The assessment of the origin of the anthropogenic contamination in marine regions impacted by other sources than global fallout is a challenge. This is the case of the west coast of Sweden, influenced by the liquid effluents released by the European Nuclear Reprocessing Plants through North Sea currents and by Baltic Sea local and regional sources, among others. This work focused on the study of anthropogenic actinides (${}^{236}$U, ${}^{237}$Np and ${}^{239,240}$Pu) in seawater and biota from a region close to Gothenburg where radioactive wastes with an unknown composition were dumped in 1964. To this aim, a radiochemical procedure for the sequential extraction of U, Np and Pu from biota samples and the subsequent analysis of ${}^{236}$U, ${}^{237}$Np, ${}^{239}$Pu and ${}^{240}$Pu by Accelerator Mass Spectrometry was developed. The method was validated through the study of two reference materials provided by the International Atomic Energy Agency (IAEA): IAEA-446 (Baltic Sea seaweed) and IAEA-437 (Mediterranean Sea mussels). The ${}^{233}$U$/{}^{236}$U atom ratio was also studied in the seawater samples. The obtained results indicate that the North Sea currents and global fallout are the major sources for ${}^{236}$U, ${}^{237}$Np and ${}^{239,240}$Pu to the studied area, with any clear evidence of other local sources. Complementary, information on the Concentrations Factors (CF) in biota was obtained, for which the available information is very scarce. For seaweed, CF values of $(4.1 \pm 1.1) \times 10^{3}$, $88 \pm 51$ and $70 \pm 20$ have been obtained for Pu, Np and U, respectively. Lower CF values of $(3.6 \pm 1.2) \times 10^{2}$, $37 \pm 15$ and $15.1 \pm 5.2$ for Pu, Np and U, respectively, have been obtained for mussels.

Rapid and accurate estimation of post-earthquake ground failures and building damage is critical for effective post-disaster responses. Progression in remote sensing technologies has paved the way for rapid acquisition of detailed, localized data, enabling swift hazard estimation through analysis of correlation deviations between pre- and post-quake satellite imagery. However, discerning seismic hazards and their impacts is challenged by overlapping satellite signals from ground failures, building damage, and environmental noise. Previous advancements introduced a novel causal graph-based Bayesian network that continually refines seismic ground failure and building damage estimates derived from satellite imagery, accounting for the intricate interplay among geospatial elements, seismic activity, ground failures, building structures, damages, and satellite data. However, this model's neglect of spatial heterogeneity across different locations in a seismic region limits its precision in capturing the spatial diversity of seismic effects. In this study, we pioneer an approach that accounts for spatial intricacies by introducing a spatial variable influenced by the bilateral filter to capture relationships from surrounding hazards. The bilateral filter considers both spatial proximity of neighboring hazards and their ground shaking intensity values, ensuring refined modeling of spatial relationships. This integration achieves a balance between site-specific characteristics and spatial tendencies, offering a comprehensive representation of the post-disaster landscape. Our model, tested across multiple earthquake events, demonstrates significant improvements in capturing spatial heterogeneity in seismic hazard estimation. The results highlight enhanced accuracy and efficiency in post-earthquake large-scale multi-impact estimation, effectively informing rapid disaster responses.

TomoATT is an open-source software package, aiming at determining seismic velocity and azimuthal anisotropy based on adjoint-state traveltime tomography methods. Key features of TomoATT include Eikonal equation modeling, adjoint-state method, sensitivity kernel regularization, and multi-level parallelization. Through several toy experiments, we demonstrate TomoATT's capability in accurate forward modeling, handling multipathing phenomenon, delivering reliable tomographic results, and achieving high-performance parallelization. Additionally, TomoATT is benchmarked with a synthetic experiment and two real-data applications in central California near Parkfield and Thailand. The successful recovery of the synthetic model, along with the imaging results that are consistent with previous studies and regional tectonics, verifies the effectiveness of TomoATT. Each inversion starts with only three simple input files (about model, data, and parameters) and completes within 2 hours using 64 processors. Overall, TomoATT offers an efficient and user-friendly tool for regional and teleseismic traveltime tomography, empowering researchers to image subsurface structures and deepen our understanding of the Earth's interior.

The integration of non-commutative geometry and Gauss-Bonnet corrections in an action and the study of their black hole responses can provide highly intriguing insights. Our primary motivation for this study is to understand the interplay of these two parameters on the geodesics of spacetime, including photon spheres and time-like orbits. In this study, we found that this integration, in its initial form, can limit the value of the Gauss-Bonnet parameter ($\alpha$), creating a critical threshold beyond which changes in the non-commutative parameter ($\Xi$) become ineffective, and the structure can only manifest as a naked singularity. Furthermore, we found that using a more complex model, which includes additional factors such as a cloud of strings and linear charge, as a sample for studying spacetime geodesics, yield different and varied results. In this scenario, negative $\alpha$ values can also play a role, notably preserving the black hole form even with a super-extremal charge ($q > m$). For $\alpha> 0.1$, the black hole mass parameter becomes significantly influential, with a critical mass below which the impact of other parameter changes is nullified. Interestingly, considering a more massive black hole, this high-mass state also maintains its black hole form within the super-extremal charge range. The existence of these two models led us to our main goal. By examining the temperature for these two cases, we find that both situations are suitable for studying the WGC. Finally, based on the behavior of these two models, we will explain how the WGC acts as a logical solution and a protector for the WCCC.

The p-type point-contact germanium (pPCGe) detectors have been widely adopted in searches for low energy physics events such as neutrinos and dark matter. This is due to their enhanced capabilities of background rejection, sensitivity at energies as low as the sub-keV range and particularly fine energy resolution. Nonetheless, the pPCGe is subject to irregular behaviour caused by surface effects for events near the passivated surface. These surface events can, in general, be distinguished from events that occur in the germanium crystal bulk by its slower pulse rise time. Unfortunately, the rise-time spectra of bulk and surface events starts to convolve with each other at sub-keV energies. In this work, we propose a novel method based on cross-correlation shape-matching combined with a low-pass filter to constrain the initial parameter estimates of the signal pulse. This improvement at the lowest level leads to a 50% reduction in computation time and refinements in the rise-time resolution, which will, in the end, enhance the overall analysis. To evaluate the performance of the method, we simulate artificial pulses that resembles bulk and surface pulses by using a programmable pulse generator module (pulser). The pulser-generated pulses are then used to examine the pulse behaviours at near-threshold energies, suggesting a roughly 70% background-leakage reduction in the bulk spectrum. Finally, the method is tested on data collected from the TEXONO experiment, where the results are consistent with our observations in pulser and demonstrated the possibility of lowering the analysis threshold by at least 10eV.

The resolution of low-frequency resistance noise measurements is increased by amplitude modulation, shifting the spectrum of the resistance fluctuations away from the 1/f noise contributed by measurement instruments. However, commercial lock-in amplifiers used for de-modulating the fluctuations exhibit a problematic 1/f noise contribution, which imposes a hard lower limit on the relative resistance noise that can be detected. We replace the lock-in amplifier hardware by equivalent digital signal processing performed using open-source software and inexpensive data acquisition systems. Our solution offers superior low-frequency noise performance with a reduction of the voltage power spectral density by about two orders of magnitude compared to high-end commercial instruments.

We use experiment-supported dimensional analysis to further bolster our arguments that crucial information on the emergence and/or nature of space could be extracted from the combination of the properties of gravitational and strong interactions.

Manipulating particles, such as cells and tissues, in a flowing liquid environment is crucial for life science research. Traditional contactless tweezers, although widely used for single-cell manipulation, face several challenges. These include potential damage to the target, restriction to static environments, complex excitation setups, and interference outside the target area. To address these issues, we propose an ``invisible hydrodynamic tweezer'' utilizing near-zero index hydrodynamic metamaterials. This metamaterial-based device creates an equipotential resistance zone, effectively immobilizing particles in flowing fluids without disturbing the external flow field and without causing damage to the targets. Unlike traditional active control methods, our tweezer passively captures and releases particles by adjusting the flow channel, eliminating the need for continuous and stable excitation devices, thereby significantly simplifying the setup complexity. Furthermore, these tweezers can be modularly designed in different sizes to flexibly accommodate various application needs. Simulations and experimental validations demonstrated the non-interfering, stable trapping, and precise movement capabilities of these tweezers. This proposed technique holds significant potential for applications in biomedicine, microfluidics, and environmental monitoring.

The results of the analysis of solid-state track detectors CR39 and nuclear photoemulsion plates irradiated in beams of accelerated xenon ions with energies of 3.2 MeV/n and 3.8 GeV/n at the NICA accelerator complex are presented.

Achieving high-throughput, comprehensive analysis of single nanoparticles to determine their size, shape, and composition is essential for understanding particle heterogeneity with applications ranging from drug delivery to environmental monitoring. Existing techniques are hindered by low throughput, lengthy trapping times, irreversible particle adsorption, or limited characterization capabilities. Here, we introduce Interferometric Electrohydrodynamic Tweezers (IET), an integrated platform that rapidly traps single nanoparticles in parallel within three seconds. IET enables label-free characterization of particle size and shape via interferometric imaging and identifies molecular composition through Raman spectroscopy, all without the need for fluorescent labeling. We demonstrate the platform's capabilities by trapping and imaging colloidal polymer beads, nanoscale extracellular vesicles (EVs), and newly discovered extracellular nanoparticles known as supermeres. By monitoring their interferometric contrast images while trapped, we accurately determine the sizes of EVs and supermeres. Our IET represents a powerful optofluidics platform for comprehensive characterization of nanoscale objects, opening new avenues in nanomedicine, environmental monitoring, and beyond.

The prospects of using last version of a compact supermirror transmission polarizer TRUNPOSS on silicon in modern neutron research facilities of the instrumental base being created for the PIK reactor (PNPI, Gatchina, Russia) will discuss in the paper. The results of calculations of the parameters and main characteristics of this polarizer for using it in IN2, IN3, SEM, DEDM, TENZOR facilities are discussed in detail.

Turbulence grounds aircraft and combating it in flight requires energy, yet volant wildlife fly effortlessly even on windy days. The nature of the interactions between soaring birds and transient turbulent gusts is not clear, especially when compared with our understanding of flight in larger and steadier airflows during thermal or dynamic soaring. We show that soaring golden eagles (Aquila chrysaetos) experienced short upward accelerations indicative of preferential engagement with strong and intermittent turbulent updrafts. The vertical accelerations reflect changes in lift that were as large as 25 standard deviations from the mean, or more than three times the acceleration of gravity, and so large as not to be consistent with gust mitigation or avoidance. These extreme events occurred in short bursts that mimic movement with turbulent vortices. The burst statistics and their symmetries approach those of turbulence toward longer timescales. On the shortest timescales, the bursts break the symmetry of small-scale turbulence in favor of upward accelerations that are more intermittent than turbulence. We introduce a simple nonlinear model that predicts the scale at which symmetry breaks and the stronger intermittency on the smaller scales. These findings suggest a ratcheting mechanism on turbulent gusts and constitute the first quantitative evidence in favor of turbulent gust harvesting by wildlife. An implications is that turbulence is so strong and pervasive as to make unsteady and nonlinear aerodynamics an intrinsic and beneficial aspect of both flapping and soaring flight in the atmospheric boundary layer - one that we need to incorporate in our understanding of the energetics of flight.

Shock tubes are instrumental in studying high-temperature kinetics and simulating high-speed flows. They swiftly elevate the thermodynamic conditions of test gases, making them ideal for examining rapid chemical reactions and generating high-enthalpy flows for aerodynamic research. However, non-ideal effects, stemming from factors like diaphragm opening processes and viscous effects, can significantly influence thermodynamic conditions behind the shock wave. This study investigates the impact of various diaphragm opening patterns on the shock parameters near the driven section end-wall. Experiments were conducted using helium and argon as driver and driven gases, respectively, at pressures ranging from 1.32 to 2.09 bar and temperatures from 1073 to 2126 K behind the reflected shock. High-speed imaging captured different diaphragm rupture profiles, classified into four distinct types based on their dynamics. Results indicate that the initial stages of diaphragm opening, including the rate and profile of opening, play crucial roles in resulting incident shock Mach number and test time. A sigmoid function was employed to fit the diaphragm opening profiles, allowing for accurate categorization and analysis. New correlations were developed to predict the incident shock attenuation rate and post-shock pressure rise, incorporating parameters such as diaphragm opening time, rupture profile constants, and normalized experimental Mach number. The results emphasize the importance of considering diaphragm rupture dynamics in shock tube experiments to achieve accurate predictions of shock parameters.

The stochastic dynamics of small elastic objects in fluid are central to many important and emerging technologies. It is now possible to measure and use the higher modes of motion of elastic structures when driven by Brownian motion alone. Although theoretical descriptions exist for idealized conditions, computing the stochastic multimodal dynamics for the complex conditions of experiment is very challenging. We show that this is possible using deterministic finite element calculations with the fluctuation dissipation theorem by exploring the multimodal stochastic dynamics of a doubly-clamped nanobeam. We use a very general, and flexible, finite-element computational approach to quantify the stochastic dynamics of multiple modes simultaneously using only a single deterministic simulation. We include the experimentally relevant features of an intrinsic tension in the beam and the influence of a nearby rigid boundary on the dynamics through viscous fluid interactions. We quantify the stochastic dynamics of the first eleven flexural modes of the beam when immersed in air or water. We compare the numerical results with theory, where possible, and find excellent agreement. We quantify the limitations of the computational approach and describe its range of applicability. These results pave the way for computational studies of the stochastic dynamics of complex 3D elastic structures in a viscous fluid where theoretical descriptions are not available.

Earthquake early warning systems are crucial for protecting areas that are subject to these natural disasters. An essential part of these systems is the detection procedure. Traditionally these systems work with seismograph data, but high rate GNSS data has become a promising alternative for the usage in large earthquake early warning systems. Besides traditional methods, deep learning approaches have gained recent popularity in this field, as they are able to leverage the large amounts of real and synthetic seismic data. Nevertheless, the usage of deep learning on GNSS data remains a comparatively new topic. This work contributes to the field of early warning systems by proposing an autoencoder based deep learning pipeline that aims to be lightweight and customizable for the detection of anomalies viz. high magnitude earthquakes in GNSS data. This model, DetEQ, is trained using the noise data recordings from nine stations located in Chile. The detection pipeline encompasses: (i) the generation of an anomaly score using the ground truth and reconstructed output from the autoencoder, (ii) the detection of relevant seismic events through an appropriate threshold, and (iii) the filtering of local events, that would lead to false positives. Robustness of the model was tested on the HR GNSS real data of 2011 Mw 6.8 Concepcion earthquake recorded at six stations. The results highlight the potential of GNSS based deep learning models for effective earthquake detection.

Designing magnets for three-dimensional plasma confinement is a key task for advancing the stellarator as a fusion reactor concept. Stellarator magnets must produce an accurate field while leaving adequate room for other components and being reasonably simple to construct and assemble. In this paper, a framework for coil design and optimization is introduced that enables the attainment of sparse magnet solutions with arbitrary restrictions on where coils may be located. The solution space is formulated as a "wireframe" consisting of a mesh of interconnected wire segments enclosing the plasma. Two methods are developed for optimizing the current distribution on a wireframe: Regularized Constrained Least Squares (RCLS), which uses a linear least-squares approach to optimize the currents in each segment, and Greedy Stellarator Coil Optimization (GSCO), a fully discrete procedure in which loops of current are added to the mesh one by one to achieve the desired magnetic field on the plasma boundary. Examples will be presented of solutions obtainable with each method, some of which are able to achieve high field accuracy while obeying spatial constraints that permit easy assembly.

We report on experimental investigation of potential high-performance cavity length stabilization using odd-indexed higher-order spatial modes. Schemes based on higher-order modes are particularly useful for micro-cavities that are used for enhanced fluorescence detection of a few emitters, which need to minimize photons leaking from a stabilization beam. We describe the design and construction of an assembly for a microcavity setup with tunable high passive stability. In addition, different types of active stabilization techniques based on higher-order modes, are then implemented and characterized based on their performance. We achieved a stability of about 0.5 pm RMS, while the error photons leaking from the continuous locking beam to a fluorescence detector are suppressed by more than 100-fold. We expect these results to be important for quantum technology implementations of various emitter-cavity setups, where these techniques provide a useful tool to meet the highly challenging demands.

A structure of a laser pulse may significantly influence the dynamics of interacting particles. In the case of dilute plasma the particle dynamics may be considered in the single particle approximation. In this paper the problem of the angular momentum gain of a single particle in a focused structured pulse is considered for some certain cases, including radial and azimuthal polarizations, in the frameworks of the theoretical model, presented in [E. Dmitriev and Ph. Korneev, 'Angular momentum gain by electrons under the action of intense structured light', Physical Review A, vol. 110, p. 013514, (2024)], which includes solving of Maxwell's equations with a required accuracy and the high order perturbation theory. The obtained analytical results are compared to the results of numerical simulations. Angular dependence in the structure of the laser pulse is shown to be critically important for the orbital angular momentum gain of an ensemble of charged particles in a structured wave.

Young's modulus is an important parameter for characterizing the strength of, and wave propagation through, a given material. This study improves the estimation of Young's modulus using the impulse excitation (IE) technique based on an experimental analysis of 19 borosilicate glass bars. Analysis of the frequency equations relating Young's modulus to the out of plane and in plane flexural resonant frequencies of rectangular prisms has been conducted for both the fundamental frequency and its overtones at higher orders of vibration. The Young's modulus of three novaculite rocks with various porosities were then measured up to the seventh order of vibration to validate the optimum shear constant equation for estimating Young's modulus. Young's modulus was found to be nearly frequency independent for these rock samples.

The Monte Carlo differential operator sampling method is applied to the computation of sensitivity coefficients of unresolved resonance probability table cross sections. Three new analytical benchmarks for verifying unresolved resonance treatments and sensitivity coefficient computations are developed. The method and its research-code implementation are verified against these benchmarks and agreement is observed. Numerical results for unresolved resonance sensitivity coefficients are obtained for the Big Ten benchmark and a simplified Molten Chloride Fast Reactor model. Energy-integrated eigenvalue sensitivity coefficients for the unresolved resonance range agree with MCNP6.2 calculations of these two models.

The effects leading to generation of quasi-stationary and propagating electromagnetic fields during the propagation of a laser-driven current discharge pulse in extended targets are considered. The results of numerical modeling describe the interaction of a relativistically intense ultrashort laser pulse with an extended dense target and its transformation into a powerful discharge current pulse. It is shown that under certain conditions such a pulse can be used to generate strong electromagnetic fields with certain spatio-temporal properties. Particular attention is paid to the influence of the discharge pulse parameters on the properties of the induced electromagnetic fields. Analysis of the discharge current profile in dependence on the target thickness is provided and the dynamics of discharge current pulse profile evolution is studied on the time scale $\sim 10$ ps, when the propagation distance is $\sim 3$ mm.

We present high-resolution rovibronic spectroscopy of calcium monohydride molecular ions (CaH$^+$) co-trapped in a Coulomb crystal with calcium ions ($^{40}$Ca$^+$), focusing on rotational transitions in the $|X^1\Sigma^+, \nu" = 0> \rightarrow |A^1\Sigma^+, \nu' = 2>$ manifold. By resolving individual P and R branch transitions with record precision and using Fortrat analysis, we extract key spectroscopic constants for the excited state $|A^1\Sigma^+, \nu' = 2>$, specifically, the band origin, the rotational constant, and the centrifugal correction. Additionally, we demonstrate the application of high-resolution rotational spectroscopy of CaH$^+$ presented here as an in-situ probe of local environmental temperature. We correlate the relative amplitudes of the observed transitions to the underlying thermalized ground-state rotational population distribution and extract the black-body radiation (BBR) temperature.

Basis set incompleteness error (BSIE) is a common source of error in quantum chemistry (QC) calculations, but it has not been comprehensively studied in fixed-node Diffusion Monte Carlo (FN-DMC) calculations. FN-DMC, being a projection method, is often considered minimally affected by basis set biases. Here, we show that this assumption is not always valid. While the relative error introduced by a small basis set in the total FN-DMC energy is minor, it can become significant in binding energy ($E_{\rm b}$) evaluations of weakly interacting systems. We systematically investigated BSIEs in FN-DMC-based binding energy ($E_{\rm b}$) evaluations using the A24 dataset, a well-known benchmark set of 24 non-covalently bound dimers. Contrary to common expectations, we found that BSIEs in FN-DMC evaluations of $E_{\rm b}$ are indeed significant when small localized basis sets, such as cc-pVDZ, are employed. We observed that BSIEs are larger in dimers with hydrogen-bonding interactions and smaller in dispersion-dominated interactions. We also found that augmenting the basis sets with diffuse orbitals, using counterpoise (CP) correction, or both, effectively mitigates BSIEs.

