Papers

Magnetic particle imaging (MPI) is a tracer-based imaging modality that detects superparamagnetic iron oxide nanoparticles in vivo, with applications in cancer cell tracking, lymph node mapping, and cell therapy monitoring. We introduce a new 3D image reconstruction framework for MPI data acquired using multi-angle field-free line (FFL) scans, demonstrating improvements in spatial resolution, quantitative accuracy, and high dynamic range performance over conventional sequential reconstruction pipelines. The framework is built by combining a physics-based FFL signal model with tomographic projection operators to form an efficient 3D forward operator, enabling the full dataset to be reconstructed jointly rather than as a series of independent 2D projections. A harmonic-domain compression step is incorporated naturally within this operator formulation, reducing memory overhead by over two orders of magnitude while preserving the structure and fidelity of the model, enabling volumetric reconstructions on standard desktop GPU hardware in only minutes. Phantom and in vivo results demonstrate substantially reduced background haze and improved visualization of low-intensity regions adjacent to bright structures, with an estimated $\sim$11$\times$ improvement in iron detection sensitivity relative to the conventional X-space CT approach. These advances enhance MPI image quality and quantitative reliability, supporting broader use of MPI in preclinical and future clinical imaging.

The convergence of statistical learning and molecular physics is transforming our approach to modeling biomolecular systems. Physics-informed machine learning (PIML) offers a systematic framework that integrates data-driven inference with physical constraints, resulting in models that are accurate, mechanistic, generalizable, and able to extrapolate beyond observed domains. This review surveys recent advances in physics-informed neural networks and operator learning, differentiable molecular simulation, and hybrid physics-ML potentials, with emphasis on long-timescale kinetics, rare events, and free-energy estimation. We frame these approaches as solutions to the "biomolecular closure problem", recovering unresolved interactions beyond classical force fields while preserving thermodynamic consistency and mechanistic interpretability. We examine theoretical foundations, tools and frameworks, computational trade-offs, and unresolved issues, including model expressiveness and stability. We outline prospective research avenues at the intersection of machine learning, statistical physics, and computational chemistry, contending that future advancements will depend on mechanistic inductive biases, and integrated differentiable physical learning frameworks for biomolecular simulation and discovery.

Networks of radiation detectors provide a platform for real-time radioactive source detection and identification in urban environments. Detection algorithms in these systems must adapt to naturally-occurring changes in background, which requires well-characterized relationships between precipitation events and their corresponding radiological signature. We present a description of rain-induced radon progeny deposition events occurring in Chicago from September 2023 through February 2024. We measure ambient gamma radiation levels, precipitation rate, temperature, pressure and relative humidity in a network of sensor nodes. For each identified precipitation period, we decompose spectra into static- and radon-associated components as defined by a non-negative matrix factorization (NMF) algorithm. We find a consistent power-law relationship between a precipitation-dependent peak of the radon progeny proxy (RPP) and the peak strength of radon-associated NMF component for most precipitation events. We conduct a case study of a rainfall period with abnormally high levels of implied radon progeny concentration and describe its temporal and spatial evolution. We hypothesize this phenomenon is due to the air mass path that intersects a uranium-rich region of Wyoming. Finally, we cluster precipitation events into three distinct categories. One category roughly corresponds to events with deep low-pressure systems and high relative radon concentration, while another is characteristic of light stratiform rain with slightly higher temperatures and intermediate relative radon concentration. The third category contains a weak-gradient or lake breeze convection showers with intermittent precipitation and low relative radon concentration. These findings suggest that radiological anomaly detection could be improved by training unique background models corresponding to each category of meteorological event.

