Papers

Resonant cavity LEDs (RCLEDs) exhibit excellent temporal and spatial coherence with narrow spectral linewidth and small divergence angle, which is of great importance for micro-displays. In this paper, we demonstrate a novel method to create GaN-based RCLEDs by using photo-electrochemical etching and micro-transfer printing (MTP) technology. Through systematic optimizing of the etching conditions, highly selective etching of an InGaN multiple quantum well sacrificial layer is achieved with parasitic etching of adjacent layers being completely suppressed. The roughness of the underside of the exfoliated GaN film is only 3.3 nm. Raman spectroscopy shows that the residual stress in the released material is reduced from 0.74 GPa for the as-grown sample to -0.15 GPa. Using the MTP method, GaN coupons with a deposited upper dielectric mirror were transferred onto target substrates covered with either an Al mirror or dielectric distributed Bragg reflector to form two types of blue RCLEDs. The electroluminescence spectra of the two RCLEDs show a much narrower linewidth, reduced from 32 nm in the conventional LED to ~5 nm, together with stable peak wavelength with increasing current density, with the shift reduced from 9.3 nm to less than 1 nm. The far-field pattern is influenced by the bottom mirror, and the far-field divergence angle can be decreased to only 52{\deg} by matching the cavity and the quantum well exciton modes. This scalable approach is highly promising for the realization of compact resonant cavity devices and their use in displays and in communications.

This study experimentally investigates the influence of CaCO3 polymorphs on the thermal conductivity (TC) of biocement and biocemented sand. Calcite-rich biocements consistently showed higher TC than vaterite-dominated ones, regardless of density or saturation. Vaterite-rich biocement, resulting from rapid precipitation and elevated organic content, yields inherently lower TC. This study shed light on the limited TC improvement observed in vaterite-rich biocemented sand.

The Tokamak à Configuration Variable (TCV) is equipped with an advanced set of diagnostics for studying suprathermal electron dynamics. Among these, the vertical electron cyclotron emission (VECE) diagnostic offers valuable insights into the electron energy distribution by measuring electron cyclotron emission (ECE) along a vertical line-of-sight. However, reconstructing the electron distribution from ECE measurements is inherently challenging due to harmonic overlap and thermal radiation noise. A more practical approach leverages forward modeling of ECE based on kinetic simulations. To this end, we introduce YODA, a novel synthetic ECE diagnostic framework that simulates emission and (re)absorption of electron cyclotron radiation for arbitrary electron distributions and antenna geometries. The framework is validated against the well-established synthetic ECE code SPECE, using an ohmic TCV discharge as a reference case. In this study, the 3D bounce-averaged Fokker-Planck code LUKE is used to model electron distributions in two electron cyclotron current drive (ECCD) experiments. The synthetic spectra generated using the combined LUKE-YODA framework successfully reproduce the main features of the experimental VECE measurements in both simulated discharges. The combination of kinetic and synthetic ECE simulations allow the identification of the features in the electron distribution function which give rise to certain signatures in the VECE signal.

We all know that the first laser device was realised by Theodore Maiman at Hughes Labs in 1960. Less known is that the very first computer simulations of the relaxation oscillations displayed by Maiman's laser were also performed in 1960 on a digital IBM 704 computer. The reason is that lasers and almost all photonic devices are described by nonlinear equations that are more often than not impossible to be solved analytically, i.e. on a piece of paper. Since then the development and applications of lasers and photonic devices has progressed hand in hand with computer simulations and numerical programming. In this review we introduce and numerically solve the model equations for a variety of devices, lasers, lasers with modulated parameters, lasers with injection, Kerr resonators, saturable absorbers and optical parametric oscillators. By using computer simulations we demonstrate stability and instability of nonlinear solutions in these photonic devices via pitchfork, saddle-node, Hopf and Turing bifurcations; bistability, nonlinear oscillations, deterministic chaos, Turing patterns, conservative solitons; bright, dark and grey cavity solitons; frequency combs, spatial disorder, spatio-temporal chaos, defect mediated turbulence and even rogue waves. There has been a one-to-one correspondence between computer simulations of all these nonlinear features and laboratory experiments with applications in ultrafast optical communications, optical memories, neural networks, frequency standards, optical clocks, future GPS, astronomy and quantum technologies. All of this has been made possible by 'novel insights into spatio-temporal dynamics of lasers, nonlinear and quantum optical systems, achieved through the development and application of powerful techniques for small-scale computing' (2011 Occhialini Medal and Prize of the Institute of Physics and Societa' Italiana di Fisica).

