We introduce 6-dimensional irreducible vector and helical representations of the Lorentz group especially tailored for transforming 6-component electromagnetic and magnetic field vectors, and we show that mixture of the density matrices of helical states is equivalent to the electromagnetic stress-energy tensor.

The high-performance computing (HPC) community has recently seen a substantial diversification of hardware platforms and their associated programming models. From traditional multicore processors to highly specialized accelerators, vendors and tool developers back up the relentless progress of those architectures. In the context of scientific programming, it is fundamental to consider performance portability frameworks, i.e., software tools that allow programmers to write code once and run it on different computer architectures without sacrificing performance. We report here on the benefits and challenges of performance portability using a field-line tracing simulation and a particle-in-cell code, two relevant applications in computational plasma physics with applications to magnetically-confined nuclear-fusion energy research. For these applications we report performance results obtained on four HPC platforms with server-class CPUs from Intel (Xeon) and AMD (EPYC), and high-end GPUs from Nvidia and AMD, including the latest Nvidia H100 GPU and the novel AMD Instinct MI300A APU. Our results show that both Kokkos and OpenMP are powerful tools to achieve performance portability and decent "out-of-the-box" performance, even for the very latest hardware platforms. For our applications, Kokkos provided performance portability to the broadest range of hardware architectures from different vendors.

Fatigue fracture is one of the main causes of failure in structures. However, the simulation of fatigue crack growth is computationally demanding due to the large number of load cycles involved. Metals in the low cycle fatigue range often show significant plastic zones at the crack tip, calling for elastic-plastic material models, which increase the computation time even further. In pursuit of a more efficient model, we propose a simplified phase-field model for ductile fatigue fracture, which indirectly accounts for plasticity within the fatigue damage accumulation. Additionally, a cycle-skipping approach is inherent to the concept, reducing computation time by up to several orders of magnitude. Essentially, the proposed model is a simplification of a phase-field model with elastic-plastic material behavior. As a reference, we therefore implement a conventional elastic-plastic phase-field fatigue model with nonlinear hardening and a fatigue variable based on the strain energy density, and compare the simplified model to it. Its approximation of the stress-strain behavior, the neglect of the plastic crack driving force and consequential range of applicability are discussed. Since in fact the novel efficient model is similar in its structure to a phase-field fatigue model we published in the past, we include this older version in the comparison, too. Compared to this model variant, the novel model improves the approximation of the plastic strains and corresponding stresses and refines the damage computation based on the Local Strain Approach. For all model variants, experimentally determined values for elastic, plastic, fracture and fatigue properties of AA2024 T351 aluminum sheet material are employed.

Localization and classification of scattered nonlinear ultrasonic signatures in 2 dimensional complex damaged media using Time Reversal based Nonlinear Elastic Wave Spectroscopy (TR-NEWS) approach is extended to 3 dimensional complex damaged media. In (2+1)D, i.e. space 2 dimensional time 1 dimensional spacetime, we used quaternion bases for analyses, while in (3+1)D, we use biquaternion bases. The optimal weight function of the path of ultrasonic wave in (3+1)D lattice is obtained by using the Echo State Network (ESN) which is a Machine Learning technique. The hysteresis effect is incorporated by using the Preisach-Mayergoyz model.

The accurate prediction of solvation free energy is of significant importance as it governs the behavior of solutes in solution. In this work, we apply a variety of machine learning techniques to predict and analyze the alchemical free energy of small molecules. Our methodology incorporates an ensemble of machine learning models with feature processing using the K-nearest neighbors algorithm. Two training strategies are explored: one based on experimental data, and the other based on the offset between molecular dynamics (MD) simulations and experimental measurements. The latter approach yields a substantial improvement in predictive accuracy, achieving a mean unsigned error (MUE) of 0.64 kcal/mol. Feature analysis identifies molecular geometry and topology as the most critical factors in predicting alchemical free energy, supporting the established theory that surface tension is a key determinant. Furthermore, the feature analysis of offset results highlights the relevance of charge distribution within the system, which correlates with the inaccuracies in force fields employed in MD simulations and may provide guidance for improving force field designs. These results suggest that machine learning approaches can effectively capture the complex features governing solvation free energy, offering novel pathways for enhancing predictive accuracy.

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high signal-to-noise ratio (SNR) images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a 3-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing and model fitting, and tractography. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing, and point towards open-source software and databases specific to small animal and ex vivo imaging.

We present a novel approach to address the challenges of variable occupation numbers in direct optimization of density functional theory (DFT). By parameterizing both the eigenfunctions and the occupation matrix, our method minimizes the free energy with respect to these parameters. As the stationary conditions require the occupation matrix and the Kohn-Sham Hamiltonian to be simultaneously diagonalizable, this leads to the concept of ``self-diagonalization,'' where, by assuming a diagonal occupation matrix without loss of generality, the Hamiltonian matrix naturally becomes diagonal at stationary points. Our method incorporates physical constraints on both the eigenfunctions and the occupations into the parameterization, transforming the constrained optimization into an fully differentiable unconstrained problem, which is solvable via gradient descent. Implemented in JAX, our method was tested on aluminum and silicon, confirming that it achieves efficient self-diagonalization, produces the correct Fermi-Dirac distribution of the occupation numbers and yields band structures consistent with those obtained with SCF methods in Quantum Espresso.

We present the "First Light Advanced Ignition Model" (FLAIM), a reduced model for the implosion, adiabatic compression, volume ignition and thermonuclear burn of a spherical DT fuel capsule utilising a high-Z metal pusher. FLAIM is characterised by a highly modular structure, which makes it an appropriate tool for optimisations, sensitivity analyses and parameter scans. One of the key features of the code is the 1D description of the hydrodynamic operator, which has a minor impact on the computational efficiency, but allows us to gain a major advantage in terms of physical accuracy. We demonstrate that a more accurate treatment of the hydrodynamics plays a primary role in closing most of the gap between a simple model and a general 1D rad-hydro code, and that only a residual part of the discrepancy is attributable to the heat losses. We present a detailed quantitative comparison between FLAIM and 1D rad-hydro simulations, showing good agreement over a large parameter space in terms of temporal profiles of key physical quantities, ignition maps and typical burn metrics.

We introduce the LUNA neutron detector array developed for the investigation of the 13C(a,n)16O reaction towards its astrophysical s-process Gamow peak in the low-background environment of the Laboratori Nazionali del Gran Sasso (LNGS). Eighteen 3He counters are arranged in two different configurations (in a vertical and a horizontal orientation) to optimize neutron detection effciency, target handling and target cooling over the investigated energy range Ea;lab = 300 - 400 keV (En = 2.2 - 2.6 MeV in emitted neutron energy). As a result of the deep underground location, the passive shielding of the setup and active background suppression using pulse shape discrimination, we reached a total background rate of 1.23 +- 0.12 counts/hour. This resulted in an improvement of two orders of magnitude over the state of the art allowing a direct measurement of the 13C(a,n)16O cross-section down to Ea;lab = 300 keV. The absolute neutron detection efficiency of the setup was determined using the 51V(p,n)51Cr reaction and an AmBe radioactive source, and completed with a Geant4 simulation. We determined a (34+-3) % and (38+-3) % detection efficiency for the vertical and horizontal configurations, respectively, for En = 2.4 MeV neutrons.

A parametric study is conducted to quantify the effect of the keeper electrode geometry on the neutral flow quantities within orificed hollow cathodes, prior to cathode ignition. The keeper impinges directly on the flow out of the cathode orifice and its geometry strongly influences the product between the pressure in the orifice-keeper region and the distance between cathode and keeper, $P_{ko}\cdot D_{ko}$, a key parameter for successful cathode ignition. A representative cathode equipped with a keeper is simulated using the Direct Simulation Monte Carlo method. The numerical model is first validated with computational results from the literature, and a parametric study is then conducted. Parameters include the cathode pressure-diameter in the range of 1-5 Torr-cm and the following geometric ratios: cathode orifice-to-cathode inner radii in the range of 0.1-0.7, keeper orifice-to-cathode orifice radii in the range of 1-5, and keeper distance-to-cathode-orifice diameter, in the range of 0.5-10. If both keeper and cathode have identical orifice radii, the flow remains subsonic in the cathode orifice-to-keeper region. In most cases, however, the flow becomes underexpanded and supersonic, and the static pressure within the cathode orifice-keeper region is, on average, 4% that of the upstream pressure value. The orifice-keeper region pressure increases with either a decrease in the keeper orifice diameter or an increase in the distance between the cathode orifice plate and the keeper plate, in agreement with literature data. Trends are explained through control-volume-based conservation laws. The ratio of ignition-to-nominal mass flow rates is found to be in the range of 9-120, with a most probable value of 40, in agreement with literature data. This suggests that heater-less cathode ignition at a minimum DC voltage may be achieved by increasing the input mass flow rate by a factor of 40.

Device-to-device variations in ferroelectric (FE) hafnium oxide-based devices pose a crucial challenge that limits the otherwise promising capabilities of this technology. Earlier simulation-based studies have identified polarization (P) domain nucleation and polycrystallinity as key contributors to these variations. In this work, we experimentally investigate the effect of these two factors on remanent polarization (PR) variation in Hf0.5Zr0.5O2 (HZO) based metal-ferroelectric-insulator-metal (MFIM) capacitors for different set voltages (VSET) and FE thicknesses (TFE). Our measurements reveal a non-monotonic behavior of PR variations with VSET, which is consistent with previous simulation-based predictions. For low and high-VSET regions, we find that PR variations are dictated primarily by saturation polarization (PS) variations and are associated with the polycrystallinity in HZO. Our measurements also reveal that PR variations peak near the coercive voltage (VC), defined as the mid-VSET region. We attribute the increase of PR variation around VC to the random nature and sharp P switching associated with domain nucleation, which is dominant near VC. Further, we observe a reduction in the peak PR variation as HZO thickness (TFE) is scaled. We validate our arguments by establishing the correlation between the measured values of PR with VC and PS. Our results display that a strong correlation exists between PR and VC in the mid-VSET region and between PR and PS in the low and high-VSET regions across various TFE.

Placing an organic material on top of a Bragg mirror can significantly enhance exciton transport. Such enhancement has been attributed to strong coupling between the evanescent Bloch surface waves (BSW) on the mirror, and the excitons in the material. In this regime, the BSW and excitons hybridize into Bloch surface wave polaritons (BSWP), new quasi-particles with both photonic and excitonic character. While recent experiments unveiled a mixed nature of the enhanced transport, the role of the material degrees of freedom in this process remains unclear. To clarify their role, we performed atomistic molecular dynamics simulations of an ensemble of Methylene blue molecules, a prototype organic emitter, strongly coupled to a BSW. In contrast to the established static models of polaritons, even with disorder included, our dynamic simulations reveal a correlation between the photonic content of the BSWP and the nature of the transport. In line with experiment, we find ballistic motion for polaritons with high photonic character, and enhanced diffusion if the photonic content is low. Our simulations furthermore suggest that the diffusion is due to thermally activated vibrations that drive population transfer between the stationary dark states and mobile bright polaritonic states.

Understanding boundary layer flows in high Reynolds number (Re) turbulence is crucial for advancing fluid dynamics in a wide range of applications, from improving aerodynamic efficiency in aviation to optimizing energy systems in industrial processes. However, generating such flows requires complex, power-intensive large-scale facilities. Furthermore, the use of local probes, such as hot wires and pressure sensors, often introduces disturbances due to the necessary support structures, compromising measurement accuracy. In this paper, we present a solution that leverages the vanishingly small viscosity of liquid helium to produce high Re flows, combined with an innovative Particle Levitation Velocimetry (PLV) system for precise flow-field measurements. This PLV system uses magnetically levitated superconducting micro-particles to measure the near-wall velocity field in liquid helium. Through comprehensive theoretical analysis, we demonstrate that the PLV system enables quantitative measurements of the velocity boundary layer over a wall unit range of $44\le y^{+}\le 4400$, with a spatial resolution that, depending on the particle size, can reach down to about 10~$\mu$m. This development opens new avenues for exploring turbulence structures and correlations within the thin boundary layer that would be otherwise difficult to achieve.