At a wind farm in Croatia, lightning activity is monitored across the entire site using a lightning location system, and on a single wind turbine equipped with a Rogowski-coil-based current measurement system and a high-speed camera, all independently GPS-synchronized. In addition to recording lightning flash currents on the monitored turbine, the system is frequently triggered by electromagnetic disturbances caused by nearby lightning flashes. These include direct flashes to two neighboring turbines that share the same cable connection to the substation and have interconnected grounding systems with buried bare conductor, as well as cloud-to-ground flashes to soil near cable routes, where the resulting electromagnetic fields couple onto the cables, causing surges to propagate to the monitored turbine. The camera occasionally captures failed upward connecting leaders from the monitored turbine during these lightning events. This paper presents three cases of flashes to two neighboring wind turbines and two cases of cloud-to-ground flashes to nearby soil, all of which induced electromagnetic transients that propagated to the monitored turbine. Failed upward connecting leaders were observed in some of these cases. This paper provides observational analysis, providing Rogowski measurements of electromagnetic disturbances and failed leader currents, complemented by high-speed camera and lightning location system data.

This study investigates temporal variability in U.S. climate using harmonic decomposition techniques, specifically Fourier and wavelet transforms. Monthly temperature, precipitation, and drought index data from the National Oceanic and Atmospheric Administration (NOAA) U.S. Climate Divisional Dataset (nClimDiv, 1895–2024) were analyzed to detect periodic structures and their evolution over time. By comparing harmonic-based models with linear regression trends, this research evaluates the explanatory power of cyclic components in reproducing and predicting observed variability. Results show that U.S. climate records exhibit dominant periodicities near one year (seasonal) and 2–7 years (associated with the El Nino–Southern Oscillation, ENSO), and that incorporating harmonic terms significantly improves model performance across most states and variables. The findings indicate that U.S. climate fluctuations are characterized by quasi-stationary oscillations rather than purely monotonic trends. Overall, the main implication is that frequency-aware models provide measurably better predictive skill than trend-only approaches and should be incorporated into seasonal outlooks, drought monitoring, and resource planning.

Multiphase flow simulation is critical in science and engineering but incurs high computational costs due to complex field discontinuities and the need for high-resolution numerical meshes. While Neural Operators (NOs) offer an efficient alternative for solving Partial Differential Equations (PDEs), they struggle with two core challenges unique to multiphase systems: spectral bias caused by spatial heterogeneity at phase interfaces, and the persistent scarcity of expensive, high-resolution field data. This work introduces the Interface Information Aware Neural Operator (IANO), a novel architecture that mitigates these issues by leveraging readily obtainable interface data (e.g., topology and position). Interface data inherently contains the high-frequency features not only necessary to complement the physical field data, but also help with spectral bias. IANO incorporates an interface-aware function encoding mechanism to capture dynamic coupling, and a geometry-aware positional encoding method to enhance spatial fidelity for pointwise super-resolution. Empirical results across multiple multiphase flow cases demonstrate that IANO achieves significant accuracy improvements (up to $\sim$10%) over existing NO baselines. Furthermore, IANO exhibits superior generalization capabilities in low-data and noisy settings, confirming its utility for practical, data-efficient $\text{AI}$-based multiphase flow simulations.

From the non-equilibrium thermodynamic perspective of the origin of life as a photochemical dissipative structuring (entropy driven) process, we assess the probability of carbon-based life arising on Earth-like analogues orbiting different main-sequence stellar types (O7 V to M2 V). Using black-body spectra normalized to Earth's solar constant, we calculate surface photon fluxes for an atmosphere like early Archean Earth's in the productive dissipative structuring (P, soft UV-C + UV-B, 205-320 nm) and destructive ionization (D, hard UV-C + EUV, <205 nm) regions. Stationary concentrations of fundamental molecules and times to reach 99% of these are computed for different chemical degradation (e.g., deamination, hydrolysis, oxidation, etc.) rate constants of k = 10^{-7}, 10^{-6}, and 10^{-5} s^{-1}. For a nominal chemical degradation rate constant of k = 10^{-6} s^{-1} (t_{1/2}= 8 days), results show F-, G-, and K-type stars provide the highest stationary concentrations of fundamental molecules and short rise times (weeks to months), while quiescent M-type stars yield extremely low concentrations (~10^{-7} relative to G stars) and require years to reach even these values. Flaring M stars improve stationary concentrations by about an order of magnitude (~10^{-6} relative to G stars) but produce adverse planet surface environments for complex evolution through dissipative structuring. From this non-equilibrium thermodynamic perspective, carbon-based life like Earth's is to be found most probably on F-, G-, and high-mass K-type stars, with intelligent life arising only on G-type stars. Low mass K- and M-dwarfs are highly unlikely to harbor life unless seeded via panspermia. Biosignatures related to the thermodynamic imperative of photon dissipation are proposed.