Joint communication and sensing applications require devices that can analyze multiple electromagnetic waves and process them in real time directly in the analog domain. In optics, the growing maturity of photonic integrated platforms allows the fabrication of complex circuits that can perform such operations, but their large number of sensors and actuators requires scalable control strategies to efficiently monitor and actively stabilize their functionality at runtime. In this work, we report on a multi-aperture silicon photonic programmable circuit that operates both as a coherent beam adder and a multi-aperture beam analyzer. The circuit consists of a reconfigurable mesh of Mach-Zehnder interferometers controlled through monolithically integrated electronic circuits, which are used to serialize/deserialize the readout of integrated sensors and the driving of actuators with a time-multiplexed addressing scheme. The circuit operation is validated in a communication and sensing scenario, where the photonic chip is used to simultaneously receive a 25 Gbit/s high-speed transmission and to measure the phase difference between the input light beams.

Smoothed dissipative particle dynamics (SDPD) is a widely used particle-based method for modelling soft matter systems at mesoscopic and macroscopic scales, offering thermodynamic consistency and direct control over the fluid's transport properties. Here, we present an SDPD model that incorporates the transport of reactants on scales smaller than the discretising particles, including the evolution of compositional fields. The proposed methodology is well-suited for modelling complex systems governed by advection-diffusion-reaction (ADR) dynamics. Implemented in LAMMPS, the model is validated using a range of benchmark problems spanning diffusion-dominated, reaction-dominated, and coupled ADR regimes. Our simulation results demonstrate that the implemented SDPD model effectively captures complex behaviours, such as Turing pattern formation. The proposed model holds promise for applications across various fields, including biology, chemistry, materials science, and environmental engineering.

Complex fluids transition from laminar to transitory flow above a critical control parameter, akin to their Newtonian counterparts. In a continuum mechanics sense, fluid elements follow the ensuing complex trajectories, giving rise to secondary flows in terms of macroscopic vortices and patterns thereof. However, if we replace idealized fluid elements with actual anisotropic nanoparticles, would their trajectories still reveal the same spatiotemporal behavior as the macroscopic flow field? This question is fundamental for complex fluids, where fully developed turbulence is suppressed by high viscosities and where understanding particle-flow coupling is central to transport, processing, and structure formation. To address this question, we develop small-angle x-ray scatter microscopy of unprecedented temporal resolution combined with polarized light imaging, thereby bridging seven orders of magnitude in lengtscales. Proof-of-principle is demonstrated on a classical stability problem, Taylor-Couette flow, of platelet-like graphene oxide nanoparticle and rod-like cellulose nanocrystal suspensions. The analysis shows hitherto hidden, markedly different multiscale dynamics underlying flow stability; while the platelet-like particles follow the wavy motion of the macroscopic, secondary flow field, the rod-like particles exhibit high-frequency motion that is uncorrelated with the vortex instabilities.

Thermocouples are in widespread use in industry, but they are particularly susceptible to calibration drift in harsh environments. Self-validating thermocouples aim to address this issue by using a miniature phase-change cell (fixed-point) in close proximity to the measurement junction (tip) of the thermocouple. The fixed point is a crucible containing an ingot of metal with a known melting temperature. When the process temperature being monitored passes through the melting temperature of the ingot, the thermocouple output exhibits a "plateau" during melting. Since the melting temperature of the ingot is known, the thermocouple can be recalibrated in situ. Identifying the melting plateau to determine the onset of melting is reasonably well established but requires manual intervention involving zooming in on the region around the actual melting temperature, a process which can depend on the shape of the melting plateau. For the first time, we present a novel machine learning approach to recognize and identify the characteristic shape of the melting plateau and once identified, to quantity the point at which melting begins, along with its associated uncertainty. This removes the need for human intervention in locating and characterizing the melting point. Results from test data provided by CCPI Europe show 100% accuracy of melting plateau detection. They also show a cross-validated R2 of 0.99 on predictions of calibration drift.

