Papers

A large-scale molecular dynamics (MD) simulation of acoustic cavitation in a simple liquid was performed using the supercomputer Fugaku. The system, consisting of approximately 100 billion atoms, was subjected to ultrasonic irradiation. Direct observation of multi-bubble dynamics has been challenging in both experimental measurements and conventional numerical fluid mechanics simulations. Moreover, previous MD simulations involving only hundreds of millions of atoms were unable to generate multiple bubbles within a system. Our results reveal that cavitation bubbles nucleate and grow near the ultrasonic horn, forming a large bubble cluster that periodically splits into multiple small clusters and subsequently merges again. This cycle is synchronized with the oscillation period of the horn. Pressure and temperature inside the bubbles exhibit sharp increases during cluster fragmentation, and their oscillation amplitudes vary on a timescale longer than the driving period of the horn, indicating the presence of subharmonic behavior consistent with experimental observations. Despite bubble formation, the effect on the acoustic properties of the sound wave was almost negligible, indicating that cavitation near the horn surface has limited influence on bulk acoustic properties. These findings provide new insights into the molecular-scale mechanisms of cavitation and offer guidance for optimizing ultrasonic systems in chemical and biomedical applications. Future work will focus on quantifying long-period oscillations, analyzing attenuation effects, and extending simulations to complex fluids.

China's Tianwen-2 mission, launched on 29 May 2025, targets the near-Earth object (469219) Kamo`oalewa, an Earth quasi-satellite trapped in a 1:1 mean-motion resonance with our planet. Determining the origin of Kamo`oalewa is central to understanding the formation pathways and dynamical evolution of Earth's quasi-satellite population. Here we show a strong possibility of main-belt origin for Kamo`oalewa using long-term dynamical simulations. We examine three candidate source regions: the $\nu_6$ secular resonance ($\nu_6$), the 3:1 mean-motion resonance with Jupiter (3:1J MMR), and the Flora family. A total of 42,825 test particles were integrated over 100 Myr. We find that asteroids from all three regions can be transported onto Kamo`oalewa-like orbits, albeit with markedly different efficiencies. Particles originating near the $\nu_6$ show the highest transfer probability (3.31%), followed by the Flora family (2.54%) and the 3:1J MMR (0.39%). We further identify representative dynamical pathways linking these source regions to Earth quasi-satellite orbits. The Tianwen-2 spacecraft is expected to rendezvous with Kamo`oalewa in 2026, performing close-proximity operations and returning samples. The mission will provide decisive observational constraints on the asteroid's composition and physical properties, offering a critical test of its proposed origin.

This paper presents AAFIYA (Antenna Analysis in Frequency-domain for Impedance and Yield Assessment), a modular Python toolkit for automated characterization of radio-frequency antennas using measurement and simulation data. The toolkit provides a unified workflow for processing S-parameters, impedance, realized gain, beam patterns, polarization metrics, and calibration-based yield estimation, with support for standard Touchstone files and beam pattern data. AAFIYA is validated using measurements from an electromagnetic anechoic chamber involving Log Periodic Dipole Array (LPDA) reference antennas and Askaryan Radio Array (ARA) Bottom Vertically Polarized antennas over 100-850 MHz. Extracted metrics, including impedance matching, realized gain patterns, vector effective lengths, and cross-polarization ratio, are compared against full-wave simulations from HFSS and WIPL-D, showing good agreement across frequency and angle. The results demonstrate that AAFIYA enables accurate, reproducible, and publication-ready antenna analysis, and provides a flexible foundation for future extensions, including automated optimization and data-driven antenna design.