Semiconductor lasers merge coherent light emission with photodetection and, owing to third-order nonlinearities in their active region, function as sensitive room temperature two-photon absorption (TPA) detectors. Here, we leverage these capabilities offered by a commercially available InGaAsP semiconductor laser diode with an integrated InGaAs monitor photodiode to measure the duration of femtosecond pulses at telecom wavelengths. The nonlinear and linear signals obtained from both detectors enable the retrieval of pulse duration with femtosecond accuracy without moving parts. This capability is demonstrated down to 2 $\mu$W average optical power for a 250 MHz repetition rate laser.

The CICLoPE facility at the University of Bologna in Forli, Italy, is a unique facility that provides fully developed pipe flow up to Reynolds numbers of about $Re_\tau$ of 50,000 with exceptional spatial resolution and stable operating conditions. Measurements obtained over the last two years, on the centerline of the pipe in the fully developed test section, with Pitot probes for the mean velocity in the stream ($\pm 0.2\%$ precision) and hot wires for the intensity of turbulence in the streamwise and the normal stress ( $\pm~5\%$ precision) are reported here and compared to other experimental results and some recent direct numerical simulation (DNS) data. The comparisons reveal that high Reynolds number conditions are only reached when $Re_\tau$ is at least larger than $10,000$. The centerline von Kàrmàn constant, $\kappa_{CL}$ for pipe flow is confirmed with high confidence to be $0.44$. Unlike previously reported measurements on the pipe centerline of the streamwise turbulence intensity and normal stress reported in the literature with various conflicting trends, both quantities are found in CICLoPE to reach a level trend beyond $Re_\tau$ around $25,000$ with values of approximately $2.9\%$ and $0.85$, respectively. Normalized streamwise velocity spectra on the centerline, with high resolution over seven decades of energy, exhibit a clear and extended $-5/3$ inertial range. Skewness and kurtosis of streamwise velocity are found to be independent of Reynolds number even down to $Re_\tau = 5,000$ and equal to $-0.5$ and $3.5$, respectively.

We determine the resonating-valence-bond (RVB) state in graphene using real-space quantum Monte Carlo with correlated variational wave functions. Variational and diffusion quantum Monte Carlo (DMC) calculations with Jastrow-Slater-determinant and Jastrow-antisymmetrized-geminal-power ansatze are employed to evaluate the RVB pairing energy. Using a rectangular graphene sample that lacks $\pi/3$ rotational symmetry, we found that the single-particle energy gap near the Fermi level depends on the system size along the $x$-direction. The gap vanishes when the length satisfies $L_x=3n\sqrt{3}d$, where $n$ is an integer and $d$ is the carbon-carbon bond length, otherwise, the system, exhibits a finite gap. Our DMC results show no stable RVB pairing in the zero-gap case, whereas the opening of a finite gap near the Fermi level stabilizes the electron pairing. The DMC predicted absolute value of pairing energy at the thermodynamic limit for a finite-gap system is $\sim 0.48(1)$ mHa/atom. Our results reveal a feometry-driven electron pairing mechanism in the confined graphene nanostructure.

