By combining AI and fluid physics, we discover a closed-form closure for 2D turbulence from small direct numerical simulation (DNS) data. Large-eddy simulation (LES) with this closure is accurate and stable, reproducing DNS statistics including those of extremes. We also show that the new closure could be derived from a 4th-order truncated Taylor expansion. Prior analytical and AI-based work only found the 2nd-order expansion, which led to unstable LES. The additional terms emerge only when inter-scale energy transfer is considered alongside standard reconstruction criterion in the sparse-equation discovery.

The flow behavior in the continuous casting tundish plays a critical role in steel quality and is typically characterized via residence time distribution (RTD) curves. This study investigates the fluid flow behaviour in a single-strand tundish using numerical and experimental approaches. Full-order model (FOM) steady-state simulations were conducted under both isothermal and non-isothermal conditions to assess the influence of thermal buoyancy on the flow characteristics. The results show that buoyancy effects under non-isothermal conditions have a negligible impact on the overall velocity field. The converged flow fields serve as initial conditions for transient tracer transport simulations, enabling evaluation of RTD curves and volume partitioning. A Galerkin projection-based reduced-order model (ROM) is developed to efficiently derive RTD curves. Comparison of RTD curves from experiments, FOM simulations, and ROM predictions demonstrates strong agreement, with both computational approaches closely matching experimental data. A parameter-time dependent ROM is subsequently developed using Galerkin projection and operator interpolation. This enables efficient evaluation of RTD curves across varying parameter values with significantly reduced computational cost compared to full-order simulations. The ROM framework is well-suited for real-time analysis, design processes, optimization, and digital twin applications in metallurgical processes.

An exact analogy between wave mechanics in quantum theory and the scalar wave treatment of optics emerges from the marriage of Newtonian formulation of geometrical optics [1] and the ``formal quantum theory of light rays'' [2]. Here the incidence of a ray of light on the interface between two media is treated as the incidence of a wavefunction on a potential barrier. This leads to the coefficient of reflection identical to Fresnel's formula for s-polarised light [3], although there is no concept of the polarisation of light in this model. In the present work, we apply this model to the total internal reflection of light and evanescent waves. We also deduce a one-to-one correspondence between the transmission coefficient in wave mechanics and frustrated total internal reflection for all angles of incidence. Further, we demonstrate that this model can also be used to derive the Goos-Hänchen shift for s-polarised light. This work augments the discussion on these topics found in the standard texts in optics [4].

Teaching and learning in advanced materials science are often limited by two barriers: the technical complexity of quantum-mechanical simulations and the lack of individualized support in inquiry-based education. Here, we introduce the Neuromorphic Materials Calculator 2025 (NMC2025), a command-line platform that integrates a conversational artificial intelligence (AI) tutor with automated simulation workflows. NMC2025 combines large language model (LLM) guidance, real-time literature feedback, and domain-specific computation to create an adaptive learning environment. The system includes modular Python components for material discovery, simulation parameter optimization, and automated input generation for Quantum ESPRESSO (QE). Grounded in constructivist pedagogy, the tool enables students to carry out authentic research tasks such as identifying candidate materials for neuromorphic memristors or tuning density functional theory (DFT) inputs, while receiving context-aware explanations from the AI tutor. A case study illustrates how iterative, AI-guided refinement of hypotheses and calculations enhances both accuracy and understanding. NMC2025 fosters deeper conceptual insight, independent exploration, and smooth transfer of research methods into the classroom. This approach highlights the potential of AI-augmented education to reduce barriers to complex simulations and to expand access to computational modeling across science, technology, engineering, and mathematics (STEM).

We report results of optical characterizations of new wavelength-shifting and scintillating-wavelength-shifting fibers EJ-182 and EJ-160 from Eljen Technology and compare them to the wavelength-shifting fiber BCF-91A from Saint-Gobain. The wavelength-dependence of attenuation was derived from spectral measurements confirming that the long attenuation length increases with wavelength, while short attenuation effects become less significant at longer wavelengths. The impact of environmental refractive index was measured by immersing a fiber in water. Immersing the fibers in water reduced the light yield and led to a suppression of the short attenuation length, consistent with the expected decrease in the refractive index contrast.

For nearly two decades after 2001, Afghanistan's higher education sector expanded rapidly, with Kabul University serving as a central site of women's academic participation. Drawing on administrative records of student populations from 2016-2019 (Islamic calendar 1395-1398), this study examines gender distributions across shifts, faculties, and departments, with particular attention to STEM versus non-STEM fields. At Kabul University, the morning shift refers to the main daytime cohort (including some midday classes), while the evening shift is a separate program with its own classes and students; the two cohorts are administratively and academically distinct. Results show steady growth in the overall female student population, but with marked disparities between morning and evening shifts. Women were concentrated in non-STEM and "socially acceptable" disciplines such as literature, law, and psychology, while within STEM they were relatively well represented in the life sciences but remained significantly underrepresented in technical fields such as engineering, computer science, and physics. Gender parity improved modestly across most faculties, yet the Gender Parity Index (GPI) rarely approached 1.0, and the STEM GPI consistently remained below 0.5. These findings highlight both progress and persistent structural inequalities, documenting a critical historical benchmark before the 2021 ban on women's university education.

This lecture presents an overview of the basic concepts and fundamentals of Engineering Materials within the framework of accelerator applications. After a short introduction, main concepts relative to the structure of matter are reviewed, like crystalline structures, defects and dislocations, phase diagrams and transformations. The microscopic description is correlated with physical properties of materials, focusing in metallurgical aspects like deformation and strengthening. Main groups of materials are addressed and described, namely, metals and alloys, ceramics, polymers, composite materials, and advanced materials, where brush-strokes of tangible applications in particle accelerators and detectors are given. Deterioration aspects of materials are also presented, like corrosion in metals and degradation in plastics.

We describe, and test, a method of obtaining spectrally and spatially sampled Stokes polarimetry measurements based on star test polarimetry enabled by a stress engineered optic (SEO). When the SEO is placed in the pupil plane of an imaging system, the space-variant birefringence of the SEO combined with the intrinsic wavelength dependence of the retardance allows for point spread functions from distinct laser wavelengths to be both spectrally and polarimetrically distinguished in a single frame measurement designed to enable single shot spectropolarimetry of individual laser pulses. In the experiment, the beam is sampled using a lenslet array typical of that used in a Shack-Hartmann sensor with the PSF of each spot relayed to a sensor. Experiments show angular errors as small as 100 mRad on the Poincare sphere, with red and blue measurements outperforming green. Potential applications include polarization sampling of extreme lasers as well as spectropolarimetry of display devices based on liquid crystals.

$\textit{Ab initio}$ calculations of the parallel component of the magnetic dipole hyperfine structure (HFS) constant have been carried out for hydroxyl radical isotopologues ($^{16,17}$OH(D)) over the internuclear distance range $R \in [0.6, 1.8]$ Å. For the ground electronic state $X^2\Pi$, the HFS functions were evaluated for contributions induced by both oxygen and hydrogen nuclei. In addition, the hydrogen-induced HFS curve was calculated for the excited $A^2\Sigma^+$ state. The quantum-chemistry study employs a four-component relativistic coupled-cluster (CC) method, including excitations up to the triple level, namely: the contribution of triple-cluster amplitudes was studied both perturbatively (CCSD(T)) and through fully iterative calculations (CCSDT). The resulting oxygen- and hydrogen-induced HFS functions represent the most accurate and reliable theoretical predictions to date exhibiting excellent agreement with semiempirical curve for hydrogen-induced HFS derived from high-resolution spectroscopic data for the lowest vibrational levels ($v\in [0,2]$) of the electronic $X^2\Pi$ state. Vibrationally averaged $\textit{ab initio}$ values are consistent with experimental values within $1\%$ for all states considered. Furthermore, the internuclear distance range over which the HFS curves are defined has been extended beyond that of previous studies, thereby providing a robust foundation for accurate HFS treatments of higher-lying rovibrational levels of OH isotopologues within both adiabatic and non-adiabatic frameworks.

Bistability in nanomechanical resonators can be exploited for sensing, signal processing, and memory applications due to its potential for switching and high sensitivity to external stimuli. External vibration can be used to drive a doubly-clamped nanowire into the nonlinear regime of bistable oscillation. Here, we experimentally demonstrate that the bistable oscillation of such a nonlinear nanomechanical resonator can be controlled, switched and read by an electron beam.

We present a passive ultraviolet charge management system for the fused silica test masses in ground-based laser interferometric gravitational wave detectors. The system uses photoelectron emission from low work-function gold coatings illuminated by 275 to 285 nm UV light to neutralize unwanted electric charges on the electrically floating test masses, maintaining them at zero potential relative to surrounding components. Two implementation schemes are described: 1. distributed discharge tabs illuminated by UV LEDs mounted at the eight surrounding earthquake stops, and 2. a conductively linked tab design enabling a centralized discharge on the test mass barrel, with potential extension to annular coatings around the high-reflectivity and anti-reflective surfaces. Experimental results show photoelectric currents greater or equal to 10 pA for 1.0 mW of incident UV, enabling discharge rates greater than 10V/s for a 1 pF capacitance. With 0.2 mW UV power, charge neutralization to less or equal to 1 pC can be achieved in 5 to 75 minutes. This technique offers a vacuum-compatible alternative to ion sprayers, significantly reducing operational downtime. For the conductive-barrel configuration, we also propose a balanced electrostatic actuator using four parallel-plate capacitors for combined control of TM axial displacement, tilt, and azimuth, with reduced drive voltages.

A new method was recently proposed for axion dark matter searches which employs scalable, permanently magnetized, flat conversion surface (or magnetized converter) generating an electromagnetic signal from dark matter particles passing through them. BRASS-p is an experimental setup which applies this method to direct searches of axion/ALP dark matter in the frequency range from 12 to 18 GHz (corresponding to the range of particle mass from 50 to 74 $\mu$eV. The conversion surface of BRASS-p will consist of 24 individual panels, each containing a 483mm $\times$ 483mm array of high-grade permanent magnets organized in a pattern capable of generating a magnetic field with an average strength of $\approx$ 0.9 T, for the field component parallel to the surface of the panel. This paper describes the development and optimization of a design for these panels and discusses the resulting sensitivity of BRASS-p measurements to the axion/ALP dark matter.

Driven by the growing demand for large-scale artificial intelligence applications, disaggregated compute nodes and high-radix switches in next-generation computing clusters are set to surpass the capacity of current optical interconnect technologies. Such a surge turns several aspects of transmitters into critical bottlenecks: shoreline bandwidth density and energy efficiency are effectively limiting the scalability. We present a comb-driven coherent optical transmitter architecture on a Si/SiN platform that provides the bandwidth density, energy efficiency, and compact footprint required for such co-packaged-enabled optical interconnects. We evaluate scalability through critical building blocks, including ultra-compact microring-assisted Mach--Zehnder modulators (MRA-MZMs) and dense wavelength-division multiplexing (DWDM) interleavers. Single-tone experiments demonstrate a net line rate of 400 Gbps per polarization (16-QAM, 120 GBd) in silicon within the O-band, achieving a record shoreline density of 4 Tbps/mm while consuming only 10 fJ/bit for modulation. We also demonstrate transmission rates of up to 160 GBd QPSK in back-to-back and 100 GBd over 7 km of fiber without dispersion compensation. Using a quantum-dot frequency comb, six 100 GHz-spaced WDM channels transmit 1.08 Tbps over 5 km. System-level analyses show that by leveraging advanced modulation formats through the integration of wavelength and polarization multiplexing, our proposed architecture can realistically support combined transmission rates exceeding 10 Tbps per fiber within practical limits of power consumption and packaging, outlining a clear path toward future petabit-scale interconnects.

We demonstrate that in pair plasma weakly nonlinear electromagnetic waves, $a_0 \leq 1$, experience Anderson self-localization. The beat between the driver and a back-scattered wave creates random, charge-neutral, large density fluctuations $\delta \rho/\rho \gg 1$, and corresponding random fluctuations of the dielectric permittivity $\epsilon$. Propagating in quasi-1D, waves in a medium with spatially random, time-varying, self-created fluctuations of dielectric permeability experience localization. Anderson self-localization of light leads to (i) reflection of EM waves by the under-dense pair plasma; (ii) a wave already present inside the plasma separates into bright trapped pockets and dark regions. Mild initial thermal spread restores wave propagation by suppressing the seeds of parametrically unstable density fluctuations. A circularly polarized driver produces linearly polarized structures, with position angle varying randomly between the bright pulses. We discuss possible applications to astrophysical Fast Radio Bursts.

Predicting the quantum dynamics of promising solid-state and molecular quantum technology candidates remains a formidable challenge. Yet, accessing these dynamics is key to understanding and controlling decoherence mechanisms -- a prerequisite for designing better qubits, sensors, and memories. We leverage a matrix product state representation to introduce a numerically exact and scalable method to achieve this goal. We demonstrate that our method accurately predicts coherence and population dynamics of spin networks across a wide range of parameter regimes, encompassing nuclear spin sensors and qubits in solid-state semiconductors and molecular magnets. Our method further predicts spin dynamics under the influence of repeated light pulses, which are commonly used to mitigate decoherence and perform quantum sensing experiments. Our method thus provides reliable results for moderately-sized spin platforms spanning molecular magnets and solid-state spins that can guide the development of approximate but efficient quantum dynamics methods and enable principled inquiry into decoherence mechanisms.

We report a 100 kHz linewidth for the 1S0 to 3P1 intercombination line of 88Sr atoms at 689nm in a microfabricated 9x14 x4.4 mm3 vapor cell. This puts an upper bound on the residual gas pressure in the vapor cell of 10 mTorr. The microfabricatd Sr vapor cell offers an inexpensive and scalable way to lock lasers in state-of-the-art cold-atom based atomic clocks and quantum computing experiments. It also paves the way for a compact and precise optical frequency references based on alkaline earth vapor cells.

Machine learning interatomic potentials (MLIPs) have revolutionized molecular and materials modeling, but existing benchmarks suffer from data leakage, limited transferability, and an over-reliance on error-based metrics tied to specific density functional theory (DFT) references. We introduce MLIP Arena, a benchmark platform that evaluates force field performance based on physics awareness, chemical reactivity, stability under extreme conditions, and predictive capabilities for thermodynamic properties and physical phenomena. By moving beyond static DFT references and revealing the important failure modes of current foundation MLIPs in real-world settings, MLIP Arena provides a reproducible framework to guide the next-generation MLIP development toward improved predictive accuracy and runtime efficiency while maintaining physical consistency. The Python package and online leaderboard are available at this https URL.

In the design of optical devices and components, geometric structures and optical properties of materials, such as absorption, refraction, reflection, diffraction, scattering, and trapping, have been utilized. Finding the ideal material with certain optical and geometric characteristics is essential for a customized application. Here, we fabricated unoxidizable achromatic copper films (ACFs) on Al2O3 substrates utilizing an atomic sputtering epitaxy apparatus. ACFs are made up of two regions vertically: a comparatively flat layer region and a three-dimensional (3D) porous nanostructured region on top of the flat region. The measured specular reflectance displayed low-pass filter behaviour with a sharp cutoff frequency in the infrared spectrum. Furthermore, the measured diffusive reflectance spectra showed light-trapping behaviour in the spectral region above the cutoff frequency, where there are no known absorption mechanisms, such as phonons and interband transitions. A focused ion beam scanning electron microscope was utilized to study the thin film's nanostructured region through 3D tomographic analysis in order to comprehend the phenomena that were observed. This work will shed fresh light on the design and optimization of optical filters and light-trapping employing porous nanostructured metallic thin films.