Graphitic carbon nitride ($g$-CN) has attracted vast interest as a promising inexpensive metal-free photocatalyst for water splitting with solar photons. The heptazine (Hz) molecule is the building block of graphitic carbon nitride. The photochemistry of the Hz molecule and derivatives thereof in protic environments has been the subject of several recent experimental and computational studies. In the present work, the hydrogen-bonded Hz$\cdots$H$_2$O complex was adopted as a model system for the exploration of photoinduced electron and proton transfer processes in this complex with quasi-classical nonadiabatic trajectory simulations, using the $ab$ $initio$ ADC(2) electronic-structure method and a computationally efficient surface-hopping algorithm. The population of the optically excited bright $^1\pi\pi^*$ state of the Hz chromophore relaxes through three $^1n\pi^*$ states and a low-lying charge-transfer state, which drives proton transfer from H$_2$O to Hz, to the long-lived optically dark S$_1$($\pi\pi^*$) state of Hz. The imaging of this ultrafast and complex dynamics with femtosecond time-resolved transient absorption (TA) pump-probe (PP) spectroscopy and two-dimensional (2D) electronic spectroscopy (ES) was computationally explored in the framework of the quasi-classical doorway-window approximation. By comparison of the spectra of the Hz$\cdots$H$_2$O complex with those of the free Hz molecule, the effects of the hydrogen bond on the ultrafast internal conversion dynamics can be identified in the spectroscopic signals. Albeit the TA PP and 2D ES spectroscopies are primarily sensitive to electronic excited-state dynamics and less so to proton transfer dynamics, they nevertheless can provide mechanistic insights which can contribute to the acceleration of the optimization of photocatalysts for water splitting.

The International Muon Collider Collaboration (IMCC) is engaged in a design study for a future facility intended to collide muons. Subsequent to the initial linear acceleration, the counter-rotating muons and anti-muons are accelerated in a chain of rapid cycling synchrotrons (RCS) up to the multi-TeV collision energy. To maximise the number of muons available in the collider, it is essential to exploit the time dilation of the muon lifetime by employing a large accelerating gradient. The 1.3 GHz TESLA cavity serves as the baseline for the RCS chain. Considering the high bunch population and the small aperture of the cavity, the resulting beam-induced voltage per bunch passage is considerable, resulting in a substantial perturbation of the cavity voltage for subsequent bunch passages. In this contribution, the effects of beam loading during the acceleration cycle on the muons are calculated with the objective of determining the optimum parameters for minimising the cavity voltage transients. The interaction of the induced voltages, considering the counter-rotating beams, is studied.

Most widely used machine learned (ML) potentials for condensed phase applications rely on many-body permutationally invariant polynomial (PIP) or atom-centered neural networks (NN). However, these approaches often lack chemical interpretability in atomistic energy decomposition and the computational efficiency of traditional force fields has not been fully achieved. Here, we present a novel method that combines aspects of both approaches, and achieves state-of-the-art balance of accuracy and force field-level speed. This method utilizes a monomer-centered representation, where the potential energy is decomposed into the sum of chemically meaningful monomeric energies. Without sophisticated neural network design, the structural descriptors of monomers are described by 1-body and 2-body effective interactions, enforced by appropriate sets of PIPs as inputs to the feed forward NN. We demonstrate the performance of this method through systematic assessments of models for gas-phase water trimer, liquid water, and also liquid CO2. The high accuracy, fast speed, and flexibility of this method provide a new route for constructing accurate ML potentials and enabling large-scale quantum and classical simulations for complex molecular systems.

We present a method for engineering harmonic frequency combs (HFCs) in the terahertz spectral region. This approach involves interfacing a quantum cascade laser (QCL) with an external reflector featuring frequency-dependent reflectivity. A notable advantage of this method over existing ones is its dual functionality in shaping HFCs, allowing for control over both the frequency offset and comb spacing based on the external reflectivity profile. Moreover, the resulting HFCs manifest as sequences of short pulses in the time domain. Consequently, our method enables the generation of ultrashort picosecond pulses passively, providing a distinct alternative to conventional pulse generation systems reliant on active bias current modulation, which struggle with modulation frequencies significantly higher than the first beatnote. This offers intriguing prospects for utilizing HFCs in pump and probe spectroscopy, a field already recognized in the literature as one of the most compelling applications of these states. Furthermore, we demonstrate that these HFCs can be triggered from an initial condition of free-running unlocked dynamics, eliminating the need for assuming free-running comb emission. Thus, the utilization of an external, frequency-dependent reflector is capable of enhancing the coherence of the QCL emission.

Liquid composites moulding is an important manufacturing technology for fibre reinforced composites, due to its cost-effectiveness. Challenges lie in the optimisation of the process due to the lack of understanding of key characteristic of textile fabrics - permeability. The problem of computing the permeability coefficient can be modelled as the well-known Stokes-Brinkman equation, which introduces a heterogeneous parameter $\beta$ distinguishing macropore regions and fibre-bundle regions. In the present work, we train a Fourier neural operator to learn the nonlinear map from the heterogeneous coefficient $\beta$ to the velocity field $u$, and recover the corresponding macroscopic permeability $K$. This is a challenging inverse problem since both the input and output fields span several order of magnitudes, we introduce different regularization techniques for the loss function and perform a quantitative comparison between them.

Simulating complex hydraulic conditions, particularly two-phase flows over spillway chutes, can be achieved with high accuracy using three-dimensional numerical models. This study investigates the potential for vacuum generation and cavitation phenomena on the Aghchai Dam service spillway through numerical simulations conducted in Flow-3D. The analysis focuses on two specific flow rates, 4400 and 1065 cubic meters per second, as determined by experimental data. The Volume of Fluid (VOF) method is employed to accurately calculate the free surface flow. Simulation results at a discharge rate of 4400 cubic meters per second indicate a high likelihood of cavitation at critical locations, including the ogee curve and the angle transition in the chute channel. These areas require specific mitigation measures to prevent cavitation-induced damage. In contrast, at the lower flow rate of 1065 cubic meters per second, the risk of cavitation is minimal due to reduced flow velocity and the absence of flow separation from the bed. The numerical findings align closely with empirical observations, demonstrating the reliability of the simulation approach in predicting cavitation behavior.

Densely arranged optical vortices are natural solutions of high-symmetry Bloch modes in photonic crystals. However, strict symmetry constraints limit the potential spatial configurations of nearfield vortices, restricting the control over light-matter interaction. Here, we demonstrate a nearfield vortex dynamic within a supercell photonic crystal. By introducing paired rotations of triangular structures, we achieve high-quality-factor Bloch mode transition from evanescent valley modes, to quasi-bound states in the continuum, frustrated modes, and quasi-valleys. Each stage exhibits distinct nearfield vortex distributions, nonlinear overlap properties, and quality factors, revealing diverse physical behaviors for tailoring light-matter interaction. Notably, the asymmetric vortex configuration of frustrated modes enhances second harmonic generation, driven by an optimized nonlinear overlap factor. Our paired-rotation strategy offers a versatile design framework for creating supercell photonic crystals with unique nearfield vortex properties, presenting promising applications in lasing, nonlinear optics and optical forces.

We propose a one-dimensional mixing model to investigate the impact of Ablative Rayleigh-Taylor Instability (ARTI) on compression, addressing the limitations of high-dimensional simulations. In this model, the scale of the mixed region is predicted using an ablative buoyancy drag model and the mass-weighted average field distributions in the direction of acceleration are obtained by interpolating between the initial and final isothermal molecule mixing states based on the mixing extent, defined by the percentage of maximum specific turbulent kinetic energy and a free multiplier f, which varies from chunk mixing (0) to atomic mixing (1). After validation through two-dimensional simulations, the model is integrated into a Lagrangian framework and applied to spherical implosion scenarios. The results indicate that the compression of the inflight shell is diminished, leading to a reduced time interval between the convergence of the main shock and stagnation, potentially providing a measurable signal in single-shot experiments.

A Mesoscale Convective System (MCS) is a collection of thunderstorms that function as a system, representing a widely discussed phenomenon in both the natural sciences and visual effects industries, and embodying the untamed forces of nature.In this paper, we present the first interactive, physically inspired mesoscale thunderstorms simulation model that integrates Grabowski-style cloud microphysics with atmospheric electrification processes. Our model simulates thunderclouds development and lightning flashes within a unified meteorological framework, providing a realistic and interactive approach for graphical applications. By incorporating key physical principles, it effectively links cloud formation, electrification, and lightning generation. The simulation also encompasses various thunderstorm types and their corresponding lightning activities.

Photoacoustic imaging (PAI) suffers from inherent limitations that can degrade the quality of reconstructed results, such as noise, artifacts, and incomplete data acquisition caused by sparse sampling or partial array detection. In this study, we proposed a new optimization method for both two-dimensional (2D) and three-dimensional (3D) PAI reconstruction results, called regularized iteration method with shape prior. The shape prior is a probability matrix derived from the reconstruction results of multiple sets of random partial array signals in a computational imaging system using any reconstruction algorithm, such as Delay-and-Sum (DAS) and Back-Projection (BP). In the probability matrix, high-probability locations indicate high consistency among multiple reconstruction results at those positions, suggesting a high likelihood of representing the true imaging results. In contrast, low-probability locations indicate higher randomness, leaning more towards noise or artifacts. As a shape prior, this probability matrix guides the iteration and regularization of the entire array signal reconstruction results using the original reconstruction algorithm (the same algorithm for processing random partial array signals). The method takes advantage of the property that the similarity of the object to be imitated is higher than that of noise or artifact in the results reconstructed by multiple sets of random partial array signals of the entire imaging system. The probability matrix is taken as a prerequisite for improving the original reconstruction results, and the optimizer is used to further iterate the imaging results to remove noise and artifacts and improve the imaging fidelity. Especially in the case involving sparse view which brings more artifacts, the effect is remarkable. Simulation and real experiments have both demonstrated the superiority of this method.

Well log analysis is crucial for hydrocarbon exploration, providing detailed insights into subsurface geological formations. However, gaps and inaccuracies in well log data, often due to equipment limitations, operational challenges, and harsh subsurface conditions, can introduce significant uncertainties in reservoir evaluation. Addressing these challenges requires effective methods for both synthetic data generation and precise imputation of missing data, ensuring data completeness and reliability. This study introduces a novel framework utilizing sequence-based generative adversarial networks (GANs) specifically designed for well log data generation and imputation. The framework integrates two distinct sequence-based GAN models: Time Series GAN (TSGAN) for generating synthetic well log data and Sequence GAN (SeqGAN) for imputing missing data. Both models were tested on a dataset from the North Sea, Netherlands region, focusing on different sections of 5, 10, and 50 data points. Experimental results demonstrate that this approach achieves superior accuracy in filling data gaps compared to other deep learning models for spatial series analysis. The method yielded R^2 values of 0.921, 0.899, and 0.594, with corresponding mean absolute percentage error (MAPE) values of 8.320, 0.005, and 151.154, and mean absolute error (MAE) values of 0.012, 0.005, and 0.032, respectively. These results set a new benchmark for data integrity and utility in geosciences, particularly in well log data analysis.

A simulation workflow has been developed to study dark current (DC) radiation effects using ACE3P and Geant4. The integrated workflow interfaces particle data transfer and geometry between the electromagnetic (EM) cavity simulation code ACE3P and the radiation code Geant4, targeting large-scale problems using high-performance computing. The process begins by calculating the operating mode in the vacuum region of an accelerator structure and tracking field-emitted electrons influenced by the EM fields of the mode calculated by ACE3P. It then transfers particle data at the vacuum-wall interface for subsequent radiation calculations within the wall enclosure materials through Geant4 calculation. The whole integrated simulation workflow will be demonstrated through large-scale dark current radiation calculations for the KEK 56-cell traveling-wave structure, and the efficiency of performing these simulations on the NERSC supercomputer Perlmutter will be presented.

The goal of this work is to investigate the capability of a neural operator (DeepONet) to accurately capture the complex deformation of a platelet's membrane under shear flow. The surrogate model approximated by the neural operator predicts the deformed membrane configuration based on its initial configuration and the shear stress exerted by the blood flow. The training dataset is derived from particle dynamics simulations implemented in LAMMPS. The neural operator captures the dynamics of the membrane particles with a mode error distribution of approximately 0.5\%. The proposed implementation serves as a scalable approach to integrate sub-platelet dynamics into multi-scale computational models of thrombosis.

Living cells presumably employ optimized information transfer methods, enabling efficient communication even in noisy environments. As expected, the efficiency of chemical communications between cells depends on the properties of the molecular messenger. Evidence suggests that proteins from narrow ranges of molecular masses have been naturally selected to mediate cellular communications, yet the underlying communication design principles are not understood. Using a simple physical model that considers the cost of chemical synthesis, diffusion, molecular binding, and degradation, we show that optimal mass values exist that ensure efficient communication of various types of signals. Our findings provide insights into the design principles of biological communications and can be used to engineer chemically communicating biomimetic systems.

Polar polyatomic molecules provide an ideal but largely unexplored platform to encode qubits in rotational states. Here, we trap cold (100-600 mK) formaldehyde (H$_2$CO) inside an electric box and perform a Ramsey-type experiment to observe long-lived (~100 $\mu$s) coherences between symmetry-protected molecular states with opposite rotation but identical orientation, representing a quasi-hidden molecular degree of freedom. As a result, the observed qubit is insensitive to the magnitude of an external electric field, and depends only weakly on magnetic fields. Our findings provide a basis for future quantum and precision experiments with trapped cold molecules.

Continuum damage mechanics (CDM) is a popular framework for modelling crack propagation in solids. The CDM uses a damage parameter to quantitatively assess what one loosely calls `material degradation'. While this parameter is sometimes given a physical meaning, the mathematical equations for its evolution are generally not consistent with such physical interpretations. Curiously, degradation in the CDM may be viewed as a change of measures, wherein the damage variable appears as the Radon-Nikodym derivative. We adopt this point of view and use a probabilistic measure-valued description for the random microcracks underlying quasi-brittle damage. We show that the evolution of the underlying density may be described via killed diffusion as in the Feynman-Kac theory. Damage growth is then interpreted as the reduction in this measure over a region, which in turn quantifies the disruption of bonds through a loss of force-transmitting mechanisms between nearby material points. Remarkably, the evolution of damage admits an approximate closed-form solution. This brings forth substantive computational ease, facilitating fast yet accurate simulations of large dimensional problems. By selecting an appropriate killing rate, one accounts for the irreversibility of damage and thus eliminates the need for ad-hoc history-dependent routes typically employed, say, in phase field modelling of damage. Our proposal FeynKrack (a short form for Feynman-Kac crack propagator) is validated and demonstrated for its efficacy through several simulations on quasi-brittle damage. It also offers a promising stochastic route for future explorations of non-equilibrium thermodynamic aspects of damage.

Negative index metamaterials (NIMs) can be achieved with uniaxial hyperbolic metamaterials (HMMs) featuring $\epsilon_{parallel}>0$ and $\epsilon_{perpendicular}<0$. This type of approach has been traditionally realized using stacked doped/undoped semiconductor layers. Only recently surface phonon polaritons (SPhPs) have emerged as a promising low-loss alternative to surface plasmon polaritons (SPPs). Despite this advantage, the SPhP-based approach has been underexplored due to the challenges associated with ensuring high crystal quality in the heterostructure when using alloys with different phonon frequencies. In this work, we design a phononic-driven NIM using a ZnO/(Zn,Mg)O heterostructure, demonstrating control over its hyperbolic behavior through the precise selection of the Mg content and the relative layer thicknesses. Our study shows that increasing the Mg content in the ternary layers enhances the type I behavior, and that the optimal layer thickness varies depending on the Mg content. After analyzing the conditions for achieving type I hyperbolic dispersion, we experimentally demonstrate this concept with a sample featuring equal layer thicknesses and a 32% Mg concentration. We characterize the structure by means of polarized reflectance spectroscopy and use attenuated total reflectance spectroscopy to report the presence of a SPhP mode located within the type I hyperbolic region. By employing the transfer matrix method, we demonstrate that this mode exhibits negative frequency dispersion, a hallmark of type I hyperbolic modes, and isofrequency curve calculations further confirm this behavior. Controlling the design of a phononic hyperbolic type I metamaterial lays the groundwork for exploring its potential applications in attaining low-loss, sub-diffraction-limited optical modes using SPhP excitations.

We study the nonlinear optical response of argon to a four-wave-mixing pulse sequence consisting of an extreme ultraviolet pulse, an overlapping collinear IR and an non-collinear delayed IR pulses. Absorption of an extreme ultraviolet and an IR photon from the collinear beams excites, sequentially, the $3s^{-1}4p$ bright state and the {$3s^{-1}3d/5s$} dark states. The subsequent absorption of an IR photon from the non-collinear beam results in an angled extreme ultraviolet emission whose variation with delay encodes coupling between autoionizing-states, dark-state lifetimes, and non-perturbative effects. Both our measurements and \emph{ab initio} simulations of the angled four-wave-mixing signal show a double-peak structure in delay dependence, in excellent agreement with each other. We attribute the minimum at the center of the signal to the rapid Rabi cycling, driven by the IR pulse, between dark states and the $3s^{-1}4p$ resonance, which results in the destructive interference in the final transition amplitude.

Ground motion prediction (GMP) models are critical for hazard reduction before, during and after destructive earthquakes. In these three stages, intensity forecasting, early warning and interpolation models are corresponding employed to assess the risk. Considering the high cost in numerical methods and the oversimplification in statistical methods, deep-learning-based approaches aim to provide accurate and near-real-time ground motion prediction. Current approaches are limited by specialized architectures, overlooking the interconnection among these three tasks. What's more, the inadequate modeling of absolute and relative spatial dependencies mischaracterizes epistemic uncertainty into aleatory variability. Here we introduce QuakeFormer, a unified deep learning architecture that combines these three tasks in one framework. We design a multi-station-based Transformer architecture and a flexible masking strategy for training QuakeFormer. This data-driven approach enables the model to learn spatial ground motion dependencies directly from real seismic recordings, incorporating location embeddings that include both absolute and relative spatial coordinates. The results indicate that our model outperforms state-of-the-art ground motion prediction models across all three tasks in our research areas. We also find that pretraining a uniform forecasting and interpolation model enhances the performance on early warning task. QuakeFormer offers a flexible approach to directly learning and modeling ground motion, providing valuable insights and applications for both earthquake science and engineering.

Augmented reality display performance depends strongly on features of the human visual system. This is especially true for retinal scan glasses, which use laser beam scanning and transparent holographic optical combiners. Human-centered approaches allow us to go beyond conventional optical metrology and evaluate display performance as it is perceived in actual augmented reality use cases. Here, we first present a theoretical formula for the retinal scan luminance and ambient contrast ratio calculated from optical powers, wavelengths, field of view, and human pupil diameter. As a promising insight we found that the pupil diameter dependence is beneficial in assimilating the virtual image luminance to the ambient luminance. Second, we designed and performed a psychophysical experiment to assess perceived resolution in augmented reality settings using a fully functional retinal scan glasses prototype. We present the results of the trials and illustrate how this approach can be useful in the further development of augmented reality smart glasses.

Despite significant advancements in wireless smart implants over the last two decades, current implantable devices still operate passively and require additional electronic modules for wireless transmission of the stored biological data. To address these challenges, we propose an innovative wireless force sensing paradigm for implantable systems through the integration of mechanical metamaterials and nano energy harvesting technologies. We demonstrate composite mechanical metamaterial implants capable of serving as all-in-one wireless force sensing units, incorporating functions for power generation, sensing and transmission with ultra-low power requirements. In this alternative communication approach, the electrical signals harvested by the implants from mechanical stimuli are utilized directly for the wireless transmission of the sensed data. We conduct experimental and theoretical studies to demonstrate the wireless detection of the generated strain-induced polarization electric field using electrodes. The feasibility of the proposed wireless force sensing approach is evaluated through a proof-of-concept orthopedic implant in the form of a total knee replacement. The findings indicate that the created wireless, electronic-free metamaterial implants with a power output as low as 0.1 picowatts enable direct, self-powered wireless communication during force sensing across air, simulated body fluid and animal tissue. We validate the functionality of the proposed implants through a series of experiments conducted on an ex vivo human cadaver knee specimen. Furthermore, the effect of electrode size and placement on the strength of the received signals is examined. Finally, we highlight the potential of our approach to create a diverse array of mechanically-tunable wireless force sensing implants without relying on any external power sources.

