Papers

A Full Turbulence Simulation (FTS) of turbulent channel flow at friction Reynolds number (Re_tau) approx 1000 was performed by resolving the Kolmogorov wavenumber in all spatial directions. At this Reynolds number, the intermediate layer attains a physically meaningful width and is fully resolved in the present computation, providing the reference dataset that captures its turbulence and dissipation characteristics with high fidelity. The wall-normal grid spacing of the FTS also confirms that, when the Kolmogorov length scale is sufficiently resolved, the second-order central-difference scheme introduces no adverse numerical effects in the wall-normal direction. In the wall-parallel directions, two resolution criteria were identified based on the present FTS: a first-approximation DNS resolution that resolves more than 99 percent of the turbulent kinetic energy and dissipation rate (Delta x+ approx 19, Delta y+ approx 8, where Delta x+ and Delta y+ denote the streamwise and spanwise spatial resolutions in wall units) and a full dissipation-resolution criterion (Delta x+ approx 7.5, Delta y+ approx 5.0). The first-approximation resolution by means of a spectral method reproduces the essential turbulence statistics within 1 percent accuracy while requiring only one-eighth of the grid points used in the FTS, demonstrating its practical efficiency. In contrast, even the highest-resolution second-order central-difference case (Delta x+ approx 5.0, Delta y+ approx 4.5) fails to match the accuracy of the first-approximation spectral resolution. These findings provide important resolution guidelines for high-Reynolds-number DNS, particularly for simulations at Re_tau = O(10^4).

As first shown by Shafranov, toroidal plasmas in magnetohydrodynamic equilibrium tend to expand in major radius when the pressure is increased. Here, an average measure of the resulting Shafranov shift is introduced, and its properties are discussed for various classes of stellarator configurations. It is shown to be particularly small in quasi-helical and quasi-isodynamic stellarators with a large number of field periods, which are thus relatively robust to variations in the plasma pressure.

In 2020, Peter Larsen reported flaws in the methods for centrosymmetry parameter computation in the existing molecular dynamics and analysis packages. He proposed an intuitive an mathematically rigorous formulation for centrosymmetry parameter in terms of minimal-weight matching (MWM) on a fully-connected graph of atomic neighbors. He proposed using Edmonds' blossom algorithm for computing such a matching. In this paper, we investigate an alternative algorithm for MWM CSP computation using path finding approach and A* algorithm.

Living cells and tissues can generate complex patterns of electric fields and fluid flows which can play important role in physiology. Both, fields and flows are rooted in ion transport across biological interfaces: cell membranes and epithelial cell layers. Here we develop a unified electrohydraulic framework that combines electric fields, osmotic pressures, and fluid flows, emphasising their couplings. We consider an active, permeable interface that drives electrohydraulic fields in the surrounding bulk. We show that spatially heterogeneous ion transport acts as a distributed current source, generating long-range electric fields, osmotic gradients, and fluid flows. Using this framework, we show that patterns of ion pumping at cell and tissue boundaries can simultaneously produce large-scale electric fields and fluid flows due to electrohydraulic coupling. A key insight is that an external electric field and an internal dipolar pumping pattern can be physically equivalent and can generate the same pattern of ion current and fluid flows. The induced dipolar osmotic pressure can drive self-propulsion through bulk osmotic coupling, with a mobility determined by interfacial permeability and system size, a mechanism distinct from classical electrophoresis or electro-osmosis. We further show that for strong fields a new effect emerges. Nonlinear coupling can lead to isotropic swelling of a hollow ball of cells. This can explain recent experiments on epithelial organoids. Finally, we show that feedback between ion transport and resulting electric fields can drive spontaneous symmetry breaking, generating dipolar or multipolar fields and patterns. Our work highlights the importance of electrohydraulic coupling in the emergence in currents and fields in the biological systems.

