties. Conventional dMR metrics are shown to be redundant, while most of the invariants provide novel complementary information. Our complete set of invariants for the kurtosis tensor improves multiple sclerosis classification in a cohort of 1189 subjects. We design acquisitions based on icosahedral vertices guaranteeing minimal number of measurements to determine the most used invariants in only 1--2 minutes for the whole brain. Representing dMR signals via scalar invariant maps with definite symmetries will underpin machine learning classifiers of brain pathology, development, and aging, while fast protocols will enable translation of advanced dMR into clinical practice.

One of the important features of non-Hermitian Hamiltonians is the existence of a unique type of singularities, the so-called exceptional points (EPs). When the corresponding systems operate around such singularities, they exhibit ultrasensitive behavior that has no analog in conservative systems. An alternative way to realize such ultra-sensitivity relies on asymmetric couplings. Here we provide a comprehensive analysis based on pseudospectra, that shows the origin of exponential sensitivity, without relying on topological zero modes or the localization of all eigenstates (skin effect), but on the underlying extreme lattice non-normality. In particular, we consider four different types of lattices (Hatano-Nelson, Sylvester-Kac, non-Hermitian Su-Schrieffer-Heeger and a non-Hermitian random lattice) and identify the conditions for exponential sensitivity as a function of the lattice size. Complex and structured pseudospectra reveal the signatures of exponential sensitivity both on the eigenvalue spectra and on the underlying dynamics. Our study, may open new directions on studies related to the exploitation of non-normality for constructing ultra-sensitive systems that do not rely on the existence of EPs.

Models of how things spread often assume that transmission mechanisms are fixed over time. However, social contagions--the spread of ideas, beliefs, innovations--can lose or gain in momentum as they spread: ideas can get reinforced, beliefs strengthened, products refined. We study the impacts of such self-reinforcement mechanisms in cascade dynamics. We use different mathematical modeling techniques to capture the recursive, yet changing nature of the process. We find a critical regime with a range of power-law cascade size distributions with non-universal scaling exponents. This regime clashes with classic models, where criticality requires fine tuning at a precise critical point. Self-reinforced cascades produce critical-like behavior over a wide range of parameters, which may help explain the ubiquity of power-law distributions in empirical social data.

Shear-thinning fluids flowing through pipes are crucial in many practical applications, yet many unresolved problems remain regarding their turbulent transition. Using highly robust numerical tools for the Carreau-Yasuda model, we discovered that linear instability can arise when the power-law index falls below 0.35. This inelastic non-axisymmetric instability can universally arise in generalised Newtonian fluids that extend the power-law model. The viscosity ratio from infinite to zero shear rate can significantly impact instability, even if it is small. Two branches of finite-amplitude travelling wave solutions bifurcate subcritically from the linear critical point. The solutions exhibit sublaminar drag reduction, a phenomenon not possible in the Newtonian case.

A new three-dimensional, non-linear Magnetohydrodynamics (MHD) model has been extended in MIPS code, incorporating parallel heat diffusivity. The model has been benchmarked against the former MHD model used in MIPS code. A preliminary study of the core density collapse event (CDC) observed in the Large Helical Device (LHD) plasma has been performed using the developed model. The equilibrium has been constructed using HINT code for a typical super dense core discharge in LHD, with vacuum magnetic axis configuration RaxV = 3.85 m and magnetic axis beta \beta0 = 4% plasma. This configuration corresponds to a plasma with a steep pressure gradient and strong Shafranov shift, which makes the plasma potentially unstable in the LHD. The model shows preliminary characteristics of the CDC event. The plasma is destabilized by high-n ballooning modes in the low-field side region during the linear regime, eventually leading to the collapse of the pressure and density profiles, together with the stochastization of the magnetic field and a shift to low-n modes centered at the core of the plasma after the non-linear coupling at the relaxation regime.

The advent of high-intensity radioactive ion beams has opened new avenues for nuclear structure research. By studying exotic ions, phenomena such as shell evolution, halos, and the limits of stability have been studied. In particular, Quasi-Free Scattering (QFS) experiments on hydrogen targets have proven to be a valuable tool to investigate the structure of exotic ions. Recently, a series of QFS experiments were performed at the Radioactive Isotope Beam Factory (RIBF) of the RIKEN Nishina Center employing the MINOS liquid hydrogen system. A fundamental part of the success of these experiments was the use of dedicated devices to measure the gamma-rays, charged particles, and neutrons emitted in the reactions. The experience gained during the past campaigns, as well as the upcoming upgrade of the RIBF facility call for improvements on the existing devices, as well as for the development of new detection systems. Here we review the main detection devices used at the RIBF for QFS experiments, and give an overview of the ongoing and upcoming developments.

Delayed luminescence (DL) is a quantized signal that is characteristic of photoexcited molecules entering a relaxed state. Studying DL provides critical insight into photophysical mechanisms through the analysis of specific spatiotemporal dynamics. In this study, we developed a high-sensitivity DL imaging system using a quantitative scientific complementary metal-oxide-semiconductor (qCMOS) camera and a single-photon counting resolution. By optimizing the optical architecture and signal processing algorithms together, we achieved full-field spatiotemporal DL imaging at megapixel resolution (i.e., $2304 \times 4096$ pixels). Key findings include the following: (1) we observed spatial heterogeneity in DL intensity across the leaves of Arabidopsis thaliana, with stronger signals detected in veins and at sites of mechanical injury; (2) species-specific DL responses occur in response to oxidative stress, with Hydrocotyle vulgaris and Ginkgo biloba showing enhanced central DL activity; (3) excitation using white light induced maximum DL intensity, while red and blue light differentially modulated decay kinetics. Finally, we develop a two-level quantum model that links DL dynamics to the populations of excited-state electrons, thereby developing a theoretical framework for future photophysical research. Collectively, this work establishes a theoretical and technological framework for advancing plant phenotyping under stress conditions and optimizing light environments.

We reveal a nonlinear magnetic dynamo in a Taylor-Couette flow at small magnetic Prandtl numbers $Pm\leq 1$, which has been previously believed to exist only at higher $Pm\gtrsim 10$ in this flow. The amplitude of initial perturbations, $Pm$ and domain aspect ratio play a key role in the onset and evolution of the dynamo. It exists in two main states -- a weak state dominated by large-scale modes and a strong, turbulent state with higher amplitude dominated by small-scale modes. These findings can be important for dynamo processes in various astrophysical objects with small $Pm$.

This paper explores decaying turbulence beneath surface waves that is initially isotropic and shear-free. We start by presenting phenomenology revealed by wave-averaged numerical simulations: an accumulation of angular momentum in coherent vortices perpendicular to the direction of wave propagation, suppression of kinetic energy dissipation, and the development of depth-alternating jets. We interpret these features through an analogy with rotating turbulence (Holm 1996), wherein the curl of the Stokes drift, $\boldsymbol{\nabla}\times\boldsymbol{u}^S$, takes on the role of the background vorticity (for example, $(f_0 + \beta y) \hat{\boldsymbol{z}}$ on the beta plane). We pursue this thread further by showing that a two-equation model proposed by Bardina et al. (1985) for rotating turbulence reproduces the simulated evolution of volume-integrated kinetic energy. This success of the two-equation model -- which explicitly parametrizes wave-driven suppression of kinetic energy dissipation -- carries implications for modeling turbulent mixing in the ocean surface boundary layer. We conclude with a discussion about a wave-averaged analogue of the Rossby number appearing in the two-equation model, which we term the "pseudovorticity number" after the pseudovorticity $\boldsymbol{\nabla}\times\boldsymbol{u}^S$. The pseudovorticity number is related to the Langmuir number in an integral sense.