Brownian systems are characterized by spatiotemporal disorder, which arises from the erratic motion of particles driven by thermal fluctuations. When light interacts with such systems, it typically produces unpolarized and uncorrelated fields. Here, we report the observation of a large-scale spin-locking effect of light within a Brownian medium. In an observation direction perpendicular to the incident wave momentum, scattering naturally divides into two diffusion regions, each associated with an opposite spin from the Brownian nanoparticles. This effect arises from the intrinsic spin-orbit interactions of scattering from individual nanoparticles, which ubiquitously generate radiative spin fields that propagate through the Brownian medium with multiple incoherent scattering. It offers a novel experimental platform for exploring macroscale spin behaviors of diffused light, with potential applications in precision metrology for measuring various nanoparticle properties. Our findings may inspire the study of analogous phenomena for different waves from novel spin-orbit interactions in complex disordered systems.

This work presents a comprehensive framework for the efficient implementation of finite-volume-based reacting flow solvers, specifically tailored for high speed propulsion applications. Using the exascale computing project (ECP) based AMReX framework, a compressible flow solver for handling high-speed reacting flows is developed. This work is complementary to the existing PeleC solver, emphasizing specific applications that include confined shock-containing flows, stationary and moving shocks and detonations. The framework begins with a detailed exposition of the numerical methods employed, emphasizing their application to complex geometries and their effectiveness in ensuring accurate and stable numerical simulations. Subsequently, an in-depth analysis evaluates the solver's performance across canonical and practical geometries, with particular focus on computational cost and efficiency. The solver's scalability and robustness are demonstrated through practical test cases, including flow path simulations of scramjet engines and detailed analysis of various detonation phenomena.

Microdosimetry and nanodosimetry study the track structure of charged particles, i.e., the stochastics of radiation interaction on the microscopic scale. The (different) concepts developed in micro- and nanodosimetry are matched to the experimental approaches in the two fields. This work reviews the concepts of an event used in the two fields and explores a common denominator between the two methodologies to characterize particle track structure at the nanometric level. An approach to harmonize the concepts of microdosimetry and nanodosimetry for nanometric sites and linking them to macroscopic fluence is theoretically formulated and tested on proton tracks with energies between 1 MeV and 100 MeV. Unification of microdosimetry and nanodosimetry for the same target seems to require three elements: (1) the definition of an event as relating to a beam volume within which interactions of the primary particle can result in energy deposits or ionizations in the site, (2) the definition of a nanodosimetric analogue to a microdosimetric event, and (3) the redefinition of the microdosimetric concept of an event to include only events in which an ionization occurs in the site. The range of impact parameters of particle tracks that contributes to energy imparted in a site appears not to depend on whether any energy deposits or only energy deposits by ionizations are considered. Since there is no longer a one-to-one correspondence between tracks passing a site and the occurrence of an event, it is proposed to use the fluence for which on average one event occurs as a substitute for single events.

Linear stability of a locally parallel annular swirling jet laden with particles in a swirl flow combustor is considered. At low Stokes numbers, the eigenspectra of the particle-laden jet with uniform particle concentration shows three unstable modes namely centre, sinuous and varicose modes. As the Stokes number is increased to unity, the growth rates of the centre and shear layer modes reduces compared to that of the unladen swirling jet. The magnitude of the velocity eigenmodes peaks in the vortex core and decays radially outward. The variation in particle concentration occurs mostly in the vortex core and almost none in the shear layer. The strength of flow reversal at the jet centreline is given by the backflow parameter. An increase in the backflow parameter increases the growth rate of the centre mode. Non-uniformity in the base-state particle concentration is introduced using a Gaussian function varying in the radial direction and a reduction in the growth rate of the centre mode is seen compared to the uniform particle concentration profile. When the location of the peak of the base-state particle concentration profile is inside the vortex core, the centre modes are stable. Linearized vorticity budget analysis reveals that this is accompanied by a decrease in the net generation of perturbation vorticity in the axial direction and increased radial and azimuthal perturbation vorticity.

It is widely recognized that reciprocal interactions between cells and their microenvironment, via mechanical forces and biochemical signaling pathways, regulate cell behaviors during normal development, homeostasis and disease progression such as cancer. However, it is still not well understood how complex patterns of tissue growth emerge. Here, we propose a framework for the chemomechanical regulation of growth based on thermodynamics of continua and growth-elasticity to predict growth patterns. Combining the elastic and chemical energies, we use an energy variational approach to derive a novel formulation that incorporates an energy-dissipating stress relaxation and biochemomechanical regulation of the volumetric growth rate. We validate the model using experimental data from growth of tumor spheroids in confined environments. We also investigate the influence of model parameters, including tissue rearrangement rate, tissue compressibility, strength of mechanical feedback and external mechanical stimuli, on the growth patterns of tumor spheroids.

Bound states in the continuum (BICs) in planar photonic structures have attracted broad scientific interest owing to their exceptional capability to confine light. Topological robustness of certain BICs allows them to be moved in the momentum space by tuning the geometric parameters of the structure. In this work, we study such a BIC in a one-dimensional periodic grating, and find that its momentum-space position can be made a non-monotonic function of a geometric parameter, forming a locally bent ''V''-shaped trajectory. We show that, near the turning point of this trajectory, the robustness of the BIC and its $Q$ factor can be greatly enhanced. We tune such ''V-BICs'' to almost merge with a symmetry-protected BIC at the $\Gamma$-point. This creates a ''K''-shaped ultrahigh-$Q$ region containing a BIC with a much higher and more stable $Q$ factor compared to the ordinary merging BICs. The ''K-BICs'' are also found to provide a strong enhancement of the $Q$ factor in finite gratings over an extremely wide range of geometric parameters. Our findings enable further advancements in the development of ultrahigh-$Q$ BICs and their applications.

In the forecast diagnostic and verification community, there exists a need for smoothing methods that would work in the global domain. For limited-area domains, fast smoothing methods already exist, but the problem is that these approaches cannot be used with global fields as a global grid defined on a sphere is inherently non-equidistant and/or irregular. Another potential issue is the variability of grid point area sizes and the presence of missing data in the field, which can also be problematic to deal with for existing smoothing methods. Here, we present two new approaches for area-size-informed smoothing on a sphere. The first approach is based on k-d trees, and the second one is based on overlap detection. While each has its strengths and weaknesses, both are potentially fast enough to make the smoothing of high-resolution global fields feasible, as demonstrated by the smoothing of an operational global high-resolution precipitation forecast from the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecasts. Both approaches can also handle missing data in an appropriate manner and can also be used in non-rectangularly-shaped limited-area domains defined on non-equidistant and/or irregular grids.

The ALPHA-g experiment at CERN aims to perform the first-ever direct measurement of the effect of gravity on antimatter, determining its weight to within 1% precision. This measurement requires an accurate prediction of the vertical position of annihilations within the detector. In this work, we present a novel approach to annihilation position reconstruction using an ensemble of models based on the PointNet deep learning architecture. The newly developed model, PointNet Ensemble for Annihilation Reconstruction (PEAR) outperforms the standard approach to annihilation position reconstruction, providing more than twice the resolution while maintaining a similarly low bias. This work may also offer insights for similar efforts applying deep learning to experiments that require high resolution and low bias.

Precision optical filters are key components for current and future photonic technologies. Here, we demonstrate a low loss spectral filter consisting of an ultrasteep bandpass feature with a maximum gradient of (90.6$\pm$0.7) dB/GHz, centred within a notch filter with (128$\pm$6) dB of suppression. The filter consists of a fiber Bragg grating with multiple $\pi$-phase discontinuities inscribed into a single mode photosensitive fiber. The measured performance closely matches the simulated spectrum calculated from the design parameters indicating a high degree of confidence in the repeatability and manufacture of such devices. These filters show great promise for applications reliant on high-frequency resolution noise suppression, such as quantum networking, and highlight the opportunities for the versatility, efficiency, and extreme suppression offered by high-performance fiber Bragg grating devices.

We focus on three distinct lines of recent developments: edge modes and boundary charges in gravitational physics, relational dynamics in classical and quantum gravity, and quantum reference frames. We argue that these research directions are in fact linked in multiple ways, and can be seen as different aspects of the same research programme. This research programme has two main physical goals and one general conceptual aim. The physical goals are to move beyond the two idealizations/approximations of asymptotic or closed boundary conditions in gravitational physics and of ideal reference frames (coded in coordinate frames or gauge fixings), thus achieving a more realistic modeling of (quantum) gravitational physical phenomena. The conceptual aim is to gain a better understanding of the influence of observers in physics and the ensuing limits of objectivity.

Thermo-optical nonlinearities (TONL) in metasurfaces enable dynamic control of optical properties like transmission, reflection, and absorption through external stimuli such as laser irradiation or temperature. As slow thermal dynamics of extended systems are expected to limit modulation speeds ultimately, research has primarily focused on steady-state effects. In this study, we investigate photo-driven TONL in amorphous silicon (a-Si) metasurfaces both under steady-state and, most importantly, dynamic conditions (50 kHz modulation) using a 488 nm continuous-wave pump laser. First, we show that a non-monotonic change in the steady-state transmission occurs at wavelengths longer than the electric-dipole resonance (800 nm). In particular, at 815 nm transmission first decreases by 30% and then increases by 30% as the laser intensity is raised to 5 mW/{\mu}m2. Next, we demonstrate that TONL decouple the thermal and optical characteristic times, the latter being up to 7 times shorter in the tested conditions (i.e {\tau}opt =0.5 {\mu}s vs {\tau}th =3.5 {\mu}s). Most remarkably, we experimentally demonstrate that combining these two effects enables optical modulation at twice the speed (100 kHz) of the excitation laser modulation. We finally show how to achieve all-optical transmission modulation at MHz speeds with large amplitudes (85%). Overall, these results show that photo-driven TONL produce large and fully reversible transmission modulation in dielectric metasurfaces with fast and adjustable speeds. Therefore, they open completely new opportunities toward exploiting TONL in dynamically reconfigurable systems, from optical switching to wavefront manipulation.

The concept of photonic bound states in the continuum (BICs), introduced in structured metallic surface cavities, provides a crucial mechanism for designing plasmonic open-resonant cavities with high quality (high-Q) factors, making significant advances in plasmonic nanophotonics. However, the two major bottlenecks for plasmonic nanocavities: enhancing emission and big beam divergence for quantum emitters, due to the strong intrinsic Ohmic losses of metals. Here, we propose and realize a {\sigma}h symmetry-breaking plasmonic honeycomb nanocavities (PHC) that support quasi-BIC resonance modes with high-Q factors. Our anodic oxidation-engineered strategy breaks out-of-plane symmetry while preserving in-plane symmetry, enabling the PHC to exhibit collective plasmonic lattice resonances (PLR) couplings and achieve Q-factors exceeding 106. Experimentally, we couple perovskite quantum dots (PQDs) to the PHC, demonstrating effective tuning of their emission properties and beam quality in the blue spectral region, achieving a 32-fold emission enhancement by suppress Ohmic loss and the life time of quantum emitters, simultaneously realize vertical emission in the 2.556 - 2.638 eV region, with a far-field hexagonal beam shape and a full width at half maximum of 12.6 degree under optimal coupling conditions. Furthermore, we demonstrate topological band inversion characterized by Zak phase transitions by continuously tuning the system parameters, confirming that the PHC supports topologically non-trivial q-BIC due to PLR coupling. The PHC presents itself as a promising next-generation, high-brightness nanoscale light source matrix, which can be directly scaled up to cover a wide wavelength range from UV to IR.

We propose to expand the territory of density functional theory to strongly-correlated electrons by reformulating the Kohn-Sham ansatz in the representation of fractionalized particles. We call it the ''KS* ansatz''. Using inhomogeneous t-J chains as a test bed, we show that the KS* ansatz with simple local density approximtion is able to achieve accurate ground state energy and density distribution comparable to the density matrix renormalization group method, while the computational complexity is much lower.

Carbon dioxide (CO2), an important trace species that is gradually increasing in the atmosphere due to anthropogenic activities, causes enhanced warming in the lower atmosphere. The increased concentration of CO2 in the upper atmosphere results in enhanced radiative cooling rates leading to the contraction of the upper atmosphere. Due to its long lifetime and large vertical gradient, CO2 concentration is also influenced by large dynamic events. We report a startling case of variability in CO2 density and its infrared radiative cooling rates in the mesosphere and lower thermospher during a major sudden stratospheric warming (SSW) event. A counter-intuitive connection between CO2 density and resulting CO2 radiative cooling has been observed during the 2009 major SSW event. The behaviour of CO2 cooling rates during such a dramatic events draw attention to our current understanding of CO2 infrared cooling variation and its connection to changes in CO2 concentration. The significance of temperature and atomic oxygen variability in the observed cooling patterns despite changes in CO2 concentration, is also highlighted.

This item from the News & Views category, to be published in Light: Science & Applications, aims to provide a summary of theoretical and experimental results recently published in Ref. [24], which demonstrate the creation of corner modes in nonlinear optical waveguides of the higher-order topological-insulator (HOTI) type. Actually, these are second-order HOTIs, in which the transverse dimension of the topologically protected edge modes is smaller than the bulk dimension (it is 2, in the case of optical waveguide) by 2, implying zero dimension of the protected modes, that are actually realized as corner or defect ones. Work [24] reports prediction and creation of various forms of the corner modes in a HOTI with a fractal transverse structure, represented by the Sierpinski gasket (SG). The self-focusing nonlinearity of the waveguide's material transforms the corner modes into corner solitons, almost all of which are stable. The solitons may be attached to external or internal corners created by the underlying SG. This N&V item offers an overview of these new findings reported in Ref. [24] and other recent works, and a brief discussion of directions for the further work on this topic.

Oceanic wave propagation through Earth's sea ice covers is a critical component of accurate ice and climate modeling. Continuum models of the polar ocean surface layer are characterized rheologically by the effective complex viscoelasticity of the composite of ice floes and sea water. Here we present the first rigorous theory of this parameter, and distill its dependence on mixture geometry into the spectral properties of a self-adjoint operator analogous to the Hamiltonian in quantum physics. Bounds for the complex viscoelasticity are obtained from the sea ice concentration and the contrast between the elastic and viscous properties of the ice and water/slush constituents. We find that several published wave attenuation datasets in both laboratory and field settings fall well within the bounds for specific contrast values of the ice/ocean composite.

Motivated by the Hamilton$-$Jacobi approach of fields with constraints, we analyse the classical structure of three different constrained field systems: (i) the scalar field coupled to two flavors of fermions through Yukawa couplings (ii) the scalar field coupled minimally to the vector potential (iii) the electromagnetic field coupled to a spinor. The equations of motion are obtained as total differential equations in many variables. The integrability conditions are investigated. The second and third constrained systems are quantized using canonical path integral formulation based on the Hamilton$-$Jacobi treatment.

We theoretically propose a new approach to in-situ monitor the altered states of pixels in VO2 based active THz amplitude metagrating by using surface acoustic waves (SAWs) generated in LiNbO3 substrate. A single broadband RF response of the SAW device consists of N narrowband frequency channels which code the feedback information on the current pixels states. This way the resistance alteration due to metal-to-insulator transition of all VO2 based pixels of the active THz grating is monitored in advance even without incident radiation. The method provides important new options of monitoring a metasurface aging as well as incorrect switching related to technological defects. Using of the SAW device in all-electrically addressed VO2 based binary grating with tunable period as well as advanced gradient type gratings are modeled.

We present calculations of the self-energy correction to the $E1$ transition amplitudes in hydrogen-like ions, performed to all orders in the nuclear binding strength parameter. Our results for the $1s$-$2p_{1/2}$ transition for the hydrogen isoelectronic sequence show that the perturbed-orbital part of the self-energy correction provides the dominant contribution, accounting for approximately 99\% of the total correction for this transition. Detailed calculations were performed for $ns$-$n'p$ and $np$-$n'd$ transitions in H-like caesium. We conclude that the perturbed-orbital part remains dominant also for other $ns$-$n'p$ transitions, whereas for the $np$-$n'd$ matrix elements this dominance no longer holds. Consequently, the self-energy corrections for the $np$-$n'd$ one-electron matrix elements cannot be well reproduced by means of effective QED operators constructed for energy levels.

Full-vectorial finite difference method with perfectly matched layers boundaries is used to identify the single mode operation region of submicron rib waveguides fabricated using sili-con-on-insulator material system. Achieving high mode power confinement factors is emphasized while maintaining the single mode operation. As opposed to the case of large cross-section rib waveguides, theoretical single mode conditions have been demonstrated to hold for sub-micron waveguides with accuracy approaching 100%. Both, the deeply and the shallowly etched rib waveguides have been considered and the single mode condition for entire sub-micrometer range is presented while adhering to design specific mode confinement requirements.

Dielectric barrier discharge (DBD) plasma actuator arrays have been suggested as active flow control devices due to the robust electrohydrodynamic (EHD) force generation in variable atmospheric conditions. DBD plasma augmentation schemes allow for significant performance improvements. However, the transitions to sliding discharge or counter-flow discharge limit their use in high-power arrays. Here, we experimentally demonstrate the performance of a scalable DBD array for two alternating phases of air-exposed electrode configuration. Plasma emissions, direct thrust, velocity profiles, and power consumption measurements of the DBD array reveal that cross-talk between DBD stages can be eliminated to create high-power density actuators. AC augmentation of plasma provides additional gains in thrust; however, the transition to sliding and filamentary discharge reveals geometric limits when increasing the array power density. Introducing a segmented electrode with a resistor delays the onset of adverse sliding and filamentary discharge, allowing it to operate at higher voltage inputs. An optimized four-stage DBD array generated thrust > 250 mN/m with a wall jet thickness > 15 mm, enabling a broader range of flow control applications.

In this work we propose an efficient and accurate multi-scale optical simulation algorithm by applying a numerical version of slowly varying envelope approximation in FEM. Specifically, we employ the fast iterative method to quickly compute the phase distribution of the electric field within the computational domain and construct a novel multi-scale basis function that combines the conventional polynomial basis function together with numerically resolved phase information of optical waves. Utilizing this multi-scale basis function, the finite element method can significantly reduce the degrees of freedom required for the solution while maintaining computational accuracy, thereby improving computational efficiency. Without loss of generality, we illustrate our approach via simulating the examples of lens groups and gradient-index lenses, accompanied with performance benchmark against the standard finite element method. The results demonstrate that the proposed method achieves consistent results with the standard finite element method but with a computational speed improved by an order of magnitude.

When numerically solving partial differential equations, for a given problem and operating condition, adaptive mesh refinement (AMR) has proven its efficiency to automatically build a discretization achieving a prescribed accuracy at low cost. However, with continuously varying operating conditions, such as those encountered in uncertainty quantification, adapting a mesh for each evaluated condition becomes complex and computationally expensive. To enable more effective error and cost control, this work introduces a novel approach to mesh adaptation. The method consists in building a unique adapted mesh that aims at minimizing the average error for a continuous set operating conditions. In the proposed implementation, this unique mesh is built iteratively, informed by an estimate of the local average error over a reduced set of sample conditions. The effectiveness and performance of the method are demonstrated on a one-dimensional Burgers equation and a two-dimensional Euler scramjet shocked flow configurations.

In invasive breast cancer, cell clusters of varying sizes and shapes are embedded in the fibrous extracellular matrix (ECM). Although the prevailing view attributes this structure to increasing disorder resulting from loss of function and dedifferentiation, our findings reveal that it arises through a process of active self-organization driven by cancer cell motility. Simulations and histological analyses of tumours from over 2,000 breast cancer patients reveal that motile, aligned cancer cells within clusters move as active nematic aggregates through the surrounding highly aligned ECM fibres, which form a confining, passive nematic phase. Cellular motion leads to cluster splitting and coalescence. The degree of cluster activity, combined with heterogeneity in cell motility, is reflected in specific scaling behaviours for cluster shape, size distribution, and the distance between cluster boundaries and nematic defects in ECM alignment. Increased activity estimates correlate with tumour progression and are associated with a poorer prognosis for patients.

Silicon is a nonlinear material and optics based on silicon makes use of these nonlinearities to realize various functionalities required for on-chip communications. This article describes foundations of these nonlinearities in silicon at length. Particularly, self phase modulation and cross phase modulation in the context of integrated on-board and on-chip communications are presented. Important published results and principles of working of these nonlinearities are presented in considerable detail for non-expert readers.