A super-resolution (SR) method for the reconstruction of Navier-Stokes (NS) flows from noisy observations is presented. In the SR method, first the observation data is averaged over a coarse grid to reduce the noise at the expense of losing resolution and, then, a dynamic observer is employed to reconstruct the flow field by reversing back the lost information. We provide a theoretical analysis, which indicates a chaos synchronization of the SR observer with the reference NS flow. It is shown that, even with noisy observations, the SR observer converges toward the reference NS flow exponentially fast, and the deviation of the observer from the reference system is bounded. Counter-intuitively, our theoretical analysis shows that the deviation can be reduced by increasing the lengthscale of the spatial average, i.e., making the resolution coarser. The theoretical analysis is confirmed by numerical experiments of two-dimensional NS flows. The numerical experiments suggest that there is a critical lengthscale for the spatial average, below which making the resolution coarser improves the reconstruction.

This study presents an extension of the corrected Smagorinsky model, incorporating advanced techniques for error estimation and regularity analysis of far-from-equilibrium turbulent flows. A new formulation that increases the model's ability to explain complex dissipative processes in turbulence is presented, using higher-order Sobolev spaces to address incompressible and compressible Navier-Stokes equations. Specifically, a refined energy dissipation mechanism that provides a more accurate representation of turbulence is introduced, particularly in the context of multifractal flow regimes. Furthermore, we derive new theoretical results on energy regularization in multifractal turbulence, contributing to the understanding of anomalous dissipation and vortex stretching in turbulent flows. The work also explores the numerical implementation of the model in the presence of challenging boundary conditions, particularly in dynamically evolving domains, where traditional methods struggle to maintain accuracy and stability. Theoretical demonstrations and analytical results are provided to validate the proposed framework, with implications for theoretical advances and practical applications in computational fluid dynamics. This approach provides a basis for more accurate simulations of turbulence, with potential applications ranging from atmospheric modeling to industrial fluid dynamics.

A new 2,400 L liquid scintillator has been produced for the COSINE-100 Upgrade, which is under construction at Yemilab for the next COSINE dark matter experiment phase. The linear-alkyl-benzene-based scintillator is designed to serve as a veto for NaI(Tl) crystal targets and a separate platform for rare event searches. We measured using a sample consisting of a custom-made 445 mL cylindrical Teflon container equipped with two 3-inch photomultiplier tubes. Analyses show activity levels of $0.091 \pm 0.042$ mBq/kg for $^{238}$U and $0.012 \pm 0.007$ mBq/kg for $^{232}$Th.

Understanding the interactions between the El Nino-Southern Oscillation (ENSO) and the Madden-Julian Oscillation (MJO) is essential to studying climate variabilities and predicting extreme weather events. Here, we develop a stochastic conceptual model for describing the coupled ENSO-MJO phenomenon. The model adopts a three-box representation of the interannual ocean component to characterize the ENSO diversity. For the intraseasonal atmospheric component, a low-order Fourier representation is used to describe the eastward propagation of the MJO. We incorporate decadal variability to account for modulations in the background state that influence the predominant types of El Nino events. In addition to dynamical coupling through wind forcing and latent heat, state-dependent noise is introduced to characterize the statistical interactions among these multiscale processes, improving the simulation of extreme events. The model successfully reproduces the observed non-Gaussian statistics of ENSO diversity and MJO spectra. It also captures the interactions between wind, MJO, and ENSO.

During intracranial aneurysm (IA) treatment with Diverters (FDs), the device/parent artery diameters ratio may influence the ability of the device to induce aneurysm healing response. Oversized FDs are safer to deploy but may not induce enough hemodynamic resistance to ensure aneurysm occlusion. Methods based on Computational Fluid Dynamics (CFD) could allow optimal device selection but are time-consuming and inadequate for intra-operative guidance. To address this limitation, we propose to investigate a method for optimal FD selection using Angiographic Parametric Imaging (API) and machine learning (ML). We selected 128 pre-treatment angiographic sequences of IAs which demonstrated full occlusion at six months follow-up. For each IA, we extracted five API parameters from the aneurysm dome and normalized them to the feeding artery corresponding parameters. We dichotomized the dataset based on the FD/ proximal artery diameter ratio as undersized, if the ratio<1 or if multiple FDs were used and oversized otherwise. Single API parameter and ML analysis were used to determine whether API parameters could be used to determine the need for FD under-sizing (i.e., increased flow resistance). Classification accuracy was assessed using area under the receiver operator characteristic (AUROC). In total we identified 51 and 77 cases for the undersized and oversized cohorts respectively. Single API parameter analysis yielded an inadequate AUROC ~0.5 while machine learning using all five API parameters yielded and AUROC of 0.72.

In Angiographic Parametric Imaging (API), accurate estimation of parameters from Time Density Curves (TDC) is crucial. However, these estimations are often marred by errors arising from factors such as patient motion, procedural preferences, image noise, and injection variability. While fitting methods like gamma-variate fitting offer a solution to recover incomplete or corrupted TDC data, they might also introduce unforeseen biases. This study investigates the trade-offs and benefits of employing gamma-variate fitting on virtual angiograms to enhance the precision of API biomarkers. Utilizing Computational Fluid Dynamics (CFD) in patient specific 3D geometries, we generated a series of high-definition virtual angiograms at distinct inlet velocities: 0.25m/s, 0.35m/s, and 0.45m/s. These velocities were investigated across injection durations ranging from 0.5s to 2.0s. From these angiograms, TDCs for aneurysms and their corresponding inlets were constructed. To emulate typical clinical challenges, we introduced noise, simulated patient motion, and generated temporally incomplete data sets. These modified TDCs underwent gamma-variate fitting. We quantified both the original and fitted TDC curves using standard angiography metrics such as Cross-Correlation (Cor), Time to Peak (TTP), Mean Transit Time (MTT), Peak Height (PH), Area Under the Curve (AUC), and Maximum Gradient (Max-Gr) for a comprehensive comparison. TDCs enhanced by gamma-variate fitting exhibited a robust correlation with vascular flow dynamics. Our results affirm that gamma-variate fitting can adeptly restore TDCs from fragmentary sequences, elevating the precision of derived API parameters.

Optical tweezer arrays have emerged as a key experimental platform for quantum computation, quantum simulation, and quantum metrology, enabling unprecedented levels of control over single atoms and molecules. Existing methods to generate tweezer arrays mostly rely on active beam-shaping devices, such as acousto-optic deflectors or liquid-crystal spatial light modulators. However, these approaches have fundamental limitations in array geometry, size, and scalability. Here we demonstrate the trapping of single atoms in optical tweezer arrays generated via holographic metasurfaces. We realize two-dimensional arrays with more than 250 tweezer traps, arranged in arbitrary geometries with trap spacings as small as 1.5 um. The arrays have a high uniformity in terms of trap depth, trap frequency, and positional accuracy, rivaling or exceeding existing approaches. Owing to sub-micrometer pixel sizes and high pixel densities, holographic metasurfaces open a path towards optical tweezer arrays with >100,000 traps.

We investigate the possibility and current limitations of flow computations using quantum annealers by solving a fundamental flow problem on Ising machines. As a fundamental problem, we consider the one-dimensional advection-diffusion equation. We formulate it in a form suited to Ising machines (i.e., both classical and quantum annealers), perform extensive numerical tests on a classical annealer, and finally test it on an actual quantum annealer. To make it possible to process with an Ising machine, the problem is formulated as a minimization problem of the residual of the governing equation discretized using either the spectral method or the finite difference method. The resulting system equation is then converted to the Quadratic Unconstrained Binary Optimization (QUBO) form though quantization of variables. It is found in the numerical tests using a classical annealer that the spectral method requiring smaller number of variables has a particular merit over the finite difference method because the accuracy deteriorates with the increase of the number of variables. We also found that the computational error varies depending on the condition number of the coefficient matrix. In addition, we extended it to a two-dimensional problem and confirmed its fundamental applicability. From the numerical test using a quantum annealer, however, it turns out that the computation using a quantum annealer is still challenging due largely to the structural difference from the classical annealer, which leaves a number of issues toward its practical use.

Social networks inherently exhibit complex relationships that can be positive or negative, as well as directional. Understanding balance in these networks is crucial for unraveling social dynamics, yet traditional theories struggle to incorporate directed interactions. This perspective presents a comprehensive roadmap for understanding balance in signed directed networks, extending traditional balance theory to account for directed interactions. Balance is indicated by the enrichment of higher-order patterns like triads compared to an adequate null model, where the network is randomized with some key aspects being preserved. Finding appropriate null models has been a challenging task even without considering directionality, which largely expands the space of potential null models. Recently, it has been shown that in the undirected case both the network topology and the signed degrees serve as key factors to preserve. Therefore, we introduce a maximally constrained null model that preserves the directed topology as well as node-level features given by the signed unidirectional, reciprocated, and conflicting node degrees. Our null model is based on the maximum-entropy principle and reveals consistent patterns across large-scale social networks. We also consider directed generalizations of balance theory and find that the observed patterns are well aligned with two proposed directed notions of strong balance. Our approach not only unveils balance in signed directed networks but can also serve as a starting point towards generative models of signed directed social networks, advancing our understanding of complex social systems and their dynamics.

Backscattering in micro-ring cavities induces mode mixing and limits device performance. Existing methods to mitigate backscattering often involve complex fabrication processes or are insufficient for complete suppression. In this work, we introduce a novel method to eliminate backscattering by operating the cavity at an exceptional point (EP). By engineering non-conservative coupling between degenerate clockwise (CW) and counter-clockwise (CCW) modes, we achieve chiral transmission that prevents degeneracy lifting and suppresses unwanted mode coupling. Unlike previous approaches that rely on precise gain-loss balance or complex structures, our method utilizes non-conservative coupling between the counterpropgating cavity modes. Using this method, we further show significant enhancement in the cavity performance in Floquet mode conversion efficiency at the EP. Our highly adaptable approach enables seamless integration into various photonic platforms with electro-optic modulators. This advancement mitigates backscattering and improves the precision of light-matter interactions, offering promising applications in quantum communication and information processing.

The maximum deposition eigenchannel provides the largest possible power delivery to a target region inside a diffusive medium by optimizing the incident wavefront of a monochromatic beam. It originates from constructive interference of scattered waves, which is frequency sensitive. We investigate the spectral width of maximum deposition eigenchannels over a range of target depths using numerical simulations of a 2D diffusive system. Compared to tight focusing into the system, power deposition to an extended region is more sensitive to frequency detuning. The spectral width of enhanced delivery to a large target displays a rather weak, non-monotonic variation with target depth, in contrast to a sharp drop of focusing bandwidth with depth. While the maximum enhancement of power deposited within a diffusive system can exceed that of power transmitted through it, this comes at the cost of a narrower spectral width. We investigate the narrower deposition width in terms of the constructive interference of transmission eigenchannels within the target. We further observe that the spatial field distribution inside the target region decorrelates slower with spectral detuning than power decay of the maximum deposition eigenchannel. Additionally, absorption increases the spectral width of deposition eigenchannels, but the depth dependence remains qualitatively identical to that without absorption. These findings hold for any diffusive waves, including electromagnetic waves, acoustic waves, pressure waves, mesoscopic electrons, and cold atoms.

The Moir\'e superlattice has attracted growing interest in the electromagnetic and optical communities. Here, we extend this concept to time-varying photonic systems by superposing two binary modulations on the refractive index with different modulation periods, i.e., the Moir\'e photonic time crystal (PTC). Such a Moir\'e PTC leads to extreme narrow bands in momentum space which supports temporal localized modes, exhibiting periodically self-reconstructing pulse in time domain. We investigate how the modulation parameters change the bandstructure of the Moir\'e PTC and the temporal localization behavior. Moreover, we explore mode-locking mechanism in frequency space in the Moir\'e PTC, which points towards potential applications in mode-locked lasers with tunable time width of the emitted pulses. Our work therefore extends the study of PTC to complex modulation patterns, and unveils new possibility in wave manipulation with time-varying systems.