Multiphase fluid flows comprising of mesoscale solid particles, liquid droplets, or gas bubbles are common in both natural and man-made systems, but quantifying the energy transfer is challenging due to complex bubble-particle interactions. In this study, we used ultrafast synchrotron X-ray imaging to study the mesoscale dynamic interactions among ultrasonic cavitation bubbles and hydrophobic particles or clusters. Critical dynamic information and data were extracted from the vast amount of X-ray images and then fed into the hybrid analytical-numerical model for calculating the energy transfer from the oscillating bubble and the imploding bubble to the nearby hydrophobic particles. Using the Ni spherical microparticles as an example, at bubble oscillation approximately 16% (80-320 nJ) of the local energy was transferred to the particle. At bubble implosion, the transferred energy increased approximately 26% (0.135-1.09 uJ). Local energy transfer occurred on timescales of 1 us to 1 ms and length scales of 1 um to 1 mm. Within each ultrasound cycle, kinetic and potential energy underwent complex exchanges, with local energy exhibiting a stepwise decay at the end of each cycle. The transferred energy was mainly consumed for enabling highly efficient particle dispersion. This research provides quantitative insights into optimizing hydrophobic nanomaterial dispersion and has broader implications for interfacial energy transfer processes such as making suspensions, composite materials and exfoliated 2D materials.

Treatment delivery is largely determined by capabilities of the beam delivery system (BDS), where faster delivery can have many potential benefits including improved dosimetric quality, utility, cost effectiveness, patient throughput and comfort. Despite significant developments in accelerators, delivery methodologies, dose optimisation and more, the energy layer switching time (ELST) is still a persisting limitation in existing BDS. The ELST can contribute significantly to beam delivery time (BDT) and extend treatment times, requiring compensation by optimisation planning approaches, motion mitigation strategies, or active beam modification. This fundamental constraint can be addressed by increasing the narrow energy acceptance range of conventional beamlines to minimise the ELST, enabling ultra-fast delivery. A large energy acceptance (LEA) BDS has the potential to revolutionise PBT through immediate improvements to current treatment delivery and emerging delivery modalities: the complete exploitation of PBT - and unlocking its full potential - can only be made possible with advances in beam delivery technologies. We review the abundant opportunities offered by an ultra-fast BDS: shorter treatment times, reduced motion induced dose degradation, improved effectiveness of motion management techniques, possibilities for volumetric rescanning, bidirectional delivery, further planning optimisation, and novel delivery strategies. We overview the design concepts of several LEA proposals, technology requirements, and also discuss the remaining challenges and considerations with realising a LEA BDS in practice. There are multiple avenues requiring further development and study, however the clinical potential and benefits of this enabling technology are clear: ultra-fast delivery offers both immediate and future improvements to PBT treatments.

Accurate and efficient rumor detection is critical for information governance, particularly in the context of the rapid spread of misinformation on social networks. Traditional rumor detection relied primarily on manual analysis. With the continuous advancement of technology, machine learning and deep learning approaches for rumor identification have gradually emerged and gained prominence. However, previous approaches often struggle to simultaneously capture both the sequential and the global structural relationships among topological nodes within a social network. To tackle this issue, we introduce a hybrid model for detecting rumors that integrates a Graph Convolutional Network (GCN) with a Transformer architecture, aiming to leverage the complementary strengths of structural and semantic feature extraction. Positional encoding helps preserve the sequential order of these nodes within the propagation structure. The use of Multi-head attention mechanisms enables the model to capture features across diverse representational subspaces, thereby enhancing both the richness and depth of text comprehension. This integration allows the framework to concurrently identify the key propagation network of rumors, the textual content, the long-range dependencies, and the sequence among propagation nodes. Experimental evaluations on publicly available datasets, including Twitter 15 and Twitter 16, demonstrate that our proposed fusion model significantly outperforms both standalone models and existing mainstream methods in terms of accuracy. These results validate the effectiveness and superiority of our approach for the rumor detection task.