Forward and backward scattering provide complementary volumetric and interfacial information, yet conventional three-dimensional (3D) imaging typically accesses only one. In this Letter, we present a substrate-enhanced diffraction tomography approach that simultaneously recovers both channels under multi-angle epi-illumination.

The immense computational cost of simulating turbulence has motivated the use of machine learning approaches for super-resolving turbulent flows. A central challenge is ensuring that learned models respect physical symmetries, such as rotational equivariance. We show that standard convolutional neural networks (CNNs) can partially acquire this symmetry without explicit augmentation or specialized architectures, as turbulence itself provides implicit rotational augmentation in both time and space. Using 3D channel-flow subdomains with differing anisotropy, we find that models trained on more isotropic mid-plane data achieve lower equivariance error than those trained on boundary layer data, and that greater temporal or spatial sampling further reduces this error. We show a distinct scale-dependence of equivariance error that occurs regardless of dataset anisotropy that is consistent with Kolmogorov's local isotropy hypothesis. These results clarify when rotational symmetry must be explicitly incorporated into learning algorithms and when it can be obtained directly from turbulence, enabling more efficient and symmetry-aware super-resolution.

Purpose: Convolutional neural networks (CNNs) are promising in predicting treatment outcome for pediatric craniopharyngioma while the decision mechanisms are difficult to interpret. We compared the activation maps of CNN with hand crafted radiomics features of a densely connected artificial neural network (ANN) to correlate with clinical decisions. Methods: A cohort of 100 pediatric craniopharyngioma patients were included. Binary tumor progression was classified by an ANN and CNN with input of T1w, T2w, and FLAIR MRI. Hand-crafted radiomic features were calculated from the MRI using the LifeX software and key features were selected by Group lasso regularization, comparing to the activation maps of CNN. We evaluated the radiomics models by accuracy, area under receiver operational curve (AUC), and confusion matrices. Results: The average accuracy of T1w, T2w, and FLAIR MRI was 0.85, 0.92, and 0.86 (ANOVA, F = 1.96, P = 0.18) with ANN; 0.83, 0.81, and 0.70 (ANOVA, F = 10.11, P = 0.003) with CNN. The average AUC of ANN was 0.91, 0.97, and 0.90; 0.86, 0.88, and 0.75 of CNN for the 3 MRI, respectively. The activation maps were correlated with tumor shape, min and max intensity, and texture features. Conclusions: The tumor progression for pediatric patients with craniopharyngioma achieved promising accuracy with ANN and CNN model. The activation maps extracted from different levels were interpreted with hand-crafted key features of ANN.

Purpose: We developed an artificial neural network (ANN) combining radiomics with clinical and dosimetric features to predict the extent of body mass index (BMI) increase after surgery and proton therapy, with advantage of improved accuracy and integrated key feature selection. Methods and Materials: Uniform treatment protocol composing of limited surgery and proton radiotherapy was given to 84 pediatric craniopharyngioma patients (aged 1-20 years). Post-treatment obesity was classified into 3 groups (<10%, 10-20%, and >20%) based on the normalized BMI increase during a 5-year follow-up. We developed a densely connected 4-layer ANN with radiomics calculated from pre-surgery MRI (T1w, T2w, and FLAIR), combining clinical and dosimetric features as input. Accuracy, area under operative curve (AUC), and confusion matrices were compared with random forest (RF) models in a 5-fold cross-validation. The Group lasso regularization optimized a sparse connection to input neurons to identify key features from high-dimensional input. Results: Classification accuracy of the ANN reached above 0.9 for T1w, T2w, and FLAIR MRI. Confusion matrices showed high true positive rates of above 0.9 while the false positive rates were below 0.2. Approximately 10 key features selected for T1w, T2w, and FLAIR MRI, respectively. The ANN improved classification accuracy by 10% or 5% when compared to RF models without or with radiomic features. Conclusion: The ANN model improved classification accuracy on post-treatment obesity compared to conventional statistics models. The clinical features selected by Group lasso regularization confirmed our practical observation, while the additional radiomic and dosimetric features could serve as imaging markers and mitigation methods on post-treatment obesity for pediatric craniopharyngioma patients.

In-line holography offers high space-bandwidth product imaging with a simplified lens-free optical system. However, in-line holographic reconstruction is troubled by twin images arising from the Hermitian symmetry of complex fields. Twin images disrupt the reconstruction in solving the ill-posed phase retrieval problem. The known parameters are less than the unknown parameters, causing phase ambiguities. State-of-the-art deep-learning or non-learning methods face challenges in balancing data fidelity with twin-image disturbance. We propose the Gaussian splatting holography (GSH) for twin-image-suppressed holographic reconstruction. GSH uses Gaussian splatting for optical field representation and compresses the number of unknown parameters by a maximum of 15 folds, transforming the original ill-posed phase retrieval into a well-posed one with reduced phase ambiguities. Additionally, the Gaussian splatting tends to form sharp patterns rather than those with noisy twin-image backgrounds as each Gaussian has a spatially slow-varying profile. Experiments show that GSH achieves constraint-free recovery for in-line holography with accuracy comparable to state-of-the-art constraint-based methods, with an average peak signal-to-noise ratio equal to 26 dB, and structure similarity equal to 0.8. Combined with total variation, GSH can be further improved, obtaining a peak signal-to-noise ratio of 31 dB, and a high compression ability of up to 15 folds.

Advancements in remote sensing have led to development of several satellite-derived precipitation products; however, their accuracy must be evaluated before use in scientific and operational studies. This study comprehensively assesses six widely used datasets PERSIANN CCS, CHIRPS, MSWEP, IMERG, AgERA5, and GSMaP ISRO against a dense rain gauge network across Karnataka, a southern Indian state characterized by diverse climatic conditions and complex topography. The analysis focuses on the Indian summer monsoon season for 2011 to 2022. To complement traditional metrics, tools from complex network theory were applied to investigate spatial organization and connectivity patterns of rainfall. A functional climate network approach was used to construct rainfall correlation networks, while event synchronization, a nonlinear measure, quantified the co occurrence of extreme events. Most products reproduced large scale monsoon features, yet their ability to represent intensity categories and extremes varied. GSMaP ISRO showed the highest correlation, lowest bias, and RMSE across subregions, whereas PERSIANN CCS exhibited systematic errors, particularly in Western Ghats, though correlations improved over interior plains. Network-based analysis reaffirmed GSMaP ISROs skill in replicating spatial correlation structures, capturing high coherence in regions dominated by large-scale processes and lower coherence in areas influenced by localized dynamics. The observed rainfall network revealed strong synchronization between the coastal region and central Karnataka, indicating broad spatial co occurrence of extremes, while the Malnad region showed weaker connectivity, suggesting localized events. GSMaP ISRO closely reproduced this degree distribution, reflecting corrections using IMD gridded dataset. Future work should improve sub-daily and localized rainfall estimates, especially in complex terrain.

Strain has been extensively employed to tailor graphene's properties and has emerged as a powerful tool for engineering gauge fields and exploring fundamental phenomena in artificial platforms like photonic graphene. Here we discover that, in graphene flakes with custom boundaries, one can create or destroy edge states depending on the direction of the applied uniaxial strain. This is experimentally demonstrated in a photonic platform with two specific examples: one flake structure with pairs of twig and zigzag edges, and the other with pairs of armchair and bearded edges. We find that the existence of the edge states and their positions in momentum space are accurately predicted with appropriate winding numbers, unveiling the underlying topology of such edge states. Furthermore, when a graphene flake supports the maximum number of edge states along boundaries after a semimetal-to-insulator transition, both compact localized edge and corner states emerge, indicating the realization of a photonic minimal-model higher-order topological insulator based on such strained graphene flakes.

The increasing complexity and scale of photonic and electromagnetic devices demand efficient and accurate numerical solvers. In this work, we develop a parallel overlapping domain decomposition method (DDM) based on the finite-difference frequency-domain (FDFD) formulation to model the electromagnetic response of large-scale complex nanostructures. The global computational domain is partitioned into multiple overlapping subdomains terminated with perfectly matched layers (PMLs), enabling seamless source transfer between adjacent subdomains. A multi-frontal preconditioner is employed to accelerate the iterative solution process, while an OpenMP-based parallel implementation ensures high scalability. Several numerical examples are provided to validate the efficiency and accuracy of the proposed algorithm. The results demonstrate excellent agreement with analytical and commercial COMSOL solutions. Notably, the method achieves up to an order of magnitude reduction in computation time, highlighting its potential as a powerful tool for large-scale photonic and electromagnetic modeling.

Designing nanophotonic devices with minimal human intervention has gained substantial attention due to the complexity and precision required in modern optical technologies. While inverse design techniques typically rely on conventional electromagnetic solvers as forward models within optimization routines, the substantial electrical size and subwavelength characteristics of nanophotonic structures necessitate significantly accelerated simulation methods. In this work, we introduce a forward modeling approach based on the volume integral equation (VIE) formulation as an efficient alternative to traditional finite-difference (FD)-based methods. We derive the adjoint method tailored specifically for the VIE framework to efficiently compute optimization gradients and present a novel unidirectional mode excitation strategy compatible with VIE solvers. Comparative benchmarks demonstrate that our VIE-based approach provides multiple orders of magnitude improvement in computational efficiency over conventional FD methods in both time and frequency domains. To validate the practical utility of our approach, we successfully designed two representative nanophotonic components: a selective mode reflector and a 3 dB power splitter. Our results underscore the significant runtime advantages offered by the VIE-based framework, highlighting its promising role in accelerating inverse design workflows for next-generation nanophotonic devices.

A general synthetic iterative scheme is proposed to solve the Enskog equation within a Monte Carlo framework. The method demonstrates rapid convergence by reducing intermediate Monte Carlo evolution and preserves the asymptotic-preserving property, enabling spatial cell sizes much larger than the mean free path in near-continuum flows. This is realized through mesoscopic-macroscopic two-way coupling: the mesoscopic Monte Carlo simulation provides high-order constitutive relations to close the moment (synthetic) equation, while the macroscopic synthetic equation, once solved toward steady state, directs the evolution of simulation particles in the Monte Carlo method. The accuracy of the proposed general synthetic iterative scheme is verified through one-dimensional normal shock wave and planar Fourier heat transfer problems, while its fast-converging and asymptotic-preserving properties are demonstrated in the force-driven Poiseuille flow and two-dimensional hypersonic cylinder flow and low-speed porous media flow, where the simulation time is reduced by several orders of magnitude in near-continuum flows. With the proposed method, a brief analysis is conducted on the role of the adsorption layer in porous media flow, mimicking shale gas extraction.

The practical deployment of high-capacity free-space optical communication is fundamentally limited by atmospheric turbulence, a challenge that conventional mode-sorting techniques have failed to overcome. While engineered optical computers like diffractive networks offer a potential solution, their design complexity remains a significant barrier. Here, we introduce and experimentally validate a methodology for task-dependent hardware design, demonstrating a simple, continuous-wave-driven multimode fibre reservoir that is physically configured to solve this specific, high-impact problem. We first establish a set of physical design principles, showing that recurrent dynamics are optimal for structural data (MNIST), whereas high mode-mixing is superior for textural data (FMNIST). By treating turbulence-induced wavefront distortion as a complex textural feature, we configure a physically optimised reservoir for classifying orbital angular momentum (OAM) modes. In moderate to high-turbulence regimes, our system outperforms an ideal modal decomposition by an average of 20.32~$\pm$~3.00\%, succeeding precisely where the conventional approach fails. This work not only demonstrates a practical, low-complexity solution for turbulence mitigation but also reframes the optical receiver as a physical likelihood processor, thereby offering a path towards significantly reduced digital signal processing burdens by offloading much of the computational load to the physical optical front-end. We establish a framework for co-designing physical hardware with computation, enabling simpler, more robust optical machine learning systems tailored for real-world challenges.

We demonstrate a reconfigurable photonic platform capable of implementing both optical logic gates and a 2-bit decoder on the same device. The architecture is based on cascaded multiport directional couplers interleaved with thermo-optic phase shifters, enabling programmable $N \times N$ unitary transformations. Optimal heater voltages for phase control are determined through Bayesian optimization. The devices operate reliably across multiple wavelengths with 50 GHz spacing, suggesting potential for wavelength-parallel optical processing. These results highlight a pathway toward compact, programmable and integrated photonic circuits.

We present the components of a high-order accurate Navier-Stokes solver designed to simulate high-Reynolds-number stratified flows. The proposed numerical model addresses some of the numerical and computational challenges that high-Reynolds-number simulations pose, facilitating the reproduction of stratified turbulent fluid dynamics typically observed in oceanic and atmospheric flows, namely the development of thin regions of high vertical shear, strongly layered turbulence at high Reynolds numbers and internal wave radiation. This Navier-Stokes solver utilizes a Fourier pseudo-spectral method in the horizontal direction and a modal spectral element discretization in the vertical. We adopt an implicit-explicit time discretization scheme that involves solving several one-dimensional Helmholtz problems at each time step. Static condensation and modal boundary-adapted basis functions result in an inexpensive algorithm based on solving many small tridiagonal systems. A series of benchmark studies is presented to demonstrate the robustness of the flow solver. These include two-dimensional and three-dimensional problems, concluding with a turbulent stratified wake generated by a sphere in linear stratification.

High-order harmonic generation (HHG), a robust tabletop source for producing attosecond pulses, has been extensively utilized in attosecond metrology. Traditionally, HHG driven by classical laser fields involves two typical quantum paths (short and long quantum paths) contributing to harmonic emission. Here, we demonstrate that these quantum paths in HHG can be selectively controlled using squeezed lights, a form of non-classical light. Our results indicate that the long (short) quantum path of HHG will be dramatically suppressed in the phase (amplitude)-squeezed fields. The time-frequency analysis reveals that this quantum path control stems from the quantum fluctuations in the squeezed light, which modify the phase matching of harmonic emission from different quantum states of the squeezed light. Such a quantum path selection can be achieved for the whole harmonic plateau, which has great potential to generate ultrashort isolated attosecond pulse with duration less than one atomic unit of time.

Nanoimprint lithography (NIL) is a widely used high-throughput fabrication technique for photonic devices, yet its reliability is often compromised by the inevitable imperfections that arise during the demolding process. Topological photonics, which harnesses topologically nontrivial structures to support defect-robust photonic states, offers a promising solution to this challenge. Here, we demonstrate a topological laser that is one-step nanoimprinted upon colloidal perovskite nanocrystals. This laser features multiple higher-order topological corner states, with topological protection provided by the structure effectively mitigating imperfections caused by the nanoimprinting process. This property enables the reliable detection of these states, which is particularly challenging to achieve in the visible spectrum. Our work establishes topological photonics as a viable pathway to enhance the reliability of NIL-based manufacturing, providing a scalable and practical route for mass-producing topological lasers with low-index materials.