We investigate the use of an atomic Fabry-Perot interferometer (FPI) with a pulsed non-interacting Bose-Einstein condensate (BEC) source as a space-based acceleration sensor. We derive an analytic approximation for the device's transmission under a uniform acceleration, which we use to compute the device's attainable acceleration sensitivity using the classical Fisher information. In the ideal case of a high-finesse FPI and an infinitely narrow momentum width atomic source, we find that when the total length of the device is constrained to small values, the atomic FPI can achieve greater acceleration sensitivity than a Mach-Zender (MZ) interferometer of equivalent total device length. Under the more realistic case of a finite momentum width atomic source, We identify the ideal cavity length that gives the best sensitivity. Although the MZ interferometer now offers enhanced sensitivity within currently-achievable experimental parameter regimes, our analysis demonstrates that the atomic FPI holds potential as a promising alternative in the future, provided that narrow momentum width atomic sources can be engineered.

Quantum entanglement is a captivating phenomenon in quantum physics, characterized by intricate and non-classical correlations between particles. This phenomenon plays a crucial role in quantum computing and measurement processes. In this tutorial we explore the dynamics of quantum systems with up to three spins, providing an introductory guide to understanding how entanglement evolves and transfers within such systems. Through detailed examples, simulations, and analyses, the tutorial offers insights into the fundamental principles of entanglement; . We also provide \code{python} modules for reproducing the presented results and as a basis for further projects. The target audience of this tutorial is physics enthusiasts among high school students and students in their first semesters.

Generating high output powers while achieving narrow line single mode lasing are often mutual exclusive properties of commercial laser diodes. For this reason, efficient and scalable amplification of narrow line laser light is still a major driving point in modern laser system designs. Commonly, injection locking of high-power semiconductor laser diodes are used for this purpose. However, for many laser diodes it is very challenging to achieve stable operation of the injection locked state due to a complex interplay of non-linearities and thermal effects. Different approaches of active or passive stabilization usually require a large overhead of optical and electrical equipment and are not generally applicable. In our work we present a passive stabilization scheme, that is generally applicable, technically easy to implement and extremely cost-effective. It is based on the externally synchronized automatic acquisition of the optimal injection state. Central to our simple but powerful scheme is the management of thermalization effects during lock acquisition. By periodical relocking, spectrally pure amplified light is maintained in a quasi-CW manner over long timescales. We characterize the performance of our method for laser diodes amplifying 671 nm light and demonstrate the general applicability by confirming the method to work also for laser diodes at 401 nm, 461 nm and 689 nm. Our scheme enables the scaled operation of injection locks, even in cascaded setups, for the distributed amplification of single frequency laser light.

In this study, we report a detailed calculation of the static dipole polarizabilities for group 12 elements using the finite-field approach combined with the relativistic coupled-cluster method, including single, double, and perturbative triple excitations. We examine three types of relativistic effects on dipole polarizabilities: scalar-relativistic, spin-orbit coupling (SOC), and fully relativistic Dirac-Coulomb contributions. The final recommended polarizability values, with their associated uncertainties, are $37.95 \pm 0.72$ for Zn, $45.68 \pm 1.16$ for Cd, $34.04 \pm 0.67$ for Hg, and $27.92 \pm 0.24$ for Cn. Our results align closely with the recommended values in the 2018 Table of static dipole polarizabilities for neutral atoms [Mol. Phys. \textbf{117}, 1200 (2019)], while providing reduced uncertainties for Cd and Cn. The analysis indicates that scalar-relativistic effects are the dominant relativistic contributions to atomic dipole polarizabilities for these atoms, with SOC effects found to be negligible. Furthermore, we evaluate the influence of electron correlation across all relativistic regimes, underscoring its critical role in the precise determination of dipole polarizabilities.

The sheath plasma resonance (SPR) in an inverted fireball (IFB) system is semi-analytically investigated using a generalized hydrodynamic isothermal model formalism. It incorporates the constitutive ionic fluid viscosity, inter-species collisions, and geometric curvature effects. The SPR stability is studied for an anodic (hollow, meshed) IFB for the first time against the traditional cathode-plasma arrangements of regular electrode (solid, smooth) fireballs. The SPR develops near a spherical electrode enclosed by a plasma sheath amid a given electric potential. A generalized linear quartic dispersion relation (DR) with diverse plasma multi-parametric coefficients is methodically derived using a standard normal mode analysis. The mathematical construction of the obtained DR roots confirms that only one feasible nonzero frequency mode exists (emerging in the IFB). This root existence is confirmed both analytically and numerically. This consequent SPR creates trapped acoustic fluctuations in the IFB plasmas because of the internal reflections at the sheath plasma boundary. Also, sensible parametric changes in the SPR features, with both plasma density and viscosity, are seen. A local condition for the SPR excitation and its subsequent transition to collective standing wave-like patterns in the IFBs is illustrated. A fair corroboration of our results with the earlier SPR experimental observations of standing wave-like eigenmode patterns (evanescent) strengthens the reliability of our study alongside new applicability.

A new approach using scalar metasurfaces for the design of linearly polarized antennas is presented. The proposed method is based on the construction of the surface impedance Zs using a technique called "phase-matching," which employs the sum of two circular polarizations in phase opposition. This process allows for the achievement of good performance of the synthesized antenna, such as the reduction of side lobe levels and the attainment of an almost symmetric main lobe regardless of the pointing direction. Numerical and measurement results are also presented.

The amount of air entrained by vertical water jets impacting a large pool is revisited. To test available phenomenological models, new data on the jet deformation at impact and on the entrained air flow rate were collected both on a small-scale (height of fall H about 1 m, jet diameter D0 = 7.6 mm) and a large-scale (H up to 9 m, D0 up to 213 mm) facilities. Conditions for which jet break-up occurred were not considered. For short heights of fall (H less than a few D0), the jet deformation remains smaller than 0.1 jet diameter, and the entrained air flow rate happens to grow as Ui^3/2, where Ui is the jet velocity at impact. This scaling agrees with the air film model proposed by Sene, 1988. At larger fall heights, even though conditions leading to jet break-up were avoided, the jets exhibited complex topologies, including strong deformations and/or interface stripping and/or jet aeration. Further, the roughness model initiated by Henderson, McCarthy and Molloy, 1970 which stipulates that the entrained air flow rate corresponds to the air trapped within jet corrugations, was found valid for these conditions. More precisely, for corrugated jets, the effective roughness amounts to the maximum jet deformation (as measured from the 90% detection probability on the diameter pdf) or equivalently to about two times the total deformation of one side of the jet (where the total deformation of one side of the jet is experimentally evaluated as the standard deviation of the position of one jet edge). However, for jets experiencing strong stripping or aeration (the latter being identified by a threshold on the growth of the jet diameter with the falling distance), the effective roughness amounts to about 0.8 times the maximum jet deformation or equivalently to 1.1 times the total deformation of one side of the jet. Compared with corrugated jets, the effective roughness is thus diminished by half.

The Purcell effect describes the enhancement of the spontaneous emission rate of an emitter near a resonant structure. However, evaluating the Purcell factor quantitatively and empirically is difficult due to the difficulties in measuring the electromagnetic nearfield of an optical resonance for calculation of the exact effective modal volume, especially with non-Hermitian resonators. Therefore, we propose a new analytical approach to circumvent the need to measure the nearfield and predict the Purcell enhancement with an analytical model and spectrally measurable parameters. Our proposed model predicts the averaged Purcell enhancement by metasurfaces on a photoluminescence medium, and is verified with experimental measurements and numerical simulations of nanoparticle arrays coupled to a fluorescent thin film. The model directly analyzes the photoluminescence enhancement and extraction efficiency of metasurface, and can be generalized to work with arbitrarily-shaped photoluminescent medium that is coupled to a resonator. This discovery will facilitate optimization of metasurfaces that support open cavity modes for efficient extraction of enhanced luminescence.

We combine the theory of slow spectral closure for linearized Boltzmann equations with Maxwell's kinetic boundary conditions to derive non-local hydrodynamics with arbitrary accommodation. Focusing on shear-mode dynamics, we obtain explicit steady state solutions in terms of Fourier integrals and closed-form expressions for the mean flow and the stress. We demonstrate that the exact non-local fluid model correctly predicts several rarefaction effects with accommodation, including the Couette flow and thermal creep in a plane channel.

Urban development is shaped by historical, geographical, and economic factors, presenting challenges for planners in understanding urban form. This study models commute flows across multiple U.S. cities, uncovering consistent patterns in urban population distributions and commuting behaviors. By embedding urban locations to reflect mobility networks, we observe that population distributions across redefined urban spaces tend to approximate log-normal distributions, in contrast to the often irregular distributions found in geographical space. This divergence suggests that natural and historical constraints shape spatial population patterns, while, under ideal conditions, urban organization may naturally align with log-normal distribution. A theoretical model using preferential attachment and random walks supports the emergence of this distribution in urban settings. These findings reveal a fundamental organizing principle in urban systems that, while not always visible geographically, consistently governs population flows and distributions. This insight into the underlying urban structure can inform planners seeking to design efficient, resilient cities.

In a previous publication [J. Chem. Phys., 161, 044105 (2024)], it has been shown that Rothe's method can be used to solve the time-dependent Schr\"odinger equation (TDSE) for the hydrogen atom in a strong laser field using time-dependent Gaussian wave packets. Here, we generalize these results, showing that Rothe's method can propagate arbitrary numbers of thawed, complex-valued, explicitly correlated Gaussian functions (ECGs) with dense correlation matrices for systems with varying dimensionality. We consider the multidimensional Henon-Heiles potential, and show that the dynamics can be quantitatively reproduced using only 30 Gaussians in 2D, and that accurate spectra can be obtained using 20 Gaussians in 2D and 30 to 40 Gaussians in 3D and 4D. Thus, the relevant multidimensional dynamics can be described at high quality using only a small number of ECGs that give a very compact representation of the wave function. This efficient representation, along with the demonstrated ability of Rothe's method to propagate Gaussian wave packets in strong fields and ECGs in complex potentials, paves the way for accurate molecular dynamics calculations beyond the Born-Oppenheimer approximation in strong fields.

The paper introduces a novel dual-port dual-polarized magneto-electric dipole (MED) antenna with orthogonal Gamma and inverted-Gamma shape probes, which was fabricated by means of an additive 3D metal printing process. Electromagnetic wave simulation and RF measurement report a resonance bandwidth from 3 GHz to 4 GHz at both MED's ports with respect to a standing wave ratio of less than 2. The cross-polarization isolation (XPI) between the MED's ports was also measured to be greater than 50 dB across its entire resonance bandwidth. The paper also thoroughly examines the impact of misalignments in the polarization of the MED probes on the XPI level. The broadband resonance and excellent isolation between the MED ports make it a strong candidate for a full-duplex wireless transceiver in network infrastructure.

Surrogate networks can constitute suitable replacements for real networks, in particular to study dynamical processes on networks, when only incomplete or limited datasets are available. As empirical datasets most often present complex features and interplays between structure and temporal evolution, creating surrogate data is however a challenging task, in particular for data describing time-resolved interactions between agents. Here we propose a method to generate surrogate temporal networks that mimic such observed datasets. The method is based on a decomposition of the original dataset into small temporal subnetworks encoding local structures on a short time scale. These are used as building blocks to generate a new synthetic temporal network that will hence inherit the shape of local interactions from the dataset. Moreover, we also take into account larger scale correlations on structural and temporal dimension, using them to inform the process of assembling the building blocks. We showcase the method by generating surrogate networks for several datasets of social interactions and comparing them to the original data on two complementary aspects. First, we show that the surrogate data possess complex structural and temporal features similar to the ones of the original data. Second, we simulate several dynamical processes, describing respectively epidemic spread, opinion formation and emergence of norms in a population, and compare the outcome of these processes on the generated and original datasets. We describe the method in detail and provide an implementation so that it can be easily used in future works based on temporally evolving networks.