Time delays increase the effective dimensionality of reservoirs, thus suggesting that time delays in reservoirs can enhance their performance, particularly their memory and prediction abilities. We find new closed-form expressions for memory and prediction functions of linear time-delayed reservoirs in terms of the power spectrum of the input and the reservoir transfer function. We confirm this relationship numerically for some time-delayed reservoirs using simulations, including when the reservoir can be linearized but is actually nonlinear. Finally, we use these closed-form formulae to address the utility of multiple time delays in linear reservoirs in order to perform memory and prediction, finding similar results to previous work on nonlinear reservoirs. We hope these closed-form formulae can be used to understand memory and predictive capabilities in time-delayed reservoirs.

In this work, we study the behavior of elementary electromagnetic sources, i.e., point-like electric charges and intrinsic magnetic dipoles, in the presence of homogeneous electromagnetic fields in a classical and covariant setting. We show that the respective evolution equations for both kinds of sources can be formulated using a single Lorentz-like transformation with suitably adjusted parameters, thereby unifying the fundamental behavior of these intrinsic particle properties. We arrive at this description by expressing the evolution of the electric sources, governed by the Lorentz force, as a series of infinitesimal boosts and rotations acting on them, with the electric and magnetic field as the corresponding parameters. Upon suitable adjustments, we find a new effective Lorentz-like transformation, applicable to both electric and magnetic sources. We provide the results using both the tensorial and pseudovector representation of the magnetic sources. Finally, we obtain a non-relativistic limit of the evolution equation for magnetic sources.

Hibernation is an adaptation to extreme environmental seasonality that has been studied for almost 200 years, but our understanding of the underlying physiological system remains lacking due to the partially observed nature of the system. During hibernation, small mammals, such as the Arctic ground squirrel, exhibit dramatic oscillations in body temperature, typically one of the only physiological states measured, of up to 40 $^{\circ}$C. These spikes are known as interbout arousals and typically occur 10-20 times throughout hibernation. The physiological process that drives interbout arousals is unknown, but two distinct macro-scale mechanisms have been hypothesized. Using model selection for partially observed systems and classical dynamical systems theory, we are able to differentiate between these two hypotheses using only body temperature data recorded from a free-ranging Arctic ground squirrel, and show that our model can capture the broad features of the observed seasonal physiological transitions. We then modify our discovered physiological model of Arctic ground squirrel to include environmental information and find that we can qualitatively match body temperature data recorded from a wide range of species, including a bird, a shrew, and a bear, which also dynamically modulate body temperature. Our results suggest that a universal, environmentally sensitive core regulator could control body temperature across a diverse range of species -- a restructuring of our current understanding of the physiological organization across species. While the findings presented here are applicable to thermophysiology, the general modeling procedure is applicable to time series data collected from partially observed biological, chemical, physical, mechanical, and cosmic systems for which the goal is to elucidate the underlying mechanism or control structure.

The equations of 2D incompressible dissipationless extended magnetohydrodynamics (XMHD) extend the equations of incompressible Hall MHD (HMHD) by retaining finite-electron inertia. These XMHD equations couple the fluid velocity ${\bf V} = \wh{\sf z}\btimes\nabla\phi + V_{z}\,\wh{\sf z}$ with the magnetic field ${\bf B} = \nabla\psi\btimes\wh{\sf z} + B_{z}\,\wh{\sf z}$ in a process that is known to support dissipationless solutions that exhibit finite-time singularities associated with magnetic X-point collapse in the magnetic plane $(B_{x} = \partial\psi/\partial y, B_{y} = -\,\partial\psi/\partial x)$. Here, by adopting a 2D self-similar model for the four XMHD fields $(\phi,\psi,V_{z},B_{z})$, we obtain five coupled ordinary differential equations that are solved in terms of the Jacobi elliptic functions based on an orbital classification associated with particle motion in a quartic potential. Excellent agreement is found when these analytical solutions are compared with numerical solutions, including the precise time of a magnetic X-point collapse.

Here, we introduce the Directional Quantum Evolution Theory (DQET), a covariant reformulation of quantum mechanics where evolution takes place along a four-vector-defined arbitrary timelike direction. This method restores space-time symmetry and provides a geometric interpretation of energy as a frame-dependent projection by substituting a directional derivative for the traditional time derivative. DQET establishes a conserved probability current, supports proper-time evolution, and recovers the Schrodinger in suitable bounds. It provides a covariant solution to the quantum twin conundrum and predicts observable phase discrepancies between systems traveling along distinct trajectories. With encouraging extensions to curved spacetime, the theory offers a cohesive framework for relativistic quantum evolution.

We present ab initio calculations of one-electron quantum electrodynamical corrections to the second-order Zeeman splitting for the $1s_{1/2}$, $2s_{1/2}$, and $2p_{1/2}$ states in highly charged hydrogen-like ions. The self-energy correction is evaluated using the rigorous QED approach. The vacuum polarization correction is evaluated within the electric-loop approximation. Calculations are performed for the wide range of nuclear charge number: $Z = 14 - 92$.

The combinatorial scaling of configuration interaction (CI) has long restricted its applicability to only the simplest molecular systems. Here, we report the first numerically exact CI calculation exceeding one quadrillion ($10^{15}$) determinants, enabled by categorical compression within the small-tensor-product distributed active space (STP-DAS) framework. As a demonstration, we converged the relativistic complete active space CI (CASCI) ground state of HBrTe involving over $10^{15}$ complex-valued 2-spinor determinants in under 34.5 hours (time-to-completion) using 1000 nodes, representing the largest CASCI calculation reported to date. Additionally, we achieved $\boldsymbol{\sigma}$-build times of just 5 minutes for systems with approximately 150 billion complex-valued 2-spinor determinants using only a few compute nodes. Extensive benchmarks confirm that the method retains numerical exactness with drastically reduced resource demands. Compared to previous state-of-the-art CI calculations, this work represents a 3-orders-of-magnitude increase in CI space, a 6-orders-of-magnitude increase in FLOP count, and a 6-orders-of-magnitude improvement in computational speed. By introducing a numerically exact, categorically compressed representation of the CI expansion vectors and reformulating the $\boldsymbol{\sigma}$-build accordingly, we eliminate memory bottlenecks associated with storing excitation lists and CI vectors while significantly reducing computational cost. A compression-compatible preconditioner further enhances performance by generating compressed CI expansion vectors throughout Davidson iterations. This work establishes a new computational frontier for numerically exact CI methods, enabling chemically and physically accurate simulations of strongly correlated, spin-orbit coupled systems previously thought to be beyond reach.

Atmospheric turbulence degrades the performance of free-space optical (FSO) communication and remote sensing systems by introducing phase and intensity distortions. While a majority of research focuses on mitigating these effects to ensure robust signal transmission, an underexplored alternative is to leverage the transformation of structured light to characterize the turbulent medium itself. Here, we introduce a deep learning framework that fuses post-propagation intensity speckle patterns and orbital angular momentum (OAM) spectral data for atmospheric turbulence parameter inference. Our architecture, based on a modified InceptionNet backbone, is optimized to extract and integrate multi-scale features from these distinct optical modalities. This multimodal approach achieves validation accuracies exceeding 80%, substantially outperforming conventional single-modality baselines. The framework demonstrates high inference accuracy and enhanced training stability across a broad range of simulated turbulent conditions, quantified by varying Fried parameters (r0) and Reynolds numbers (Re). This work presents a scalable and data-efficient method for turbulence characterization, offering a pathway toward robust environmental sensing and the optimization of dynamic FSO systems.

