In this paper, we proposes a thermodynamically consistent two-fluid magnetohydrodynamic model based on the Helmholtz free energy framework, which strictly satisfies both the principles of energy conservation and entropy increase in the two-fluid model. By constructing the convexe-concave characteristics of the free energy density (convexity with respect to plasma number density and concavity with respect to temperature), the model self-consistently derives key thermodynamic quantities such as chemical potential, entropy density, and internal energy. Based on the proposed modeling framework and the convex-concave properties of the Helmholtz free energy density, we develop a temporally discrete numerical scheme with thermodynamic consistency. We rigorously prove that the proposed method satisfies the first law of thermodynamics (global energy conservation) and the second law (non-decreasing total entropy). Additionally, we provide spatiotemporal error estimates for the 2D degenerate system used in practical computations. Numerical experiments validate the effectiveness of the proposed method in capturing key plasma phenomena.

NUCLEUS is a cryogenic detection experiment which aims to measure Coherent Elastic Neutrino-Nucleus Scattering (CE$\nu$NS) and to search for new physics at the Chooz nuclear power plant in France. This article reports on the prediction of particle-induced backgrounds, especially focusing on the sub-keV energy range, which is a poorly known region where most of the CE$\nu$NS signal from reactor antineutrinos is expected. Together with measurements of the environmental background radiations at the experimental site, extensive Monte Carlo simulations based on the Geant4 package were run both to optimize the experimental setup for background reduction and to estimate the residual rates arising from different contributions such as cosmic ray-induced radiations, environmental gammas and material radioactivity. The NUCLEUS experimental setup is predicted to achieve a total rejection power of more than two orders of magnitude, leaving a residual background component which is strongly dominated by cosmic ray-induced neutrons. In the CE$\nu$NS signal region of interest between 10 and 100 eV, a total particle background rate of $\sim$ 250 d$^{-1}$kg$^{-1}$keV$^{-1}$ is expected in the CaWO$_4$ target detectors. This corresponds to a signal-to-background ratio $\gtrsim$ 1, and therefore meets the required specifications in terms of particle background rejection for the detection of reactor antineutrinos through CE$\nu$NS.

Accurately modelling orbital angular momentum (OAM) mode crosstalk in turbulent environments is challenging yet essential for developing free-space optical systems that employ OAM modes for multiplexing or diversity. Turbulence induces tip/tilt aberrations and lateral displacement, which significantly degrade system performance. Existing analytical models describe the transformation from an input Gaussian mode to an output OAM spectrum; however, our feed forward neural network model generalizes this approach by accounting for the effects of these aberrations on arbitrary input OAM modes. We validate the model experimentally by estimating turbulence-induced tilt and lateral displacement using a dual-camera setup and comparing the estimated spectrum with the actual modal decomposition. With a typical root mean square error of less than 11%, our results indicate that the model could serve as a reliable source of meta-information for digital signal processing, soft-decision forward error correction or perhaps dynamic mode hopping in future systems.

Neural networks have promise as surrogate partial differential equation (PDE) solvers, but it remains a challenge to use these concepts to solve problems with high accuracy and scalability. In this work, we show that neural network surrogates can combine with iterative algorithms to accurately solve PDE problems featuring different scales, resolutions, and boundary conditions. We develop a subdomain neural operator model that supports arbitrary Robin-type boundary condition inputs, and we show that it can be utilized as a flexible preconditioner to iteratively solve subdomain problems with bounded accuracy. We further show that our subdomain models can facilitate the construction of global coarse spaces to enable accelerated, large scale PDE problem solving based on iterative multilevel domain decomposition. With two-dimensional Maxwell's equations as a model system, we train a single network to simulate large scale problems with different sizes, resolutions, wavelengths, and dielectric media distribution. We further demonstrate the utility of our platform in performing the accurate inverse design of multi-wavelength nanophotonic devices. Our work presents a promising path to building accurate and scalable multi-physics surrogate solvers for large practical problems.

We report on efficient rovibrational state-selective loading of single H$_2^+$ molecular ions into a cryogenic linear Paul trap using (2+1) resonance-enhanced multi-photon ionization (REMPI). The H$_2^+$ ions are created by resonant two-photon excitation of H$_2$ molecules from the $X\;^1\Sigma_g^+$ state to the $E,F\;^1\Sigma_g^+$ state, followed by non-resonant one-photon ionization. The H$_2^+$ ions are produced from residual gas and sympathetically cooled by a co-trapped, laser-cooled $^9$Be$^+$ ion. By tuning the wavelength of the REMPI laser, we observe the loading of single H$_2^+$ ions via the ($\nu' = 0$, $L' = 0, 1, 2, 3$) rovibrational levels of the $E,F\;^1\Sigma_g^+$ intermediate state. We measure the success probability for the production of H$_2^+$ in the ($\nu^+ = 0$, $L^+ = 1$) state via the ($\nu' = 0$, $L' = 1$) level to be 85(6)% by quantum logic spectroscopy (QLS) of the hyperfine structure of this rovibrational state. Furthermore, we load an H$_2^+$ ion via the ($\nu' = 0$, $L' = 2$) level and confirm its rovibrational state to be ($\nu^+ = 0$, $L^+ = 2$) by QLS. We perform QLS probes on the ion over 19 h and observe no decay of the rotationally excited state. Our work demonstrates an efficient state-selective loading mechanism for single-ion, high-precision spectroscopy of hydrogen molecular ions.

We introduce SeisDiff-denoNIA, a novel diffusion-based seismic denoising framework that trains directly on field noise, eliminating the reliance on synthetic datasets. Unlike conventional denoising methods that require clean signal labels, our approach leverages field noise extracted prior to first arrivals as training targets, allowing the diffusion model to explicitly learn the true noise distribution. The model demonstrates robust performance on field DAS-VSP data contaminated by different noise types, and significantly outperforms traditional signal-based diffusion models under low SNR conditions in synthetic tests. The results suggest that explicitly modeling noise is not only viable but advantageous for seismic denoising tasks.

We present a perturbative triples correction to the relativistic quadratic unitary coupled cluster singles and doubles (qUCCSD) method, denoted as qUCCSD[T]. The method builds upon the Hermitian structure of the unitary ansatz and employs a many-body perturbation theory framework to consistently include the effects of triple excitations without the need for computationally intensive iterative procedures. Relativistic effects are incorporated using the exact two-component atomic mean-field (X2CAMF) Hamiltonian, and the computational cost is further reduced through the frozen natural spinor (FNS) and Cholesky decomposition (CD) approximations. Benchmark results demonstrate that qUCCSD[T] outperforms previously proposed triples corrections to the unitary coupled cluster method in the clasical computing regime and yields excellent agreement with experimental data and Full CI benchmarks. Specifically, the method shows high accuracy in computing bond dissociation enthalpies, molecular geometries, vibrational frequencies, ionization potentials, and electron affinities of heavy-element-containing systems.

The nonperturbative guiding-center model provides an exact alternative to full-orbit simulations of charged particle dynamics in situations where traditional guiding-center theory may fail. We demonstrate that the charged particle motion in a homogeneous, time-varying magnetic field is a solvable example of the nonperturbative guiding-center model. This entails showing that the exact magnetic moment of Qin and Davidson can be constructed to be asymptotic to the adiabatic invariant series of Kruskal. In contrast to the perturbative invariant, the exact invariant contains information about parametric resonances. These resonances destroy the conservation of the usual magnetic moment over very long times. This refutes some previous claims about the all-time invariance of the magnetic moment.

Understanding human mobility during disastrous events is crucial for emergency planning and disaster management. Here, we develop a methodology involving the construction of time-varying, multilayer networks in which edges encode observed movements between spatial regions (census tracts) and network layers encode different movement categories according to industry sectors (e.g., visitations to schools, hospitals, and grocery stores). This approach provides a rich characterization of human mobility, thereby complementing studies examining the risk-aversion activities of evacuation and sheltering in place. Focusing on the 2021 Texas winter storm as a case study which led to many casualties, we find that people largely reduced their movements to ambulatory healthcare services, restaurants, and schools, but prioritized movements to grocery stores and gas stations. Additionally, we study the predictability of nodes' in- and out-degrees in the multilayer networks, which encode movements into and out of census tracts. We find that inward movements are harder to predict than outward movements, and even more so during this winter storm. Our findings about the reduction, prioritization, and predictability of sector-specific human movements could inform mobility-related decisions arising from future extreme weather events.

We reproduce the consistently-seen experimental voltage versus pressure (V-P) dependence of DC magnetron sputtering (DCMS) with 2D-RZ particle-in-cell (PIC) simulation. Informed by PIC simulation, we develop a steady-state, 1D-axial fluid model of the sheath and presheath that also reproduces this V-P dependence. The V-P dependence is the relationship between the steady-state voltage needed to maintain a constant discharge current and the neutral gas pressure. V-P dependence is fundamental to device performance, but has not previously been reproduced with simulation or satisfactorily explained. In this work, we compare the V-P curve of our simulated device and fluid model with past experiments and then present a theoretical explanation for this V-P dependence. We find that the decrease in voltage with increasing pressure is not due to electron recapture at the cathode. Rather, the constant current dictates a constant global ionization rate, so the voltage decrease compensates for the increase in neutral gas density by lowering the energy of the plasma electrons, which decreases their ionization probability. The PIC simulations also reveal that the presheath and bulk plasma are unaffected by the electron reflection coefficient at the cathode; the only effect of increasing reflection is a reduction in the sheath voltage and width. In addition to the potential structure, we explore how pressure affects the plasma density, particle drifts, and particle energy distributions.

In this paper, we propose a microflow velocimetry based on particle tracking with the aid of the optical trapping of tracers, namely, optically-trapped particle tracking velocimetry (ot-PTV). The ot-PTV has two phases: a trap phase, in which individual tracers are trapped by an optical force and held at a measurement position; a release phase, in which the tracer is released and advected by the fluid flow, without interference from the optical force. The released tracer is subsequently trapped again by the optical force. By repeating the set of trap and release phases, we can accumulate the sequential images of the tracer that have the same initial position. In this way, we can reduce the effect of statistical noise inherent in the Brownian motion of small tracers, thereby achieving both high spatial resolution and a high signal-to-noise ratio, which are necessary for the analysis of extremely slow microflows, such as near-wall creeping flows. The concept of ot-PTV is validated using a benchmark experiment, i.e., a pressure-driven flow in a straight microchannel with a square cross-section.

This paper reports an 11.7 GHz compact filter based on Scandium Aluminum Nitride (ScAlN) Film Bulk Acoustic Resonators (FBARs) and provides a detailed analysis of the fundamental challenges that limit performance when scaling to higher frequencies. A 50 {\Omega} ladder filter based on single-layer thin film ScAlN with Platinum (Pt) electrodes was demonstrated at 11.7 GHz with a 3 dB fractional bandwidth (FBW) of 4.0% and an out of band rejection greater than 23.1 dB. However, the filter exhibits a moderate insertion loss (IL) of 6.8 dB, which is attributed to a limited Quality (Q) factor in the constituent resonators. Consequently, we identify and analyze three primary challenges in frequency scaling ScAlN FBARs with thinner film stacks: 1) degradation of piezoelectric thin-film crystal quality, 2) increased series resistance in ultra-thin electrodes, and 3) residual film stress that limits device size and structural integrity. By establishing a clear relationship between these fabrication-level challenges and the resulting device performance, this work provides critical insights for the future development of low-loss acoustic filters for millimeter-wave applications.

Understanding planetary core convection dynamics requires the study of convective flows in which the Coriolis and Lorentz forces attain a leading-order, so-called magnetostrophic balance. Experimental investigations of rotating magnetoconvection (RMC) in the magnetostrophic regime are therefore essential to broadly characterize the properties of local-scale planetary core flow. Towards this end, we present here the first thermovelocimetric measurements of magnetostrophic, liquid metal convection, which are made using liquid gallium as the working fluid, at moderate rotation rates (Ekman numbers $10^{-4} \leq Ek\leq 10^{-5}$) and in the presence of dynamically strong magnetic fields (Elsasser number $\Lambda=1$). Complementary rotating convection (RC) experiments are performed at the same rotation rates to serve as reference cases. Our RMC velocity measurements adequately follow a geostrophic turbulent scaling for cases in which local-scale convective inertial forces exceed the Lorentz forces in the fluid bulk. In cases where Lorentz forces exceed local-scale inertia ($N_\ell \gtrsim 3$), the root-mean-square RMC velocities are magnetically damped, yielding values below the geostrophic turbulent RC scaling prediction. An enhancement in heat transfer is observed, which we attribute to the increased coherence of vertically aligned magnetostrophic convective flow. Extrapolating these laboratory results, we predict that convection-scale flows in Earth's core occur in the magnetically damped $N_\ell \gtrsim 3$ regime with Rayleigh number values between $10^{24}$ and $10^{26}$.

Expressions of dissent against authority are an important feature of most societies, and efforts to suppress such expressions are common. Modern digital communications, social media, and Internet surveillance and censorship technologies are changing the landscape of public speech and dissent. Especially in authoritarian settings, individuals must assess the risk of voicing their true opinions or choose self-censorship, voluntarily moderating their behavior to comply with authority. We present a model in which individuals strategically manage the tradeoff between expressing dissent and avoiding punishment through self-censorship while an authority adapts its policies to minimize both total expressed dissent and punishment costs. We study the model analytically and in simulation to derive conditions separating defiant individuals who express their desired dissent in spite of punishment from self-censoring individuals who fully or partially limit their expression. We find that for any population, there exists an authority policy that leads to total self-censorship. However, the probability and time for an initially moderate, locally-adaptive authority to suppress dissent depend critically on the population's willingness to withstand punishment early on, which can deter the authority from adopting more extreme policies.

MTV is increasingly recognized as an accurate estimate of disease burden, which has prognostic value, but its implementation has been hindered by the time-consuming need for manual segmentation of images. Automated quantitation using AI-driven approaches is promising. AI-driven automated quantification significantly reduces labor-intensive manual segmentation, improving consistency, reproducibility, and feasibility for routine clinical practice. AI-enhanced radiomics provides comprehensive characterization of tumor biology, capturing intratumoral and intertumoral heterogeneity beyond what conventional volumetric metrics alone offer, supporting improved patient stratification and therapy planning. AI-driven segmentation of normal organs improves radioligand therapy planning by enabling accurate dose predictions and comprehensive organ-based radiomics analysis, further refining personalized patient management.

Nonlinear optical phenomena are generally governed by geometry in matter systems, as they depend on the spatial arrangement of atoms within materials or molecules. Metasurfaces, through precisely designed geometries on a subwavelength scale, allow tailoring the optical response of a material far beyond its natural properties. Therefore, metasurfaces are highly appealing to enable the engineering of nonlinear optical interactions at an unprecedented level. Current studies on nonlinear metasurfaces predominantly focus on the phase control of the generated light. Nonetheless, investigating the tensorial nature of the nonlinearity of metasurfaces and its effect on the polarization of the generated light is critical to fully unlock a range of applications, such as nonlinear vector beam generation and nonlinear polarization imaging. Here, we study the artificial optical nonlinearity of a dielectric metasurface originating from its meta-atom symmetry and describe the third-order nonlinear behavior by considering the polarization degree of freedom. We establish an effective nonlinear medium model that serves as a design toolbox for developing amorphous silicon-based geometric metasurfaces with customizable features in third harmonic generation. We further extract quantitative values of the artificial nonlinear susceptibility tensor elements related to the investigated nonlinear process and geometry. The implemented functional devices demonstrate the versatility of dielectric metasurfaces in shaping the emitted light in terms of amplitude, phase, and polarization, for the precise engineering of novel nonlinear architectures targeting applications in nonlinear imaging and complex light generation.

Biologists and physicists have a rich tradition of modeling living systems with simple models composed of a few interacting components. Despite the remarkable success of this approach, it remains unclear how to use such finely tuned models to study complex biological systems composed of numerous heterogeneous, interacting components. One possible strategy for taming this biological complexity is to embrace the idea that many biological behaviors we observe are ``typical'' and can be modeled using random systems that respect biologically-motivated constraints. Here, we review recent works showing how this approach can be used to make close connection with experiments in biological systems ranging from neuroscience to ecology and evolution and beyond. Collectively, these works suggest that the ``random-with-constraints'' paradigm represents a promising new modeling strategy for capturing experimentally observed dynamical and statistical features in high-dimensional biological data and provides a powerful minimal modeling philosophy for biology.

Coulomb explosion imaging (CEI) is a powerful technique for capturing the real-time motion of individual atoms during ultrafast photochemical reactions. CEI generates high-dimensional data with naturally embedded correlations that allow mapping the coordinated motion of nuclei in molecules. This enables reliable separation of competing reaction pathways and makes this approach uniquely suited for characterizing weak reaction channels. However, rich information contained in experimental CEI patterns remains largely underexploited due to challenges in visualizing correlations between multiple observables in multi-dimensional parameter space. Here we present a new approach to CEI of intermediate-sized polyatomic molecules, detecting up to eight ionic fragments in coincidence and leveraging machine-learning-based analysis to identify patterns and correlations in the resulting high-dimensional momentum-space data, enabling robust molecular structure identification and differentiation. Our approach provides high-dimensional background-free data encoding exceptionally rich structural information and establishes an automated, scalable framework for extracting insightful information from the data. As a demonstration, we apply this method to image and distinguish dichloroethylene isomers, showcasing its potential for broader applications in molecular imaging. Our results pave the way for channel-specific analysis of ultrafast structural dynamics in chemically relevant systems, particularly for disentangling mixed reaction pathways and detecting contributions from weak channels and minority species.