The design and optimization of optical components, such as Bragg gratings, are critical for applications in telecommunications, sensing, and photonic circuits. To overcome the limitations of traditional design methods that rely heavily on computationally intensive simulations and large datasets, we propose an integrated methodology that significantly reduces these burdens while maintaining high accuracy in predicting optical response. First, we employ a Bayesian optimization technique to strategically select a limited yet informative number of simulation points from the design space, ensuring that each contributes maximally to the model's performance. Second, we utilize singular value decomposition to effectively parametrize the entire reflectance spectra into a reduced set of coefficients, allowing us to capture all significant spectral features without losing crucial information. Finally, we apply XGBoost, a robust machine learning algorithm, to predict the entire reflectance spectra from the reduced dataset. The combination of Bayesian optimization for data selection, SVD for full-spectrum fitting, and XGBoost for predictive modeling provides a powerful, generalizable framework for the design of optical components.

Nearly fifty years ago, Roberts (1978) postulated that Earth's magnetic field, which is generated by turbulent motions of liquid metal in its outer core, likely results from a subcritical dynamo instability characterised by a dominant balance between Coriolis, pressure and Lorentz forces. Here we numerically explore the generation of subcritical geomagnetic fields using techniques from optimal control and dynamical systems theory to uncover the nonlinear dynamical landscape underlying dynamo action. Through nonlinear optimisation, via direct-adjoint looping, we identify the minimal seed - the smallest magnetic field that attracts to a nonlinear dynamo solution. Additionally, using the Newton-hookstep algorithm, we converge stable and unstable travelling wave solutions to the governing equations. By combining these two techniques, complex nonlinear pathways between attracting states are revealed, providing insight into a potential subcritical origin of the geodynamo. This paper showcases these methods on the widely studied benchmark of Christensen et al. (2001), laying the foundations for future studies in more extreme and realistic parameter regimes. We show that the minimal seed reaches a nonlinear dynamo solution by first attracting to an unstable travelling wave solution, which acts as an edge state separating a hydrodynamic solution from a magnetohydrodynamic one. Furthermore, by carefully examining the choice of cost functional, we establish a robust optimisation procedure that can systematically locate dynamo solutions on short time horizons with no prior knowledge of its structure.

Acoustic tweezers comprising a surface acoustic wave chip and a disposable silicon microfluidic chip are potentially advantageous to stable and cost-ffective acoustofluidic experiments while avoiding the cross-contamination by reusing the surface acoustic wave chip and disposing of the microfluidic chip. For such a device, it is important to optimize the chip-to-chip bonding and the size and shape of the microfluidic chip to enhance the available acoustic pressure. In this work, aiming at studying samples with the size of a few tens of microns, we explore the device structure and assembly method of acoustic tweezers. By using a polymer bonding layer and shaping the silicon microfluidic chip via deep reactive ion etching, we were able to attain the acoustic pressure up to 2 MPa with a corresponding acoustic radiation pressure of 0.2 kPa for 50 MHz ultrasound, comparable to reported values at lower ultrasound frequencies. We utilized the fabricated acoustic tweezers for non-contact viscoelastic deformation experiments of soft matter and trapping of highly motile cells. These results suggests that the feasibility of the hybrid chip approach to attaining the high acoustic force required to conduct acoustomechanical testing of small soft matters and cells.

We investigate the dependence of the yield of positive secondary ions created upon impact of primary He, B and Ne ions on geometry and electronic and nuclear energy deposition by the projectiles. We employ pulsed beams in the medium energy regime and a large position-sensitive, time-of-flight detection system to ensure accurate quantification. As a target, we employ a single crystalline Si(100) self-supporting 50 nm thick membrane thus featuring two identical surfaces enabling simultaneous measurements in backscattering and transmission geometry. Electronic sputtering is identified as the governing mechanism for the desorption of hydrogen and molecular species found on the surfaces. Nevertheless, larger energy deposition to the nuclear subsystem by heavier projectiles as well as due to the directionality of the collision cascade appears to act in synergy with the electronic energy deposition leading to an overall increase in secondary ion yields. A higher yield of ions sputtered from the matrix is observed in transmission geometry only for B and Ne ions, consistent with the observed role of nuclear stopping.

We designed a new artificial neural network by modifying the neural ordinary differential equation (NODE) framework to successfully predict the time evolution of the 2D mode profile in both the linear growth and nonlinear saturated stages. Starting from the magnetohydrodynamic (MHD) equations, simplifying assumptions were applied based on physical properties and symmetry considerations of the energetic-particle-driven geodesic acoustic mode (EGAM) to reduce complexity. Our approach embeds physical laws directly into the neural network architecture by exposing latent differential states, enabling the model to capture complex features in the nonlinear saturated stage that are difficult to describe analytically, and thus, the new artificial neural network is named as ExpNODE (Exposed latent state Neural ODE). ExpNODE was evaluated using a data set generated from first-principles simulations of the EGAM instability, focusing on the pre-saturated stage and the nonlinear saturated stage where the mode properties are most complex. Compared to state-of-the-art models such as ConvLSTM, ExpNODE with physical information not only achieved lower test loss but also converged faster during training. Specifically, it outperformed ConvLSTM method in both the 20-step and 40-step prediction horizons, demonstrating superior accuracy and efficiency. Additionally, the model exhibited strong generalization capabilities, accurately predicting mode profiles outside the training data set. Visual comparisons between model predictions and ground truth data showed that ExpNODE with physical information closely captured detailed features and asymmetries inherent in the EGAM dynamics that were not adequately captured by other models. These results suggest that integrating physical knowledge into neural ODE frameworks enhances their performance, and provides a powerful tool for modeling complex plasma phenomena.

We propose a technique for transforming the intensity of quasi-monochromatic recoilless radiation with a photon energy of 93.3 keV, emitted by radioactive M\"ossbauer sources 67Ga or 67Cu, into a sequence of short pulses with individually and independently controlled, on demand, the moments of appearance of pulses, as well as the peak intensity, duration and shape of each pulse. The technique is based on the transmission of recoilless (M\"ossbauer) 93.3-keV photons from the source through a medium containing resonantly absorbing 67Zn nuclei. The pulses are formed due to the rapid reciprocating movement of the source relative to the absorber at specified moments in time along the direction of photon propagation at a distance not exceeding half the radiation wavelength. The produced sequences of $\gamma$-ray pulses are similar to the digitization of information carried by electromagnetic waves. They can also be used to develop M\"ossbauer spectroscopy of the atomic and subatomic structures, as well as open novel opportunities for $\gamma$-ray quantum optics.

In gas evolving electrolysis, bubbles grow at electrodes due to a diffusive influx from oversaturation generated locally in the electrolyte by the electrode reaction. When considering electrodes of micrometer-size resembling catalytic islands, bubbles are found to approach dynamic equilibrium states at which they neither grow nor shrink. Such equilibrium states are found at low oversaturation for both, pinning and expanding wetting regimes of the bubbles and are based on the balance of local influx near the bubble foot and global outflux. Unlike the stability of pinned nano-bubbles studied earlier, the Laplace pressure plays a minor role only. We extend the analytical solution of Zhang & Lohse (2023) by taking into account the non-uniform distribution of dissolved gas around the bubble obtained from direct numerical simulation. This allows us to identify the parameter regions of bubble growth, dissolution and dynamic equilibrium as well as to analyze the stability of the equilibrium states. Finally, we draw conclusions on how to possibly enhance the efficiency of electrolysis.

An isolated attosecond vortex $\gamma$-ray pulse is generated by using a relativistic spatiotemporal optical vortex (STOV) laser in particle-in-cell simulations. A $\sim$ 300-attosecond electron slice with transverse orbital angular momentum (TOAM) is initially selected and accelerated by the central spatiotemporal singularity of the STOV laser. This slice then collides with the laser's reflected Gaussian-like front from a planar target, initiating nonlinear Compton scattering and resulting in an isolated, attosecond ($\sim$ 300 as), highly collimated ($\sim$ 4$\degree$), ultra-brilliant ($\sim 5\times 10^{24}$ photons/s/mm$^2$/mrad$^2$/0.1\%BW at 1 MeV) $\gamma$-ray pulse. This STOV-driven approach overcomes the significant beam divergence and complex two-laser requirements of prior Gaussian-based methods while introducting TOAM to the attosecond $\gamma$-ray pulse, which opens avenues for ultrafast imaging, nuclear excitation, and detection applications.

Skyrmions are topological defects belonging to nontrivial homotopy classes in particle theory, and are recognized as a suitable unit in the high-density, low-dissipation microelectronic devices in condensed matter physics. Their remarkably stable topology has been observed in electromagnetic waves recently. For the evanescent fields near a surface, this has been realized so far only for elementary optical skyrmions, with a fixed skyrmion number. Here we introduce the concept of moir\'e optical skyrmion clusters-multiskyrmions are nested to form a large optical skyrmion cluster-crystallized or quasi-crystallized as a consequence of the twisted nanostructures. The rapid inverting of optical skyrmion number is achieved in the imperfectly aligned composite nanostructures. This moir\'e optical skyrmion interaction mechanism is described by a lattice model. Further, the nucleation and collapse of optical skyrmion are studied, where their nanoscale dynamics are revealed with a tiny change of the twist angle. The sudden reversal of the on-chip skyrmion can serve as a precise beacon of the relative alignment deviation between twisted composite nanostructures.

In this work we propose a method for probing the chirality of nanoscale electromagnetic near fields utilizing the properties of a coherent superposition of free-electron vortex states in electron microscopes. Electron beams optically modulated into vortices carry orbital angular momentum, thanks to which they are sensitive to the spatial phase distribution and topology of the investigated field. The sense of chirality of the studied specimen can be extracted from the spectra of the electron beam with nanoscale precision owing to the short picometer de Broglie wavelength of the electron beam. We present a detailed case study of the interaction of a coherent superposition of electron vortex states and the optical near field of a golden nanosphere illuminated by circularly polarized light as an example, and we examine the chirality sensitivity of electron vortex beams on intrinsically chiral plasmonic nanoantennae.

Dual-comb ranging has emerged as an effective technology for long-distance metrology, providing absolute distance measurements with high speed, precision, and accuracy. Here, we demonstrate a dual-comb ranging method that utilizes a free-space transceiver unit, enabling dead-zone-free measurements and simultaneous ranging with interchanged comb roles to allow for long-distance measurements even when the target is moving. It includes a GPU-accelerated algorithm for real-time signal processing and a free-running single-cavity solid-state dual-comb laser with a carrier wavelength $\lambda_c \approx$ 1055 nm, a pulse repetition rate of 1 GHz and a repetition rate difference of 5.06 kHz. This combination offers a fast update rate and sufficient signal strength to reach a single-shot time-of-flight precision of around 0.1 $\mu$m (i.e. $< \lambda_c/4$) on a cooperative target placed at a distance of more than 40 m. The free-running laser is sufficiently stable to use the phase information for interferometric distance measurements, which improves the single-shot precision to $<$20 nm. To assess the ranging accuracy, we track the motion of the cooperative target when moved over 40 m and compare it to a reference interferometer. The residuals between the two measurements are below 3 $\mu$m. These results highlight the potential of this approach for accurate and dead-zone-free long-distance ranging, supporting real-time tracking with nm-level precision.