The black hole shadow, a direct probe of the event horizon's gravitational influence, has been observationally confirmed by the Event Horizon Telescope (EHT). While theoretical models of shadows in vacuum are mature, real astrophysical black holes like M87* and Sgr A* are enveloped in plasma, which can alter photon trajectories through dispersion. Current understanding, based on foundational work, indicates that only specific plasma distributions allow for an analytical treatment via the separation of the Hamilton-Jacobi equation. In this work, we build upon this framework to systematically investigate the propagation of light rays in Kerr spacetime surrounded by a pressureless, non-magnetized cold plasma. We explicitly derive the separability condition, identifying the exact class of plasma densities that permit a generalized Carter constant. For these models, we compute the photon regions and shadow boundaries, characterizing how the shadow's size and shape deviate from the vacuum case in a frequency-dependent manner. Our results provide analytical benchmarks for the distortion of shadows in dispersive media and determine the critical plasma frequency beyond which the shadow is erased, offering a direct link between observable shadow features and the properties of the ambient plasma environment and providing a foundation for studying more dynamic, non-separable plasma distributions.

We consider the integrable dynamics of a Kepler billiard in the plane bounded by a branch of a conic section focused at the Kepler center. We show that in this case, for non-zero-energy orbits, the lines of consecutive second orbital foci along a billiard trajectory are all tangent to a fixed circle. Based on this observation we analyse in details the integrable dynamics of a planar Kepler billiard inside or outside an elliptic reflection wall, with the Kepler center occupying one of its foci. We identify the associated elliptic curve on which the dynamics is linearized, and the shift defined thereon. We also discuss explicit conditions on $n$-periodicity using Cayley's criteria.

The interdisciplinary field of astrochemistry arose during the 1970s as observations in previously unexplored parts of the electromagnetic spectrum began to reveal the extent of a molecular component of interstellar matter with a surprisingly rich chemistry. Astrochemistry expanded further in order to explain the role of atomic and molecular processes in a broad range of phenomena in the universe. It is instructive to describe the current scope of astrochemistry using the career and accomplishments of Alexander Dalgarno as an organizing principle. Dalgarno helped to establish a self-sustaining community of astrochemists around the world. His own research interests highlight the early development of astrochemistry and anticipate much of its later evolution. His theoretical investigations of fundamental atomic and molecular processes lie at the heart of the subject.

Equal-time scaling exponents in fully developed turbulence typically exhibit non anomalous scaling in the inverse cascade of two-dimensional (2D) turbulence and anomalous scaling in three dimensions. We demonstrate that multiscaling is not confined to longitudinal, scalar velocity increments, but also emerges in increments associated with the magnitude and orientation of the velocity vector. This decomposition uncovers a multiscaling in the 2D inverse cascade, which remains obscured when using conventional structure functions. Our results highlight a decoupling between velocity amplitude and flow geometry, offering new insight into the statistical structure of turbulent cascades as well as showing how different classes of multiscaling emerge.

Replica symmetry breaking (RSB) underlies the complex organization of disordered systems, yet quantitative validation beyond $N \sim 100$ spins has remained computationally challenging. We use quantum annealing to access ground states of the Sherrington-Kirkpatrick model up to $N = 4000$ spins, enabling the most extensive test of Parisi's Nobel Prize-winning RSB solution to date. Five independent observables confirm RSB predictions: ground-state energies converge to Parisi's value with characteristic $N^{-2/3}$ corrections, energy fluctuations scale as $N^{-3/4}$ ($\gamma = 0.739 \pm 0.036$), the chaos exponent $\theta = 0.51 \pm 0.02$ ($R^2 = 0.989$) confirms mean-field universality, the overlap distribution exhibits hierarchical structure ($\sigma_q = 0.19$), and the complexity remains invariant under 36% network dilution. Beyond a critical threshold $0.8 < D_c < 0.9$, the hierarchy collapses discontinuously through a cooperative avalanche that converts the entire system to vacancies within a narrow parameter window $\Delta D = 0.1$. These findings establish quantum computation as a tool for probing emergent many-body phenomena and uncover the topological foundations of complexity in disordered systems, with implications for neural networks, optimization, and materials science.

