Accurate multi-decadal wind power predictions are crucial for sustainable energy transitions but are challenged by the coarse spatial resolution of global climate models (GCMs). This study examines the impact of spatial resolution on wind power forecasts by analyzing historical wind speed outputs from ten CMIP6 GCMs in Germany, using ERA5 reanalysis as a reference. Results show that the choice of GCM is the primary influence on wind speed output, with higher resolution models partly, but not consistently, improving predictions. While high-resolution models better capture extreme wind speeds, they do not systematically improve the prediction of the whole wind speed distribution. The data set MPI-ESM1-2-HR (MPI-HR) was found to represent the wind speed distribution particularly faithfully, while the MIROC6 (JAP) data set showed substantial underestimation for the German region compared to ERA5. These findings underscore the complexity of wind speed modeling for power predictions and emphasize the need for careful GCM selection and appropriate downscaling and bias correction methods.

Electrolytes mediate interactions between the cathode and anode and determine performance characteristics of batteries. Mixtures of multiple solvents are often used in electrolytes to achieve desired properties, such as viscosity, dielectric constant, boiling point, and melting point. Conventionally, multi-component electrolyte properties are approximated with linear mixing, but in practice, significant deviations are observed. Excess quantities can provide insights into the molecular behavior of the mixture and could form the basis for designing high-performance electrolytes. Here we investigate the excess density of commonly used Li-ion battery solvents such as cyclic carbonates, linear carbonates, ethers, and nitriles with molecular dynamics simulations. We additionally investigate electrolytes consisting of these solvents and a salt. The results smoothly vary with mole percent and are fit to permutation-invariant Redlich-Kister polynomials. Mixtures of similar solvents, such as cyclic-cyclic carbonate mixtures, tend to have excess properties that are lower in magnitude compared to mixtures of dissimilar substances, such as carbonate-nitrile mixtures. We perform experimental testing using our robotic test stand, Clio, to provide validation to the observed simulation trends. We quantify the structure similarity using SOAP fingerprints to create a descriptor for excess density, enabling the design of electrolyte properties. To a first approximation, this will allow us to estimate the deviation of a mixture from ideal behavior based solely upon the structural dissimilarity of the components.

Terahertz microscopy has attracted attention owing to distinctive characteristics of the THz frequency region, particularly non-ionizing photon energy, spectral fingerprint, and transparency to most nonpolar materials. Nevertheless, the well-known Rayleigh diffraction limit imposed on THz waves commonly constrains the resultant imaging resolution to values beyond the millimeter scale, consequently limiting the applicability in numerous emerging applications for chemical sensing and complex media imaging. In this theoretical and numerical work, we address this challenge by introducing a new imaging approach, based on acquiring high-spatial frequencies by adapting the Fourier synthetic aperture approach to the terahertz spectral range, thus surpassing the diffraction-limited resolution. Our methodology combines multi-angle terahertz pulsed illumination with time-resolved field measurements, as enabled by the state-of-the-art time-domain spectroscopy technique. We demonstrate the potential of the approach for hyperspectral terahertz imaging of semi-transparent samples and show that the technique can reconstruct spatial and temporal features of complex inhomogeneous samples with subwavelength resolution.

Predicting the ground-state 3D molecular conformations from 2D molecular graphs is critical in computational chemistry due to its profound impact on molecular properties. Deep learning (DL) approaches have recently emerged as promising alternatives to computationally-heavy classical methods such as density functional theory (DFT). However, we discover that existing DL methods inadequately model inter-atomic forces, particularly for non-bonded atomic pairs, due to their naive usage of bonds and pairwise distances. Consequently, significant prediction errors occur for atoms with low degree (i.e., low coordination numbers) whose conformations are primarily influenced by non-bonded interactions. To address this, we propose REBIND, a novel framework that rewires molecular graphs by adding edges based on the Lennard-Jones potential to capture non-bonded interactions for low-degree atoms. Experimental results demonstrate that REBIND significantly outperforms state-of-the-art methods across various molecular sizes, achieving up to a 20\% reduction in prediction error.

Neutrinoless double-beta decay ($0\nu\beta\beta$) is a rare nuclear process that, if observed, will provide insight into the nature of neutrinos and help explain the matter-antimatter asymmetry in the universe. The Large Enriched Germanium Experiment for Neutrinoless Double-Beta Decay (LEGEND) will operate in two phases to search for $0\nu\beta\beta$. The first (second) stage will employ 200 (1000) kg of High-Purity Germanium (HPGe) enriched in $^{76}$Ge to achieve a half-life sensitivity of 10$^{27}$ (10$^{28}$) years. In this study, we present a semi-supervised data-driven approach to remove non-physical events captured by HPGe detectors powered by a novel artificial intelligence model. We utilize Affinity Propagation to cluster waveform signals based on their shape and a Support Vector Machine to classify them into different categories. We train, optimize, test our model on data taken from a natural abundance HPGe detector installed in the Full Chain Test experimental stand at the University of North Carolina at Chapel Hill. We demonstrate that our model yields a maximum physics event sacrifice of $0.024 ^{+0.004}_{-0.003} \%$ when performing data cleaning cuts. Our model is being used to accelerate data cleaning development for LEGEND-200.

Simulations have acted as a cornerstone to understand MOF/polymer interface structure, however, no molecular-level simulation has yet been performed at the nanoparticle scale. In this work, a hybrid MARTINI/Force Matching (FM) force field was developed and successfully implemented to model the ZIF-8/PVDF composite at a coarse grained resolution. Inter-phase interactions were modeled using FM potentials, which strive to reasonably reproduce the forces from an atomistic benchmark model, while intraphase interactions are modeled using the general-purpose MARTINI potentials. Systems made of a ZIF-8 nanoparticle embedded into a PVDF matrix were considered to evaluate the effect of nanoparticle size and morphology in the polymer structure and in the CO2 adsorption. Results show that simulations at the nanoparticle level are crucial for depicting the polymer penetration. Notably, the smallest nanoparticle exhibited the least extent of polymer penetration, while the cubic nanoparticle exhibited the highest amount. Polymer conformation and local density values change similarly in all ZIF-8/PVDF systems depending on whether the polymer lies inside or outside of the nanoparticle domain. All composite models present more significant CO2 adsorption in the nanoparticle domain than in the PVDF phase, in agreement with experiments. More remarkably, the small rhombic dodecahedron ZIF-8/PVDF system presents a larger equilibrium amount of gas adsorbed at ambient condition compared to the other two systems, in alignment with the observed polymer penetration trend. On the other hand, the amount of CO2 adsorbed at equilibrium is lower for the rhombic dodecahedron morphology than for the cubic one, contrary to the intuitive expectation founded in the polymer penetration trend. This result could be a reflection of a difference in the number of surface adsorption sites.

It is shown that condensation and precipitation do not disturb the hydrostatic equilibrium if the local pressure sink (condensation rate expressed in pressure units) is proportional to the local pressure, with a proportionality coefficient $k$ that is independent of altitude. In the real atmosphere, condensation rate is controlled, among other factors, by the vertical velocity that can vary freely over height. This means that, in general, condensation disturbs hydrostatic equilibrium and thus causes pressure adjustments through redistribution of air masses. It is proposed that $k$ maximised in the upper atmosphere results in additional upward motion, which leads to cyclone strengthening. Conversely, $k$ maximised closer to the surface produces additional downward motion, which causes cyclone's weakening. The maximum scale of both effects should be set by the strength of the mass sink (precipitation). Using observational data, it is found that the mean intensification and weakening rates ($8$ and $6$ hPa~day$^{-1}$, respectively) in Atlantic tropical cyclones constitute about two thirds of their maximum concurrent precipitation (multiplied by gravity). The implications of these results for recent studies evaluating the (de-)intensification process based on a mass continuity equation that neglects the mass sink are discussed.

For over half a century, computer-aided structural elucidation systems (CASE) for organic compounds have relied on complex expert systems with explicitly programmed algorithms. These systems are often computationally inefficient for complex compounds due to the vast chemical structural space that must be explored and filtered. In this study, we present a transformer based generative chemical language artificial intelligence (AI) model, an innovative end-to-end architecture designed to replace the logic and workflow of the classic CASE framework for ultra-fast and accurate spectroscopic-based structural elucidation. Our model employs an encoder-decoder architecture and self-attention mechanisms, similar to those in large language models, to directly generate the most probable chemical structures that match the input spectroscopic data. This approach demonstrates the potential of transformer based generative AI to accelerate traditional scientific problem-solving processes. The model's ability to iterate quickly based on new data highlights its potential for rapid advancements in structural elucidation.