Building on our prior work, where our team transcended self assembled molecular monolayers (SAMs) research from a 2D configuration to 3D structured materials and successfully introduced the molecular self assembled 3D printer to fabricate technomolecular materials hybrid carbon metal nanosheets that mimic biological self assembly through cooperative organic inorganic interactions these materials promise advances in nanotechnology by enabling seamless integration of molecular systems with metallic electrodes. Here we show that electron beam irradiation induces direct self patterning of silver fractal nanoelectrodes on the technomolecular nanosheets, with formation influenced by molecular structure: saturated variants yield localized nanoparticles, while conjugated ones produce propagated fractals via electron delocalization and cross linking. In situ transmission electron microscopy reveals dynamic diffusion aggregation mechanisms, allowing controlled circuit patterns through resist free electron beam lithography. This approach advances flexible electronics, bioelectronics, and energy conversion, including fractal antennas and unclonable identifiers.

Quantification of protoporphyrin IX (PpIX) fluorescence in human brain tumours has the potential to significantly improve patient outcomes in neuro-oncology, but represents a formidable imaging challenge. Protoporphyrin is a biological molecule which interacts with the tissue micro-environment to form two photochemical states in glioma. Each exhibits markedly different quantum efficiencies, with distinct but overlapping emission spectra that also overlap with tissue autofluorescence. Fluorescence emission is known to be distorted by the intrinsic optical properties of tissue, coupled with marked intra-tumoural heterogeneity as a hallmark of glioma tumours. Existing quantitative fluorescence systems are developed and validated using simplified phantoms that do not simultaneously mimic the complex interactions between fluorophores and tissue optical properties or micro-environment. Consequently, existing systems risk introducing systematic errors into PpIX quantification when used in tissue. In this work, we introduce a novel pipeline for quantification of PpIX in glioma, which robustly differentiates both emission states from background autofluorescence without reliance on a priori spectral information, and accounts for variations in their quantum efficiency. Unmixed PpIX emission forms are then corrected for wavelength-dependent optical distortions and weighted for accurate quantification. Significantly, this pipeline is developed and validated using novel tissue-mimicking phantoms replicating the optical properties of glioma tissues and photochemical variability of PpIX fluorescence in glioma. Our workflow achieves strong correlation with ground-truth PpIX concentrations (R2 = 0.918+-0.002), demonstrating its potential for robust, quantitative PpIX fluorescence imaging in clinical settings.

This paper investigates the non-linear dynamics of horizontal shear instability in an incompressible, stratified and rotating fluid in the non-traditional $f$-plane, i.e. with the full Coriolis acceleration, using direct numerical simulations. The study is restricted to two-dimensional horizontal perturbations. It is therefore independent of the vertical (traditional) Coriolis parameter. However, the flow has three velocity components due to the horizontal (non-traditional) Coriolis parameter. Three different scenarios of non-linear evolution of the shear instability are identified, depending on the non-dimensional Brunt-Väisälä frequency $N$ and the non-dimensional non-traditional Coriolis parameter $\tilde{f}$ (non-dimensionalized by the maximum shear), in the range $\tilde{f}<N$ for fixed Reynolds and Schmidt numbers $Re=2000$, $Sc=1$. When the stratification is strong $N\gg 1$, the shear instability generates stable Kelvin-Helmholtz billows like in the traditional limit $\tilde{f}=0$. Furthermore, when $N\gg1$, the governing equations for any $\tilde{f}$ can be transformed into those for $\tilde{f}=0$. This enables us to directly predict the characteristics of the flow depending on $\tilde{f}$ and $N$. When $N$ is around unity and $\tilde{f}$ is above a threshold, the primary Kelvin-Helmholtz vortex is destabilised by secondary instabilities but it remains coherent. For weaker stratification, $N\leqslant0.5$ and $\tilde{f}$ large enough, secondary instabilities develop vigorously and destroy the primary vortex into small-scales turbulence. Concomitantly, the enstrophy rises to high values by stretching/tilting as in fully three-dimensional flows. A local analysis of the flow prior to the onset of secondary instabilities reveals that the Fjortoft necessary condition for instability is satisfied, suggesting that they correspond to shear instabilities.