Magnetoencephalography measures the magnetic fields generated by neural currents using quantum sensors such as superconducting quantum interference devices and atomic magnetometers. Here we combine the energy resolution limit of magnetic sensing with the metabolic power available to neural currents to derive a technology-independent bound on the information capacity of MEG. The bound factorizes into geometry, metabolism, and Planck's constant, and gives an estimated maximum information rate of 2.2~Mbit/s for representative human-brain parameters. Further, we show that the externally measurable magnetic field has a finite angular bandwidth, with high multipole components being geometrically attenuated and falling below the quantum-limited noise floor. This yields an information-limited spatial scale of order $1~{\rm cm}$ and renders the accessible measurement space effectively finite-dimensional. The energy resolution limit therefore defines an information-theoretic Nyquist scale for magnetoencephalography, beyond which denser spatial sampling provides redundant measurements rather than additional recoverable information. Since the energy resolution limit also makes the noise variance grow linearly with measurement bandwidth, temporal and spatial bandwidths compete, producing a fundamental spatio-temporal trade-off. These results show how quantum-limited measurements constrain the observable complexity and information content of noninvasive brain imaging, providing a quantitative link between fundamental physics and neuroscience.

Epidemics expose critical tensions between protecting public health and maintaining essential urban mobility. Public transport systems face this dilemma most acutely: they enable access to jobs, education, and services, yet also facilitate close contact among travelers. We develop an integrated modeling framework that couples agent-based epidemic simulation (EpiSim) with an optimization-based public transport flow model under capacity constraints. Using Munich as a case study, we analyze how combinations of facility closures and transport restrictions shape epidemic outcomes and accessibility. The results reveal three key insights. First, epidemic interventions redistribute rather than simply reduce infection risks, shifting transmission to households. Second, epidemic and transport policies interact nonlinearly - moderate demand suppression can offset large capacity cuts. Third, epidemic pressures amplify temporal and spatial inequalities, disproportionately affecting peripheral and peak-hour travelers. These findings highlight that blanket restrictions are both inefficient and inequitable, calling for targeted, time- and space-differentiated measures to build epidemic-resilient and socially fair transport systems.

We discuss the conceptual design of a spatially-resolved spectroscopy system of Thomson scattering with high wavelength resolution capable of measuring the shape of electron velocity distribution functions in magnetically confined plasmas. We design a spatially-resolved spectrometer with 2560 wavelength channels. The estimated number of scattered photons in a single spectrometer channel is much larger than unity under the experimental setup and plasma parameters of the Compact Helical Device (CHD), indicating sufficient photon statistics for single-shot measurements. Simulations of the scattered spectra show that the signal-to-noise ratio exceeds 5 even under the most unfavorable conditions expected in CHD at full spectral resolution, and further improves with post-processing pixel binning. Bayesian inference applied to the simulated spectra demonstrates that the inferred plasma parameters agree with the input values within the estimated uncertainties. Comparisons between spectra generated from non-Maxwellian electron velocity distribution functions and their Maxwellian fits indicate that deviations from Maxwellian distributions can be identified using the proposed system.

The process of preparing heterogeneous catalysts on porous supports includes a drying stage, in which the porous material, impregnated with an aqueous solution of the catalyst precursor, is dried, and the precursor is precipitated on the pore walls. The precipitate distribution throughout the support volume strongly influences the catalyst performance and durability within industrial reactors. This paper presents a mathematical model for simulating the precipitation distribution during the drying of a porous sphere. Examples of calculations are given demonstrating the influence of some parameters on the distribution by space and by pore size.17