Designing fluorescent small molecules with tailored optical and physicochemical properties requires navigating vast, underexplored chemical space while satisfying multiple objectives and constraints. Conventional generate-score-screen approaches become impractical under such realistic design specifications, owing to their low search efficiency, unreliable generalizability of machine-learning prediction, and the prohibitive cost of quantum chemical calculation. Here we present LUMOS, a data-and-physics driven framework for inverse design of fluorescent molecules. LUMOS couples generator and predictor within a shared latent representation, enabling direct specification-to-molecule design and efficient exploration. Moreover, LUMOS combines neural networks with a fast time-dependent density functional theory (TD-DFT) calculation workflow to build a suite of complementary predictors spanning different trade-offs in speed, accuracy, and generalizability, enabling reliable property prediction across diverse scenarios. Finally, LUMOS employs a property-guided diffusion model integrated with multi-objective evolutionary algorithms, enabling de novo design and molecular optimization under multiple objectives and constraints. Across comprehensive benchmarks, LUMOS consistently outperforms baseline models in terms of accuracy, generalizability and physical plausibility for fluorescence property prediction, and demonstrates superior performance in multi-objective scaffold- and fragment-level molecular optimization. Further validation using TD-DFT and molecular dynamics (MD) simulations demonstrates that LUMOS can generate valid fluorophores that meet various target specifications. Overall, these results establish LUMOS as a data-physics dual-driven framework for general fluorophore inverse design.

This paper constructs a multilayer recursive game model to demonstrate that in a rule vacuum environment, hierarchical predatory structures inevitably collapse into a monolithic political strongman system due to the conflict between exponentially growing rent dissipation and the rigidity of bottom-level survival constraints. We propose that the rise of a monolithic political strongman is essentially an "algorithmic entropy reduction" achieved through forceful means by the system to counteract the "informational entropy increase" generated by multilayer agency. However, the order gained at the expense of social complexity results in the stagnation of social evolutionary functions.

The replica-exchange Monte-Carlo (RXMC) method is a powerful Markov-chain Monte-Carlo algorithm for sampling from multi-modal distributions, which are challenging for conventional methods. The sampling efficiency of the RXMC method depends highly on the selection of the temperatures, and finding optimal temperatures remains a challenge. In this study, we propose a refined online temperature selection method by extending the gradient-based optimization framework proposed previously. Building upon the existing temperature update approach, we introduce a reparameterization technique to strictly enforce physical constraints, such as the monotonic ordering of inverse temperatures, which were not explicitly addressed in the original formulation. The proposed method defines the variance of acceptance rates between adjacent replicas as a loss function, estimates its gradient using differential information from the sampling process, and optimizes the temperatures via gradient descent. We demonstrate the effectiveness of our method through experiments on benchmark spin systems, including the two-dimensional ferromagnetic Ising model, the two-dimensional ferromagnetic XY model, and the three-dimensional Edwards-Anderson model. Our results show that the method successfully achieves uniform acceptance rates and reduces round-trip times across the temperature space. Furthermore, our proposed method offers a significant advantage over recently proposed policy gradient method that require careful hyperparameter tuning, while simultaneously preventing the constraint violations that destabilize optimization.

Multi-turn charge-exchange injection is the primary method of creating high-intensity hadron beams in circular accelerators, and phase space painting during injection enables tailoring of the accumulated phase space distribution. A technique we call eigenpainting allows injection of particles into a single mode of a coupled ring, providing full four-dimensional control of the phase space distribution. Under ideal conditions, uniform eigenpainting generates a linear-force equilibrium distribution in the transverse plane, with zero volume in four-dimensional transverse phase space, even including space charge. We have implemented eigenpainting for the first time in the Spallation Neutron Source (SNS) Accumulator Ring. Injecting 8.8 $\mu$C of 800 MeV beam, we obtain a final ratio of intrinsic transverse emittances of $\approx$2.4. We analyze the effect of space charge on the final distribution through comparison of the reconstructed phase space to particle-in-cell simulations.