In this article, we present a detailed characterisation of a multicusp-assisted inductively coupled RF plasma source for plasma immersion ion implantation (PIII). Using laser-induced fluorescence (LIF) and RF-compensated Langmuir probe diagnostics, we measured ion temperature T i and drift velocity v z in argon plasmas near an immersed electrode. The multicusp configuration enhances plasma density at low pressure, enabling stable operation down to 0.8 mTorr. Timeaveraged measurements show no detectable perturbation near the pulsed electrode, indicating full plasma recovery between high-voltage pulses. LIF-derived potential profiles match Riemann's presheath theory, and ion velocity distributions reveal acceleration consistent with sheath dynamics. These results support the use of LIF for steady-state characterisation of the bulk and presheath regions in PIII systems.

In this paper, the results of numerical experiments verifying a novel setup for laser beam profiling are presented. The experimental setup is based on infrared thermography and includes laser beam illuminating a thin metal plate. The method allows to determine four parameters of the short high-power laser pulse, namely the Super-Gaussian profile coefficient, laser power, pulse start time and duration. The unknown parameters are retrieved based on temporal and spatial temperature distributions at the rear side of the illuminated plate. The applied inverse method is based on Levenberg-Marquardt technique and it is implemented in the GNU Octave environment. Solutions of the forward problem are obtained numerically, with the aid of three-dimensional transient heat transfer model implemented in the commercial software ANSYS Fluent. The paper presents the results of the sensitivity analysis as well as calibration and verification of the developed inverse algorithm through application of numerically-generated synthetic data. Strengths and weaknesses of the applied approach are widely discussed.

Whilst surface-stress integration remains the standard approach for fluid force evaluation, control-volume integral methods provide deeper physical insights through functional relationships between the flow field and the resultant force. In this work, by introducing a second-order tensor weight function into the Navier-Stokes equations, we develop a novel weighted-integral framework that offers greater flexibility and enhanced capability for fluid force diagnostics in incompressible flows. Firstly, in addition to the total force and moment, the weighted integral methods establish, for the first time, rigorous quantitative connections between the surface-stress distribution and the flow field, providing potential advantages for flexible body analyses. Secondly, the weighted integral methods offer alternative perspectives on force mechanisms, through vorticity dynamics or pressure view, when the weight function is set as divergence-free or curl-free, respectively. Thirdly, the derivative moment transformation (DMT)-based integral methods (Wu et al., J. Fluid Mech. vol. 576, 2007, 265-286) are generalised to weighted formulations, by which the interconnections among the three DMT methods are clarified. In the canonical problem of uniform flow past a circular cylinder, weighted integral methods demonstrate advantages in yielding new force expressions, improving numerical accuracy over original DMT methods, and enhancing surface-stress analysis. Finally, a force expression is derived that relies solely on velocity and acceleration at discrete points, without spatial derivatives, offering significant value for experimental force estimation. This weighted integral framework holds significant promise for flow diagnostics in fundamentals and applications.

Interpretable scientific machine learning often trades predictive performance for structural transparency. When physical targets arise from hierarchical and nonlinear descriptor entanglement, weakly interacting white-box models underfit, whereas highly expressive black-box models obscure physical insight. Here I introduce GoodRegressor, a hierarchical depth-controlled symbolic regression framework that systematically assembles nonlinear descriptor interactions through lexicographically-ordered expansion. Despite effective compositional search spaces approaching $\sim 10^{400}$ structures, disciplined depth control enables tractable and reproducible exploration under realistic computational constraints. Across oxygen-ion conductors, NASICONs, and superconducting oxides, as representative high-complexity testbeds, predictive performances match or exceed state-of-the-art black-box models, retaining explicit functional form. Moreover, interaction-depth evolution reveals system-dependent optimal windows, providing an empirical taxonomy of hierarchical complexity in scientific datasets. These results establish hierarchical inductive bias with explicit depth control as a design principle for interpretable artificial intelligence in high-dimensional compositional spaces, and position interaction depth as a structural axis for diagnosing hierarchical complexity in scientific systems.