We present a Machine Learning approach based on Symbolic Regression to derive, from either numerically generated or experimentally measured spectral data, closed-form expressions that model the optical properties of biological materials. To evaluate the performance of our approach, we consider three case studies with the aim of retrieving the refractive index of the materials that constitute the biological structures considered. The results obtained show that, in addition to retrieving readable and dimensionally homogeneous dispersion models, the expressions found have a physical meaning and their algebraic form is similar to that of the models used to characterize the dispersive behavior of transparent dielectrics in the visible region.

Following a recent work [E. Fromager, J. Phys. Chem. A 2025, 129, 4, 1143-1155] on the ensemble density functional theory (DFT) of excited electronic energy levels, we derive in this paper the ensuing static linear response theory, thus allowing for an in-principle exact evaluation of excited-state density-density linear response functions in a completely frequency-independent setting. Once individual-state components of the inverse ensemble linear response function have been introduced, a working Dyson-type equation naturally emerges for each state, individually. By considering the zero-weight limit of the theory, which infinitesimally deviates from standard Kohn--Sham DFT, exact excited-state corrections to ground-state linear response DFT can be identified. They involve the first-order weight derivatives of the ensemble Hartree-exchange-correlation (Hxc) potential and kernel, thus confirming the importance in ensemble DFT of both weight and density-functional derivatives of the ensemble Hxc energy functional.

Salient aspects of the commissioning, calibration, and performance of the CMS silicon strip tracker are discussed, drawing on experience during operation with proton-proton collisions delivered by the CERN LHC. The data were obtained with a variety of luminosities. The operating temperature of the strip tracker was changed several times during this period and results are shown as a function of temperature in several cases. Details of the system performance are presented, including occupancy, signal-to-noise ratio, Lorentz angle, and single-hit spatial resolution. Saturation effects in the APV25 readout chip preamplifier observed during early Run 2 are presented, showing the effect on various observables and the subsequent remedy. Studies of radiation effects on the strip tracker are presented both for the optical readout links and the silicon sensors. The observed effects are compared to simulation, where available, and they generally agree well with expectations.

During its earth-bound phase of the Aditya-L1 spacecraft of India, the Supra-Thermal and Energetic Particle Spectrometer (STEPS) of the Aditya Solar wind Particle EXperiment (ASPEX) was operated whenever the orbit was above 52000 km during 11 - 19 September 2023. This phase of operation provided measurements of energetic ions (with energies 0.1--2 MeV) in the magnetosphere, magnetosheath, and interplanetary medium. Three interplanetary coronal mass ejections (ICME) hit the magnetosphere during this period. This provided opportunity to examine the relative roles of ICME-generated solar energetic particles (SEPs) and substorm generated energetic ions on the magnetosphere. We approach this objective by detailed spectral analyses of energetic ion fluxes measured by two units of ASPEX-STEPS. We identify three distinctly different conditions of the north-south component of the interplanetary magnetic field (IMF $B_z$ = 0, $>$ 0, and $<$ 0) and use the derived spectral indices to understand this relative role. By combining these with the simultaneous energetic ion flux variations from the Advanced Composition Explorer (ACE) around the Sun-Earth first Lagrangian (L1) point and the Geostationary Operational Environmental Satellite (GOES) in the Earth's magnetosphere, we show that the polarity of IMF $B_z$ influences the energetic ion spectra in the magnetosphere by modulating the interplay of the ICME-generated SEP with the energetic particles generated inside the magnetosphere by substorms. Interestingly, ASPEX-STEPS observations also indicate towards directional anisotropy based on spectral indices. This suggests spatially inhomogeneous mixing of energetic ions coming from different source processes.

We study the dynamics of salt fingers in the regime of slow salinity diffusion (small inverse Lewis number) and strong stratification (large density ratio), focusing on regimes relevant to Earth's oceans. Using three-dimensional direct numerical simulations in periodic domains, we show that salt fingers exhibit rich, multiscale dynamics in this regime, with vertically elongated fingers that are twisted into helical shapes at large scales by mean flows and disrupted at small scales by isotropic eddies. We use a multiscale asymptotic analysis to motivate a reduced set of partial differential equations that filters internal gravity waves and removes inertia from all parts of the momentum equation except for the Reynolds stress that drives the helical mean flow. When simulated numerically, the reduced equations capture the same dynamics and fluxes as the full equations in the appropriate regime. The reduced equations enforce zero helicity in all fluctuations about the mean flow, implying that the symmetry-breaking helical flow is spontaneously generated by strictly non-helical fluctuations.

Radiative effects of aerosol-cloud interactions constitute the most uncertain climate forcing of the Earth system, making it important to understand how they may change with climate. We conduct 3-day-long large-eddy simulations of a stratocumulus-to-cumulus transition along an airmass-following trajectory over the Northeast Pacific Ocean. By perturbing boundary layer aerosol concentrations, we simulate aerosol-cloud interactions in both present-day and doubled-CO2 conditions. Aerosol-induced cloud changes, including the Twomey effect and adjustments of cloud fraction and liquid water path, are inhibited in a doubled-CO2 climate. Decomposing the aerosol-induced cloud radiative effect change ($\Delta$CRE) reveals that aerosol-induced cloud fraction changes dominate $\Delta$CRE. Overall, doubling CO2 attenuates aerosol-induced $\Delta$CRE (i.e., cooling) by >30% in our simulations. Our results also show that low cloud feedbacks are sensitive to the background aerosol concentration, highlighting the interplay between climate forcings and feedbacks. These results may aid in predicting the cooling potential of marine cloud brightening in a changing climate.

There is a global need for BioImage Analysis (BIA) as advances in life sciences increasingly rely on cutting-edge imaging systems that have dramatically expanded the complexity and dimensionality of biological images. Turning these data into scientific discoveries requires people with effective data management skills and knowledge of state-of-the-art image processing and data analysis, in other words, BioImage Analysts. The Global BioImage Analysts' Society (GloBIAS) aims to enhance the profile of BioImage Analysts as a key role in science and research. Its vision encompasses fostering a global network, democratising access to BIA by providing educational resources tailored to various proficiency levels and disciplines, while also establishing guidelines for BIA courses. By collaboratively shaping the education of BioImage Analysts, GloBIAS aims to unlock the full potential of BIA in advancing life science research and to consolidate BIA as a career path. To better understand the needs and geographical representation of the BIA community, a worldwide survey was conducted and 291 responses were collected across people from all career stages and continents. This work discusses how GloBIAS aims to address community-identified shortcomings in work environment, funding, and scientific activities. The survey underscores a strong interest from the BIA community in activities proposed by GloBIAS and their interest to actively contribute. With 72% of respondents willing to pay for membership, the community's enthusiasm for both online and in-person events is set to drive the growth and sustainability of GloBIAS.

In the article titled "The Bouc-Wen Model for Binary Direct Collinear Collisions of Convex Viscoplastic Bodies" and published in the Journal of Computational and Nonlinear Dynamics (Volume 20, Issue 6, June 2025), the authors studied mathematical models of binary direct collinear collisions of convex viscoplastic bodies that employed two incremental collision laws based on the Bouc-Wen differential model of hysteresis. It was shown that the models possess favorable analytical properties, and several model parameter identification studies were conducted, demonstrating that the models can accurately capture the nature of a variety of collision phenomena. In this article, the aforementioned models are augmented by modeling the effects of external forces as time-dependent inputs that belong to a certain function space. Furthermore, the range of the parameters under which the models possess favorable analytical properties is extended to several corner cases that were not considered in the prior publication. Finally, the previously conducted model parameter identification studies are extended, and an additional model parameter identification study is provided in an attempt to validate the ability of the augmented models to represent the effects of external forces.