Coarse-grained (CG) modeling simplifies molecular systems by mapping groups of atoms into representative units. However, traditional CG approaches rely on fixed mapping rules, which limit their ability to handle diverse chemical systems and require extensive manual intervention. Thus, supervised learning-based CG methods have been proposed, enabling more automated and adaptable mapping. Nevertheless, these methods suffer from limited labeled datasets and the inability to control mapping resolution, which is essential for multiscale modeling. To overcome these limitations, we propose MolCluster, an unsupervised model that integrates a graph neural network and a community detection algorithm to extract CG representations. Additionally, a predefined group pair loss ensures the preservation of target groups, and a bisection strategy enables precise, customizable resolution across different molecular systems. In the case of the downstream task, evaluations on the MARTINI2 dataset demonstrate that MolCluster, benefiting from its label-free pretraining strategy, outperforms both traditional clustering and supervised models. Overall, these results highlight the potential of MolCluster as a core model for customizable and chemically consistent CG mapping.

Harnessing bound states in the continuum (BICs) for guiding light in leaky environments has unlocked new possibilities in photonic integrated circuits. BIC confinement enables low-loss waveguiding of leaky transverse-magnetic (TM) modes in etchless waveguides based on dielectric wires loaded on plane slabs. We have recently reported BIC slow light waveguides by introducing one-dimensional photonic crystals into such etchless waveguides. However, they were restricted to a high-symmetry point ($X$ point), limiting their applicability. In this Letter, we propose and numerically demonstrate BIC slow light waveguides at off-high-symmetry points by exploiting Friedrich-Wintgen BICs, arising from the interband coupling of two guided modes sharing a radiation continuum. We identified a systematic approach for tuning the loss minimum position in momentum space and simultaneously achieved a high group index over $100$ and a low propagation loss of less than $5 \times 10^{-2}~\mathrm{dB/cm}$ at an off-high-symmetry point. Our findings pave the way for advanced control of light-matter interactions in non-Hermitian photonic systems.

We theoretically investigate a quantum spin-Hall topological laser formed by an array of dielectric ring resonators endowed of saturable gain. The system preserves time-reversal symmetry, the clockwise and counter-clockwise modes in each ring resonator acting as two pseudospin states that experience opposite synthetic magnetic fields. We consider ring resonators featuring an internal S-shaped waveguide asymmetrically coupling the two pseudospin states. In spite of the non-magnetic nature of the configuration, we show that an effective breaking of reciprocity is induced by the interplay of spatial parity breaking with saturable gain and a Kerr optical non-linearity. This enables robust single-mode topological lasing even in the presence of realistic levels of backscattering.

Doubly resonant optical parametric oscillators (OPOs) under continuous wave (CW) pumping are particularly notable for their low threshold and narrow linewidth. Backward OPOs (BOPOs) realized through backward quasi-phase matching which exhibit unique tuning properties compared with conventional forward OPOs have been demonstrated under pulse pumping. In this work, a doubly resonant BOPO was implemented in a semi-monolithic cavity under CW pumping, and its tuning properties were characterized. By tuning the pump wavelength, the forward and backward waves exhibited tuning ranges of 56.85 nm and 0.89 nm, respectively. Adjusting the crystal temperature resulted in tuning ranges of 59.7 GHz and 59.4 GHz for the forward and backward waves, respectively. This research establishes the BOPO as a promising candidate for applications in the field of CW OPOs.

We investigate the equilibration time to attain steady-state for a system of liquid molecules under boundary-driven planar Couette flow via nonequilibrium molecular dynamics (NEMD) simulation. In particular, we examine the equilibration time for the two common types of boundary driven flow: one in which both walls slide with equal and opposite velocity, and the other in which one wall is fixed and the other moves with twice the velocity. Both flows give identical steady-state strain rates, and hence flow properties, but the transient behaviour is completely different. We find that in the case of no-slip boundary conditions, the equilibration times for the counter-sliding walls flow are exactly 4 times faster than those of the single sliding wall system, and this is independent of the atomistic nature of the fluid, i.e., it is an entirely hydrodynamic feature. We also find that systems that exhibit slip have longer equilibration times in general and the ratio of equilibration times for the two types of boundary-driven flow is even more pronounced. We analyse the problem by decomposing a generic planar Couette flow into a linear sum of purely symmetric and antisymmetric flows. We find that the no-slip equilibration time is dominated by the slowest decaying eigenvalue of the solution to the Navier-Stokes equation. In the case of slip, the longest relaxation time is now dominated by the transient slip velocity response, which is longer than the no-slip response time. In the case of a high-slip system of water confined to graphene channels, the enhancement is over two orders of magnitude.

Compared to cryogenically cooled conventional detectors, up-conversion detection enables efficient room-temperature short-wave infrared (SWIR) imaging. Although quasi-phase-matching (QPM) in periodically poled crystals offers advantages, the small crystal aperture (typically 1 mm$\times$3 mm) limits resolution. Non-poled crystals enable larger apertures but suffer walk-off aberrations. This work overcomes these limitations by using a noncritical phase matched (NCPM) KTiOPO$_4$ crystal (6 mm$\times$7 mm aperture, 0.5 mm length). Results show resolutions 6$\times$ and 2$\times$ higher than periodically poled crystals in orthogonal directions, with broad conversion band (1.3-2.2 $\mu$m) covering biological and atmospheric windows. The absence of walk-off ensures better image fidelity in up-conversion process. This study presents the first comprehensive characterization of NCPM-based broadband up-conversion imaging, demonstrating performance at the theoretical resolution limit while circumventing drawbacks inherent in alternative up-conversion schemes and conventional detectors.

Computational ultrasound imaging (cUSi) with few elements and spatial field encoding can provide high-resolution volumetric B-mode imaging. In this work, we extend its application to 4D carotid artery (CA) flow imaging using a custom large-aperture 240-element matrix probe. We implemented a frequency band-based matched filtering strategy that balances resolution and contrast. The system's inherent imaging capabilities were evaluated and validated in flow phantom and human CA experiments. In the phantom study, 3D/4D power Doppler image and speckle-tracking analyses confirmed the system's ability to resolve flow structures and hemodynamics. In the human study, the CA bifurcation flow structure and its local pulsatile flow dynamics were successfully reconstructed. These results demonstrate the feasibility of using a large-footprint, few-element cUSi system for 4D CA flow assessment.

Pipeline transport of dense-phase CO2-rich mixtures is a crucial component in carbon capture and storage (CCS). Accurate modeling requires coupling of fluid dynamics and thermodynamics, especially during transient events such as depressurization. In this work, we present a unified framework for two-phase multicomponent transport in pipelines that integrates both aspects. Specifically, we employ the homogeneous equilibrium model (HEM) for modeling the transport of two-phase CO2-rich mixture, with thermodynamic closure provided by a Helmholtz energy-based equation of state. Phase equilibrium calculations are performed using UVN-flash, supplemented with a stability analysis procedure to detect phase separation and generate initial guesses for the phase-equilibrium calculations. Specifically, we introduce a novel tailored UVN-flash routine that aligns with the fluid dynamics formulation. This is achieved by introducing an alternative and better-scaled set of variables for the phase-equilibrium calculations. The proposed framework is applied to the depressurization of tanks and pipelines containing CO2-rich mixtures, demonstrating its effectiveness for CCS-relevant applications.

The inversion of surface wave dispersion curves poses significant challenges due to the non-uniqueness, nonlinear, & ill-posed nature of the problem. Local search methods get trapped in suboptimal minima, whereas global search methods are computationally intensive. The CPU time becomes challenging when dealing with a large number of traces, such as 2D/3D surface wave surveys or DAS surveys. Several attempts have been made to perform inversion using machine learning to improve accuracy and reduce CPU times. Current machine learning methods rely on a fixed number of soil layers in the training dataset to maintain a consistent output size, limiting these models to predicting only a narrow range of soil profiles. Consequently, no single machine learning model can effectively predict soil profiles with a wide range of shear wave velocities and varying numbers of layers. The present study introduces a novel soil profile standardization technique and proposes a regression-based XGBoost algorithm to efficiently estimate shear wave velocity profiles for stratified media with a varying number of layers. The proposed model is trained using 10 million synthetic soil profiles. This extensive dataset enables our XGBoost model to learn effectively across a wide range of shear wave velocities. Additionally, the study proposes constraints on the differences in shear wave velocities between consecutive layers and on their ratio with layer thickness, preventing the formation of unrealistic layers and ensuring the predictive model reflects real-world conditions. The effectiveness of our proposed algorithm is demonstrated by adopting a wide range of soil profiles from published literature and comparing the results with traditional inversion methods. The model performs well in a wide range of S-wave velocities and can accurately capture any number of layers of the soil profile during the inversion process

The ion channel laser (ICL) is a plasma-based alternative to the free electron laser (FEL) that uses the electric field of a uniform-density ion channel rather than the magnetic field of an undulator to induce transverse oscillations of a relativistic electron bunch and thereby produce coherent radiation via a collective electromagnetic instability. The powerful focusing of the ion channel generally yields significantly higher gain parameters in the ICL as compared to the FEL. This permits lasing in extremely short distances using electron bunches with an energy spread as large as a few percent; a value readily achievable with current plasma-based accelerators. ICLs, however, impose stringent transverse phase space requirements on the electron beam beyond what is required in FELs. In this work, we present a novel 3D theory of the off-axis planar ICL that accounts for a number of effects including diffraction, transverse guided mode shape, frequency and betatron phase detuning, and nonzero spread in energy and undulator parameter. We derive the ICL pendulum and field equations, which we use to write down the 3D Maxwell-Klimontovich equations. After linearizing, we obtain an integro-differential equation describing the $z$-evolution of the radiation mode. The 3D ICL dispersion relation is obtained using a Van Kampen normal mode expansion. We numerically solve the $z$-evolution equation to compute radiation power growth rates and transverse guided mode profiles over a range of different ICL parameters. We note the ICL's large gain bandwidth and examine the gain reduction due to 3D effects, energy spread, and emittance. Electron beam phase space and emittance requirements for lasing are derived. Finally, we make general observations about the general performance and feasibility of the ICL and discuss future prospects.

Accurate, efficient prediction of wind flow with wake effects is crucial for wind-farm layout and power forecasting. Existing approaches-physical measurements, numerical simulations, physics-based models, and data-driven models-face trade-offs: the first two are time- and resource-intensive; physics-based models can lack accuracy due to limited physics; data-driven methods leverage abundant, high-quality data and are increasingly popular. We propose a rapid, data-driven wake-flow model inspired by video-frame interpolation and the principle of similarity. Field data are transformed into images; multi-scale feature recognition then identifies, matches, and interpolates wake structures using Scale-Invariant Feature Transform (SIFT) and Dynamic Time Warping (DTW) to generate intermediate flow fields. Six representative mini wind-farm cases validate the approach, spanning variations in turbine spacing, turbine size, combined spacing-size variations, different turbine counts, and wind-direction misalignment. Across cases, the method achieves a mean absolute percentage error (MAPE) of 0.68-2.28%. Because it flexibly computes both 2D and 3D wake fields, the method offers substantial computational-efficiency gains over large-eddy simulation (LES) and Meteodyn WT when 2D accuracy suffices for industrial needs. Accordingly, it provides a practical alternative to measurements, high-fidelity simulations, and simplified physics-based models, enabling efficient expansion of wake-flow databases for wind-farm design and power prediction while balancing speed and accuracy.

This study presents an integrated approach to understanding epidemic dynamics through a stochastic spatio-temporal simulation model on a multiplex network, blending physical and informational layers. The physical layer maps the geographic movement of individuals, while the information layer tracks the spread of knowledge and health behavior via social interactions. We explore the interplay between physical mobility, information flow, and epidemic outcomes by simulating disease spread within this dual-structured network. Our model employs stochastic elements to mirror human behavior, mobility, and information dissemination uncertainties. Through simulations, we assess the impact of network structure, mobility patterns, and information spread speed on epidemic dynamics. The findings highlight the crucial role of effective communication in curbing disease transmission, even in highly mobile societies. Additionally, our agent-based simulation allows for real-time scenario analysis through a user interface, offering insights into leveraging physical and informational networks for epidemic control. This research sheds light on designing strategic interventions in complex social systems to manage disease outbreaks.

Selective control over the emission pattern of valley-polarized excitons in monolayer transition metal dichalcogenides is crucial for developing novel valleytronic, quantum information, and optoelectronic devices. While significant progress has been made in directionally routing photoluminescence from these materials, key challenges remain: notably, how to link routing effects to the degree of valley polarization, and how to distinguish genuine valley-dependent routing from spin-momentum coupling - an optical phenomenon related to electromagnetic scattering but not the light source itself. In this study, we address these challenges by experimentally and numerically establishing a direct relationship between the intrinsic valley polarization of the emitters and the farfield emission pattern, enabling an accurate assessment of valley-selective emission routing. We report valley-selective manipulation of the angular emission pattern of monolayer tungsten diselenide mediated by gold nanobar dimer antennas at cryogenic temperature. Experimentally, we study changes in the system's emission pattern for different circular polarization states of the excitation, demonstrating a valley-selective circular dichroism in photoluminescence of 6%. These experimental findings are supported by a novel numerical approach based on the principle of reciprocity, which allows modeling valley-selective emission in periodic systems. We further show numerically, that these valley-selective directional effects are a symmetry-protected property of the nanoantenna array owing to its extrinsic chirality for oblique emission angles, and can significantly be enhanced when tailoring the distribution of emitters. This renders our nanoantenna-based system a robust platform for valleytronic processing.

Coherent, self-sustained oscillations of the flow over aircraft wings can lead to unsteady loads that detrimentally affect aircraft safety and stability, thus limiting the flight envelope. Two such types of oscillations are the low-frequency oscillations (LFO) observed in flow over airfoils close to stall in the incompressible regime and transonic buffet, which occurs at high speeds and involves oscillating shock waves. The possibility that these two are linked has been explored only recently at low Reynolds numbers (Re ~ O(1e4)) and natural transition conditions (Moise et al., J. Fluid Mech., vol. 981, 2024, p. A23). However, the shock wave structure in the transonic regime under these conditions differs substantially when compared to high Reynolds number flows, and it is unknown whether a connection can be established at high Reynolds numbers. This study investigates this possibility by performing incompressible and compressible URANS simulations at Re = 1e7. We show that transonic buffet exists for a narrow range of freestream Mach numbers across a wide range of angles of attack, and that buffet-like oscillations are observed at higher angles even in the absence of shock waves. Using a spectral proper orthogonal decomposition (SPOD), we show that the dominant modes associated with these oscillations are strongly correlated for all cases, even in the absence of shock waves. Furthermore, using a fully incompressible URANS framework, we capture LFO at the same Reynolds number and confirm the connection between these two phenomena using SPOD. These results imply that neither shock waves nor compressibility is necessary to sustain such low-frequency oscillations, suggesting that the fundamental mechanism governing them is related to flow separation. This can potentially help in improved control strategies to extend the flight envelope by mitigating buffet or LFO.

In "arXiv:2312.13429" Lackner et al. use standard methods to decide if it is possible to ignite mixed fuels. They correctly identify that the increased radiation losses make ignition significantly more challenging than for pure DT fuels, since this leads to higher ignition temperatures. Further, they conclude that at those temperatures the reduced electronic $\alpha$-stopping makes ignition impossible. We show that this conclusion is not correct. The model used for $\alpha$-stopping by Lackner et al. is only approximately correct for low temperatures and hydrogen isotopes. By extending the $\alpha$-stopping model to include ionic $\alpha$-stopping we show in \cite{ruhlkornarXiv5} that the contribution of ionic $\alpha$-particle stopping cannot be neglected. The ionic $\alpha$-stopping together with the neutron stopping, which is also neglected by Lackner et al., lead to elevated ion temperatures implying $kT_i > kT_e$. Those three effects combined lead us to the conclusion, that ignition of mixed fuels is indeed possible with far reaching implications, contrary to the analysis by Lackner.