In fluid flow imaging, intensity gradients are a good measure of spatial variations in scalar properties, which play an important role in controlling transport processes. However, current flow imaging techniques exhibit system-limited spatial resolutions, thus inhibiting the ability to accurately detect intensity gradients. To address this challenge, we present a method and system, inspired by Structured Illumination Microscopy (SIM), which can be implemented in dynamic flow imaging to enhance pixel resolution and, thereby, the estimation of scalar gradients. We utilize sub-pixel-scale patterned light matching the system pixel scale and multi-frame imaging that creates quasi-static images over four frames, with scalability for high-speed imaging. These multi-frame images are then processed using a bespoke recombination algorithm that produces a new image with twice the pixel resolution compared to the original images. The sub-pixel spatial-resolution enhancement capabilities are shown with static images and dynamic fluid flow, for which enhancement in the flow gradient is demonstrated.

The present study extends the analysis of turbulence-affected beam statistics through a manifold-based statistical framework that unifies probabilistic modeling with geometric interpretation. The spatial intensity distributions, distorted by dynamic turbulence, are represented using Gaussian Mixture Models (GMMs), whose probability landscapes are refined via Kernel Density Estimation (KDE) applied to pixel-level intensity data across temporal frames. The temporal evolution of turbulence is quantified by monitoring the variation of the unnormalized volumetric integrals under the GMM surfaces, providing a continuous measure of power redistribution within the optical field. Experimental investigations were carried out under four propagation conditions: turbulence-free reference, turbulence only, turbulence with a single PMMA compensator, and turbulence with dual PMMA compensators. To assess statistical dissimilarity, Affine-Invariant Riemannian Metric (AIRM) distances were computed between the covariance representations of successive frames, capturing the geometric evolution of beam topology in the space of Symmetric Positive Definite (SPD) matrices. Complementary topological distance analyses, performed with respect to the initial frame of each set, revealed distinct signatures of turbulence-induced aberrations and their progressive mitigation through dielectric compensation. The observed reduction in inter-frame distance metrics confirms the ability of coupled dipole interactions within PMMA slabs to partially restore statistical coherence in turbulence-distorted optical beams.

Natural gradient is an advanced optimization method based on information geometry, where the Fisher metric plays a crucial role. Its quantum counterpart, known as quantum natural gradient (QNG), employs the symmetric logarithmic derivative (SLD) metric, one of the quantum Fisher metrics. While quantization in physics is typically well-defined via the canonical commutation relations, the quantization of information-theoretic quantities introduces inherent arbitrariness. To resolve this ambiguity, monotonicity has been used as a guiding principle for constructing geometries in physics, as it aligns with physical intuition. Recently, a variant of QNG, which we refer to as nonmonotonic QNG in this paper, was proposed by relaxing the monotonicity condition. It was shown to achieve faster convergence compared to conventional QNG. In this paper, we investigate the properties of nonmonotonic QNG. To ensure the paper is self-contained, we first demonstrate that the SLD metric is locally optimal under the monotonicity condition and that non-monotone quantum Fisher metrics can lead to faster convergence in QNG. Previous studies primarily relied on a specific type of quantum divergence and assumed that density operators are full-rank. Here, we explicitly consider an alternative quantum divergence and extend the analysis to non-full-rank cases. Additionally, we explore how geometries can be designed using Petz functions, given that quantum Fisher metrics are characterized through them. Finally, we present numerical simulations comparing different quantum Fisher metrics in the context of parameter estimation problems in quantum circuit learning.