Flexure of Antarctic ice shelves under excitation from long ocean waves induces mechanical ice shelf stresses that amplify fractures and, hence, contribute to calving events. Here, a solution method is developed for a hydroelastic mathematical model of wave-induced ice shelf flexure, based on the conventional theory of a Kirchoff-Love plate floating on shallow water under linearised conditions, but allowing wave forcing of ice shelves with variations in both horizontal dimensions, and where the ice shelves are of arbitrary shape, including non-uniform thickness. The method uses finite elements specifically designed for the high-order hydroelastic system, and a Dirichlet-to-Neumann map to bound the computational domain in the open ocean. Following verification, the method is used to conduct novel studies on how the ice-shelf deflection is affected by the ice shelf shape, the incident wave direction and the proportion of the shelf that is grounded. The efficiency of the method allows the studies to be conducted over a broad frequency range, such that resonant responses are identified.

We study theoretically the lasing synchronization of the two arrays of lasers with the complex mode dispersion, separated by a spectrally detuned barrier. We demonstrate that for lasing at the Dirac point, the synchronization persists for an order of magnitude higher barriers than in the arrays with a usual parabolic dispersion or a purely dissipative coupling. We interpret this effect as the Klein tunneling of the laser coherence through the barrier. Our numerical findings are supported by an analysis of the delocalization of the linearized eigenmodes of the arrays, which enhances the synchronization.

Ultrasonic backscatter measurements are widely used for microstructural characterisation. However, in materials containing strong anisotropy and spatial heterogeneity, the interpretation of backscatter signals becomes challenging because distance-dependent propagation effects can obscure genuine microstructural variations across depth. In this paper, a cross-directional compensation method is presented for ultrasonic backscatter measurements acquired from opposing inspection surfaces. The method exploits the reciprocal constraint that the dominant through-thickness propagation bias should contain a shared component between opposing inspection directions. A shared distance-dependent baseline is estimated in the logarithmic amplitude domain using an anchor-based fitting approach and subsequently used to compensate the measured backscatter profiles with depth. The method is demonstrated on two macrozone-containing Ti–6Al–4V samples, where conventional attenuation-based compensation is shown to be insufficient to consistently reconcile opposing-face backscatter profiles. Across six opposing-face signal pairs, the proposed method reduces the mean standard deviation of the directional mismatch profile from $0.367$ to $0.120$ and the mean absolute fitted gradient from $0.171$ to $0.0067$, outperforming conventional attenuation compensation. These results demonstrate that reciprocity-based compensation can reduce propagation-related bias while preserving local direction-dependent scattering variations, providing a practical signal-normalisation framework for backscatter analysis in heterogeneous anisotropic materials.

Stenting is among the most common transcatheter interventions for congenital heart disease (CHD). Patient-specific computational fluid dynamics (CFD) simulations can predict hemodynamic outcomes of intervention scenarios but require post-operative vascular geometries that reflect stent-induced shape changes, which existing tools either model inadequately or require extensive time or manual effort to generate. We present SDFStent, a signed distance function (SDF) based mesh deformation method for virtual stenting that operates in real time, maintains mesh integrity, and preserves junction geometry. The stent is modeled as a pipe surface composed of piecewise-capsule SDFs joined by a smooth-minimum operator. Mesh vertices near the expanding SDF surface are displaced along the SDF gradient with a compactly supported fall-off function and an alpha blending mask. SDFStent was benchmarked against three existing approaches and validated on three tetralogy of Fallot (ToF) patients and three coarctation of the aorta (CoA) patients using rigid-wall steady-state CFD simulations against clinical catheterization measurements. Against a prescribed diameter of 6.0 mm, the method produced a mean stented diameter of 5.92 $\pm$ 0.08 mm in 1.5 s, over 100$\times$ faster than the best stenting-specific comparator. All output meshes were watertight and self-intersection-free. CFD-simulated post-operative pressure drops agreed with clinical measurements within 4 mmHg (mean error 2 mmHg). SDFStent produces simulation-ready post-stent models that match prescribed stent dimensions at interactive speeds, from pre-operative anatomy and catheterization data alone. The implementation is open-source and available in 3D Slicer. Its scriptable architecture enables automated generation of large synthetic cohorts for data-driven surrogate modeling.