This work presents a fast, scalable, and uncertainty-aware methodology for real-time estimation of key aerodynamic states, including instantaneous vorticity fields and aerodynamic loads, during severe gust encounters where strong flow separation and vortex-gust interactions dominate the dynamics. The methodology, trained and tested on high-fidelity simulations of two-dimensional wing-gust encounters with sparse pressure data, combines physics-based ensemble filtering with data-driven surrogate modeling in a learned low-order space. Within this reduced space, latent dynamics and sensor observation operators are learned using neural networks, enabling efficient modeling of complex aerodynamic responses. These components are embedded within a low-rank Ensemble Kalman Filter (LREnKF), yielding a computationally efficient scheme that combines data-driven expressivity with physical interpretability in the original high-dimensional space. Because assimilation occurs entirely within the latent space, updates are fast enough for real-time use, ensuring that aerodynamic states can be continuously estimated from streaming pressure data. The filter corrects predictions predominantly in directions that are both dynamically significant and observable, improving efficiency and reducing spurious adjustments. An observability analysis shows how sensor informativeness evolves during wing-gust interactions, and sensor dropout experiments demonstrate that the LREnKF adaptively re-weights neighboring sensors to preserve estimation quality under degraded sensing.

Global climate models parameterize a range of atmospheric-oceanic processes like gravity waves, clouds, moist convection, and turbulence that cannot be sufficiently resolved. These subgrid-scale closures for unresolved processes are a leading source of model uncertainty. Here, we present a new approach to developing machine learning parameterizations of small-scale climate processes by fine-tuning a pre-trained AI foundation model (FM). FMs are largely unexplored in climate research. A pre-trained encoder-decoder from a 2.3 billion parameter FM (NASA and IBM Research's Prithvi WxC) -- which contains a latent probabilistic representation of atmospheric evolution -- is fine-tuned (or reused) to create a deep learning parameterization for atmospheric gravity waves (GWs). The parameterization captures GW effects for a coarse-resolution climate model by learning the fluxes from an atmospheric reanalysis with 10 times finer resolution. A comparison of monthly averages and instantaneous evolution with a machine learning model baseline (an Attention U-Net) reveals superior predictive performance of the FM parameterization throughout the atmosphere, even in regions excluded from pre-training. This performance boost is quantified using the Hellinger distance, which is 0.11 for the baseline and 0.06 for the fine-tuned model. Our findings emphasize the versatility and reusability of FMs, which could be used to accomplish a range of atmosphere- and climate-related applications, leading the way for the creation of observations-driven and physically accurate parameterizations for more earth-system processes.

Turbulence Models represent the workhorse for simulations used in engineering design and analysis. Despite their low computational cost and robustness, these models suffer from substantial predictive uncertainty, most of which is epistemic. At present, the Eigenspace Perturbation Method (EPM) is the only approach to estimate these turbulence model uncertainties, using physics based perturbation to the predicted Reynolds stresses. While the EPM address the question of how to perturb the Reynolds stresses for uncertainty estimation, it does not address how much to perturb. This shortcoming leads to very generous uncertainty bounds that result in sub-optimal designs. In this investigation, we use Convolutional Neural Networks (CNN) to predict the discrepancy between predicted and actual turbulent flows. These can be utilized to modulate the degree of the perturbations in the EPM leading to a Physics Constrained Deep Learning approach for Reynolds Averaged Navier Stokes model uncertainty quantification. We test this approach on turbulent flows over aero-foils and periodic hills to show the efficacy of our approach.

Free space optical (FSO) communication is considered a promising solution in next_generation communication networks. However, its performance is significantly influenced by atmospheric turbulence. To enhance system robustness to turbulence, we propose a turbulence_robust end_to_end FSO communication system (TRFSO) that integrates a data-driven channel model with joint source_channel coding modulation (JSCCM). Specifically, a bidirectional long short-term memory (BiLSTM)_based channel model is developed and trained on data collected over a physical FSO link under varying turbulence conditions. This model accurately captures real_world channel distortions, achieving a minimum Kullback_Leibler (KL) divergence of 0.0019 in amplitude distribution matching. Experimental results show that the TRFSO system trained with the BiLSTM_based channel model outperforms the same architecture trained under the additive white Gaussian noise (AWGN) channel, achieving an average 3.5 dB improvement in multi-scale structural similarity (MS_SSIM) under strong atmospheric turbulence. These results demonstrate the effectiveness of the proposed TRFSO in achieving robust and reliable transmission under dynamic atmospheric turbulence.

A liquid crystal (LC) lens array based on double-layer composite electrodes, characterized by a large aperture, short focal length, and low operating voltage is demonstrated. The lens array consists of an LC layer, a top common electrode, a bottom double-layer composite electrode layer, and an oxide layer. The bottom double-layer composite electrode layer comprises the pixel electrodes and the auxiliary electrode. In focusing mode, the pixel electrodes receive operational voltage to establish the LC layer's electric field, with the auxiliary electrode applying reduced voltage for field optimization. Experiment results show that the proposed LC lens array achieves the shortest focal length of 3.3 mm when the pixel electrodes are set at 5.2 Vrms and the auxiliary electrode is set at 2.6 Vrms. This design addresses the technical challenge of achieving larger apertures (800 {\mu}m or more), offering enhanced viewing zones and improved 3D performance. This configuration provides an ideal refractive index distribution in a relatively thick LC layer, enabling 2D/3D switchable display with performance superior to current LC lens arrays of equivalent aperture. Furthermore, the proposed structure demonstrates excellent tolerance to manufacturing errors.

Sensitive to scattering from unresolved sample structures, the dark-field channel in full-field X-ray imaging provides complementary information to that offered by conventional attenuation and phase-contrast methods. A range of experimental dark-field techniques and retrieval algorithms have been recently developed to extract this signal by directly resolving dark-field-associated local image blurring with a high-resolution camera. While the underlying physical mechanism that generates dark-field contrast is generally defined similarly across the methods, no comparison of these dark-field techniques using identical samples has been conducted. In this paper, dark-field imaging data from two samples were acquired using three emerging dark-field setups at a synchrotron: propagation-based, single-grid, and speckle-based X-ray imaging. Dark-field images were then reconstructed using a variety of retrieval algorithms--some requiring only a single sample exposure, others multiple; some performing local, pixel-wise analysis, and others operating globally on entire images. We find that the dominant contribution to dark-field contrast--arising from diffuse scattering from unresolved microstructures or multiple refractions through larger, potentially resolved structures--is consistently recovered across all approaches, demonstrating mutual agreement. However, some differences emerge for structures that are spatially varying. We attribute these differences to the idea that each technique has a different internal ruler, a sensitivity scale for dark-field retrieval influenced by both the experimental design and algorithmic assumptions of the technique. This study is intended to guide dark-field imaging users in selecting the most appropriate technique for their imaging goals and to motivate future research into dark-field sensitivity and sources of dark-field contrast across different methods.

The sixth generation (6G) wireless networks require dynamic spectrum management to optimize the utilization of scarce spectral resources and support emerging integrated sensing and communication (ISAC) applications. This necessitates real-time spectrum sensing (RT-SS) capability with ultrawide measurement range, compact size, and low latency. Conventional electronic RT-SS solutions face critical challenges in operating across the millimeter-wave and sub-terahertz bands, which are essential spectra for 6G wireless. While photonic RT-SS has the potential to surpass this limitation, the current implementations feature limited bandwidths below 50 GHz and mostly rely on bulky dispersive fibers with high latency. Here, we address these challenges by developing an integrated photonic RT-SS system capable of ultrabroadband measurement from microwave to sub-terahertz bands, covering the full spectrum for 6G wireless. The photonic RT-SS circuit integrates a broadband electro-optic (EO) modulator for unknown signal loading, an EO tunable microring filter bank for high-speed parallel frequency-to-time mapping, as well as an EO comb for precise channel frequency referencing, all realized on a single thin-film lithium niobate chip. We achieve an unprecedented spectral measurement range beyond 120 GHz at a low latency of less than 100 ns. To validate the effectiveness of our photonic RT-SS system in future 6G scenes, we further propose a heuristic spectro-temporal resource allocation algorithm and conduct a proof-of-concept ISAC demonstration, where a radar adaptively access RT-SS-informed spectrally underutilized regions for high-quality target sensing under dynamic communication interferences. Our work presents a compact and cost-effective solution for efficient spectrum sharing and dynamic management in future 6G ISAC networks.

Commemorating the 2024 S. Chandrasekhar Prize, this review provides a retrospective on the genesis and evolution of plasma wakefield acceleration. It traces the journey from prehistory and the invention of the Plasma Wakefield Accelerator (PWFA), the establishment of its theoretical cornerstones, to its profound reverberations across fundamental physics, including astrophysics and analog gravity. The narrative emphasizes conceptual evolution, key theoretical breakthroughs, and future outlook, culminating in a vision for hybrid schemes and next-generation colliders. In addition to application to particle accelerators and high energy collider physics, it is found that plasma wakefield, with its ultra-intense acceleration, can also be applied to investigate gravity effects in the laboratory based on Einstein's equivalence principle. A specific example is accelerating flying relativistic plasma mirrors to investigate the celebrated black hole Hawking evaporation and the associated information loss paradox. We describe an ongoing experiment, AnaBHEL (Analog Black Hole Evaporation via Lasers), which aims at shedding some lights on the black hole information loss paradox.

We present an efficient numerical implementation of the quasi-classical doorway-window approximation, specifically designed for on-the-fly simulations of time-resolved nonlinear spectroscopic signals in the Julia package this http URL. The package contains modules that facilitate standard tasks such as input/output data handling, data filtering, and post-processing, among others. this http URL includes implementations of various methods developed by the authors, including integral and dispersed transient absorption pump-probe signals and two-dimensional spectra. By developing this http URL we aim to create a versatile platform to perform simulations and develop methodologies within the quasi-classical doorway-window approximation framework.

This work presents an exact, analytical derivation of the ensemble-averaged orbital angular momentum (OAM) power spectrum for a circularly polarized Gaussian beam traversing a statistical Q-plate with Gaussian spatial disorder. Utilizing the Gaussian moment theorem, a new closed-form expression for the averaged mutual coherence is obtained. This coherence function is then rigorously projected onto OAM modes, yielding an exactly normalized series representation whose absolute convergence is formally proven. The analysis meticulously resolves limiting disorder regimes: for coarse disorder, an ideal OAM spectrum, sharply peaked at its nominal OAM mode, is demonstrated. Conversely, for fine disorder, a specific fraction of total power exponentially decays with disorder variance into the nominal OAM mode, with remaining power distributed among nearby OAM modes, exhibiting an effective width inversely proportional to the dimensionless correlation length. Crucially, a new universal scaling framework, defined by a master control parameter and a universal coordinate, is introduced. This framework, rigorously derived from the exact solution, unifies the spectrum's description across all relevant disorder regimes and enables robust data collapse. Monte Carlo simulations, implemented with the same normalization and OAM projection, substantiate these claims by corroborating the predicted scaling and universal behavior, offering a foundational, device-level perspective on OAM universality in complex media.

This study experimentally examines the entry and penetration of a superhydrophobic sphere into a quiescent deep pool, with special emphasis placed on the primary and secondary pinch-off of the air cavity existing in its wake. Two aspects are novel in this study. For one, the experiments are performed for a large range of dimensionless sphere densities, where lighter spheres, with their air cavity, exhibit a terminally ascending trajectory and heavier spheres a terminally descending trajectory. The second novel result is a strong correlation of primary and secondary pinch-off times with the Froude number at impact and the dimensionless density. A semi-empirical correlation for the air cavity volume following the primary pinch-off shows excellent agreement with measurements over all dimensionless densities. A scalar force balance predicts a drastic decrease of buoyancy upon pinch-off, reflected also in the abrupt change of deceleration, measured using two orthogonally placed high-speed cameras to capture the time resolved trajectory of the sphere in the pool. Comparisons are drawn between the trajectories of superhydrophobic spheres and those of hydrophilic spheres, measured in a previous study.

A tridiagonal matrix algorithm (TDMA), Pipelined-TDMA, is developed for multi-GPU systems to resolve the scalability bottlenecks caused by the sequential structure of conventional divide-and-conquer TDMA. The proposed method pipelines multiple tridiagonal systems, overlapping communication with computation and executing GPU kernels concurrently to hide non-scalable stages behind scalable compute stages. To maximize performance, the batch size is optimized to strike a balance between GPU occupancy and pipeline efficiency: larger batches improve throughput for solving tridiagonal systems, while excessively large batches reduce pipeline utilization. Performance evaluations on up to 64 NVIDIA A100 GPUs using a one-dimensional (1D) slab-type domain decomposition confirm that, except for the terminal phase of the pipeline, the proposed method successfully hides most of the non-scalable execution time-specifically inter-GPU communication and low-occupancy computation. The solver achieves ideal weak scaling up to 64 GPUs with one billion grid cells per GPU and reaches 74.7 percent of ideal performance in strong scaling tests for a 4-billion-cell problem, relative to a 4-GPU baseline. The optimized TDMA is integrated into an ADI-based fractional-step method to remove the scalability bottleneck in the Poisson solver of the flow solver (Ha et al., 2021). In a 9-billion-cell simulation on 64 GPUs, the TDMA component in the Poisson solver is accelerated by 4.37x, contributing to a 1.31x overall speedup of the complete flow solver.

Skyrmions are topologically stable vector textures as potential information carriers for high-density data storage and communications, especially boosted by the recently emerging meta-generators of skyrmions in electromagnetic fields. However, these implementations always rely on planar, rigid designs with stringent fabrication requirements. Here, we propose the free-form conformal metasurface generating skyrmions towards future wearable and flexible devises for topological resilience light fields. Furthermore, we experimentally tested the outstanding topological robustness of the skyrmion number under different disorder degrees on the metasurface. This work promotes the development of flexible compact skyrmion-based communication devices and demonstrates their potential to improve the quality of space information transmission.

Beam monitoring is crucial for particle accelerators to achieve high luminosity. We describe an upgrade of the LABM, a beam monitoring device utilizing observation of the beamstrahlung, radiation emitted by a beam of charged particles when it accelerates in the electromagnetic field of another beam of charged particles.

Traditional sources of quantum-entangled photons, such as spontaneous parametric down-conversion, suffer from low flux, limiting clinical applications. We investigated whether clinical megavoltage (MV) radiotherapy beams can serve as dual-purpose sources, delivering therapeutic dose while simultaneously generating high-flux entangled photon pairs for quantum ghost imaging, quantum theranostics (QTX), and related applications. Using GEANT4 Monte Carlo simulations, we modeled water-equivalent phantoms containing 2 cm spherical tumors loaded with gold nanoparticles (AuNPs, 10 mg/mL). Clinical spectra at 6, 10, and 15 MV were simulated. Key metrics included 511 keV photon-pair yield, positronium lifetimes (time-resolved), Doppler and energy broadening (energy-resolved), signal-to-noise ratios (SNR), and entanglement retention at depths from 5 to 15 cm. Pair yields scaled with beam energy and AuNP concentration, reaching about 10^8 pairs per Gy per cm^3 in AuNP-loaded tumors. Positronium lifetime shifts (about 100 ps) reflected tumor oxygenation and reactive oxygen species, offering potential functional biomarkers. Doppler (1-3 keV) and AuNP-induced line broadening (0.5-1 keV) produced distinct spectroscopic signatures of electron density, tissue composition, and nanoparticle uptake. Time-resolved QTX revealed oxygenation and ROS levels, energy-resolved QTX probed tissue density and atomic composition, and combined modes enabled comprehensive theranostic imaging. Depth-dependent simulations achieved voxel SNRs exceeding 100. These findings demonstrate that clinical MV beams can serve as practical, high-flux entangled photon sources. Integrated time- and energy-resolved QTX supports functional and spectroscopic tumor imaging, enables nanoparticle optimization, and expands quantum imaging platforms from optical (eV) to therapeutic (hundreds of keV) regimes.

We report a mid-infrared ring laser via fiber-to-waveguide hybrid integration enabled by directional couplers fabricated in fluoride glasses. Directional couplers were inscribed into fluoride glass using femtosecond laser direct writing, forming low-loss waveguides with a high positive refractive index change. The waveguides were optimized for efficient guidance at a wavelength of 2.7 {\textmu}m. Directional couplers with systematically varied coupling gap and interaction lengths were fabricated and experimentally characterized, demonstrating controllable power splitting ratios spanning from complete transmission (100:0) to balanced coupling (50:50). A directional coupler with a 7 {\textmu}m coupling gap and a 1.5 mm interaction length, providing an 80:20 power splitting ratio, was integrated with a cladding-pumped Er:ZBLAN fiber to realize a ring laser cavity. The resulting device produced stable lasing at 2716.7 nm with an output power of 14.2 mW. This work represents the first realization of a fully fiber-chip integrated mid-infrared laser source and opens new avenues for 3D photonic integration in fluoride glasses, enabling design flexibility previously unattainable in traditional fiber-optics architectures.