We describe design studies for a pulsed quasi-monoenergetic 2-keV neutron source for calibration of sub-keV nuclear recoils. Such a calibration is required for detectors sensitive to sub-GeV dark matter and also the coherent elastic scattering of reactor neutrinos. In our design, neutrons from a commercial deuterium-tritium generator are moderated to the keV scale and then filtered to the monoenergetic spectrum using a feature in the neutron cross section of scandium. In this approach, unmoderated high-energy neutrons form a challenging background, along with gammas from neutron capture in the moderator materials. We describe the optimization of the moderator+filter and shielding geometry, and find a geometry that in simulation achieves both the target neutron flux at 2 keV and subdominant rates of background interactions. Lastly, we describe a future path to lower-energy (few eV scale) calibrations using time-of-flight and sub-keV neutrons.

Artificial intelligence (AI) has become a buzz word since Google's AlphaGo beat a world champion in 2017. In the past five years, machine learning as a subset of the broader category of AI has obtained considerable attention in the research community of granular materials. This work offers a detailed review of the recent advances in machine learning-aided studies of granular materials from the particle-particle interaction at the grain level to the macroscopic simulations of granular flow. This work will start with the application of machine learning in the microscopic particle-particle interaction and associated contact models. Then, different neural networks for learning the constitutive behaviour of granular materials will be reviewed and compared. Finally, the macroscopic simulations of practical engineering or boundary value problems based on the combination of neural networks and numerical methods are discussed. We hope readers will have a clear idea of the development of machine learning-aided modelling of granular materials via this comprehensive review work.

The Discrete Element Method is widely employed for simulating granular flows, but conventional integration techniques may produce unphysical results for simulations with static friction when particle size ratios exceed $R \approx 3$. These inaccuracies arise because some variables in the velocity-Verlet algorithm are calculated at the half-timestep, while others are computed at the full timestep. To correct this, we develop an improved velocity-Verlet integration algorithm to ensure physically accurate outcomes up to the largest size ratios examined ($R=100$). The implementation of this improved integration method within the LAMMPS framework is detailed, and its effectiveness is validated through a simple three-particle test case and a more general example of granular flow in mixtures with large size-ratios, for which we provide general guidelines for selecting simulation parameters and accurately modeling inelasticity in large particle size-ratio simulations.

We investigate seismic motion propagation through a passively isolated mechanical system, using Wiener filters and convolutional neural networks with time-dilation layers. The goal of this study was to explore the capabilities of neural networks and Wiener filters in characterizing a mechanical system from the measurements. The mechanical system used is a testbed facility for technology development for current and future gravitational wave detectors, "VATIGrav", currently being commissioned at University of Hamburg. It consists of a large vacuum chamber mounted on four active vibration isolators with an optical table inside, mounted on four passive vibration isolators. In this paper we have used seismic data recorded on the ground and on the optical table inside the chamber. The data were divided in 6 hours for training and another 6 hours for validation, focusing on inferring 150-second stretches of time series of table motion from the ground motion in the frequency range from $0.1~\mathrm{Hz}$ to about $50~\mathrm{Hz}$. We compare the performance of a neural network with FTT-based loss function and with Huber loss function to single-input, single-output (SISO) and multiple-input, single-output (MISO) Wiener filters. To be able to compute very large MISO Wiener filters (with 15,000 taps) we have optimized the calculations exploiting block-Toeplitz structure of the matrix in Wiener-Hopf equations. We find that for the given task SISO Wiener filters outperform MISO Wiener filters, mostly due to low coherence between different motion axes. Neural network trained with Huber loss performs slightly worse than Wiener filters. Neural network with FFT-based loss outperforms Wiener filters in some frequency regions, particularly with low amplitudes and reduced coherence, while it tends to slightly underestimate the peaks, where Wiener filters perform better.

Intrinsic bulk viscosity of the one-component plasma (OCP) is computed and analyzed using equilibrium molecular dynamics simulations and the Green-Kubo formalism. It is found that bulk viscosity exhibits a maximum at $\Gamma \approx 1$, corresponding to the condition that the average kinetic energy of particles equals the potential energy at the average inter-particle spacing. The weakly coupled and strongly coupled limits are analyzed and used to construct a model that captures the full range of coupling strengths simulated: $\Gamma \approx 10^{-2} - 10^2$. Simulations are also run of the Yukawa one-component plasma (YOCP) in order to understand the impact of electron screening. It is found that electron screening leads to a smaller bulk viscosity due to a reduction in the excess heat capacity of the system. Bulk viscosity is shown to be at least an order of magnitude smaller than shear viscosity in both the OCP and YOCP. The generalized frequency-dependent bulk viscosity coefficient is also analyzed. This is found to exhibit a peak near twice the plasma frequency in strongly coupled conditions, which is associated with the oscillatory decay observed in the bulk viscosity autocorrelation function. The generalized shear and bulk viscosity coefficients are found to have a similar magnitude for $\omega \gtrsim 2\omega_p$ at strongly coupled conditions.

The ability to prepare molecular ions in selected quantum states enables studies in areas such as chemistry, metrology, spectroscopy, quantum information, and precision measurements. Here, we demonstrate (2+1) resonance-enhanced multiphoton ionization (REMPI) of oxygen, both in a molecular beam and in an ion trap. The two-photon transition in the REMPI spectrum is rotationally resolved, allowing ionization from a selected rovibrational state of ${\rm O}_2$. Fits to this spectrum determine spectroscopic parameters of the ${\rm O}_2$ $d\,^1\Pi_g$ state and resolve a discrepancy in the literature regarding its band origin. The trapped molecular ions are cooled by co-trapped atomic ions. Fluorescence mass spectrometry nondestructively demonstrates the presence of the photoionized ${\rm O}_2^+$. We discuss strategies for maximizing the fraction of ions produced in the ground rovibrational state. The combination of state-selective loading and nondestructive detection of trapped molecular ions has applications in optical clocks, tests of fundamental physics, and control of chemical reactions.

We have been developing a novel interferometer formed directly between a Very Long Baseline Interferometry (VLBI) radio telescope and a Global Navigation Satellite Systems (GNSS) antenna/receiver. This interferometer is enabled by the High Rate Tracking Receiver (HRTR), a high-performance GNSS software-defined receiver that records baseband data similar to a VLBI receiving system. Of particular interest is the potential of the technique to produce precise local tie vectors directly between the geodetic reference points of VLBI and GNSS antennas for use in the determination of combination terrestrial reference frames. In this paper, we will describe the unique setup of this interferometer before discussing current developmental work in estimating local tie vectors from experimental data collected using this technique. In particular, much recent work has been in exploring the possibility of direct comparison of the GNSS and VLBI processing techniques. A significant advantage of GNSS-VLBI co-observation of GNSS satellites comes from the ability to cross-check position and clock estimates derived from differential measurements in VLBI-style processing with differential GNSS processing and absolute GNSS positioning with Precise Point Positioning (PPP). We show a preliminary VLBI analysis of interferometric data including a differential positioning solution using phase delays.

Optical-based terahertz sources are important for many burgeoning scientific and technological applications. Among such applications is precision spectroscopy of molecules, which exhibit rotational transitions at terahertz frequencies. Stemming from precision spectroscopy is frequency discrimination and stabilization of terahertz sources. Because many molecular species exist in the gas phase at room temperature, their transitions are prime candidates for practical terahertz frequency references. We demonstrate the stabilization of a low phase-noise, dual-wavelength Brillouin laser (DWBL) terahertz oscillator to a rotational transition of carbonyl sulfide (\ce{OCS}). We achieve an instability of $1.2\times10^{-12}/\sqrt{\tau}$, where $\tau$ is the averaging time in seconds. The signal-to-noise ratio and intermodulation limitations of the experiment are also discussed. We thus demonstrate a highly stable and spectrally pure terahertz frequency source. Our presented architecture will likely benefit metrology, spectroscopy, precision terahertz studies, and beyond.