Plasmonic nanoparticles generate strongly localized and enhanced light field through localized surface plasmon resonance, thereby playing a central role in plasmonics and nanophotonics. Because the optical properties of plasmonic nanoparticles are highly sensitive to their size and shape, nanoscale visualization of nanoparticle growth is crucial for detailed understanding of growth mechanisms and precise control of particle geometry. However, it is not possible to visualize the rapid growth dynamics using conventional imaging techniques. In this study, we demonstrate in-situ real-time observation of silver nanoparticle (AgNP) growth dynamics at the single-particle level using high-speed atomic force microscopy (HS-AFM). We employed a photoreduction method, which enables reliable control of AgNP formation by laser irradiation. By integrating a stand-alone tip-scan HS-AFM with an optical setup for photoreduction, we successfully captured real-time movies showing the nucleation and subsequent growth of AgNPs at the single-particle level. Furthermore, quantitative single-particle analysis revealed particle-to-particle variations in growth dynamics. The growth dynamics were further studied at different laser intensities, revealing intensity-dependent growth rates and the balance between nucleation and growth. This study establishes HS-AFM as a novel microscopic platform for in-situ visualization of plasmonic nanoparticle growth and will contribute to advances in plasmonics and materials science.

This study investigates the elastic response of a two-dimensional semi-infinite medium subjected to a moving surface load with a prescribed displacement profile. As a fundamental step, we derive analytical Green's functions for the displacement and stress fields generated by a point load traveling at a constant velocity along the surface, explicitly incorporating elastodynamic effects through Mach number dependence. These moving-load solutions serve as building blocks for constructing more general loading scenarios via linear superposition. Based on Green's functions, an inverse problem is formulated to reconstruct the unknown surface traction responsible for a given surface displacement. The inverse analysis is performed through a Fourier-domain inversion with regularization, which enables a direct and computationally efficient determination of the contact pressure without iterative forward simulations. This framework is applied to a rigid wheel-ground contact problem, where the imposed displacement is dictated by the wheel geometry. The reconstructed surface traction exhibits a smooth, symmetric distribution within the contact region, while the resulting subsurface stress fields are obtained in closed analytical form and involve dilogarithm functions. The principal stress difference reveals characteristic spatial patterns similar to photoelastic fringes, and their asymmetry increases with the Mach number, reflecting the dynamic nature of the moving contact.

The input pump light field can be split into two transverse modes, after entering a AIN microring, which can generate rich nonlinear effects. The cross-phase modulation (XPM) effect in magnetic(TM) polarization mode can cause a refractive index alteration of the micro-ring, the electric(TE) polarization mode and TM polarization mode will display different values and generate a phase change. By adjusting the magnitude of the input TE polarization mode and TM polarization mode, we can achieve a series of phase distributions. By controlling the phase of the electromagnetic field, we can control orbital angular momentum (OAM). The traditional LLE does not take phase into account, in this paper, we obtain a generalized LLE includes phase case. Our research suitable for precision spectroscopy, optical communication links, and coherent information processing.

The contemporary scientific landscape is characterized by a "curse of dimensionality," where our capacity to collect high-dimensional network data frequently outstrips our ability to computationally simulate or intuitively comprehend the underlying dynamics. This review provides a comprehensive synthesis of the methodologies developed to resolve this paradox by extracting low-dimensional "macroscopic theories" from complex systems. We classify these approaches into three distinct methodological lineages: Structural Coarse-Graining, which utilizes spectral and topological renormalization to physically contract the network graph; Analytical-Based Reduction, which employs rigorous ansatzes (such as Watanabe-Strogatz and Ott-Antonsen) and moment closures to derive reduced differential equations ; and Data-Driven Reduction, which leverages manifold learning and operator-theoretic frameworks (e.g., Koopman analysis) to infer latent dynamics from observational trajectories. We posit that the selection of a reduction strategy is governed by a fundamental "No Free Lunch" theorem, establishing a Pareto frontier between computational tractability and physical fidelity. Furthermore, we identify a growing epistemological schism between equation-based derivations that preserve causal mechanisms and black-box inference that prioritizes prediction. We conclude by discussing emerging frontiers, specifically the necessity of Higher-Order Laplacian Renormalization for simplicial complexes and the development of hybrid "Scientific Machine Learning" architectures-such as Neural ODEs-that fuse analytical priors with deep learning to solve the closure problem.