Collisionless shocks are common in astrophysical systems and stand as sites of particle acceleration. While particles at perpendicular shocks may not return to the upstream region, at oblique shocks a fraction of energetic electrons manage to escape the shock and travel upstream. An extended region known as the electron foreshock is formed, where these reflected particles drive various instabilities that may promote electron acceleration. Here we present the first 2D3V particle-in-cell (PIC) simulations of electron-ion non-relativistic oblique shocks that explore the interaction of the foreshock with pre-existing compressive turbulence with relative amplitude of 15% based on interstellar medium estimates. We find that pre-existing turbulence influences the emergence and behavior of the whistler-wave instability, as it enhances the amplitudes of the magnetic-field fluctuations and leads to larger nonlinear structures. This impacts the dynamics of the reflected electrons, resulting in a shorter and hotter electron foreshock. At the end of our simulations, with pre-existing upstream turbulence we observe non-thermal electrons that are more numerous, reach higher energies, and carry a larger portion of the total energy.

Computational imaging enables compact infrared systems, but deep-learning pipelines that combine image reconstruction and object detection often introduce substantial inference latency. Most existing acceleration strategies compress the reconstruction network while overlooking physical priors from the optical path, leaving a trade-off between accuracy and speed. We present Physics-aware Dual-Integrated Network (PDI-Net), a low-latency framework that integrates infrared reconstruction with object detection and further embeds optical priors into the learning process. PDI-Net uses a supervised U-Net during training, while a semi-U-Net encoder shares features directly with a YOLO-based detector during inference, avoiding full image reconstruction. To bridge the gap between fidelity-oriented reconstruction features and detection-oriented semantics, we introduce a physics-aware large-small bridge (PALS-Bridge), which uses field-dependent point spread function priors to adaptively modulate multiscale convolutional branches. A physics-informed optical degradation simulation pipeline is also developed for training and validation. The method is deployed on a single-lens infrared camera, reducing system weight by about 50% compared with traditional multi-lens designs. On the M3FD benchmark under low-SNR conditions, PDI-Net reduces inference time by 84.06% compared with the Rec+Det with pruning strategy while improving mAP@0.5:0.95 by 5.07%. These results demonstrate compact, low-latency computational infrared imaging for real-time object detection on resource-constrained platforms.

We investigate the fast ion effects on the threshold conditions of ion temperature gradient (ITG) mode and electron temperature gradient (ETG) mode both analytically and numerically using gyrokinetic equation. The onset condition for ITG mode shows a strong and monotonic favorable dependence on the fraction of fast ions, and mostly favorable but non-monotonic dependence on the fast ions' normalized temperature $T_f/T_i$ ($T_f$ is the effective temperature of fast ions, $T_i$ is the temperature of thermal ions). Overall favorable parametric trends are consistent with those for the linear growth rate reported in previous papers, as they are largely determined by kinetic wave-particle resonance effects. While general analytic expressions for the critical normalized thermal ion temperature gradient scale length $(R/L_{T_i})_c$ are quite complicated, an explicit compact expression $\left(\frac{R}{L_{T_i}}\right)_c=\left(\frac{4}{3}+\frac{3}{2}\sqrt{\frac{\pi}{2}}\frac{|\hat{s}|}{q}\right)\left(1+\frac{T_i}{Z_i(1-f_h)T_e}\right)$ has been derived for the mode with its perpendicular scale larger than thermal ion gyroradius, but much smaller than the fast ion gyroradius so that finite Larmor radius effects are manifested in opposite asymptotic limits depending on ion species when $T_f\gg T_i$, and weak density gradient. Here, $q$ is safety factor, $\hat{s}$ is magnetic shear, $Z_i$ is thermal ions' charge, and $f_h$ is fast ion charge density fraction. In this limit, only the fast-ion-induced thermal ion dilution effects persist as fast ion density response becomes unmagnetized and negligible. On the other hand, the fast ion effects on ETG-threshold are found to be unfavorable.