This article answers the question of whether homogenization of discrete fine-scale mechanical models, such as particle or lattice models, gives rise to an equivalent continuum that is of Cauchy-type or Cosserat-type. The study employs the machinery of asymptotic expansion homogenization to analyze discrete mechanical models with rotational degrees of freedom commonly used to simulate the mechanical behavior of heterogeneous solids. The proposed derivation has general validity in both stationary (steady-state) and transient conditions (assuming wavelength much larger that particle size) and for arbitrary nonlinear, inelastic fine-scale constitutive equations. The results show that the unit cell problem is always stationary, and the only inertia term appears in the linear momentum balance equation at the coarse scale. Depending on the magnitude of the local bending stiffness, mathematical homogenization rigorously identifies two limiting conditions that correspond to the Cauchy continuum and the Cosserat continuum. A heuristic combination of these two limiting conditions provides very accurate results also in the transition from one limiting case to the other. Finally, the study demonstrates that cases for which the Cosserat character of the homogenized response is significant are associated with non-physically high fine-scale bending stiffness and, as such, are of no interest in practice.

Thermoacoustic tomography (TAT) is an imaging technique based on the thermoacoustic effect, combining electromagnetic contrast and high resolution of ultrasound imaging. In TAT, a short micro- or radio wave pulse is directed to the imaged target. Energy of this pulse is absorbed depending on the dielectric parameters of the target, resulting in a spatially varying pressure distribution via the thermoacoustic effect. This pressure, known as the initial pressure distribution, propagates as acoustic waves that are measured on the boundary of the target using ultrasound sensors. In the inverse problem of TAT, the initial pressure is estimated from the measured ultrasound waves. TAT can further be extended to quantitative TAT (QTAT), where the aim is to estimate the dielectric parameters of the target from the measured ultrasound waves, utilizing a model for electromagnetic wave propagation. In this work, we study the inverse problem of QTAT, and propose an approach for simultaneous estimation of electrical conductivity and permittivity from the ultrasound waves. This problem is approached in the framework of Bayesian inverse problems, enabling incorporation of prior and noise models. The forward model describing electromagnetic and acoustic wave propagation is based on the Maxwell's equations and the acoustic wave-equation, respectively. The approach is evaluated with numerical simulations. The results show that the dielectric parameters can be estimated using the proposed approach with good precision. However, the ultrasound sensor geometry and the number of electromagnetic pulses have a significant effect on the accuracy of the estimated parameters.

We derive the beam tracing and profile evolution for the propagation of any localised beam with arbitrary profile through an inhomogeneous cold plasma. We recover standard Gaussian beam-tracing, with an additional PDE describing the evolution of the beam's profile as it propagates through the plasma. We then solve for generic families of solutions to the PDE using ladder operators, which can be chosen to reduce to Gauss-Hermite beams in homogeneous media. We importantly obtain an exact expression for the resulting beam profile, demonstrating that Hermite modes will generally evolve into a superposition of different modes during propagation through inhomogeneous plasmas, contrary to prior work on the subject. Importantly, this approach facilitates future analysis of the diagnostic signal received from arbitrary beams.

We present an isothermal Global Buckley–Leverett framework for multicomponent, multiphase flow in porous and fractured media that retains the interpretability of classical Buckley–Leverett while incorporating essential physics: equation of state-based phase behavior, multicomponent Maxwell–Stefan diffusion, dynamic capillarity, stress-sensitive permeability, and non-Darcy fracture flow. The formulation yields a single global-pressure equation driving the total Darcy flux and an exact fractional-flow decomposition of phase velocities with buoyancy and capillary drifts; inertial effects enter as per-phase damping that renormalizes mobilities. Crucially, the combination of Maxwell–Stefan diffusion and dynamic capillarity renders transport pseudo-parabolic, resolving the loss of strict hyperbolicity that plagues three-phase Buckley–Leverett and ensuring a well-posed initial-value problem. In practice, each time step solves the scalar global-pressure equation, reconstructs phase fluxes via the split, and advances strictly conservative component balances; axisymmetric (cylindrical) forms for radial injection with vertical buoyancy are provided. The model reduces exactly to classical Buckley–Leverett when added physics are disabled, making it a practical backbone for carbon storage, geothermal exchange, and contaminant transport in fractured, compositionally complex reservoirs.