The importance of singlet fission as a fundamental process with a variety of implications in energy harvesting cannot be overstated. The challenge is in characterizing the energy states of these large singlet fission molecular aggregates that participate in the process. Large dimensionality and essential multi-configuration nature of the electronic states of interest combine to make accurate ab initio calculations prohibitively difficult. We present a spin-resolved tight-binding excitonic model for singlet fission that can be parameterized based on ab initio calculations on monomers and dimers of molecules, and is highly suitable for the study of aggregates using tensor network methods such as the density matrix renormalization group. This tensor network coarse-grained model is demonstrated specifically on the pentacene crystal, where we evaluate the spectra and density of states. We show the natural emergence of bands of states in some cases, and characterize them. Through an analysis of entanglement entropy of the eigenstates, we gain crucial insight into the extent of their multireference character. This method is useful in understanding not just the structure of these extended aggregates, but also being the cornerstone for incorporation of vibronic features and simulation of the singlet fission dynamics.

Cities are experiencing significant warming and more frequent climate extremes, raising risks for over 90% of Australians living in cities. Yet many of our tools for climate prediction and projection lack accurate representations of these environments. We also lack the observations and datasets needed to evaluate model performance. This paper identifies critical gaps in Australias current capability, showing how they undermine climate impact and risk assessments in cities and may lead to poorly designed adaptation and mitigation strategies. These gaps, and the recommendations to address them, were identified through consultation with experts across research institutes, universities, two ARC Centres of Excellence, federal and state governments, and private agencies. Our recommendations span four key areas: city descriptive datasets, integrated observations, fit for purpose models, and a coordinated community of research and practice. Urgent action is needed to tailor models to Australia's unique urban landscapes and climates. This requires comprehensive, nationally consistent, high resolution datasets that capture the form, fabric, and function of contemporary and future cities. It also requires filling systematic gaps in integrated networks of urban climate observations for evaluation and benchmarking. At the same time, scientific understanding of key urban processes that influence weather and climate must advance, alongside improvements in their representation in physical models. This can be achieved through a national community of research and practice that codesigns and oversees an implementation plan, integrated with infrastructure such as ACCESS NRI and AURIN. Building this capability will enable us to answer critical questions about the interaction between cities and climate, protecting Australias urban populations and ensuring a resilient future.

Although speckle is a powerful tool for high-precision metrology, large datasets and cumbersome training are always required to learn from the encoded speckle patterns, which is unfavorable for rapid deployment and multi-dimensional metrology. To enable high accuracy and fast training, physics-informed machine learning enforces physical laws to address high-dimensional problems. Here, we harness the modal fields in a few-mode fiber, which follow the law of beam propagation, to enable high-accuracy and fast-training parameter estimation. Anti-noise fast mode decomposition is implemented to retrieve the modal fields from the speckles. The accuracy is enhanced since the modal fields enable parameter estimation at random points in the continuous space-time domain. Artificial tactile perception and multi-dimensional metrology are achieved with high accuracy because the modal fields respond diversely to different parameters. Meanwhile, the number of specklegrams for training is reduced by around 5 times. The training time of machine learning is significantly reduced by 800 times, from 9 hours and 45 minutes to 40 seconds. Therefore, harnessing the modal fields paves a new way for the speckle-based metrology to develop efficient, low-cost, multi-dimensional sensors, making it suitable for intelligent wearable devices, industrial robots and healthcare applications.

Flat-optics platforms offer new opportunities for the generation of entangled photons by relaxing traditional phase-matching constraints, enabling the use of a broader range of nonlinear materials. Among these, gallium arsenide and aluminum gallium arsenide stand out for their exceptionally high second-order nonlinearities, but their conventional orientation (001) has limited their applicability for photon-pair generation. By transitioning to crystals with (111) surface orientation, we overcome these limitations. We demonstrate a flat-optics-based telecom-range SPDC source using Al0.30Ga0.70As that achieves a high photon-pair generation rate per pump power and bandwidth of up to 0.24 Hz/mW/nm. The choice of 30% aluminum concentration allowed us to reduce pump absorption and photoluminescence background for photon pairs generation at telecom wavelengths by at least an order of magnitude compared to that of GaAs. The specific layer orientation facilitates the generation of orthogonally polarized entangled photons, a prerequisite for polarization-entangled states. Rather than directly probing entanglement, we observe the effect of hidden polarization. Our results highlight AlGaAs (111) as a promising platform for scalable quantum photonic sources and shed light on nonclassical polarization effects accessible through flat-optics engineering.

Despite generally lacking ferromagnetic properties or strong spin-orbit coupling, electrons in chiral molecules exhibit unique spin-dependent transport behavior, known as chiral-induced spin selectivity (CISS). This phenomenon implies a profound connection between chirality and spin, and draws attention to the link between chirality and magnetism. Experiments in recent years have shown that chirality can induce spin-polarizations and magnetizations, providing fresh insights into interpreting chirality-related biochemical processes and designing nano-magnetic devices. In this paper, we present a dynamical theoretical model aimed at elucidating how charge-polarization combined with the CISS leads to spin-polarization and magnetization. Our theoretical model successfully explains the spin-polarization and magnetization observed in three types of experiments, where the charge-polarization is induced in the chiral molecules by the dispersion interaction, gate voltage, and molecular adsorption. The model simulates a clear time evolution process and provides a comprehensive theoretical framework for this field.

The chiral-induced spin selectivity (CISS) effect, a phenomenon where the chirality of molecules imparts significant spin selectivity to electron transfer processes, has garnered increasing interest among the chemistry, biology, and physics communities. Although this effect was discovered more than a decade ago, the dynamical process of how electron spin polarization is caused by chiral molecules is still unclear. Here, we propose a dynamical theory of electron transfer in donor-chiral molecule bridge-acceptor systems without electrodes or substrates based on the Lindblad-type master equation. We demonstrate that the molecular spin-orbit coupling generates unequal spin velocities and achieves steady spin polarization with the help of dephasing. Our work elucidates the dynamical process of CISS and may promote the applications of chiral-based spintronic devices.

Molecular chirality and electron spin are intricately intertwined via the fascinating phenomenon of chiral-induced spin selectivity (CISS), which has garnered considerable attention due to its extensive potential applications. A recent experiment has revealed that chiral molecules self-assembled on the gold surface can modulate the inverse spin Hall effect, providing an alternative platform for studying the interplay between chirality and spin transport. Our study uncovers that this modulation stems from the CISS effect, which enhances spin currents of one spin orientation while suppressing those of the opposite spin orientation. We provide numerical results that are highly consistent with the experimental phenomena and further investigate the influence of various factors on this modulation. This work offers a theoretical explanation of previously unexplained experimental findings, and the underlying physical mechanism broadens current perspectives on understanding and applying CISS.

Chiral molecules have attracted significant multidisciplinary interest and extensive research owing to their remarkable ability to achieve charge-to-spin conversion, known as the chiral-induced spin selectivity (CISS). A recent experiment has revealed that chiral molecules also exhibit an unexpected capability for spin-to-charge conversion, referred to as the inverse CISS (ICISS), opening unprecedented avenues for the study and application of chiral molecules. Here, we propose a specific theoretical explanation, suggesting that ICISS arises from the spin-dependent deflection of electrons caused by the interaction between the spin and chiral structure. Our numerical results are in excellent agreement with experimental observations, demonstrating that ICISS persists under strong disorder. Our model also reproduces the inverse spin Hall effect (ISHE) in this experiment. Comparative analysis reveals that ICISS employs an unconventional spin-to-charge conversion mechanism distinct from conventional ISHE approaches. We provide a comprehensive explanation of ICISS, elucidate experimental phenomena, and pave the way for organic-based spintronics.

This work presents a biventricular electromechanical human heart model that is comprehensive and clinically relevant, integrating a realistic 3D heart geometry with both systemic and pulmonary hemodynamics. The model uses a two-way fluid-structure-interaction (FSI) formulation with actual 3D blood meshes to accurately investigate the effect of blood flow on the myocardium. It couples a reaction-diffusion framework and a voltage-dependent active stress term to replicate the link between electrical excitation and mechanical contraction. Additionally, the model incorporates innovative epicardial boundary conditions to mimic the stiffness and viscosity of neighboring tissues. The model's ability to replicate physiological heart motion was validated against Cine magnetic resonance imaging (MRI) data, which demonstrated a high degree of consistency in regional displacement patterns. The analysis of the right ventricle showed that the basal and mid free walls experience the largest motion, making these regions ideal for implanting motion-driven energy harvesting devices. This validated model is a robust tool for enhancing our understanding of cardiac physiology and optimizing therapeutic interventions before clinical implementation.

This study explores heated wavy wall shape design in channel flow using machine learning, aiming to minimize temperature variation ($\sigma_T$) while limiting pressure loss ($\Delta p$). A cost function $J$ defined as a product of $\sigma_T$ and $\Delta p$ balances these competing objectives. Optimization is performed via Bayesian optimization (BO) coupled with Reynolds-Averaged Navier-Stokes (RANS) computations in an active learning loop involving up to 1000 subsequent iterations. Two shaping strategies are considered: a sinusoidal-type function defined by four parameters (two waviness amplitudes, wave count, and tilt), and a higher-dimensional approach employing a Piecewise Cubic Hermite Interpolation Polynomial (PCHIP) with 19 control points. Results show the sinusoidal design reduces $\sigma_T$ over $60$-fold but increases $\Delta p$ fourfold, while the PCHIP shape offers only a $15$-fold $\sigma_T$ reduction but with a twofold $\Delta p$ increase. Flow characteristics such as turbulent kinetic energy, pressure, temperature, and Nusselt number are examined for both optimal and suboptimal shapes along the Pareto front. The insights gained motivated a human-aided refinement of the BO result, leading to a further $17.7$\% reduction in $J$. This was achieved by replacing small-amplitude waviness periods with flat segments, which additionally significantly facilitates manufacturability.

Molecular visualization software has long supported research and education in chemical and structural sciences, but consumer devices constrained to 2D inputs and outputs pose two major challenges: they poorly convey 3D nature, and 3D manipulation is very difficult. eXtended Reality (XR, including AR and VR) offers new ways to see and interact with molecules in three dimensions. This chapter presents the "MolecularWeb" ecosystem (this https URL), a set of web-based tools for immersive visualization, modeling, and simulations, already widely used in education and science communication and now expanding toward research applications. We cover moleculARweb, which provides AR educational activities via phones, tablets, and computers; MolecularWebXR, a multiuser WebXR platform accessible from both headsets and simpler devices, supporting immersive education, outreach, and scientific discussion; and PDB2AR, which enables users to generate custom content for MolecularWebXR and standalone AR/VR. Finally, we introduce a prototype and an upcoming version of HandMol, our latest WebXR software which allows concurrent multiuser immersive visualization and modeling of molecules with bare hands supported by real-time molecular mechanics, natural language input via a language model, and access through both high-end headsets or consumer devices like smartphones and laptops. Together, these tools demonstrate the present and near-future of accessible, interactive molecular science on the web.

The simple 3-D radiative transfer model in the atmosphere of the Earth is built for numerical comparison of direct solar radiation and limb scattering background at the definite layer during the deep twilight period at the middle and upper atmosphere. This allows finding the contribution of multiple scattering in the field of high-altitude clouds such as polar stratospheric and noctilucent clouds. This is the factor that can influence the results of altitude and microphysical study of the particles based on the approximation of single scattering. The possible errors caused by this effect are estimated together with the contribution of multiple scattering for different altitudes of the clouds, wavelengths and solar zenith angles. The results are interpreted geometrically and optically, color effects observed for noctilucent clouds are described.

The rigorous QED approach is employed to calculate the energies of the $2p2p\,^3P_{0,1,2}$, $2p2p\,^1D_2$, and $2p2p\,^1S_0$ states of selected Be-like highly charged ions over a wide range of nuclear-charge numbers, $18 \leqslant Z \leqslant 92$. Combined with the previously reported energies of the $2s2p \, ^3P_{0,1,2}$ and $2s2p \, ^1P_1$ states [A. V. Malyshev et al., Phys. Rev. A 110, 062824 (2024)], the obtained results are used to study various intra-$L$-shell transition energies. Strong level mixing, caused by the proximity of states with the same symmetry, is overcome by means of the QED perturbation theory for quasi-degenerate levels. The applied approach merges a rigorous perturbative QED treatment up to the second order with the consideration of electron-electron correlation contributions of the third and higher orders evaluated within the Breit approximation. The higher-order screened QED effects are estimated using the model-QED-operator approach. The nuclear-recoil and nuclear-polarization effects are also taken into account. The obtained predictions represent the most accurate theoretical description of the electronic structure of Be-like ions to date and demonstrate good agreement with available experimental data.

This study investigates the Lagrangian properties of ion turbulent transport driven by drift-type turbulence in tokamak plasmas. Despite the compressible and inhomogeneous nature of Eulerian gyrocenter drifts, numerical simulations with the T3ST code reveal approximate ergodicity, stationarity, and time-symmetry. These characteristics are attributed to broad initial phase-space distributions that support ergodic mixing. Moreover, relatively minor constraints on the initial distributions are found to have negligible effects on transport levels.

Graphene field-effect transistors (GFETs) are among the most promising platforms for ultrasensitive chemical and biological sensing due to their high carrier mobility, large surface area, and low intrinsic noise. However, conventional single-gate GFET sensors in liquid environments suffer from severe limitations, including signal drift, charge trapping, and insufficient signal amplification. Here, we introduce a dual-gate GFET architecture that integrates a high-k hafnium dioxide local back gate with an electrolyte top gate, coupled with real-time feedback biasing. This design enables capacitive signal amplification while simultaneously suppressing gate leakage and low-frequency noise. By systematically evaluating seven distinct operational modes, we identify the Dual Mode Fixed configuration as optimal, achieving up to 20x signal gain, > 15x lower drift compared with gate-swept methods, and up to 7x higher signal to noise ratio across a diverse range of analytes, including neurotransmitters, volatile organic compounds, environmental contaminants, and proteins. We further demonstrate robust, multiplexed detection using a PCB-integrated GFET sensor array, underscoring the scalability and practicality of the platform for portable, high-throughput sensing in complex environments. Together, these advances establish a versatile and stable sensing technology capable of real-time, label-free detection of molecular targets under ambient and physiological conditions, with broad applicability in health monitoring, food safety, agriculture, and environmental screening.

I present a simple, inexpensive, and engaging laboratory activity designed for undergraduate chemistry education that illustrates the Beer-Lambert law using everyday materials. The choice of tomato juice as the colored analyte, due to its strong visible absorption from lycopene, a carotenoid pigment, makes this activity relevant to real-world applications. To prepare a concentration series, I diluted tomato juice with tap water to yield seven samples (0, 2.5, 5, 10, 25, 50, and 100%). Transmission spectra for each sample were recorded by passing light from a halogen lamp through a 1 cm cuvette and measuring the transmitted intensity with a spectrometer covering the visible region. The raw spectra revealed clear attenuation in the 480-520 nm region, consistent with the absorption band of lycopene. By converting the data to absorbance and plotting against concentration, I observed a linear relationship in the dilute region, confirming the Beer-Lambert law. At higher concentrations, deviations from linearity were evident, which can be attributed to scattering effects, molecular aggregation, and instrumental limitations. These deviations offer valuable opportunities for classroom discussions about the practical boundaries of the law, thereby preparing students for real-world applications. The activity requires only standard laboratory glassware and a broadband lamp, making it highly accessible. Its combination of visual impact, quantitative analysis, and discussion of limitations enhances student understanding of both the power and the boundaries of spectroscopic methods.

Two-step photon upconversion solar cells (TPU-SCs) belong to the class of solar cells that in principle can exceed the Shockley--Queisser limit for single-junction solar cells. A TPU-SC basically consists of a wide-gap and a narrow-gap semiconductor layer, where intraband absorption of infrared (IR) photons by electrons accumulated at the conduction-band-edge discontinuity leads to an increase in the output current and voltage. This IR-induced upconversion process enables a better utilization of the broad solar spectrum, and quantum dots (QDs) can be added to improve the intraband transition rates in actual devices. In this study, we added an n--p--n double-tunnel-junction structure to an $\mathrm{Al_{0.3}Ga_{0.7}As}$/GaAs TPU-SC including a QD layer to further enhance the contribution of intraband absorption to the output current. The double-tunnel-junction structure should suppress carrier recombination at the heterointerface and improve the extraction efficiency of electrons after intraband absorption. We performed two-color excitation experiments using IR and 784-nm light, and we particularly found that 784-nm light causes a significant amount of intraband transitions in this device. By employing rate equations, we clarify that this result demonstrates a higher intraband absorption efficiency due to the double-tunnel-junction structure. This highlights the potential of double-tunnel-junction structures for the realization of high-efficiency TPU-SCs.

This study investigates the effect of increasing strain rate on thermodiffusively unstable, lean premixed hydrogen flames in a 2D counterflow configuration through detailed-chemistry numerical simulations for the first time. The analysis of transient flame dynamics without imposed perturbations reveals that a steady-state flame front is achieved only when the strain rate exceeds a certain threshold. Below this threshold, the flame exhibits unstable oscillatory behavior. When subjected to a range of perturbation wavelengths, the flame front exhibits an exponentially increasing wavelength over time, driven by the flame-tangential velocity component, with the applied strain rate acting as the amplification factor. It is shown that any perturbation is damped at sufficiently high applied strain rate conditions after a transient phase. At these high strain regimes, the growth rate transient follows a characteristic onset that depends uniquely on the initial perturbation wavelength and exhibits a linear dependence on the applied strain rate.