Rotating magnets have recently emerged as an efficient method for producing ultra-low frequency signals for through-earth and through-seawater communications. Magneto-mechanical resonator (MMR) arrays, which are magnetized torsional rotors with a restoring torque, are a promising implementation of this idea that use resonance to enhance the magnetic signal generation. The fundamental challenge for MMR design is to have a suspension system for the rotors capable of resisting large transverse magnetic forces while allowing for a large angle of motion at low dissipation and hence high efficiency. Here, we study flexure-based pivot bearings as compliant support elements for MMR rotors which address this challenge and demonstrate their efficient low damping operation. A crossed-flexure configuration enables large angular rotation around a central axis, large transverse stiffness, and compact assembly of closely spaced rotor arrays via geometric flexure interlocking. We characterize the eigen frequency performance and structural damping of MMR supported by these proposed pivot bearings. We develop analytical and numerical models to study their static and dynamic behaviors, including their coupled dynamic modes. We demonstrate that their damping coefficient is up to 80 times lower than corresponding ball bearing MMR. This study is broadly applicable to various systems that leverage arrays of coupled torsional oscillators such as magneto-mechanical transmitters, metamaterials, and energy harvesters.

The Particle-In-Cell (PIC) and Monte Carlo Collisions (MCC) methods are workhorses of many numerical simulations of physical systems. Recently, it was pointed out that, while the two methods can be exactly - or nearly - energy-conserving independently, combining the two is leading to anomalous numerical heating. This paper reviews the standard explicit PIC-MCC algorithm, elucidates the origins of the anomalous numerical heating and explains how to couple the two methods such that the anomalous numerical heating is avoided.

The high-luminosity upgrade of the LHC (HL-LHC) brings unprecedented requirements for precision bunch-by-bunch luminosity measurement and beam-induced background monitoring in real time. A key component of the CMS Beam Radiation Instrumentation and Luminosity detector system is a stand-alone luminometer, the Fast Beam Condition Monitor (FBCM), which is able to operate independently at all times with a triggerless asynchronous readout. FBCM utilizes a dedicated front-end ASIC to amplify the signals from CO$_2$-cooled silicon-pad sensors with 1 ns timing resolution. Front-end (FE) electronics are subject to high-radiation conditions, thus all components are radiation hardened: sensors, ASICs, transceivers, etc. The FBCM ASIC contains 6 channels, each outputting a high-speed binary signal carrying the time-of-arrival and time-over-threshold information. This signal is sent via a gigabit optical link to the back-end electronics for analysis. A dedicated test system is designed for the FBCM FE electronics with a modular setup for all testing needs of the project from initial ASIC validation test to system-level testing with the full read-out chain. The paper reports on the design, read-out architecture, and testing program for the FBCM electronics.

Cellular membrane dynamics play an important role in a variety of physiological processes. However, due to the stringent light-coupling conditions required for exciting evanescent waves, label-free mapping of cellular membrane dynamics on curved substrates remains challenging. Here, we report surface-emitting resonator interference microscopy (SERIM), which employs the evanescent wave naturally present in the near-field region of a whispering gallery mode (WGM) resonator to probe the subcellular membrane dynamics. The WGM resonator provides strong optical feedback for enhancing the light-mattering interaction and also provides a biomimetic curvature interface to investigate the membrane dynamics. The interaction of the evanescent wave with the cell membrane caused significant scattering, forming a highly sensitive interference pattern. We found that the time-resolved interference patterns can be utilized to extract subcellular membrane dynamics with a diffractive limited spatial resolution. We further employed the SERIM to investigate the spatial heterogeneity of membrane dynamics during migration, and also stimulus responses to temperature and drugs. In striking contrast to the conventional label-free methods, the easy excitation of the evanescent wave makes SERIM a versatile label-free strategy for studying cellular behavior on a curved surface. Our work holds promise for sensitive detection of subtle biochemical and biophysical information during cell-substrate interaction.

Predicting gray swan weather extremes, which are possible but so rare that they are absent from the training dataset, is a major concern for AI weather/climate models. An important open question is whether AI models can extrapolate from weaker weather events present in the training set to stronger, unseen weather extremes. To test this, we train independent versions of the AI model FourCastNet on the 1979-2015 ERA5 dataset with all data, or with Category 3-5 tropical cyclones (TCs) removed, either globally or only over the North Atlantic or Western Pacific basin. We then test these versions of FourCastNet on 2018-2023 Category 5 TCs (gray swans). All versions yield similar accuracy for global weather, but the one trained without Category 3-5 TCs cannot accurately forecast Category 5 TCs, indicating that these models cannot extrapolate from weaker storms. The versions trained without Category 3-5 TCs in one basin show some skill forecasting Category 5 TCs in that basin, suggesting that FourCastNet can generalize across tropical basins. This is encouraging and surprising because regional information is implicitly encoded in inputs. No version satisfies gradient-wind balance, implying that enforcing such physical constraints may not improve generalizability to gray swans. Given that current state-of-the-art AI weather/climate models have similar learning strategies, we expect our findings to apply to other models and extreme events. Our work demonstrates that novel learning strategies are needed for AI weather/climate models to provide early warning or estimated statistics for the rarest, most impactful weather extremes.

Turbulence has remained the greatest challenge in fluid mechanics. Especially, randomness is one of the most important characteristics of turbulence, but its origin is still an open question until now. In this paper, by means of several ingeniously designed ``clean'' numerical experiments based on Navier-Stokes equations for the two-dimensional turbulent Kolmogorov flow, we reveal such a new phenomenon, namely ``noise-expansion cascade'', that all micro-level noises/disturbances at different orders of magnitude in initial condition of Navier-Stokes equations enlarge consistently, say, one by one like an inverse cascade, to macro-level, and besides each of them might greatly change macro-level characteristic of turbulence. The ``noise-expansion cascade'' closely connects randomness of micro-level noise/disturbance and macro-level disorder of turbulence and thus reveals the origin of randomness of turbulence. In addition, it clearly indicates that unavoidable thermal fluctuation must be considered for turbulence, even if it is several orders of magnitude smaller than other external environmental disturbances.

Basement relief gravimetry is crucial in geophysics, especially for oil exploration and mineral prospecting. It involves solving an inverse problem to infer geological model parameters from observed data. The model represents basement relief with constant-density prisms, and the data reflect gravitational anomalies from these prisms. Inverse problems are often ill-posed, meaning small data changes can lead to large solution variations. To mitigate this, regularization techniques like Tikhonov's are used to stabilize solutions. This study compares regularization methods applied to gravimetric inversion, including Smoothness Constraints, Total Variation, Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT) using Daubechies D4 wavelets. Optimization, particularly with Genetic Algorithms (GA), is used to find prism depths that best match observed anomalies. GA, inspired by natural selection, selects the best solutions to minimize the objective function. The results, evaluated through fit metrics and error analysis, show the effectiveness of all regularization methods and GA, with the Smoothness constraint performing best in synthetic models. For the real data model, all methods performed similarly.

Superhydrophobic surfaces (SHS) are textured hydrophobic surfaces which have the ability to trap air pockets when immersed in water. This can result in significant drag reduction, due to substantially lower viscosity of air resulting in substantial effective slip velocity at the interface. Past studies of both laminar and turbulent flows model this slip velocity in terms of a homogenized Navier slip boundary condition with a slip length relating the wall slip velocity to the wall-normal velocity gradient. In this work, we seek to understand the effects of superhydrophobic surfaces in the context of mean scalar and momentum mixing. We use the macroscopic forcing method (Mani and Park, 2021) to compute the generalized eddy viscosity and slip length operators of a turbulent channel over SHS, implemented as both a pattern-resolved boundary condition and homogenized slip length boundary condition, for several pattern sizes and geometries. We present key differences in the mixing behavior of both boundary conditions through quantification of their near-wall eddy viscosity. Analysis of transport in turbulent flow over pattern-resolved surfaces reveals substantial nonlocality in teh measured homogenized slip length for both scalar and momentum mixing when the Reynolds and Peclet numbers based on pattern size are finite. We present several metrics to quantify this nonlocality and observe possible trends relating to Reynolds number, texture size, and pattern geometry. The importance of nonlocality in the slip length operator and in the eddy diffusivity operator is demonstrated by examining impact on Reynolds-averaged solutions for the mean scalar and velocity fields.