Medical synchrotrons are often used for testing instrumentation in high-energy physics or non-clinical research in medical physics. In many applications of medical synchrotrons, such as microdosimetry and ion imaging, precise knowledge of the spill structure and instantaneous particle rate is crucial. Conventional ionization chambers, while omnipresent in clinical settings, suffer from limitations in charge resolution and integration time, making single-particle detection at high dose rates unfeasible. To address these limitations, we present a beam detection setup based on a silicon carbide (SiC) sensor and a monolithic microwave integrated circuit (MMIC), capable of detecting single particles with a full width at half maximum (FWHM) pulse duration of 500 ps. At the MedAustron ion therapy center, we characterized the spill structure of proton and carbon-ion beams delivered to the irradiation room beyond the timescale of the maximum ion revolution frequency in the synchrotron. The resulting data offer valuable insights into the beam intensity at small time scales and demonstrate the capabilities of SiC-based systems for high-flux beam monitoring.

Cavity electro-optic (EO) modulation plays a pivotal role in optical pulse and frequency comb synthesis, supporting a wide range of applications including communication, computing, ranging, and quantum information. The ever-growing demand for these applications has driven efforts in enhancing modulation coupling strength and bandwidth towards advanced pulse-comb synthesis. However, the effects of strong-coupling and high-bandwidth cavity EO modulation remain underexplored, due to the lack of a general, unified model that captures this extreme condition. In this work, we present a universal framework for pulse-comb synthesis under cavity EO modulation, where coupling strength and modulation bandwidth far exceed the cavity's free spectral range (FSR). We show that, under such intense and ultrafast driving conditions, EO-driven frequency combs and pulses exhibit rich higher-order nonlinear dynamics, including temporal pulse compression and comb generation with arbitrary pump detuning. Leveraging this framework, we reveal a direct link between the higher-order dynamics of EO pulse-comb generation and the band structure of synthetic dimension. Furthermore, we demonstrate arbitrary comb shaping via machine-learning-based inverse microwave drive design, achieving a tenfold enhancement in cavity electro-optic comb flatness by exploring the synergistic effects of high-bandwidth driving and detuning-induced frequency boundaries. Our findings push cavity electro-optic modulation into a new frontier, unlocking significant potential for universal and machine-learning-programmable electro-optic frequency combs, topological photonics, as well as photonic quantum computing in the strong-coupling and high-bandwidth regimes.

The chiral(ity)-induced spin selectivity (CISS) effect, where electrons passing through a chiral medium acquire significant spin-polarization at ambient temperatures, has been widely observed experimentally, yet its theoretical foundations remain actively debated. Open questions persist regarding whether CISS originates from helical geometry or more general chirality, and whether a unified mechanism can account for phenomena across solid-state and soft-matter systems, mesoscopic films, and single molecules. Clarifying the interrelations between existing models is essential to determine if a universal picture of CISS can be found or whether system-specific models are required, and if so, where their common starting point should lie for a workable classification of CISS manifestations. Despite this theoretical fragmentation, recent studies of CISS effects in electron transfer systems, magnetic field sensitivity and coherence of radical pair reactions, polarized electroluminescence in chiral hybrid perovskites, DNA-based biosensors, and enantioselective detection, highlight its broad conceptual relevance and potential applications in spintronics, molecular sensors, and quantum information processing. In this review, we help bridge the gap between theory, experiment, and implementation, with a particular focus on prospects for quantum sensing and metrology. We outline fundamental frameworks of CISS, clarifying what constitutes the `chiral', the `induced', and the `spin-selectivity' that makes up CISS, before going on to survey key model realizations and their assumptions. We examine some of the emerging quantum sensing applications and assess the model-specific implications, in particular exemplifying these in the context of spin-correlated radical pairs, which offer a promising, tunable, and biomimetic platform for emerging molecular quantum technologies.

A wide range of methods currently exist for testing the presence of malaria, each with its own advantages and disadvantages. New technologies are urgently needed to develop more effective diagnosis tools to fight and eradicate malaria. Optical biosensors that employ surface plasmon resonance (SPR) techniques are a promising category of devices for detecting malaria biomarkers. One such biomarker is plasmodium lactate dehydrogenase (pLDH), a protein produced during the life cycle of the malaria parasite, which is a metabolic enzyme found in all plasmodium species, including the most widespread falciparum. This work reports on the design, probing, and experimental performance of an optical biosensor for detecting pLDH based on SPR and extraordinary optical transmission. The biosensor is composed of an aluminum metasurface made from an array of nanoholes. The sensor operates in the visible spectral region and achieves label-free sensing of plasmodium falciparum LDH (pfLDH) spiked in phosphate-buffered saline. The sensor has a spectral sensitivity of 360 nm/RIU and an LOD of 1.3 nM, equivalent to 45.6 ng/mL of pfLDH. This type of optical biosensor may offer a cost-effective and high sensitivity method for active infection diagnosis.

The appearance of angular momentum in the nuclear motion of molecular systems lacking inversion symmetry under imposed thermal gradients presents a novel mechanism with potential implications for spintronics, magnetic response, and energy transport in such systems. Here we explore this phenomenon, using theoretical analysis and numerical simulations to study angular momentum generation in several driven chiral molecular models. We demonstrate that significant vibrational angular momentum can be induced under both mechanical and thermal driving, with magnitude comparable to that induced in optically driven chiral phonons. We find that generation of angular momentum is a common and general phenomenon in driven chiral structures, highlighting the role of symmetry-breaking in the (non-equilibrium) internal atomic motion of such systems.

Acoustic holography in the megahertz frequency range can impact numerous applications, including manufacturing, non-destructive testing, and transcranial ultrasound. However, designing lens topologies for complex acoustic holograms in the megahertz range poses a significant challenge, as weave propagation effects through the lens cannot be ignored. Here, we show that the inherent ability of heterogeneous angular spectrum approach to incorporate in plane varying speed-of-sound maps and support rapid differentiable optimization of lens thickness profiles can generate lens topologies for high fidelity acoustic holography. Crucially, we show that this framework can also account for wavefront aberrations in the propagation media, providing the opportunity to reconfigure this disruptive technology for high precision neuro-interventions. Our investigations also revealed that low frequency acoustic feedback generated by nonlinear mixing of high frequency waves allows attaining accurate skull-compensating lens alignment and creates the possibility to monitor CSF fluid build-up and removal in hydrocephalus. Together, our findings support the design of simple, economical, and high-performance ultrasound systems.

Reliable long-lead forecasting of the El Nino Southern Oscillation (ENSO) remains a long-standing challenge in climate science. The previously developed Multimodal ENSO Forecast (MEF) model uses 80 ensemble predictions by two independent deep learning modules: a 3D Convolutional Neural Network (3D-CNN) and a time-series module. In their approach, outputs of the two modules are combined using a weighting strategy wherein one is prioritized over the other as a function of global performance. Separate weighting or testing of individual ensemble members did not occur, however, which may have limited the model to optimize the use of high-performing but spread-out forecasts. In this study, we propose a better framework that employs graph-based analysis to directly model similarity between all 80 members of the ensemble. By constructing an undirected graph whose vertices are ensemble outputs and whose weights on edges measure similarity (via RMSE and correlation), we identify and cluster structurally similar and accurate predictions. From which we obtain an optimized subset of 20 members using community detection methods. The final prediction is then obtained by averaging this optimized subset. This method improves the forecast skill through noise removal and emphasis on ensemble coherence. Interestingly, our graph-based selection shows robust statistical characteristics among top performers, offering new ensemble behavior insights. In addition, we observe that while the GNN-based approach does not always outperform the baseline MEF under every scenario, it produces more stable and consistent outputs, particularly in compound long-lead situations. The approach is model-agnostic too, suggesting that it can be applied directly to other forecasting models with gargantuan ensemble outputs, such as statistical, physical, or hybrid models.