Resistive pulse sensing has been widely used to characterize and count single particles in solution moving through channels under an electric bias, with nanoscale pores more recently providing enough spatial resolution for nucleic acid sequencing at the single-molecule level. At its core, this technique relies on measuring the drop in ionic current through the pore induced by the passage of a molecule and, through conductance models, translating the blockage signal to molecular dimensions. However, there exists no model considering the resistive contributions of the pore exterior, i.e. the access regions, when obstructed by a molecule. This is becoming increasingly important for low aspect ratio pores, with the advent of 2D materials and ultrathin membranes. In this work, a general method by which to model the resistance of the access regions of a pore in the presence of an insulating obstruction is presented. Thin oblate spheroidal slices are used to partition access regions and infer their conductance when blocked by differently shaped objects. We show that our model accurately estimates the blocked-state conductance of 2D and finite-length pores as a function of the distance from the pore in the presence of simple obstructions geometries (e.g. cylindrical and spherical objects) or complex structures (i.e. sequence of simple obstruction sub-units). The model is further shown to capture off-axis effects by predicting deeper blockages for obstructions offset from the pore's central axis. A web-based tool is created to predict the electrical signatures of a wide range of molecule geometries translocating through differently shaped pores. The introduced model will help guide experimental designs and thus presents a straightforward way to extend the quantification of the resistive pulse technique at the nanoscale.

A wall-resolved large-eddy simulation (LES) of the fluid flow around a 30P30N airfoil is conducted at a Reynolds number of Rec=750,000 and an angle of attack (AoA) of 9 degrees. The simulation results are validated against experimental data from previous studies and further analyzed, focusing on the suction side of the wing main element. The boundary layer development is investigated, showing characteristics typical of a zero-pressure-gradient turbulent boundary layer (ZPG TBL). In particular, the boundary layer exhibits limited growth, and the outer peak of the streamwise Reynolds stresses is virtually absent, distinguishing it from an adverse-pressure-gradient turbulent boundary layer (APG TBL). A proper orthogonal decomposition (POD) analysis is performed on a portion of the turbulent boundary layer, revealing a significant energy spread across higher-order modes. Despite this, TBL streaks are identified, and the locations of the most energetic structures correspond to the peaks in the Reynolds stresses.

We experimentally demonstrate the emergence of flat-band-induced compact-localized modes in acoustic Kagome lattices. Compact localized states populate singular dispersion bands characterized by band crossing, where a quadratic and a flat-band dispersion coalesce into a singularity. These conditions enable intriguing wave phenomena when the Hilbert Schmidt quantum distance, measuring the strength of the singularity, is nonzero. We report numerically and experimentally the formation of compact localized states (CLS), extremely localized in space and protected by dispersion flatness. In our system of coupled acoustic waveguides, sound waves are confined to propagate within tightly localized sites positioned both at the boundaries and within the interior of the lattice, achieving broadband and sustained confinement over time. This framework opens new avenues for the manipulation and transport of information through sound waves, with potential application in mechanics and acoustics, including communication, signal processing, and sound isolation. This work also expands the exploration of flat-band lattice physics within the realm of acoustics.

We have developed a neutrino detector with threshold energies from ~0.115 to 105 MeV in a clean detection mode almost completely void of accidental backgrounds. It was initially developed for the NASA $\nu$SOL project to put a solar neutrino detector very close to the Sun with 1,000 to 10,000 times higher solar neutrino flux than on Earth. Similar interactions have been found for anti-neutrinos, which were initially intended for Beta decay neutrinos from reactors, geological sources, or for nuclear security applications. These techniques work at the 1 to 100 MeV region for neutrinos from the ORNL Spallation Neutron Source or low energy accelerator neutrino and anti-neutrino production targets less than $\sim$100 MeV. The identification process is clean, with a double pulse detection signature within a time window between the first interaction producing the conversion electron or positron and the secondary gamma emission 100 ns to ~1 $\mu$s, which removes most accidental backgrounds. These new modes for neutrino and anti-neutrino detection of low energy neutrinos and anti-neutrinos could allow improvements to neutrino interaction measurements from an accelerator beam on a target.

Recent investigations have established the physical relevance of spatially-localized instability mechanisms in fluid dynamics and their potential for technological innovations in flow control. In this letter, we show that the mathematical problem of identifying spatially-localized optimal perturbations that maximize perturbation-energy amplification can be cast as a sparse (cardinality-constrained) optimization problem. Unfortunately, cardinality constrained optimization problems are non-convex and combinatorially hard to solve in general. To make the analysis viable within the context of fluid dynamics problems, we propose an efficient iterative method for computing sub-optimal spatially-localized perturbations. Our approach is based on a generalized Rayleigh quotient iteration algorithm followed by a variational renormalization procedure that reduces the optimality gap in the resulting solution. The approach is demonstrated on a sub-critical plane Poiseuille flow at Re = 4000, which has been a benchmark problem studied in prior investigations on identifying spatially-localized flow structures. Remarkably, we find that a subset of the perturbations identified by our method yield a comparable degree of energy amplification as their global counterparts. We anticipate our proposed analysis tools will facilitate further investigations into spatially-localized flow instabilities, including within the resolvent and input-output analysis frameworks.

The complete second-order hyperfine-interaction correction is calculated for centroid energy levels of H, D, and $^3$He atoms. For $^3$He, the corrections of $-2.075$ kHz and $-0.305$ kHz beyond the leading hyperfine-mixing contribution are obtained for the $2^1S$ and $2^3S$ states, respectively. These results shift the nuclear charge radii difference derived from the $^3$He - $^4$He isotope shift and largely resolve the previously reported disagreement between the muonic and electronic helium determinations [Y. van der Werf et al., arXiv:2306.02333 (2023); K. Schuhmann et al., arXiv:2305.11679 (2023)].

In view of recently demonstrated joint use of novel Fourier-transform techniques and effective high-accuracy frequency domain solvers related to the Method of Moments, it is argued that a set of transformative innovations could be developed for the effective, accurate and efficient simulation of problems of wave propagation and scattering of broadband, time-dependent wavefields. This contribution aims to convey the character of these methods and to highlight their applicability in computational modeling of electromagnetic configurations across various fields of science and engineering.

We study the effects of the velocity distribution functions of the plasma particles on the equilibrium charge of dust grains, acquired through inelastic collisions of the particles with the grains. This paper is the second in a series of two papers on the subject. Here, we consider the charging process when the plasma particles are statistically described by the recently proposed regularized Kappa distribution functions, which allow for extreme suprathermal states, characterized by extremely low values of the kappa index, previously forbidden to the standard Kappa distributions, whose effects on dust charging were studied in Paper I of this series. We analyse the effects that extreme suprathermal states of the plasma particles have on dust charging and verify conditions for the uncommon result of positive equilibrium charge, employing two different models for the regularized Kappa distributions.

Computational cardiovascular flow modeling plays a crucial role in understanding blood flow dynamics. While 3D models provide acute details, they are computationally expensive, especially with fluid-structure interaction (FSI) simulations. 1D models offer a computationally efficient alternative, by simplifying the 3D Navier-Stokes equations through axisymmetric flow assumption and cross-sectional averaging. However, traditional 1D models based on finite element methods (FEM) often lack accuracy compared to 3D averaged solutions. This study introduces a novel physics-constrained machine learning technique that enhances the accuracy of 1D blood flow models while maintaining computational efficiency. Our approach, utilizing a physics-constrained coupled neural differential equation (PCNDE) framework, demonstrates superior performance compared to conventional FEM-based 1D models across a wide range of inlet boundary condition waveforms and stenosis blockage ratios. A key innovation lies in the spatial formulation of the momentum conservation equation, departing from the traditional temporal approach and capitalizing on the inherent temporal periodicity of blood flow. This spatial neural differential equation formulation switches space and time and overcomes issues related to coupling stability and smoothness, while simplifying boundary condition implementation. The model accurately captures flow rate, area, and pressure variations for unseen waveforms and geometries. We evaluate the model's robustness to input noise and explore the loss landscapes associated with the inclusion of different physics terms. This advanced 1D modeling technique offers promising potential for rapid cardiovascular simulations, achieving computational efficiency and accuracy. By combining the strengths of physics-based and data-driven modeling, this approach enables fast and accurate cardiovascular simulations.

This paper enhances the classic Smagorinsky model by introducing an innovative, adaptive dissipation term that adjusts dynamically with distance from boundary regions. This modification addresses a known limitation of the standard model over dissipation near boundaries thereby improving accuracy in turbulent flow simulations in confined or wall-adjacent areas. We present a rigorous theoretical framework for this adaptive model, including two foundational theorems. The first theorem guarantees existence and uniqueness of solutions, ensuring that the model is mathematically well-posed within the adaptive context. The second theorem provides a precise bound on the energy dissipation rate, demonstrating that dissipation remains controlled and realistic even as boundary effects vary spatially. By allowing the dissipation coefficient to decrease near boundary layers, this approach preserves the finer turbulent structures without excessive smoothing, yielding a more physically accurate representation of the flow. Future work will focus on implementing this adaptive model in computational simulations to empirically verify the theoretical predictions and assess performance in scenarios with complex boundary geometries.

Metasurfaces offer a powerful platform for effective light manipulation, which is crucial for advanced optical technologies. While designs of polarization-independent structures have reduced the need for polarized illumination, they are often limited by either low Q factors or low resonance modulation. Here, we design and experimentally demonstrate a metasurface with polarization-independent quasi-bound state in the continuum (quasi-BIC), where the unit cell consists of four silicon squares arranged in a two-dimensional array and the resonance properties can be controlled by adjusting the edge length difference between different squares. Our metasurface experimentally achieves a Q factor of approximately 100 and a resonance modulation of around 50%. This work addresses a common limitation in previous designs, which either achieved high Q factors exceeding 200 with a resonance modulation of less than 10%, leading to challenging signal-to-noise ratio requirements, or achieved strong resonance modulation with Q factors of only around 10, limiting light confinement and fine-tuning capabilities. In contrast, our metasurface ensures that the polarization-independent signal is sharp and distinct within the system, reducing the demands on signal-to-noise ratio and improving robustness. Experiments show the consistent performance across different polarization angles. This work contributes to the development of versatile optical devices, enhancing the potential for the practical application of BIC-based designs in areas such as optical filtering and sensing.

Photonic integrated circuits find ubiquitous use in various technologies, from communication, to computing and sensing, and therefore play a crucial role in the quantum technology counterparts. Several systems are currently under investigation, each showing distinct advantages and drawbacks. For this reason, efforts are made to effectively combine different platforms in order to benefit from their respective strengths. In this work, 3D laser written photonic wire bonds are employed to interface triggered sources of quantum light, based on semiconductor quantum dots embedded into etched microlenses, with low-loss silicon-nitride photonics. Single photons at telecom wavelengths are generated by the In(Ga)As quantum dots which are then funneled into a silicon-nitride chip containing single-mode waveguides and beamsplitters. The second-order correlation function of g(2)(0) = 0.11+/-0.02, measured via the on-chip beamsplitter, clearly demonstrates the transfer of single photons into the silicon-nitride platform. The photonic wire bonds funnel on average 28.6+/-8.8% of the bare microlens emission (NA = 0.6) into the silicon-nitride-based photonic integrated circuit even at cryogenic temperatures. This opens the route for the effective future up-scaling of circuitry complexity based on the use of multiple different platforms.

We develop a full-wave electromagnetic (EM) theory for calculating the multipole decomposition in two-dimensional (2-D) structures consisting of isolated, arbitrarily shaped, inhomogeneous, anisotropic cylinders or a collection of such. To derive the multipole decomposition, we first solve the scattering problem by expanding the scattered electric field in divergenceless cylindrical vector wave functions (CVWF) with unknown expansion coefficients that characterize the multipole response. These expansion coefficients are then expressed via contour integrals of the vectorial components of the scattered electric field evaluated via an electric field volume integral equation (EFVIE). The kernels of the EFVIE are the products of the tensorial 2-D Green's function (GF) expansion and the equivalent 2-D volumetric electric and magnetic current densities. We validate the theory using the commercial finite element solver COMSOL Multiphysics. In the validation, we compute the multipole decomposition of the fields scattered from various 2-D structures and compare the results with alternative formulations. Finally, we demonstrate the applicability of the theory to study an emerging photonics application on oligomers-based highly directional switching using active media. This analysis addresses a critical gap in current literature, where multipole theories exist primarily for three-dimensional (3-D) particles of isotropic materials. Our work enhances the understanding and utilization of the optical properties of 2-D, inhomogeneous, and anisotropic cylindrical structures, contributing to advancements in photonic and meta-optics technologies.