Most recently, some substitutional solute atoms in {\alpha}-Ti have been predicted to occupy unexpectedly the low-symmetry (LS) positions away from the high-symmetry (HS) lattice site, which was speculated to result in enhanced solid solution hardening (SSH). In the present work, the SSH induced by the LS off-center solute atom is evaluated within the framework of continuum elasticity theory, in comparison with that induced by its HS lattice-site counterpart. The interaction energy and force between the solute atom and the basal/prismatic edge/screw <a> dislocations in {\alpha}-Ti solid solution are calculated with the elastic dipole model, with which the strength increments induced by the solute atoms are evaluated with the Labusch model. We show that, in general, the LS solute atom interacts much more strongly with the dislocations than its HS counterpart does. The calculated interaction energies suggest that the LS solute atom forms atmosphere above/below the slip plane of the basal <a> dislocations but on the slip plane of the prismatic <a> dislocations regardless of the dislocation types (edge or screw). The strength increments caused by most of the LS solute atoms are more than an order of magnitude higher than those by their HS counterparts. The SSH effect induced by the LS solute atom is mainly determined by the strength of the Jahn-Teller splitting of the d-orbitals of the solute atom, dissimilar to that induced by HS solute atom where the atomic size mismatch dominates.

Performance optimization associated with optical modulators requires reasonably accurate predictive models for key figures of merit. Interleaved PN-junction topology offers the maximum mode/junction overlap and is the most efficient modulator in depletion-mode of operation. Due to its structure, the accurate modelling process must be fully three-dimensional, which is a nontrivial computational problem. This paper presents a rigorous 3D model for the modulation efficiency of silicon-on-insulator interleaved junction optical phase modulators with submicron dimensions. Solution of Drift-Diffusion and Poisson equations were carried out on 3D finite-element-mesh and Maxwell equations were solved using Finite-Difference-Time-Domain (FDTD) method on 3D Yee-cells. Whole of the modelling process has been detailed and all the coefficients required in the model are presented. Model validation suggests < 10% RMS error.

We demonstrate the advantages of THz frequency combs for high-resolution spectroscopy. This benefits from wide spectral coverage and the exact knowledge of the frequency position of each comb component. Heterodyne detection combined with a fast Fourier spectrometer enables rapid and simultaneous measurement of more than 80 frequency comb modes covering a 7.5 GHz bandwidth. A spectrum is obtained in under 20 minutes yielding a uniform resolution of 70 kHz. This new setup has been validated by recording more than 150 lines of methanol around 723 GHz, and represents a new solution to exploit THz frequency combs for high-resolution spectroscopy.

High resolution rotational Terahertz (THz) spectroscopy has been widely applied to the studies of numerous polar gas phase molecules, in particular volatile organic compounds (VOCs). During the storage of foodstuffs packed under a protective atmosphere, microbial activity will lead to the generation of a complex mixture of trace gases that could be used as food spoilage indicators. Here we have demonstrated that the THz instrumentation presently available provides sufficient sensitivity and selectivity to monitor the generation of hydrogen sulfide (H2S) in the headspace of packed Atlantic salmon (Salmo salar) fillet portions. A comprehensive comparison was made by selective-ion flow-tube mass spectrometry (SIFT-MS) in order to validate the THz measurements and protocol. The detectivity of a range of alternative compounds for this application is also provided, based on the experimental detection limit observed and molecular spectroscopic properties. Molecules like ethanol, methyl mercaptan and ammonia are suitable indicators with the presently available sensitivity levels, while dimethyl sulfide, acetone and butanone may be considered with a sensitivity improvement of 2 orders of magnitude.

The emergence of conversational natural language processing models presents a significant challenge for Higher Education. In this work, we use the entirety of a UK physics undergraduate (BSc with Honours) degree including all examinations and coursework to test if ChatGPT (GPT-4) can pass a degree. We adopt a "maximal cheating" approach wherein we permit ourselves to modify questions for clarity, split questions up into smaller sub-components, expand on answers given - especially for long form written responses, obtaining references, and use of advanced coaching, plug-ins and custom instructions to optimize outputs. In general, there are only certain parts of the degree in question where GPT-4 fails. Explicitly these include compulsory laboratory elements, and the final project which is assessed by a viva. If these were no issue, then GPT-4 would pass with a grade of an upper second class overall. In general, coding tasks are performed exceptionally well, along with simple single-step solution problems. Multiple step problems and longer prose are generally poorer along with interdisciplinary problems. We strongly suggest that there is now a necessity to urgently re-think and revise assessment practice in physics - and other disciplines - due to the existence of AI such as GPT-4. We recommend close scrutiny of assessment tasks: only invigilated in-person examinations, vivas, laboratory skills testing (or "performances" in other disciplines), and presentations are not vulnerable to GPT-4, and urge consideration of how AI can be embedded within the disciplinary context.

Rheology of bubble suspensions is critical for the prediction and control of bubbly flows in a wide range of industrial processes. It is well-known that the bubble suspension exhibits a shear-thinning behavior due to the bubble shape deformation under pure shear, but how the shear rheology response to dilatation remains unexplored. Here, we report a compression-thinning behavior that the bubble suspension exhibits a decreasing shear viscosity upon compressing. This peculiar rheological behavior is microscopically due to that a shrinking bubble surface effectively weakens the flow resistance of the surrounding liquid. We theoretically propose a constitutive equation for dilute bubble suspensions considering both shear and dilatation effects, and demonstrate that the contribution of dilatation effect on the shear viscosity can be significant at a changing pressure.

The spark plasma sintering (SPS) process, a key technology for advanced material manufacturing, demands accurate and efficient simulation tools to capture the complex electro-thermal-mechanical interactions inherent in powder materials. This paper introduces a novel concurrent multiscale framework employing the Direct FE$^2$ method, designed for fully coupled electro-thermal-mechanical simulations in SPS. The model integrates microscale powder characteristics into a macroscopic analysis through multi-point constraints within a 3D finite element (FE) solver. This approach enables, for the first time, a direct and seamless coupling of micro- and macroscale physical phenomena, enhancing both accuracy and computational efficiency by capturing interactions across scales. The proposed method achieves a temperature and displacement error margin below 1% compared to full FE analysis while reducing computational degrees of freedom by a factor of 8, resulting in a 70-fold acceleration in simulation time. Additionally, the methodology provides robust flexibility in accommodating diverse powder morphologies without compromising precision, enabling degree-of-freedom reductions of up to 44 times. This combination of enhanced efficiency and accuracy establishes the proposed Direct FE$^2$ approach as a highly effective tool for realistic and scalable simulations of the SPS process.

Optical brain imaging technologies are promising due to their relatively high temporal resolution, portability and cost-effectiveness. However, the highly scattering nature of near-infrared light in human tissue makes it challenging to collect photons emerging from more than 4 cm below the scalp, or with source-detector separation larger than several centimeters. We explore the physical limits of photon transport in the head and show that despite an extreme attenuation of ~10^(18), we can experimentally detect light that is transmitted diametrically through the entire adult human head. Analysis of various photon migration pathways through the head also indicates how the source-detector configuration can be used to isolate photons interacting with deep regions of the brain that are inaccessible with current optical techniques.

When the variations of surface temperature are measured both spatially and temporally, analytical expressions that correctly account for multi-dimensional transient conduction can be applied. To enhance the accessibility of these accurate multi-dimensional methods, expressions for converting between surface temperature and heat flux are presented as the sum of the one-dimensional component plus the multi-dimensional component. Advantage arises herein because potential numerical challenges are isolated within the one-dimensional component and practitioners are already familiar with well-established one-dimensional methods. The second derivative of the surface heat flux distribution scaled by the thermal diffusivity and the duration of the experiment delivers an approximation of the multi-dimensional conduction term. For the analysis of experiments in which multi-dimensional effects are significant, a simplified numerical approach in which the temperature within each pixel is treated as uniform is demonstrated. The approach involves convolution of temperature differences and pixel-based impulse response functions, followed by a summation of results across the region of interest, but there are no singularities that require special treatment in the multi-dimensional component. Recovery of heat flux distributions to within 1% is demonstrated for two-dimensional heat flux distributions discretized using several tens of elements, and for a three-dimensional distribution discretized using several hundred pixels. Higher accuracy can be achieved by using finer spatial resolution, but the level of discretization used herein is likely sufficient for practical applications since typical experimental uncertainties are much larger than 1%.

We present tabulated data for numerical calculations of relative para- and diamagentic contributions to the magnetic shielding constant ($\sigma$) of the Dirac one-electron atoms with a pointlike, spinless and motionless nuclei of charge $Ze$. Exploiting the analytical formulas for the diamagnetic ($\sigma_{d}$) and paramagnetic ($\sigma_{p}$) terms of $\sigma$, valid for an arbitrary discrete energy state, recently derived by us with the aid of the Gordon decomposition technique, we have found the numerical values of $\sigma_{d}/\sigma$ and $\sigma_{p}/\sigma$ for the ground state and for the first two sets of excited states (i.e.: $2s_{1/2}$, $2p_{1/2}$, $2p_{3/2}$, $3s_{1/2}$, $3p_{1/2}$, $3p_{3/2}$, $3d_{3/2}$, and $3d_{5/2}$) of the relativistic hydrogenic ions with the nuclear charge number from the range $1 \leqslant Z \leqslant 137$. The comparisons of our results with those reported by other authors for some atomic states are also presented. We also compile here the numerical values of the total magnetic shielding constants for the ground state $1s_{1/2}$ and for each state belonging to the first set of excited states of selected hydrogenlike ions, obtained with the use of three different values of the fine-structure constant, i.e.: $\alpha^{-1}=137.035 \: 999 \: 139$ (from CODATA 2014), $\alpha^{-1}=137.035 \: 999 \: 084$ (from CODATA 2018) and $\alpha^{-1}=137.035 \: 999 \: 177$ (from CODATA 2024).

Results of research are presented concerning operative modes of a high-voltage (relativistic) pulsed magnetron for the 8 mm wavelength range. Technical solutions are proposed for improving the output system of the device, such as to increase the efficiency of power extraction from the field-particle interaction space. The stability of the magnetron operation has been increased, enhanced quantitative indices of the output power to 381 kW in each of the two linear polarizations. And a stabilized frequency spectrum from 35.7 to 40 GHz of the EHF radiation generated.

Enhancement of the bremsstrahlung X-ray radiation (BSXR) power generated by the high-current relativistic electron beam (REB) of a pulsed direct-action accelerator TEMP-B is being curried out to use in particular for studying the radiation resistance of walls material of radioactive waste containers. For this the magnetic field in which REB is transported in the drift chamber is taken higher at some distance before the converter to provide REB diameter on the converter the same as in the drift chamber. Scheme of power supply and creation of the required magnetic field for the REB current transportation through the whole distance from the diode to the converter is developed. Higher BSXR dose per REB current pulse at the same energy resource is obtained.

We provide a reply to the Argument from Intimacy on behalf of defenders of emergent spacetime in theories of quantum gravity. We argue that if one accepts that spacetime regions are nowhere in the sense that they are locations but do not have locations, then the Argument from Intimacy can be resolved. We go on to consider a problem with this response, namely that it is unavailable to super-substantivalists. We argue that this is right for identity but not priority super-substantivalists. We then suggest that there is no cost for our solution here, since identity versions of super-substantivalism face severe challenges in the context of spacetime emergence and so should be rejected anyway.

Experimental data and results of numerical simulations are presented, concerning excitation of narrowband gigahertz-range wave trains in coaxial guiding structures that are partially filled with ferromagnetic material and may involve periodically arranged metal inserts. The experiments performed confirm the possibility of exciting weakly damped electromagnetic waves by feeding high voltage, unilateral electromagnetic pulses of short duration into the line. The coax line was of outer diameter 50.5 mm, filled with an isotropic dielectric (relative dielectric constant {\epsilon} = 2.25) and a set of ferrite rings with {\epsilon}=16 and saturated-state {\mu} about 4 to 5. With a peak voltage of the primary pulse close to 160 kV and a magnetizing field of 17.5 kA/m, the parameters of the waves excited reached magnitudes as: frequency 1.89 GHz to 2.1 GHz; bandwidth 16%; VHF power at the output about 20 MW.

Ultrafast and highly efficient dynamic optical metasurfaces enabling truly spatiotemporal control over optical radiation are poised to revolutionize modern optics and photonics, but their practical realization remains elusive. In this work, we demonstrate highly efficient electro-optic metasurfaces based on quasi-bound states in the continuum (qBIC) operating in reflection that are amenable for ultrafast operation and thereby spatiotemporal control over reflected optical fields. The material configuration consists of a lithium niobate thin film sandwiched between an optically thick gold back-reflector and a grating of gold nanoridges also functioning as control electrodes. Metasurfaces for optical free-space intensity modulation are designed by utilizing the electro-optic Pockels effect in combination with an ultra-narrow qBIC resonance, whose wavelength can be finely tuned by varying the angle of light incidence. The fabricated electro-optic metasurfaces operate at telecom wavelengths with the modulation depth reaching 95 % (modulating thereby 35 % of the total incident power) for a bias voltage of +/-30 V within the electrical bandwidth of 125 MHz. Leveraging the highly angle-dependent qBIC resonance realized, we demonstrate electrically tunable phase contrast imaging using the fabricated metasurface. Moreover, given the potential bandwidth of 39 GHz estimated for the metasurface pixel size of 22 $\mu$m, the demonstrated electro-optic metasurfaces promise successful realization of unique optical functions, such as harmonic beam steering and spatiotemporal shaping as well as nonreciprocal operation.

While photodissociation of molecular systems has been extensively studied, the photoinduced formation of chemical bonds remains largely unexplored. Especially for larger aggregates, the electronic and nuclear dynamics involved in the cluster formation process remain elusive. This limitation is rooted in difficulties to prepare reactants at well-defined initial conditions. Here, we overcome this hurdle by exploiting the exceptional solvation properties of helium nanodroplets. We load the droplets with Mg atoms and investigate the dynamical response of the formed Mg$_n$ aggregates to photoexcitation with time-resolved photoelectron spectroscopy. Beside the response expected for conventional Mg$_n$ clusters, consisting of a prompt signal rise and a decay characteristic for van der Waals bonds, the transient spectra also show a delayed photoelectron band peaking at 1.2 ps. This delayed signal rise is characteristic for nuclear dynamics and represents the transition of Mg$_n$ aggregates from a metastable, foam-like configuration, where Mg atoms are stabilized with a previously predicted interatomic spacing of 9.5 A, to a compact cluster. With global fitting analysis and ion-electron coincidence detection, the concerted electronic and nuclear dynamics can be tracked on a fs timescale. We find that cluster formation, proceeding with a ($450\pm180$) fs time constant, is accompanied by the population of highly-excited atomic states. We propose an energy pooling reaction in collisions of two or more excited Mg atoms during cluster formation as the mechanism leading to population of these high-lying Mg states. Additionally, conversion to kinetic energy through electronic relaxation leads to fragmentation and ejection of ionic cluster fragments from the He droplet. These results underline the potential of He droplets for time-resolved studies of bond formation and to uncover involved processes.

We demonstrate that including the second viscosity of an electron gas in the hydrodynamic model allows for highly accurate modeling of the optical response of heavily doped semiconductors. In our setup, which improves resonance visibility compared to previous approaches, plasmon resonances become more distinct, allowing for detailed analysis of the underlying physics. With advanced fitting techniques based on a physics-informed cost function and a tailored optimization algorithm, we obtain close agreement between simulations and experimental data across different sample thicknesses. This enhanced resonance visibility, combined with our integrated approach, shows that key parameters such as doping level and effective electron mass can be retrieved from a single optical measurement. The spatial dispersion taken into account in the hydrodynamic framework is essential for accurately describing the optical response of plasmonic materials in this frequency range and is likely to become a standard modeling approach.

In this study, we designed a spiral-shaped milli-reactor with a T-junction microchannel to generate digital droplets for studying and observing the digital freezing process of droplets. During the study of the recalescence and solidification processes of digital droplets dynamically moving in microchannels, we found that although the digital generation of droplets in our channel aligns well with the literature, achieving the digitalization of the droplet freezing process is very challenging. Even the initial phase of freezing (the recalescence process) exhibits significant randomness. A key feature of the randomness in the freezing process is the nucleation position of droplets within the channel, which significantly impacts the digital characteristics and hinders digital freezing. During the investigation of freezing randomness, we identified five distinct nucleation profiles, which largely determine the evolution of the freezing front and the duration of the recalescence phase. However, upon studying the motion velocity of the freezing front, we found that these velocities are temperature-dependent. This aligns with the results of our phase-field simulations and experimental findings, indicating that the release of latent heat during the recalescence process is stable. Additionally, the randomness in freezing may also stem from the deformation of droplets during the solidification process. In this study, we identified two distinct solidification modes during the freezing phase: one initiating from the droplet's head or tail and the other starting from the middle, with the latter causing significant droplet deformation. Through statistical analysis, we further explored the influence of flow rate variation on the digital clustering of droplet freezing and discovered flow rate parameters that optimize freezing digitalization.

Understanding the dynamics of pedestrian crowds is an outstanding challenge crucial for designing efficient urban infrastructure and ensuring safe crowd management. To this end, both small-scale laboratory and large-scale real-world measurements have been used. However, these approaches respectively lack statistical resolution and parametric controllability, both essential to discovering physical relationships underlying the complex stochastic dynamics of crowds. Here, we establish an investigation paradigm that offers laboratory-like controllability, while ensuring the statistical resolution of large-scale real-world datasets. Using our data-driven neural crowd simulator (\modelname{}), which we train on large-scale data and validate against key statistical features of crowd dynamics, we show that we can perform effective surrogate crowd dynamics experiments without training on specific scenarios. We not only reproduce known experimental results on pairwise avoidance, but also uncover the vision-guided and topologic nature of N-body interactions. These findings show how virtual experiments based on neural simulation enable data-driven scientific discovery.

Accurate lifetime measurements of excited states of highly charged ions (HCIs) are essential for advancing diagnostics in both laboratory and astrophysical plasmas, especially in the X-ray regime. The Hanle effect, which utilizes external magnetic fields to modify photon scattering patterns, provides a powerful technique for these measurements. Previously, this method has been successfully employed for He-like ions. Here, we present a theoretical study of the prospects of the Hanle effect for lifetime determinations of Li-like ions. Our results highlight the potential for plasma diagnostics and X-ray spectral analysis.

This paper describes the performance of the LABDOS01, a silicon diode-based spectrometer suitable for dose measurements in mixed radiation fields. The instrument is currently being used in two high-altitude environmental dose monitoring projects: SAMADHA (South Atlantic Magnetic Anomaly Dosimetry at High Altitude) at Chacaltaya (Bolivia, 5240 m a.s.l.) and CORDIAL (COsmic Rays Dosimetry In Antarctic Latitudes) at the Concordia station (Antarctica, 3233 m a.s.l.). Before installing two of these devices at the measurement sites, the detectors were tested on flight routes covering a wide range of geomagnetic latitudes. The collected dosimetric data were compared with the expectations derived by the CARI-7A software, which provides the absorbed dose rate in silicon due to cosmic ray secondaries at a given position on the Earth. The measured dose rates along the flights at variable altitude and rigidity cutoff agree well with the simulated ones. By analyzing the spectrum of the energy deposited in the silicon layer, we derive an empirical method to approximately evaluate the ambient dose equivalent $H^{*}(10)$, a quantity directly related to the biological damage caused by environmental radiation.

This study explores the suitability of hydrogen-based plasma in direct power extraction (DPE) as a non-conventional electricity generation method. We apply computational modeling and principles in physics and chemistry to estimate different thermal and electric properties of a water-vapor/nitrogen/cesium-vapor (H2O/N2/Cs) gas mixture with different levels of cesium (Cs) at a fixed temperature of 2300 K (2026.85 {\deg}C). This gas mixture and temperature are selected because they resemble the stoichiometric combustion of hydrogen with air, followed by the addition of the alkali metal element cesium to allow ionization, thus converting the gas mixture into electrically conducting plasma. We vary the cesium mole fraction in the gas mixture by two orders of magnitude, from a minute amount of 0.0625% (1/1600) to a major amount of 16% (0.16). We use these results to further estimate the theoretical upper limit of the electric power output from a unit volume of a high-speed magnetohydrodynamic (MHD) channel, with the plasma accelerated inside it to twice the local speed of sound (Mach number 2) while subject to an applied magnetic field of 5 T (5 teslas). We report that there is an optimum cesium mole fraction of 3%, at which the power output is maximized. Per 1 m3 of plasma volume, the estimated theoretical electric power generation at 1 atm (101.325 kPa) pressure of the hydrogen-combustion mixture is extraordinarily high at 360 MW/m3, and the plasma electric conductivity is 17.5 S/m. This estimated power generation even reaches an impressive level of 1.15 GW/m3 (11500 MW/m3) if the absolute pressure can be decreased to 0.0625 atm (6.333 kPa), at which the electric conductivity exceeds 55 S/m (more than 10 times the electric conductivity of seawater).