We reveal an instantaneous optical valley selection rule that illuminates the coupling between the instantaneous optical chirality of the driving laser field and the chirality of valley systems. Building on this principle, we propose and demonstrate that a single chirality-separated optical field, in which oppositely signed instantaneous optical chiralities are separated within an optical cycle, enables independent manipulation of currents from K and K' valleys. Based on this scheme, we highlight two key example applications: (1) complete separation of currents from different valleys, yielding 100%-purity valley-polarized currents, and (2) generation of pure valley current with zero net charge flow. Our work offers a robust and highly controllable all-optical strategy for ultrafast engineering valley currents in the optical cycle timescale, paving a new avenue for valleytronics and quantum information technologies.

The fluid dynamics community has increasingly adopted machine learning to analyze, model, predict, and control a wide range of flows. These methods offer powerful computational capabilities for regression, compression, and optimization. In some cases, machine learning has even outperformed traditional approaches. However, many fluid mechanics problems remain beyond the reach of current machine learning techniques. As the field moves from its current state toward a more mature paradigm, this article offers a critical assessment of the key challenges that must be addressed. Tackling these technical issues will not only deepen our understanding of flow physics but also expand the applicability of machine learning beyond fundamental research. We also highlight the importance of community-maintained datasets and open-source code repositories to accelerate progress in this area. Furthermore, the future success of machine learning in fluid dynamics will depend on effective training -- not only for the next generation of researchers but also for established fluid mechanicians adapting to this evolving landscape. Data-driven fluid dynamics is in its critical transitional state over the next few years to shape its future. This perspective article aims to spark discussions and encourage collaborative efforts to advance the integration of machine learning in fluid dynamics.

We present a compact, noise-resilient reconstructive spectrometer-on-a-chip that achieves high-resolution hyperspectral imaging across an extended near-infrared (NIR) range up to 1100nm. The device integrates monolithically fabricated silicon photodiodes enhanced with photon-trapping surface textures (PTST), enabling improved responsivity in the low-absorption NIR regime. Leveraging a fully connected neural network, we demonstrate accurate spectral reconstruction from only 16 uniquely engineered detectors, achieving <0.05 RMSE and 8nm resolution over a wide spectral range of 640nm to 1100nm. Our system outperforms conventional spectrometers, maintaining signal-to-noise ratio above 30dB even with 40dB of added detector noise; extending functionality to longer wavelengths up to 1100nm, while the traditional spectrometers fail to perform beyond 950nm due to poor detector efficiency and noise performance. With a footprint of 0.4mm2, dynamic range of 50dB, ultrafast time response (57ps), and high photodiode gain (>7000), this AI-augmented silicon spectrometer is well-suited for portable, real-time, and low-light applications in biomedical imaging, environmental monitoring, and remote sensing. The results establish a pathway toward fully integrated, high-performance hyperspectral sensing in a CMOS-compatible platform.

This paper investigates the suppression of two stream instabilities in electron ion plasmas when the individual species attain relativistic velocities. This suppression of the growth rate of two stream instability is consistent even when the ions form a neutralizing background. It is found that the parameter space of the growth rate reasonably squeezes for relativistic electrons at higher plasma frequencies. We further report the suppression of the growth rate of the said instability as the ion electron mass ratio reaches the realistic limit. Our results have implications for high-energy plasmas, laser-plasma interactions, and relativistic particle beam physics, providing insights into the complex interplay of linear and nonlinear processes governing the two-stream instability. Our unified four-regime analysis extends previous understanding of how realistic mass ratios fundamentally modify relativistic suppression effects, providing essential scaling laws for high-energy plasma applications.

Bound states in the continuum (BICs) have emerged as powerful tools for realizing ultra-high-Q resonances in nanophotonics. While previous implementations have primarily relied on dielectric metasurfaces, they remain limited by the diffraction limit. In this work, we theoretically and numerically demonstrate and experimentally validate the existence of topologically protected phonon-polaritonic BICs in periodic arrays of cylindrical nanoresonators composed of isotopically enriched hexagonal boron nitride (h11BN), which have the availability of two restrahlen bands (lower (type-I) and upper (type II)), operating in the lower Reststrahlen band (RB-1). Owing to the uniaxial anisotropy of hBN and the rotational symmetry of the structure, these systems support topologically symmetry-protected BICs at the {\Gamma}-point, where radiative losses are fully suppressed. The total quality factor is ultimately bounded by the intrinsic phonon damping of h11BN, enabling high-Q polaritonic modes with minimal radiation leakage. When cylindrical symmetry is broken via angular tilting of incident light away from the normal incidence, these BICs transition into quasi-BICs (q-BICs) with strong field confinement and tunable radiation leakage. This topological protection enables robust control over mode lifetimes and confinement, paving the way toward scalable polaritonic platforms for mid-infrared optoelectronics, sensing, and quantum nanophotonics.

We present a numerical method for grand canonical density functional theory (DFT) tailored to solid-state systems, employing Gaussian-type orbitals as the primary basis. Our approach directly minimizes the grand canonical free energy using the density matrix as the sole variational parameter, while self-consistently updating the electron number between self-consistent field iterations. To enable realistic electrochemical modeling, we integrate this approach with implicit solvation models. Our solvation scheme introduces less than 50% overhead relative to gas-phase calculations. Compared to existing plane wave-based implementations, our method shows improved robustness in grand canonical simulations. We validate the approach by modeling corrosion at silver surfaces, finding excellent agreement with previous studies. This work lays the groundwork for future wavefunction-based simulations beyond DFT under electrochemical operando conditions.

We present a result derived from the optical chirality continuity equation that shows the existence of a source term describing optical chirality generation through the interactions of the magnetic induction field with a source current, without contributions from the electric field. This framework is validated through a metasurface-based methodology. Using a lossless, all-dielectric nanoparticle array, we engineer an in-plane rotating electric field that generates out-of-plane optical magnetism. This array exhibits a far-field circular dichroism of 0.47 and a high-quality factor on the order of 10^6. The presented findings demonstrate the feasibility of inducing chiral optical responses from a magnetic source. This work establishes a new paradigm for structured photonic media, offering insights into the design of nanophotonic devices that exploit optical magnetism for chiral light-matter interactions.