This study examines the role of stratification in the formation and persistence of eastward jets (like the Gulf Stream and Kuroshio currents). Using a wind-driven, two-layer quasi-geostrophic model in a double-gyre configuration, we construct a phase diagram to classify flow regimes. The parameter space is defined by a criticality parameter $\xi$, which controls the emergence of baroclinic instability, and the ratio of layer depths $\delta$, which describes the surface intensification of stratification. Eastward jets detaching from the western boundary are observed when $\delta \ll 1$ and $\xi \sim 1$, representing a regime transition from a vortex-dominated western boundary current to a zonostrophic regime characterized by multiple eastward jets. Remarkably, these surface-intensified patterns emerge without considering bottom friction. The emergence of the coherent eastward jet is further addressed with complementary 1.5-layer simulations and explained through both linear stability analysis and turbulence phenomenology. In particular, we show that coherent eastward jets emerge when the western boundary layer is stable, and find that the asymmetry in the baroclinic instability of eastward and westward flows plays a central role in the persistence of eastward jets, while contributing to the disintegration of westward jets.

Complex systems, such as economic, social, biological, and ecological systems, usually feature interactions not only between pairwise entities but also among three or more entities. These multi-entity interactions are known as higher-order interactions. Hypergraph, as a mathematical tool, can effectively characterize higher-order interactions, where nodes denote entities and hyperedges represent interactions among multiple entities. Meanwhile, all higher-order interactions can also be projected into a number of lower-order interactions or even some pairwise interactions. Whether it is necessary to consider all higher-order interactions, and whether it is with little loss to replace them by lower-order or even pairwise interactions, remain a controversial issue. If the role of higher-order interactions is insignificant, the complexity of computation and the difficulty of analysis can be drastically reduced by projecting higher-order interactions into lower-order or pairwise interactions. We use link prediction, a fundamental problem in network science, as the entry point. Specifically, we evaluate the impact of higher-order interactions on link predictive accuracy to explore the necessity of these structures. We propose a method to decompose the higher-order structures in a stepwise way, thereby allowing to systematically explore the impacts of structures at different orders on link prediction. The results indicate that in some networks, incorporating higher-order interactions significantly enhances the accuracy of link prediction, while in others, the effect is insignificant. Therefore, we think that the role of higher-order interactions varies in different types of networks. Overall, since the improvement in predictive accuracy provided by higher-order interactions is significant in some networks, we believe that the study of higher-order interactions is both necessary and valuable.

This study presents wall-resolved large-eddy simulations (WRLES) of a high-lift airfoil, based on high-order flux reconstruction (FR) commercial software Dimaxer, which runs on consumer level GPUs. A series of independence tests are conducted, including various Ffowcs Williams-Hawkings sampling surfaces, different mesh densities, simulations at 4th and 5th order accuracies, and varying spanwise lengths, to establish best practice for predicting slat noise through high-order WRLES. The results show excellent agreement with experimental data while requiring significantly fewer computational resources than traditional second-order methods. An investigation on the effects of Reynolds number (Re) is performed by scaling the airfoil size, with Reynolds numbers ranging from 8.55e5 to a real aircraft level of 1.71e7. By applying simple scaling through Strouhal number (St), spanwise correction, and distance from the receiver, the far-field noise spectra for different Reynolds numbers can be coincided. Additionally, simulations are performed at four angles of attack: 3{\deg}, 5.5{\deg}, 9.5{\deg}, and 14{\deg}. The results indicate that higher angles of attack lead to a less intense feedback loop, resulting in lower tonal noise frequencies and reduced noise amplitude. The maximum noise reduction observed is over 14dB when comparing 14{\deg} to 3{\deg}. Furthermore, an improved formula is proposed to enhance the prediction of slat noise tonal frequencies and to better elucidate the mechanism behind tonal noise generation.

Recent advances in high-precision spectroscopy of highly charged ions necessitate an understanding of energy shifts of ionic levels caused by external electric and magnetic fields. Beyond the well-known Stark and linear Zeeman shifts, trapped ions may also exhibit quadratic Zeeman and electric quadrupole shifts. In this contribution, we present a systematic approach for the theoretical analysis of these shifts for arbitrary many-electron ions. Based on the derived expressions and making use of the multiconfigurational Dirac-Fock approach, we performed calculations of quadratic Zeeman shift coefficients and quadrupole moments for various ionic states in Ca$^{14+}$, Ni$^{12+}$ and Xe$^{q+}$ ions. These ions attract particular interest for ongoing and future experiments in optical clocks and tests of fundamental physics.

With the versatile manipulation capability, programmable metasurfaces are rapidly advancing in their intelligence, integration, and commercialization levels. However, as the programmable metasurfaces scale up, their control configuration becomes increasingly complicated, posing significant challenges and limitations. Here, we propose a multiple-partition cross-modulation (MPCM) programmable metasurface to enhance the wireless communication coverage with low hardware complexity. We firstly propose an innovative encoding scheme to multiply the control voltage vectors of row-column crossing, achieving high beamforming precision in free space while maintaining low control hardware complexity and reducing memory requirements for coding sequences. We then design and fabricate an MPCM programmable metasurface to confirm the effectiveness of the proposed encoding scheme. The simulated and experimental results show good agreements with the theoretically calculated outcomes in beam scanning across the E and H planes and in free-space beam pointing. The MPCM programmable metasurface offers strong flexibility and low complexity by allowing various numbers and combinations of partition items in modulation methods, catering to diverse precision demands in various scenarios. We demonstrate the performance of MPCM programmable metasurface in a realistic indoor setting, where the transmissions of videos to specific receiver positions are successfully achieved, surpassing the capabilities of traditional programmable metasurfaces. We believe that the proposed programmable metasurface has great potentials in significantly empowering the wireless communications while addressing the challenges associated with the programmable metasurface's design and implementation.

The discrete time Vicsek model confined by a harmonic potential explains many aspects of swarm formation in insects. We have found exact solutions of this model without alignment noise in two or three dimensions. They are periodic or quasiperiodic (invariant circle) solutions with positions on a circular orbit or on several concentric orbits and exist for quantized values of the confinement. There are period 2 and period 4 solutions on a line for a range of confinement strengths and period 4 solutions on a rhombus. These solutions may have polarization one, although there are partially ordered period 4 solutions and totally disordered (zero polarization) period 2 solutions. We have explored the linear stability of the exact solutions in two dimensions using the Floquet theorem and verified the stability assignements by direct numerical simulations.

Photonic neural networks capable of rapid programming are indispensable to realize many functionalities. Phase change technology can provide nonvolatile programmability in photonic neural networks. Integrating direct laser writing technique with phase change material (PCM) can potentially enable programming and in-memory computing for on-chip photonic neural networks. Sb2Se3 is a newly introduced ultralow-loss phase change material with a large refractive index contrast over the telecommunication transmission band. Compact, low-loss, rewritable, and nonvolatile on-chip phase-change metasurfaces can be created by using direct laser writing on a Sb2Se3 thin film. Here, by cascading multiple layers of on-chip phase-change metasurfaces, an ultra-compact on-chip programmable diffractive deep neural network is demonstrated at the wavelength of 1.55um and benchmarked on two machine learning tasks of pattern recognition and MNIST (Modified National Institute of Standards and Technology) handwritten digits classification and accuracies comparable to the state of the art are achieved. The proposed on-chip programmable diffractive deep neural network is also advantageous in terms of power consumption because of the ultralow-loss of the Sb2Se3 and its nonvolatility which requires no constant power supply to maintain its programmed state.

Extraction of hot carriers (HCs) over the band-edge is a key to harvest solar energy beyond Shockley-Queisser limit1. Graphene is known as a HC-layered material due to phonon bottleneck effect near Dirac point, but limited by low photocarrier density2. Graphene/transition metal dichalcogenide (TMD) heterostructures circumvent this issue by ultrafast carrier transfer from TMD to graphene2,3. Nevertheless, efficient extraction of photocurrent by means of HCs together with carrier multiplication (CM) is still missing. Here, we introduce an ultrathin broadband resonant tunneling (BRT) barrier, TiOX to efficiently extract photocurrent with simultaneous CM and HC measurements in MoS2/graphene/TiOX heterostructure. The BRT layer gives rise to boosting open circuit voltage which is linearly proportional to incident photon energy. Meanwhile, short circuit current rises rapidly over 2Eg with obvious CM feature. This was explained by defining the joint density of states between graphene and TiOX layer over positive and negative voltage. The broadband resonant tunneling states inherently constructed from oxidation states varying from Ti3+ to Ti4+ allow the ultrafast HCs to efficiently transfer from graphene to TiOX layer. We find that the number of available tunneling states is directly proportional to short circuit current, which is well corroborated with TiOX and MoS2 thickness variance. We obtained an optimum thickness of BRT layer of ~2.8 nm, yielding cascade open circuit voltage as high as ~0.7 V, two orders of magnitude higher than that without BRT layer to reach a record efficiency of 5.3% with improved fill factor owing to synergistic HC and CM conversion under 1-SUN with long-term stability.

We compare methods for signal classification applied to voltage traces from transition edge sensors (TES) which are photon-number resolving detectors fundamental for accessing quantum advantages in information processing, communication and metrology. We quantify the impact of numerical analysis on the distinction of such signals. Furthermore, we explore dimensionality reduction techniques to create interpretable and precise photon number embeddings. We demonstrate that the preservation of local data structures of some nonlinear methods is an accurate way to achieve unsupervised classification of TES traces. We do so by considering a confidence metric that quantifies the overlap of the photon number clusters inside a latent space. Furthermore, we demonstrate that for our dataset previous methods such as the signal's area and principal component analysis can resolve up to 16 photons with confidence above $90\%$ while nonlinear techniques can resolve up to 21 with the same confidence threshold. Also, we showcase implementations of neural networks to leverage information within local structures, aiming to increase confidence in assigning photon numbers. Finally, we demonstrate the advantage of some nonlinear methods to detect and remove outlier signals.

Singular optics aims to understand and manipulate light's topological defects, pioneered by the discovery that phase vortex lines, strands of destructive interference, naturally occur in scalar wave fields. Monochromatic electromagnetic fields, however, are described by complex three-dimensional vectors that make individual scalar phase vortices in their vector components, which depend on the choice of co-ordinate basis, less meaningful. Instead, polarisation singularities can capture the vector texture of complicated, even non-paraxial light, with separate spatial descriptions for the electric $\mathbf{E}$ and magnetic $\mathbf{H}$ fields. But polarisation textures, too, are basis-dependent, because the laws of electromagnetism can be expressed not only by separate $\mathbf{E}$ and $\mathbf{H}$ fields, but by linear combinations of the two. We instead propose fundamental, basis-independent topological features generic in monochromatic electromagnetic fields: one- and two-dimensional structures that relate to time-averaged symmetries, including parity, duality and time-reversal, held locally by the combined electric and magnetic field polarisation geometry.

Diffractive optical neural networks (DONNs), leveraging free-space light wave propagation for ultra-parallel, high-efficiency computing, have emerged as promising artificial intelligence (AI) accelerators. However, their inherent lack of reconfigurability due to fixed optical structures post-fabrication hinders practical deployment in the face of dynamic AI workloads and evolving applications. To overcome this challenge, we introduce, for the first time, a multi-dimensional reconfigurable hybrid diffractive ONN system (MDR-HDONN), a physically composable architecture that unlocks a new degree of freedom and unprecedented versatility in DONNs. By leveraging full-system learnability, MDR-HDONN repurposes fixed fabricated optical hardware, achieving exponentially expanded functionality and superior task adaptability through the differentiable learning of system variables. Furthermore, MDR-HDONN adopts a hybrid optical/photonic design, combining the reconfigurability of integrated photonics with the ultra-parallelism of free-space diffractive systems. Extensive evaluations demonstrate that MDR-HDONN has digital-comparable accuracy on various task adaptations with 74x faster speed and 194x lower energy. Compared to prior DONNs, MDR-HDONN shows exponentially larger functional space with 5x faster training speed, paving the way for a new paradigm of versatile, composable, hybrid optical/photonic AI computing. We will open-source our codes.