Biophotonics-and more recently, biointegrated photonics-offer transformative tools for probing cellular processes with unprecedented precision. Among these, whispering gallery mode (WGM) resonators-optical microcavities formed in spherical structures-have emerged as powerful biosensors and intracellular barcodes. Lipid droplets (LDs), with their high refractive index and intrinsic spherical geometry, are ideal candidates for supporting intracellular lasing. Although lasing in LDs has been previously demonstrated, it has not yet been harnessed to study live cell biology. Here, we report the first use of WGM resonances in LDs of live primary adipocytes, employing a continuous-wave (CW) laser at powers below the biological damage threshold. By measuring these resonances, we achieved nanometer-scale precision in size estimation, enabling real-time observation of rapid LD dynamics and deformations on the minute scale, far beyond the spatio-temporal resolution of conventional microscopy. We systematically characterized this photonic sensing approach, demonstrating its ability to resolve adipocyte heterogeneity, monitor lipolytic responses to forskolin and isoproterenol, and detect early signs of cell viability loss, well before conventional assays. This proof-of-concept establishes intracellular LD WGM resonances as a robust platform for investigating live single-cell metabolism. The technique enables rapid, cost-effective assessment of adipocyte function, reveals cell-to-cell variability obscured by bulk assays, and lays the foundation for high-throughput analysis of metabolism- and obesity-related diseases at both cellular and tissue levels.

We present a comprehensive sensitivity study of future CE$\nu$NS detectors, focusing on a cryogenic cesium iodide detector and a tonne-scale liquid argon one, currently being developed by the COHERENT Collaboration. These setups will enable precision measurements of the weak mixing angle at low energies and allow accurate extraction of the neutron nuclear distribution radius. We also demonstrate that next-generation detectors will place constraints on the neutrino charge radius comparable to or better than current global fits. In addition, we explore the sensitivity to non standard neutrino electromagnetic properties, such as magnetic moments and millicharges, as well as new mediators. These findings reinforce the role of CE$\nu$NS experiments in the upcoming precision era, with future detectors playing a key role in advancing our understanding of neutrino interactions and electroweak physics at low energies.

Heat propagation is governed by phonon interactions and mathematically described by partial differential equations (PDEs), which link thermal transport to the intrinsic properties of materials. Conventional experimental techniques infer thermal responses based on surface emissions, limiting their ability to fully resolve subsurface structures and internal heat distribution. Additionally, existing thermal tomographic techniques can only shoot one frame from each layer. Physics-informed neural networks (PINNs) have recently emerged as powerful tools for solving inverse problems in heat transfer by integrating observational data with physical constraints. However, standard PINNs are primarily focused on fitting the given external temperature data, without explicit knowledge of the unknown internal temperature distribution. In this study, we introduce a Helmholtz-informed neural network (HINN) to predict internal temperature distributions without requiring internal measurements. The time-domain heat diffusion equation was converted to the frequency-domain and becomes the pseudo-Helmholtz equation. HINN embeds this pseudo-Helmholtz equation into the learning framework, leveraging both real and imaginary components of the thermal field. Finally, an inverse Fourier transform brings real-part and imagery-part back to the time-domain and can be used to map 3D thermal fields with interior defects. Furthermore, a truncated operation was conducted to improve computational efficiency, and the principle of conjugate symmetry was employed for repairing the discarded data. This approach significantly enhances predictive accuracy and computational efficiency. Our results demonstrate that HINN outperforms state-of-the-art PINNs and inverse heat solvers, offering a novel solution for non-invasive thermography in applications spanning materials science, biomedical diagnostics, and nondestructive evaluation.

The development of advanced diagnostic systems to measure and optimize emerging energetic material performance is critical for the defeat of Chemical Warfare Agents (CWA). This study presents an integrated multi-spectroscopic approach to monitor the interaction between a CWA simulant, Diisopropyl Methyl Phosphonate (DIMP), and combusting composite metal particles. A custom benchtop Polygonal Rotating Mirror Infrared Spectrometer (PRiMIRS), equipped with a customizable experimental chamber, is employed to observe DIMP decomposition. Tunable Diode Laser Absorption Spectroscopy (TDLAS) is used to measure path-averaged gas temperature profiles during combustion. In the experiment, the chamber is preheated to evaporate liquid DIMP. Various composite metal powders (Al-8Mg):3Zr, (Al-8Mg):Zr, 2(Al-8Mg):Zr, and 4(Al-8Mg):Zr are placed on a stainless steel mount and ignited using 3Al-2Ni sputter-deposited nanolayered foils. The combusting metal particles mix with the DIMP vapor, initiating chemical and thermal interactions. PRiMIRS captures DIMP spectral evolution, while TDLAS simultaneously monitors gas temperature. A spectral defeat parameter was developed to enable quantitative real-time assessment of the DIMP destruction. It uses infrared light absorption by both from DIMP and its immediate decomposition products Isopropyl Methyl Phosphonate (IMP) and Isopropyl Alcohol (IPA). Fourier Transform Infrared Spectroscopy (FTIR) serves as a secondary verification tool quantifying the decomposition products over extended timeframes, and Transmission Electron Microscopy (TEM) confirms the expected metal oxide dispersion within the reaction space. This study reports variability in DIMP defeat as a function of metal powder stoichiometry, metal powder loading, and path-averaged gas temperature profiles, offering critical insights into optimizing reactive materials for effective CWA neutralization.

Modeling political structures by simplicial complexes, we investigate whether introducing a mediator into a substructure increases or decreases the stability of the overall structure. We prove theorems that quantify the stability of a political structure when $n$ mediators are introduced, either one by one or simultaneously. We also examine how the stability is affected when a single agent is split into two. In addition, stability is expressed in terms of the $h$-vector, and special attention is given to a class of political structures modeled by shellable simplicial complexes. In the latter context, we analyze weighted political structures and examples of political structures modeled by independence complexes of graphs.

Photo-thermal methods for measuring thermal diffusivity inherently pose an ill-posed inverse problem, affected by factors such as sample thickness, heating or cooling time, and excitation energy. Measurement accuracy becomes particularly challenging under non-impulsive pulsed excitation when the observation timescale is comparable to the pulse duration. This is often due to poorly defined pulse shapes, broadened thermal responses, and the absence of clear boundary conditions, especially under significant interfacial temperature gradients where natural convection dominates. The classic Parker solution, while widely used, is physically unrealistic as it assumes adiabatic heat flux and shallow-region heat absorption. In this study, we prove that Parker's assumption is equivalent to the Dirac pulse boundary condition in mathematics. Then, we present comprehensive analytical solutions for thermal/cooling responses under Dirac and rectangular pulse excitations. By comparing with eigenfunction-based solutions for well-posed boundary conditions, we show that Parker's solution is only valid before the thermal peak. Through dimensionless processing, we further demonstrate that Parker's solution can be regarded as a limiting case of the rectangular pulse solution as the heating duration approaches zero. Furthermore, we propose a novel excitation approach, evaporative cryocooling, for thermal diffusivity measurement. This method offers a compact, low-cost, and easy-to-implement alternative to conventional excitation schemes. The theoretical model was further validated through comparison with experimental results.

Results from the 2023 negative triangularity campaign on DIII-D demonstrate encouraging energy confinement properties, similar to or exceeding the scaling of the IPB98(y,2) law. This paper describes the procedure with which a new scaling law was regressed specifically from the data from the DIII-D campaign. Given the relatively small size of the single-machine dataset, measures were taken to minimize sampling bias and give a realistic estimate of the large uncertainties from the regression. The resulting power law shows a robustly stronger dependence on plasma current and more severe power degradation as compared to the H-mode scaling law.

Molecular dynamics simulations hold great promise for providing insight into the microscopic behavior of complex molecular systems. However, their effectiveness is often constrained by long timescales associated with rare events. Enhanced sampling methods have been developed to address these challenges, and recent years have seen a growing integration with machine learning techniques. This review provides a comprehensive overview of how they are reshaping the field, with a particular focus on the data-driven construction of collective variables. Furthermore, these techniques have also improved biasing schemes and unlocked novel strategies via reinforcement learning and generative approaches. In addition to methodological advances, we highlight applications spanning different areas such as biomolecular processes, ligand binding, catalytic reactions, and phase transitions. We conclude by outlining future directions aimed at enabling more automated strategies for rare-event sampling.

A phononic frequency comb consists of equally spaced components in the mechanical frequency domain and holds promise for numerous applications. Yet, prior demonstrations have been limited in spectral range due to the inherently low mechanical frequencies. In this work, we report a phononic comb with a record span from 1 to 70 GHz. This result is achieved by harnessing the strong mechanical nonlinearity of a $2.5$-$\mu$m-radius silicon carbide microdisk, which supports a radial breathing mode at $1.655$ GHz with a mechanical quality factor of 13,500. With just 1 mW of dropped optical power, radiation pressure from a continuous-wave pump drives strong phonon lasing, generating 42 phase-locked harmonics with $1.655$ GHz spacing. The combination of such broad bandwidth, low phase noise (-132 dBc/Hz at 1 MHz offset frequency) and frequency stability ($<10^{-7}$ at 1 second of averaging time) positions this ultracompact phononic comb as a powerful platform for diverse applications.

The prompt identification of pancreatic cancer symptoms is an ongoing clinical challenge, often leading to late diagnosis and poor prognosis. Tumor 'hijacking' of the pancreatic stromal structure limits the uptake of systemic chemotherapeutics. Localized drug delivery systems (DDS) using endoluminal techniques are widely utilized, with positive early results for improved control over tumor growth. There is a need for technologies that integrate endoluminal resources and intelligent material systems to better control the spatiotemporal delivery of chemotherapeutics. We demonstrate the ultrasound (US)-triggered localized release of therapeutics through the design of solvent traceless drug-loaded vinylbenzyl-functionalized gelatin (gel4vbc) nanoparticles (NPs) integrated with an electrospun fabric. Albumin-loaded NPs encapsulated into a poly(vinyl alcohol) (PVA) coating of a poly(epsilon-caprolactone) fabric evidence tunable triggered NP delivery controlled by regulating PVA concentration (0-1 wt.%) and US intensity (0-3 W/cm2). The fixation of the NP-coated fabric to a magnetic tentacle robot (MTR) enables the automated manipulation into a phantom pancreatic duct before the US-triggered release of NP-loaded albumin and MTR retraction. Albumin release is controlled by varying the surface area of the NP-loaded MTR-coating fabric. Herein we have designed a novel DDS capable of facile integration into soft robotics and US-triggered delivery of therapeutic-loaded NPs.

The present study investigates the dynamics of nonlocal beams by establishing a consistent stress-driven integral elastic using the Physics-Informed Neural Network (PINN) approach. Specifically, a PINN is developed to compute the first eigenfunction and eigenvalue arising from the underlying sixth-order ordinary differential equation. The PINN is based on a feedforward neural network, with a loss function composed of terms from the differential equation, the normalization condition, and both boundary and constitutive boundary conditions. Relevant eigenvalues are treated as separate trainable variables. The results demonstrate that the proposed method is a powerful and robust tool for addressing the complexity of the problem. Once trained, the neural network is less computationally intensive than analytical methods. The obtained results are compared with benchmark analytical solutions and show strong agreement.

Zinc sulfide (ZnS:Ag) scintillators are widely used for ultracold neutron (UCN) detection, but their application is limited by long decay times and pronounced phosphorescence. We tested two possible replacement scintillators: yttrium aluminum perovskite (YAP:Ce) and lutetium yttrium orthosilicate (LYSO:Ce). Both have decay times on the order of 30-40 ns, which can help reduce dead time in high count rate experiments. YAP:Ce showed a 60% lower phosphorescence when compared to ZnS:Ag after 2 days and outperformed ZnS:Ag in counting UCN by about 20%. On the other hand, LYSO:Ce exhibited more phosphorescence and produced fewer UCN counts compared to both ZnS:Ag and YAP:Ce. Both of these scintillators are viable UCN detectors for high count rate experiments, but YAP:Ce outperformed LYSO:Ce by every tested metric.

Spatially-periodic channels are increasingly attracting attention as an efficient alternative to packed columns for a number of analytical and engineering processes. In incompressible flows, the periodic geometry allows to compute the flow structure by solving the Navier-Stokes (NS) equations in the minimal periodic cell of the structure, however large the pressure gradient. Besides, when gas flow under large pressure drops are dealt with, the velocity field is not periodic because of the density dependence on pressure. In this case, the momentum balance equations must be solved numerically on the entire channel, thereby requiring massive computational effort. Based on the marked separation of scales between the length of the periodic cell and the overall channel length characterizing many applications we develop a general method for predicting both the large-scale pressure and velocity profiles, and the small-scale flow structure. The approach proposed is based on the assumption that the local dimensionless pressure drop as a function of the Reynolds number, say $g({\rm Re})$, can be estimated from the solution of the incompressible NS equations within the minimal periodic cell of the channel. From the knowledge of $g({\rm Re})$, the pressure profile $P(Z)$ vs the large scale axial coordinate $Z$ is derived analytically by quadratures. We show how qualitatively different profiles $P(Z)$ can be obtained depending on the equation of state the gas. The approach is validated by comparing the predicted profiles with the full-scale numerical solution of the compressible NS equations in different axially-symmetric periodic channel geometries.

The number and nature of atomic configurations are cornerstones of atomic spectroscopy, especially for the calculation of hot-plasma radiative properties. The knowledge of the distributions of magnetic quantum number $M$ and angular momentum $J$ for $N$ identical fermions in a subshell with half-integer spin $j$ is a prerequisite to the determination of the structure of such configurations. The problem is rather complicated, since the possible occurrence of a specific values of $J$ is governed by the Pauli exclusion principle. Several methods, such as generating functions, recurrence relations or algebraic number theory, for instance via Gaussian polynomials, have proven effective in addressing this issue. However, up to now, no general formula was known. In the present work, we present exact and compact explicit formulas for the number of atomic configurations and for the distributions of the total magnetic quantum number $M$ and angular momentum $J$.

Time-dependent, predictive simulations were performed with the 1.5D tokamak integrated modeling code TRANSP on a large set of well-analyzed, high performing discharges from the National Spherical Torus Experiment (NSTX) in order to evaluate how well modern reduced transport models can reproduce experimentally observed temperature profiles in spherical tokamaks. Overall, it is found that simulations using the Multi-Mode Model (MMM) more consistently agree with the NSTX observations than those using the Trapped Gyro-Landau Fluid (TGLF) model, despite TGLF requiring orders of magnitude greater computational cost. When considering all examined discharges, MMM has median overpredictions of electron temperature ($T_e$) and ion temperature ($T_i$) profiles of 28% and 27%, respectively, relative to the experiment. TGLF overpredicts $T_e$ by 46%, with much larger variance than MMM, and underpredicts $T_i$ by 25%. As $\beta$ is increased across NSTX discharges, TGLF predicts lower $T_e$ and significant flattening of the $T_i$ profile, conflicting with NSTX observations. When using an electrostatic version of TGLF, both $T_e$ and $T_i$ are substantially overpredicted, underscoring the importance of electromagnetic turbulence in the high $\beta$ spherical tokamak regime. Additionally, calculations with neural net surrogate models for TGLF were performed outside of TRANSP with a time slice flux matching transport solver, finding better agreement with experiment than the TRANSP simulations, highlighting the impact of different transport solvers and simulation techniques. Altogether, the reasonable agreement with experiment of temperature profiles predicted by MMM motivates a more detailed examination of the sensitivities of the TRANSP simulations with MMM to different NSTX plasma regimes in a companion paper, in preparation for self-consistent, time-dependent predictive modeling of NSTX-U scenarios.

The Multi-Mode Model (MMM) for turbulent transport was applied to a large set of well-analyzed discharges from the National Spherical Torus Experiment (NSTX) in order to evaluate its sensitivities to a wide range of plasma conditions. MMM calculations were performed for hundreds of milliseconds in each discharge by performing time-dependent predictive simulations with the 1.5D tokamak integrated modeling code TRANSP. A closely related study concluded that MMM predicted electron ($T_e$) and ion ($T_i$) temperature profiles that were in reasonable agreement with NSTX observations, generally outperforming a different reduced transport model, TGLF, motivating a more thorough investigation of the characteristics of the MMM predictions. The simulations with MMM have electron energy transport dominated by electron temperature gradient modes for relatively low plasma $\beta$ and high collisionality, transitioning to a mixture of different modes for higher $\beta$ and lower collisionality. The thermal ion diffusivity predicted by MMM is much smaller than the neoclassical contribution, in line with previous experimental analysis of NSTX. Nonetheless, the $T_e$ and $T_i$ profiles are coupled via collisional energy exchange and thus sensitive to which transport channels are predicted. The simulations with MMM are robust to the simulation start time, converging to remarkably similar temperatures later during the discharge. MMM typically overpredicts confinement relative to NSTX observations, leading to the prediction of overly steep profiles. Plasmas with spatially broader $T_e$ profiles, higher $\beta$, and longer energy confinement times tend to be predicted by MMM with better agreement with the experiment. These findings provide useful context for understanding the regime-dependent tendencies of MMM in anticipation of self-consistent, time-dependent predictive simulations of NSTX-U discharges.