Free-electron-based nanoscopy enables the study of optical excitations in materials with deep-subwavelength spatial resolution, with cathodoluminescence (CL) being one of the most useful resulting signals. When combined with an optical collection system, CL measurements can access multidimensional information of light; yet the phase of the emitted optical fields has remained largely elusive. Here, we demonstrate a reference-free phase retrieval approach for far-field CL wavefronts using the Gerchberg-Saxton algorithm implemented with real-space and angular-space CL intensity data. Applying this approach to representative nanostructures, including a planar surface, nanosphere, plasmonic crystal, and nanowire, we reconstruct distinct phase distributions that reveal their underlying radiation mechanisms. This reference-free framework offers a robust and flexible route for retrieving the phase of electron-beam-excited optical fields without relying on a reference wave, making it readily extendable to a wide range of nanostructures.

Cryogenic calorimetric detectors are a powerful tool in the search for rare events such as neutrinoless double beta decay ($0\nu\beta \beta$), due to their excellent energy resolution and low intrinsic background. The performance of these detectors depends critically on a precise understanding of their energy scale and energy resolution. Recent studies suggest that both energy scale and energy resolution may vary depending on the spatial location and topology of energy deposition within the detector, indicating the presence of previously uncharacterized systematic effects. The Cryogenic Experiment to Reconstruct Energy Systematics (CERES) is a dedicated experiment designed to directly measure the position dependence of calorimetric response in Tellurium Dioxide (TeO$_{2}$) crystals. This paper details the experimental design, current status, and future upgrade plans for CERES.

We present the results of a combined theoretical and experimental investigation into the vertical dynamics of floating discs subjected to an imposed time-periodic forcing. The axisymmetric and inviscid wavefield is governed by a linear elliptic boundary value problem with mixed boundary conditions, wherein the no-penetration boundary condition is satisfied under the disc while the free surface boundary conditions are enforced away from it. The problem is solved by recasting the system of partial differential equations as a second-kind Fredholm integral equation which is then solved numerically. The solution furnishes a prediction for the dependence of the disc's oscillation amplitude on the forcing frequency, which exhibits good agreement with experiments. We interpret our results physically by computing the added mass, wave damping and effective spring coefficients of the disc, both numerically for a range of forcing frequencies and analytically in the low-frequency limit.

Unidirectional electromagnetic absorption provides a powerful approach for controlling light and heat, yet broadband realization in the infrared spectral region remains experimentally unexplored. Here, we report a non-Hermitian multilayer structure that enables robust broadband infrared unidirectional absorption. By combining low- and high-loss materials and engineering their thicknesses using the transfer-matrix formulation, the structure exhibits nearly perfect absorption spectrally matched to the blackbody radiation at 373 K under forward illumination, while suppressing backward absorption below 30%. Spectral analysis indicates that the observed unidirectionality originates from non-Hermitian physics near an exceptional point. Notably, broadband unidirectional absorption is achieved even without strict exceptional-point condition. This indicates that the observed unidirectionality is governed by the combination effect of loss distribution and optical interference, rather than a singular condition, ensuring robustness against film thickness variations. Furthermore, thermal shielding experiments demonstrate that the structure enables unidirectional control of thermal radiation, resulting in a temperature difference of up to 21 0C between forward and backward configurations. These results establish a robust strategy for broadband directional control of infrared radiation, with potential applications in passive thermal management, including thermal smart windows and infrared heat-shielding devices.

This paper utilizes the Black Hole Vision smartphone application to catalyze a pedagogical shift in General Relativity education through the quantitative analysis of simulated black hole imaging. Presented here for the Schwarzschild spacetime, the investigation is designed with a hierarchical modularity suitable for undergraduate students, with an expanded version intended for graduate courses in General Relativity or Relativistic Astrophysics. By transforming the mobile device into an educational relativistic imaging tool, we triangulate the simulated Schwarzschild mass through independent probes and characterize anisotropic coordinate transformations via a Jacobian map. Global numerical consistency is investigated through integrated coordinate length, while the exponential instability of nearly bound orbits is quantified through a measurement of the simulated Lyapunov exponent. Finally, symmetry is constrained through a sub-pixel constraint on eccentricity in the simulated spacetime. By integrating this statistical framework, the paper enables students to explore the distinction between physical signatures and instrumental noise using established metrological protocols.