The particle-in-cell (PIC) method is a well-established and widely used kinetic plasma modelling approach that provides a hybrid Lagrangian-Eulerian approach to solve the plasma kinetic equation. Despite its power in capturing details of the underlying physics of plasmas, conventional PIC implementations are associated with a significant computational cost, rendering their applications for real-world plasma science and engineering challenges impractical. The acceleration of the PIC method has thus become a topic of high interest, with several approaches having been pursued to this end. Among these, the concept of reduced-order (RO) PIC simulations, first introduced in 2023, provides a uniquely flexible and computationally efficient framework for kinetic plasma modelling - characteristics verified extensively in various plasma configurations. In this two-part article, we report the latest progress achieved on RO-PIC. Part I article revisits the original RO-PIC formulation and introduces refinements that substantially enhance the cost-efficiency and accuracy of the method. We discuss these refinements in comparison against the original formulation, illustrating the progression to a "first-order" implementation from the baseline "zeroth-order" one. In a detailed step-by-step verification, we first test the newly updated reduced-dimension Poisson solver (RDPS) in the first-order RO-PIC against its zeroth-order counterpart using test-case Poisson problems. Next, comparing against the zeroth-order version, we examine the performance of the complete first-order RO-PIC code in two-dimensional plasma problems. The detailed verifications demonstrate that the improvements in the RO-PIC formulation enable the approach to provide full-2D-equivalent results at a substantially lower (up to an order of magnitude) computational cost compared to the zeroth-order RO-PIC.

In combustion theory, flames are usually described in terms of the dynamics of iso-surfaces of a specific scalar. The flame displacement speed is then introduced as a local variable quantifying the progression of these iso-surfaces relative to the flow field. While formally defined as a scalar, the physical meaning of this quantity allows relating it with a vector pointing along the normal direction of the scalar iso-surface. In this work, this one-dimensional concept is extended by the introduction of a generalized flame displacement velocity vector, which is associated with the dynamics of iso-surfaces of two generic scalars, $\alpha$ and $\beta$. It is then shown how a new flamelet paradigm can be built around this velocity vector, which leads to a very compact and generic set of two-dimensional flamelet equations for thermochemical quantities and the conditioning scalar gradients, $g_{\alpha} = \lvert \nabla \alpha \rvert$ and $g_{\beta} = \lvert \nabla \beta \rvert$. The most important features of the developed framework are discussed in the context of partially-premixed flames, which provides significant insights into several aspects of the theory, including the nature of the different contributions to the flamelet equations for the conditioning scalar gradients and the fact that different flamelet coordinate systems (orthogonal and non-orthogonal) can be characterized by the same flame displacement velocity vector. This approach opens an entire spectrum of possibilities for the definition of new two-dimensional composition spaces, which represents a very promising basis for the development of new variants of flamelet theory.

Across many plasma applications, the underlying phenomena and interactions among the involved processes are known to exhibit three-dimensional characteristics. Furthermore, the global properties and evolution of plasma systems are often determined by a process called inverse energy cascade, where kinetic plasma processes at the microscopic scale interact and lead to macroscopic coherent structures. These structures can have a major impact on the stability of plasma discharges, with detrimental effects on the operation and performance of plasma technologies. Kinetic particle-in-cell (PIC) methods offer a sufficient level of fidelity to capture these processes and behaviors. However, three-dimensional PIC simulations that can cost-effectively overcome the curse of dimensionality and enable full-scale simulations of real-world time significance have remained elusive. Tackling the enormous computational cost issue associated with conventional PIC schemes, the computationally efficient reduced-order (RO) PIC approach provides a viable path to 3D simulations of real-size plasma systems. This part II paper builds upon the improvements to the RO-PIC's underpinning formulation discussed in part I and extends the novel "first-order" RO-PIC formulation to 3D. The resulting Quasi-3D (Q3D) implementation is rigorously verified in this paper, both at the module level of the Q3D reduced-dimension Poisson solver (RDPS) and at the global PIC code level. The plasma test cases employed correspond to 3D versions of the 2D configurations studied in Part I, including a 3D extension to the Diocotron instability problem. The detailed verifications of the Q3D RO-PIC confirm that it maintains the expected levels of cost-efficiency and accuracy, demonstrating the ability of the approach to indistinguishably reproduce full-3D simulation results at a fraction of the computational cost.

Weakly collisional plasmas contain a wealth of information about the dynamics of the plasma in the particle velocity distribution functions, yet our ability to exploit fully that information remains relatively primitive. Here we aim to present the fundamentals of a new technique denoted Plasma Seismology that aims to invert the information from measurements of the particle velocity distribution functions at a single point in space over time to enable the determination of the electric field variation over an extended spatial region. The fundamental mathematical tool at the heart of this technique is the Morrison $G$ Transform. Using kinetic numerical simulations of Langmuir waves in a Vlasov-Poisson plasma, we demonstrate the application of the standard Morrison $G$ Transform, which uses measurements of the particle velocity distribution function over all space at one time to predict the evolution of the electric field in time. Next, we introduce a modified Morrison $G$ Transform which uses measurements of the particle velocity distribution function at one point in space over time to determine the spatial variation of the electric field over an extended spatial region. We discuss the limitations of this approach, particularly for the numerically challenging case of Langmuir waves. The application of this technique to Alfven waves in a magnetized plasma holds the promise to apply the technique to existing spacecraft particle measurement instrumentation to determine the electric fields over an extended spatial region away from the spacecraft.

Time perception is crucial for a coherent human experience. As life progresses, our perception of the passage of time becomes increasingly non-uniform, often feeling as though it accelerates with age. While various causes for this phenomenon have been theorized, a comprehensive mathematical and theoretical framework remains underexplored. This study aims to elucidate the mechanisms behind perceived time dilation by integrating classical and revised psychophysical theorems with a novel mathematical approach. Utilizing Weber-Fechner laws as foundational elements, we develop a model that transitions from exponential to logarithmic functions to represent changes in time perception across the human lifespan. Our results indicate that the perception of time shifts significantly around the age of mental maturity, aligning with a proposed inversion point where sensitivity to temporal stimuli decreases, eventually plateauing out at a constant rate. This model not only explains the underlying causes of time perception changes but also provides analytical values to quantify this acceleration. These findings offer valuable insights into the cognitive and neurological processes influencing how we experience time as we go through life.

Positron Emission Tomography (PET) is a vital imaging modality widely used in clinical diagnosis and preclinical research but faces limitations in image resolution and signal-to-noise ratio due to inherent physical degradation factors. Current deep learning-based denoising methods face challenges in adapting to the variability of clinical settings, influenced by factors such as scanner types, tracer choices, dose levels, and acquisition times. In this work, we proposed a novel 3D ControlNet-based denoising method for whole-body PET imaging. We first pre-trained a 3D Denoising Diffusion Probabilistic Model (DDPM) using a large dataset of high-quality normal-dose PET images. Following this, we fine-tuned the model on a smaller set of paired low- and normal-dose PET images, integrating low-dose inputs through a 3D ControlNet architecture, thereby making the model adaptable to denoising tasks in diverse clinical settings. Experimental results based on clinical PET datasets show that the proposed framework outperformed other state-of-the-art PET image denoising methods both in visual quality and quantitative metrics. This plug-and-play approach allows large diffusion models to be fine-tuned and adapted to PET images from diverse acquisition protocols.

This study introduces a new method for synthesizing Cu+-containing metastable phases through ion exchange. Traditionally, CuCl has been used as a Cu+ ion source for solid-state ion exchanges; however, its thermodynamic driving force is often insufficient for complete ion exchange with Li+-containing precursors. First-principles calculations have identified Cu2SO4 and Cu3PO4 as more powerful alternatives, providing a higher driving force than CuCl. It has been experimentally demon-strated that these ion sources can open up new reaction pathways through experimental ion exchanges, such as from \beta-LiGaO2 to \beta-CuGaO2, which were previously unattainable. An important perspective provided by this study is that the poten-tial of such basic compounds to act as powerful ion sources has been overlooked, and that they were identified through straightforward first-principles calculations. This work presents the initial strategic design of an ion exchange reaction by exploring suitable ion sources, thereby expanding the potential for synthesizing metastable materials.

Alkali-noble-gas comagnetometers have become an essential tool for tests of fundamental physics and offer a compact platform for precision gyroscopy. They are, however, limited by technical noise at low frequencies, commonly due to their limited suppression of magnetic noise. Here we investigate a new method for co-magnetometry between a single noble gas and alkali species. While similar to well-known devices using self-compensation, our scheme introduces magnetic pulses that controllably perturb the noble gas and pulsed optical pumping to polarise the alkali atoms. These applied pulses allow our scheme to measure, rather than just suppress, the effect of magnetic noise thereby offering reduced cross-talk. We show numerically that our scheme retrieves four signals (rotations and magnetic fields on two transverse axes) with similar sensitivity to a single axis device. We also present a proof-of-principle experiment based on a 87Rb-129Xe cell. Our data shows a low magnetic-rotation cross-talk of $0.2 \pm 0.1\mu$Hz$/$pT, which is already on par with the most sensitive devices relying on self-compensation.

The freely jointed chain model with reversible hinges (rFJC) is the simplest theoretical model that captures reversible transitions of the local bending stiffness along the polymer chain backbone, e.g. helix-coil-type of local conformational changes or changes due to the binding/unbinding of ligands). In this work, we analyze the bending fluctuations and the bending response of a grafted rFJC in the Gibbs (fixed-force) ensemble. We obtain a recursion relation for the partition function of the grafted rFJC under bending force, which allows, in principle, exact-numerical calculation of the behavior of a rFJC of arbitrary size. In contrast to stretching, we show that under sufficiently stiff conditions, the differential bending compliance and the mean fraction of closed hinges are non-monotonic functions of the force. We also obtain the persistence length $L_p$ of the rFJC, the moments $\langle R^2 \rangle$ (mean-square end-to-end distance), and $\langle z^2 \rangle$ (mean-square transverse deflection) for the discrete chain and take the continuum limit. The tangent vector auto-correlation decays exponentially, as in the wormlike chain model (WLC). Remarkably, the expression of $\langle R^2 \rangle$ as a function of the contour length $L$ becomes the same as that in the WLC. In the thermodynamic limit, we have calculated the exact bending response analytically. As expected, for $L\gg L_p$, the boundary conditions do not matter, and the bending becomes equivalent to stretching. In contrast, for $L_p\gg L$, we have shown the non-monotonicity of the bending response (the compliance and mean fraction of closed hinges).

The Earth's weather system encompasses intricate weather data modalities and diverse weather understanding tasks, which hold significant value to human life. Existing data-driven models focus on single weather understanding tasks (e.g., weather forecasting). Although these models have achieved promising results, they fail to tackle various complex tasks within a single and unified model. Moreover, the paradigm that relies on limited real observations for a single scenario hinders the model's performance upper bound. In response to these limitations, we draw inspiration from the in-context learning paradigm employed in state-of-the-art visual foundation models and large language models. In this paper, we introduce the first generalist weather foundation model (WeatherGFM), designed to address a wide spectrum of weather understanding tasks in a unified manner. More specifically, we initially unify the representation and definition of the diverse weather understanding tasks. Subsequently, we devised weather prompt formats to manage different weather data modalities, namely single, multiple, and temporal modalities. Finally, we adopt a visual prompting question-answering paradigm for the training of unified weather understanding tasks. Extensive experiments indicate that our WeatherGFM can effectively handle up to ten weather understanding tasks, including weather forecasting, super-resolution, weather image translation, and post-processing. Our method also showcases generalization ability on unseen tasks.