This paper proposes a novel numerical method based on Godunov Smoothed Particle Hydrodynamics for special relativistic fluid dynamics. Our method utilizes a Riemann solver to describe shock, enhancing accuracy in strong shock waves. The formulation maintains conservation laws and achieves higher accuracy through convolution integrals that define physical quantities for SPH particles. We also propose the number density calculation method that uses a non-equal baryon number in each SPH particle and variable smoothing length in a way different from the conventional method. Numerical experiments demonstrate the method's robustness across one- and two-dimensional relativistic shock tube problems, as well as its ability to simulate Kelvin-Helmholtz instabilities accurately, validating SRGSPH as a reliable approach for high-resolution relativistic simulations.

Light experiences drag effects when it propagates through a moving medium. The study of light drag has provided foundational insights into light-matter interactions. While longitudinal drag has been extensively characterized, transverse drag, where the medium moves perpendicular to the light's propagation, is subtler and requires advanced techniques for detection. In this work, we experimentally investigate transverse drag in a highly dispersive slow-light medium using non-degenerate Zeeman electromagnetically induced transparency (EIT) in rubidium vapor. By systematically comparing configurations where the light beam and the medium serve as the moving frame, we leverage Galilean invariance to analyze transverse light-drag in this optical context. Thus, we provide a platform for future tests of fundamental principles on strong experimental grounds, which offers promising applications in precision velocimetry, accelerometry, quantum information, and light storage technologies.

The growing demands of artificial intelligence and immersive media require communication beyond bit-level accuracy to meaning awareness. Conventional optical systems that focused on syntactic precision suffer significant inefficiencies. Here, we introduce a multi-dimensional semantic communication framework that bridges this gap by directly mapping high-level semantic features onto the orthogonal physical dimensions of light, frequency, polarization, and intensity, within a multimode fiber. This synergistic co-design of semantic logic and the photonic channel achieve an unprecedented equivalent spectral efficiency approaching 1000 bit/s/Hz. Moreover, it demonstrates profound resilience, maintaining high-fidelity reconstruction even when the physical-layer symbol error rate exceeds 36%, a condition under which conventional communication systems fail completely. Crucially, this deeply integrated co-design of semantic encoding and physical-layer modulation enables full semantic demodulation with only single-ended intensity detection, therefore significantly reducing system complexity and cost. This work establishes a validated pathway toward hyper-efficient, error-resilient optical networks for the next generation of data-intensive computing.

A p-n junction is the basic building block for various semiconductor devices. A Schottky diode has characteristics that are essentially similar to those of the p-n junction diode. As is known, the leakage currents of p-n and Schottky junctions affect the overall performance of the devices and reduce the reliability of the devices. In order to achieve optimum device design, it is essential to fully understand the physical principle of the leakage currents. In traditional theory, defects provide a path for leakage current to travel. In this study, a novel theoretical model based on impurity-photovoltaic-effect is proposed to explain the leakage currents. It is well known that any object at a room-temperature emits infrared (IR) photons due to blackbody radiation. As is also well known, there is no absolutely pure material, and any material contains unavoidable defects associated with impurities. The self-absorption of the IR emission could be achieved through the sub-band-gap excitations due to defect-related intermediate levels in forbidden energy band-gap, creating carriers (electrons and holes). Some of the carriers diffuse into the built-in electric field of the junction. The built-in field then sweeps out electrons and holes in opposite directions, forming IR-generated photocurrent. Therefore, the leakage current is regarded as photocurrent. In addition to p-n junctions, some junctions exist in many semiconductor devices such as p-i-n diode and charge-coupled device (CCD), and these junctions also have built-in field due to contact potential difference. In fact, every semiconductor device contains at least one junction. The novel model is expected to explain the leakage for all kinds of junctions with semiconductor built-in fields.

In this paper, we propose a space-dependent eddy thermal diffusivity model for turbulent vertical natural convection in a fluid between two infinite vertical walls at different temperatures. Using this model, we derive analytical results for the mean temperature profile. Our results reveal that mean temperature profiles for different Rayleigh and Prandtl numbers are described by two universal scaling functions in the inner region next to the walls and the outer region near the centerline between the two walls and the characteristic temperature scales in the inner and outer regions are expressed in terms of the two parameters of the model which determine the characteristic velocities for heat transfer in the two regions. We show that these results are in good agreement with direct numerical simulation data.