This manuscript reports on the development and qualification of an ECDL-based, fiber-coupled laser system at a wavelength of {\lambda} = 698 nm for space applications. We designed and developed the optical and mechanical configuration, along with the laser driving and thermal management electronics, to meet space compatibility requirements. Validation tests were conducted on off-the-shelf components to assess their suitability for satellite deployment. The final system integrates all components into a compact design optimized for CubeSat platforms.

This paper presents a three-dimensional framework for simulating dynamic wetting phenomena using the volume of fluid (VOF) method, implemented in Basilisk. A geometric interpolation scheme is developed to obtain an accurate and reliable value of the contact line velocity. To capture realistic wetting dynamics, a dynamic contact angle model is integrated that considers also contact angle hysteresis (CAH). The approach is validated against various experimental results, including droplet spreading, splashing, and sliding and demonstrates quantitative agreement with the three-dimensional wetting behavior observed. Additionally, a comparative analysis between dynamic and static contact angle models is performed.

Immersed reflection gratings improve spectral resolving power by enabling diffraction within a high refractive index medium. This principle has been widely adopted to make grating spectrometers more compact. Conventional immersed gratings have blazed profiles which typically show the highest efficiency for one main design wavelength. In addition, the blazed profiles tend to cause significant polarization sensitivity. In this work, we propose an alternative approach for designing an immersed grating composed of sub-wavelength structures, designed to increase diffraction efficiency and reduce polarization dependence. For a theoretical demonstration, a reflective metagrating immersed in silicon is optimized over the short-wave infrared band-3 (SWIR-3, here $2.304~\mu$m-$2.405~\mu$m), targeting the same diffraction angles as the immersion grating used in the Sentinel-5 Earth observation mission. The structure is optimized using a modified Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The optimized immersed metagrating achieves an average efficiency of (over the SWIR-3 band) $\sim 78\%$, compared to $\sim 62\%$ for the conventional immersed blazed grating, and reduces polarization sensitivity from roughly $\sim 15\%$ to $\sim 5\%$. A manufacturing tolerance analysis is also conducted to evaluate the design's performance under systematic manufacturing errors, which revealed a degradation of $\sim 10\%$ efficiency at feature size errors of $\pm 25{nm}$ and almost negligible effect on the efficiency at $-10{nm}$ and of $\sim 5\%$ at $+10{nm}$.

We present the first experimental confirmation that a laser-wakefield accelerator produced by a flying focus pulse is able to maintain the coherent structures necessary to accelerate electrons to relativistic energies. Through a combination of spatio-temporal near-field shaping of the beam and focusing with an axiparabola - a long-focal-depth mirror that produces a quasi-Bessel beam - the propagation velocity of the wakefield is tuned to control the maximum electron energy achievable. The experimental data are supported by advanced optical and particle-in-cell simulations and are aligned with a simplified analytical model. Together, the results significantly strengthen the case for the flying-focus wakefield as a strategy for mitigating dephasing in laser-wakefield acceleration.

Future high-luminosity hadron colliders demand tracking detectors with extreme radiation tolerance, high spatial precision, and sub-nanosecond timing. 3D diamond pixel sensors offer these capabilities due to diamond's radiation hardness and high carrier mobility. Conductive electrodes, produced via femtosecond IR laser pulses, exhibit high resistivity that delays signal propagation. This effect necessitates extending the classical Ramo-Shockley weighting potential formalism. We model the phenomenon through a 3rd-order, 3+1D PDE derived as a quasi-stationary approximation of Maxwell's equations. The PDE is solved numerically and coupled with charge transport simulations for realistic 3D sensor geometries. A Mixture-of-Experts Physics-Informed Neural Network, trained on Spectral Method data, provides a meshless solver to assess timing degradation from electrode resistance.

Nanophotonics has recently gained new momentum with the emergence of a novel class of nanophotonic systems consisting of resonant dielectric nanostructures integrated with single or few layers of transition metal dichalcogenides (2D-TMDs). Thinned to the single layer phase, 2D-TMDs are unique solid-state systems with excitonic states able to persist at room temperature and demonstrate notable tunability of their energies in the optical range. Based on these properties, they offer important opportunities for hybrid nanophotonic systems where a nanophotonic structure serves to enhance the light-matter interaction in the 2D-TMDs, while the 2D-TMDs can provide various active functionalities, thereby dramatically enhancing the scope of nanophotonic structures. In this work, we combine 2D-TMD materials with resonant photonic nanostructures, namely, metasurfaces composed of high-index dielectric nanoparticles. The dependence of the excitonic states on charge carrier density in 2D-TMDs leads to an amplitude modulation of the corresponding optical transitions upon changes of the Fermi level, and thereby to changes of the coupling strength between the 2D-TMDs and resonant modes of the photonic nanostructure. We experimentally implement such a hybrid nanophotonic system and demonstrate voltage tuning of its reflectance as well as its different polarization-dependent behavior. Our results show that hybridization with 2D-TMDs can serve to render resonant photonic nanostructures tunable and time-variant $-$ important properties for practical applications in optical analog computers and neuromorphic circuits.

String field theory is supposed to stand to perturbative string theory as quantum field theory stands to single-particle quantum theory; as such, it purports to offer a substantially more general and powerful perspective on string theory than the perturbative approach. In addition, string field theory has been claimed for several decades to liberate string theory from any fixed, background spatiotemporal commitments -- thereby (if true) rendering it `background independent'. But is this really so? In this article, we undertake a detailed interrogation of this claim, finding that the verdict is sensitive both to one's understanding of the notion of background independence, and also to how one understands string field theory itself. Although in the end our verdicts on the question of the background independence are therefore somewhat mixed, we hope that our study will elevate the levels of systematicity and rigour in these discussions, as well as equip philosophers of physics with a helpful introduction to string field theory and the variety of interesting conceptual questions which it raises.

This roadmap provides a comprehensive and forward-looking perspective on the individualized application and safety of non-ionizing radiation (NIR) dosimetry in diagnostic and therapeutic medicine. Covering a wide range of frequencies, i.e., from low-frequency to terahertz, this document provides an overview of the current state of the art and anticipates future research needs in selected key topics of NIR-based medical applications. It also emphasizes the importance of personalized dosimetry, rigorous safety evaluation, and interdisciplinary collaboration to ensure safe and effective integration of NIR technologies in modern therapy and diagnosis.

In quantum physics, classical optics, and many other wave systems, wave confinement in a potential well produces is associated with discrete oscillatory states, and the ground state is typically assumed to vanish uniformly. An open question is whether the ground state can counterintuitively support a flat-top, nonzero envelope, offering new opportunities for quantum emitters, optical antennas, and lasers. Here, we show that by applying the Byers-Yang theorem with an artificial gauge field, energy levels can be continuously shifted, driving eigenstates to morph into a ground state with a uniform yet nontrivial wave envelope. We implement this concept in a photonic crystal slab where a central bulk region is surrounded by heterogeneous bandgaps that engineer reflective phases acting as an artificial local gauge field. By inducing lasing, we probe directly the evolution of the energy levels, demonstrating wave morphing toward a flat-top ground state via near- and far-field measurements.

Purpose: This work proposes a method for the simultaneous estimation of the exchange-dependent relaxation rate Rex and the longitudinal relaxation time T1 from a single acquisition. Methods: A novel acquisition scheme was developed that combines CEST saturation with an inversion pulse and a Look-Locker readout to capture the magnetization evolution starting from the inverse transient Z-spectrum. The corresponding signal model, derived from the Bloch-McConnell equations, describes both the transient Z-spectrum and the Look-Locker dynamics. A model-based reconstruction approach is employed to jointly estimate Rex and T1. The proposed method was validated using a numerical phantom and benchmarked against conventional CEST and Look-Locker T1 mapping in phantom and in vivo on a clinical 3T scanner. Results: The joint estimation approach demonstrated strong agreement with ground truth and conventional methods across a wide range of T1 and CEST parameters. The acquisition time was reduced by 20-30% compared to standard CEST protocols, while providing higher signal-to-noise ratio (SNR) in parameter maps. Conclusion: The proposed technique enables robust and efficient simultaneous quantification of CEST Rex and T1 in a single acquisition. It improves parameter map quality and reduces scan time, making it suitable for both phantom and in vivo imaging across a wide range of physiological conditions.

Background and Purpose: Voxel-based analysis (VBA) helps to identify dose-sensitive regions by aligning individual dose distributions within a common coordinate system (CCS). Accurate deformable image registration (DIR) is essential for addressing anatomical variability across patients. To improve both global and region-specific alignment, we enhanced our in-house DIR algorithm (CPT-DIR) with contour-informed regularisations. We tested its performance for head-and-neck (HN) CT images. Materials and Methods: We developed and evaluated contour-informed CPT-DIR on 37 HN CTs, including 7 with ground-truth dose for warped dose validation. Bone contours were generated using TotalSegmentator, while other organs at risk (OARs) were manually delineated. Contour-based constraints, such as Dice Similarity, were integrated to enhance registration outcome. The global registration results were evaluated using MAE, SSIM and PSNR. Geometric accuracy and warped dose accuracy were assessed using Dice Similarity Coefficient (DSC) and Dose-Organ Overlap (DOO). Constrained and unconstrained CPT-DIR were compared to B-spline. Results: CPT-DIR achieved superior accuracy with a MAE of 98.9\pm6.3 HU, lower than 179.1\pm17.8 HU for B-spline. Incorporating brainstem contours as regularisation improved the DSC from 0.604\pm0.116 to 0.878\pm0.017 and DOO from 0.430\pm0.117 to 0.753\pm0.043 for brainstem. Across all metrics, the enhanced CPT-DIR outperformed the B-spline, confirming its advantages in geometric accuracy. Conclusions: The integration of contour-informed regularisation in CPT-DIR improved DIR accuracy, particularly in dosimetrically relevant regions. This enhanced spatial alignment enabled more precise dose mapping for VBA and demonstrated strong potential for advancing reliable inter-patient dosimetric studies in HN radiotherapy.

Inverse photoelectron spectroscopy (IPES) is a powerful technique for probing the unoccupied electronic states of materials. It can be regarded as the inversion process of photoelectron spectroscopy (PES), which examines the occupied states. Recently developed low-energy inverse photoelectron spectroscopy (LEIPS) can significantly advance the study of unoccupied states, owing to an improved signal-to-noise ratio and minimal sample damage compared to conventional IPES. However, the instrumental resolution remains at 0.2 eV, which is one order of magnitude lower than that of PES. Spectral broadening caused by the low instrumental resolution often results in overlapping peaks. Peak separation is therefore crucial in the analysis of LEIPS spectra. In this study, we compared three peak separation methods: second derivative, curve fitting, and deconvolution. These methods were applied to modeled and experimental LEIPS spectra of the lowest unoccupied molecular orbital-derived band of pentacene, which consists of two splitting peaks due to the two inequivalent molecules in the unit cell. We systematically and quantitatively evaluated the performance of each method in terms of analysis parameters and discussed its robustness to noise as well as its peak separation capability. This work offers a practical framework for peak separation in LEIPS, with extensions to PES and a wide range of spectroscopies.

This study assesses air quality (AQ) and greenhouse gas (GHG) emissions from the rapid expansion of data centers in Texas, a major hub due to infrastructure, electricity markets, and business conditions. AQ impacts were separated from GHG emissions to clarify sources, regulations, and mitigation strategies. Electricity consumption and cooling systems dominate GHG emissions, with a 10 megawatt data center generating about 37,668 metric tons CO2 annually, while construction materials and IT equipment add substantial embodied emissions. Local AQ impacts, often overlooked, arise from diesel backup generators, construction equipment, and commuting. Generator testing alone can emit about 12 metric tons of NOx annually per facility, worsening ozone issues in regions such as Houston and Dallas-Fort Worth. Mitigation strategies include advanced cooling, renewable energy procurement, cleaner backup power (fuel cells, batteries), sustainable construction, and standardized reporting. ERCOT forecasts project 39 to 78 gigawatts of new data center load by 2030, potentially leading to 170 to 205 million metric tons of annual CO2 emissions. Aggressive adoption of renewables and advanced technologies could cut emissions by 50 to 80 percent, avoiding 85 to 165 million metric tons of CO2. The study identifies research and policy gaps, including the need for cumulative air dispersion modeling, AQ-specific regulations, and mandatory efficiency standards. Findings underscore the importance of aligning Texas digital infrastructure growth with environmental and community health protections.

Judging by its omnipresence in the literature, the hysteresis observed in the transfer characteristics of emerging transistors based on 2D-materials is widely accepted as an important metric related to the device quality. The hysteresis is often reported with attributes like "negligible" or "small" without giving any specifics as to how this was determined and against what reference the measured values were compared to. Quite surprisingly, there appears to be only a fragmentary understanding of the mechanisms actually contributing to hysteresis and the sensitivity of the actual measurement on various experimental parameters. We attempt to close this gap by first providing a comprehensive theoretical analysis of the dominant mechanisms contributing to hysteresis: charge trapping by defects from the channel or the gate, the drift of mobile charges, and eventually ferroelectricity. We continue by suggesting methods to experimentally distinguishing between these phenomena. Based on these discussions it becomes clear that previously reported hysteresis values have little meaning as they have been non-systematically recorded under arbitrary conditions. In order to resolve this predicament, we propose a standardized hysteresis measurement scheme to establish the hysteresis as a comparable metric for the assessment of device stability. Our standardized scheme ensures that hysteresis data can be effectively compared across different technologies and, most importantly, provide a means to extrapolate data obtained on thicker prototypes to subnanometer equivalent oxide thicknesses. This facilitates the systematic benchmarking of insulator/channel combinations in terms of stability, which thereby enables the screening of material systems for more stable and reliable 2D-material-based MOSFETs.

A reduced order asymptotic homogenization based multiscale technique which can capture damage and inelastic effects in composite materials is proposed. This technique is based on two scale homogenization procedure where eigen strain representation accounts for the inelastic response and the computational efforts are alleviated by reduction of order technique. Macroscale stress is derived by calculating the influence tensors from the analysis of representative volume element (RVE). At microscale, the damage in the material is modeled using continuum damage mechanics (CDM) based framework. To solve the problem of strain localization a method of the alteration of stress-strain relation of micro con- stituents based on the dissipated fracture energy in a crack band is implemented. The issue of spurious post failure artificial stiffness at macroscale is discussed and effect of increasing the order to alleviate this problem is checked. Verification studies demonstrated the proposed formulation predicts the macroscale response and also captures the damage and plasticity induced inelastic strains.