Dielectric resonant metasurfaces can be utilized for efficient high harmonic generation, which has been explored here in this effort for a high-efficient Germanium metasurface for upconverted full-Stokes imaging at 1.33-micron wavelength by the third harmonic generation (THG) from the 4-micron fundamental wavelength. The internal TH conversion efficiency for the Ge film is about 1x10-5 % to 5.6x10-3 % and for the Ge-metasurface 1.5x10-4 % to 1.05x10-1 % for linearly polarized (LP) incidence, and 1.3x10-4 % to 6.1x10-2 % for circularly polarized (CP) incidence, for the optical intensity range 0.47 GW/cm2 to 16.8 GW/cm2 respectively. The effect of the Ge film height variation has been discussed and compared with transmission line theory for the nonlinear medium. The metasurface design pitch is suitably tuned for the anisotropic cross-triangles nanoantenna for analyzing the polarization states of the fundamental beam simultaneously. The effect of source intensity on TH conversion among the LP and CP states, flipping the nonlinear diffraction orders and incipient of new harmonics with intensity dependence and design limitations, has also been addressed. To my knowledge, with this type of metasurface design, this is the first approach for upconverted full-Stokes imaging. The benefit will be upconverted polarimetry of MWIR at SWIR, where uncooled efficient detectors are available for high-resolution thermal imaging. This efficient Ge-metasurface is exempt from TH transmission loss due to the size effect of assigning a phase gradient for polarization-dependent TH diffraction order generation and orthogonal transmission disparity. Integration of this type of metasurface for infrared image upconversion will open new possibilities for intense heat signature identification, especially for target recognition in next-generation infrared homing devices in surveillance and missile defense systems.

Traditional cosmological hydrodynamical simulations usually assume equal-numbered but unequal-mass dark matter and baryonic particles, which can lead to spurious collisional heating due to energy equipartition. To avoid such a numerical heating effect, a simulation setup with equal-mass dark matter and baryonic particles, which corresponds to a particle number ratio of $N_{\rm DM}:N_{\rm gas} = \Omega_{\rm cdm} / \Omega_{\rm b}$, is preferred. However, previous studies have typically used grid-based particle loads to prepare such initial conditions, which can only reach specific values for $N_{\rm DM}:N_{\rm gas}$ due to symmetry requirements. In this study, we propose a method based on the glass approach that can generate two-component particle loads with more general $N_{\rm DM}:N_{\rm gas}$ ratios. The method simultaneously relaxes two Poisson particle distributions by introducing an additional repulsive force between particles of the same component. We show that the final particle load closely follows the expected minimal power spectrum, $P(k) \propto k^{4}$, exhibits good homogeneity and isotropy properties, and remains sufficiently stable under gravitational interactions. Both the dark matter and gas components individually also exhibit uniform and isotropic distributions. We apply our method to two-component cosmological simulations and demonstrate that an equal-mass particle setup effectively mitigates the spurious collisional heating that arises in unequal-mass simulations. Our method can be extended to generate multi-component uniform and isotropic distributions. Our code based on Gadget-2 is available at https://github.com/liaoshong/gadget-2glass .

Single-photon emitters are fundamental building blocks for quantum information processing, communication and sensing. However, unwanted interactions with bulk phonons in their host environment strongly limit their coherence and controllability. We report single color centers in nanodiamonds that are strongly and comprehensively decoupled from the bulk phononic environment. The color centers feature record-narrow linewidths down to 0.3 nm at room temperature and stable, bright emission, exceeding 10 Mcps in saturation. Notably, the bulk phonon sideband is almost entirely suppressed, revealing the presence of a single localized vibrational mode outside the diamond phonon band. Our observations and simulations point towards a unique mechanism for phonon decoupling in common wide-gap materials, based on a strongly radiative orbital transition coupled to a localized vibrational mode. The new color center enables qualitatively higher performance for applications in quantum networks and nanoscale sensing, and the exploration of new physical resources associated with vibrational states.