Over the years, various scenarios -- such as the stability-limit conjecture (SLC), two critical point (TCP), critical point-free (CPF), and singularity-free (SF) -- have been proposed to explain the thermodynamic origin of supercooled waters anomalies. However, direct experimental validation is challenging due to the rapid phase transition from metastable water. In this study, we explored whether the phase transition pathways from metastable water provide insight into the thermodynamic origin of these anomalies. Using a classical density functional theory approach with realistic theoretical water models, we examined how different thermodynamic scenarios influence vapor nucleation kinetics at negative pressures. Our findings show significant variations in nucleation kinetics and mechanism during both isobaric and isochoric cooling. In the TCP scenario, the nucleation barrier increases steadily during isobaric cooling, with a slight decrease near the Widom line at lower temperatures (Ts). In contrast, the SF scenario shows a monotonic increase in the nucleation barrier. For the CPF scenario, we observed a non-classical mechanism, such as wetting-mediated nucleation (where the growing vapor nucleus is wetted by the intermediate low-density liquid phase) and the Ostwald step rule at low temperatures. Isochoric cooling pathways also revealed notable differences in T-dependent nucleation barrier trends between the TCP and CPF scenarios. Overall, this study underscores the importance of analyzing phase transition kinetics and mechanism to understand the precise thermodynamic origin of supercooled waters anomalies.

We study the nonlinear inverse source problem of detecting, localizing and identifying unknown accidental disturbances on forced and damped transmission networks. A first result is that strategic observation sets are enough to guarantee detection of disturbances. To localize and identify them, we additionally need the observation set to be absorbent. If this set is dominantly absorbent, then detection, localization and identification can be done in "quasi real-time". We illustrate these results with numerical experiments.

In this paper, we revisit the concept of noncommuting common causes; refute two objections raised against them, the triviality objection and the lack of causal explanatory force; and explore how their existence modifies the EPR argument. More specifically, we show that 1) product states screening off all quantum correlations do not compromise noncommuting common causal explanations; 2) noncommuting common causes can satisfy the law of total probability; 3) perfect correlations can have indeterministic noncommuting common causes; and, as a combination of the above claims, 4) perfect correlations can have noncommuting common causes which are both nontrivial and satisfy the law of total probability.

Planetesimal formation models often invoke the gravitational collapse of pebble clouds to overcome various barriers to grain growth and propose processes to concentrate particles sufficiently to trigger this collapse. On the other hand, the geochemical approach for planet formation constrains the conditions for planetesimal formation and evolution by providing temperatures that should be reached to explain the final composition of planetesimals, the building blocks of planets. To elucidate the thermal evolution during gravitational collapse, we used numerical simulations of a self-gravitating cloud of particles and gas coupled with gas drag. Our goal is to determine how the gravitational energy relaxed during the contraction is distributed among the different energy components of the system, and how this constrains a thermal and dynamical planetesimal's history. We identify the conditions necessary to achieve a temperature increase of several hundred kelvins, and as much as 1600 K. Our results emphasise the key role of the gas during the collapse.

Clarifying the physical origin of valley polarization and exploring new ferrovalley materials are conducive to the application of valley degrees of freedom in the field of information storage. Here, we explore two new-type altermagnetic semiconductors (monolayers Nb2Se2O and Nb2SeTeO) with above-room-temperature N\'eel temperature based on first-principles calculations. It reveals that uniaxial strain induces valley polarization without spin-orbital coupling (SOC) in altermagnets owing to the piezovalley effect, while uniaxial compressive strain transforms from ferrovalley semiconductor to semimetal, half metal and metal. Moreover, moderate biaxial strain renders Janus monolayer Nb2SeTeO to robust Dirac-like band dispersion. The SOC and intrinsic in-plane magnetocrystalline anisotropy energy induces Dirac-like altermagnets to generate apparent valley polarization through magnetovalley coupling. In terms of SOC perturbation, we elucidate the physical mechanism behind in-plane-magnetization induced valley polarization and point out the magnitude of valley polarization is positively correlated with the square of SOC strength and negatively correlated with the bandgap. The present work reveals the physical origin of valley polarization in altermagnets and expands the application of ferrovalley at room temperature in valleytronics.

This paper presents a deep reinforcement learning (DRL) framework for active flow control (AFC) to reduce drag in aerodynamic bodies. Tested on a 3D cylinder at Re = 100, the DRL approach achieved a 9.32% drag reduction and a 78.4% decrease in lift oscillations by learning advanced actuation strategies. The methodology integrates a CFD solver with a DRL model using an in-memory database for efficient communication between

Quantum magnetometers based on spin defects in solids enable sensitive imaging of various magnetic phenomena, such as ferro- and antiferromagnetism, superconductivity, and current-induced fields. Existing protocols primarily focus on static fields or narrow-band dynamical signals, and are optimized for high sensitivity rather than fast time resolution. Here, we report detection of fast signal transients, providing a perspective for investigating the rich dynamics of magnetic systems. We experimentally demonstrate our technique using a single nitrogen-vacancy (NV) center magnetometer at room temperature, reaching a best-effort time resolution of 1.1 ns, an instantaneous bandwidth of 0.9 GHz, and a time-of-flight precision better than 20 ps. The time resolution can be extended to the picosecond range by use of on-chip waveguides. At these speeds, NV quantum magnetometers will become competitive with time-resolved synchrotron X-ray techniques. Looking forward, adding fast temporal resolution to the spatial imaging capability further promotes single-spin probes as powerful research tools in spintronics, mesoscopic physics, and nanoscale device metrology.

This study aimed to investigate the evolutionary dynamics of a three-strategy game that combines snowdrift and stag hunt games. This game is motivated by an experimental study, which found that individual solution lowers cooperation levels. Agents adopting this option aim to address a problem to the extent necessary to remove negative impact on themselves, although they do not free ride on cooperation effort provided by others. This property of the individual solution is similar to that of option defection in the stag hunt. Thus, the role of the interplay of defection in the snowdrift game and individual solution was examined in this study. The well-mixed population has two asymptotically stable rest points, one wherein the individual solution occupies the population, and the other wherein cooperation and defection coexist. The interactions on a square lattice enlarge the parameter region wherein cooperation survives, and the three strategies often coexist. The scrutinization of the evolutionary process shows that multiple mechanisms lead to the coexistence of the three strategies depending on parameter values. Our analysis suggests that considering the individual solution adds complexity to the evolutionary process, which might contribute to our understanding on the evolution of cooperation.

We employ deep reinforcement learning methods to investigate shortest-time navigation strategies for smart active Brownian particles (agents) that self-propel through a rotating, localized potential barrier in an otherwise static and viscous fluid background. The particle motion is prescribed to begin from a specified start point and terminate at a specified destination point. The potential barrier is modeled as a repulsive Gaussian potential with a finite range that rotates with given angular velocities around a fixed center within the plane of motion. We use an advantage actor-critic approach to train the agents and demonstrate that, by leveraging this approach, the rotating potential can be utilized for size-based sorting and separation of the agents. In other words, agents of different (hydrodynamic) radii reach the end-point at sufficiently well-separated times, enabling their separation. The efficiency of particle separation in this context is discussed using specific criteria. We also study the effect of rotational Brownian noise on the quality of the proposed size-based sorting mechanism. Additionally, we demonstrate how the use of noise-induced training can enhance this mechanism in a noisy background within the range of parameters explored in our work. Reinforcement learning in the context of active particles thus offers promising avenues to unravel optimal navigation strategies within complex environments, with potential applications that can be utilized in microfluidic settings.

A critical component of particle acceleration in astrophysical shocks is the non-resonant (Bell) instability, where the streaming of cosmic rays (CRs) leads to the amplification of magnetic fields necessary to scatter particles. In this work we use kinetic particle-in-cells simulations to investigate the high-CR current regime, where the typical assumptions underlying the Bell instability break down. Despite being more strongly driven, significantly less magnetic field amplification is observed compared to low-current cases, an effect due to the anisotropic heating that occurs in this regime. We also find that electron-scale modes, despite being fastest growing, mostly lead to moderate electron heating and do not affect the late evolution or saturation of the instability.

This work investigates the solid-state reaction between iridium and zirconium carbide, resulting in the formation of carbon and $\mathrm{ZrIr}_{3}$ -- an intermetallic compound of great interest for modern high-temperature materials science. We have found a transition of kinetic regimes in this reaction: from linear kinetics (when the chemical reaction is a limiting stage) at 1500 and 1550{\deg}C to `non-parabolic kinetics' at 1600{\deg}C. Non-parabolic kinetics is characterized by thickness of a product layer being proportional to a power of time less than 1/2. The nature of non-parabolic kinetics was still an open question, which motivated us to develop a model of this kinetic regime. The proposed model accounts for the grain growth in the product phase and how it leads to the time dependence of the interdiffusion coefficient. We have obtained a complete analytic solution for this model and an equation that connects the grain-growth exponent and the power-law exponent of non-parabolic kinetics. The measurements of the thickness of the product layer and the average grain size of the intermetallic phase confirm the results of the theoretical solution.

The adsorption of charged clay nanoplatelets plays an important role in stabilizing emulsions by forming a barrier around the emulsion droplets and preventing coalescence. In this work, the adsorption of charged clay nanoplatelets on a preformed Latex microsphere in an aqueous medium is investigated at high temporal resolution using optical tweezer-based single-colloid electrophoresis. Above a critical clay concentration, charged clay nanoplatelets in an aqueous medium self-assemble gradually to form gel-like networks that become denser with increasing medium salinity. In a previous publication [R. Biswas et. al., Soft Matter, 2023, 19, 24007-2416], some of us had demonstrated that a Latex microsphere, optically trapped in a clay gel medium, is expected to attach to the network strands of the gel. In the present contribution, we show that for different ionic conditions of the suspending medium, the adsorption of clay nanoplatelets increases the effective surface charge on an optically trapped Latex microsphere while also enhancing the drag experienced by the latter. Besides the ubiquitous contribution of non-electrostatic dispersion forces in driving the adsorption process, we demonstrate the presence of an electrostatically-driven adsorption mechanism when the microsphere was trapped in a clay gel. These observations are qualitatively verified via cryogenic field emission scanning electron microscopy and are useful in achieving colloidal stabilisation, for example, during the preparation of clay-armoured Latex particles in Pickering emulsion polymerisation.

We report on the emergence of a highly non-classical collective behavior in quantum parametric oscillators, which we name quantum hyperspin, induced by a tailored nonlinear interaction. This is the second quantized version of classical multidimensional spherical spins, as XY spins in two dimensions, and Heisenberg spins in three dimensions. In the phase space, the quantum hyperspins are represented as spherical shells whose radius scales with the number of particles in a way such that it cannot be factorized even in the limit of large particle number. We show that the nonlinearly coupled quantum oscillators form a high-dimensional entangled state that is surprisingly robust with respect to the coupling with the environment. Such a behavior results from a properly engineered reservoir. Networks of entangled quantum hyperspins are a new approach to quantum simulations for applications in computing, Ising machines, and high-energy physics models. We analyze from first principles through ab initio numerical simulations the properties of quantum hyperspins, including the interplay of entanglement and coupling frustration.

A numerical scheme is presented for solving the Helmholtz equation with Dirichlet or Neumann boundary conditions on piecewise smooth open curves, where the curves may have corners and multiple junctions. Existing integral equation methods for smooth open curves rely on analyzing the exact singularities of the density at endpoints for associated integral operators, explicitly extracting these singularities from the densities in the formulation, and using global quadrature to discretize the boundary integral equation. Extending these methods to handle curves with corners and multiple junctions is challenging because the singularity analysis becomes much more complex, and constructing high-order quadrature for discretizing layer potentials with singular and hypersingular kernels and singular densities is nontrivial. The proposed scheme is built upon the following two observations. First, the single-layer potential operator and the normal derivative of the double-layer potential operator serve as effective preconditioners for each other locally. Second, the recursively compressed inverse preconditioning (RCIP) method can be extended to address "implicit" second-kind integral equations. The scheme is high-order, adaptive, and capable of handling corners and multiple junctions without prior knowledge of the density singularity. It is also compatible with fast algorithms, such as the fast multipole method. The performance of the scheme is illustrated with several numerical examples.