The African easterly jet (AEJ) is a prominent circulation feature in the tropical atmosphere. It transports mineral dust and generates easterly waves that serve as seeds for hurricanes. Conventional wisdom holds that the AEJ is in thermal wind balance with the positive meridional temperature gradient over North Africa. Here, using reanalysis data, we show that the negative meridional moisture gradient substantially counteracts the effect of the temperature gradient on density in that balance, diagnostically accounting for a 30% reduction of the AEJ magnitude. Using CMIP6 data, we further show that this effect of vapor buoyancy on the AEJ strengthens under global warming, highlighting the critical role of the spatial distribution of moisture on large-scale circulation. Analysis of the AEJ in CMIP6 models confirms that some models do not include vapor buoyancy in their governing equations, raising questions about the relevance of their projections of climate change in that region.

The effect of a moderate adverse pressure gradient (APG) on the structure of a high-Reynolds-number turbulent boundary layer (TBL) was investigated experimentally using complementary multi-point measurements. Unlike many previous studies, the present work focuses on the wake region and aims to characterise the turbulent motions that are energised by local APG conditions. Simultaneous two-point hot-wire measurements of the streamwise velocity were used to estimate the linear coherence spectrum (LCS), quantifying the wall-normal coherence between a wake-region reference point and the rest of the TBL. LCS-based decomposition of the spectral energy and variance showed that motions coherent with the wake reference account for a significant part of the APG-induced increase at large time scales, but not all of the enhanced energy. The remaining increase is associated with relatively smaller-scale motions that are not correlated with the selected wake location. High-spatial-resolution snapshot PIV measurements were then used to examine this broader range of energetic motions, which are associated with spanwise vortices in the wake region. Spanwise vorticity statistics were evaluated over 0.2 < z/{\delta} < 0.4, where the largest APG-induced change in spectral energy was observed. Under APG, both the mean and variance of spanwise vorticity increased significantly in this region, while swirling-strength distributions confirmed a relative increase in both the population and magnitude of spanwise vortices. Finally, dynamically significant clockwise rotating spanwise vortices were identified using different swirling-strength thresholds. Higher thresholds produced conditionally averaged velocity fields that best captured the key wake-region dynamics, motivating their use for vortex-based conditional averaging in future analyses.

Understanding how typhoons respond to localized perturbations in their environmental fields is fundamental to assessing the limits of predictability and exploring the potential for track or intensity intervention. This study develops a dedicated simulation methodology testbed for typhoon sensitivity analysis by integrating the Pangu weather model, a high-precision AI forecasting system, with Proportional-Integral-Derivative (PID) closed-loop techniques. The testbed is constructed with modular functional blocks including a meteorological prediction module, an artificial perturbation input interface, a typhoon quantitative modeling module, and a PID closed-loop test module, implemented via a cross-platform MATLAB/ONNX technical framework. A Single-Input Single-Output (SISO) test system was built, with velocity and thermal perturbations set as the core inputs and typhoon track and intensity as the key output targets, to perform controlled perturbation-response experiments. The experiments reveal the feasible perturbation-response range, the parameter tuning behavior of the PID module, and the energy-scale response characteristics under different perturbation modes, and quantify the input-output coupling relationships of the test system. By constructing this testbed on an operational AI weather forecasting model, this study provides a framework that goes beyond idealized sensitivity studies typically validated only on low-order dynamical models. The testbed offers an expandable platform for investigating typhoon sensitivity to artificial environmental perturbations and provides a foundation for subsequent expansion toward multi-input multi-output architectures and advanced analysis strategies such as nonlinear PID or model predictive control.