Statistical fluctuations of local tensorial fields beyond the mean are relevant to predict localized failure or overall behavior of the inelastic composites. The expression for second moments of the local fields can be established using Hill-Mandel condition. Complete estimation of statistical fluctuations via second moments is usually ignored despite its significance. In Eshelby-based mean-field approaches, the second moments are evaluated through derivatives of Hill's Polarization tensor $ \left ( \mathbb{P}_{\mathrm{o}} \right )$ using a singular approximation. Typically, semi-analytical procedures using numerical integration are used to evaluate the derivatives of the polarization tensor $\mathbb{P}_{\mathrm{o}}$. Here, new analytically derived explicit expressions are presented for calculating the derivatives, specifically for unidirectional fibrous composites with isotropic phases. Full-field homogenization using finite element is used to compute the statistical distribution of local fields (exact solution) for the class of random fibrous microstructures. The mean-field estimates are validated with the exact solution across different fiber volume fractions and aspect ratios. The results indicate that the fiber volume fraction significantly influences the fluctuation of stress tensor invariants, whereas the aspect ratio has minimal effect.

We investigate enstrophy variations by collapse of point vortices in an inviscid flow and, in particular, focus on the enstrophy dissipation that is a significant property characterizing 2D turbulent flows. Point vortex is an ideal vortex whose vorticity is concentrated on a point and the dynamics of point vortices on an inviscid flow is described by the point-vortex system. The point-vortex system has self-similar collapsing solutions, which are expected to cause the anomalous enstrophy dissipation, but this collapsing process of point vortices cannot be described by the 2D Euler equations. In this study, we consider point-vortex solutions of the 2D filtered Euler equations, which are a regularized model of the 2D Euler equations, and the filtered-point-vortex system describing the dynamics of them. The preceding studies have proven that there exist solutions to the three filtered-point-vortex system such that they converge to self-similar collapsing orbits of the point-vortex system and dissipate the enstrophy at the event of collapse in the zero limit of a filter scale. In this study, we numerically show that the enstrophy dissipation by the collapse of point vortices could occur for the four and five vortex problems.

Effective spatio-temporal measurements of water surface elevation (water waves) in laboratory experiments are crucial for scientific and engineering research. Existing techniques are often cumbersome, computationally heavy and generally suffer from limitations in wavenumber/frequency response. To address these challenges, we propose Wave (from) Polarized Light Learning (WPLL), a learning based remote sensing method for laboratory implementation, capable of inferring surface elevation and slope maps in high resolution. The method uses the polarization properties of light reflected from the water surface. The WPLL uses a deep neural network (DNN) model that approximates the water surface slopes from the polarized light intensities. Once trained on simple monochromatic wave trains, the WPLL is capable of producing high-resolution and accurate 2D reconstruction of the water surface slopes and elevation in a variety of irregular wave fields. The method's robustness is demonstrated by showcasing its high wavenumber/frequency response, its ability to reconstruct wave fields propagating at arbitrary angles relative to the camera optical axis, and its computational efficiency. This developed methodology is an accurate and cost-effective near-real time remote sensing tool for laboratory water surface waves measurements, setting the path for upscaling to open sea application for research, monitoring, and short-time forecasting.

We found that the specific dispersion of metal-insulator-metal (MIM) waveguide allows the hybridization of surface plasmon polaritons (SPPs) and the waveguide, which is not possible with dielectric waveguides. The SPP-MIM hybridization structure forms such a meta-film that integrates the previously incompatible respective merits of SPR and LSPR, including flat interfaces, high sensitivities, short evanescent fields and easy coupling with confined light. On the other hand, to achieve stable and reproducible performance is one of the greatest unresolved challenges for the development of nanophotonic biosensors. We point out that the key is to obtain well-controlled biomolecular behaviors using simple physical interfaces, for which the SPP-MIM meta-film provides a capable solution. We embed the SPP-MIM meta-film with a plasmonic crystal cavity and integrate it on a single-mode fiber's end-facet to detect biomolecular interactions. This device demonstrates highly reproducible sensorgrams and convincing detection of biotinylated proteins at down to 30 fM, with the sensorgrams following the Langmuir model. By unprecedentedly having both high sensitivity and high reproducibility, our device proposal provides a comprehensive solution for optical fiber-tip plasmonic devices to turn into a useful industrial biosensing technology.

One of the exceptional features of non-Hermitian systems is the unidirectional wave interactions. Simultaneous modulation of the real and the imaginary part of the interaction potentials (of the refractive index and the gain/loss in the case of optical systems) can result in unequal coupling coefficients between the fields of different parts of the system. The unidirectional coupling can also be arranged not only between the internal fields of the system but also between internal fields and external radiation. At a particular (exceptional) point the situation can be achieved, that the external radiation is efficiently coupled into the system, but the internal radiation cannot escape backwards. In this way, the incident radiation can be trapped inside the non-Hermitian system and, eventually, can be efficiently absorbed there. We realize this idea in non-Hermitically modulated thin films. The modulation consists of a Hermitian part - the periodic corrugation of the surfaces of a thin film, and a non-Hermitian part - the modulation of losses along the film. We prove numerically and demonstrate experimentally that the incident radiation, coupled with such a non-Hermitian thin film, is unidirectionally trapped into a planar mode of the film, does not escape from the film (or escape weakly due to experimental imperfections), and is efficiently absorbed there.

The quest to enhance the sensitivity of electron spin resonance (ESR) is an ongoing challenge. One potential strategy involves increasing the frequency, for instance, moving from Q-band (approximately 35 GHz) to W-band (approximately 94 GHz). However, this shift typically results in higher transmission and switching losses, as well as increased noise in signal amplifiers. In this work, we address these shortcomings by employing a W-band probehead integrated with a cryogenic low-noise amplifier (LNA) and a microresonator. This configuration allows us to position the LNA close to the resonator, thereby amplifying the acquired ESR signal with minimal losses. Furthermore, when operated at cryogenic temperatures, the LNA exhibits unparalleled noise levels that are significantly lower than those of conventional room temperature LNAs. We detail the novel probehead design and provide some experimental results at room temperature as well as cryogenic temperatures for representative paramagnetic samples. We find, for example, that spin sensitivity of ~3*10^5 spins/sqrt(Hz) is achieved for a sample of phosphorus doped 28Si, even for sub-optimal sample geometry with potential improvement to <10^3 spins/sqrt(Hz) in more optimal scenarios.

The demand for structured light with a reconfigurable spatial and polarization distribution has been increasing across a wide range of fundamental and advanced photonics applications, including microscopy, imaging, sensing, communications, and quantum information processing. Nevertheless, the unique challenge in manipulating light structure after optical fiber transmission is the necessity to dynamically address the inherent unknown fiber transmission matrix, which can be affected by factors like variations in the fiber stress and inter-modal coupling. In this study, we demonstrated that the beam structure at the fiber end including its spatial and polarization distribution can be precisely and adaptively reconfigured by a programmable silicon photonic processor, without prior knowledge of the optical fiber systems and their changes in the transmission matrices. Our demonstrated photonic chip can generate and control the full set of spatial and polarization modes or their superposition in a two-mode few-mode optical fiber. High-quality beam structures can be obtained in experiments. In addition, efficient generation is achieved by our proposed chip-to-fiber emitter while using a complementary metal-oxide-semiconductor compatible fabrication technology. Our findings present a scalable pathway towards achieving a portable and reliable system capable of achieving precise control, efficient emission, and adaptive reconfiguration for structured light in optical fibers.

Purpose: To develop an MRI technique for free-breathing 3D whole-liver quantification of water T1, water T2, proton density fat fraction (PDFF), R2*. Methods: An Eight-echo spoiled gradient echo pulse sequence with spiral readout was developed by interleaving inversion recovery and T2 magnetization preparation. We propose a neural network based on a 4D and a 3D implicit neural representation (INR) which simultaneously learns the motion deformation fields and the static reference frame MRI subspace images respectively. Water and fat singular images were separated during network training, with no need of performing retrospective water-fat separation. T1, T2, R2* and proton density fat fraction (PDFF) produced by the proposed method were validated in vivo on 10 healthy subjects, using quantitative maps generated from conventional scans as reference. Results: Our results showed minimal bias and narrow 95% limits of agreement on T1, T2, R2* and PDFF values in the liver compared to conventional breath-holding scans. Conclusions: INR-MRF enabled co-registered 3D whole liver T1, T2, R2* and PDFF mapping in a single free-breathing scan.