This paper describes a novel homogenization methodology for analyzing the failure of elastoplastic composite materials based on elastic and eigen influence tensors-driven transformation field analysis ($\mathtt{E}^2$-TFA). The proposed technique considers the microscopic eigenstrain field accounting for intra-phase damage and inelastic strains. This results in realistic computations by alleviating the post-damage stiffness response, which is a drawback of TFA-based methods. We attain computational efficiency by identifying the preprocessing data solely from the elastic and eigen transformation functions and adopting a reduced order modelling technique with a piecewise constant eigenstrain field throughout the subdomains. The performance of the model is assessed by simulating the response for (a) the representative volume element (RVE) as a homogenized continuum and (b) the various composites under complex load histories with intricate macroscale morphologies. Furthermore, the nonlinear shear stress-strain response of a glass fiber composite is calculated and compared to experimentally measured fracture initiation parameters, failure plane orientation, and strain histories. Finally, we show that $\mathtt{E}^2$-TFA can accurately and efficiently capture damage and inelastic deformations in order to estimate the mechanical response of composite materials in a better way.

A novel homogenization methodology is proposed for analyzing the failure of fiber-reinforced composite materials, utilizing elastic and eigen influence tensors within a damage informed transformation field analysis (D-TFA) framework. This approach includes a technique for calculating macroscopic damage under uniform stress and strain conditions, offering more realistic simulations. Computational efficiency is enhanced through a reduced-order modeling strategy, while elastic and eigen strain distribution driven k-means clustering methods are employed to partition the microscale domain. The model's performance is assessed by simulating the response of a representative volume element (RVE) treated as a homogenized continuum. Subsequently, a comparative assessment is carried out to check the efficacy of two clustering schemes. Damage morphologies are calculated using proposed framework and compared with predictions obtained using finite element method. Furthermore, open-hole specimen tests are simulated and failure paths are predicted for the domains with different fiber layups. Ultimately, we show that D-TFA can accurately capture damage patterns and directional strengths, providing improved predictions of the mechanical behavior of composite materials. It has been demonstrated that higher cluster counts are crucial for capturing a more accurate stress-strain response, especially for complex microstructures.

Atmospheric ozone is a crucial absorber of solar radiation and an important greenhouse gas. However, most climate models participating in the Coupled Model Intercomparison Project (CMIP) still lack an interactive representation of ozone due to the high computational costs of atmospheric chemistry schemes. Here, we introduce a machine learning parameterization (mloz) to interactively model daily ozone variability and trends across the troposphere and stratosphere in standard climate sensitivity simulations, including two-way interactions of ozone with the Quasi-Biennial Oscillation. We demonstrate its high fidelity on decadal timescales and its flexible use online across two different climate models -- the UK Earth System Model (UKESM) and the German ICOsahedral Nonhydrostatic (ICON) model. With atmospheric temperature profile information as the only input, mloz produces stable ozone predictions around 31 times faster than the chemistry scheme in UKESM, contributing less than 4 percent of the respective total climate model runtimes. In particular, we also demonstrate its transferability to different climate models without chemistry schemes by transferring the parameterization from UKESM to ICON. This highlights the potential for widespread adoption in CMIP-level climate models that lack interactive chemistry for future climate change assessments, particularly when focusing on climate sensitivity simulations, where ozone trends and variability are known to significantly modulate atmospheric feedback processes.

We present a detailed analysis of a 3D MHD simulation of a subset of the magnetic flux in an active region. The simulation models the generation of nanoflares and response of the plasma to imposed photospheric motions. Our study focuses on characterizing the energy distribution and occurrence frequency of the nanoflares in the simulation that self-consistently heat the corona. This field line based analysis reveals that the nanoflare energy distribution (energy per unit cross sectional area) follows a log-normal profile, where low energy nanoflares are significantly more prevalent than those with high energy. When compared with the plasma cooling time, different energy nanoflares tend to repeat with different frequencies. Low energy nanoflares repeat at high frequencies, while high energy nanoflares repeat at low frequencies. However, the thermal evolution of plasma along individual field lines is governed predominantly by the high energy nanoflares. These findings provide critical insights into the role of small-scale magnetic reconnection events in heating the solar corona.

This paper investigates quantum dots (QDs), which are miniature semiconductor structures with remarkable optical and electrical properties due to quantum confinement processes. Traditional QDs, such as CdTe, have been extensively investigated; however, they frequently exhibit toxicity and stability issues. Graphene quantum dots (GQDs) are emerging as a safer and more stable alternative to traditional QDs. GQDs are honeycomb-lattice carbon atoms with unique electronic and optical properties that make them promising candidates for biomedical, electronic, and energy storage applications. GQD synthesis methods (top-down and bottom-up) and their advantages over standard QDs include better photostability, biocompatibility, and configurable band gaps. GQDs are perfect for real-world uses like sensitive biosensing, real-time food safety monitoring, and smart packaging because of their low toxicity, high sensitivity, and affordability. These uses are all essential for cutting down on food grain waste. This emphasizes the growing significance of GQDs in advancing nanotechnology and their potential integration with quantum technologies, paving the door for creative solutions in biosensing, food safety, environmental monitoring, and future quantum electronics.

A tethered multirotor autogyro can function as an unmanned aerial vehicle for energy-efficient and prolonged deployment, as it uses the available wind energy to sustain flight. This article presents an adaptive altitude control strategy for such a device. At a constant wind speed, the equilibrium altitude can be approximated by a quadratic function of the pitch angle. The proposed adaptive control estimates the coefficients of this quadratic function. The estimates are used for altitude control and to attain the maximum altitude (and minimum horizontal drift) for a given wind speed. A feedback controller based on regenerative differential rotor braking is used as the actuation to modulate the autogyro's pitch angle. Implementation of the controller using a control-oriented, higher-order dynamic model demonstrates the controller's capability to regulate the altitude and maintain stable flights under varying wind speeds. Based on the system's maximum altitude tracking performance, the adaptive control is adjusted to improve performance under substantial changes in wind speeds.

Fluctuations in the Sun's photospheric magnetic field are the primary source of the turbulence that can heat and accelerate the solar atmosphere, and thus play an important role in the production and evolution of the solar wind that permeates the heliosphere. A key parameter that characterizes this turbulence is the correlation scale of fluctuations, which determines the injection of turbulent energy into the plasma and influences the diffusive transport of solar energetic particles. This study employs magnetogram data acquired by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory to characterize an ensemble of spatial autocorrelation functions (ACFs) of magnetic fluctuations in the photosphere. It is shown that the two-point ACFs satisfy the similarity-decay hypothesis of von Kármán and Howarth, a fundamental property of turbulent systems: following a rescaling of the ACFs by energy and correlation lengths, a quasi-universal functional form is obtained demonstrating exponential decay of correlations. The probability distribution function of transverse correlation lengths (\(\lambda\)) is shown to be approximately log-normal. A mosaic of the spatial distribution of \(\lambda\) over the photosphere is presented; the ``quiet Sun'' tends to have \(\lambda\sim 1500\) km (albeit with a wide distribution), which is close to the scale of solar granulation; systematically longer lengths are associated with active regions. A positive correlation is observed between mean magnetic field magnitude and \(\lambda\), and empirical fits are derived to quantify this relationship. These results improve our understanding of the nature of turbulence in the solar photosphere and the origin of coronal and solar-wind turbulence, while providing observational constraints for models that describe the transport of turbulence from solar and stellar photospheres into their atmospheres.

Rydberg atom based sensors allow for SI traceable measurements and show promise for applications in the field of communication and radar technologies. In this article, we investigate the bandwidth and sensitivity of a Rydberg atom-based sensor in a rubidium vapor cell using Rydberg electromagnetically induced transparency (EIT) spectroscopy. We employ a radio-frequency (RF) heterodyne measurement technique in combination with an optical homodyne setup to extend the achievable range between sensitivity and bandwidth in a Rydberg sensor. While the bandwidth of Rydberg sensors are limited by the transit time of atoms and the Rabi frequency of the coupling field, achieving higher bandwidth through smaller beam sizes is thought to compromise sensitivity due to reduced EIT signal strength. Using optical homodyne detection, we demonstrate that sensitivity is preserved while achieving a response bandwidth of 8 MHz. In addition, using the Rydberg sensor, we receive digital communication signals and present error vector magnitude (EVM) measurements as a function of varying symbol rates and bandwidth of the Rydberg sensor. Furthermore, the sensor's performance is compared with a conventional RF mixer. We establish that the bandwidth of a Rydberg sensor when receiving a pure tone is not the same as the bandwidth of the sensor when receiving a modulated signal. This difference results from the spreading of symbols in the frequency domain, leading to a reduction of the signal to noise ratio (SNR) and an accumulation of noise over the total span of the modulated signal.

Collisionless dissipation of turbulence is important for heating plasmas in astrophysical, space physics, and laboratory environments, controlling energy, momentum and particle transport. We analyze Parker Solar Probe observations to understand the collisionless heating of the sub-Alfvénic solar wind, which is connected to the solar corona. Our results show that linear resonant heating through parallel-propagating cyclotron waves cannot account for turbulent dissipation in sub-Alfvénic region, which observations suggest may dissipate turbulence at distances further from the Sun. Instead, we find that stochastic heating can account for the observed ion energization; however, because the dominant contributions arise from infrequent, large-amplitude events, turbulent intermittency must be explicitly incorporated. These observations directly connect stochastic heating via breaking of the proton magnetic moment with the intermittent and inhomogeneous heating of turbulence reported in many previous studies. Our identification of stochastic heating as a dynamic mechanism responsible for intermittent heating of the solar wind has significant implications for turbulent dissipation in the lower corona, other astrophysical environments, and laboratory plasma.

Suspension thermal modes in interferometric gravitational-wave detectors produce narrow, high-Q spectral lines that can contaminate gravitational searches and bias parameter estimation. In KAGRA, cryogenic mirrors are held by thick suspension fibers, designed to sustain such low-temperature environment, which may further affects inharmonicity modes, fiber dimensions, and mechanical behavior compared to typical interferometers. Remaining a prominent source of narrowband contamination, we implement a Kalman filter to model and track violin lines, building on the methodology introduced in [1], and apply subtraction to KAGRA O3GK data. Using gravitational-wave template injections, we validate that the subtraction preserves matched-filter SNR while effectively suppressing line power. Comparisons of power spectral densities and residual analyses confirm that the method removes deterministic line contributions without introducing waveform distortions. This approach provides a cleaner strain channel for searches and parameter estimation and will become increasingly important for future low-temperature detectors with higher-Q suspensions, such as the Einstein Telescope.

We analyze and develop numerical methods for time-harmonic wave scattering in metallic waveguide structures of infinite extent. We show that radiation boundary conditions formulated via projectors onto outgoing modes determine the coefficients of propagating modes uniquely, even when the structure supports trapped modes. Building on this, we introduce a fast divide-and-conquer solver that constructs solution operators on subdomains as impedance-to-impedance maps and couples them by enforcing continuity conditions across their interfaces. For Dirichlet waveguides, the computation of impedance-to-impedance maps requires the solution of mixed Dirichlet-Impedance boundary value problems. We construct a second-kind Fredholm integral equation that avoids near-hypersingular operators, requiring only integral operators whose kernels are at most weakly singular. Numerical experiments on large structures with many circuit elements demonstrate substantial efficiency gains: the proposed approach typically outperforms state-of-the-art fast iterative and fast direct solvers by one to two orders of magnitude.

The search for extraterrestrial intelligence (SETI) is largely limited by the vastness of the signaling parameter space. The concurrent signaling scheme offers a framework in which civilizations can coordinate their transmission and reception by referring to a common astrophysical event. Building on this idea, I propose a hybrid strategy that combines the Galactic Center as a spatial reference with an extragalactic burst as a temporal marker. If such a scheme is indeed employed, the sky area to be surveyed in SETI could be reduced by more than two orders of magnitude, based solely on existing astronomical data. I examine records of three types of extragalactic bursts (supernovae, neutron star mergers, and gamma-ray bursts [GRBs]) to identify suitable temporal markers. Among them, GRB 221009A is particularly notable due to its high fluence and favorable sky location.

We introduce an entanglement distillation (purification) protocol for supersinglet states composed of N qubits. The supersinglet state we target is a total spin zero state with zero spin variance, and has a fully entangled structure involving all qubits. In our distillation protocol, three copies of an initial spin zero state are measured in the local total spin basis such that a higher fidelity supersinglet state is generated upon postselection. The initial state can be prepared using conventional Bell state distillation methods distributed in a way to target the supersinglet symmetries. The protocol uses only local operations and classical communications, and is suitable for long-distance applications such as quantum clock synchronization and cryptography, and avoids a high dimensional Schur transform such that it can be used for tasks such as quantum metrology.

Isoprene and its oligomers, terpenes, are expected to be present, along with other complex organic molecules, in the diverse environments of the ISM and in our solar system. Due to insufficient spectral information of these molecules at low temperature, the detection and understanding of the importance of these molecules has been rather incomplete. For this purpose, we have carried out the vacuum ultraviolet (VUV) photoabsorption measurements on pure molecular ices of isoprene and a few simple terpenes: limonene, $\alpha$-pinene and $\beta$-pinene by forming icy mantles on cold dust analogs. From these experiments, we report the first low-temperature (10 K) VUV spectra of isoprene and its oligomers, limonene, $\alpha$-pinene, and $\beta$-pinene. VUV photoabsorption spectra of all the molecules reported here reveal similarities in the ice and gas phase, as expected, except for isoprene, where a prominent red shift is observed in the ice phase absorption. This unique property of isoprene, along with distinctive absorption at longer wavelengths, supports its candidature for detection on icy bodies.

The ability to optically trap and manipulate artificial microswimmers such as active Janus particles (JPs) provides a breakthrough in active matter research and applications. However, it presents significant challenges because of the asymmetry in the optical properties of JPs and remains incomprehensible. Illustrating the interplay between optical and thermophoretic forces, we demonstrate dynamically stable optical trapping of Pt-silica JPs, where the force-balanced position evolves spontaneously within a localized volume around the focal point and in a vertically shifted annular confinement at low and high laser powers, respectively. Intriguingly, the orientational and orbital dynamics of JP remain strongly coupled in the delocalized confinement. Furthermore, we demonstrate simultaneous optical trapping of multiple JPs. This first report on thermophoresis of Pt-silica JPs and localized-to-delocalized crossover in the position distributions of an optically trapped active JP, verifying theoretical predictions, advances our understanding on confined active matter and their experimental realizations.

Strain localization in granular materials arises from complex microscale dynamics, including intermittent particle rearrangements and spatiotemporally correlated deformation. While dynamic heterogeneity (DH) and dynamic facilitation (DF) have been widely studied in two-dimensional amorphous materials, their prevalence in three-dimensional (3D) granular systems remains unclear. Here, we performed a 3D triaxial compression test with in-situ X-ray computed tomography to track particle-scale kinematics across small and large strain increments. We analyzed deviatoric strain, volumetric strain, and non-affine motion fields, computed four-point spatial dynamic correlation functions to probe DH, quantified DF through a facilitation ratio, and assessed temporal persistence of local dynamics using four-point temporal dynamic correlations. Across large strain increments, DH and DF emerge strongly in the transition regime between the initially elastic response and the critical state regime, but weaken or become statistically insignificant within the shear band at the critical state, indicating a qualitative change in microscale dynamics upon localization. In contrast, under small increments, both measures are suppressed across all regimes. These results demonstrate that correlated dynamics depend strongly on both strain increment and deformation regime. This work provides the first comprehensive investigation of DH and DF in 3D granular materials and highlights their strain-increment and regime-dependent behaviors, establishing a connection to glassy dynamics in amorphous solids.