Cosmology and GR remain largely inaccessible to high-school teaching due to the advanced prerequisites to master these topics. Integrating them into upper secondary teaching is a significant challenge that remains unresolved. This contribution reports on an implementation study of a GR and cosmology course for upper secondary school students as part of an educational project launched during the centenary of GR and tested ever since for several years. The course aimed to expand students' knowledge to include current physics topics while highlighting their foundations in areas of classical physics such as Newtonian mechanics, electromagnetism, and waves. Targeted teaching and learning materials are focused on conceptual and qualitative understanding, while systematically combined with a mathematical treatment accessible at the upper secondary level, avoiding oversimplification. A key element is an active learning approach, incorporating activities and tasks such as engaging applications related to current research, reflective exercises, thought experiments, and hands-on tasks. The main research objective was to explore whether a conceptually deep and educationally effective GR and cosmology course could be successfully implemented for non-specialist upper secondary students. A pre-post study assessed both conceptual learning and affective outcomes, including interest, curiosity, self-concept, and perceived relevance of science. Results showed encouraging gains in both learning and motivation, with large to very large effect sizes for conceptual learning of core principles. Additionally, no or small effects of predictors such as gender were observed. We conclude that the integration of GR and cosmology into upper secondary physics teaching, in the form of courses and materials that are engaging, comprehensible, and impactful, is feasible.

We introduce the $\Sigma^{\text{BSE}}@L^{\text{BSE}}$ self-energy in the quasi-particle self-consistent $GW$ (qs$GW$) framework (qs$\Sigma^{\text{BSE}}@L^{\text{BSE}}$). Here, $L$ is the two-particle response function which we calculate by solving the Bethe-Salpeter equation with the static, first-order $GW$ kernel. The same kernel is added to $\Sigma$ directly. For a set of medium organic molecules, we show that including the vertex both in $L$ and $\Sigma$ is crucial. This approach retains the good performance of qs$GW$ for predicting first ionization potentials and fundamental gaps, while it greatly improves the description of electron affinities. Its good performance places qs$\Sigma^{\text{BSE}}@L^{\text{BSE}}$ among the best-performing electron propagator methods for charged excitations. Adding the vertex in $L$ only, as commonly done in the solid state community, leads to devastating results for electron affinities and fundamental gaps. We also test the performance of BSE@qs$GW$ and qs$\Sigma^{\text{BSE}}@L^{\text{BSE}}$ for neutral charge-transfer excitation and find both methods to perform similar. We conclude that $\Sigma^{\text{BSE}}@L^{\text{BSE}}$ is a promising approximation to the electronic self-energy beyond $GW$. We hope that future research on dynamical vertex effects, second-order vertex corrections, and full self-consistency will improve the accuracy of this method, both for charged and neutral excitation energies.

Ab initio calculation of low energy scattering of electrons (muons) off hydrogen (muonic hydrogen) are performed on the basis of Faddeev-Merkuriev (FM) equations. The explicit contribution of induced dipole interaction in the asymptotic behavior of the wave function components has been incorporated into FM formalism. Elastic and inelastic cross sections have been calculated with high energy resolution in the vicinity of $n=2,3$ exited states thresholds of respective atoms. The Gailitis-Dumburg oscillations are discovered in some of calculated cross sections.

The Wasatch Front in Utah, USA, is currently a non-attainment area for ozone according to the Environmental Protection Agency's (EPA) National Ambient Air Quality Standards (NAAQS). Nitrogen oxides ($\mathrm{NO_x = NO_2 + NO}$) and volatile organic compounds (VOCs), in the presence of sunlight, lead to ozone formation in the troposphere. When the rate of oxidant production, defined as the sum of $\mathrm{O_3}$ and $\mathrm{NO_2}$, is faster than the rate of $\mathrm{NO_x}$ production, a region is said to be $\mathrm{NO_x}$limited, and ozone formation will be limited by the concentration of $\mathrm{NO_x}$ species in the region. The inverse of this situation makes the region VOC-limited. Knowing whether a region is $\mathrm{NO_x}$-limited or VOC-limited can aid in generating effective mitigation strategies. Understanding the background or regional contributions to ozone in a region, whether from the transport of precursors or of ozone, provides information about the lower limit for ozone concentrations that a region can achieve through regulation of local precursors. In this paper, measured oxidant and $\mathrm{NO_x}$ concentrations are analyzed from 14 counties in the state of Utah to calculate the regional and local contributions to ozone for each region. This analysis is used to determine the nature of the atmosphere in each county by identifying whether the region is VOC or $\mathrm{NO_x}$-limited. Furthermore, this analysis is performed for each county for the years 2012 and 2022 to assess changes in the oxidative nature and quantify regional and local contributions to ozone over a 10-year period. All studied counties--except for Washington County--in Utah were found to be VOC-limited in 2012. This shifted in 2022, with most counties being either in a transitional state or $\mathrm{NO_x}$-limited. Local contributions to ozone increased in two major counties, Cache and Salt Lake Counties, but decreased in Carbon, Davis, Duchesne, Uinta, Utah, Washington, and Weber Counties. Generally, the regional contributions to oxidant concentrations decreased across the state. A summertime spike in both regional and local contributions to oxidants was observed. Smoke from wildfires was found to increase regional contributions to oxidants and shift the local regime to be more $\mathrm{NO_x}$-limited.

A light-emitting-diodes (LEDs)-integrated silicon carbide (SiC) insulated gate bipolar transistors (LI-IGBT) is proposed in this paper. The novelty of the LI-IGBT depends on the photogeneration effect of III-nitride LEDs embedded in the poly-Si regions of IGBT. Then, the photogenerated carriers are formed in the JFET region and the drift layer, indicating the increase of the conductivity in LI-IGBT as compared with the SiC IGBT with hole-barrier layer (H-IGBT) and the SiC IGBT with charge storage layer (CSL-IGBT). The static simulation results show that the electron density of the LI-IGBT at the middle of the drift layer is separately 17.44 times and 15.81 times higher than those of the H-IGBT and CSL-IGBT, yielding 40.91% and 37.38% reduction of forward voltage drop, respectively, and also, the LI-IGBT shows 304.59% and 263.67% improvements in BFOM as compared with CSL-IGBT and H-IGBT, respectively. For the dynamic simulation in one cycle, the loss of LI-IGBT is separately reduced by 6.57% and 8.57% compared to H-IGBT and CSL-IGBT. Meanwhile, the relationship between VC(sat) and Eturn-off can be optimized by adjusting collector doping and minority carrier lifetime. These results reveal that the proposed SiC IGBT will be more suitable for ultra-high voltage application.

Electron beam (e-beam) generated plasmas with applied cross electric and magnetic $\left( \textbf{E} \times \textbf{B} \right)$ fields are promising for low-damage material processing. However, these plasmas can be subject to the formation of azimuthally propagating structures that enhance the radial transport of energetic charged species, which can harm the gentle processing capability of the plasma. In this work we investigate the azimuthal structure formation in an e-beam generated $\textbf{E} \times \textbf{B}$ plasma using experimental diagnostics and 2D3V particle-in-cell simulations. Our findings demonstrate the formation of multiple simultaneously occurring azimuthally propagating modes that exhibit a nontrivial radial dependence. It is suggested that the multimodal azimuthal spectrum is caused by the complex nature of the ion dynamics in the plasma.

Exciton-polariton condensation occurs at the extrema of the underlying dispersion where the density of states diverges and carriers can naturally accumulate. The existence of multiple such points leads to coupling and competition between the associated modes and dynamical redistribution of the carriers in the dispersion. Here, we directly engineer the above situation via subwavelength periodic patterning of a two-dimensional nanostructure. This leads to multimode condensation into a pair of symmetric condensates that form at high-momenta, accidental-coupling points, and a high-symmetry $\Gamma$-point with a bound-in-the-continuum (BiC) state. The dynamical behaviour of the system reveals the non-simultaneous appearance of these condensates and the interplay of non-trivial gain and relaxation mechanisms. We fully characterise the quasi-static and dynamical regime of this artificial crystal and the properties of the different condensates. This understanding is necessary when band-structure engineering techniques are used to achieve precise control of condensate formation with given energy and momentum.

We integrate numerically the Schr\"odinger equation for the model helium atom irradiated by intense few-cycle laser pulse and find the emitted XUV spectra. They demonstrate resonant peaks at the frequencies of transitions from the doubly-excited autoionizing states (AISs) to the ground state. We study the properties of these peaks depending on the laser pulse duration and find that the decay of the AISs due to photoionization by the laser field affects them. Moreover, we consider the classical system of two coupled oscillators and find that both the quantum (the atom with AIS in the field) and the classical (the coupled oscillators with friction) systems demonstrate Fano-like resonant peak described by an essentially complex asymmetry parameter. We find a remarkable similarity in the behavior of these systems and conclude that the classical system of coupled oscillators with friction is an analogy of the AIS having an extra decay channel in addition to the autoionization one.

In this study, the influence of temperature $(40-80 ^\circ\mathrm{C})$ and light exposure on the dissolution of combusted iron particles in aqueous oxalic acid $(0.45~\mathrm{mol/L})$ is experimentally investigated. Unlike previous studies, real combusted iron particles with varying fuel-to-air equivalence ratios were used instead of model oxides. In-situ video recordings reveal the evolution of particle size and morphology. Increasing temperature and short-wavelength light exposure enhance the reaction rate, with light-induced effects only becoming significant above $40 ^\circ$C for the duration of the experiments. This behavior differs significantly from hematite/maghemite oxides, attributed to the internal Fe phase structure of the combusted iron particles. At $80^\circ$C with additional light irradiation, a sudden decrease in reaction rate is observed due to solid ferrous oxide formation. While the fuel-to-air ratio induces differences in iron oxide phase composition, it does not affect the dissolution combusted iron particles significantly.

Sensitive avalanche photodetectors (APDs) that operate within the ultraviolet spectrum are critically required for applications in detecting fire and deep-space exploration. However, the development of such devices faces significant challenges, including high avalanche breakdown voltage, the necessity for complex quenching circuits, and thermal runaway associated with Geiger-mode avalanche operation. To mitigate these issues, we report on a 4H-SiC APD design utilizing micro-holes (MHs) structures and Al nano-triangles (NTs) to enhance surface electric field driven by strong localized surface plasmon excitations and lightning-rod effect. The device demonstrates a record low avalanche breakdown voltage of approximately 14.5 V, a high detectivity of 7E13 Jones, a nanosecond-level response time, and repeated stable detections without the requirement of a quenching circuit. Collectively, when compared with the conventional wide-bandgap-based APDs, this device achieves a reduction in avalanche breakdown voltage by an order of magnitude and exhibits a substantial increase in detectivity. Consequently, the proposed APD configuration presents a promising candidate for ultraviolet detection and integrated optoelectronic circuits.

Spatiotemporal optical vortices (STOV) are space-time structured light pulses with a unique topology that couples spatial and temporal domains and carry transverse orbital angular momentum (OAM). Up to now, their generation has been limited to the visible and infrared regions of the spectrum. During the last decade, it was shown that through the process of high-order harmonic generation (HHG) it is possible to up-convert spatial optical vortices that carry longitudinal OAM from the near-infrared into the extreme-ultraviolet (EUV), thereby producing vortices with distinct femtosecond and attosecond structure. In this work we demonstrate theoretically and experimentally the generation of EUV spatiotemporal and spatiospectral vortices using near infrared STOV driving laser pulses. We use analytical expressions for focused STOVs to perform macroscopic calculations of HHG that are directly compared to the experimental results. As STOV beams are not eigenmodes of propagation, we characterize the highly-charged EUV STOVs both in the near and far fields, to show that they represent conjugated spatiotemporal and spatiospectral vortex pairs. Our work provides high-frequency light beams topologically coupled at the nanometer/attosecond scales domains with transverse OAM, that could be suitable to explore electronic dynamics in magnetic materials, chiral media, and nanostructures.

We present a spectrally accurate bounce-averaging operator implemented as a part of the automatically differentiable DESC stellarator optimization suite. Using this operator, we calculate the proxy for neoclassical transport coefficient $\epsilon_{\mathrm{eff}}^{3/2}$ in the $1/\nu$ regime and benchmark it against the NEO code. Ultimately, by employing this differentiable approximation, for the first time, we directly optimize a finite-$\beta$ stellarator to enhance neoclassical transport using reverse-mode differentiation. This ensures that the computational cost of determining the gradients does not depend on the number of input parameters.

This study investigates the dynamics of pedestrian crossing flows with varying crossing angles $\alpha$ to classify different scenarios and derive implications for crowd management. Probability density functions of four key features$-$velocity $v$, density $\rho$, avoidance number $Av$, and intrusion number $In$$-$were analyzed to characterize pedestrian behavior. Velocity-density fundamental diagrams were constructed for each $\alpha$ and fitted with functional forms from existing literature. Classification attempts using $Av$-$In$ and $v$-$\rho$ phase spaces revealed significant overlaps, highlighting the limitations of these metrics alone for scenario differentiation. To address this, machine learning models, including logistic regression and random forest, were employed using all four features. Results showed robust classification performance, with $v$ and $Av$ contributing most significantly. Insights from feature importance metrics and classification accuracy offer practical guidance for managing high-density crowds, optimizing pedestrian flow, and designing safer public spaces. These findings provide a data-driven framework for advancing pedestrian dynamics research.

Modeling of collisionless galactic systems is based on the N-body model, which requires large computational resources due to the long-range nature of gravitational forces. The most common method for calculating gravity is the TreeCode algorithm, which provides a faster calculation of the force compared to the direct summation of contributions from all particles for N-body simulation. An analysis of the computational efficiency is performed for models with the number of particles up to $10^{8}$. We considered several processors with different architectures in order to determine the performance of parallel simulations based on the OpenMP standard. An analysis of the use of extra threads in addition to physical cores shows an increase in simulation performance only when all logical threads are loaded, which doubles the total number of threads. This gives an increase in the efficiency of parallel computing by 20 percent on average.

Physical motion has long been at the heart of what humans identify as life. Early thinkers about what differentiates life from inanimate matter connected agency to autonomous motion, a line of thinking that continued until Robert Brown's experiments showed decisively that autonomous motion, and many other physical behaviors and organizational principles, belong to inanimate matter. An incredible trajectory of inquiry for the next 100 years showed that life is characterized by the ability to reproduce, to inherit, to adapt. But throughout this arc, a persistent search continued for that fundamental process, that gain in function from matter to life. Subsequent decades showed that genetic sequence is not enough. The leap occurs somewhere in cells' watery milieu - physicochemical functions unique to life. In this Review, we explore how advances in the last decade have embraced the deep coupling of physics and cellular biochemistry to shed light on the matter/life nexus. Brownian flow physics are more than implicated but rather help instantiate life. The reader will observe in this review many references to works in cell biology - as many or more than in biophysics or fluid mechanics. This is perhaps not as intentional as it is unavoidable in the effort to see how matter becomes life inside the watery compartment of the cell.

A thulium-doped fiber laser operating quasi-continuous-wave generated 198 W of instantaneous output power in 0.2-ms pulses at 50 Hz repetition rate. The duty cycle becomes 1% and the average output power 2.0 W. This was cladding-pumped with 408 W from 0.79-um diode lasers (average pump power 4.1 W). The pump switch-on time was ~10 us at full power, with the laser exhibiting relaxation oscillations starting ~5 us after the start of the pumping and lasting for ~5 us. The slope efficiency with respect to absorbed pump power was 67% up to 200 W of absorbed pump power and 52% at the full 352 W of absorbed pump power. The low duty cycle simplified the heatsinking, despite the high instantaneous power and thermal load.

We analyse the collisionless tearing mode instability of a current sheet with a strong shear flow across the layer. The growth rate decreases with increasing shear flow, and is completely stabilized as the shear flow becomes Alfv\'enic. We also show that in the presence of strong flow shear, the tearing mode growth rate decreases with increasing background ion-to-electron temperature ratio, the opposite behaviour to the tearing mode without flow shear. We find that even a relatively small flow shear is enough to dramatically alter the scaling behaviour of the mode, because the growth rate is small compared to the shear flow across the ion scales (but large compared to shear flow across the electron scales). Our results may explain the relative absence of reconnection events in the near-Sun Alfv\'enic solar wind observed recently by NASA's Parker Solar Probe.

Objective: This study explores a semi-supervised learning (SSL), pseudo-labeled strategy using diverse datasets to enhance lung cancer (LCa) survival predictions, analyzing Handcrafted and Deep Radiomic Features (HRF/DRF) from PET/CT scans with Hybrid Machine Learning Systems (HMLS). Methods: We collected 199 LCa patients with both PET & CT images, obtained from The Cancer Imaging Archive (TCIA) and our local database, alongside 408 head&neck cancer (HNCa) PET/CT images from TCIA. We extracted 215 HRFs and 1024 DRFs by PySERA and a 3D-Autoencoder, respectively, within the ViSERA software, from segmented primary tumors. The supervised strategy (SL) employed a HMLSs: PCA connected with 4 classifiers on both HRF and DRFs. SSL strategy expanded the datasets by adding 408 pseudo-labeled HNCa cases (labeled by Random Forest algorithm) to 199 LCa cases, using the same HMLSs techniques. Furthermore, Principal Component Analysis (PCA) linked with 4 survival prediction algorithms were utilized in survival hazard ratio analysis. Results: SSL strategy outperformed SL method (p-value<0.05), achieving an average accuracy of 0.85 with DRFs from PET and PCA+ Multi-Layer Perceptron (MLP), compared to 0.65 for SL strategy using DRFs from CT and PCA+ K-Nearest Neighbor (KNN). Additionally, PCA linked with Component-wise Gradient Boosting Survival Analysis on both HRFs and DRFs, as extracted from CT, had an average c-index of 0.80 with a Log Rank p-value<<0.001, confirmed by external testing. Conclusions: Shifting from HRFs and SL to DRFs and SSL strategies, particularly in contexts with limited data points, enabling CT or PET alone to significantly achieve high predictive performance.

Muon Space (Muon) is building a constellation of small satellites, many of which will carry global navigation satellite system-reflectometry (GNSS-R) receivers. In preparation for the launch of this constellation, we have developed a generalized deep learning retrieval pipeline, which now produces operational GNSS-R near-surface soil moisture retrievals using data from NASA's Cyclone GNSS (CYGNSS) mission. In this article, we describe the input datasets, preprocessing methods, model architecture, development methods, and detail the soil moisture products generated from these retrievals. The performance of this product is quantified against in situ measurements and compared to both the target dataset (retrievals from the Soil Moisture Active-Passive (SMAP) satellite) and the v1.0 soil moisture product from the CYGNSS mission. The Muon Space product achieves improvements in spatial resolution over SMAP with comparable performance in many regions. An ubRMSE of 0.032 cm$^3$ cm$^{-3}$ for in situ soil moisture observations from SMAP core validation sites is shown, though performance is lower than SMAP's when comparing in forests and/or mountainous terrain. The Muon Space product outperforms the v1.0 CYGNSS soil moisture product in almost all aspects. This initial release serves as the foundation of our operational soil moisture product, which soon will additionally include data from Muon Space satellites.

Born in Punjab (India) in December 1941, Balraj Singh is not only the single most prolific nuclear data evaluator and disseminator of nuclear structure and decay data with 148 evaluations in Nuclear Data Sheets -- 85 as the first and often only author -- plus other journals, but his upmost curiosity and dedication brought him to be one of the finest nuclear physicists, with an everlasting influence on many of us. Balraj passed away about a year ago on 9 October 2023 in Ottawa, Ontario (Canada) at the age of 81, and at Atomic Data and Nuclear Data Tables we would like to commemorate some of his scientific achievements.

For decades, researchers have developed task-specific models to address scientific challenges across diverse disciplines. Recently, large language models (LLMs) have shown enormous capabilities in handling general tasks; however, these models encounter difficulties in addressing real-world scientific problems, particularly in domains involving large-scale numerical data analysis, such as experimental high energy physics. This limitation is primarily due to BPE tokenization's inefficacy with numerical data. In this paper, we propose a task-agnostic architecture, BBT-Neutron, which employs a binary tokenization method to facilitate pretraining on a mixture of textual and large-scale numerical experimental data. The project code is available at https://github.com/supersymmetry-technologies/bbt-neutron. We demonstrate the application of BBT-Neutron to Jet Origin Identification (JoI), a critical categorization challenge in high-energy physics that distinguishes jets originating from various quarks or gluons. Our results indicate that BBT-Neutron achieves comparable performance to state-of-the-art task-specific JoI models. Furthermore, we examine the scaling behavior of BBT-Neutron's performance with increasing data volume, suggesting the potential for BBT-Neutron to serve as a foundational model for particle physics data analysis, with possible extensions to a broad spectrum of scientific computing applications for Big Science experiments, industrial manufacturing and spacial computing.