The present work is devoted to the study of the thermostability of native DNA during binding to TiO$_{2}$ nanoparticles (NPs) at various concentrations under conditions close to physiological (0.1 M Na $^+$, pH 7) using thermal denaturation method and dynamic light scattering (DLS). The analysis of the DNA melting curves in the presence of TiO$_{2}$ NPs revealed a temperature range in which the light absorption of DNA decreases. This is explained by the formation of an ordered structure of partially unwound DNA strands on the NP surface. The DNA:TiO$_{2}$ NP nanoassemblies formed at the initial stages remained stable in the wide temperature range. The performed temperature-dependent DLS measurements of the DNA:TiO$_{2}$ NP suspension at pH 7 have not revealed the aggregation of the DNA:TiO$_{2}$ NP nanoassemblies that had been observed at pH 5.

The currents in the ocean have a serious impact on ocean dynamics, since they affect the transport of mass and thus the distribution of salinity, nutrients and pollutants. In many physically important situations the current depends quadratic-ally on the depth. We consider a single layer of fluid and study the propagation of the surface waves in the presence of depth-dependent current with quadratic profile. We select the scale of parameters and quantities, which are typical for the Boussinesq propagation regime (long wave and small amplitude limit) and we also derive the well known KdV model for the surface waves interacting with current.

Receptor occupancy (RO) studies using PET neuroimaging play a critical role in the development of drugs targeting the central nervous system (CNS). The conventional approach to estimate drug receptor occupancy consists in estimation of binding potential changes between two PET scans (baseline and post-drug injection). This estimation is typically performed separately for each scan by first reconstructing dynamic PET scan data before fitting a kinetic model to time activity curves. This approach fails to properly model the noise in PET measurements, resulting in poor RO estimates, especially in low receptor density regions. Objective: In this work, we evaluate a novel joint direct parametric reconstruction framework to directly estimate distributions of RO and other kinetic parameters in the brain from a pair of baseline and post-drug injection dynamic PET scans. Methods: The proposed method combines the use of regularization on RO maps with alternating optimization to enable estimation of occupancy even in low binding regions. Results: Simulation results demonstrate the quantitative improvement of this method over conventional approaches in terms of accuracy and precision of occupancy. The proposed method is also evaluated in preclinical in-vivo experiments using 11C-MK-6884 and a muscarinic acetylcholine receptor 4 positive allosteric modulator drug, showing improved estimation of receptor occupancy as compared to traditional estimators. Conclusion: The proposed joint direct estimation framework improves RO estimation compared to conventional methods, especially in intermediate to low-binding regions. Significance: This work could potentially facilitate the evaluation of new drug candidates targeting the CNS.

The global demand for unconventional energy sources such as geothermal energy and white hydrogen requires new exploration techniques for precise subsurface structure characterization and potential reservoir identification. Magnetotelluric (MT) inversion is crucial for these tasks, providing critical information on the distribution of subsurface electrical resistivity at depths ranging from hundreds to thousands of meters. However, traditional iterative algorithm-based inversion methods require the adjustment of multiple parameters, demanding time-consuming and exhaustive tuning processes to achieve proper cost function minimization. Although recent advances have incorporated deep learning algorithms for MT inversion, these have been primarily based on supervised learning, which needs large labeled datasets for training. Therefore, it causes issues in generalization and model characteristics that are restricted to the neural network's features. This work utilizes TensorFlow operations to create a differentiable forward MT operator, leveraging its automatic differentiation capability. Moreover, instead of solving for the subsurface model directly, as classical algorithms perform, this paper presents a new deep unsupervised inversion algorithm guided by physics to estimate 1D MT models. Instead of using datasets with the observed data and their respective model as labels during training, our method employs a differentiable modeling operator that physically guides the cost function minimization, making the proposed method solely dependent on observed data. Therefore, the optimization problem is updating the network weights to minimize the data misfit. We test the proposed method with field and synthetic data at different acquisition frequencies, demonstrating that the resistivity models are more accurate than other results using state-of-the-art techniques.

Magnesium sulfate (${ \rm MgSO_4 }$) is used as an additive to reduce capacity fading in rechargeable zinc-ion batteries. This study investigates the ion pairing and solvation structure of ${ \rm Zn^{2+}-SO_4^{2-} }$ and ${ \rm Mg^{2+}-SO_4^{2-} }$ in mixtures of ${ \rm [ZnSO_4]{2M} + [MgSO_4]{0M} }$, ${ \rm [ZnSO_4]{1M} + [MgSO_4]{1M} }$, and ${ \rm [ZnSO_4]{0M} + [MgSO_4]{2M} }$. Using molecular dynamics simulations, we evaluated dipole dynamics, spatial distributions, and coordination of ions through radial distribution functions, running coordination numbers, and potentials of mean force. The results reveal that increasing ${ \rm MgSO_4 }$ concentration disrupts ${ \rm Zn^{2+} }$ self-association, while ${ \rm Mg^{2+} }$ shows enhanced association. The analysis indicates different solvation structures, with ${ \rm Zn^{2+} }$ maintaining a more ordered coordination with ${ \rm SO_4^{2-} }$ than ${ \rm Mg^{2+} }$. Preferential binding analysis highlights stronger ${ \rm Zn^{2+} }$ affinity for ${ \rm SO_4^{2-} }$, shedding light on ion interaction dynamics in mixed electrolytes.

Complex contagion models that involve contagion along higher-order structures, such as simplicial complexes and hypergraphs, yield new classes of mean-field models. Interestingly, the differential equations arising from many such models often exhibit a similar form, resulting in qualitatively comparable global bifurcation patterns. Motivated by this observation, we investigate a generalized mean-field-type model that provides a unified framework for analysing a range of different models. In particular, we derive analytical conditions for the emergence of different bifurcation regimes exhibited by three models of increasing complexity -- ranging from three- and four-body interactions to two connected populations with both pairwise and three-body interactions. For the first two cases, we give a complete characterisation of all possible outcomes, along with the corresponding conditions on network and epidemic parameters. In the third case, we demonstrate that multistability is possible despite only three-body interactions. Our results reveal that single population models with three-body interactions can only exhibit simple transcritical transitions or bistability, whereas with four-body interactions multistability with two distinct endemic steady states is possible. Surprisingly, the two-population model exhibits multistability via symmetry breaking despite three-body interactions only. Our work sheds light on the relationship between equation structure and model behaviour and makes the first step towards elucidating mechanisms by which different system behaviours arise, and how network and dynamic properties facilitate or hinder outcomes.

A universal mathematical description of various micro-optical schemes related with Fano resonances is missing. This concerns, for instance, the novel micro-optical designs, such as micro-ring resonators, or especially thin films, couple to a continuum, and thus showing Fano resonances. Usually, such micro-optic circuits are simulated numerically, frequently by the use of commercial software. We fill this gap of the lack of universal analytical model. by introducing and exploring a simple mechanical equivalent, the oscillator chain, to simulate such schemes involving Fano resonances. The model does not necessary provide the rigorous description of more complicated micro-optical schemes, however does capture the main properties of such Fano-related optical systems. The model captures different modifications of thin film: thin film with amplification, non-Hermitical thin films, and others. It also covers the case of multiple Fano resonances in thin film. The model is compared with the rigorously calculated (Rigorous Coupled Wave analysis) wave propagation in thin films.

We report on continuous high-harmonic generation at 1 kHz repetition rate from a liquid-sheet plasma mirror driven by relativistic-intensity near-single-cycle light transients. Through precise control of both the surface plasma density gradient and the driving light waveform, we can produce highly stable and reproducible extreme ultraviolet spectral quasi-continua, corresponding to the generation of stable kHz-trains of isolated attosecond pulses in the time domain. This confirms the exciting potential of liquid sheet targets as one of the building blocks of future high-power attosecond lasers.

We consider emergence from the perspective of dynamics: states of a system evolving with time. We focus on the role of a decomposition of wholes into parts, and attempt to characterize relationships between levels without reference to whether higher-level properties are "novel" or "unexpected." We offer a classification of different varieties of emergence, with and without new ontological elements at higher levels.