Background: Voxel-based analysis (VBA) is an analytic approach to evaluate correlations between local dose and the development of different toxicities. DVHs are used for toxicity prediction as well. Compared with DVH, no contours are required for VBA technique and results tell specific voxels that may be related to the toxicity instead of the whole contoured area. The VBA has been used on different cancer sites and for different toxicities. Most of these studies included patients treated with photon, all published studies were based on planned dose and VBA tools used were developed in house. In our study, patient cohort were treated with proton, our VBA tool was developed based on RayStation and doses fed to the VBA tool were delivered doses with constant and two variable RBE models.

Integrated photonics has undergone tremendous development in the past few decades, transforming many fields of study in science and technology. Loss and gain are two fundamental elements in photonic circuits and have direct impacts on nearly all key performance metrics. Surprisingly, the tools to characterize the optical loss and gain inside photonic integrated circuits (PICs) are very limited. This is because, unlike free-space or fiber optics, integrated circuits cannot be nondestructively disassembled. Here, we report a universal method to see inside the photonic integrated circuits and measure loss and gain on the component level nondestructively. The method leverages nonlinear optical devices as optical power discriminators to retrieve the loss and gain information inside the PICs. Our method has a precision better than 0.1 dB, and can characterize the loss of individual fiber-chip coupling facet and general unknown devices under test. As a demonstration of applications, we measured the true on-chip quantum efficiency of a quantum PIC consisting of heterogeneously integrated balanced photodiodes, a critical building block for integrated quantum technology. Our method can be implemented on different photonic platforms, and can be used to understand gain and loss in complex photonic circuits, which is essential to optimize circuit design and to create large-scale systems with predictable, reproducible performance.

Density inhomogeneities are ubiquitous in space and astrophysical plasmas, particularly at magnetic reconnection sites, shock fronts, and within compressible turbulence. The gradients associated with these inhomogeneous plasma regions serve as free energy sources that can drive plasma instabilities, including the lower-hybrid drift instability (LHDI). Notably, lower-hybrid waves are frequently observed in magnetized space plasma environments, such as Earth's magnetotail and magnetopause. Previous studies have primarily focused on modeling particle acceleration via LHDI in these regions using a quasilinear approach. This study expands the investigation of LHDI to a broader range of environments, spanning weakly to strongly magnetized media, including interplanetary, interstellar, intergalactic, and intracluster plasmas. To explore the applicability of LHDI in various astrophysical settings, we employ two key parameters: (1) plasma magnetization, characterized by the plasma beta parameter, and (2) the spectral slope of suprathermal electrons following a power-law distribution. Using a quasilinear model, we determine the critical values of plasma beta and spectral slope that enable efficient electron acceleration via LHDI by comparing the rate of growth of instability and the damping rate of the resulting fluctuations. We further analyze the time evolution of the electron distribution function to confirm these critical conditions. Our results indicate that electron acceleration is generally most efficient in low-beta plasmas ($\beta < 1$). However, the presence of suprathermal electrons significantly enhances electron acceleration via LHDI, even in high-beta plasmas ($\beta > 1$). Finally, we discuss the astrophysical implications of our findings, highlighting the role of LHDI in electron acceleration across diverse plasma environments.