As the global shift toward transportation electrification has accelerated, capacity planning for electric vehicle (EV) charging infrastructure has become a critical challenge in the development of low-carbon urban energy systems. This study proposes the first demand-driven, multi-objective planning model for optimizing city-scale capacity allocation of EV charging infrastructure. The model employs a bottom-up approach to estimate charging demand differentiated by vehicle type-battery electric vehicles (BEVs), extended-range electric vehicles (EREVs), and plug-in hybrid electric vehicles (PHEVs). Chongqing, a rapidly expanding EV hub in China with a strong industrial base, supportive policies, and diverse urban morphologies, is selected as the case study. The results show that (1) monthly EV electricity consumption in Chongqing rose from 18.9 gigawatt-hours (GWh) in June 2022 to 57.5 GWh in December 2024, with associated carbon emissions increasing from 9.9 kilotons of carbon dioxide (ktCO2) to 30 ktCO2, driven primarily by BEVs; (2) 181,622 additional charging piles were installed between 2022 and 2024, concentrated in densely populated areas, reflecting a demand-responsive strategy that prioritizes population density over geographic coverage; and (3) between 2025 and 2030, EV electricity demand is projected to reach 1940 GWh, with the number of charging piles exceeding 1.4 million, and charging demand from EREVs and PHEVs expected to overtake BEVs later in the period. While Chongqing serves as the pilot area, the proposed planning platform is adaptable for application in cities worldwide, enabling cross-regional comparisons under diverse socio-economic, geographic, and policy conditions. Overall, this work offers policymakers a versatile tool to support sustainable, cost-effective EV infrastructure deployment aligned with low-carbon electrification targets in the transportation sector.

The development of new electronic structure methods is a very time consuming and error prone process when done by hand. SpinAdaptedSecondQuantization is an open-source Julia package we have developed for working with automated electronic structure theory development. The code focuses on being user-friendly and extensible, allowing for easy use of both user- and pre-defined fermionic and/or bosonic operators, tensors, and orbital spaces. This allows the code to be used to efficiently investigate and prototype new electronic structure methods for many different types of systems. This includes both exotic systems with wave functions consisting of different kinds of particles at once, as well as new parametrizations for traditional many-electron systems. The code is spin-adapted, working directly with spin-adapted fermionic operators, and can easily be used to derive common electronic structure theory equations and expressions, such as the coupled cluster energy, ground and excited state equations, one- and two-electron density matrices, etc. Additionally, the code can translate expressions into code, accelerating the process of going from ideas to implemented methods.

We study charge $\Delta_T$ noise, followed by an examination of spin $\Delta_T$ noise, in the normal metal-spin flipper-normal metal-insulator-superconductor (N-sf-N-I-S) junction. Our analysis reveals a key contrast: while charge $\Delta_T$ noise remains strictly positive, spin $\Delta_T$ noise undergoes a sign reversal from positive to negative, driven by the interplay between spin-flip scattering as well as Andreev reflection. In contrast, charge quantum shot noise remains positive and sign-definite, which is valid for spin quantum shot noise also. The emergence of negative spin $\Delta_T$ noise has two major implications. First, it establishes a clear distinction between spin resolved $\Delta_T$ noise and quantum shot noise: the former is dominated by opposite-spin correlations, whereas the latter is led by same-spin correlations. Second, it provides access to scattering mechanisms that are not captured by quantum shot noise alone. Thus, negative spin $\Delta_T$ noise serves as a unique probe of the cooperative effects of Andreev reflection and spin flipping. We further place our results in context by comparing them with earlier reports of negative $\Delta_T$ noise in strongly correlated systems, such as fractional quantum Hall states, and in multiterminal hybrid superconducting junctions. Overall, this work offers new insights into the mechanisms governing sign reversals in $\Delta_T$ noise and highlights their role as distinctive fingerprints of spin-dependent scattering in superconducting hybrid devices.

Measuring the forces of individual muscles in a muscle group around a joint is non-trivial, and researchers have suggested using surrogates for individual muscle forces instead. Traditionally, experimentalists have shown that the force output of the skeletal muscle tissue can be correlated to the intra-muscular pressure (IMP) generated by the muscle belly. However, IMP proves difficult to measure in vivo, due to variations from sensor placement and invasiveness of the procedure. Numerical biomechanical simulations offer a tool to analyze muscle contractions, enabling new insights into the correlations among non-invasive experimentally measurable quantities such as strains, and the force output. In this work, we investigate the correlations between the muscle force output, the principal, shear and volumetric strains experienced by the muscle, as well as the pressure developed within the muscle belly as the tissue undergoes isometric contractions with varying activation profiles and magnitudes. It is observed that pressure does not correlate well with force output under higher sub-maximal and maximal activation levels, especially at locations away from the center of the muscle belly due to pressure relaxation effects. This study reveals strong correlations between force output and the strains at all locations of the belly, irrespective of the type of activation considered. This observation offers evidence for further in vivo studies using experimentally measurable principal and volumetric strains in the muscle belly as proxies for the force generation by the individual muscle and consequently enables the estimation on the contribution of various muscle groups to the total force.

We theoretically propose a method for implementing the Hamiltonian incorporating Heisenberg and Dzyaloshinskii-Moriya (DM) interactions within Rydberg atoms arranged in a two-dimensional square lattice, utilizing Floquet engineering. In our scheme, we use both global and local operations of the spins. The global operations can be realized by applying the microwave and the local operations can be realized by the locally addressing lasers, which yields the ac-Stark shift. Since our engineered Hamiltonian contains bond-dependent DM interactions, we expect the emergence of quantum skyrmions in the ground state.

Discoveries of fundamental limits for the rates of physical processes, from the speed of light to the Lieb-Robinson bound for information propagation, are conceptual breakthroughs that often challenge our understanding of the underlying physics. Here we observe such a limit for a paradigmatic many-body phenomenon, the spreading of coherence during formation of a weakly interacting Bose-Einstein condensate. We study condensate formation in an isolated homogeneous atomic gas that is initially far from equilibrium, in an incoherent low-energy state, and condenses as it relaxes towards equilibrium. Tuning the inter-atomic interactions that drive condensation, we show that the spreading of coherence through the system is initially slower for weaker interactions, and faster for stronger ones, but always eventually reaches the same limit, where the square of the coherence length grows at a universal rate given by the ratio of Planck's constant and the particle mass, or equivalently by the quantum of velocity circulation associated with a quantum vortex. These observations are robust to changes in the initial state, the gas density, and the system size. Our results provide benchmarks for theories of universality far from equilibrium, are relevant for quantum technologies that rely on large-scale coherence, and invite similar measurements in other systems.

Urgent applications like wildfire management and renewable energy generation require precise, localized weather forecasts near the Earth's surface. However, forecasts produced by machine learning models or numerical weather prediction systems are typically generated on large-scale regular grids, where direct downscaling fails to capture fine-grained, near-surface weather patterns. In this work, we propose a multi-modal transformer model trained end-to-end to downscale gridded forecasts to off-grid locations of interest. Our model directly combines local historical weather observations (e.g., wind, temperature, dewpoint) with gridded forecasts to produce locally accurate predictions at various lead times. Multiple data modalities are collected and concatenated at station-level locations, treated as a token at each station. Using self-attention, the token corresponding to the target location aggregates information from its neighboring tokens. Experiments using weather stations across the Northeastern United States show that our model outperforms a range of data-driven and non-data-driven off-grid forecasting methods. They also reveal that direct input of station data provides a phase shift in local weather forecasting accuracy, reducing the prediction error by up to 80% compared to pure gridded data based models. This approach demonstrates how to bridge the gap between large-scale weather models and locally accurate forecasts to support high-stakes, location-sensitive decision-making.

Architecting mechanisms of damage in metamaterials by leveraging lattice topology and geometry poses a vital yet complex challenge, essential for engineering desirable mechanical responses. Of these metamaterials, Maxwell lattices, which are on the verge of mechanical stability, offer significant potential for advanced functionality. By leveraging their robust topological features, they enable precise control of effective elastic properties, manipulation of stress localisation and delocalisation across specific domains, and targeted global damage that follows local fracture events. In this work, we identify topology and geometry-dependent parameters that establish a simple, yet precise, framework for designing the behaviour of non-idealised Maxwell lattices and their damage processes. We numerically explore the underlying phenomenology to demonstrate how this framework can guide or arrest damage in lattices, both with and without domain walls and additional boundary constraints. Our approach uncovers a robust way to manipulate the mechanisms of damage and the path they follow in metamaterials, with further insight into crack arrest, diversion, and shielding.