Non-Gaussian states, described by Wigner quasi-probability distribution taking negative values, are of great interest for various applications of quantum physics. It is known however that they are highly vulnerable to dissipation. In this paper, we show that the robustness of the non-Gaussian states to losses can be significantly improved by pre-squeezing of the quantum state, and find the optimal parameters of the squeezing. As specific examples, we consider such well-known quantum states as Schrodinger cat, Fock , and ``banana'' ones.

Discrete time quasicrystals (DTQC) constitute a class of non-equilibrium matter characterized by temporal order without strict periodicity, in contrast to conventional time crystals. Investigating these phenomena is essential for expanding our fundamental understanding of far-from-equilibrium quantum matter and spontaneous symmetry breaking beyond periodic regimes. Here, we experimentally observe a DTQC in a driven-dissipative ensemble of strongly interacting Rydberg atoms, displaying non-equilibrium dynamical response with a different finite Abelian group symmetry $\mathbb{Z}{_m} \times \mathbb{Z}{_n}$. By applying a quasi-periodic drive using a dual-frequency drive with incommensurate frequencies, we demonstrate that the system exhibits a robust subharmonic response at multiple incommensurate frequencies, signifying the emergence of a DTQC phase. We map the full phase diagram of the system, which includes the DTQC phase, and demonstrated its rigidity against perturbations in both RF field intensity and laser detuning. Moreover, we observe a cyclic group symmetry effect that constrains the construction of $\mathbb{Z}{_2} \times \mathbb{Z}{_3}$-symmetric DTQC. This work establishes a versatile platform for studying non-equilibrium phases of matter and provides insights into the dynamics of time-translation symmetry breaking in quantum many-body systems.

Snakes can bend their elongate bodies in various forms to traverse various environments. We understand how snakes use lateral bending to push against asperities on flat ground for propulsion, and snake robots can do so effectively. However, snakes can also use vertical bending to push against terrain of large height variation for propulsion, and they can adjust it to adapt to novel terrain presumably using mechano-sensing feedback control. Although some snake robots can traverse uneven terrain, few have used vertical bending for propulsion, and how to control it in novel environments is poorly understood. Here we systematically studied a snake robot with force sensors pushing against large bumps using vertical bending to understand the role of sensory feedback control. We compared a feedforward controller and four feedback controllers that use different senses and generate distinct bending patterns and body-terrain interaction. We challenged the robot with increasing backward load and novel terrain geometry that break its contact with the terrain. We further varied how much the feedback control modulated bending to conform to or push against the terrain to test their effects. Feedforward propagation of vertical bending generated large propulsion when the shape matched terrain geometry. However, when perturbations caused loss of contact, the robot easily lost propulsion or had motor overload. Contact feedback control resolved these by improving contact. Yet excessive conformation interrupted propagation and excessive pushing stalled motors. Unlike that using lateral bending, for propulsion generation using vertical bending, body weight can help maintain contact with the environment but may also overload motors. Our results will help snake robots better traverse terrain with large height variation and can inform how snakes use sensory feedback to control vertical bending for propulsion.

Using real-space view of high harmonic generation (HHG) in solids, we develop a physically transparent and gauge-invariant approach for distinguishing intraband and interband HHG mechanisms. Our approach relies on resolving the harmonic emission according to the separation between Wannier states involved in radiative transitions. We show that the intra- and inter-band HHG emission exhibit striking qualitative differences in their dependence on this separation and can be clearly distinguished using the Wannier basis.

Cell tracking is a subject of active research gathering great interest in medicine and biology. Positron emission tomography (PET) is well suited for tracking radio-labeled cells in vivo due to its exceptional sensitivity and whole-body capability. For validation, ground-truth data are desirable that realistically mimic the flow of cells in a clinical situation. This study develops a workflow (CeFloPS) for simulating moving radio-labeled cells in a human phantom. From the XCAT phantom, the blood vessels are reduced to nodal networks along which cells can move and distribute to organs and tissues. The movement is directed by the blood flow, which is calculated in each node using the Hagen-Pooiseuille equation and Kirchhoff's laws assuming laminar flow. Organs are voxelized and movement of cells from artery entry to vein exit is generated via a biased 3D random walk. The probabilities of cells moving or remaining in tissues are derived from rate constants of tracer kinetic-based compartment modeling. PET listmode data is generated using the Monte-Carlo simulation framework GATE based on the definition of a large-body PET scanner with cell paths as moving radioactive sources and the XCAT phantom providing attenuation data. From the flow simulation of 100,000 cells, 100 sample cells were further processed by GATE and listmode data was reconstructed into images for comparison. As demonstrated by comparisons of simulated and reconstructed cell distributions, CeFloPS is capable of simulating cell behavior in whole-body PET. It achieves this simulation in a way that is anatomically and physiologically reasonable, thereby providing valuable data for the development and validation of cell tracking algorithms.

The molecular motion in heterogeneous media displays anomalous diffusion by the mean-squared displacement $\langle X^2(t) \rangle = 2 D t^\alpha$. Motivated by experiments reporting populations of the anomalous diffusion parameters $\alpha$ and $D$, we aim to disentangle their respective contributions to the observed variability when this last is due to a true population of these parameters and when it arises due to finite-duration recordings. We introduce estimators of the anomalous diffusion parameters on the basis of the time-averaged mean squared displacement and study their statistical properties. By using a copula approach, we derive a formula for the joint density function of their estimations conditioned on their actual values. The methodology introduced is indeed universal, it is valid for any Gaussian process and can be applied to any quadratic time-averaged statistics. We also explain the experimentally reported relation $D\propto\exp(\alpha c_1+c_2)$ for which we provide the exact expression. We finally compare our findings to numerical simulations of the fractional Brownian motion and quantify their accuracy by using the Hellinger distance.

We report lifetime measurements of the metastable $6\mathrm{d}\, ^{2}\mathrm{D}_{5/2}$ and $6\mathrm{d}\, ^{2}\mathrm{D}_{3/2}$ states of Ra$^+$. The measured lifetimes, $\tau_{5} = $ 303.8(1.5) ms and $\tau_{3} = $ 642(9) ms, are important for optical frequency standards and for benchmarking high-precision relativistic atomic theory. Independent of the reported measurements, the D state lifetimes were calculated using the coupled-cluster single double triple method, in which the coupled-cluster equations for both core and valence triple excitations were solved iteratively. The method was designed for precise prediction of atomic properties, especially for heavy elements, where relativistic and correlation corrections become large, making their treatment more challenging. This Letter presents the first tests of the method for transition properties. Our prediction agrees with experimental values within the uncertainties. The ability to accurately predict the atomic properties of heavy elements is important for many applications, from tests of fundamental symmetries to the development of optical clocks.

We develop a matter-space framework in which particles including photons are modeled as residing on a three-dimensional matter space, while the familiar electromagnetic fields in four-dimensional spacetime arise as projections. A set of constraint equations, implied by the absence of nontrivial 4-forms on a three-dimensional space, yields a ``matter-space gauge symmetry'', which manifests on a subset of the 1-form fields in 4-dimensional spacetime. Employing the electro-magnetic duality, the gauge symmetry enforces a precise relation between the potential $A_a$ and the field strength $F_{ab}$, reproducing one half of Maxwell's equations. The remaining Maxwell equation follows from an action principle for relativistic fluids formulated on matter space. Notably, in this construction the dynamics of electromagnetism emerges from the set of constraints, rather than being added ad hoc. This formulation not only ensures consistency with conventional electromagnetism but also provides deeper insights on the particle nature of the electromagnetic field. To support this claim, we demonstrate that i) the Aharonov-Bohm effect arises solely from the matter-space gauge potential. ii) the helicity is shown to be conserved automatically. iii) nonlinear corrections analogous to mass renormalization are intrinsically incorporated. This framework offers a fundamental interplay between matter and electromagnetic fields.

Establishing a resilient food trade system is an international consensus on safeguarding food security amid growing disruptions. However, a unified resilience framework has yet to be established, leading to the proliferation of diverse measures. Here, we conceptualize resilience as a trade-off between efficiency and redundancy and employ an entropy-based approach to quantify the dynamic structural resilience of international trade networks for maize, rice, soybean, and wheat from 1986 to 2022. Using index decomposition analysis, we also investigate the relative contributions of internal components to resilience dynamics. Within this framework, despite heterogeneity across different food commodities, we find that current trade networks are relatively redundant, with improvements in efficiency being the dominant driver of changes in resilience. In addition, we reveal a historically pronounced impact of flow concentrations on resilience, while trade interactions have become increasingly important in recent years. Following the leave-one-out approach, we furthermore identify critical economies and trade relationships that disproportionately affect the overall resilience, some of which are less well-focused in previous studies. Moreover, we highlight that overconcentration of flows along core trade relationships may undermine both efficiency and resilience, whereas peripheral trade networks may play strategic alternative roles in sustaining resilience, underscoring the importance of concentrating on developing economies and promoting broader trade links. These findings not only provide new insights for assessing the resilience of international food trade systems but also propose directions for strengthening resilience through both regional cooperation and more inclusive trade relations.

We study a near-field thermophotonic (NF-TPX) refrigerating device, consisting of a lightemitting diode and a photovoltaic cell in close proximity. Calculations are performed in the frame of the detailed-balance approach. We study how thermal radiation, separation distance and LED temperature can affect both cooling power and coefficient of performance. More specifically, we assess the impact of bandgap energy and external quantum efficiency for an artificial material on those cooling performances. For a particular device made of GaAs and/or AlGaAs we show that, in the near-field regime, the cooling power can be increased by one order of magnitude compared to far field. However, a 10% reduction of the external quantum efficiency can lead to a decrease of the cooling power by two orders of magnitude. Finally, we compare existing literature data on electroluminescent, TPX and thermoelectric cooling with our detailed balance prediction, which highlights design-rule requirements for NF-TPX cooling devices.

Photolithography conventionally requires flat, rigid and stable substrates, limiting its applications in flexible, curved, and transient electronics. In this study, a breakthrough approach is reported that employs a reversibly adhesion-switchable phase-changing polymer to universally transfer commercial photoresists onto previously inaccessible substrates, overcoming fundamental limitations of conventional photolithography. Remarkably, it achieves wafer-scale (4-inch) transfer with global registration error below 60 microns and support precise patterning on solvent-sensitive, curved, microtextured or delicate surfaces. Combined with dry etching, this study demonstrates a novel route for high-resolution patterning of susceptible materials (e.g. quantum dots and organic semiconductors). The transfer method also supports a sustainable "dry lift-off" for patterning functional materials. The reusability of both the transfer carrier and photoresist introduces a new level of sustainability and scalability, establishing a significant advancement in microfabrication. Additionally, this unprecedented capability is further demonstrated by fabricating a micro-sized UV-photodetector array with wide-angle perception directly on a curved glass bottle.

Networks representing social, biological, technological or other systems are often characterized by higher-order interaction involving any number of nodes. Temporal hypergraphs are given by ordered sequences of hyperedges representing sets of nodes interacting at given points in time. In this paper we discuss how a recently proposed model family for time-stamped hyperedges - relational hyperevent models (RHEM) - can be employed to define tailored null distributions for temporal hypergraphs and to test and control for complex dependencies in hypergraph dynamics. RHEM can be specified with a given vector of temporal hyperedge statistics - functions that quantify the structural position of hyperedges in the history of previous hyperedges - and equate expected values of these statistics with their empirically observed values. This allows, for instance, to analyze the overrepresentation or underrepresentation of temporal hyperedge configurations in a model that reproduces the observed distributions of possibly complex sub-configurations, including but going beyond node degrees. Concrete examples include, but are not limited to, preferential attachment, repetition of subsets of any given size, triadic closure, homophily, and degree assortativity for subsets of any order.

We investigated resonant multiphoton excitation of high-angular-momentum planetary states of the strontium atom, a quasi-three-body Coulomb system, both experimentally and theoretically. A series of highly doubly excited electronic states converging to the double ionization threshold was identified and reproduced by first-principles calculations, which provided access to the two-electron wavefunctions of the resonances. In these states, the outer electron is dynamically localized at large distances by pendulum-like oscillations of the inner electron across the nucleus. A complementary simple molecular-like adiabatic model offers a clear interpretation of the correlated two-electron motion and suggests that the series is a general feature of highly doubly excited states of atoms and molecules.

Scalable photonic optimization holds the promise of significantly enhancing the performance of diffractive lenses across a wide range of photonic applications. However, the high computational cost of conventional full three-dimensional electromagnetic solvers has thus far been a major obstacle to large-scale-domain optimization. Here, we address this limitation by integrating the convergent Born series with the adjoint-field optimization framework, enabling inverse design with its domain size up to a $110 \times 110 \times 46\ \mu\text{m}^3$ volume$-$corresponding to 0.1 gigavoxels$-$using a single, cost-effective graphics card. The optimized lens achieves a 9% improvement in axial resolution and a 20% increase in focusing efficiency compared to a standard Fresnel lens of identical diameter and numerical aperture. These gains point to immediate application opportunities for optimizing high-performance microscopy, photolithography, and optical trapping systems using modest computational resources.

We model carrier transport in photonic crystal lasers with lateral current injection through two-dimensional (2D) finite-volume simulations. Though such lasers can achieve ultra-low threshold currents, leakage paths reduce the carrier injection efficiency. The design is evaluated through its performance in terms of injection efficiency, internal quantum efficiency, and IV characteristics. Our model predicts the presence of unconventional leakage paths, explaining experimental observations of low injection efficiencies and enhanced spontaneous recombination at doping interfaces. Carrier leakage paths arise due to insufficient injection of holes into the active region, leading to an electric field that increases the energy barrier for electrons, thereby reducing the injection efficiency. The spatial profile of the p-doped region is shown to play a critical role in achieving a high electrical injection efficiency and low-threshold lasing. The model is an important step towards modelling and optimizing properties of 2D photonic crystal membrane lasers.

Accurate and efficient numerical simulation of ammonia combustion is critical for advancing ammonia-based energy systems, where turbulent flame dynamics and pollutant formation strongly affect practical applicability. However, such simulations are hindered by the need to solve high-dimensional stiff chemical ordinary differential equations (ODEs), which constitute the primary computational bottleneck. To address this challenge, this study explores Deep learning for solving Flame chemical kinetics with stiff ODEs (DFODE) in ammonia/natural gas combustion. Thermochemical training data are obtained from one-dimensional (1D) freely propagating premixed laminar flames, and a physics-aware augmentation strategy combining interpolation of neighboring states with constrained random perturbations is introduced to overcome sampling imbalance near steep flame-front gradients. In addition, transformation strategies for model target formulation were evaluated, and the prediction accuracy in low-temperature regimes was notably enhanced through scale separation for targets spanning multiple orders of magnitude. Validation in 1D laminar flames confirms the effectiveness of these refinements, while a posteriori evaluation in a two-dimensional (2D) propagating flame under homogeneous isotropic turbulence (HIT) demonstrates that the trained models generalize to unseen conditions. The DNN surrogates reproduce flame characteristics with high fidelity and deliver up to a 20x speedup in end-to-end CFD simulations. These results highlight the potential of deep learning-based chemical kinetics to accelerate ammonia/natural gas combustion modeling, supporting efficient and scalable high-fidelity simulations for emerging zero-carbon energy systems.