Recent developments in materials informatics and artificial intelligence has led to the emergence of foundational energy models for material chemistry, as represented by the suite of MACE-based foundation models, bringing a significant breakthrough in universal potentials for inorganic solids. As to all method developments in computational materials science, performance benchmarking against existing high-level data with focusing on specific applications, is critically needed to understand the limitations in the models, thus facilitating the ongoing improvements in the model development process, and occasionally, leading to significant conceptual leaps in materials theory. Here, using our own published DFT (Density Functional Theory) database of room-temperature dynamic stability and vibrational anharmonicity for $\sim2000$ cubic halide double perovskites, we benchmarked the performances of four different variants of the MACE foundation models for screening the dynamic stabilities of inorganic solids. Our analysis shows that, as anticipated, the model accuracy improves with more training data. The dynamic stabilities of weakly anharmonic materials (as predicted by DFT) are more accurately reproduced by the foundation model, than those highly anharmonic and dynamically unstable ones. The predominant source of error in predicting the dynamic stability arises predominantly from the amplification of errors in atomic forces when predicting the harmonic phonon properties through the computation of the Hessian matrix, less so is the contribution from possible differences in the range of the configurational spaces that are sampled by DFT and the foundation model in molecular dynamics. We hope that our present findings will stimulate future works towards more physics-inspired approaches in assessing the accuracy of foundation models for atomistic modelling.

The Madden-Julian Oscillation (MJO) is an important driver of global weather and climate extremes, but its prediction in operational dynamical models remains challenging, with skillful forecasts typically limited to 3-4 weeks. Here, we introduce a novel deep learning framework, the Physics-guided Cascaded Corrector for MJO (PCC-MJO), which acts as a universal post-processor to correct MJO forecasts from dynamical models. This two-stage model first employs a physics-informed 3D U-Net to correct spatial-temporal field errors, then refines the MJO's RMM index using an LSTM optimized for forecast skill. When applied to three different operational forecasts from CMA, ECMWF and NCEP, our unified framework consistently extends the skillful forecast range (bivariate correlation > 0.5) by 2-8 days. Crucially, the model effectively mitigates the "Maritime Continent barrier", enabling more realistic eastward propagation and amplitude. Explainable AI analysis quantitatively confirms that the model's decision-making is spatially congruent with observed MJO dynamics (correlation > 0.93), demonstrating that it learns physically meaningful features rather than statistical fittings. Our work provides a promising physically consistent, computationally efficient, and highly generalizable pathway to break through longstanding barriers in subseasonal forecasting.

Data fusion is an essential task in various domains, enabling the integration of multi-source information to enhance data quality and insights. One key application is in satellite remote sensing, where fusing multi-sensor observations can improve spatial and temporal resolution. In this study, we explore the design space of diffusion and flow models for data fusion, focusing on the integration of Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) and Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime lights data. Our approach leverages a diverse set of 2D image-to-image generative models, including UNET, diffusion, and flow modeling architectures. We evaluate the effectiveness of these architectures in satellite remote sensing data fusion, identifying diffusion models based on UNet as particularly adept at preserving fine-grained spatial details and generating high-fidelity fused images. We also provide guidance on the selection of noise schedulers in diffusion-based models, highlighting the trade-offs between iterative solvers for faster inference and discrete schedulers for higher-quality reconstructions. Additionally, we explore quantization techniques to optimize memory efficiency and computational cost without compromising performance. Our findings offer practical insights into selecting the most effective diffusion and flow model architectures for data fusion tasks, particularly in remote sensing applications, and provide recommendations for leveraging noise scheduling strategies to enhance fusion quality.

We treat the Mechanics of point particles as a 1-dimensional general-relativistic gauge field theory, which may be referred to as Mechanical Field Theory (MFT), exploiting the bundle geometry of Mechanical Field Space (MFS). The diffeomorphism covariance of MFT encodes its relational character, arising - as in all general-relativistic physics - via the conjunction of a hole and a point-coincidence argument. Any putative "boundary problem", meaning the claim that 'spacetime' boundaries break diffeomorphism and gauge symmetries, thereby dissolves. It is highlighted that the standard path integral (PI) on the MFS, the exact analogue of the PI used in gauge field theory, is conceptually and technically distinct from the standard PI of Quantum Mechanics. We then use the Dressing Field Method to give a manifestly invariant and relational reformulation of MFT, which reproduces the standard textbook formulation when a clock field is chosen as a (natural) dressing field. The dressed, or basic, PI on the MFS, defining Relational Quantization - i.e. the quantization of invariant relational d.o.f. - is shown to reproduce the standard PI of Quantum Mechanics. This establishes the soundness of Relational Quantization as a general guiding principle: We outline it for general-relativistic gauge field theories.