We introduce a utility-driven bounded-confidence model of opinion dynamics in which opinions associated with higher utility exert stronger social influence. In the regime where all agents belong to a single opinion cluster, we derive a stochastic differential equation for the mean opinion and show that its stationary distribution is Gibbs-like, with an effective potential determined by the utility landscape and an inverse temperature controlled by the learning rate and the number of agents. For multimodal utility functions, the dynamics exhibit metastability and spontaneous switching between competing opinion states. The reduced stochastic description also captures the evolution and merging of multiple opinion clusters, in agreement with agent-based simulations.

We present a simplified method for isolating the edges of a phase object from the edges of an amplitude object using a 4f system with an off-axis q-plate. Instead of the four off-axis shifts of the q-plate required in previous work, we need only two shifts (along +/- x) combined with linear polarizers at 45 degrees and 135 degrees. The number of measurements is reduced by half, potentially doubling the acquisition speed. We derive the theoretical basis, showing that the resulting intensity corresponds to the phase gradient squared, with amplitude-object contributions eliminated. Experiments on two phase-amplitude object samples demonstrate amplitude-edge reduction up to 97.6% and correlation coefficients up to 0.78 (sample 1) and 0.75 (sample 2). In overlapping regions, the phase edge is partially recovered; full recovery would require additional processing such as inverse filtering. This research is useful for biological imaging applications where fast and simple phase-edge isolation is desired.

Cerebral aneurysm rupture has long been associated with abnormal hemodynamic conditions, particularly wall shear stress (WSS)-related flow behavior. Although aneurysm rupture is widely recognized as a multifactorial phenomenon involving complex interactions between geometry, flow structures, and multiple hemodynamic quantities, WSS-based parameters remain among the most commonly reported metrics in computational aneurysm studies. In the present study, computational fluid dynamics simulations were performed using an in-house parallel C++ finite element solver to investigate velocity streamlines, wall shear stress (WSS), and time-averaged wall shear stress (TAWSS) in patient-specific ruptured and unruptured cerebral aneurysm models. The computed flow fields demonstrated substantial hemodynamic overlap between the two groups, with elevated WSS and TAWSS regions observed in both ruptured and unruptured aneurysms. Similar high-velocity inflow structures and localized shear concentration patterns were identified across different cases, demonstrating that elevated shear-related magnitudes can be present in aneurysms with different rupture statuses. The results show that comparable WSS- and TAWSS-based hemodynamic characteristics may exist in aneurysms with different clinical outcomes, emphasizing the complexity and case-dependent nature of cerebral aneurysm hemodynamics. Rather than proposing rupture prediction criteria, the present study provides a focused computational assessment of hemodynamic similarities between ruptured and unruptured aneurysms and highlights the importance of careful interpretation of isolated WSS- and TAWSS-based analyses in rupture assessment.

The homopolar or unipolar generator, which is sometimes referred to as a Faraday Paradox, is and experiment that shows an apparent contradiction between different predictions for induced emfs. I present a simple, handheld version of the experiment and a suggested resolution.

This study investigates the effect of long-term UV radiation exposure on the mechanical and structural properties of selected polymeric materials - PLA, ABS, ABST, PETG, HIPS and PC - fabricated using 3D-print based Fused Filament Fabrication (FFF) method. Existing research in the field of polymers weathering has predominantly examined combined degradation factors (UV irradiation, humidity and thermal cycling) over the timescales rarely exceeding a few months, which prevents attribution of property changes to individual agents. To isolate the contribution of UV radiation specifically, samples were exposed up to 7744 consecutive hours (i.e. 322 days) of UV irradiation in an environmental cell with controlled humidity and temperature. The mechanical properties were characterized using nanoindentation, which allows for spatially resolved measurements of hardness and elastic modulus in the near-surface region, thereby enabling the detection of degradation gradients on the order of tens of micrometers. The lack of methodological consistency in the current literature hinders the cross-material comparison; this study addresses the gap by applying nanoindentation uniformly across wide range of selected materials prepared and tested under identical conditions, while studying these properties after a very long exposure to UV radiation.