Spin polarization and spin transport are common phenomena in many quantum systems. Relativistic spin hydrodynamics provides an effective low-energy framework to describe these processes in quantum many-body systems. The fundamental symmetry underlying relativistic spin hydrodynamics is angular momentum conservation, which naturally leads to inter-conversion between spin and orbital angular momenta. This inter-conversion is a key feature of relativistic spin hydrodynamics, closely related to entropy production and introducing ambiguity in the construction of constitutive relations. In this article, we present a pedagogical introduction to relativistic spin hydrodynamics. We demonstrate how to derive the constitutive relations by applying local thermodynamic laws and explore several distinctive aspects of spin hydrodynamics. These include the pseudo-gauge ambiguity, the behavior of the system in the presence of strong vorticity, and the challenges of modeling the freeze-out of spin in heavy-ion collisions. We also outline some future prospects for spin hydrodynamics.

Our research is focused on the missing, but important and necessary Draconic gearing of the Antikythera Mechanism. The three Lunar cycles Sidereal, Synodic and Anomalistic are represented on the Mechanism by correlating the Fragments A and C (part of the Front plate), whereas the fourth Lunar cycle Draconic results after correlating the unplaced Fragment D with Fragment A. Considering the deformation of the Mechanism s parts during 2000 years underwater and their shrinkage after their retraction from the sea bottom, we present a revised gearing scheme of the Draconic scale. The existence of the Draconic gearing is crucial, because both the preserved and the missing eclipse events can be precalculated by the phase correlation of three pointers: of the Lunar Disc, of the Golden sphere/Sun-ray and the Draconic. This means that the eclipse events are calculated by pure mechanical processing and that they are not documented observed events. The phase coordination of the three lunar cycles can be used as a quality criterion for a functional model of the Mechanism. Eudoxus papyrus was the key for the lost words completion of the Back Plate inscriptions eclipse events classification of the Antikythera Mechanism.

We present fast parallel rearrangement of single atoms in optical tweezers into arbitrary geometries by updating holograms displayed by an ultra fast spatial light modulator. Using linear interpolation of the tweezer position and the optical phase between the start and end arrays, we can calculate and display holograms every few ms, limited by technology. To show the versatility of our method, we sort the same atomic sample into multiple geometries with success probabilities of 0.996(2) per rearrangement cycle. This makes the method a useful tool for rearranging large atom arrays for quantum computation and quantum simulation.

Over the past several years, there have been many studies demonstrating the ability of deep neural networks to identify phase transitions in many physical systems, notably in classical statistical physics systems. One often finds that the prediction of deep learning methods trained on many ensembles below and above the critical temperature $T_{\rm c}$ behaves similarly to an order parameter, and this analogy has been successfully used to locate $T_{\rm c}$ and estimate universal critical exponents. In this work, we pay particular attention to the ability of a convolutional neural network to capture these critical parameters for the 2-$d$ Ising model when the network is trained on configurations at $T=0$ and $T=\infty$ only. We directly compare its output to the same network trained at multiple temperatures below and above $T_{\rm c}$ to gain understanding of how this extreme restriction of training data can impact a neural network's ability to classify phases. We find that the network trained on two temperatures is still able to identify $T_{\rm c}$ and $\nu$, while the extraction of $\gamma$ becomes more challenging.

We describe a model that accounts for the phase rotation that occurs when a receiver or transmitter changes orientation while observing or emitting circularly polarized electromagnetic waves. This model extends work detailing Global Navigation Satellite Systems (GNSS) carrier phase wind-up to allow us to describe the interaction of changing satellite orientation with phase rotation in observing radio telescopes. This development is motivated by, and a critical requirement of, unifying GNSS and Very Long Baseline Interferometry (VLBI) measurements at the observation level. The model can be used for either stationary choke ring antennas or steerable radio telescopes observing either natural radio sources or satellites. Simulations and experimental data are used to validate the model and to illustrate its importance. In addition, we rigorously lay out the feed rotation correction for radio telescopes with beam waveguide and full Nasmyth focuses and validate the correction by observing the effect with dual polarization observations. Using this feed rotation model for beam waveguide telescopes, we produce the first phase delay solution for the VLBI baseline WARK30M-WARK12M. We provide a practical guide to using the feed rotation model in Appendix D.

Taylor dispersion, the gravity-induced enhancement of translational diffusion during the steady settling of a Brownian particle, has so far been analyzed only for torque-free bodies (H. Brenner, J. Colloid Interface Sci., 71(2): 189-208, 1979). In this work, we extend the theory to a non-centrosymmetric, axisymmetric particle whose centers of mass and buoyancy are offset from its hydrodynamic center, so that the gravitational torque acts in addition to the net gravitational force. We study the Taylor dispersion of such particles as a function of non-dimensional parameter $\alpha$ representing the strength of the gravitational torque: $\alpha$ is zero when the centers of mass and buoyancy are at the hydrodynamic center and increases when they are offset from it. Analytical calculations show that for small $\alpha$, the Taylor dispersion is always amplified: the effective diffusivities created by sedimentation always increase as $\alpha^2$, but they start to decrease at certain values of $\alpha$ and approach to zero for large values of $\alpha$. We further analyze the transient regime of the mean-square displacement (MSD). At short times, the MSD grows quadratically with time before crossing over to the diffusive regime. The ballistic regime persists to relatively high sedimentation Péclet numbers (the ratio of the rotational relaxation time to the sedimentation time), even in the presence of a gravitational torque, indicating that such a torque does not considerably alter sustained ballistic motion.

The sliding motion of aqueous droplets on hydrohobic surfaces leads to charge separation at the trailing edge, with implications from triple-line friction to hydrovoltaic energy generation. Charges deposited on the solid surface have been attributed to ions or electrons ripped off from the liquid drop. However, the dynamics and exact physicochemical nature of these surface-trapped charges remains poorly explored. Here, we take advantage of a scanning-based electrostatic mapping technique, to directly quantify the spatiotemporal dynamics of surface deposited charges in the wake of droplets sliding on hydrophobic surfaces. We confirm the ionic nature of these interfacially trapped charges, and evidence that they undergo very fast bidimensional diffusive transport, gliding with low friction at the solid/gas interface. We interpret our observations in the framework of molecular dynamics simulation of hydrated ions adsorbed on solid surfaces, revealing a peculiar transport mechanism limited by purely interfacial friction of the ionic solvation shell with the solid surface. By uncovering the unexpected dynamics of these ionic puddles - a distinct state of interfacial ionic matter - our findings have general implications for molecular-scale ionic transport, electrified matter and wetting dynamics at interfaces.

Accurate finite element analysis of refined shell theories is crucial but often hindered by membrane and shear locking effects. While various element-based locking-free techniques exist, this work addresses the problem at the theoretical level by utilizing results from asymptotic analysis. A formulation of a 2D refined shell theory incorporating transverse shear is developed using rescaled coordinates and angles of rotation, ensuring equal asymptotic orders of magnitude for extension, bending, and rotation measures and their respective stiffnesses. This novel approach, implemented via isogeometric analysis, is shown to be both asymptotically accurate relative to the underlying refined shell theory and inherently free from membrane and shear locking. Numerical simulations of semi-cylindrical shells show excellent agreement between the analytical solutions, 2D refined shell theory predictions, and 3D elasticity theory, validating the effectiveness and accuracy of the proposed formulation.