We describe and analyze a new class of direct energy conversion schemes based on the adiabatic magnetic drift of charged particles in axisymmetric magnetic fields. The efficiency of conversion as well as the geometrical and dynamical limitations of the recoverable power are calculated. The geometries of these axisymmetric field configurations are suited for direct energy conversion in radiating advanced aneutronic reactors and in advanced divertors of deuterium-tritium tokamak reactors. The E cross B configurations considered here do not suffer from the classical drawbacks and limitations of thermionic and magnetohydrodynamic high temperature direct energy conversion devices.

Background/Objectives: Cataract surgery, a very common and critical procedure for restoring vision, has outcomes that can vary based on patient demographics. This study aimed to elucidate the effects of age and sex on the risk factors, intraoperative complications, and postoperative outcomes of cataract surgery. Subjects/Methods: Conducted as a single-center retrospective cohort study, it analyzed 691 eyes from 589 individuals who underwent surgery at a tertiary referral center, utilizing data from electronic medical records to assess preoperative risk factors, intraoperative complications, and pre- and post-operative best corrected visual acuity (BCVA) along with demographic data. Results: The main results highlighted that males aged 65-75 years exhibited significantly higher rates of functional postoperative BCVA (91% for males vs. 79% for females, p=0.007), a disparity that is not explained by differences in surgical complications or risk factor prevalence. Furthermore, the study identified age-specific thresholds where BCVA improvements significantly declined beyond 65 years for females and 75 years for males. The likelihood of worsened BCVA post-surgery increased with age for both sexes, with a significant decline in BCVA improvement transitioning from 55-65 years to 65-75 years age groups. Conclusions: The findings underscore the critical influence of both sex and age on cataract surgery outcomes, revealing significant sex-specific age thresholds that signal lesser improvements in postoperative BCVA. These insights advocate for the integration of patient age and sex into preoperative evaluations to better tailor the timing and planning of cataract surgery, ultimately aiming to optimize clinical outcomes.

We introduce an all-optical system, termed the "lying mirror", to hide input information by transforming it into misleading, ordinary-looking patterns that effectively camouflage the underlying image data and deceive the observers. This misleading transformation is achieved through passive light-matter interactions of the incident light with an optimized structured diffractive surface, enabling the optical concealment of any form of secret input data without any digital computing. These lying mirror designs were shown to camouflage different types of input image data, exhibiting robustness against a range of adversarial manipulations, including random image noise as well as unknown, random rotations, shifts, and scaling of the object features. The feasibility of the lying mirror concept was also validated experimentally using a structured micro-mirror array along with multi-wavelength illumination at 480, 550 and 600 nm, covering the blue, green and red image channels. This framework showcases the power of structured diffractive surfaces for visual information processing and might find various applications in defense, security and entertainment.

In the N$\nu$DEx collaboration, a high-pressure gas TPC is being developed to search for the neutrinoless double beta decay. The use of electronegative $\mathrm{^{82}SeF_{6}}$ gas mandates an ion-TPC. The reconstruction of $z$ coordinate is to be realized exploiting the feature of multiple species of charge carriers. As the initial stage of the development, we studied the properties of the $\mathrm{SF_{6}}$ gas, which is non-toxic and has similar molecular structure to $\mathrm{SeF_{6}}$. In the paper we present the measurement of drift velocities and mobilities of the majority and minority negative charge carriers found in $\mathrm{SF_{6}}$ at a pressure of 750 Torr, slightly higher than the local atmospheric pressure. The reduced fields range between 3.0 and 5.5 Td. It was performed using a laser beam to ionize the gas inside a small TPC, with a drift length of 3.7 cm. A customized charge sensitive amplifier was developed to read out the anode signals induced by the slowly drifting ions. The reconstruction of $z$ coordinate using the difference in the velocities of the two carriers was also demonstrated.

We present a widefield fluorescence microscope that integrates an event-based image sensor (EBIS) with a CMOS image sensor (CIS) for ultra-fast microscopy with spectral distinction capabilities. The EBIS achieves temporal resolution of $\sim10\thinspace\mu$s ($\sim\thinspace$50,000 frames/s), while the CIS provides diffraction-limited spatial resolution. A diffractive optical element encodes spectral information into a diffractogram, which is recorded by the CIS. The diffractogram is processed using a deep neural network to resolve the fluorescence of two beads, whose emission peaks are separated by only 7 nm and exhibit an 88\% spectral overlap. We validate our microscope by imaging the capillary flow of fluorescent beads, demonstrating a significant advancement in ultra-fast spectral microscopy. This technique holds broad potential for elucidating foundational dynamic biological processes.

This paper introduces the design and comprehensive characterization of a novel three-layer metamaterial absorber, engineered to exploit the unique optical properties of gold, vanadium dioxide, and silicon dioxide. At the core of this design, silicon dioxide serves as a robust substrate that supports an intricately structured layer of gold and a top layer of vanadium dioxide. This configuration is optimized to harness and enhance absorption capabilities effectively across a broadband terahertz (THz) spectrum. The absorber demonstrates an extensive absorption bandwidth of 3.00 THz, spanning frequencies from 2.414 THz to 5.417 THz. Remarkably, throughout this range, the device maintains a consistently high absorption efficiency, exceeding 90%. This efficiency is characterized by two sharp absorption peaks located at 2.638 THz and 5.158 THz, which signify the precise tuning of the metamaterial structure to interact optimally with specific THz frequencies. The absorbance of the proposed model is almost equal to 99%. This absorber is polarization insensitive. The development of this absorber involved a series of theoretical simulations backed by experimental validations, which helped refine the metamaterial's geometry and material composition. This process illuminated the critical role of the dielectric properties of silicon dioxide and the plasmonic effects induced by gold and vanadium dioxide layers, which collectively contribute to the high-performance metrics observed.

Free-induction-decay (FID) magnetometers have evolved as simple magnetic sensors for sensitive detection of unknown magnetic fields. However, these magnetometers suffer from a fundamental problem known as a "dead zone," making them insensitive to certain magnetic field directions. Here, we demonstrate a simple experimental scheme for the dead-zone-free operation of a FID atomic magnetometer. Using a single laser beam containing equal strength of linear- and circular-polarization components and amplitude-modulation at a low-duty cycle, we have synchronously pumped the rubidium-87 atoms with both first- and second-order frequency harmonics. Such a novel pumping scheme has enabled us to observe the free Larmor precession of atomic spins at a frequency of $\Omega_L$ (orientation) and/or 2$\Omega_L$ (alignment) in a single FID signal, depending on the direction of the external magnetic field. We observed that the amplitude of the FID signal does not go to zero for any magnetic field direction, proving the absence of dead zones in the magnetometer. The magnetometer has a sensitivity in the range of 3.5 - 7.8 pT/$\sqrt{Hz}$ in all directions. Such a generic experimental scheme can play a crucial role in developing miniaturized atomic magnetometers for various practical applications, including geomagnetic applications.

A transverse radiative heat flux induced by the gradient of spin angular momentum of photons in non-reciprocal systems is predicted. This thermal analog of the inverse spin Hall effect is analyzed in magneto-optical networks exhibiting C4 symmetry, under the action of spatially variable external magnetic fields. This finding opens new avenues for thermal management and energy conversion with non-reciprocal systems through a localized and dynamic control of the spin angular momentum of light.

Akinetic swept-sources are essential for high-speed optical coherence tomography (OCT) imaging. Time-stretched supercontinuum (TSSC) lasers have proven to be efficient for multi-MHz swept-sources. However, lack of low-noise broadband lasers and of large dispersion devices in the water low-absorption band at 1060 nm have limited the biomedical applications of TSSC lasers. In this letter, an approach to tune the wavelength around 1050 nm over 90 nm with low-noise at 10 MHz is presented. This is based on all-normal dispersion (ANDi) supercontinuum dynamics, and employs a long chirped fiber Bragg grating (CFBG) to time-stretch a broadband pulse with a duty cycle of 93 %. Retinal images are demonstrated, with a sensitivity of 84 dB - approaching the shot noise limit. We believe this high-speed low-noise swept-source will greatly promote the development of OCT techniques for biomedical applications.

In this study, we analyze chemical reaction systems by applying large deviation theory (LDT) and demonstrate that the fluctuation theorem (FT) in nonequilibrium reaction systems can be derived based on the symmetry of the cumulant generating function (CGF) defined through the rate function. Furthermore, we redefine heat through this rate function, establishing a direct connection between heat and Shannon entropy. This redefinition allows for the evaluation of Landauer's principle, which concerns the minimum energy cost of information erasure. The results obtained highlight the effectiveness of LDT as a framework for various nonequilibrium systems.