The detection and characterization of lunar water are critical for enabling sustainable human and robotic exploration of the Moon. Orbital neutron spectrometers, such as instruments on Lunar Prospector and the Lunar Reconnaissance Orbiter, have revealed hydrogen-rich regions near the poles but are limited by coarse spatial resolution and low counting efficiency. We present a compact, lightweight, and low-power epithermal neutron detector based on boron-coated silicon imagers, designed to probe subsurface hydrogen at decimeter scales from mobile platforms such as lunar rovers. This instrument leverages the high neutron capture cross-section of $^{10}$B to convert epithermal neutrons into detectable $\alpha$ and $^{7}$Li ions in a fully-depleted silicon imager, providing a unique event topology to identify neutrons while suppressing backgrounds. Monte Carlo simulations demonstrate that a 3 $\mu$m boron layer achieves optimal neutron detection efficiency, further enhanced with polyethylene moderation to improve sensitivity to the 0.4 eV-500 keV epithermal energy range. For a 10 cm$^2$ active area, the detector achieves sensitivity to H$_2$O weight fractions as low as 0.01 wt % in a 15 minute measurement. This scalable, portable, low-mass design is well-suited for integration into upcoming Artemis and commercial lunar rovers, providing a transformative capability for in-situ resource prospecting and ground-truth validation of orbital measurements.

While dissipation in collisional plasma is defined in terms of viscosity and resistivity, the exact functional form of dissipation i.e., the so-called dissipation function in nearly collisionless plasma is unknown. Nevertheless, previous studies have suggested that there exists viscous-like energy conversion in collisionless plasma with scaling characteristics analogous to collisional plasma, and in particular that the average dissipation is proportional to the square of the rate of strain as in hydrodynamics. In this study, using 2.5D kinetic particle-in-cell (PIC) simulation of collisionless plasma turbulence, we provide an estimate of effective viscosity at each scale, obtained via a scale-filtering approach. We then compare the turbulent dynamics of the PIC simulation with that from MHD and two-fluid simulations in which with the viscosity is equal to the effective viscosity estimate obtained from the PIC simulation. We find that the global behavior in these MHD and two-fluid simulations has a striking similarity with that in its kinetic/PIC counterpart. In addition, we explore the scale dependence of the effective viscosity, and discuss implications of this approach for space plasmas.

As an introductory exercise of elementary electrodynamics, we consider the static magnetic field induced by the electric currents flow along the straight wires, which are equidistantly put on the hyperboloid. The distribution of the magnetic field and the force acting on the wires are calculated under several typical setups, including the continuum limit.

Ytterbium-171 is a versatile atomic species often used in quantum optics, precision metrology, and quantum computing. Consolidated atomic data is essential for the planning, execution, and evaluation of experiments. In this reference, we present physical and optical properties of neutral $^{171}$Yb relevant to these applications. We emphasize experimental results and supplement these with theoretical estimates. We present equations to convert values and derive important parameters. Tabulated results include key parameters for commonly used transitions in $^{171}$Yb (${}^1\mathrm{S}_0\rightarrow{}^1\mathrm{P}_1$, ${}^1\mathrm{S}_0\rightarrow{}^3\mathrm{P}_{0,1,2}\,$, ${}^3\mathrm{P}_{0,2}\rightarrow{}^3\mathrm{S}_1$, and ${}^3\mathrm{P}_0\rightarrow{}^3\mathrm{D}_1$). This dataset serves as an up-to-date reference for studies involving fermionic $^{171}$Yb.

We propose two new diagnostics for the degree to which static correlation impacts the quality of a coupled cluster calculation. The first is the change in the Matito static correlation diagnostic $\overline{I_{ND}}$ between CCSD and CCSD(T), $\Delta I_{ND}[\textrm{(T)}]=\overline{I_{ND}}[\textrm{CCSD(T)}]-\overline{I_{ND}}[\textrm{CCSD}]$. The second is the ratio of the same and of the corresponding change in the total correlation diagnostic $\overline{I_{T}}=\overline{I_{ND}}+\overline{I_{D}}$, i.e., $r_I[(T)]=\Delta I_{ND}[\textrm{(T)}]/\Delta I_{T}[\textrm{(T)}]$. The first diagnostic can be extended to higher-order improvements in the wave function, e.g., $\Delta I_{ND}[\textrm{(Q)}]=\overline{I_{ND}}[\textrm{CCSDT(Q)}]-\overline{I_{ND}}[\textrm{CCSDT}]$. In general, a small $\Delta I_{ND}$[\textrm{level$_1$}] value indicates that at this level$_1$ of theory, the density is converged and any further changes to the energy come from dynamical correlation, while larger $\Delta I_{ND}$[\textrm{level$_2$}] indicates that the density is still not converged at level$_2$ and some static correlation remains. $r_I[(T)]$ is found to be a moderately good predictor for the importance of post-CCSD(T) correlation effects.

Machine-learning interatomic potentials (MLIPs) have become a mainstay in computationally-guided materials science, surpassing traditional force fields due to their flexible functional form and superior accuracy in reproducing physical properties of materials. This flexibility is achieved through mathematically-rigorous basis sets that describe interatomic interactions within a local atomic environment. The number of parameters in these basis sets influences both the size of the training dataset required and the computational speed of the MLIP. Consequently, compressing MLIPs by reducing the number of parameters is a promising route to more efficient simulations. In this work, we use low-rank matrix and tensor factorizations under fixed-rank constraints to achieve this compression. In addition, we demonstrate that an algorithm with automatic rank augmentation helps to find a deeper local minimum of the fitted potential. The methodology is verified using the Moment Tensor Potential (MTP) model and benchmarked on multi-component systems: a Mo-Nb-Ta-W medium-entropy alloy, molten LiF-NaF-KF, and a glycine molecular crystal. The proposed approach achieves up to 50% compression without any loss of MTP accuracy and can be applied to compress other MLIPs.

For a transition between two stable states, the committor is the probability that the dynamics leads to one stable state before the other. It can be estimated from trajectory data by minimizing an expression for the transition rate that depends on a lag time. We show that an existing such expression is minimized by the exact committor only when the lag time is a single time step, resulting in a biased estimate in practical applications. We introduce an alternative expression that is minimized by the exact committor at any lag time. Numerical tests on benchmark systems demonstrate that our committor and resulting transition rate estimates are much less sensitive to the choice of lag time. We derive an additional expression for the transition rate, relate the transition rate expression to a variational approach for kinetic statistics based on the mean-squared residual, and discuss further numerical considerations with the aid of a decomposition of the error into dynamic modes.

In recent years, the introduction of deep learning into the field of single-pixel imaging has garnered significant attention. However, traditional networks often operate within the pixel space. To address this, we innovatively migrate single-pixel imaging to the latent space, naming this framework LSSPI (Latent Space Single-Pixel Imaging). Within the latent space, we conduct in-depth explorations into both reconstruction and generation tasks for single-pixel imaging. Notably, this approach significantly enhances imaging capabilities even under low sampling rate conditions. Compared to conventional deep learning networks, LSSPI not only reconstructs images with higher signal-to-noise ratios (SNR) and richer details under equivalent sampling rates but also enables blind denoising and effective recovery of high-frequency information. Furthermore, by migrating single-pixel imaging to the latent space, LSSPI achieves superior advantages in terms of model parameter efficiency and reconstruction speed. Its excellent computational efficiency further positions it as an ideal solution for low-sampling single-pixel imaging applications, effectively driving the practical implementation of single-pixel imaging technology.

The TianQin(TQ) program is to deploy three satellites that can form an equilateral triangle in about 100,000 km Earth orbit to capture gravitational wave signals in the low-frequency band. In order to ensure accurate capture, noise needs to be analyzed and compensated. In this paper, we model and analyze the acceleration noise generated by the test mass affected by the magnetic field in space. In this paper, we use the Tsyganenko model as the background magnetic field of the TQ orbit, calculate the magnetic field and magnetic field gradient of the satellite orbit from 1997 to 2023, analyze the acceleration noise due to the coupling of the residual magnetic moment, the induced magnetic moment with the magnetic field in space and the acceleration noise due to the Lorentz force, and calculate the acceleration integrated noise of the influence of the magnetic field on the test mass from the power spectral densities of the modeled magnetic field and the magnetic field gradient. The acceleration integrated noise of the magnetic field influence on the test mass is calculated from the power spectral density of the magnetic field and the magnetic field gradient obtained by the model. Through the simulation study, the acceleration of the test mass induced by the magnetic field in the space of the TQ orbit reaches the magnitude of $10^{-16}ms^{-1}Hz^{-1/2}$, which is an important source of the influencing noise. The acceleration noise induced by the magnetic field and the Lorentz force is relatively higher than that induced by the magnetic field gradient.

We present constraints on low mass dark matter-electron scattering and absorption interactions using a SuperCDMS high-voltage eV-resolution (HVeV) detector. Data were taken underground in the NEXUS facility located at Fermilab with an overburden of 225 meters of water equivalent. The experiment benefits from the minimizing of luminescence from the printed circuit boards in the detector holder used in all previous HVeV studies. A blind analysis of $6.1\,\mathrm{g\cdot days}$ of exposure produces exclusion limits for dark matter-electron scattering cross-sections for masses as low as $1\,\mathrm{MeV}/c^2$, as well as on the photon-dark photon mixing parameter and the coupling constant between axion-like particles and electrons for particles with masses $>1.2\,\mathrm{eV}/c^2$ probed via absorption processes.

In physics geometrical connections are the mean to create models with local symmetries (gauge connections), as well as general diffeomorphisms invariance (affine connections). Here we study the irreducible tensor decomposition of connections on the tangent bundle of an affine manifold as used in the polynomial affine model of gravity. This connection is the most general linear connection, which allows us to build metric independent, diffeomorphism invariant models. This set up includes parts of the connection that are associated with conformal and projective transformations.

This paper builds on a recently introduced dynamical networking framework, applying it to model motor-driven transport along cytoskeletal filament networks. Within this approach, the networking functional describes the periodic binding and unbinding of motors to available filament sites,whilst accounting for all possible pairing, enabling a field-theoretic treatment of constrained motion in complex networks. In this application, the dynamical networking theory is introduced into a Martin-Siggia-Rose representation of the Langevin dynamics describing the motion of a motor protein and its cargo. Results are presented in a collective description of motors on a network, for two different scenarios, namely homogeneous and non-homogeneous networks. A diffusion coefficient is presented for homogeneous networks, whilst it is shown that various possibilities remain for disordered averaging over network densities for non-homogeneous networks.

Employing non-Hermitian Floquet theory, the strong-field process of high harmonic (HH) generation by a classical continuous-wave field irradiating ground-state atom is discovered to be controllable by placing the irradiated atom inside a single-mode quantum cavity initiated even with a single photon. Judicious cavity coupling of cavity-free photo-induced atomic Floquet states forms polaritonic Floquet states that generate side harmonics around the (standard no-cavity) odd harmonics. The different possible cavity-controlled HH spectra, including also the ones resulting from several cavities in a row, enable attosecond-pulse sequences different from the one produced without a cavity. Moreover, the present study sets the framework and opens the way for further cavity control over the HH generation process as well as over other strong-field processes

Dislocations are line defects in crystals that multiply and self-organize into a complex network during strain hardening. The length of dislocation links, connecting neighboring nodes within this network, contains crucial information about the evolving dislocation microstructure. By analyzing data from Discrete Dislocation Dynamics (DDD) simulations in face-centered cubic (fcc) Cu, we characterize the statistical distribution of link lengths of dislocation networks during strain hardening on individual slip systems. Our analysis reveals that link lengths on active slip systems follow a double-exponential distribution, while those on inactive slip systems conform to a single-exponential distribution. The distinctive long tail observed in the double-exponential distribution is attributed to the stress-induced bowing out of long links on active slip systems, a feature that disappears upon removal of the applied stress. We further demonstrate that both observed link length distributions can be explained by extending a one-dimensional Poisson process to include different growth functions. Specifically, the double-exponential distribution emerges when the growth rate for links exceeding a critical length becomes super-linear, which aligns with the physical phenomenon of long links bowing out under stress. This work advances our understanding of dislocation microstructure evolution during strain hardening and elucidates the underlying physical mechanisms governing its formation.

The nuclides ^{92}Zr are produced and transported by using a radioactive beam line to a lowbackground detection station. After a flight time of about 1.14 {\mu}s, the ions are implanted into a carbon foil, and four {\gamma} rays deexciting the 8+ state in ^{92}Zr are observed in coincidence with the implantation signals within a few nanoseconds. We conjecture that there exists an isomer located slightly above the 8^{+} state in ^{92}Zr. The isomeric lifetime in highly charged states is extended significantly due to the blocking of internal conversion decay channels, enabling its survival over the transportation. During the slowing-down process in the carbon foil, the ^{92}Zr ions capture electron and evolve toward neutral atoms, and consequently the lifetime is restored to a normal short value. Such a high-lying isomer depopulated by a low-energy transition may provide unique opportunity to develop nuclear {\gamma} laser.

Turbulent radiative mixing layers (TRMLs), where shear-driven turbulence between dense and diffuse phases produces intermediate-temperature gas with short cooling times, are ubiquitous in the interstellar and circumgalactic media. Drawing an analogy with Reynolds' decomposition, we perform a quasi-steady-state analysis of TRMLs, separating fields into mean and turbulent components. In the quasi-isobaric TRML, upstream gas cools and compresses before streamwise momentum is fully mixed across the shear layer, which exhibits the expected negative turbulent shear stress $R_{xz}$ that steadily spreads into the cold phase. The thermal pressure reaches a minimum within the TRML, compensated by a positive vertical compressive stress $R_{zz}$. The sum of upstream ram and thermal pressures balances the downstream thermal pressure. Radiative losses are offset by enthalpy dissipation, with a negative turbulent heat flux $Q_{z}$ indicating thermal transport within the TRML. The volume-averaged temperature profile is well fit by a tanh function -- an emergent property that, for the first time, enables a predictive theory for TRMLs consistent with numerical experiments, including the universality of their temperature-dependent emissivity distributions.

Quantum frequency conversion (QFC) plays a crucial role in constructing seamless interconnection between quantum systems operating at different wavelengths. To advance future quantum technology, chip-scale integrated QFC components, featuring high efficiency, small footprint, low power consumption and high scalability, are indispensable. In this work, we demonstrate the first hybrid integrated QFC chip on thin film lithium niobate platform that connects the telecom and visible bands. Benefiting from the periodically poled microring resonator with ulta-high normalized conversion efficiency of 386,000 %/W, an ultra-low pump power of 360 {\mu}W is achieved which is more than two orders of magnitude lower than traditional straight waveguide scheme. By injecting current into the chip, an on-chip quantum efficiency of 57% and a noise count of ~ 7k counts per second are achieved. Such an electrically pumped, integrated and scalable QFC chip would significantly advancing the integration of quantum network and the development of chip-scale quantum optical systems.

We develop a scattering theory formalism and use it to predict that a resonantly driven atomic ensemble weakly coupled to an optical mode can generate light with non-Gaussian correlations. Our approach -- based on a perturbative diagrammatic expansion of multi-photon interactions -- shows that photon-photon interaction mediated by the emitters causes the transmitted light to have a non-vanishing connected third-order correlation function $g_c^{(3)}$. We explain the temporal pattern of $g_c^{(3)}$ using the interaction processes in our diagrammatic expansion. A quantitative comparison with cascaded master equation simulations for small ensembles with optical depth $\mathrm{OD}\leq 2$ confirms that the perturbative results remain accurate across experimentally relevant optical depths and for drive strengths large enough to make the predicted non-Gaussian signatures detectable. We anticipate that state-of-the-art nanofibre-coupled atomic ensembles can experimentally demonstrate our predictions.

The prediction of complex dynamics remains an open problem across many domains of physics, where nonlinearities and multiscale interactions severely limit the reliability of conventional forecasting methods. Quantum reservoir computing (QRC) has emerged as a promising paradigm for information processing by exploiting the high dimensionality of the Hilbert space, where the dynamics of quantum systems take place. Here, we introduce a hybrid quantum-classical reservoir architecture capable of handling multivariate time series through quantum evolution combined with classical memory enhancement. Our model employs a five-qubit transverse-field Ising Hamiltonian with input-modulated dynamics and temporal multiplexing, enabling the encoding of input signals over multiple timescales. We apply this framework to two paradigmatic models of chaotic behavior in fluid dynamics, where multiscale dynamics and nonlinearities play a dominant role: a low-dimensional truncation of the two-dimensional Navier-Stokes equations and the Lorenz-63 system. By systematically scanning the quantum system's parameter space, we identify regions that maximize forecasting performance, as measured by the Valid Prediction Time. The observed robustness and reliable performances for both dynamical systems suggest that this hybrid quantum approach offers a flexible platform for modelling complex nonlinear time series.

$\alpha$-Sn exhibits a rich topological phase diagram, yet experimental methods to tune and distinguish these phases remain limited. Here, we investigated the helicity-dependent photocurrent (HDPC) in $\alpha$-Sn films of varying thickness grown on CdTe(110) by molecular beam epitaxy. The HDPC of the 5 nm $\alpha$-Sn film shows an odd-function dependence on incident angle, whereas that of the 10 and 30 nm films exhibit an even-function dependence. Combined with high-resolution transmission electron microscopy (HR-TEM), point-group symmetry analysis, and first-principles calculations, it is revealed that a thickness-driven topological phase transition from a two dimensional (2D) to a three dimensional (3D) topological insulator occurs between 5 and 10 nm. These results demonstrate that HDPC serves as a sensitive diagnostic tool for topological phase transitions. The tunable electronic properties of $\alpha$-Sn(110) films enable thickness- and strain-mediated control of topological states, establishing a versatile platform for exploring emerging topological phenomena and developing spin-based devices.