In a brief reflection on the principles of human society, P. A. M. Dirac articulated a structural tension between two widely affirmed norms: that it is good and natural for parents to improve the prospects of their own children, and that justice requires that all children have equal opportunities in life. These principles, each compelling on its own, cannot be fully realized together. This paper reconstructs Dirac's dilemma, connects it to the dynamics of compounding advantage and inheritance, and situates it within the broader history of political philosophy, including the work of Rawls, Dworkin, Cohen, Brighouse and Swift, Nozick, Murphy and Nagel, and others. The paper argues that attempts to eliminate the resulting injustices entirely risk damaging the non–zero–sum structures that generate general prosperity, and defends a position of "managed inequality": a robust social floor and real mobility, combined with limits on extreme dynastic accumulation and an explicit acceptance of some residual, but constrained, inherited advantage.

Electron paramagnetic resonance (EPR) is a valuable tool for physics, chemistry, biology and medicine, providing complementary spectroscopic information to NMR. It has long been known that EPR at high magnetic fields offers greater spectral resolution, but limitations in the THz instrumentation have prevented the full realization of these opportunities. Here we describe an EPR spectrometer at the high magnetic field of 14 T using 396 GHz excitation, which adapts techniques from liquid-state NMR to obtain sharp EPR resonances with a width of 210 ppb (full-width half-maximum). We use this to measure resonance positions, and hence g-factors, with a precision that reaches $\pm$16 ppb. Our use of in-situ liquid-state NMR of our solvent within the same sample improves the accuracy of these measurements: it allows us to reference our EPR measurement back to dilute gas 3He NMR for which quantum calculations are accurate. We measure the g-factor of N@C60 in deuterated toluene as g = 2.002 099 09 (3), where the 3 in brackets means that the uncertainty on the last digit is $\pm$3.

The rise of social media and recommendation algorithms has sparked concerns about their role in fostering opinion polarization and echo chambers. We study these phenomena using an adaptive voter model to compare two connection mechanisms: "free" global rewiring, where individuals connect with anyone sharing their opinion, and "friend-of-a-friend" local rewiring, which mimics algorithmic link recommendations on platforms like Facebook or LinkedIn. Simulations across different network topologies reveal that local rewiring increases final-state polarization of the system and fragments social networks into many disconnected components. The usual phase transition into two disconnected components turns into a fragmentation of smaller components, leading to an increase in echo chambers as well as many isolated nodes. This effect is most pronounced in clustered networks with high homophily in rewiring, illustrating how recommendation algorithms can intensify social fragmentation by changing the very structure of the network.

In this paper we approach the problem of superdeterminism from a novel point of view, highlighting its character as a more metaphysical than scientific proposition. First, we introduce a distinction between two types of superdeterministic theories, naïve (NSD) and metaphysical (MSD), and argue how NSD presents significant epistemic flaws. We show how NSD justifies itself through claims to fundamentality, thus connoting itself as a metaphysical theory rather than a scientific one. We finally illustrate that the most developed MSD model so far, Invariant Set Theory, implicitly proposes a confused form of priority monism. Our paper thus reinforces the thesis that theories should demonstrate rather than assume fundamentality and that it is methodologically flawed for a theory to assume its own fundamentality for the sole purpose of defending against criticisms.

Single-photon polymerization ensures rapid photopolymerization of centimeter-scale structures with features on the order of tens of micrometers, whereas 2PP provides sub-micrometer features at sub-millimeter scales. Existing hybrid approaches combining these techniques typically rely on stitched or layer-by-layer fabrication and often require separate printing platforms, making mesoscale manufacturing time-consuming. Here, we introduce a hybrid unified 3D printer that leverages the complementary strengths of both printing mechanisms to bridge this scale resolution-fabrication time gap. We propose integrating 2PP for high-resolution, localized spatial control with single-photon TVAM for enabling rapid, high-throughput 3D fabrication. In this approach, TVAM first forms millimeter-scale volumetric structures attached on a glass rod, via overprinting, which is then accessible, on the same platform, for subsequent high-resolution 2PP. Without needing to change the photoresin or introducing intermediate post-processing steps, we proceed to demonstrate finely printed structures via 2PP, fabricated both inside (embedded within) and on the surface of the millimeter-scale 3D objects printed with TVAM. Here, TVAM contributes in two distinct ways: by generating a pre-polymerized volume that facilitates subsequent 2PP, and by directly driving layer-less volumetric polymerization in designated regions within seconds. We experimentally demonstrate that this dual-mode strategy provides a mesoscale approach spanning three orders of magnitude in scale for rapid fabrication of millimeter-scale structures featuring 830 nm details. For applications such as micro-optics, biomedical scaffolds and tissue engineering, tens-of-micrometer features are sufficient across the majority of the volume, with higher resolution confined to localized functional regions.