The propagation of waves through the marginal ice zone (MIZ) and deeper into pack ice is a key phenomenon that influences the breakup and drift of sea ice. When waves in ice propagate through a solid, non-cracked, thick enough sea ice cover, significant flexural elastic effects can be present in the dispersion relation. This results in a dispersion relation that opens up for 3-wave interactions, also known as wave triads. Here, we report the observation of high-frequency spectral peaks in the power spectral density of waves in ice spectra. We show, in two timeseries datasets, that the presence of these high-frequency peaks is accompanied by high values for the spectral bicoherence. This is a signature that the high-frequency peak is phase-locked with frequency components in the main spectral energy peak, and a necessary condition for nonlinear coupling to take place. Moreover, we show for a timeseries dataset that includes several closely located sensors that the dispersion relation recovered from a cross-spectrum analysis is compatible with the possible existence of wave triads at the same frequencies for which the bicoherence peak is observed. In addition to these observations in timeseries datasets, we show that similar high-frequency peaks are observed from additional, independent datasets of waves in ice power spectrum densities transmitted over iridium from autonomous buoys. These results suggest that nonlinear energy transfers between wave in ice spectral components are likely to occur in some waves and sea ice conditions. This may enable redistribution of energy from weakly damped low-frequency waves to more strongly attenuated higher-frequency spectral components, which can contribute to energy dissipation in the ice.

The ability to quantify information transmission is crucial for the analysis and design of natural and engineered systems. The information transmission rate is the fundamental measure for systems with time-varying signals, yet computing it is extremely challenging. In particular, the rate cannot be obtained directly from experimental time-series data without approximations, because of the high dimensionality of the signal trajectory space. Path Weight Sampling (PWS) is a computational technique that makes it possible to obtain the information rate exactly for any stochastic system. However, it requires a mathematical model of the system of interest, be it described by a master equation or a set of differential equations. Here, we present a technique that employs Machine Learning (ML) to develop a generative model from experimental time-series data, which is then combined with PWS to obtain the information rate. We demonstrate the accuracy of this technique, called ML-PWS, by comparing its results on synthetic time-series data generated from a non-linear model against ground-truth results obtained by applying PWS directly to the same model. We illustrate the utility of ML-PWS by applying it to neuronal time-series data.

Objective: Research on eye lens dosimetry for radiation workers has increased after the 2012 ICRP118 update on eye lens dose limits. However, corneal dosimetry remains underexplored due to historical focus and measurement challenges. This study uses a high-resolution digital eye phantom in Monte Carlo simulations to estimate corneal and lens doses for nuclear medicine staff, with and without lead glasses. Method: The Monte Carlo code GATE (version 9.0) based on GEANT4 (version 10.6) was used to estimate and compare doses in a digital eye phantom, accounting for primary and scattered radiation from common radionuclides (F18, I131, Tc99m) with varying lead glass shielding (0 to 0.75 mm). Results: Across all radionuclides, the dose to the cornea was consistently higher than the dose to the lens. Notably, the ratio of corneal to lens dose increased with thicker lead glasses, indicating a greater dose reduction to the lens compared to the cornea. Conclusion: The findings show that corneal doses from all studied radionuclides exceeded lens doses. Although increasing lead glass thickness reduced both, the reduction was more significant for the lens, raising the cornea-to-lens dose ratio. This trend suggests that while thicker lead glasses enhance lens protection, their practicality may be limited due to diminishing returns and potential discomfort. Keywords: Corneal Dosimetry, Lens Dosimetry, Monte Carlo, GATE, Nuclear Medicine, Simulation

We report a microwave spectroscopy measurement of the muonium $2S_{1/2}-2P_{3/2}$ fine structure transition, yielding a transition frequency of $9871.0 \pm 7.0~\mathrm{MHz}$, in agreement with state-of-the-art QED predictions within one standard deviation. In combination with the recent Lamb shift result, the $2P_{1/2}-2P_{3/2}$ splitting is determined, improving the spectroscopic characterization of the $n=2$ manifold in muonium. These results provide a stringent test of bound-state QED in a purely leptonic system and establish a path toward future searches for Lorentz violation and muon-specific new physics.

Phononic circuits constructed from high tensile stress membranes offer a range of desirable features such as high acoustic confinement, controllable nonlinearities, low mass, compact footprint, and ease of fabrication. This tutorial presents a systematic approach to modelling and designing phononic integrated circuits on this platform, beginning with acoustic confinement, wave propagation and dispersion, mechanical and actuation nonlinearities, as well as resonator dynamics. By adapting coupled mode theory from optoelectronics to suspended membranes, and validating this theory with several numerical techniques (finite element modelling, finite difference time domain simulations, and the transfer matrix method), we then provide a comprehensive framework to engineer a broad variety of phononic circuit building blocks. As illustrative examples, we describe the implementation of several acoustic circuit elements including resonant and non-resonant variable-ratio power splitters, mode converters, mode (de)multiplexers, and in-line Fabry-Perot cavities based on evanescent tunnel barriers. These building blocks lay the foundation for phononic integrated circuits with applications in sensing, acoustic signal processing, and power-efficient and radiation-hard computing.

Ji \etal [New J. Phys. {\bf 26}, 093014 (2024)] established a direct link between the photoionization cross section and the attosecond time delay near Cooper minima (CM) in the valence shells of noble-gas atoms. This link is based on the analytic properties of the ionization amplitude in the complex plane of the photoelectron energy, and is particularly sensitive to the winding number of the amplitude around the origin of the complex energy plane. % Here, we demonstrate an analogous relation for photoionization of the valence $ns$ shells of alkali-metal atoms (AMA), from Na ($n=3$) to Cs ($n=6$), as well as alkaline-earth-metal atoms (AEMA), from Mg ($n=3$) to Ba ($n=6$). To this end, we employ a fully relativistic formalism that separates the two complementary $ns_{1/2} \to Ep_{1/2}$ and $Ep_{3/2}$ ionization channels. Each of these channels exhibits a phase variation close to $\pi$, but in opposite directions, near their respective Cooper minima. This phase variation vanishes in a nonrelativistic formulation, where the two channels become degenerate % For AMA, due to the threshold proximity of the CM, the universal Coulomb contribution to the time delay must be subtracted. The remaining component of the time delay is target-specific, angular-dependent, and accessible through comparative measurements.

Configuring clinical linear accelerators (linacs) for ultra-high dose rate (UHDR) electron experiments typically requires invasive hardware manipulation and/or irreversible manufacturer modifications, limiting broader implementation. We present an independently developed UHDR electron configuration of a clinical TrueBeam linac that allows reversible switching between preclinical UHDR and conventional (CONV) modes using only non-invasive software settings. UHDR mode was achieved via service mode software with RF and beam current settings typical of a photon beam, the photon target and monitor chamber retracted, and a clinically unused low-energy scattering foil inserted. An external AC current transformer (ACCT) for beam monitoring, anatomy-specific collimator, and sample holder were mounted on the accessory tray, with external ion chamber in solid water for exit dose monitoring. Percent depth dose (PDD) was measured for UHDR and CONV beams. Dose-per-pulse (DPP) was varied by adjusting gun voltage and quantified with radiochromic film at different source-to-surface distances (SSD). Beam profiles assessed dose uniformity and usable field size. Dose calibration was established between film, ACCT, and ion chamber, and day-to-day reproducibility was tested. PDD confirmed similar energies for UHDR (12.8MeV) and CONV (11.9MeV) beams with matching profiles through mouse thickness. Maximum DPP exceeded 0.5Gy, reaching ~1.5Gy for collimated in vivo setups and ~0.7Gy at extended SSD for tissue culture. Field flatness and symmetry were maintained, supporting organ-specific irradiations and up to 5cm fields for culture. Calibration showed strong linearity across detectors, and output variation was <4%. We demonstrated accurate, reproducible UHDR delivery on a widely available clinical linac with no invasive hardware manipulation, enabling preclinical FLASH research on a clinical treatment machine.

Originally envisioned as a solution for the capacity crunch in telecommunications networks, multicore fibers (MCF) are contributing to scientific fields beyond telecom, such as sensing and metrology. Confined within the same cladding, the cores of MCF have a high degree of noise correlation which can be harnessed for a variety of applications. Here, we investigate MCF as a solution to the challenging problem of quantum and classical light co-existence in quantum networks by operating the quantum and stabilization light in separate but highly correlated cores of a 7-core MCF. Over 40 km of spooled fiber, we achieved 100 attosecond integrated jitter on one core by using phase information derived from another core. This allows for 100% duty cycle on a quantum channel while maintaining a low spurious photon rate from crosstalk between stabilization and quantum channels. With cycle-slip-free stabilization over 6 hours, frequency detuning between designated stabilization and quantum channels, and an additional 40 dB rejection of noise photons provided by the low optical crosstalk between cores, we achieved a Raman scattering-induced spurious photon rate of only 0.01 photons/s in 100 GHz bandwidth. Our results with MCF are a promising approach to ultra-stable quantum networks with 100% duty cycle on the quantum channel.

We present a quantum computing algorithm for the smoothed particle hydrodynamics (SPH) method. We use a normalization procedure to encode the SPH operators and domain discretization in a quantum register. We then perform the SPH summation via an inner product of quantum registers. Using a one-dimensional function, we test the approach in a classical sense for the kernel sum and first and second derivatives of a one-dimensional function, using both the Gaussian and Wendland kernel functions, and compare various register sizes against analytical results. Error convergence is exponentially fast in the number of qubits. We extend the method to solve the one-dimensional advection and diffusion partial differential equations, which are commonly encountered in fluids simulations. This work provides a foundation for a more general SPH algorithm, eventually leading to highly efficient simulations of complex engineering problems on gate-based quantum computers.

Despite advances in a diversity of environments, snake robots are still far behind snakes in traversing complex 3-D terrain with large obstacles. This is due to a lack of understanding of how to control 3-D body bending to push against terrain features to generate and control propulsion. Biological studies suggested that generalist snakes use contact force sensing to adjust body bending in real time to do so. However, studying this sensory-modulated force control in snakes is challenging, due to a lack of basic knowledge of how their force sensing organs work. Here, we take a robophysics approach to make progress, starting by developing a snake robot capable of 3-D body bending with contact force sensing to enable systematic locomotion experiments and force measurements. Through two development and testing iterations, we created a 12-segment robot with 36 piezo-resistive sheet sensors distributed on all segments with compliant shells with a sampling frequency of 30 Hz. The robot measured contact forces while traversing a large obstacle using vertical bending with high repeatability, achieving the goal of providing a platform for systematic experiments. Finally, we explored model-based calibration considering the viscoelastic behavior of the piezo-resistive sensor, which will for useful for future studies.

Ultralight axion-like particles are well-motivated relics that might compose the cosmological dark matter and source anomalous time-dependent magnetic fields. We report on terrestrial bounds from the Noble And Alkali Spin Detectors for Ultralight Coherent darK matter (NASDUCK) collaboration on the coupling of axion-like particles to neutrons and protons. The detector uses nuclei of noble-gas and alkali-metal atoms and operates in the Spin-Exchange Relaxation-Free~(SERF) regime, achieving high sensitivity to axion-like dark matter fields. Conducting a month-long search, we cover the mass range of $1.4\times 10^{-12}$~eV/$c^2$ to $2\times 10^{-10}$~eV/$c^2$ and provide limits which supersede robust astrophysical bounds, and improve upon previous terrestrial constraints by over two orders of magnitude for many masses within this range for protons, and up to two orders of magnitude for neutrons. These are the first reliable terrestrial bounds reported on the coupling of protons with axion-like dark matter, covering an unexplored terrain in its parameter space.

Quantum computing promises to provide the next step up in computational power for diverse application areas. In this review, we examine the science behind the quantum hype, and the breakthroughs required to achieve true quantum advantage in real world applications. Areas that are likely to have the greatest impact on high performance computing (HPC) include simulation of quantum systems, optimization, and machine learning. We draw our examples from electronic structure calculations and computational fluid dynamics which account for a large fraction of current scientific and engineering use of HPC. Potential challenges include encoding and decoding classical data for quantum devices, and mismatched clock speeds between classical and quantum processors. Even a modest quantum enhancement to current classical techniques would have far-reaching impacts in areas such as weather forecasting, aerospace engineering, and the design of "green" materials for sustainable development. This requires significant effort from the computational science, engineering and quantum computing communities working together.

In multi-parameter quantum metrology, the resource of entanglement can lead to an increase in efficiency of the estimation process. Entanglement can be used in the state preparation stage, or the measurement stage, or both, to harness this advantage; here we focus on the role of entangling measurements. Specifically, entangling or collective measurements over multiple identical copies of a probe state are known to be superior to measuring each probe individually, but the extent of this improvement is an open problem. It is also known that such entangling measurements, though resource-intensive, are required to attain the ultimate limits in multi-parameter quantum metrology and quantum information processing tasks. In this work we investigate the maximum precision improvement that collective quantum measurements can offer over individual measurements for estimating parameters of qudit states, calling this the 'collective quantum enhancement'. We show that, whereas the maximum enhancement can, in principle, be a factor of $n$ for estimating $n$ parameters, this bound is not tight for large $n$. Instead, our results prove an enhancement linear in dimension of the qudit is possible using collective measurements and lead us to conjecture that this is the maximum collective quantum enhancement in any local estimation scenario.

We present a quantum interference phenomenon in which four-photon quantum states generated by two independent sources are used to create a two-photon interference pattern without detecting two of the photons. Contrary to the common perception, the interference pattern can be made fully independent of phases acquired by the photons detected to construct it. However, it still contains information about spatially dependent phases acquired by the two undetected photons. This phenomenon can also be observed with fermionic particles. We show that the phenomenon can be applied to develop an interferometric, quantum phase imaging technique that is immune to uncontrollable phase fluctuations in the interferometer and allows image acquisition without detecting the photons illuminating the object.

Identifying reaction conditions that are broadly applicable across diverse substrates is a longstanding challenge in chemical and pharmaceutical research. While many methods are available to generate conditions with acceptable performance, a universal approach for reliably discovering effective conditions during reaction exploration is rare. Consequently, current reaction optimization processes are often labor-intensive, time-consuming, and costly, relying heavily on trial-and-error experimentation. Nowadays, large language models (LLMs) are capable of tackling chemistry-related problems, such as molecule design and chemical reasoning tasks. Here, we report the design, implementation and application of Chemma-RC, a text-augmented multimodal LLM to identify effective conditions through task-specific dialogue and condition generation. Chemma-RC learns a unified representation of chemical reactions by aligning multiple modalities-including text corpus, reaction SMILES, and reaction graphs-within a shared embedding module. Performance benchmarking on datasets showed high precision in identifying optimal conditions, with up to 17% improvement over the current state-of-the-art methods. A palladium-catalysed imidazole C-H arylation reaction was investigated experimentally to evaluate the functionalities of the Chemma-RC in practice. Our findings suggest that Chemma-RC holds significant potential to accelerate high-throughput condition screening in chemical synthesis.

As a pinning-controlled permanent magnet, tailoring the cellular nanostructure of samarium-cobalt-based 1:7-type (SmCo-1:7) magnets remains crucial for improving magnetic performance. Jointing forward and inverse machine learning models with the high-throughput micromagnetic simulations (42,300 runs), we identify the nanostructural and magnetic features that are most effective for coercivity, combining both nucleation and pinning mechanisms. Sensitivity analyses reveal that the 1:5-phase enhances coercivity by providing high anisotropy, and the Z-phase strengthens pinning through fluctuations in domain wall energy. Cu additions in the 1:5-phase significantly reduce coercivity, while Fe substitutions in the 2:17-phase modestly reduce coercivity but improve pinning locally and increase saturation magnetization. Among all examined features, magnetocrystalline misorientation emerges as the dominant factor. Finally, the framework enables the inverse design of nanostructures with prescribed coercivity, demonstrating a computationally cost-effective toolkit for guiding the performance tailoring of SmCo-1:7 magnets.