We systematically investigated the growth of Si-doped $\beta$-Ga$_2$O$_3$ films using LPCVD system, achieving high electron mobilities of 162 cm$^2$/V.s and 149 cm$^2$/V.s at carrier concentrations of $1.51 \times 10^{17}$ cm$^{-3}$ and $1.15 \times 10^{17}$ cm$^{-3}$, respectively, for homoepitaxial (010) $\beta$-Ga$_2$O$_3$ films grown on $\beta$-Ga$_2$O$_3$ substrates and heteroepitaxial (-201) $\beta$-Ga$_2$O$_3$ films grown on off-axis c-sapphire substrates with 6{\deg} miscut, representing the highest mobilities reported for LPCVD-grown $\beta$-Ga$_2$O$_3$ materials. Carrier concentrations were precisely tuned by varying SiCl$_4$ flow rates at a growth temperature of 1000{\deg}C, resulting in concentrations ranging from $1.15 \times 10^{17}$ to $1.19 \times 10^{19}$ cm$^{-3}$, as confirmed by both Hall and C-V measurements. The films exhibited high crystalline quality, confirmed by high-resolution XRD and Raman spectroscopy, indicating phase purity and structural integrity. Surface morphologies characterized by FESEM and AFM imaging showed a strong correlation between carrier concentrations and surface smoothness, with lower concentrations resulting in reduced RMS roughness. SIMS analysis revealed uniform Si incorporation, with low carbon, hydrogen, and chlorine impurities below detection limits, indicating high purity of the films. A high low-temperature peak mobility exceeding 843 cm$^2$/V$\cdot$s was achieved for (-201) $\beta$-Ga$_2$O$_3$ films at 80 K, highlighting the high purity and low compensation of these films. These findings emphasize the potential of LPCVD growth system for producing high-purity $\beta$-Ga$_2$O$_3$ films with thickness ranging between ~2.3-11.7 $\mu$m and faster growth rates (~4.7-17 $\mu$m/hr), promising transport properties, controllable doping, and scalability for developing high power vertical devices.

Single-molecule localization microscopy generates point clouds corresponding to fluorophore localizations. Spatial cluster identification and analysis of these point clouds are crucial for extracting insights about molecular organization. However, this task becomes challenging in the presence of localization noise, high point density, or complex biological structures. Here, we introduce MIRO (Multimodal Integration through Relational Optimization), an algorithm that uses recurrent graph neural networks to transform the point clouds in order to improve clustering efficiency when applying conventional clustering techniques. We show that MIRO supports simultaneous processing of clusters of different shapes and at multiple scales, demonstrating improved performance across varied datasets. Our comprehensive evaluation demonstrates MIRO's transformative potential for single-molecule localization applications, showcasing its capability to revolutionize cluster analysis and provide accurate, reliable details of molecular architecture. In addition, MIRO's robust clustering capabilities hold promise for applications in various fields such as neuroscience, for the analysis of neural connectivity patterns, and environmental science, for studying spatial distributions of ecological data.

This paper proposes a new way to learn Physics-Informed Neural Network loss functions using Generalized Additive Models. We apply our method by meta-learning parametric partial differential equations, PDEs, on Burger's and 2D Heat Equations. The goal is to learn a new loss function for each parametric PDE using meta-learning. The derived loss function replaces the traditional data loss, allowing us to learn each parametric PDE more efficiently, improving the meta-learner's performance and convergence.

The separation of colloidal particles is of great importance in many fields, such as chemistry, medicine, and chemical engineering. However, separating particles based on their surface physico-chemical properties remains challenging. Diffusiophoresis and diffusioosmosis have been used for the continuous separation of colloids based on particle size and zeta potential. Here, we demonstrate that diffusiophoresis and diffusioosmosis in electrolyte streams enable the separation of carboxylate polystyrene particles with similar sizes and zeta potentials but distinct surface concentrations of carboxyl groups. A double junction microfluidic device, fed with low and high electrolyte concentration streams, was used to create steady-state salt concentration gradients. As the streams mix along the channel, the particles are exposed to environments with varying salinity levels, and their dynamics are influenced by the sensitivity of their zeta potential to the local salt concentration. Typically, for carboxylate polystyrene particles, the magnitude of the electrophoretically measured zeta potential decreases with increasing salt concentrations. However, surface conductance effects, which depend on the surface concentration of the carboxyl groups, may lead to an opposite sensitivity of the zeta potential to salt. Therefore, under selected salt conditions, similarly sized polystyrene colloids with comparable zeta potentials in the low concentration stream, but different surface concentrations of carboxyl groups, can be separated in a continuous flow process when they exhibit opposite zeta potential sensitivities to salt. This mechanism has discipline-spanning potential for the continuous separation of colloids distinguished solely by surface physico-chemical properties that influence their zeta potential sensitivities, like roughness, permeability, heterogeneity, and chemical composition.

Quantum Error Correction (QEC) is widely regarded as the most promising path towards quantum advantage, with significant advances in QEC codes, decoding algorithms, and physical implementations. The success of QEC relies on achieving quantum gate fidelities below the error threshold of the QEC code, while accurately decoding errors through classical processing of the QEC stabilizer measurements. In this paper, we uncover the critical system-level requirements from a controller-decoder system (CDS) necessary to successfully execute the next milestone in QEC, a non-Clifford circuit. Using a representative non-Clifford circuit, of Shor factorization algorithm for the number 21, we convert the logical-level circuit to a QEC surface code circuit and finally to the physical level circuit. By taking into account all realistic implementation aspects using typical superconducting qubit processor parameters, we reveal a broad range of core requirements from any CDS aimed at performing error corrected quantum computation. Our findings indicate that the controller-decoder closed-loop latency must remain within tens of microseconds, achievable through parallelizing decoding tasks and ensuring fast communication between decoders and the controller. Additionally, by extending existing simulation techniques, we simulate the complete fault-tolerant factorization circuit at the physical level, demonstrating that near-term hardware performance, such as a physical error rate of 0.1% and 1000 qubits, are sufficient for the successful execution of the circuit. These results are general to any non-Clifford QEC circuit of the same scale, providing a comprehensive overview of the classical components necessary for the experimental realization of non-Clifford circuits with QEC.

In this paper we employ a configurational information measure, specifically the differential configurational complexity (DCC), to quantify the information content of lump-type solutions in various scalar field models, including two modified inverted $\phi^{4}$ models, the modified $\phi^{3}$ model, as well as two additional families of lump models. Our objective is to complement previous studies by providing an informational perspective that distinguishes different solutions based on their energy configurations. We explore how the DCC measure relates to energy and its applicability in analyzing degenerate states. Our findings indicate that DCC effectively correlates with the energy parameters of the solutions, offering significant insights into their informational properties. This study underscores the value of using informational metrics like DCC to deepen our understanding of the structural and dynamic characteristics of complex systems in theoretical physics.

The two-state solar wind paradigm is based on observations showing that slow and fast solar wind have distinct properties like helium abundances, kinetic signatures, elemental composition, and charge-state ratios. Nominally, the fast wind originates from solar sources that are continuously magnetically open to the heliosphere like coronal holes while the slow wind is from solar sources that are only intermittently open to the heliosphere like helmet streamers and pseudostreamers. The Alfv\'enic slow wind is an emerging 3rd class of solar wind that challenges the two-state fast/slow paradigm. It has slow wind speeds but is highly Alfv\'enic, i.e. has a high correlation between velocity and magnetic field fluctuations along with low compressibility typical of Alfv\'en waves, which is typically observed in fast wind. Its other properties are also more similar to the fast than slow wind. From 28 years of Wind observations at 1 AU, we derive the solar wind helium abundance ($A_\mathrm{He}$), Alfv\'enicity ($\left|\sigma_c\right|$), and solar wind speed ($v_\mathrm{sw}$). Characterizing vsw as a function of $\left|\sigma_c\right|$ and $A_\mathrm{He}$, we show that the maximum solar wind speed for plasma accelerated in source regions that are intermittently open is faster than the minimum solar wind speed for plasma accelerated in continuously open regions. We infer that the Alfv\'enic slow wind is likely solar wind originating from open-field regions with speeds below the maximum solar wind speed for plasma from intermittently open regions. We then discuss possible implications for solar wind heating and acceleration. Finally, we utilize the combination of helium abundance and normalized cross helicity to present a novel solar wind categorization scheme.

Constructing datasets representative of the target domain is essential for training effective machine learning models. Active learning (AL) is a promising method that iteratively extends training data to enhance model performance while minimizing data acquisition costs. However, current AL workflows often require human intervention and lack parallelism, leading to inefficiencies and underutilization of modern computational resources. In this work, we introduce PAL, an automated, modular, and parallel active learning library that integrates AL tasks and manages their execution and communication on shared- and distributed-memory systems using the Message Passing Interface (MPI). PAL provides users with the flexibility to design and customize all components of their active learning scenarios, including machine learning models with uncertainty estimation, oracles for ground truth labeling, and strategies for exploring the target space. We demonstrate that PAL significantly reduces computational overhead and improves scalability, achieving substantial speed-ups through asynchronous parallelization on CPU and GPU hardware. Applications of PAL to several real-world scenarios - including ground-state reactions in biomolecular systems, excited-state dynamics of molecules, simulations of inorganic clusters, and thermo-fluid dynamics - illustrate its effectiveness in accelerating the development of machine learning models. Our results show that PAL enables efficient utilization of high-performance computing resources in active learning workflows, fostering advancements in scientific research and engineering applications.

In this paper we consider a stochastic SEIQR (susceptible-exposed-infected-quarantined-recovered) epidemic model with a generalized incidence function. Using the Lyapunov method, we establish the existence and uniqueness of a global positive solution to the model, ensuring that it remains well-defined over time. Through the application of Young's inequality and Chebyshev's inequality, we demonstrate the concepts of stochastic ultimate boundedness and stochastic permanence, providing insights into the long-term behavior of the epidemic dynamics under random perturbations. Furthermore, we derive conditions for stochastic extinction, which describe scenarios where the epidemic may eventually die out, and V-geometric ergodicity, which indicates the rate at which the system's state converges to its equilibrium. Finally, we perform numerical simulations to verify our theoretical results and assess the model's behavior under different parameters.

The simulation of non-Markovian quantum dynamics plays an important role in the understanding of charge and exciton dynamics in the condensed phase environment, and yet it remains computationally expensive on classical computers. We have developed a variational quantum algorithm that is capable of simulating non-Markovian quantum dynamics. The algorithm captures the non-Markovian effect by employing the Ehrenfect trajectories in the path integral formulation and the Monte Carlo sampling of the thermal distribution. We tested the algorithm with the spin-boson model on the quantum simulator and the results match well with the exact ones. The algorithm naturally fits into the parallel computing platform of the NISQ devices and is well suited for anharmonic system-bath interactions and multi-state systems.

Multi-agent systems (MAS) utilizing multiple Large Language Model (LLM) agents with Retrieval Augmented Generation (RAG) can execute code locally and may become beneficial in cosmological data analysis. Here, we illustrate a first small step towards AI-assisted analyses and a glimpse of the potential of MAS to automate and optimize scientific workflows. The system architecture of our example package, that builds upon the autogen/ag2 framework, can be applied to MAS in any area of quantitative scientific research. Our work-in-progress code is open source and available at https://github.com/CMBAgents/cmbagent.

The oxidation of human sebum, a lipid mixture covering our skin, generates a range of volatile and semi-volatile carbonyl compounds that contribute largely to indoor air pollution in crowded environments. Kinetic models have been developed to gain a deeper understanding of this complex multiphase chemistry, but they rely partially on rough estimates of kinetic and thermodynamic parameters, especially those describing skin permeation. Here, we employ atomistic molecular dynamics simulations to study the translocation of selected skin oil oxidation products through a model stratum corneum membrane. We find these simulations to be non-trivial, requiring extensive sampling with up to microsecond simulation times, in spite of employing enhanced sampling techniques. We identify the high degree of order and stochastic, long-lived temporal asymmetries in the membrane structure as the leading causes for the slow convergence of the free energy computations. Even after tremendous simulation efforts, substantial errors in the free energy profiles remain, which we propagate to membrane permeabilities. These errors are independent of the enhanced sampling technique employed and very likely independent of the precise membrane model.

In this work, we developed a rigorous procedure for mapping the exact non-Markovian propagator to the generalized Lindblad form. It allows us to extract the negative decay rate that is the indicator of the non-Markovian effect. As a consequence, we can investigate the influence of the non-Markovian bath on the system's properties such as coherence and equilibrium state distribution. The understanding of the non-Markovian contribution to the dynamical process points to the possibility of leveraging non-Markovianity for quantum control.

Bilayers of transition-metal dichalcogenides show many exciting features, including long-lived interlayer excitons and wide bandgap tunability using strain. Not many investigations on experimental determinations of deformation potentials relating changes in optoelectronic properties of bilayer WSe$_2$ with the strain are present in the literature. Our experimental study focuses on three widely investigated high-symmetry points, K$_{c}$, K$_{v}$, and Q$_{c}$, where subscript c (v) refers to the conduction (valence) band, in the Brillouin zone of bilayer WSe$_2$. Using local biaxial strains produced by nanoparticle stressors, a theoretical model, and by performing the spatially- and spectrally-resolved photoluminescence measurements, we determine absolute deformation potential of -5.10 $\pm$ 0.24 eV for Q$_{c}$-K$_{v}$ indirect bandgap and -8.50 $\pm$ 0.92 eV for K$_{c}$-K$_{v}$ direct bandgap of bilayer WSe$_2$. We also show that $\approx$0.9% biaxial tensile strain is required to convert an indirect bandgap bilayer WSe$_2$ into a direct bandgap semiconductor. Moreover, we also show that a relatively small amount of localized strain $\approx$0.4% is required to make a bilayer WSe$_2$ as optically bright as an unstrained monolayer WSe$_2$. The bandgap deformation potentials measured here will drive advances in flexible electronics, sensors, and optoelectronic- and quantum photonic- devices through precise strain engineering.

Full waveform inversion (FWI) is able to construct high-resolution subsurface models by iteratively minimizing discrepancies between observed and simulated seismic data. However, its implementation can be rather involved for complex wave equations, objective functions, or regularization. Recently, automatic differentiation (AD) has proven to be effective in simplifying solutions of various inverse problems, including FWI. In this study, we present an open-source AD-based FWI framework (ADFWI), which is designed to simplify the design, development, and evaluation of novel approaches in FWI with flexibility. The AD-based framework not only includes forword modeling and associated gradient computations for wave equations in various types of media from isotropic acoustic to vertically or horizontally transverse isotropic elastic, but also incorporates a suite of objective functions, regularization techniques, and optimization algorithms. By leveraging state-of-the-art AD, objective functions such as soft dynamic time warping and Wasserstein distance, which are difficult to apply in traditional FWI are also easily integrated into ADFWI. In addition, ADFWI is integrated with deep learning for implicit model reparameterization via neural networks, which not only introduces learned regularization but also allows rapid estimation of uncertainty through dropout. To manage high memory demands in large-scale inversion associated with AD, the proposed framework adopts strategies such as mini-batch and checkpointing. Through comprehensive evaluations, we demonstrate the novelty, practicality and robustness of ADFWI, which can be used to address challenges in FWI and as a workbench for prompt experiments and the development of new inversion strategies.

The domain decomposition (DD) nonlinear-manifold reduced-order model (NM-ROM) represents a computationally efficient method for integrating underlying physics principles into a neural network-based, data-driven approach. Compared to linear subspace methods, NM-ROMs offer superior expressivity and enhanced reconstruction capabilities, while DD enables cost-effective, parallel training of autoencoders by partitioning the domain into algebraic subdomains. In this work, we investigate the scalability of this approach by implementing a "bottom-up" strategy: training NM-ROMs on smaller domains and subsequently deploying them on larger, composable ones. The application of this method to the two-dimensional time-dependent Burgers' equation shows that extrapolating from smaller to larger domains is both stable and effective. This approach achieves an accuracy of 1% in relative error and provides a remarkable speedup of nearly 700 times.

Light volatile elements in lunar regolith are thought to be a mixture of the solar wind and Earth's atmosphere, the latter sourced in the absence of geomagnetic field. However, the extent to which both the current and primitive geodynamo influence the transport of terrestrial ions still remains unclear, and this uncertainty is further complicated by the enigmatic composition and poorly constrained location of the Eoarchean exosphere. Here we use 3-D MHD numerical simulations with present-day magnetized and Archean unmagnetized atmospheres to investigate how Earth's intrinsic magnetic field affects this transfer, aiming to constrain how and when the lunar isotopic signature provides a record of Earth's paleoatmosphere. We find that atmospheric transfer is efficient only when the Moon is within Earth's magnetotail. The non-solar contribution to the lunar soil is best explained by implantation during the long history of the geodynamo, rather than any short, putatively unmagnetized epoch of early Earth. This further suggests the history of the terrestrial atmosphere, spanning billions of years, could be preserved in buried lunar soils. Our results indicate that the elemental abundances of Apollo samples are very sensitive to Earth's exobase altitude, which, at the time of ion implantation, was never smaller than 190 km.

Particles are accelerated to very high, non-thermal energies in space, solar, and astrophysical plasma environments. In cosmic ray physics, the "Hillas limit" is often used as a rough estimate (or the necessary condition) of the maximum energy of particles. This limit is based on the concepts of one-shot direct acceleration by a system-wide motional electric field, as well as stochastic and diffusive acceleration in strongly turbulent environments. However, it remains unclear how well this limit explains the actual observed maximum energies of particles. Here we show, based on a systematic review, that the observed maximum energy of particles -- those in space, solar, astrophysical, and laboratory environments -- often reach the energy predicted by the Hillas limit. We also found several exceptions, such as electrons in solar flares and jet-terminal lobes of radio galaxies, as well as protons in planetary radiation belts, where deviations from this limit occur. We discuss possible causes of such deviations, and we argue in particular that there is a good chance of detecting ultra-high-energy ($\sim$100 GeV) solar flare electrons that have not yet been detected. We anticipate that this study will facilitate further interdisciplinary discussions on the maximum energy of particles and the underlying mechanisms of particle acceleration in diverse plasma environments.

Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows. However, as standard datasets in this space often cover small classes of physical behavior, it can be difficult to evaluate the efficacy of new approaches. To address this gap, we introduce the Well: a large-scale collection of datasets containing numerical simulations of a wide variety of spatiotemporal physical systems. The Well draws from domain experts and numerical software developers to provide 15TB of data across 16 datasets covering diverse domains such as biological systems, fluid dynamics, acoustic scattering, as well as magneto-hydrodynamic simulations of extra-galactic fluids or supernova explosions. These datasets can be used individually or as part of a broader benchmark suite. To facilitate usage of the Well, we provide a unified PyTorch interface for training and evaluating models. We demonstrate the function of this library by introducing example baselines that highlight the new challenges posed by the complex dynamics of the Well. The code and data is available at https://github.com/PolymathicAI/the_well.

Refractory high-entropy alloy (RHEA) is a promising class of materials with potential applications in extreme environments, where the dominant failure mode is thermal creep. The design of these alloys, therefore, requires an understanding of how their microstructure and local chemical distribution affect creep behavior. In this study, we performed high-fidelity atomistic simulations using machine-learning interatomic potentials to explore the creep behavior of MoNbTaW RHEA under a wide range of stress and temperature conditions. We parametrized grain size and local chemical order (LCO) to investigate the effects of these two important design variables, which can be controlled during the alloy fabrication process. Our investigation revealed that resistance to creep deformation is enhanced with larger grain size and higher levels of LCO. This study highlights the importance of utilizing LCO in conjunction with other microstructural properties when designing RHEAs for extreme environment applications.

Invariant measures are widely used to compare chaotic dynamical systems, as they offer robustness to noisy data, uncertain initial conditions, and irregular sampling. However, large classes of systems with distinct transient dynamics can still exhibit the same asymptotic statistical behavior, which poses challenges when invariant measures alone are used to perform system identification. Motivated by Takens' seminal embedding theory, we propose studying invariant measures in time-delay coordinates, which exhibit enhanced sensitivity to the underlying dynamics. Our first result demonstrates that a single invariant measure in time-delay coordinates can be used to perform system identification up to a topological conjugacy. This result already surpasses the capabilities of invariant measures in the original state coordinate. Continuing to explore the power of delay-coordinates, we eliminate all ambiguity from the conjugacy relation by showing that unique system identification can be achieved using additional invariant measures in time-delay coordinates constructed from different observables. Our findings improve the effectiveness of invariant measures in system identification and broaden the scope of measure-theoretic approaches to modeling dynamical systems.