In high-resolution mass spectrometry, an electrospray ionization source is often paired with an ion-funnel to enhance ion transmission. Although it is established that ions experience collision-induced dissociation as they pass through this device, the impact of gas-flow dynamics on ion fragmentation remains unexplored. The present work demonstrates that the gas-flow dynamics from the capillary interface of an electrospray ionization source into an ion-funnel significantly reduces ion fragmentation. This reduction stems from the substantial decrease in the rate of increase in the internal energy of the ions resulting from the collisions with a supersonically expanding gas. The results of this study have significant consequences for systems that employ electrospray mass spectrometry and ion-mobility spectrometry.

We report the exact closed-form solutions for higher-order topological states as well as explicit energy-spectrum relationships in two-dimensional (2D) non-Hermitian multi-orbital lattices with generalized boundary conditions. These analytical solutions unequivocally confirm that topological edge states in a 2D non-Hermitian system which feature point-gap topology must undergo the non-Hermitian skin effect along the edge. Under double open boundary conditions, the occurrence of the non-Hermitian skin effect for either topological edge states or bulk states can be accurately predicted by our proposed winding numbers. We unveil that the zero-energy topological corner state only manifests itself on a corner where two nearby gapped edge states intersect, and thus can either disappear completely or strengthen drastically due to the non-Hermitian skin effect of gapped topological edge states. Our analytical results offer direct insight into the non-Bloch band topology in two or higher dimensions and trigger experimental investigations into related phenomena such as quadrupole topological insulators and topological lasing.

We introduce the Seismic Language Model (SeisLM), a foundational model designed to analyze seismic waveforms -- signals generated by Earth's vibrations such as the ones originating from earthquakes. SeisLM is pretrained on a large collection of open-source seismic datasets using a self-supervised contrastive loss, akin to BERT in language modeling. This approach allows the model to learn general seismic waveform patterns from unlabeled data without being tied to specific downstream tasks. When fine-tuned, SeisLM excels in seismological tasks like event detection, phase-picking, onset time regression, and foreshock-aftershock classification. The code has been made publicly available on https://github.com/liutianlin0121/seisLM.

The discovery of collinear symmetric-compensated altermagnets (AM) with intrinsic spin splitting provides a route towards energy-efficient and ultrafast device applications. Here, using first-principles calculations and symmetry analysis, we propose a series of AM Cr2SX (X=O, S, Se) monolayer and explore the spin splitting in Cr2SX multilayer. A general design principle for realizing the spin-layer coupling in odd/even-layer is mapped out based on the comprehensive analysis of spin group symmetry. The spin splitting behavior related with the MzUt, Mz and ML symmetries in AM multilayer can be significantly modulated by magnetic orders, crystal symmetry and external perpendicular gate field (Ez). Due to the spin-compensated bands of sublayers linked by overall Mz and interlayers ML symmetries, the Cr2S2 odd-layer exhibits the unique coexistence of spin splitting and spin degeneracy at high symmetric paths and X/Y valley, respectively. Furthermore, owing to the higher priority of overall ML symmetry compared to interlayers ML symmetry in AM even-layer, the spin-layer coupling of AM multilayer shows strong odd/even-layer dependence. Our work not only offer a new direction for manipulating spin splitting, but also greatly enrich the research on AM monolayer and multilayer.

The prevalent high intrinsic absorption in the crystalline state of phase-change materials (PCMs), typically leads to a decline in modulation efficiency for phase-change metasurfaces, underutilizing their potential for quasi-continuous phase-state tuning. This research introduces a concise design approach that maximizes the exploitation of the quasi-continuous phase transition properties of PCM, achieving high-efficiency spatial light modulation. By optimizing the metasurface design, the phase modulation process is strategically localized in a low crystallization ratio state, significantly reducing the impact of material absorption on modulation efficiency. Utilizing GSST as an example, numerical simulations demonstrate a minimum reflectance of 46.5% at the target wavelength of 1550 nm, with a phase modulation depth of 246.6{\deg}. The design is fruther extended to dynamic polarization modulation by incorporating structural anisotropy, enabling independent control of amplitude and phase modulation in orthogonal polarization directions. This strategy not only circumvents the efficiency limitations of crystalline states but also harnesses the full potential of PCMs for multifunctional photonic applications.

Tilt-to-length (TTL) noise, caused by angular jitter and misalignment, is a major noise source in the inter-satellite interferometer for gravitational wave detection. However, the required level of axis alignment of the optical components is beyond the current state of the art. A set of optical parallel plates, called beam alignment mechanism (BAM), is proposed by LISA to compensate for the alignment error. In this paper, we show a prototype design of the BAM and demonstrate its performance in a ground-based optical system. We derive the BAM theoretical model, which agrees well with the numerical simulation. Experimental results reveal that the BAM can achieve lateral displacement compensation of the optical axis with a resolution of \SI{1}{\micro\meter} across a dynamic range of about \SI{0.5}{\milli\meter}. Furthermore, the TTL coefficient is reduced from about \SI{0.3}{\milli\meter/\radian} to about \SI{5}{\micro\meter/\radian}, satisfying the preliminary requirements for LISA and TianQin. These findings confirm the efficacy of the BAM in suppressing TTL noise, offering a promising solution for space-based gravitational wave detection.

Single-molecule experiments are a unique tool to characterize the structural dynamics of biomolecules. However, reconstructing molecular details from noisy single-molecule data is challenging. Simulation-based inference (SBI) integrates statistical inference, physics-based simulators, and machine learning and is emerging as a powerful framework for analysing complex experimental data. Recent advances in deep learning have accelerated the development of new SBI methods, enabling the application of Bayesian inference to an ever-increasing number of scientific problems. Here, we review the nascent application of SBI to the analysis of single-molecule experiments. We introduce parametric Bayesian inference and discuss its limitations. We then overview emerging deep-learning-based SBI methods to perform Bayesian inference for complex models encoded in computer simulators. We illustrate the first applications of SBI to single-molecule force-spectroscopy and cryo-electron microscopy experiments. SBI allows us to leverage powerful computer algorithms modeling complex biomolecular phenomena to connect scientific models and experiments in a principled way.

Nanocrystalline pure FCC metals and some alloys are known to exhibit abnormal grain growth. Addition of solutes, such as W, has led to improved grain size stability in nanocrystalline Ni. While several groups have investigated grain growth behavior in Ni-based binary alloys, grain sizes greater than 15 nm were primarily investigated. In the present study, grain growth behavior is examined in grain size regime below 10 nm in nanocrystalline Ni-W alloys with 8 to 15 at% W at 773K. In this size regime, normal grain growth behavior was observed in these alloys, evidenced from (a) parabolic kinetics in plots of square of average grain size against annealing time, and (b) statistical self-similarity on the scaled size distribution in a log-normal plot.

Optical super-resolution microscopy is a key technology for structural biology that optimally complements electron microscopy because of its high contrast and live-cell compatibility. MINFLUX is an emerging super-resolution technique that measures the precise position of single fluorophores by targeted scanning of a patterned excitation beam featuring a minimum. It achieves unprecedented localization precision for a given number of detected photons. In combination with switchable fluorophores, it can be used for super-resolution imaging with nanometer resolution. For tracking of single fluorophores, it greatly outperforms camera-based techniques and achieves nanometer spatial and sub-millisecond temporal resolution over long tracks. Here, we introduce the basic principle of MINFLUX and explain how it can reach such high photon efficiencies. We then discuss the potential and limitations of MINFLUX for super-resolution imaging and single-fluorophore tracking, describe recent extensions and variations of MINFLUX and give an outlook to future developments.

Seismic phase picking, which aims to determine the arrival time of P- and S-waves according to seismic waveforms, is fundamental to earthquake monitoring. Generally, manual phase picking is trustworthy, but with the increasing number of worldwide stations and seismic monitors, it becomes more challenging for human to complete the task comprehensively. In this work, we explore multiple ways to do automatic phase picking, including traditional and learning-based methods.