The discovery of magnetic materials with high operating temperature ranges and optimized performance is essential for advanced applications. Current data-driven approaches are limited by the lack of accurate, comprehensive, and feature-rich databases. This study aims to address this challenge by using Large Language Models (LLMs) to create a comprehensive, experiment-based, magnetic materials database named the Northeast Materials Database (NEMAD), which consists of 67,573 magnetic materials entries(this http URL). The database incorporates chemical composition, magnetic phase transition temperatures, structural details, and magnetic properties. Enabled by NEMAD, we trained machine learning models to classify materials and predict transition temperatures. Our classification model achieved an accuracy of 90% in categorizing materials as ferromagnetic (FM), antiferromagnetic (AFM), and non-magnetic (NM). The regression models predict Curie (Néel) temperature with a coefficient of determination (R2) of 0.87 (0.83) and a mean absolute error (MAE) of 56K (38K). These models identified 25 (13) FM (AFM) candidates with a predicted Curie (Néel) temperature above 500K (100K) from the Materials Project. This work shows the feasibility of combining LLMs for automated data extraction and machine learning models to accelerate the discovery of magnetic materials.

We present a way to capture high-information posteriors from training sets that are sparsely sampled over the parameter space for robust simulation-based inference. In physical inference problems, we can often apply domain knowledge to define traditional summary statistics to capture some of the information in a dataset. We show that augmenting these statistics with neural network outputs to maximise the mutual information improves information extraction compared to neural summaries alone or their concatenation to existing summaries and makes inference robust in settings with low training data. We introduce 1) two loss formalisms to achieve this and 2) apply the technique to two different cosmological datasets to extract non-Gaussian parameter information.

This paper theoretically investigates the manipulation of the Photonic Spin Hall Effect (photonic SHE) using a four-level closed coherent control coupling scheme in a cavity. The atomic system is configured to function as a combined Tripod and Lambda (CTL), Lambda $\Lambda$, and $N$ level model by properly adjusting the control field strengths and their relative phases. The system demonstrates multiple transparency windows in the CTL configuration, allowing the tunable photonic SHE to be used over a wider range of probe field detuning. At probe field resonance under the condition of electromagnetic induced transparency (EIT), the $\Lambda$-type system exhibits photonic SHE similar to the CTL system, showing a maximum upper limit of photonic SHE equal to half of the incident beam waist. This upper limit arises due to zero absorption and dispersion. Control field strengths and atomic density do not influence photonic SHE at resonance for both atomic configurations. Our findings reveal that atomic density and strength of control fields significantly influence photonic SHE in the $N$-type model at resonance, offering additional control parameters for tuning photonic SHE. Finally, the results are equally valid and applicable to conventional $\Lambda$-type and N-type atomic systems, making the findings broadly relevant in cavity atomic systems. The results of angular photonic SHE are also discussed.

Here, we present a high-dimensional QKD protocol utilizing the position and momentum entanglement of photon pairs. The protocol exploits the fact that position and momentum form mutually unbiased bases, linked via a Fourier transform. One photon of the entangled pair is measured by the sender in a randomly chosen basis-either position or momentum - selected passively via a 50:50 beamsplitter. This projective measurement remotely prepares the partner photon in a corresponding spatial mode, which is sent to the receiver (Bob), who similarly performs a random measurement in one of the two basis. This approach combines state preparation and measurement into a single process, eliminating the need for external random number generators. In this proof-of-principle demonstration, we achieve a photon information efficiency of 5.07 bits per photon using 90 spatial modes, and a maximum bit rate of 0.9 Kb/s with 361 modes. Looking ahead, we theoretically show that using the same entangled photon source but with next-generation event-based cameras - featuring improved quantum efficiency, timing and spatial resolution - our approach could achieve 10.9 bits per photon at 2500 spatial modes, and a maximum bit rate of 3.1 Mb/s with 5100 modes. This work establishes a scalable path toward high-dimensional, spatially encoded quantum communication with both high photon efficiency and secure bit rates.

Light carrying orbital angular momentum (OAM)--known as vortex beams--has broadened the scope of understanding and applications of light's angular momentum. Optical tweezers using OAM, often referred to as optical spanners, have significantly expanded the tunability of optical manipulation. A key frontier now lies in understanding how vortex beams interact with quantum states of matter. In this work, we numerically investigate the dynamics of a superconductor under vortex beam illumination and demonstrate the transfer of angular momentum from light to the superconducting collective mode, resulting in mechanical rotation. Our findings open a pathway for optical manipulation in the quantum regime, which we term the quantum optical spanner.

We predict collective 'free-space' lasing in a dense nanoscopic emitter arrangement where dipole-dipole coupled atomic emitters synchronize their emission and exhibit lasing behavior without the need for an optical resonator. At the example of a subwavelength-spaced linear emitter chain with varying fractions of pumped and unpumped emitters, we present a comprehensive study of this mirrorless lasing phenomenon. The total radiated power transitions from subradiant suppression under weak pumping to superradiant enhancement at stronger pumping, while exhibiting directional emission confined to a narrow spatial angle. At the same time multiple independent spectral emission lines below the lasing threshold merge towards a single narrow spectral line at high pump power. The most substantial enhancement and line narrowing occur when a fraction of unpumped atoms is present. We show that this leads to superradiant lasing near the bare atomic frequency, making the system a promising candidate for a minimalist active optical frequency reference.

In this review, we present recent works on materials whose common point is the presence of electronic bands of very low dispersion, called "flat bands", which are due to specific atomic order effects without electron interactions. These states are always indicative of some form of confinement and have consequences on the electronic properties. A first part is devoted to the cases where this confinement is due to the long-range geometry of the defect-free structure. We have thus studied periodic approximant structures of quasiperiodic Penrose and octagonal tilings, and twisted bilayers of graphene (TBG) or transition metal dichalcogenides (TMDs) whose rotation angle between the two layers assumes a special value, called "magic angle". In these materials, the flat bands correspond to electronic states distributed over a very large number of atoms (several hundreds or even thousands of atoms). We have shown that their electronic transport properties cannot be described by usual Bloch-Boltzmann theories, because the interband terms of the velocity operator dominate the intraband terms as far as quantum diffusion is concerned. In the case of TBG, flat bands can induce a magnetic state and other electron-electron correlation effects. The second part focuses on two-dimensional nanomaterials in the presence of local point defects that cause resonant electronic states (vacancies, adsorbed atoms or molecules). We present studies on monolayer graphene, twisted or Bernal bilayer graphene, carbon nanotubes, monolayer and multilayer black phosphorene, and monolayer TMDs. A recent result is the discovery that the selective functionalization of a Bernal bilayer graphene sublattice leads to a metallic or insulating behavior depending on the functionalized sublattice type. This result suggests that functionalization can be a key parameter to control the electronic properties of 2D materials.

Achieving deterministic placement of self-assembled quantum dots (QDs) during epitaxial growth is essential for the reliable and efficient fabrication of high-quality single-photon sources and solid-state cavity quantum electrodynamics (cQED) systems, yet it remains a significant challenge due to the inherent stochasticity of QD nucleation processes. In this work, we theoretically and numerically demonstrate that deterministic QD nucleation within a pristine growth region, e.g., InAs on a (001)-oriented GaAs substrate, can be achieved by engineering the boundary geometry of that region. During epitaxial growth, adatoms initially move toward the boundary and promote the formation of primary QDs along the boundary, driven by curvature and diffusion anisotropy. The resulting primary QDs distribution will generate many-body interactions that dynamically reshape the chemical potential landscape for subsequently deposited adatoms, enabling the formation of secondary QDs within the pristine growth region. These findings provide a theoretical foundation for reliable patterning of high optical-quality QDs, with potential applications in next-generation quantum photonic devices.

The steep computational cost of diffusion models at inference hinders their use as fast physics emulators. In the context of image and video generation, this computational drawback has been addressed by generating in the latent space of an autoencoder instead of the pixel space. In this work, we investigate whether a similar strategy can be effectively applied to the emulation of dynamical systems and at what cost. We find that the accuracy of latent-space emulation is surprisingly robust to a wide range of compression rates (up to 1000x). We also show that diffusion-based emulators are consistently more accurate than non-generative counterparts and compensate for uncertainty in their predictions with greater diversity. Finally, we cover practical design choices, spanning from architectures to optimizers, that we found critical to train latent-space emulators.

Quantum gases of ultracold polar molecules have novel properties because of the strong dipolar forces between molecules. Current experiments shield the molecules from destructive collisions by engineering long-range repulsive interactions using microwave or static electric fields. These shielding methods produce interaction potentials with large repulsive cores that are not well described with contact potentials. In this paper we explore the anisotropic interaction potentials that arise for pairs of polar molecules shielded with static electric fields. We derive computationally inexpensive approximations for the potentials that are suitable for use in calculations of many-body properties. The interaction potentials for molecules shielded with static fields are substantially different from those that arise from microwave shielding and will produce quite different many-body physics.

We consider a quantum algorithm for ground-state preparation based on a Chebyshev series approximation to the wall function. In a classical setting, this approach is appealing as it guarantees rapid convergence. We analyze the asymptotic scaling and success probabilities of different quantum implementations and provide numerical benchmarks, comparing the performance of the wall-Chebyshev projectors with current state-of-the-art approachs. We find that this approach requires fewer serial applications of the Hamiltonian oracle to achieve a given ground state fidelity, but is severely limited by exponentially decaying success probability. However, we find that some implementations maintain non-trivial success probability in regimes where wall-Chebyshev projection leads to a fidelity improvement over other approaches. As the wall-Chebyshev projector is highly robust to loose known upper bounds on the true ground state energy, it offers a potential resource trade-off, particulary in the early fault-tolerant regime of quantum computation.

Fast and reliable validation of novel designs in complex physical systems such as batteries is critical to accelerating technological innovation. However, battery research and development remain bottlenecked by the prohibitively high time and energy costs required to evaluate numerous new design candidates, particularly in battery prototyping and life testing. Despite recent progress in data-driven battery lifetime prediction, existing methods require labeled data of target designs to improve accuracy and cannot make reliable predictions until after prototyping, thus falling far short of the efficiency needed to enable rapid feedback for battery design. Here, we introduce Discovery Learning (DL), a scientific machine-learning paradigm that integrates active learning, physics-guided learning, and zero-shot learning into a human-like reasoning loop, drawing inspiration from learning theories in educational psychology. DL can learn from historical battery designs and actively reduce the need for prototyping, thus enabling rapid lifetime evaluation for unobserved material-design combinations without requiring additional data labeling. To test DL, we present 123 industrial-grade large-format lithium-ion pouch cells, spanning eight material-design combinations and diverse cycling protocols. Trained solely on public datasets of small-capacity cylindrical cells, DL achieves 7.2% test error in predicting the average cycle life under unknown device variability. This results in savings of 98% in time and 95% in energy compared to industrial practices. This work highlights the potential of uncovering insights from historical designs to inform and accelerate the development of next-generation battery technologies. DL represents a key advance toward efficient data-driven modeling and helps realize the promise of machine learning for accelerating scientific discovery and engineering innovation.

Hardware-based security primitives have become critical to enhancing information security in the Internet of Things (IoT) era. Physical unclonable functions (PUFs) utilize the inherent variations in the manufacturing process to generate cryptographic keys unique to a device. Reconfigurable PUFs (rPUFs) can update cryptographic keys for enhanced security in dynamic operational scenarios involving huge amounts of data, which makes them suitable for implementation in CMOS-integrated spin-orbit torque magnetic random access memory (SOT-MRAM) chips. However, a key challenge is achieving real-time reconfiguration independent of the environmental conditions, particularly the operating temperature. We propose a dual-pulse reconfiguration strategy for rPUFs in CMOS-integrated SOT-MRAM chips that effectively widens the operating window and achieves resilience across a wide range of operating temperatures without the need for dynamic feedback that overly complicates circuit design. The proposed strategy lays a solid foundation for the next generation of hardware-based security primitives to protect IoT architectures.

In 1959, Kolmogorov proposed to study the instability of the shear flow $(\sin(y),0)$ in the vanishing viscosity regime in tori $\mathbb{T}_{\alpha}\times \mathbb{T}$. This question was later resolved by Meshalkin and Sinai. We extend the problem to general shear flows $(U(y),0)$ and show that every $U(y)$ exhibits long-wave instability whenever $\|\partial_y^{-1} U\|_{L^2} > \nu$ and $\alpha\ll \nu$, with $\nu$ being the kinematic viscosity. This instability mechanism confirms previous findings by Yudovich in 1966, supported also by several numerical results, and is established through two independent approaches: one via the construction of Kato's isomorphism and one via normal forms. Unlike in many other applications of the latter methods, both proofs deal with the presence of a delicate term in the linearized operator that becomes singular as $\alpha$ approaches $0$.

Global Station Weather Forecasting (GSWF) is a key meteorological research area, critical to energy, aviation, and agriculture. Existing time series forecasting methods often ignore or unidirectionally model spatial correlation when conducting large-scale global station forecasting. This contradicts the intrinsic nature underlying observations of the global weather system, limiting forecast performance. To address this, we propose a novel Spatial Structured Attention Block in this paper. It partitions the spatial graph into a set of subgraphs and instantiates Intra-subgraph Attention to learn local spatial correlation within each subgraph, and aggregates nodes into subgraph representations for message passing among the subgraphs via Inter-subgraph Attention -- considering both spatial proximity and global correlation. Building on this block, we develop a multiscale spatiotemporal forecasting model S$^2$Transformer by progressively expanding subgraph scales. The resulting model is both scalable and able to produce structured spatial correlation, and meanwhile, it is easy to implement. The experimental results show that it can achieve performance improvements up to 16.8% over time series forecasting baselines at low running costs.

Hidden interactions and components in complex systems-ranging from covert actors in terrorist networks to unobserved brain regions and molecular regulators-often manifest only through indirect behavioral signals. Inferring the underlying network structure from such partial observations remains a fundamental challenge, particularly under nonlinear dynamics. We uncover a robust linear relationship between the spectral strength of a node's behavioral time series under evolutionary game dynamics and its structural degree, $S \propto k$, a structural-behavioral scaling that holds across network types and scales, revealing a universal correspondence between local connectivity and dynamic energy. Leveraging this insight, we develop a deterministic, frequency-domain inference framework based on the discrete Fourier transform (DFT) that reconstructs network topology directly from payoff sequences-without prior knowledge of the network or internal node strategies-by selectively perturbing node dynamics. The framework simultaneously localizes individual hidden nodes or identifies all edges connected to multiple hidden nodes, and estimates tight bounds on the number of hidden nodes. Extensive experiments on synthetic and real-world networks demonstrate that our method consistently outperforms state-of-the-art baselines in both topology reconstruction and hidden component detection. Moreover, it scales efficiently to large networks, offering robustness to stochastic fluctuations and overcoming the size limitations of existing techniques. Our work establishes a principled connection between local dynamic observables and global structural inference, enabling accurate topology recovery in complex systems with hidden elements.