Generative AI has emerged as a transformative paradigm in engineering design, enabling automated synthesis and reconstruction of complex 3D geometries while preserving feasibility and performance relevance. This paper introduces a domain-specific implicit generative framework for turbine blade geometry using DeepSDF, addressing critical gaps in performance-aware modeling and manufacturable design generation. The proposed method leverages a continuous signed distance function (SDF) representation to reconstruct and generate smooth, watertight geometries with quantified accuracy. It establishes an interpretable, near-Gaussian latent space that aligns with blade-relevant parameters, such as taper and chord ratios, enabling controlled exploration and unconditional synthesis through interpolation and Gaussian sampling. In addition, a compact neural network maps engineering descriptors, such as maximum directional strains, to latent codes, facilitating the generation of performance-informed geometry. The framework achieves high reconstruction fidelity, with surface distance errors concentrated within $1\%$ of the maximum blade dimension, and demonstrates robust generalization to unseen designs. By integrating constraints, objectives, and performance metrics, this approach advances beyond traditional 2D-guided or unconstrained 3D pipelines, offering a practical and interpretable solution for data-driven turbine blade modeling and concept generation.

In this work, we investigate $CO_{2}$ activation on ZnS using Near Ambient-Pressure X-ray photoelectron spectroscopy measurements (NAP-XPS) and density functional theory calculations (DFT). Our NAP-XPS experiments reveal that $CO_{2}$ adsorbs onto a defective ZnS surface upon heating above $473 \ K$ in a $CO_{2}$ atmosphere (up to $0.55 \ mbar$). The $CO_{2}$ adsorption fingerprint is detectable even after cooling to room temperature under ultra-high vacuum. Our DFT calculations suggest that $CO_{2}$ adsorption is energetically favorable on ZnS surfaces containing zinc vacancies, highlighting defect sites as key adsorption centers. Additionally, oxygen adsorption on a defective ZnS surface is exothermic, in contrast to the endothermic behavior observed on a defect-free surface. These findings contribute to a deeper understanding of defect-driven surface reactivity and may inform ZnS-based catalyst's design for $CO_{2}$ capture and reutilization.

We demonstrate a neutral atom networking node that combines high photon collection efficiency with high atom photon entanglement fidelity in a compact, fiber integrated platform. A parabolic mirror is used both to form the trap and to collect fluorescence from a single rubidium atom, intrinsically mode matching $\sigma$ polarized emitted photons to the fiber and rendering the system largely insensitive to small imperfections or drifts. The core optics consist of millimeter scale components that are pre aligned, rigidly bonded on a monolithic in-vacuum assembly, and interfaced entirely via optical fibers. With this design, we measure an overall photon collection and detection efficiency of $5\%$, from which we infer an overall collection efficiency of $9\%$ after the single–mode fiber coupling. We generate atom photon entangled states with a raw Bell state fidelity of 0.93 and an inferred fidelity of 0.98 after correcting for atom readout errors. The same node design has been realized in two independent setups with comparable performance and is compatible with adding high NA objective lenses to create and control atomic arrays at each node. Our results establish a robust, cavity free neutral atom interface that operates near the limit set by the collection optics numerical aperture and provides a practical building block for scalable quantum network nodes and repeaters.