We present the results of a novel type of numerical simulation that realizes a rotating Universe with a shear-free, rigid body rotation inspired by a G\"{o}del-like metric. We run cosmological simulations of unperturbed glasses with various degrees of rotation in the Einstein-de Sitter and the $\Lambda$CDM cosmologies. To achieve this, we use the StePS N-body code capable of simulating the infinite Universe, overcoming the technical obstacles of classical toroidal (periodic) topologies that would otherwise prevent us from running such simulations. Results show a clear anisotropy between the polar and equatorial expansion rates with more than $1\%$ deviation from the isotropic case for maximal rotation without closed timeline curves within the horizon, $\omega_{0} \approx 10^{-3}$ Gyr$^{-1}$; a considerable effect in the era of precision cosmology.

Current theories of diffusiophoresis in porous media are limited to a porous medium saturated with a valence symmetric electrolyte. A predictive model for diffusiophoresis in porous media saturated with a valence asymmetric electrolyte, or a general mixture of valence symmetric and asymmetric electrolytes, is lacking. To close this knowledge gap, in this work we develop a mathematical model, based upon the regular perturbation method and numerical integration, to compute the diffusiophoretic mobility of a colloid in porous media saturated with a general mixture of electrolytes. We model the electrokinetics using the Poisson-Nernst-Planck equations and the fluid transport in porous media using the Brinkman equation with an electric body force. We report three novel key findings. First, we demonstrate that, in the same electrolyte concentration gradient, lowering the permeability of the porous medium can significantly weaken the colloid diffusiophoretic motion. Second, we show that, surprisingly, by using a valence asymmetric electrolyte the colloid diffusiophoretic motion in a denser porous medium can be stronger than that in a less dense porous medium saturated with a symmetric electrolyte. Third, we demonstrate that varying the composition of an electrolyte mixture does not only change the strength of the colloid diffusiophoretic motion drastically, but also qualitatively its direction. The model developed from this work can be used to understand and predict natural phenomena such as intracellular transport, as well as design technological applications such as enhanced oil recovery, nanoparticle drug delivery, and colloidal species separation.

Accurate segmentation of gross tumor volume (GTV) is essential for effective MRI-guided adaptive radiotherapy (MRgART) in head and neck cancer. However, manual segmentation of the GTV over the course of therapy is time-consuming and prone to interobserver variability. Deep learning (DL) has the potential to overcome these challenges by automatically delineating GTVs. In this study, our team, $\textit{UW LAIR}$, tackled the challenges of both pre-radiotherapy (pre-RT) (Task 1) and mid-radiotherapy (mid-RT) (Task 2) tumor volume segmentation. To this end, we developed a series of DL models for longitudinal GTV segmentation. The backbone of our models for both tasks was SegResNet with deep supervision. For Task 1, we trained the model using a combined dataset of pre-RT and mid-RT MRI data, which resulted in the improved aggregated Dice similarity coefficient (DSCagg) on an internal testing set compared to models trained solely on pre-RT MRI data. In Task 2, we introduced mask-aware attention modules, enabling pre-RT GTV masks to influence intermediate features learned from mid-RT data. This attention-based approach yielded slight improvements over the baseline method, which concatenated mid-RT MRI with pre-RT GTV masks as input. In the final testing phase, the ensemble of 10 pre-RT segmentation models achieved an average DSCagg of 0.794, with 0.745 for primary GTV (GTVp) and 0.844 for metastatic lymph nodes (GTVn) in Task 1. For Task 2, the ensemble of 10 mid-RT segmentation models attained an average DSCagg of 0.733, with 0.607 for GTVp and 0.859 for GTVn, leading us to $\textbf{achieve 1st place}$. In summary, we presented a collection of DL models that could facilitate GTV segmentation in MRgART, offering the potential to streamline radiation oncology workflows. Our code and model weights are available at https://github.com/xtie97/HNTS-MRG24-UWLAIR.

xploiting quantum interference remains a significant challenge in ultracold inelastic scattering. In this work, we propose a method to enable detectable quantum interference within the two-body loss rate resulting from various inelastic scattering channels. Our approach utilizes a ``ring-coupling" configuration, achieved by combining external radio-frequency and static electric fields during ultracold atomic collisions. We conduct close-coupling calculations for $^7$Li-$^{41}$K collisions at ultracold limit to validate our proposal. The results show that the interference profile displayed in two-body loss rate is unable to be observed with unoptimized external field parameters. Particularly, our findings demonstrate that the two-body loss rate coefficient exhibits distinct constructive and destructive interference patterns near the magnetically induced $p$-wave resonance in the incoming channel near which a rf-induced scattering resonance exists. These interference patterns become increasingly pronounced with greater intensities of the external fields. This work opens a new avenue for controlling inelastic scattering processes in ultracold collisions.

The explicit expression for the photon polarization operator in the presence of a single electron is found in the $in$-$in$ formalism in the one-loop approximation out of the photon mass-shell. This polarization operator describes the dielectric permittivity of a single electron wave packet in coherent scattering processes. The plasmons and plasmon-polaritons supported by a single electron wave packet are described. The two limiting cases are considered: the wavelength of the external electromagnetic field is much smaller than the typical scale of variations of the electron wave packet and the wavelength of the external electromagnetic field is much larger than the size of the electron wave packet. In the former case, there are eight independent plasmon-polariton modes. In the latter case, the plasmons boil down to the dynamical dipole moment attached to a point electron. Thus, in the infrared limit, the electron possesses a dynamical electric dipole moment manifesting itself in coherent scattering processes.

Ecological forecasts are model-based statements about currently unknown ecosystem states in time or space. For a model forecast to be useful to inform decision-makers, model validation and verification determine adequateness. The measure of forecast goodness that can be translated into a limit up to which a forecast is acceptable is known as the `forecast horizon'. While verification of meteorological models follows strict criteria with established metrics and forecast horizons, assessments of ecological forecasting models still remain experiment-specific and forecast horizons are rarely reported. As such, users of ecological forecasts remain uninformed of how far into the future statements can be trusted. In this work, we synthesise existing approaches, define empirical forecast horizons in a unified framework for assessing ecological predictability and offer recipes on their computation. We distinguish upper and lower boundary estimates of predictability limits, reflecting the model's potential and actual forecast horizon, and show how a benchmark model can help determine its relative forecast horizon. The approaches are demonstrated with four case studies from population, ecosystem, and earth system research.

In this paper, we introduce the proper latent decomposition (PLD) as a generalization of the proper orthogonal decomposition (POD) on manifolds. PLD is a nonlinear reduced-order modeling technique for compressing high-dimensional data into nonlinear coordinates. First, we compute a reduced set of intrinsic coordinates (latent space) to accurately describe a flow with fewer degrees of freedom than the numerical discretization. The latent space, which is geometrically a manifold, is inferred by an autoencoder. Second, we leverage tools from differential geometry to develop numerical methods for operating directly on the latent space; namely, a metric-constrained Eikonal solver for distance computations. With this proposed numerical framework, we propose an algorithm to perform PLD on the manifold. Third, we demonstrate results for a laminar flow case and the turbulent Kolmogorov flow. For the laminar flow case, we are able to identify a semi-analytical expression for the solution of Navier-Stokes; in the Kolmogorov flow case, we are able to identify a dominant mode that exhibits physical structures, which are compared with POD. This work opens opportunities for analyzing autoencoders and latent spaces, nonlinear reduced-order modeling and scientific insights into the structure of high-dimensional data.

The autoencoder model typically uses an encoder to map data to a lower dimensional latent space and a decoder to reconstruct it. However, relying on an encoder for inversion can lead to suboptimal representations, particularly limiting in physical sciences where precision is key. We introduce a decoder-only method using gradient flow to directly encode data into the latent space, defined by ordinary differential equations (ODEs). This approach eliminates the need for approximate encoder inversion. We train the decoder via the adjoint method and show that costly integrals can be avoided with minimal accuracy loss. Additionally, we propose a $2^{nd}$ order ODE variant, approximating Nesterov's accelerated gradient descent for faster convergence. To handle stiff ODEs, we use an adaptive solver that prioritizes loss minimization, improving robustness. Compared to traditional autoencoders, our method demonstrates explicit encoding and superior data efficiency, which is crucial for data-scarce scenarios in the physical sciences. Furthermore, this work paves the way for integrating machine learning into scientific workflows, where precise and efficient encoding is critical. \footnote{The code for this work is available at \url{https://github.com/k-flouris/gfe}.}

An axiomatic approach to macroeconomics based on the mathematical structure of thermodynamics is presented. It deduces relations between aggregate properties of an economy, concerning quantities and flows of goods and money, prices and the value of money, without any recourse to microeconomic foundations about the preferences and actions of individual economic agents. The approach has three important payoffs. 1) it provides a new and solid foundation for aspects of standard macroeconomic theory such as the existence of market prices, the value of money, the meaning of inflation, the symmetry and negative-definiteness of the macro-Slutsky matrix, and the Le Chatelier-Samuelson principle, without relying on implausibly strong rationality assumptions over individual microeconomic agents. 2) the approach generates new results, including implications for money flow and trade when two or more economies are put in contact, in terms of new concepts such as economic entropy, economic temperature, goods' values and money capacity. Some of these are related to standard economic concepts (eg marginal utility of money, market prices). Yet our approach derives them at a purely macroeconomic level and gives them a meaning independent of usual restrictions. Others of the concepts, such as economic entropy and temperature, have no direct counterparts in standard economics, but they have important economic interpretations and implications, as aggregate utility and the inverse marginal aggregate utility of money, respectively. 3) this analysis promises to open up new frontiers in macroeconomics by building a bridge to ideas from non-equilibrium thermodynamics. More broadly, we hope that the economic analogue of entropy (governing the possible transitions between states of economic systems) may prove to be as fruitful for the social sciences as entropy has been in the natural sciences.

This work demonstrates a systematic implementation of hybrid quantum-classical computational methods for investigating corrosion inhibition mechanisms on aluminum surfaces. We present an integrated workflow combining density functional theory (DFT) with quantum algorithms through an active space embedding scheme, specifically applied to studying 1,2,4-Triazole and 1,2,4-Triazole-3-thiol inhibitors on Al111 surfaces. Our implementation leverages the ADAPT-VQE algorithm with benchmarking against classical DFT calculations, achieving binding energies of -0.386 eV and -1.279 eV for 1,2,4-Triazole and 1,2,4-Triazole-3-thiol, respectively. The enhanced binding energy of the thiol derivative aligns with experimental observations regarding sulfur-functionalized inhibitors' improved corrosion protection. The methodology employs the orb-d3-v2 machine learning potential for rapid geometry optimizations, followed by accurate DFT calculations using CP2K with PBE functional and Grimme's D3 dispersion corrections. Our benchmarking on smaller systems reveals that StatefulAdaptVQE implementation achieves a 5-6x computational speedup while maintaining accuracy. This work establishes a workflow for quantum-accelerated materials science studying periodic systems, demonstrating the viability of hybrid quantum-classical approaches for studying surface-adsorbate interactions in corrosion inhibition applications. In which, can be transferable to other applications such as carbon capture and battery materials studies.

Dynamic metasurface antennas (DMAs), surfaces patterned with reconfigurable metamaterial elements (meta-atoms) that couple waves from waveguides or cavities to free space, are a promising technology to realize 6G wireless base stations and access points with low cost and power consumption. Mutual coupling between the DMA's meta-atoms results in a non-linear dependence of the radiation pattern on the DMA configuration, significantly complicating modeling and optimization. Therefore, mutual coupling has to date been considered a vexing nuance that is frequently neglected in theoretical studies and deliberately mitigated in experimental prototypes. Here, we demonstrate the overlooked property of mutual coupling to boost the control over the DMA's radiation pattern. Based on a physics-compliant DMA model, we demonstrate that the radiation pattern's sensitivity to the DMA configuration significantly depends on the mutual coupling strength. We further evidence how the enhanced sensitivity under strong mutual coupling translates into a higher fidelity in radiation pattern synthesis, benefiting applications ranging from dynamic beamforming to end-to-end optimized sensing and imaging. Our insights suggest that DMA design should be fundamentally rethought to embrace the benefits of mutual coupling. We also discuss ensuing future research directions related to the frugal characterization of DMAs based on compact physics-compliant models.

Machine learning-based weather models have shown great promise in producing accurate forecasts but have struggled when applied to data assimilation tasks, unlike traditional numerical weather prediction (NWP) models. This study introduces the Jacobian-Enforced Neural Network (JENN) framework, designed to enhance DA consistency in neural network (NN)-emulated dynamical systems. Using the Lorenz 96 model as an example, the approach demonstrates improved applicability of NNs in DA through explicit enforcement of Jacobian relationships. The NN architecture includes an input layer of 40 neurons, two hidden layers with 256 units each employing hyperbolic tangent activation functions, and an output layer of 40 neurons without activation. The JENN framework employs a two-step training process: an initial phase using standard prediction-label pairs to establish baseline forecast capability, followed by a secondary phase incorporating a customized loss function to enforce accurate Jacobian relationships. This loss function combines root mean square error (RMSE) between predicted and true state values with additional RMSE terms for tangent linear (TL) and adjoint (AD) emulation results, weighted to balance forecast accuracy and Jacobian sensitivity. To ensure consistency, the secondary training phase uses additional pairs of TL/AD inputs and labels calculated from the physical models. Notably, this approach does not require starting from scratch or structural modifications to the NN, making it readily applicable to pretrained models such as GraphCast, NeuralGCM, Pangu, or FuXi, facilitating their adaptation for DA tasks with minimal reconfiguration. Experimental results demonstrate that the JENN framework preserves nonlinear forecast performance while significantly reducing noise in the TL and AD components, as well as in the overall Jacobian matrix.

Making ultra-short gate-length transistors significantly contributes to scaling the contacted gate pitch. This, in turn, plays a vital role in achieving smaller standard logic cells for enhanced logic density scaling. As we push the boundaries of miniaturization, it is intriguing to consider that the ultimate limit of contacted gate pitch could be reached with remarkable 1 nm gate-length transistors. Here, we identify InSe, Bi2O2Se, and MoSi2N4 as potential two-dimensional semiconductors for 1 nm transistors with low contact resistance and outstanding interface properties. We employ a fully self-consistent ballistic quantum transport model starting from first-principle calculations. Our simulations show that the interplay between electrostatics and quantum tunneling influences the performance of these devices over the device design space. MoSi2N4 channels have the best immunity to quantum tunneling, and Bi2O2Se channel devices have the best electrostatics. We show that for a channel length of 12 nm, all the devices can deliver I_$ON$/I_$OFF$ > 10^3 , suitable for electronic applications, and Bi2O2Se is the best-performing channel material.

Adaptive network is a powerful presentation to describe different real-world phenomena. However, current models often neglect higher-order interactions (beyond pairwise interactions) and diverse adaptation types (cooperative and competitive) commonly observed in systems like the human brain and social networks. This work addresses this gap by incorporating these factors into a model that explores their impact on collective properties like synchronization. Through simplified network representations, we investigate how the simultaneous presence of cooperative and competitive adaptations influences phase transitions. Our findings reveal a transition from first-order to second-order synchronization as the strength of higher-order interactions increases under competitive adaptation. We also demonstrate the possibility of synchronization even without pairwise interactions, provided there is strong enough higher-order coupling. When only competitive adaptations are present, the system exhibits second-order-like phase transitions and clustering. Conversely, with a combination of cooperative and competitive adaptations, the system undergoes a first-order-like phase transition, characterized by a sharp transition to the synchronized state without reverting to an incoherent state during backward transitions. The specific nature of these second-order-like transitions varies depending on the coupling strengths and mean degrees. With our model, we can control not only when the system synchronizes but also the way the system goes to synchronization.

From cell development to space rockets, the mechanical stability of thin shells is crucial across many industrial and natural processes. However, predicting shells' failure properties remains an open challenge, owing to their sensitivity to imperfections inevitably present in real structures. Existing predictive methods are informed by classical stability analyses of idealized shells, which associate shell buckling with a spontaneously emerging delocalized buckling mode. Here, we show through experimental and numerical observations how realistic geometric imperfections instead result in a universal localized buckling mode. The profile of this mode is robust across shells despite their differing underlying defect structures. Further contrasting with classical predictions, a real shell's equilibrium surface deformations grow in the shape of this universal localized mode even at stable loads well before failure. Our findings show that these complex nonlinear dynamics for loads below buckling are described by a 1-D relationship for structures buckling from a saddle-node bifurcation instability. This breakthrough provides a simplified yet general understanding of buckling mechanisms, leading to improved non-destructive predictive methods. To showcase these applications, we experimentally implement a lateral-probing technique that predicts the buckling loads of several unmodified shells to within 1% of the correct value.

Complex network systems' models are designed to perfectly emulate real-world networks through the use of simulation and link prediction. Complex network systems are defined by nodes and their connections where both have real-world features that result in a heterogeneous network in which each of the nodes has distinct characteristics. Thus, incorporating real-world features is an important component to achieve a simulation which best represents the real-world. Currently very few complex network systems implement real-world features, thus this study proposes feature selection methods which utilise unsupervised filtering techniques to rank real-world node features alongside a wrapper function to test combinations of the ranked features. The chosen method was coined FS-SNS which improved 8 out of 10 simulations of real-world networks. A consistent threshold of included features was also discovered which saw a threshold of 4 features to achieve the most accurate simulation for all networks. Through these findings the study also proposes future work and discusses how the findings can be used to further the Digital Twin and complex network system field.

Following seminal work in the early 1980s that established the existence and representations of the homogenized transport coefficients for two phase random media, we develop a mathematical framework that provides Stieltjes integral representations for the bulk transport coefficients for uniaxial polycrystalline materials, involving spectral measures of self-adjoint random operators, which are compositions of non-random and random projection operators. We demonstrate the same mathematical framework also describes two-component composites, with a simple substitution of the random projection operator, making the mathematical descriptions of these two distinct physical systems directly analogous to one another. A detailed analysis establishes the operators arising in each setting are indeed self-adjoint on an $L^2$-type Hilbert space, providing a rigorous foundation to the formal spectral theoretic framework established by Golden and Papanicolaou in 1983. An abstract extension of the Helmholtz theorem also leads to integral representations for the inverses of effective parameters, e.g., effective conductivity and resistivity. An alternate formulation of the effective parameter problem in terms of a Sobolev-type Hilbert space provides a rigorous foundation for an approach first established by Bergman and Milton. We show that the correspondence between the two formulations is a one-to-one isometry. Rigorous bounds that follow from such Stieltjes integrals and partial knowledge about the material geometry are reviewed and validated by numerical calculations of the effective parameters for polycrystalline media.

The intensive study of non-collinear magnets promotes an urgent demand for the quantitative characterization of the non-collinear magnetic structures, which host numerous exotic phenomena. Here we systematically study the non-collinear magnetic structure of an artificial ferrimagnetic multilayer. The AMR measurements reveal two distinct twisted states whose magnetic structures can be quantitatively characterized with the assistance of micromagnetic simulations. Our results manifest AMR as an ideal probe of the non-collinear magnetic structure in artificial ferrimagnets.

The platinum/iridium (Pt/Ir) alloy tip for scanning probe microscopy (SPM) was successfully fabricated by amplitude-modulated alternating-current (AC) electropolishing. The clean tips with a radius of curvature less than 100 nm were reproducibly obtained by applying the 1000 Hz sinusoidal voltage with amplitude modulation of the sinusoidal wave of 100 Hz in $\mathrm{CaCl_2}$/$\mathrm{H_2O}$/acetone solution. The analyses by scanning electron microscopy with an energy-dispersive X-ray analyzer (SEM-EDX) and atom probe tomography (APT) showed that a uniform Pt/Ir alloy was exposed on the tip surface as a clean surface without O or Cl contamination. The STM imaging using the fabricated tip showed that it is more suitable for investigating rough surfaces than conventional as-cut tips and applicable for atomic-resolution imaging. Furthermore, we applied the fabricated tip to qPlus AFM analysis in liquid and showed that it has atomic resolution in both the horizontal and vertical directions. Therefore, it is concluded that the amplitude-modulated AC etching method reproducibly provides sharp STM/AFM tips capable of both atomic resolution and large-area analyses without complex etching setups.