Operational weather prediction at kilometer scales remains computationally prohibitive for traditional numerical weather prediction (NWP) models, limiting forecast access for applications in energy, agriculture, and disaster management that require fine-grained spatiotemporal detail. Here we introduce AirCast-SR, a foundation model for atmospheric super-resolution that downscales global AI weather forecasts from 0.25 degree (~28 km) to 1 km horizontal resolution at hourly temporal resolution, producing 67-hour forecasts of eight coupled surface variables simultaneously. EarthMind-SR employs a three-dimensional U-Net conditioned within a Latent Consistency Model (LCM) diffusion framework, trained on patch-based samples over the contiguous United States (CONUS) using GraphCast forecasts as input and NOAA's Analysis of Record for Calibration (AORC) as the target. The model achieves near-zero bias across all variables and lead times, and its radial power spectral density analysis demonstrates preservation of fine-scale atmospheric structure at wavelengths of 10 km to 100 km where coarser models lose spectral power. We validate EarthMind-SR across three CONUS case studies spanning winter, summer, and spring seasons, and demonstrate zero-shot global transferability over India and Germany using independent surface station observations without any retraining or fine-tuning. As an open-weights foundation model, EarthMind-SR establishes a new paradigm for kilometer-scale AI weather prediction and provides a platform for regional fine-tuning, distillation, and downstream applications in climate services and hazard forecasting.

The present paper reports a study of a unified cosmological scenario in the framework of holographic f(Q) gravity, where, in a single theoretical setup, both the early inflationary epoch and the late-time accelerated epoch are studied. Considering f(Q) = $\zeta Q^n$, we reconstruct the Hubble parameter in the presence of Barrow holographic fluid and study the inflationary behaviour through the slow-roll parameters, scalar spectral index $n_s$, and tensor-to-scalar ratio r. The obtained inflationary predictions are found to be consistent with the latest Planck 2018 observational constraints, with a very small value of the tensor-to-scalar ratio. In the next phase, we extend the study by including the matter sector. The Chevallier-Polarski-Linder (CPL) parametrization is used to connect the theoretical model with observational cosmology. Using combined Cosmic Chronometer (CC) and Baryon Acoustic Oscillation (BAO) datasets, the study constrains the model parameters through Markov Chain Monte Carlo (MCMC) analysis. From the observational results obtained this way, the study concludes that at low redshifts, the holographic f(Q) model considered here remains compatible with the standard LambdaCDM model, while mild deviations are observed at higher redshift. We have also performed the AIC and BIC analysis and commented on the goodness of fit in comparison with the LambdaCDM model. Hence, the present framework provides a viable unified description of inflation and late-time cosmic acceleration within holographic f(Q) gravity.

Phonon polaritons have attracted increasing interest as a means to offset the reduction in phonon thermal conductivity in nanostructures caused by enhanced boundary scattering. However, the interpretation of the limited experimental data on phonon polariton-mediated conduction is hindered by the lack of comprehensive full-wave models for predicting thermal electromagnetic transport in solid-state systems. Here, the radiative thermal conductivity in the diffusive regime along SiO2 nanowires near room temperature is predicted using the fluctuational electrodynamics-based discrete system Green's function method. At 400 K, for nanowire diameters of 66 nm and 132 nm, modest radiative conductivities of 0.0264 W/m-K and 0.0165 W/m-K, respectively, are obtained, with contributions dominated by surface phonon polaritons. In contrast, the kinetic theory combined with nanowire dispersion relations derived from Maxwell's equations predicts conductivities that can be nearly two orders of magnitude larger than those obtained from fluctuational electrodynamics, and are largely dominated by bulk phonon polaritons propagating within the nanowire volume. Improved agreement between the kinetic theory and fluctuational electrodynamics for the total radiative conductivity can be achieved by introducing an effective mean free path that accounts for the reduced mean free path of bulk phonon polaritons outside the Reststrahlen spectral bands of SiO2. However, even with this correction, the kinetic theory fails to accurately capture the spectral distribution of radiative conductivity. This work establishes a solid foundation for the development of phonon polariton-based systems for thermal management in micro/nanoelectronic devices.