Purpose: We developed a dynamic B0 shimming approach using a 46-channel AC/DC shim array to correct phase errors caused by eddy currents from diffusion-encoding gradients in diffusion-prepared MRI, enabling high b-value imaging without the SNR loss from the use of magnitude stabilizer. Methods: A 46-channel AC/DC shim array and corresponding amplifier system were built. Spin echo prescans with and without diffusion preparation were then used to rapidly measure eddy current induced phase differences. These phase maps were used as targets in an optimization framework to compute compensatory shim currents for multi-shot 3D diffusion-prepared acquisitions. Results: The proposed method allows flexible use of the AC/DC shim array to correct undesirable eddy current effects in diffusion-prepared MRI. Phantom and in vivo experiments demonstrate whole-brain, cardiac-gated, multi-shot 3D diffusion-prepared imaging without the use of magnitude stabilizers. The approach enables preservation of full SNR while achieving reliable diffusion encoding at b-values up to 2000 s/mm2. Conclusions: This work demonstrates a new strategy for applying an AC/DC shim array to compensate for eddy current induced phase errors in diffusion-prepared MRI. By eliminating the need for magnitude stabilizer, it enables efficient high-quality diffusion imaging with full signal sensitivity retained.

The friendship paradox – the observation that, on average, one's friends have more friends than oneself – admits two common formulations depending on whether averaging is performed over edges or over nodes. These two definitions, the "alter-based" and "ego-based" means, are often treated as distinct but related quantities. This paper establishes their exact analytical relationship, showing that the difference between them is governed by the degree-degree covariance normalized by the mean degree. Explicit examples demonstrate the three possible cases of positive, zero, and negative covariance, corresponding respectively to assortative, neutral, and disassortative mixing patterns. The derivation further connects the covariance form to the moment-based expression introduced by Kumar, Krackhardt, and Feld [Proc. Natl. Acad. Sci. 121, e2306412121 (2024)], which involves the (-1)st, 1st, 2nd, and 3rd moments of the degree distribution. The two formulations are shown to be equivalent, as they should be: the moment-based representation expands the same structural dependence that the covariance form expresses in its most compact and interpretable form. The analysis thus unifies node-level and moment-level perspectives on the friendship paradox, offering both a pedagogically transparent derivation and a direct bridge to recent theoretical developments.

Optical neural networks (ONNs) have been developed to enhance processing speed and energy efficiency in machine learning by leveraging optical devices for nonlinear activation and establishing connections among neurons. In this work, we propose a quantum optical neural network (QONN) that utilizes atom-cavity neurons with controllable photon absorption and emission. These quantum neurons are designed to replace the electronic components in ONNs, which typically introduce delays and substantial energy consumption during nonlinear activation. To evaluate the performance of the QONN, we apply it to the MNIST digit classification task, considering the effects of photon absorption duration, random atom-cavity detuning, and stochastic photon loss. Additionally, we introduce a convolutional QONN to facilitate a real-world satellite image classification (SAT-6) task. Due to its compact hardware and low power consumption, the QONN offers a promising solution for real-time satellite sensing, reducing communication bandwidth with ground stations and thereby enhancing data security.

We present a synchronization-free differential observable for one-way inter-satellite laser ranging, designed to suppress first-order Doppler effects without requiring clock synchronization between spacecraft. The observable is constructed from successive pulse-interval differences, which isolate time-varying signatures while eliminating static and slowly varying biases. Applied to GRACE-FO Laser Ranging Interferometer (LRI) Level-1B data over four seasonal epochs in 2019, the method reveals a stable, spectrally narrow modulation at the orbital frequency. The amplitude and phase of the detected signature remain consistent across all datasets, demonstrating a deterministic, mission-internal origin. The detection is independently confirmed through synthetic-signal injection, shuffle-based significance testing, and cross-comparison with K-band ranging data. These results show that the proposed observable provides a sensitive diagnostic for identifying coherent orbital-period systematics that may remain hidden in conventional range-rate analysis. The method offers a pathway toward improved characterization of instrument and dynamical effects in current and future satellite gravity missions.