We are concerned with the compressible barotropic Navier--Stokes equations for a $\gamma$-law gas with density-dependent bulk viscosity coefficient $\lambda=\lambda(\rho)=\rho^\beta$ on the two-dimensional periodic domain $\mathbb{T}^2$. The global existence of weak solutions with initial density bounded away from zero and infinity for $\beta>3$, $\gamma>1$ has been established by Va\u{\i}gant--Kazhikhov [Sib. Math. J. 36 (1995), 1283--1316]. When $\gamma=\beta>3$, the large-time behaviour of the weak solutions and, in particular, the absence of formation of vacuum and concentration of density as $t \to \infty$, has been proved by Perepelitsa [SIAM J. Math. Anal. 39 (2007/08), 1344--1365]. Huang--Li [J. Math. Pures Appl. 106 (2016), 123--154] extended these results by establishing the global existence of weak solutions and large-time behaviour under the assumptions $\beta >3/2$, $1< \gamma<4\beta-3$, and that the initial density stays away from infinity (but may contain vacuum). Improving upon the works listed above, we prove that in the regime of parameters as in Huang--Li, namely that $\beta >3/2$ and $1< \gamma<4\beta-3$, if the density has no vacuum or concentration at $t=0$, then it stays uniformly away from zero and infinity at all later time $t \in ]0,\infty]$. Moreover, under the mere assumption that $\beta>1$ and $\gamma>1$, we establish the global existence of weak solutions, thus pushing the global existence theory of the barotropic Navier--Stokes equations on $\mathbb{T}^2$ to the most general setting to date. One of the key ingredients of our proof is a novel application -- motivated by the recent work due to Danchin--Mucha [Comm. Pure Appl. Math. 76 (2023), 3437--3492] -- of Desjardins' logarithmic interpolation inequality.

Many partial differential equations (PDEs) such as Navier--Stokes equations in fluid mechanics, inelastic deformation in solids, and transient parabolic and hyperbolic equations do not have an exact, primal variational structure. Recently, a variational principle based on the dual (Lagrange multiplier) field was proposed. The essential idea in this approach is to treat the given PDE as constraints, and to invoke an arbitrarily chosen auxiliary potential with strong convexity properties to be optimized. This leads to requiring a convex dual functional to be minimized subject to Dirichlet boundary conditions on dual variables, with the guarantee that even PDEs that do not possess a variational structure in primal form can be solved via a variational principle. The vanishing of the first variation of the dual functional is, up to Dirichlet boundary conditions on dual fields, the weak form of the primal PDE problem with the dual-to-primal change of variables incorporated. We derive the dual weak form for the linear, one-dimensional, transient convection-diffusion equation. A Galerkin discretization is used to obtain the discrete equations, with the trial and test functions chosen as linear combination of either RePU activation functions (shallow neural network) or B-spline basis functions; the corresponding stiffness matrix is symmetric. For transient problems, a space-time Galerkin implementation is used with tensor-product B-splines as approximating functions. Numerical results are presented for the steady-state and transient convection-diffusion equation, and transient heat conduction. The proposed method delivers sound accuracy for ODEs and PDEs and rates of convergence are established in the $L^2$ norm and $H^1$ seminorm for the steady-state convection-diffusion problem.

Layered two-dimensional (2D) materials have revolutionized how we approach light-matter interactions, offering unprecedented optical and electronic properties with the potential for vertical heterostructures and manipulation of spin-valley degrees of freedom. The discovery of moir\'e physics in twisted heterostructures has further unlocked new possibilities for controlling the band structure of tailored semiconductor heterostructures. In parallel, the integration of 2D materials with hybrid photonic structures and ultrafast studies on their optical and spin-valley properties has revealed a wealth of novel physical phenomena. This perspective highlights the recent advances in our understanding of light-matter interactions in moir\'e and 2D systems, with a particular emphasis on ultrafast processes and the integration of these materials into photonic platforms. We explore the implications for optoelectronics and emerging photonic technologies, positioning 2D materials as a transformative tool for next-generation devices.

Non-Hermitian systems exhibit a distinctive type of wave propagation, due to the intricate interplay of non-Hermiticity and disorder. Here, we investigate the spreading dynamics in the archetypal non-Hermitian Aubry-Andr\'e model with quasiperiodic disorder. We uncover counter-intuitive transport behaviors: subdiffusion with a spreading exponent $\delta=1/3$ in the localized regime and diffusion with $\delta=1/2$ in the delocalized regime, in stark contrast to their Hermitian counterparts (halted vs. ballistic). We then establish a unified framework from random-variable perspective to determine the universal scaling relations in both regimes for generic disordered non-Hermitian systems. An efficient method is presented to extract the spreading exponents from Lyapunov exponents. The observed subdiffusive or diffusive transport in our model stems from Van Hove singularities at the tail of imaginary density of states, as corroborated by Lyapunov-exponent analysis.

The Torsion-Bar Antenna (TOBA) is a torsion pendulum-based gravitational detector developed to observe gravitational waves in frequencies between 1 mHz and 10 Hz. The low resonant frequency of the torsion pendulum enables observation in this frequency band on the ground. The final target of TOBA is to observe gravitational waves with a 10 m detector and expand the observation band of gravitational waves. In this paper, an overview of TOBA, including the previous prototype experiments and the current ongoing development, is presented.

In fault-tolerant quantum computing, the cost of calculating Hamiltonian eigenvalues using the quantum phase estimation algorithm is proportional to the constant scaling the Hamiltonian matrix block-encoded in a unitary circuit. We present a method to reduce this scaling constant for the electronic Hamiltonians represented as a linear combination of unitaries. Our approach combines the double tensor-factorization method of Burg et al. with the the block-invariant symmetry shift method of Loaiza and Izmaylov. By extending the electronic Hamiltonian with appropriately parametrized symmetry operators and optimizing the tensor-factorization parameters, our method achieves a 25% reduction in the block-encoding scaling constant compared to previous techniques. The resulting savings in the number of non-Clifford T-gates, which are an essential resource for fault-tolerant quantum computation, are expected to accelerate the feasiblity of practical Hamiltonian simulations. We demonstrate the effectiveness of our technique on Hamiltonians of industrial and biological relevance, including the nitrogenase cofactor (FeMoCo) and cytochrome P450.

We explore the use of magnonic Fabry-P\'erot resonators as programmable phase shifters for spin-wave computing. The resonator, composed of a yttrium iron garnet (YIG) film coupled with a CoFeB nanostripe, operates through dynamic dipolar coupling, leading to wavelength downconversion and the formation of a magnonic cavity. Using super-Nyquist sampling magneto-optical Kerr effect (SNS-MOKE) microscopy and micromagnetic simulations, we demonstrate that these resonators can induce a $\pi$ phase shift in the transmitted spin wave. The phase shift is highly sensitive to the magnetization alignment within the resonator, allowing for on-demand control via magnetic switching. This feature, combined with low-loss transmission, positions the magnonic Fabry-P\'erot resonator as a promising component for reconfigurable magnonic circuits and spin-wave computing devices.

The rapid advancements in machine learning have made its application to anomalous diffusion analysis both essential and inevitable. This review systematically introduces the integration of machine learning techniques for enhanced analysis of anomalous diffusion, focusing on two pivotal aspects: single trajectory characterization via machine learning and representation learning of anomalous diffusion. We extensively compare various machine learning methods, including both classical machine learning and deep learning, used for the inference of diffusion parameters and trajectory segmentation. Additionally, platforms such as the Anomalous Diffusion Challenge that serve as benchmarks for evaluating these methods are highlighted. On the other hand, we outline three primary strategies for representing anomalous diffusion: the combination of predefined features, the feature vector from the penultimate layer of neural network, and the latent representation from the autoencoder, analyzing their applicability across various scenarios. This investigation paves the way for future research, offering valuable perspectives that can further enrich the study of anomalous diffusion and advance the application of artificial intelligence in statistical physics and biophysics.

Relativistic pair beams produced in the intergalactic medium (IGM) by TeV gamma rays from blazars are expected to generate a detectable GeV-scale electromagnetic cascade, yet this cascade is absent in the observed spectra of hard-spectrum TeV emitting blazars. This suppression is often attributed to weak intergalactic magnetic fields (IGMF) deflecting electron-positron pairs out of the line of sight. Alternatively, it has been proposed that beam-plasma instabilities could drain the energy of the beam before they produce the secondary cascades. Recent studies suggest that the modification of beam distribution due to these instabilities is primarily driven by particle scattering, rather than energy loss. In this paper, we quantitatively assess, for the blazar 1ES 0229+200, the arrival time of secondary gamma rays at Earth from the beam scattering by the electrostatic instability. We first computed the production rates of electron-positron pairs at various distances using the Monte Carlo simulation CRPropa. We then simulated the feedback of the plasma instability on the beam, incorporating production rates and inverse-Compton cooling, to determine the steady-state distribution function. Our findings reveal that the time delay of the GeV secondary cascade arrival due to instability broadening is on the order of a few months. This delay is insufficient to account for the missing cascade emission in blazar spectra, suggesting that plasma instabilities do not significantly affect IGMF constraints.

The quantization of an optical field is a frontier in quantum optics with implications for both fundamental science and technological applications. Here, we demonstrate that a dinickel complex (Ni$_2$) traps and quantizes classical visible light, behaving as an individual quantum system or the Jaynes Cummings molecule.The composite system forms through coherently coupling the two level NiNi charge transfer transition with the local scattering field, which produces nonclassical light featuring photon anti bunching and squeezed states, as verified by a sequence of discrete photonic modes in the incoherent resonance fluorescence. Notably, in this Ni$_2$ system, the collective coupling of N molecule ensembles scales as N, distinct from the Tavis-Cummings model, which allows easy achievement of ultrastrong coupling. This is exemplified by a vacuum Rabi splitting of 1.2 eV at the resonance (3.25 eV) and a normalized coupling rate of 0.18 for the N = 4 ensemble. The resulting quantum light of single photonic modes enables driving the molecule field interaction in cavity free solution, which profoundly modifies the electronic states. Our results establish Ni$_2$ as a robust platform for quantum optical phenomena under ambient conditions, offering new pathways for molecular physics, polaritonic chemistry and quantum information processing.

In recent decades, significant progress has been made in construction and study of individual quantum systems consisting of the basic single matter and energy particles, i.e., atoms and photons, which show great potentials in quantum computation and communication. Here, we demonstrate that the quadruply-bonded Mo$_2$ unit of the complex can trap photons of visible light under ambient conditions, producing intense local electromagnetic (EM) field that features squeezed states, photon antibunching, and vacuum Rabi splitting. Our results show that both the electronic and vibrational states of the Mo$_2$ molecule are modified by coherent coupling with the scattered photons of the Mo$_2$ unit, as evidenced by the Rabi doublet4 and the Mollow triplet in the incoherent resonance fluorescence and the Raman spectra. The Mo$_2$ molecule, acting as an independent emitter-resonator integrated quantum system, allows optical experiments to be conducted in free space, enabling fundamental quantum phenomena to be observed through conventional spectroscopic instrumentation. This provides a new platform for study of field effects and quantum electrodynamics (QED) in the optical domain. The insights gained from this study advance our understanding in metal-metal bond chemistry, molecular physics and quantum optics, with applications in quantum information processing, optoelectronic devices and control of chemical reactivity.

We present a general-purpose algorithm for automatic production of a structure that induces a desired Casimir-Polder force. As a demonstration of the capability and wide applicability of the method, we use it to develop a geometry that leads to a repulsive Casimir-Polder force on a ground-state atom. The results turn out to be reminiscent of the ring-like geometries previously used to induce repulsion, but with some new features and -- importantly -- discovered completely independently of any input from the user. This represents a powerful new paradigm in the study of atom-surface forces -- instead of the user testing various geometries against a desired figure of merit, the goal can be specified and then an appropriate geometry created automatically.

Stabilizing nitrogen-rich compound crystals under conventional conditions is a key issue in the development and application of high-energy density materials (HEDMs). Herein, a two-dimensional double-layer interlocked Li4(N5)2 nitrogen-rich compound crystals, in which the two N5 rings are locked to by sharing four Li atoms, was found to maintain structural stability at zero pressure conditions. Dynamics studies reliably confirm crystal stability below 250 K. Furthermore, the stability of Li4(N5)2 crystal mainly arises from the ionic interaction between Li atoms and N5 rings, formed by the charge transfer from Li atoms to N5 rings. This study highlights the feasibility of stabilizing nitrogen-rich compound crystals under conventional conditions, paving the way for atomic level advancements in HEDMs.

We study the energy spectrum of three-particle systems (He-p-\mu), (He-d-\mu), (Li-p-\mu) and (Li-d-\mu) on the basis of variational approach with exponential and Gaussian basis. Using the Complex Coordinate Rotation (CCR) method we calculate energies of resonant states of listed molecules.

Due to their quantum nature, single-photon emitters generate individual photons in bursts or streams. They are paramount in emerging quantum technologies such as quantum key distribution, quantum repeaters, and measurement-based quantum computing. Many such systems have been reported in the last three decades, from Rubidium atoms coupled to cavities to semiconductor quantum dots and color centers implanted in waveguides. This review article highlights different material systems with deterministic and controlled single photon generation. We discuss and compare the performance metrics, such as purity and indistinguishability, for these sources and evaluate their potential for different applications. Finally, a new potential single-photon source, based on the Rydberg exciton in solid state metal oxide thin films, is introduced, briefly discussing its promising qualities and advantages in fabricating quantum chips for quantum photonic applications.

This paper explores how competing interactions in the intermolecular potential of fluids affect their structural transitions. The study employs a versatile potential model with a hard core followed by two constant steps, representing wells or shoulders, analyzed in both one-dimensional (1D) and three-dimensional (3D) systems. Comparing these dimensionalities highlights the effect of confinement on structural transitions. Exact results are derived for 1D systems, while the Rational-Function Approximation is used for unconfined 3D fluids. Both scenarios confirm that when the steps are repulsive, the wavelength of the oscillatory decay of the total correlation function evolves with temperature either continuously or discontinuously. In the latter case, a discontinuous oscillation crossover line emerges in the temperature--density plane. For an attractive first step and a repulsive second step, a Fisher--Widom line appears. Although the 1D and 3D results share common features, dimensionality introduces differences: these behaviors occur in distinct temperature ranges, require deeper wells, or become attenuated in 3D. Certain features observed in 1D may vanish in 3D. We conclude that fluids with competing interactions exhibit a rich and intricate pattern of structural transitions, demonstrating the significant influence of dimensionality and interaction features.

Long-range electrostatic interactions critically affect polar materials. However, state-of-the-art atomistic potentials, such as neural networks or Gaussian approximation potentials employed in large-scale simulations, often neglect the role of these long-range electrostatic interactions. This study introduces a novel model derived from first principles to evaluate the contribution of long-range electrostatic interactions to total energies, forces, and stresses. The model is designed to integrate seamlessly with existing short-range force fields without further first-principles calculations or retraining. The approach relies solely on physical observables, like the dielectric tensor and Born effective charges, that can be consistently calculated from first principles. We demonstrate that the model reproduces critical features, such as the LO-TO splitting and the long-wavelength phonon dispersions of polar materials, with benchmark results on the cubic phase of barium titanate (BaTiO$_3$).

While the accurate description of redox reactions remains a challenge for first-principles calculations, it has been shown that extended Hubbard functionals (DFT+U+V) can provide a reliable approach, mitigating self-interaction errors, in materials with strongly localized d or f electrons. Here, we first show that DFT+U+V molecular dynamics is capable to follow the adiabatic evolution of oxidation states over time, using representative Li-ion cathode materials. In turn, this allows to develop redox-aware machine-learned potentials. We show that considering atoms with different oxidation states (as accurately predicted by DFT+U+V) as distinct species in the training leads to potentials that are able to identify the correct ground state and pattern of oxidation states for redox elements present. This can be achieved, e.g., through a systematic combinatorial search for the lowest energy configuration or with stochastic methods. This brings the advantages of machine-learned potentials to key technological applications (e.g., rechargeable batteries), which require an accurate description of the evolution of redox states.

Impurity-bound excitons in II-VI semiconductors are promising optically active solid-state spin qubit systems. Previous work relied on incoherent optical excitation to generate photons from these impurities. However, many quantum applications require resonant driving to directly excite optical transitions and maintain coherence. Here, we demonstrate coherent optical emission from a resonantly driven single impurity-bound exciton in ZnSe. We observe resonance fluorescence and verify the emission coherence through polarization interferometry. Resonant excitation also enables the direct measurement of the Debye-Waller factor, determined to be 0.94, indicating high efficiency emission to the zero-phonon line. Time-resolved resonance fluorescence measurements reveal a fast optically driven ionization process attributed to Auger recombination, along with a slower spontaneous ionization process having a lifetime of 21 {\mu}s due to charge tunneling from the impurity. We demonstrate that a low-power incoherent pump laser efficiently stabilizes the charge of the impurity-bound exciton on the timescale of 9.3 ns. Our results pave the way for direct coherent optical and spin control through resonant excitation of impurity-bound excitons in II-VI semiconductors.

A 2D electromagnetic-thermal coupled numerical model has been developed using the finite element method and validated against experimental data to investigate a superconducting machine featuring high-temperature superconducting (HTS) tape stacks and cryocooled iron cores. The HTS stacks are transformed into trapped field stacks (TFSs) through pulsed field magnetisation (PFM), generating rotor fields. After PFM, the superconducting motor operates on the same principle as permanent magnet synchronous motors. This study explores the behaviour of HTS stacks by altering the stack's layer number from one to nine and adjusting the pulsed current amplitude from 250 A to 1000 A. The primary objective of this paper is to identify the optimal combination of pulsed current amplitudes and TFS layer numbers for achieving maximum magnetisation fields. The secondary objective is to evaluate the overall losses in both superconducting and non-superconducting parts of the machine during magnetisation, including heat generated in various layers of the TFS, and losses in the motor's active materials (copper windings and iron cores). Two motor configurations were proposed, and two calculation methods using linear interpolation of iron losses and steel grades were introduced to estimate the iron losses for the studied iron material, M270-35A. This pioneering study is expected to serve as a valuable reference for loss analyses and structural design considerations in developing superconducting machines.

We present a solvable scenario for 3D reconnection in a sheared magnetic field. We consider a localized external force that is applied slowly and then maintained, generating an Alfv\'{e}nic perturbation that spreads along the field lines. Separation of the sheared field lines causes the scale of the perturbation across the field to decrease, enabling magnetic diffusion to be enhanced. For a fusion-motivated equilibrium with exponential field-line separation, we find a reconnection timescale proportional to $S/\ln S$ under magnetohydrodynamics (MHD) and to $S^{1/3}$ for semi-collisional electron-only reconnection, where $S$ is the Lundquist number of the perturbed flux tube. We generalize these results to arbitrary magnetic geometries, showing that the latter is geometry-independent. Interestingly, we find that slower field-line separation yields an increased reconnection rate in MHD.

Semiconductor-superconductor hybrid devices have been proposed as promising platforms for detecting and analyzing Majorana zero modes, which find applications in topological quantum computing. In this work, we solve the Kohn-Sham Density Functional Theory and Bogoliubov-de Gennes equations to describe the normal and superconducting properties of a PbTe/Pb heterostructure. We resolve a proximity-induced superconducting gap on the PbTe side. The hybridization between PbTe and Pb causes the emergence of a soft Bardeen-Cooper-Schrieffer-like superconducting gap. We compute the anomalous charge density in real space, estimating its decay length and showing that the pairing potential is anisotropic, which is a necessary condition for unconventional superconductivity. Contrary to the models that predict Majorana zero modes in these interfaces, we find a significantly large Schottky barrier in the normal state preventing the emergence of zero modes. Our findings strengthen the understanding of the physics governing PbTe/Pb hybrid devices and their viability for Majorana zero modes applications.

Pressure-solution is a chemo-mechanical process, involving dissolution at grain/asperity contacts and precipitation away from them. It induces a compaction in time of rocks and sediments. The present study investigates numerically the impact of precipitation on the slowdown of creep behavior induced by pressure-solution. A recently published framework, called the Phase-Field Discrete Element Model, is carefully calibrated against existing indentation experiments and validated for other rate-limiting scenarios. It is shown that when precipitation is relatively slow, the slowdown of pressure-solution is due to a chemical mechanism (accumulation of solute concentration within the pore space), whereas, at fast precipitation, the slowdown is due to a mechanical mechanism (stress reduction at the contact).

The Pauli exclusion principle is fundamental to understanding electronic quantum systems. It namely constrains the expected occupancies $n_i$ of orbitals $\varphi_i$ according to $0 \leq n_i \leq 2$. In this work, we first refine the underlying one-body $N$-representability problem by taking into account simultaneously spin symmetries and a potential degree of mixedness $\boldsymbol w$ of the $N$-electron quantum state. We then derive a comprehensive solution to this problem by using basic tools from representation theory, convex analysis and discrete geometry. Specifically, we show that the set of admissible orbital one-body reduced density matrices is fully characterized by linear spectral constraints on the natural orbital occupation numbers, defining a convex polytope $\Sigma_{N,S}(\boldsymbol w) \subset [0,2]^d$. These constraints are independent of $M$ and the number $d$ of orbitals, while their dependence on $N, S$ is linear, and we can thus calculate them for arbitrary system sizes and spin quantum numbers. Our results provide a crucial missing cornerstone for ensemble density (matrix) functional theory.