Current 3D geometric molecular representations predominantly focus on discrete atomic skeletons, inherently overlooking the continuous electron density (ED) field that fundamentally governs microscopic quantum behaviors. Consequently, these purely topological models suffer from critical representational blind spots, particularly in capturing long-range electron delocalization and non-covalent interactions, imposing a severe theoretical ceiling on predicting complex quantum properties. To bridge this physical gap and standardize research in electron density-enhanced molecular learning, we first construct the large-scale UniField-ED Benchmark. Comprising the QM9-ED and QMugs-ED datasets, this benchmark provides natively aligned discrete graphs and high-fidelity ED point clouds. Building upon this data infrastructure, we introduce UniField, an SE(3)-equivariant multimodal architecture that intrinsically intertwines discrete topological graphs with continuous quantum electronic environments. Extensive empirical evaluations across all three benchmarks demonstrate that UniField establishes new state-of-the-art performance. Specifically, UniField achieves a 14.8% improvement in overall predictive performance against the leading topology-only SOTA on the ED5-OE benchmark, alongside a 37.0% performance gain over top pure-ED models. Furthermore, on the complex drug-like dataset QMugs-ED, it yields a striking 28.2% average precision improvement across frontier orbital properties. Alongside new SOTA results on QM9-ED, our method establishes a rigorous foundation for next-generation computational chemistry. Code and datasets are anonymously available at https://anonymous.4open.science/r/UniField-ED-5B1B.

The effects of compocasting process parameters on some structural and tensile characteristics of the A356-10% SiCp (volume fraction) composites were studied. Semisolid stirring was carried out at temperatures of 590, 600 and 610 C with stirring speeds of 200, 400 and 600 r/min for 10, 20 and 30 min. The distribution of the SiC particles within the matrix, porosity content and tensile properties of the obtained samples were examined. The structural evaluations show that by increasing the stirring time and decreasing the stirring temperature, the uniformity in the particle distribution is improved; however, by increasing the stirring speed the homogeneity firstly increases and then declines. It is also found that by increasing all of the processing parameters, the porosity content is enhanced. From the tensile characteristics viewpoint, the optimum values of the speed, temperature and time are found to be 400 r/min, 590 C and 30 min, respectively. The contribution of the reinforcement distribution uniformity prevails over that of the porosity level to the tensile properties.

The synergistic interpretation of anatomical information from computed tomography (CT) and metabolic information from positron emission tomography (PET) is important to oncologic imaging. However, existing deep learning methods for PET/CT remain largely task-specific, are often trained on single-center cohorts, or adopt dual-branch fusion schemes that delay cross-modal interaction and underutilize early spatial correspondence between PET and CT. To address these limitations, we present an open-source, multi-center, whole-body FDG PET/CT foundation model utilizing 4,997 harmonized scans from four public datasets. Our framework employs hierarchical UNet-shaped backbones with early channel-wise concatenation, enabling anatomical and metabolic features to interact from the first embedding layer onward. We further introduce a masked autoencoding objective based on zero-mean imputation, combined with a weighted global reconstruction loss. This design avoids non-physical intensity discontinuities at masked-region boundaries that arise from learnable mask tokens. On downstream AutoPET lesion segmentation, the proposed models demonstrate strong label efficiency: with only 10% of the labeled training data, they achieve performance comparable to models trained from scratch on the full dataset. Under extreme 5-shot linear probing, joint PET/CT pretraining also achieves higher Dice scores than separated-modality pretraining. This multi-center foundation model demonstrates label efficiency and cross-modality representation learning for PET/CT tumor segmentation. It provides a robust, open-source basis for advancing automated oncologic imaging, significantly reducing the need for large-scale manual annotations in clinical practice.