In Szilard's engine, a demon measures a one-particle gas and applies feedback to extract work from thermal fluctuations, embodying Maxwell's notion that information reduces thermodynamic entropy - an apparent second-law violation. The Landauer-Bennett Thesis resolves this paradox by requiring the demon to record the measurement, which results in an entropy increase in the demon's memory. Eventually, the demon's memory needs to be erased. The erasure costs the same work as extracted previously, hence there is no violation of the second law. Though widely accepted, the fictitious memory invoked in the thesis has drawn multiple criticisms, with debates persisting over the demon's necessity. We show that the demon is the piston that partitions the space and drives the expansion. The final position of the piston after expansion records the particle's position pre-expansion: it is an ``information-bearing degree of freedom''. In this Piston-Demon Thesis, memory register and feedback (expansion) happen simultaneously. Our exposition identifies the mischievous demon as a physical degree of freedom, and greatly simplifies Szilard's engine. It also offers educators a tangible illustration of information-thermodynamics.

MXenes, a family of 2D transition metal compounds, have emerged as promising materials due to their unique electronic properties and tunable surface chemistry. However, the translation of these nanoscale properties into macroscopic devices is constrained by suitable cross-linking strategies that enable both processability and controlled inter-flake charge transport. Herein, we demonstrate the tunability of interfaces and the inter-layer spacing between Ti$_3$C$_2$T$_x$ MXene flakes through molecular cross-linking with homologous diamines. Oleylamine was first used to stabilize MXenes in chloroform, followed by diamine-mediated cross-linking to tune precisely the interlayer spacing. Grazing incidence X-ray scattering (GIXRD/GIWAXS) confirmed the correlation between ligand chain length and inter-layer spacing, which was further supported by Density Functional Theory (DFT) calculations. Furthermore, we investigated the charge transport properties of thin films consisting of these diamine-crosslinked Ti$_3$C$_2$T$_x$ MXenes and observed a strong dependence of the conductivity on the interlayer spacing. The dominating charge transport mechanism is variable range hopping (VRH) in accordance with the structure analysis of the films. Finally, we probed chemiresistive vapor sensing in MXene composites, observing pronounced water sensitivity and selectivity, highlighting their potential for use in humidity sensors. Insights into molecular cross-linking and its impact on charge transport open avenues for next-generation MXene-based electronic devices.

Hypothesis Roughening transitions at solid-liquid interfaces govern crystal morphology in diverse systems. In ice crystallization, these transitions control interfacial faceting and surface kinetics. Faceted morphologies are often associated with ice-active molecules, which inhibit recrystallization and are essential for cryopreservation. We hypothesize that kinetic roughening transitions can induce faceting even in the absence of ice-active agents, particularly at high solute concentrations with depressed melting points, potentially complicating the interpretation of crystal morphology as an indicator of ice activity. Experiments We investigated the kinetic roughening transition of ice in dimethyl sulfoxide (DMSO) and proline-water solutions using cryomicroscopy and real-time image analysis. Crystals grew in microdroplets, maintaining near-equilibrium conditions as solute concentration increased during growth due to conversion of liquid water to ice. Antifreeze protein type III (AFPIII) was applied to distinguish intrinsic roughening from adsorption-mediated effects. Findings A distinct kinetic roughening transition temperature (TR = -16.0 +/- 0.2 oC) was identified, marking a shift from rounded disks at higher temperatures to faceted hexagonal plates at lower temperatures, independent of solute type. Recrystallization below TR revealed asymmetry between growth and melting interfaces. AFPIII promoted faceting even above TR, consistent with stabilization of step edges and elevation of the roughening transition temperature. These results clarify the interplay between intrinsic interface kinetics and molecular adsorption, with implications for interpreting ice morphology, surface roughening, and cryopreservation design.

We introduce a novel methodology for addressing systematic uncertainties in unbinned inclusive cross-section measurements and related collider-based inference problems. Our approach incorporates known analytic dependencies on parameters of interest, including signal strengths and nuisance parameters. When these dependencies are unknown, as is frequently the case for systematic uncertainties, dedicated neural network parametrizations provide an approximation that is trained on simulated data. The resulting machine-learned surrogate captures the complete parameter dependence of the likelihood ratio, providing a near-optimal test statistic. As a case study, we perform a first-principles inclusive cross-section measurement of $\textrm{H}\rightarrow\tau\tau$ in the single-lepton channel, utilizing simulated data from the FAIR Universe Higgs Uncertainty Challenge. Results in Asimov data, from large-scale toy studies, and using the Fisher information demonstrate significant improvements over traditional binned methods. Our computer code ``Guaranteed Optimal Log-Likelihood-based Unbinned Method'' (GOLLUM) for machine-learning and inference is publicly available.

Artificial intelligence (AI) is reshaping inverse design in manufacturing, enabling high-performance discovery in materials, products, and processes. However, purely data-driven approaches often struggle in realistic manufacturing settings characterized by sparse data, high-dimensional design spaces, and complex constraints. This perspective proposes an integrated framework built on three complementary pillars: domain knowledge to establish physically meaningful objectives and constraints while removing variables with limited relevance, physics-informed machine learning to enhance generalization under limited or biased data, and large language model-based interfaces to support intuitive, human-centered interaction. Using injection molding as an illustrative example, we demonstrate how these components can operate in practice and conclude by highlighting key challenges for applying such approaches in realistic manufacturing environments.

Graphs are a powerful data structure for representing relational data and are widely used to describe complex real-world systems. Probabilistic Graphical Models (PGMs) and Graph Neural Networks (GNNs) can both leverage graph-structured data, but their inherent functioning is different. The question is how do they compare in capturing the information contained in networked datasets? We address this objective by solving a link prediction task and we conduct three main experiments, on both synthetic and real networks: one focuses on how PGMs and GNNs handle input features, while the other two investigate their robustness to noisy features and increasing heterophily of the graph. PGMs do not necessarily require features on nodes, while GNNs cannot exploit the network edges alone, and the choice of input features matters. We find that GNNs are outperformed by PGMs when input features are low-dimensional or noisy, mimicking many real scenarios where node attributes might be scalar or noisy. Then, we find that PGMs are more robust than GNNs when the heterophily of the graph is increased. Finally, to assess performance beyond prediction tasks, we also compare the two frameworks in terms of their computational complexity and interpretability.

The villi and crypts of the gastrointestinal tract increase the effective surface area of the intestinal mucosa, potentially enhancing nutrient absorption. It is commonly assumed that this is their primary function, and that a higher villi density necessarily leads to improved absorption. However, when villi are packed too closely together, diffusion can be hindered, potentially offsetting this benefit. In this work, we investigate the relationship between the density of these structures and the overall efficiency of absorption. In three different simplified geometries, approximating crypts, leaf-like villi, and finger-like villi we calculate analytically the concentration profile and the absorption flux, assuming that there is only diffusion between these structures while the lumen is well mixed. When plotting the absorption flux per unit of gut length as a function of the structures' density, we observe that there is a density maximizing absorption. We study numerically this optimum. It depends weakly on the absorption properties of the given nutrient, so that a geometry optimal for one nutrient is close to optimum for another nutrient. Physiological data from various animal species align with this predicted optimal range and potentially reflect evolutionary selection for efficient nutrient uptake, supporting the model's validity.