Oscillatory recurrent networks, such as the Harmonic Oscillator Recurrent Network (HORN) model, offer advantages in parameter efficiency, learning speed, and robustness relative to traditional non-oscillating architectures. Yet, while many implementations of physical neural networks exploiting attractor dynamics have been studied, implementations of oscillatory models in analog-electronic hardware that utilize the networks' transient dynamics so far are lacking. This study explores the feasibility of implementing HORNs in analog-electronic hardware while maintaining the computational performance of the digital counterpart. Using a digital twin approach, we trained a four-node HORN in silico for sequential MNIST classification and transferred the trained parameters to an analog electronic implementation. A set of custom error metrics indicated that the analog system is able to successfully replicate the dynamics of the digital model in most test cases. However, despite the overall well-matching dynamics, when using the readout layer of the digital model on the data generated by the analog system, we only observed $28.39\%$ agreement with the predictions of the digital model. An analysis shows that this mismatch is due to a precision difference between the analog hardware and the floating-point representation exploited by the digital model to perform classification tasks. When the analog system was utilized as a reservoir with a re-trained linear readout, its classification performance could be recovered to that of the digital twin, indicating preserved information content within the analog dynamics. This proof-of-concept establishes that analog electronic circuits can effectively implement oscillatory neural networks for computation, providing a demonstration of energy-efficient analog systems that exploit brain-inspired transient dynamics for computation.

We demonstrate for the first time that a lateral impulse experienced by a granular channel can induce an inertial bulk dilation over long distances across a granular medium with a mechanically free surface. The surface dilation requires zero overburden pressure (exposure to vacuum) and is precipitated by the passing of waves traveling barely above the sound speed (> Mach 1.05). We simulate this phenomenon using open source Soft Sphere Discrete Element Method (SSDEM) software. We prepare channels of monodisperse, cohesive spherical particles exposed to vacuum and modeled as Hertzian springs. We validate our model by recreating acoustic wave, strong shock, and shear dilation behavior. We then create shocks within the channel to determine the sensitivity of surface dilation to wave speed, wave type, initial packing fraction, and boundary effects. The shocks we create undergo a rapid decay in strength and appear to propagate as solitary waves that can be sustained across the channel. We find that an inertial surface dilation is induced by compressive solitary waves, is insensitive to channel length, increases with bed height, and increases substantially with initial packing fraction. A hard subsurface floor is required to maintain this wave over the entire channel. Free surface dilation induced by laterally propagating impulse loading could be implicated in the formation of Lunar Cold Spots, distal regions of low thermal inertia surrounding young craters on the Moon.

Recurrently connected neuron populations play key roles in sensory perception and memory storage across various brain regions. While these populations are often assumed to encode information through firing rates, this method becomes unreliable with weak stimuli. We propose that in such cases, information can be transmitted via spatial spike patterns, employing a sparse or combinatorial coding based on firing rate variance. Around the critical point of a stochastic recurrent excitable network, we uncover a synergistic dual-coding scheme, enabled by single-cell threshold adaptation. This scheme optimizes variance coding for weak signals without compromising rate coding for stronger inputs, thus maximizing input/output mutual information. These optimizations are robust across adaptation rules and coupling strengths through self-suppression of internal noise, particularly around the network's phase transition, and are linked to threshold recovery times observed in hippocampal memory circuits (~$10^2$-$10^3$ms). In contrast, nonadaptive networks perform similarly only at criticality, suggesting that threshold adaptation is essential for reliable encoding of weak signals into diverse spatial patterns. Our results imply a fundamental role for near-critical latent adaptive dynamics enabled by dual coding in biological and artificial neural networks.

The shape of biological shells, such as cell nuclei, membranes, and vesicles, often deviates from a perfect sphere due to an interplay of complex interactions with a myriad of molecular structures. In particular, semiflexible biopolymers adsorbed to the surfaces of such shells seem to affect their morphological properties. While the effect of a single, long, semiflexible chain is relatively well characterized, the mechanisms by which a high density of such surface-adsorbed polymers can alter the morphology of a spherical, soft confinement, akin to biological shells, remain relatively poorly understood. Here, we use coarse-grained molecular dynamics simulations to investigate how surface adsorption of many semiflexible polymers affects the morphology of a pressurized bead-spring shell, which is spherical in the absence of these chains. By varying the attraction strength between the chains and the shell surface, chain concentration, and the polymerization degree of chains, we demonstrate that strong surface localization of the chains can induce severe shape distortions and shrinkage, depending on the chain length and concentration. Conversely, weak localization does not induce significant shape fluctuations, yet nematically ordered phases appear on the surface. Notably, these ordered phases lead to elliptic shell shapes for chains with sizes comparable to or longer than the radius of the confinement when the elastic shell is composed of extensible, harmonic bonds. Overall, our findings offer a strategy to control the shape of synthetic shells by manipulating peripheral localization and length of semiflexible polymers while suggesting a mechanism for non-spherical shapes appearing in some biological systems.

Reliable transmission of quantum optical states through real-world environments is key for quantum communication and imaging. Yet, aberrations and scattering in the propagation path can scramble the transmitted signal and hinder its use. A typical strategy is to employ a classical beacon beam to learn and then correct for the wavefront distortions. However, relying on a separate light source increases the overhead in the experimental apparatus. Moreover, the beacon light must closely match the non-classical state in polarization, wavelength, and even temporal bandwidth, which is highly challenging in practice. Here, we introduce a fast and efficient wavefront correction approach where we use the quantum state itself to correct for optical distortion. Via pump shaping, we control the degree of entanglement in the spatially-entangled two-photon state so that it behaves either as a high-dimensional entangled state or as a classical coherent state. The latter case is used to efficiently measure the transmission matrix of the propagation channel and correct its distortions with a spatial light modulator, thereby enabling the transmission of the high-dimensional entangled state with minimal errors. Our approach paves the way for the practical implementation of quantum imaging and communication protocols based on high-dimensional spatially entangled states.

This work investigates the use of stellar occultation measurements to enhance the orbit determination performance of the Lunar Meteoroid Impact Observer (LUMIO) mission, operating from a quasi-Halo orbit around the Earth-Moon L2 point. During science phases, when radiometric tracking is sparse and low illumination limits conventional optical navigation methods, occultation events, defined as precise timings of stellar appearances/disappearances behind the Moon's limb, offer a suitable alternative. A simulation tool based on JPL's MONTE library was developed to identify valid occultation events, applying geometric and illumination constraints to exclude non-observable cases. These events were integrated into a batch least-squares orbit determination filter alongside conventional radiometric data. The covariance analysis shows that occultation observables reduce the transverse and normal position uncertainties of LUMIO by up to a factor of two, especially during tracking gaps or occultation-rich arcs. This uncertainty reduction is expected to facilitate station-keeping operations and constrain the surface localization of Lunar Impact Flashes (LIFs), enhancing the mission's scientific return. Sensitivity analyses confirm that the orbit determination performance is primarily driven by the timing accuracy of occultation events, with limited dependence on lunar shape uncertainty below 100 m. These findings confirm the potential of occultation-based navigation to enhance spacecraft autonomy and robustness in low-visibility environments, making it a valuable complement to radiometric techniques for future lunar and deep-space missions.

Understanding and modeling energy transfer mechanisms in rare-earth-doped nanomaterials is essential for advancing luminescent technologies used in bioimaging, optical thermometry, and solid-state lasers. In this work, we investigate the photoluminescence dynamics of Yb3+ and Er3+ ions in Y2O3 nanoparticles over a wide concentration range (0.5-17%), using both direct and upconversion excitation. Luminescence decays of green, red, and near-infrared transitions were measured and analyzed using a rate-equation model incorporating radiative and non-radiative processes, energy transfer mechanisms, and defect-related quenching. The model successfully reproduces experimental trends across most concentrations and excitation paths. This work provides a reliable and predictive framework for modeling energy transfer in rare-earth doped materials and offers valuable insights for optimizing photoluminescent properties in nanostructured systems.

Coherent two-state oscillations are observed in numerical simulations of one-dimensional transverse- and longitudinal-field Ising model within the false vacuum decay regimes. Starting from the false vacuum state with all spins up, in moderate-sized systems with a small transverse field, we find conventional decay dynamics at resonance conditions $ h\approx 2J/n$ can change into coherent oscillations between the false vacuum and a symmetric resonant state, manifested by a sub-leading eigenstate overlap approaching $0.5$ and periodic vanishing of the Loschmidt echo. Notably, the oscillation frequency shows a superradiant-like $\sqrt{L}$ enhancement compared to the earlier Schrieffer-Wolff predictions. In larger systems, we find these oscillations persist for $n \gtrsim L/2$ (enabled by a bubble size blockade effect) or when long-range interactions lift multi-bubble degeneracies, revealing a robust many-body coherence mechanism transcending perturbative treatments and finite-size limitations.

Continuously monitored quantum systems are emerging as promising platforms for quantum metrology, where a central challenge is to identify measurement strategies that optimally extract information about unknown parameters encoded in the complex quantum state of emitted radiation. Different measurement strategies effectively access distinct temporal modes of the emitted field, and the resulting choice of mode can strongly impact the information available for parameter estimation. While a ubiquitous approach in quantum optics is to select frequency modes through spectral filtering, the metrological potential of this technique has not yet been systematically quantified. We develop a theoretical framework to assess this potential by modeling spectral detection as a cascaded quantum system, allowing us to reconstruct the full density matrix of frequency-filtered photonic modes and to compute their associated Fisher information. This framework provides a minimal yet general method to benchmark the performance of spectral measurements in quantum optics, allowing to identify optimal filtering strategies in terms of frequency selection, detector linewidth, and metrological gain accessible through higher-order frequency-resolved correlations and mean-field engineering. These results lay the groundwork for identifying and designing optimal sensing strategies in practical quantum-optical platforms.

Our work is concerned with simplicial complexes that describe higher-order interactions in real complex systems. This description allows to go beyond the pairwise node-to-node representation that simple networks provide and to capture a hierarchy of interactions of different orders. The prime contribution of this work is the introduction of geometric measures for these simplicial complexes. We do so by noting the non-commutativity of the algebra associated with their matrix representations and consequently we bring to bear the spectral triplet formalism of Connes on these structures and then notions of associated dimensions, curvature, and distance can be computed to serve as characterizing features in addition to known topological metrics.

Across development, the morphology of fluid-filled lumina enclosed by epithelial tissues arises from an interplay of lumen pressure, mechanics of the cell cortex, and cell-cell adhesion. Here, we explore the mechanical basis for the control of this interplay using the shape space of MDCK cysts and the instability of their apical surfaces under tight junction perturbations [Mukenhirn et al., Dev. Cell 59, 2886 (2024)]. We discover that the cysts respond to these perturbations by significantly modulating their lateral and basal tensions, in addition to the known modulations of pressure and apical belt tension. We develop a mean-field three-dimensional vertex model of these cysts that reproduces the experimental shape instability quantitatively. This reveals that the observed increase of lateral contractility is a cellular response that counters the instability. Our work thus shows how regulation of the mechanics of all cell surfaces conspires to control lumen morphology.

Quantum Key Distribution (QKD) stands as a revolutionary approach to secure communication, using the principles of quantum mechanics to establish unbreakable channels. Unlike traditional cryptography, which relies on the computational difficulty of mathematical problems, QKD utilizes the inherent properties of quantum states to achieve information-theoretic security. This means that the security of the key exchange is guaranteed by the laws of physics, making it theoretically unbreakable even by an adversary with unlimited computational power. Currently, one of the most viable ways to implement QKD for communication is via photonics, namely, using phase-preserving long-distance optical fibers. The objective of this project is to implement photonic QKD in a laboratory setting. This will help demonstrate the protocol's robustness and provide a feasible implementation for educational demonstrations.

Collimation in X-ray imaging restricts exposure to the region-of-interest (ROI) and minimizes the radiation dose applied to the patient. The detection of collimator shadows is an essential image-based preprocessing step in digital radiography posing a challenge when edges get obscured by scattered X-ray radiation. Regardless, the prior knowledge that collimation forms polygonal-shaped shadows is evident. For this reason, we introduce a deep learning-based segmentation that is inherently constrained to its geometry. We achieve this by incorporating a differentiable Hough transform-based network to detect the collimation borders and enhance its capability to extract the information about the ROI center. During inference, we combine the information of both tasks to enable the generation of refined, line-constrained segmentation masks. We demonstrate robust reconstruction of collimated regions achieving median Hausdorff distances of 4.3-5.0mm on diverse test sets of real Xray images. While this application involves at most four shadow borders, our method is not fundamentally limited by a specific number of edges.

The energy difference between two iso-electronic systems can be accurately approximated by the alchemical first order Hellmann-Feynmann derivative for the averaged Hamiltonian. This approximation is exact up to third order because even-order contributions cancel out. This finding holds for any iso-electronic compound pair (dubbed `alchemical diastereomers'), regardless of differences in configuration, composition, or energy, and consequently, relative energy estimates for all possible iso-electronic alchemical diastereomer pairs, require only O(1) self-consistent field cycles for any given averaging reference Hamiltonian. We discuss the relation to the Verlet algorithm, alchemical harmonic approximation (AHA) [J. Chem. Phys. 162, 044101 (2025)], relative properties such as forces, ionization potential or electron affinities, and Levy's formula for relative energies among iso-electronic systems that uses the averaged electron density of the two systems [J. Chem. Phys. 70, 1573 (1979)]. Numerical estimates accurately reflect trends in the charge-neutral iso-electronic diatomic molecule series with 14 protons (N$_2$, CO, BF, BeNe, LiNa, HeMg, HAl), with systematically increasing errors. Using alchemical Hellmann-Feynman derivatives for toluene, we demonstrate the concept's broader applicability by estimating relative energies for all 36 possible alchemical diastereomer pairs from vertical iso-electronic charge-neutral antisymmetric BN doping of toluene's aromatic ring, with mean absolute errors of a few milli-Hartrees.

A flow decomposition method based on canonical correlation analysis is proposed in this paper to optimally dissect complex flows into mutually orthogonal modes that are ranked by their cross-correlation with an observable. It is particularly suitable for identifying the observable-correlated flow structures while effectively excluding those uncorrelated, even though they may be highly energetic. Therefore, this method is capable of extracting coherent flow features under low signal-to-noise ratios. A numerical validation is conducted and shows that the method can robustly identify the observable-correlated flow events even though the underlying signal is corrupted by random noise that is four orders of magnitude stronger. The temporal sampling frequency and duration of the observable determine the maximum and minimum frequencies to be resolved in the cross-correlation respectively, while those of the flow are to ensure convergence. These criteria are validated using synthetic examples. The decomposition method is subsequently used to analyse a turbulent channel flow, a subsonic turbulent jet and an unsteady vortex shedding from a cylinder, showing the effectiveness of observable-correlated structure identification and order reduction. This decomposition represents a data-driven method of effective order reduction for highly noisy numerical and experimental data and is suitable for identifying the source and descendent events of a given observable. It is hoped that this method will join the existing flow diagnosis tools, in particular for observable-related diagnosis and control.

We present an improved method for determining guess structures for transition state searches by incorporating internal coordinates interpolation into the freezing string method (FSM). We test our method on over 40 reactions across 3 benchmark datasets covering a diverse set of chemical reactions. Our results show that incorporation of internal coordinates interpolation improves the reliability of the FSM, enabling larger interpolation step sizes and fewer optimization steps per cycle, which together yield nearly a 50\% reduction in computational cost while maintaining a 100\% success rate on benchmark chemical reaction test cases, including systems where previous attempts based on linear synchronous transit interpolation have failed. We provide an open-source Python implementation of the FSM, in addition to the reactant, product, and transition state structures of all reactions studied.

We show how to adapt the approach introduced for viscous damping in [1] to derive the approximate amplitude decay in the case of damping by a force of constant magnitude (sliding friction) and in the case of damping by a force proportional to the square of velocity (air resistance). We obtain two first-order differential equations from which we obtain the approximate time-dependent amplitudes corresponding to the considered damping forces. Our approach is suitable for first-year undergraduates, as it relies on the physical concepts and mathematical techniques they are familiar with.

Coping with prolonged periods of low availability of wind and solar power, also referred to as renewable energy droughts or "Dunkelflaute", emerges as a key challenge for realizing decarbonized energy systems based on renewable energy sources. Here we investigate the role of long-duration electricity storage and geographical balancing in dealing with such events, combining a time series analysis of renewable availability with power sector modeling of 35 historical weather years. We find that extreme droughts define long-duration storage operation and investment. Assuming policy-relevant interconnection, we find 351 TWh long-duration storage capacity or 7% of yearly electricity demand optimal to deal with the most extreme event in Europe. While nuclear power can partially reduce storage needs, the storage-mitigating effect of fossil backup plants in combination with carbon removal is limited. Policymakers and system planners should prepare for a rapid expansion of long-duration storage to safeguard the renewable energy transition in Europe.