Nitrogen vacancy centres (NVC) in diamond have been widely used for near-dc magnetometry. The intrinsic properties of diamonds make them potential candidates for tokamak fusion power diagnostics, where radiation-hard magnetometers will be essential for efficient control. An NVC magnetometer placed in a tokamak will need to operate within a $\geq$ 1 T magnetic field. In this work, we demonstrate fibre-coupled ensemble NVC optically detected magnetic resonance (ODMR) and magnetometry measurements at magnetic fields up to 1.2 T. Sensitivities of approximately 240 to 600 nT/$\sqrt{\textrm{Hz}}$ and 110 nT/$\sqrt{\textrm{Hz}}$ are achieved in a (10-150) Hz frequency range, for non-degenerate and near-$\langle$111$\rangle$ field alignments respectively.

We made a detailed review of the difference measures which have been used to judge the differences between experimentally determined color differences and theoretically defined ones, so-called line elements, for the human visual system. To eliminate the statistical errors due to variable and usually arbitrary sampling of the directions in a color point, we integrate the measures over a complete ellipsoid/ellipse. It turns out that in the limit for small deviations from circularity all proposed measures ($V_{AB}$, $\gamma-1$, $CV$ and $\mathrm{STRESS}$) are equivalent. For greater deviations the measures become distinct with $\gamma-1$ the most sensitive and $\mathrm{STRESS}$ the least. Ideally a difference measure should be coordinate independent and then it is advantageous to apply an affine transformation to both sets, e.g. turning the theoretical one into the unit ball. Although MacAdam already used this method but sampled the transformed ellipse, we integrate over the ellipsoid/ellipse. Comparing the results with the base measures we show that only $\mathrm{STRESS}$ is coordinate independent. Judging whether a single ellipsoid/ellipse resembles a unit ball can easily be done by comparing the eigenvalues with one and we show that our previously proposed error measure $d_{ev}$ (Candry e.a. Optics Express, 30, 36307, 2022) is the eigenvalue version of $\gamma-1$. We show why the short lived correlation coefficient $r$ was justly abandoned, being very coordinate dependent, but that Pant's recent geometric measure $1-R$ on the other hand is coordinate independent. All measures are routinely made scale invariant by the introduction of a scaling parameter, to be optimized. Lastly we show that from all measures the $\gamma-1$ ones are the only ones permitting the simple derivation of the globally optimized difference measure from the locally defined ones.

We study a family of lattice-gas systems defined on semiregular grids, obtained by projecting the vertices of three different geodesic icosahedra onto a spherical surface. By using couplings up to third neighbors we explore various interaction patterns, ranging from core-corona repulsion to square-well attraction and short-range attractive, long-range repulsive potentials. The relatively small number of sites in each grid ($\sim 100$) enables us to compute the exact statistical properties of the systems as a function of temperature and chemical potential by Wang-Landau sampling. For each case considered we highlight the existence of distinct low-temperature “phases”, featuring, among others, regular-polyhedral, cluster-crystal, and worm-like structures. We highlight similarities and differences between these motifs and those observed on the triangular lattice under the same conditions. Finally, we discuss the relevance of our results for the bottom-up realization of spherical templates with desired functionalities.

Four-dimensional (4D) Flow MRI can noninvasively measure cerebrovascular hemodynamics but remains underused clinically because current workflows rely on manual vessel segmentation and yield velocity fields sensitive to noise, artifacts, and phase aliasing. We present VAST (Vascular Flow Analysis and Segmentation), an automated, unsupervised pipeline for intracranial 4D Flow MRI that couples vessel segmentation with physics-informed velocity reconstruction. VAST derives vessel masks directly from complex 4D Flow data by iteratively fusing magnitude- and phase-based background statistics. It then reconstructs velocities via continuity-constrained phase unwrapping, outlier correction, and low-rank denoising to reduce noise and aliasing while promoting mass-consistent flow fields, with processing completing in minutes per case on a standard CPU. We validate VAST on synthetic data from an internal carotid artery aneurysm model across SNR = 2-20 and severe phase wrapping (up to five-fold), on in vitro Poiseuille flow, and on an in vivo internal carotid aneurysm dataset. In synthetic benchmarks, VAST maintains near quarter-voxel surface accuracy and reduces velocity root-mean-square error by up to fourfold under the most degraded conditions. In vitro, it segments the channel within approximately half a voxel of expert annotations and reduces velocity error by 39% (unwrapped) and 77% (aliased). In vivo, VAST closely matches expert time-of-flight masks and lowers divergence residuals by about 30%, indicating a more self-consistent intracranial flow field. By automating processing and enforcing basic flow physics, VAST helps move intracranial 4D Flow MRI toward routine quantitative use in cerebrovascular assessment.