The soiling level of heliostat mirrors in Concentrated Solar Power (CSP) fields is one of the key factors that significantly influences optical efficiency. State-of-the-art methods of monitoring heliostats soiling levels still face various challenges, including slow speed, labor-intensive operations, resolution and accuracy constraints or interruptions to solar field operations. We present a rapid, cost-effective, and non-intrusive method for mirror soiling detection based on polarimetric imaging, referred to as Polarimetric Imaging-based Mirror Soiling (PIMS). The compact PIMS device is designed for integration with unmanned aerial vehicles (UAVs), enabling rapid, large-area assessments of heliostat mirrors for efficient soiling detection. Our method utilizes the correlation between the Degree of Linear Polarization (DoLP) and surface soiling level based on Mie scattering theory and Monte Carlo simulations. Field deployment of the PIMS method requires minimal device installation, and its UAV-based operation allows for soiling detection without interrupting plant activities. The PIMS method holds the potential for mirror soiling detection across various concentrated solar power (CSP) plants and can be further adapted for other types of solar fields, such as parabolic trough systems.

In this paper, we present the nonmodal kinetic theory of the macroscale two-dimensional compressed-sheared non-diffusive convective flows of a magnetized plasma generated by the inhomogeneous microturbulence. This theory bases on the two-scales approach to the solution of the Vlasov-Poisson system of equations for magnetized plasma, in which the self-consistent evolution of the plasma and of the electrostatic field on the microscales, commensurable with the wavelength of the microscale instabilities and of the ion gyroradius, as well as on the macroscales of a bulk of plasma, is accounted for. It includes the theory of the formation of the macroscale spatially inhomogeneous compressed-sheared convective flows by the inhomogeneous microturbulence, the theory of the back reaction of the macroscale convected flows on the microturbulence, and of the slow macroscale responce of a bulk of plasma on the development of the compressed-sheared convective flows.

Achieving low contact resistance in advanced quantum electronic devices remains a critical challenge. With the growing demand for faster and energy-efficient devices, 2D contact engineering offers a promising solution. Beyond graphene, 1T-WTe2 has attracted attention for its excellent electrical transport, quantum phenomena, and Weyl semimetallic properties. Here, we demonstrate the direct wafer-scale growth of 1T-WTe2 via molecular beam epitaxy (MBE) and its use as a 2D contact for layered materials such as InSe. The 1T WTe2/InSe interface exhibits a barrier height nearly half that of conventional metal contacts, and its contact resistance is reduced by a factor of 21, effectively suppressing Fermi level pinning and enabling efficient electron injection. InSe/1T WTe2 photodetectors show broad photoresponsivity (0.14 to 217.58 A/W) under NIR to DUV illumination with fast rise/fall times of 42/126 ms, compared to lower responsivity (0.000865 A/W to 3.64 A/W) and slower response (150/144 ms) for InSe/Ti Au devices. The 1T WTe2/InSe devices thus exhibit approximately 60 times higher responsivity and 4 times faster response than conventional metal contacts. These results establish MBE-grown 1T-WTe2 as an effective 2D electrode, enhancing photodetection performance while simplifying device architecture, making it a strong candidate for next generation nanoelectronic and optoelectronic devices.

We propose a general scheme to investigate photon triplet generation (PTG) via third-order spontaneous parametric downconversion (TOSPDC) in $\chi^{(3)}$ nonlinear structures. Our approach leverages the quantum-classical correspondence between TOSPDC and its reverse classical process, three-wave sum-frequency generation (TSFG), to efficiently estimate the PTG rate. We apply this framework to nonlinear metasurfaces supporting quasi-bound states in the continuum (qBICs) in the optical range. From numerical analysis of non-collinear TSFG with degenerate input waves at qBIC wavelengths, we predict wavelength-tunable three-photon emission with spatio-angular correlations. These findings establish a novel method for modelling TOSPDC and also highlight the potential of nonlinear resonant metasurfaces as compact free-space photon triplet sources with quantum state control.

We present the first three-dimensional theoretical model of microparticle-assisted super-resolution imaging, enabling accurate simulation of virtual image formation. The model reveals that accounting for partial spatial coherence of illumination is a fundamental prerequisite for achieving super-resolution. We also propose a novel illumination strategy based on suppressing the normal component of incident light, which enhances image contrast and resolution. The results establish a consistent wave-optical framework that reproduces experimentally observed subwavelength imaging and clarifies the underlying physical mechanisms.

We study a novel alternative approach for the computation of transport coefficients at mesoscales. While standard nonequilibrium molecular dynamics (NEMD) approaches fix the forcing and measure the average induced flux in the system driven out of equilibrium, the so-called ``stochastic Norton dynamics'' instead fixes the value of the flux and measures the average magnitude of the forcing needed to induce it. We extend recent results obtained in Langevin dynamics to consider the generalisation of the stochastic Norton dynamics in the popular dissipative particle dynamics (DPD) at mesoscales, important for a wide range of complex fluids and soft matter applications. We demonstrate that the responses profiles for both the NEMD and stochastic Norton dynamics approaches coincide in both linear and nonlinear regimes, indicating that the stochastic Norton dynamics can indeed act as an alternative approach for the computation of transport coefficients, including the mobility and the shear viscosity, as the NEMD dynamics. In addition, based on the linear response of the DPD system with small perturbations, we derive a closed-form expression for the shear viscosity, and numerically validate its effectiveness with various types of external forces. Moreover, our numerical experiments demonstrate that the stochastic Norton dynamics approach clearly outperforms the NEMD dynamics in controlling the asymptotic variance, a key metric to measure the associated computational costs, particularly in the high friction limit.

Obstacle crossing is an essential component of human locomotion, particularly for individuals with lower limb amputations who face elevated risks of imbalance and falls. While prior studies have explored this task, they often lack a comprehensive examination of kinematic and kinetic changes throughout the entire gait cycle across varying obstacle heights. This study creates a novel dataset collected from ten healthy adults performing obstacle crossing at four different heights (7.5 cm, 15 cm, 22.5 cm, and 30 cm). Kinematic and kinetic data (angles and torques of hip, knee, and ankle) were recorded and analyzed. Results indicate that increased obstacle height leads to a longer swing phase and significant increases in both hip and knee joint angles (1.5* and 1.0*, respectively) and torques. In contrast, ankle joint angles and moments exhibited minimal variation across obstacle heights, indicating a relatively consistent movement strategy at the ankle. Furthermore, significant asymmetries were observed between the dominant and non-dominant foot: the dominant foot demonstrated larger hip and knee joint angles and more consistent ankle behavior, reflecting greater coordination. These findings offer valuable biomechanical insights for improving fall prevention strategies and informing the design of assistive devices such as prostheses and exoskeletons.

Radiative and nonradiative resonant couplings between defects are ubiquitous phenomena in photonic devices used in classical and quantum information technology applications. In this work we present a first principles approach to enable quantitative predictions of the energy transfer between defects in photonic cavities, beyond the dipole-dipole approximation and including the many-body nature of the electronic states. As an example, we discuss the energy transfer from a dipole like emitter to an F center in MgO in a spherical cavity. We show that the cavity can be used to controllably enhance or suppress specific spin flip and spin conserving transitions. Specifically, we predict that a ~10 to 100 enhancement in the resonant energy transfer rate can be gained in the case of the F center in MgO at ~10nm distances from a dipolar source, using rather moderate cavity with quality factor Q~400. We also show that a similar suppression in the transfer rate can be achieved by off-tuning the cavity resonance relative to the emitter transition energy. The framework presented here is general and readily applicable to a wide range of devices where localized emitters are embedded in micro-spheres, core-shell nanoparticles, and dielectric Mie resonators. Hence, our approach paves the way to predict how to control energy transfer in quantum memories and in ultra-high density optical memories, and in a variety of quantum information platforms.

On a Concentrated Solar Power (CSP) field, optical errors have significant impacts on the collection efficiency of heliostats. Fast, cost-effective, labor-efficient, and non-intrusive autonomous field inspection remains a challenge. Approaches using imaging drone, i.e., Unmanned Aerial Vehicle (UAV) system integrated with high resolution visible imaging sensors, have been developed to address these challenges; however, these approaches are often limited by insufficient imaging contrast. Here we report a polarimetry-based method with a polarization imaging system integrated on UAV to enhance imaging contrast for in-situ detection of heliostat mirrors without interrupting field operation. We developed an optical model for skylight polarization pattern to simulate the polarization images of heliostat mirrors and obtained optimized waypoints for polarimetric imaging drone flight path to capture images with enhanced contrast. The polarimetric imaging-based method improved the success rate of edge detections in scenarios which were challenging for mirror edge detection with conventional imaging sensors. We have performed field tests to achieve significantly enhanced heliostat edge detection success rate and investigate the feasibility of integrating polarimetric imaging method with existing imaging-based heliostat inspection methods, i.e., Polarimetric Imaging Heliostat Inspection Method (PIHIM). Our preliminary field test results suggest that the PIHIM hold the promise to enable sufficient imaging contrast for real-time autonomous imaging and detection of heliostat field, thus suitable for non-interruptive fast CSP field inspection during its operation.

We present a detailed derivation and discussion of cavity Born-Oppenheimer coupled cluster (CBO-CC) theory and address cavity-modified electron correlation in the vibrational strong coupling regime. Methodologically, we combine the recently proposed cavity reaction potential (CRP) approach with the Lagrangian formulation of CC theory and derive a self-consistent CRP-CC method at the singles and doubles excitations level (CRP-CCSD). The CRP-CC approach is formally similar to implicit solvation CC models and provides access to the CBO-CC electronic ground state energy minimized in cavity coordinate space on a CC level of theory. A hierarchy of linearisation schemes (lCRP-CCSD) similar to canonical CC theory systematically lifts the self-consistent nature of the CRP-CCSD approach and mitigates numerical cost by approximating electron correlation effects in energy minimization. We provide a thorough comparison of CRP-CCSD, lCRP-CCSD and CRP-Hartee-Fock methods for a cavity-modified Menshutkin reaction, pyridine$+$CH$_3$Br, and cavity-induced collective electronic effects in microsolvation energies of selected methanol-water clusters. We find lCRP-CCSD methods to provide excellent results compared to the self-consistent CRP-CCSD approach in the few-molecule limit. We furthermore observe significant differences between mean-field and correlated results in both reactive and collective scenarios. Our work emphasizes the non-trivial character of electron correlation under vibrational strong coupling and provides a starting point for further developments in ab initio vibro-polaritonic chemistry beyond the mean-field approximation.

We bring attention to the fact that the claim of Brambilla this http URL. [Extreme Mechanics Letters 74 (2025) 102267; arXiv:2406.14502] of discovering a novel design for pentamode materials is incorrect. Back in 2016 Briane, Harutyunyan and myself [Mathematics and Mechanics of Complex Systems 5 (2016) 41--94; arXiv:1606.03305] designed a class of stiff pentamodes, that include the high bulk modulus pentamodes of Brambilla this http URL. Our design generalized to three-dimensions, and to full anisotropy, the main aspects of a two-dimensional construction of Sigmund [Journal of the Mechanics and Physics of Solids 48 (2000) 397--428]. It is emphasized that the in depth analysis of Brambilla this http URL. goes well beyond our brief treatment.

Kinetic simulations excel at capturing microscale plasma physics phenomena with high accuracy, but their computational demands make them impractical for modeling large-scale space and astrophysical systems. In this context, we build a surrogate model, using Deep Operator Networks (DeepONets), based upon the Vlasov-Poisson simulation data to model the dynamical evolution of plasmas, focusing on the Landau damping process - a fundamental kinetic phenomenon in space and astrophysical plasmas. The trained DeepONets are able to capture the evolution of electric field energy in both linear and nonlinear regimes under various conditions. Extensive validation highlights DeepONets' robust performance in reproducing complex plasma behaviors with high accuracy, paving the way for large-scale modeling of space and astrophysical plasmas.

Hybrid integration of two-dimensional (2D) materials with nanophotonic platforms has enabled compact optoelectronic devices by leveraging the unique optical and electronic properties of atomically thin layers. While most efforts have focused on coupling 2D materials to pre-defined photonic structures, the broader potential of 2D heterostructures for actively engineering the photonic environment remains largely unexplored. In our previous work, we employed single types of 2D material and showed that even monolayer flakes can locally induce high-$Q$ nanocavities in photonic crystal (PhC) waveguides through effective refractive index modulation. Here, we extend this concept by demonstrating that further transferring of 2D material flakes onto the induced hybrid nanocavity to form heterostructures enable more flexibility for post-fabrication dielectric environment engineering of the cavity. We show that the high-$Q$ hybrid nanocavities remain robust under sequential flake stacking. Coupling optically active MoTe$_{2}$ flake to these cavities yields enhanced photoluminescence and reduced emission lifetimes, consistent with Purcell-enhanced light-matter interactions. Additionally, encapsulation with a top hBN layer leads to a significant increase in the cavity $Q$ factor, in agreement with numerical simulations. Our results show that these heterostructure stacks not only preserve the cavity quality but also introduce an additional degrees of control -- via flake thickness, refractive indices, size and interface design -- offering a richer dielectric environment modulation landscape than what is achievable with monolayers alone, providing a versatile method toward scalable and reconfigurable hybrid nanophotonic systems.

From a database of 457 experimental and numerical data from 32 machines among solid-walled tokamaks, stellarators and linear plasma devices, we derive physics-informed multi-machine scaling laws predictive of fuel and impurity puffing rates sufficient to access plasma detachment -- leading candidate for a reactor-relevant solution to the open issue of plasma-wall interaction. Validation of our laws in up to 40 plasmas in low- and high-confinement mode also featuring advanced configurations demonstrates accuracy within a factor 1.5 in up to 50% of the instances, and within a factor 2 on average. Divertor volume alone is found to correlate to fuelling. The addition of plasma opaqueness leads to the empirically-calibrated law $\Gamma_{\text{D}}\propto [n_{\text{sep}}\times a\times(S_{\text{div}}/V_{\text{div}})^{-1.5}]^{1.05}$ valid across all toroidal devices. Its simplification to $\Gamma_{\text{D}}^{\text{HDL}}\propto 0.43\times a^{1.58}\times\lambda_q^{-0.89}$ avoids a dependence on $n_{\text{sep}}$ and bears intrinsic significance provided by the H/L density limit and the power fall-off length. Non-linearities of the impurity dynamics manifest in the seeding rate -- which the present work identifies both a generalised formula for, and the Greenwald-Eich-Scarabosio simplification $\Gamma_{\text{Z}}^{\text{GES}}\propto a^{1.51}\times\lambda_q^{-0.27}$. Similar relationships are defined for stellarators, closely following the mainstream but without providing data for validation -- which should stimulate further investigations. In the era of nuclear fusion reactor design, these results find immediate applicability in informing reactor fuel cycle design and edge plasma modelling. More generally, this study demonstrates that physics-based 0D laws able to relate detachment to the engineering actuators exist.

Partial differential equations describing the dynamics of physical systems rarely have closed-form solutions. Fourier spectral methods, which use Fast Fourier Transforms (FFTs) to approximate solutions, are a common approach to solving these equations. However, mapping Fourier integrals to discrete FFTs is not straightforward, as the selection of the grid as well as the coordinate-dependent phase and scaling factors require special care. Moreover, most software packages that deal with this step integrate it tightly into their full-stack implementations. Such an integrated design sacrifices generality, making it difficult to adapt to new coordinate systems, boundary conditions, or problem-specific requirements. To address these challenges, we present FFTArray, a Python library that automates the general discretization of Fourier transforms. Its purpose is to reduce the barriers to developing high-performance, maintainable code for pseudo-spectral Fourier methods. Its interface enables the direct translation of textbook equations and complex research problems into code, and its modular design scales naturally to multiple dimensions. This makes the definition of valid coordinate grids straightforward, while coordinate grid specific corrections are applied with minimal impact on computational performance. Built on the Python Array API Standard, FFTArray integrates seamlessly with array backends like NumPy, JAX and PyTorch and supports Graphics Processing Unit acceleration. The code is openly available at this https URL under Apache-2.0 license.

This note considers a system in which three ligands can independently bind to a scaffold. Such systems arise in diverse applications, including immunotherapy and synthetic biology. It is shown that there are unique steady states in each conservation class, and these are asymptotically stable. The dependency of the steady-state amount of fully bound complex, as a function of total scaffold, is analyzed as well.

In recent years, sensors based on hot atomic vapor cells have emerged as a compact and highly sensitive means of measuring magnetic fields. Such sensors have been deployed in the field for the measurement of, e.g. biological systems, representing a promising practical application of quantum technologies. However, it remains challenging to obtain high-resolution magnetic field images from these sensors, and in most cases the spatial resolution of the system is limited by the sensor size. Here, we demonstrate the combination of single-pixel imaging (SPI) techniques with an atom vapor. Faraday magnetometer to achieve microscopic magnetic field imaging. We demonstrate magnetic field imaging with a spatial resolution of $\approx~62.5\mu m$, limited only by the resolution of our DMD projection system and the absence of magnetic shielding in our experimental setup.

The Large Hadron Collider (LHC) at CERN has been instrumental in recent advances in experimental high energy physics by colliding beams of protons and heavier nuclei at unprecedented energies. The present heavy-ion programme is based mainly on colliding lead nuclei. For future ion runs, there is strong interest to achieve a significantly higher integrated nucleon-nucleon luminosity, which might be achieved through collisions of species other than Pb. In this paper, we explore the nucleon-nucleon luminosity projections in the LHC for a selection of ion species ranging from He to Xe, and including Pb as reference. Alternative beam production schemes are investigated as a way to mitigate effects such as space charge that degrade the beam quality in the LHC injectors. In the most optimistic scenarios, we find up to about a factor~4 improvement in integrated nucleon-nucleon luminosity for a typical future one-month run, with respect to the present Pb programme. We also outline a future study programme and experiments to test the assumptions and refine the simulated projections put forward in this article.

Muon imaging, a non-invasive technique leveraging naturally occurring cosmic muons, has emerged as a promising tool for exploring underwater objects, including shipwrecks. This article examines the potential of muon imaging to investigate the contents of wrecked ships in the Baltic Sea and other marine environments. By analyzing the principles of muon tomography, its applications in underwater archaeology, and its advantages over traditional methods, we highlight its potential to reveal hidden details within shipwrecks while addressing challenges posed by the marine environment. These wrecks pose significant environmental risks due to their hazardous contents, such as explosives and crude oil products, making their detection and monitoring a matter of environmental and safety concern. Accurate modeling and imaging of such wrecks are therefore crucial for assessing potential dangers and mitigating environmental impacts. In this work, we adopted an approach similar to that used in underground muon experiments. Muon propagation through 50 meters of seawater was simulated using GEANT4, with muons transported to a depth of 35 meters and recorded in a virtual detector. The saved distributions of particle type, kinetic energy, interaction position, and angular directions were then used as tabulated input to the PrimaryGeneratorAction module. This approach enabled the generation of a realistic underwater muon flux over a 20 m x 20 m surface at 35 meters depth, which was subsequently employed to simulate different shipwreck filling scenarios. Assuming an exposure time of one week for a wreck located at a depth of 50 meters, our simulations demonstrate that muon imaging can resolve density contrasts sufficiently to distinguish between water, oil, and high-density materials, underscoring its feasibility as a practical tool for underwater hazard assessment.

We have studied the effect of microhydration on the shape resonances of uracil nucleobase. The resonance parameters were determined using the resonance via Padé approach along with the efficient wave function-based EA-EOM-DLPNO-CCSD method. Our results showed that the uracil resonances become stabilized with an increase in the extent of microsolvation. The energy of the resonances decreased, and the lifetime increased as the number of water molecules surrounding uracil was increased. It showed that ten water molecules are sufficient to make the lowest shape resonance of uracil a bound radical anionic state. Our results also indicate that the lowest energy resonance state may become a bound state under bulk solvation.

LAFOV PET/CT has the potential to unlock new applications such as ultra-low dose PET/CT imaging, multiplexed imaging, for biomarker development and for faster AI-driven reconstruction, but further work is required before these can be deployed in clinical routine. LAFOV PET/CT has unrivalled sensitivity but has a spatial resolution of an equivalent scanner with a shorter axial field of view. AI approaches are increasingly explored as potential avenues to enhance image resolution.

Two-dimensional transition metal dichalcogenides (TMDs) offer tunable optical and electronic properties, making them highly promising for next-generation optoelectronic devices. One effective approach to engineering these properties is through alloying, which enables continuous control over the bandgap energy and excitonic transitions. In this study, we perform temperature-dependent transmission spectroscopy on monolayers of Mo1-xWxS2 and Mo(S1-xSex)2 alloys, transferred onto the core of an optical fiber and measured within a cryostat over a temperature range of 20-320 K. The use of an all-fiber configuration allowed us to probe interband transitions A, B, and C with high stability and precision. We observe a systematic redshift of excitonic transitions with increasing Se content, and a blueshift when Mo is replaced with W. These spectral shifts correlate with alloy composition and enable the tuning of bandgap energies between 1.6 eV and 2.0 eV at room temperature. Furthermore, we analyze the temperature sensitivity of the excitonic transitions, revealing that Se incorporation enhances thermal response in a non-monotonic manner, while W substitution results in a more monotonic and stronger temperature dependence. The splitting between A and B transitions, associated with spin-orbit coupling, also varies with composition. Our findings underscore the potential of compositional engineering in 2D TMD alloys to achieve both spectral and thermal control of optical properties, relevant for the design of robust and tunable optoelectronic systems.

Understanding the dynamics and stability of transonic flows in quantum fluids, especially for those beyond one spatial dimension, is an outstanding challenge, with applications ranging from nonlinear optics and condensed matter to analogue gravity. One intriguing possibility is that a system with a spatially bounded supersonic flow may evolve into a self-oscillating state that periodically emits solitons, in a process originating from the well-known Landau instability. Here, we report observation of self-oscillating supersonic flows in a two-dimensional atomic superfluid. By imposing a local particle sink with strong loss, we induce a convergent radial flow forming an acoustic analogue of a black-hole horizon and an inner horizon around the sink. The observed superflow appears to be modulated by quasi-periodic bursts of superluminal signals. We measure their frequencies and find agreement with numerical simulations of soliton oscillation frequencies within the black-hole horizon. The presented experiment demonstrates a new method for creating supersonic flows in atomic superfluids, which may find applications in quantum simulations of curved spacetime, supersonic turbulence, and self-oscillating dynamics in dissipative many-body systems.

A contactless control of mean values and fluctuations of position and velocity of a nanoobject belongs among the key methods needed for ultra-precise nanotechnology and the upcoming quantum technology of macroscopic systems. An analysis of experimental implementations of such a control, including assessments of linearity and the effects of added noise, is required. Here, we present a protocol of linear amplification of mean values and fluctuations along an arbitrary phase space variable and squeezing along the complementary one, referred to as a nanomechanical state amplifier. It utilizes the experimental platform of a single optically levitating nanoparticle and the three-step protocol combines a controlled fast switching of the parabolic trapping potential to an inverted parabolic potential and back to the parabolic potential. The protocol can be sequentially repeated or extended to shape the nanomechanical state appropriately. Experimentally, we achieve amplification of position with a gain of $|G| \simeq 2$ and a classical squeezing coefficient above 4 dB in as short a timestep as one period of nanoparticle oscillations ($7.6\,\mu$s). Amplification in velocity, with the same parameters, squeezes the input noise and enhances force sensing.

Biologically-inspired computing models have made significant progress in recent years, but the conventional von Neumann architecture is inefficient for the large-scale matrix operations and massive parallelism required by these models. This paper presents Spin-NeuroMem, a low-power circuit design of Hopfield network for the function of associative memory. Spin-NeuroMem is equipped with energy-efficient spintronic synapses which utilize magnetic tunnel junctions (MTJs) to store weight matrices of multiple associative memories. The proposed synapse design achieves as low as 17.4% power consumption compared to the state-of-the-art synapse designs. Spin-NeuroMem also encompasses a novel voltage converter with a 53.3% reduction in transistor usage for effective Hopfield network computation. In addition, we propose an associative memory simulator for the first time, which achieves a 5Mx speedup with a comparable associative memory effect. By harnessing the potential of spintronic devices, this work paves the way for the development of energy-efficient and scalable neuromorphic computing systems.

Fitting models to data to obtain distributions of consistent parameter values is important for uncertainty quantification, model comparison, and prediction. Standard Markov chain Monte Carlo (MCMC) approaches for fitting ordinary differential equations (ODEs) to time-series data involve proposing trial parameter sets, numerically integrating the ODEs forward in time, and accepting or rejecting the trial parameter sets. When the model dynamics depend nonlinearly on the parameters, as is generally the case, trial parameter sets are often rejected, and MCMC approaches become prohibitively computationally costly to converge. Here, we build on methods for numerical continuation and trajectory optimization to introduce an approach in which we use Langevin dynamics in the joint space of variables and parameters to sample models that satisfy constraints on the dynamics. We demonstrate the method by sampling Hopf bifurcations and limit cycles of a model of a biochemical oscillator in a Bayesian framework for parameter estimation, and we attain performance that matches or exceeds the performance of leading MCMC approaches that require numerically integrating the ODEs forward in time. We describe numerical experiments that provide insight into the speedup. The method is general and can be used in any framework for parameter estimation and model selection.

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

We present a combined experimental and theoretical investigation of the formation and decay kinetics of vortices in two dimensional, compressible quantum turbulence. We follow the temporal evolution of a quantum fluid of exciton polaritons, hybrid light matter quasiparticles, and measure both phase and modulus of the order parameter in the turbulent regime. Fundamental topological conservation laws require that the formation and annihilation of vortices also involve critical points of the velocity field, namely nodes and saddles. Identifying the simplest mechanisms underlying these processes enables us to develop an effective kinetic model that closely aligns with the experimental observations, and shows that different processes are responsible for vortex number growth and decay. These findings underscore the crucial role played by topological constraints in shaping nonlinear, turbulent evolution of two dimensional quantum fluids.

We investigate a fifth force mediated by a light vector boson that couples to lepton spins, characterized by axial-vector couplings to leptons and vector couplings to nucleons. This interaction generates a potential proportional to the inner product of the lepton spin vector and the nucleon-lepton relative velocity vector, a feature extensively explored with precision spin sensors. Employing weak symmetry, we show that left-handed charged lepton couplings naturally extend to left-handed neutrinos, enabling this fifth force to influence neutrino oscillations. For electron-nucleon couplings, we find that solar and reactor neutrino experiments provide comparable constraints to those from spin sensors and surpass them in the short-range fifth force region. For muon-nucleon couplings, neutrino oscillation experiments exclude the fifth force as a viable explanation for the muon $ g-2 $ anomaly in the context of a vector mediator, tightening the bounds by two orders of magnitude in coupling strength by solar and atmospheric neutrino data. Our results highlight the critical role of neutrino oscillations in probing fifth forces acting across all three generations of lepton spins.

A match played in a sports tournament can be called stakeless if at least one team is indifferent to its outcome because it already has qualified or has been eliminated. Such a game threatens fairness since teams may not exert full effort without incentives. This paper suggests a novel classification for stakeless matches based on their expected outcome: they are more costly if the indifferent team is more likely to win by playing honestly. Our approach is illustrated with the 2026 FIFA World Cup, the first edition of the competition with 48 teams. We propose a novel format based on imbalanced groups, which substantially reduces the probability of stakeless matches played by the strongest teams according to Monte Carlo simulations. The new design also increases the uncertainty of match outcomes and requires fewer matches. Governing bodies in sports are encouraged to consider our innovative idea in order to enhance the competitiveness of their tournaments.

Anomalous diffusion is the fundamental ansatz of phenomenological theories of passive scalar turbulence, and has been confirmed numerically and experimentally to an extraordinary extent. The purpose of this survey is to discuss our recent result, in which we construct a class of incompressible vector fields that have many of the properties observed in a fully turbulent velocity field, and for which the associated scalar advection-diffusion equation generically displays anomalous diffusion. Our main contribution is to propose an analytical framework in which to study anomalous diffusion via a backward cascade of renormalized eddy viscosities.

The synthesis of metallic nanoparticle assemblies is nowadays well-controlled, such that these systems offer the possibility of controlling light at a sub-wavelength scale, thanks, for instance, to surface plasmons. Determining the energy dispersion of plasmons likely to couple to light within these nanostructures is, therefore, a necessary preliminary task on the way to understanding both their photonic properties and their physical nature, namely the role of the quadrupole contribution. Starting with a general model that takes account of all energy modes, we show that its low-lying energy dispersion gained numerically, can be compared to that of a minimal model that treats dipoles and quadrupoles on the same footing. The main advantage of the latter relies on the fact that its formulation is tractable, such that a semi-analytical Bogoliubov transformation allows one to access the experimentally relevant energy bands. Based on this semi-analytical derivation, we determine quantitatively the limit of validity of the dipole-only model, the presently proposed dipole and quadrupole model, compared to a full-plasmon-mode Hamiltonian. The results show that the dispersion relation, which includes dipoles and quadrupoles, is sufficient to capture the low-energy physics at play in most experimental situations. Besides, we show that at small lattice spacing, the contribution of quadrupoles is dominant around the Brillouin zone center.

We propose SILVIA (Space Interferometer Laboratory Voyaging towards Innovative Applications), a mission concept designed to demonstrate ultra-precision formation flying between three spacecraft separated by 100 m. SILVIA aims to achieve sub-micrometer precision in relative distance control by integrating spacecraft sensors, laser interferometry, low-thrust and low-noise micro-propulsion for real-time measurement and control of distances and relative orientations between spacecraft. A 100-meter-scale mission in a near-circular low Earth orbit has been identified as an ideal, cost-effective setting for demonstrating SILVIA, as this configuration maintains a good balance between small relative perturbations and low risk for collision. This mission will fill the current technology gap towards future missions, including gravitational wave observatories such as DECIGO (DECihertz Interferometer Gravitational wave Observatory), designed to detect the primordial gravitational wave background, and high-contrast nulling infrared interferometers like LIFE (Large Interferometer for Exoplanets), designed for direct imaging of thermal emissions from nearby terrestrial planet candidates. The mission concept and its key technologies are outlined, paving the way for the next generation of high-precision space-based observatories.

We investigate the relationship between solar coronal holes and open-field regions using three-dimensional radiative magnetohydrodynamic (MHD) simulations combined with remote-sensing observations from the Solar Dynamics Observatory (SDO). Our numerical simulations reveal that magnetically open regions in the corona can exhibit brightness comparable to quiet regions, challenging the conventional view that open-field regions are inherently dark coronal holes. We find that the coronal brightness is primarily determined by the total energy input from photospheric magnetic activities, such as the small-scale dynamo, rather than differences in dissipative processes within the corona. Using synthesized EUV intensity maps, we show that brightness thresholds commonly used to identify coronal holes may overlook open-field regions, especially at lower spatial resolutions. Observational analysis utilizing SDO/HMI and AIA synoptic maps supports our simulation results, demonstrating that magnetic field extrapolation techniques, such as the Potential Field Source Surface (PFSS) model, are sensitive to the chosen parameters, including the source surface height. We suggest that discrepancies in estimates of open magnetic flux (the ``open flux problem'') arise both from the modeling assumptions in coronal magnetic field extrapolation and systematic biases in solar surface magnetic field observations. Our findings indicate the need for reconsidering criteria used to identify coronal holes as indicators of open-field regions to better characterize the solar open magnetic flux.

Objective. Motion artifacts in brain MRI, mainly from rigid head motion, degrade image quality and hinder downstream applications. Conventional methods to mitigate these artifacts, including repeated acquisitions or motion tracking, impose workflow burdens. This study introduces Res-MoCoDiff, an efficient denoising diffusion probabilistic model specifically designed for MRI motion artifact this http URL-MoCoDiff exploits a novel residual error shifting mechanism during the forward diffusion process to incorporate information from motion-corrupted images. This mechanism allows the model to simulate the evolution of noise with a probability distribution closely matching that of the corrupted data, enabling a reverse diffusion process that requires only four steps. The model employs a U-net backbone, with attention layers replaced by Swin Transformer blocks, to enhance robustness across resolutions. Furthermore, the training process integrates a combined l1+l2 loss function, which promotes image sharpness and reduces pixel-level errors. Res-MoCoDiff was evaluated on both an in-silico dataset generated using a realistic motion simulation framework and an in-vivo MR-ART dataset. Comparative analyses were conducted against established methods, including CycleGAN, Pix2pix, and a diffusion model with a vision transformer backbone, using quantitative metrics such as PSNR, SSIM, and this http URL results. The proposed method demonstrated superior performance in removing motion artifacts across minor, moderate, and heavy distortion levels. Res-MoCoDiff consistently achieved the highest SSIM and the lowest NMSE values, with a PSNR of up to 41.91+-2.94 dB for minor distortions. Notably, the average sampling time was reduced to 0.37 seconds per batch of two image slices, compared with 101.74 seconds for conventional approaches.

An analytical model is developed to predict the temporal evolution of the lattice polarization in ferroelectric nanomembranes upon the excitation by a terahertz (THz) electromagnetic pulse of an arbitrary waveform, and the concurrent transmission of the THz pulse in both the linear and the nonlinear regimes. It involves the use of the perturbation method to solve the equation of motion for the lattice polarization in both unclamped and strained ferroelectric nanomembranes within the framework of Landau-Ginzburg-Devonshire theory. The model is applicable to perovskite oxides such as BaTiO3 and SrTiO3, wurtzite Al1-xScxN, and trigonal LiNbO3. Our analytical model provides a theoretical basis for determining the thermodynamic and kinetic parameters of ferroelectric materials through THz transmission experiment. The calculation results also suggest an approach to reversing the chirality of a circularly polarized THz pulse by harnessing the resonant polarization-photon coupling in ferroelectrics. This capability of chirality reversal, along with the high tunability from a strain applied along any arbitrarily oriented in-plane axis, provides new opportunities for THz wave modulation without relying on complex metasurface designs.

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

Living systems often function with regulatory interactions, but the question of how activity, stochasticity and regulations work together for achieving different goals still remains puzzling. We propose the model of an active tracking particle with information processing, based on which the entropy production, information flow, and generalised fluctuation theorem are derived. Moreover, the system performance, in terms of the first passage steps and the total energy consumption, are analysed in the variable space of (measurement error, control field), leading to discussions on the optimal control of the system. Not only elucidating the basic concepts involved in a stochastic active system with information processing, this prototypical model could also inspire more elaborated modelings of natural smart organisms and industrial designs of controllable active systems with desired physical performances in the future.