Collisional and stochastic wave-particle dynamics in plasmas far from equilibrium are complex, temporally evolving, stochastic processes which are challenging to model. In this work, we extend previous methods coupling differentiable kinetic simulators and plasma phase space diagnostics to learn collision operators that account for time-varying background distributions. We also introduce a more general integro-differential operator formulation to probe relevant terms in the collision operator. To validate the proposed methodology we use data generated by self-consistent electromagnetic Particle-in-Cell simulations. We show that both approaches recover operators that can accurately reproduce the plasma phase space dynamics while being more accurate than estimates based on particle track statistics. These results further demonstrate the potential of using differentiable simulators to infer collision operators for scenarios where no closed form solution exists or deviations from existing theory are expected.

Optimal Causation Entropy (oCSE) is a robust causal network modeling technique that reveals causal networks from dynamical systems and coupled oscillators, distinguishing direct from indirect paths. CausationEntropy is a Python package that implements oCSE and several of its significant optimizations and methodological extensions. In this paper, we introduce the version 1.1 release of CausationEntropy, which includes new synthetic data generators, plotting tools, and several advanced information-theoretical causal network discovery algorithms with criteria for estimating Gaussian, k-nearest neighbors (kNN), geometric k-nearest neighbors (geometric-kNN), kernel density (KDE) and Poisson entropic estimators. The package is easy to install from the PyPi software repository, is thoroughly documented, supplemented with extensive code examples, and is modularly structured to support future additions. The entire codebase is released under the MIT license and is available on GitHub and through PyPi Repository. We expect this package to serve as a benchmark tool for causal discovery in complex dynamical systems.

We report the magnetic and magnetotransport properties of the layered CaAl$_2$Si$_2$-type diluted magnetic semiconductor (Ba$_{1-x}$K$_x$)(Cd$_{1-y}$Mn$_y$)$_2$As$_2$ over a broad Mn (spin) substitution range of $0.05 \le y \le 0.5$. K substitution introduces hole carriers, whereas Mn provides local moments, resulting in bulk ferromagnetism with Curie temperatures up to $\sim 17$ K. Intrinsic magnetic ordering is further supported by an anomalous Hall contribution and a specific-heat anomaly near $T_{\mathrm{C}}$. A key performance feature is a colossal negative magnetoresistance: for heavily Mn-doped compositions ($y \ge 0.3$), $\mathrm{MR}=[\rho(H)-\rho(0)]/\rho(0)$ reaches approximately $-100\%$ at 2 K and nearly saturates at a relatively low magnetic field of $\sim 0.35\,\mathrm{T}$. The combination of soft ferromagnetism, strong spin-charge coupling, and low-field MR saturation highlights (Ba,K)(Cd,Mn)$_2$As$_2$ as a promising bulk platform for low-temperature magnetoresistive functionalities.

Using orbital angular momentum (OAM) in the terahertz (THz) range provides a new degree of freedom for communication and imaging systems. This study presents a compact diffractive optical neural network designed to recognize discrete and superposed OAM states at THz frequencies. The network consists of six diffractive layers trained to spatially separate nine OAM modes with topological charges from 1 to 9. Each mode is projected to a distinct position on the output plane, enabling direct recognition of its state. The structure was fabricated through low-cost 3D printing techniques with high-impact polystyrene (HIPS), allowing for scalable and practical implementations. Experimental validation at 0.3 THz demonstrates good fidelity of mode discrimination and mapping. The proposed approach offers a robust and economical pathway for OAM decoding, offering new opportunities for beam manipulation through THz systems based on diffractive optical neural networks.