This study explores the application of rectangular finite elements to model the stress-strain behavior of isotropic and orthotropic materials exhibiting negative Poisson's ratio, known as auxetic materials, under static shear conditions within linear elasticity. By employing the classical compatible shape functions for linear interpolation and the incompatible shape functions for quadratic interpolation within a displacement-based finite element framework, the research assesses the effectiveness of these approaches in capturing the mechanical response of auxetic materials. Additionally, the analytical expression for an incompatible rectangular finite element applicable to orthotropic materials is proposed. Hexachiral and re-entrant honeycomb structures, known for their auxetic behavior, are modeled as continuous media with homogenized properties using analytical expressions for their effective material constants. The findings reveal that while the classical shape functions may suffice for displacement modeling, they fall short in accurately predicting stress distributions in auxetic materials. In contrast, the incompatible shape functions demonstrate superior performance in providing appropriate stress and displacement predictions. This work underscores the relevance of using the incompatible rectangular finite elements in the modeling of advanced materials with a negative Poisson's ratio. It provides computationally efficient approaches for calculating auxetic honeycomb structures and their derived multilayer composites.

In this study, we present the first-ever direct measurements of synchrotron-emitting heliospheric traveling shocks, intercepted by the Parker Solar Probe (PSP) during its close encounters. Given that much of our understanding of powerful astrophysical shocks is derived from synchrotron radiation, these observations by PSP provide an unprecedented opportunity to explore how shocks accelerate relativistic electrons and the conditions under which they emit radiation. The probe's unparalleled capabilities to measure both electromagnetic fields and energetic particles with high precision in the near-Sun environment has allowed us to directly correlate the distribution of relativistic electrons with the resulting photon emissions. Our findings reveal that strong quasi-parallel shocks emit radiation at significantly higher intensities than quasi-perpendicular shocks due to the efficient acceleration of ultra-relativistic electrons. These experimental results are consistent with theory and recent observations of supernova remnant shocks and advance our understanding of shock physics across diverse space environments.

We simulated the Coulomb explosion dynamics due to the fast ionization induced by high-intensity X-rays in six proteins that share similar atomic content and shape. We followed and projected the trajectory of the fragments onto a virtual detector, providing a unique explosion footprint. After collecting 500 explosion footprints for each protein, we utilized principal component analysis and t-distributed stochastic neighbor embedding to classify these. The results show that the classification algorithms were able to separate proteins on the basis of explosion footprints from structurally similar proteins into distinct groups. The explosion footprints, therefore, provide a unique identifier for each of the proteins. We envision that method could be used concurrently with single particle coherent imaging experiments to provide additional information on shape, mass, or conformation.

We propose a novel scheme for controlling the injection of a high-quality electron bunch into a channel-guided laser plasma accelerator. This all-optical technique, constricted waveguide injection, creates a highly tunable controlled injection structure natively within a plasma waveguide, a key requirement for efficient acceleration of high-quality multi-GeV electron beams. We describe a simple optical setup to tailor the plasma and present start-to-end simulations showing the injection structure formation and the generation of a 1.1 GeV electron beam with 10 pC of charge and 0.35 % energy spread using 1 J of drive laser energy. Highly tunable tailored plasma sources, like those proposed here, enable fine control over the injection and acceleration processes and thus will be crucial for the development of application-focused laser plasma accelerators.

Cultural data typically contains a variety of biases. In particular, geographical locations are unequally portrayed in media, creating a distorted representation of the world. Identifying and measuring such biases is crucial to understand both the data and the socio-cultural processes that have produced them. Here we suggest to measure geographical biases in a large historical news media corpus by studying the representation of cities. Leveraging ideas of quantitative urban science, we develop a mixed quantitative-qualitative procedure, which allows us to get robust quantitative estimates of the biases. These biases can be further qualitatively interpreted resulting in a hermeneutic feedback loop. We apply this procedure to a corpus of the Soviet newsreel series 'Novosti Dnya' (News of the Day) and show that city representation grows super-linearly with city size, and is further biased by city specialization and geographical location. This allows to systematically identify geographical regions which are explicitly or sneakily emphasized by Soviet propaganda and quantify their importance.

We study the self-organization of two-dimensional turbulence in a fluid with local interactions. Using simulations and theoretical arguments, we show that the out-of-equilibrium flux to small scales, corresponding to the direct cascade, imposes a constraint on the large-scale emergent flow. As a result, instead of the unique state found in other two-dimensional models, a rich phase diagram of large-scale configurations emerges. We explain what sets the boundaries between the different phases, and show that in the infinite box limit when the range of the direct cascade is kept finite, the large-scale flow exhibits spontaneous symmetry breaking.

Single Particle Imaging techniques at X-ray lasers have made significant strides, yet the challenge of determining the orientation of freely rotating molecules during delivery remains. In this study, we propose a novel method to partially retrieve the relative orientation of proteins exposed to ultrafast X-ray pulses by analyzing the fragmentation patterns resulting from Coulomb explosions. We simulate these explosions for 45 proteins in the size range 100 -- 4000 atoms using a hybrid Monte Carlo/Molecular Dynamics approach and capture the resulting ion ejection patterns with virtual detectors. Our goal is to exploit information from the explosion to infer orientations of proteins at the time of X-ray exposure. Our results demonstrate that partial orientation information can be extracted, particularly for larger proteins. Our findings can be integrated into existing reconstruction algorithms such as Expand-Maximize-Compress, to improve their efficiency and reduce the need for high-quality diffraction patterns. This method offers a promising avenue for enhancing Single Particle Imaging by leveraging measurable data from the Coulomb explosion to provide valuable insights about orientation.

The European Organization for Nuclear Research (CERN) in Geneva today operates the world`s largest particle accelerator complex and is a much-cited prototype for high-profile international collaboration. What is less well known, however, is that when its first big accelerator was still under construction, CERN went through an experimental phase in terms of scientific fields and exchanges with similar organizations. This was reflected in a controversy surrounding a CERN-sponsored study group on controlled thermonuclear fusion that met from 1958 to 1964. Invited scientists from CERN member states attended these meetings, as did delegates from the European Atomic Energy Community (Euratom), the European Atomic Energy Agency (ENEA), and the US Atomic Energy Commission (AEC). Although the CERN Study Group on Fusion Problems created an international network for the exchange of reports and for better coordination of scientific projects to avoid duplication of work, it failed to materialize joint fusion research programmes. This article explains why, thereby providing insights into intergovernmental power dynamics and how those shaped the relationships between international organizations.

We simulated the intermittent boundary-layer flashback (BLF) of hydrogen-enriched swirling flames using large-eddy simulation (LES) with the flame-surface-density (FSD) method. Three cases of intermittent BLF, characterized by periodic flame entry and exit of the mixing tube, are presented. The intermittent BLF characteristics varied with the hydrogen volume fraction. Small flame bulges entered and exited the mixing tube in low hydrogen-enrichment cases. The duration of intermittent BLF events and BLF depth increased as the hydrogen content increased. Meanwhile, a large flame tongue penetrating deeply upstream characterised the highest hydrogen-enrichment case.The mean BLF peak depths and standard deviations obtained through simulations aligned well with experimental data for low and moderate hydrogen-enrichment cases. However, LES-FSD underestimated the average BLF peak depth for the highest hydrogen-enrichment case.Analysis of the flow-flame interaction revealed two mechanisms underlying the intermittent BLF phenomena. The flame bulges' oscillation near the outlet is caused by the reverse flow induced by the recirculation zone. At the same time, the deep intermittent BLF occurrs due to the boundary layer separation induced by the large turbulent burning velocity, resulting from the synergy of turbulence and thermo-diffusive effects.

The magnetic cloud within an interplanetary coronal mass ejection (ICME) is characterized by high magnetic field intensities. In this study, we investigate the interaction of a magnetic cloud carrying a density structure with the Earth's bow shock during ICME event on 24 April 2023. Elevated abundances of cold protons and heavier ions, namely alpha particles, and singly charged helium ions associated with the prominence plasma are observed within this structure. The plasma downstream of the bow shock exhibits an irregular compression pattern which could be due to the presence of heavy ions. Heavy ions carry a significant fraction of upstream flow energy, however, due to their different charge per mass ratio and rigidity, they are less scattered by the electromagnetic and electrostatic waves at the shock. We find that ions thermal energy is only a small fraction of the background magnetic energy density downstream of the shock. While increased ion fluxes reduce the characteristic wave speeds in the that region. As such, we observe a transition state of an unstable bow shock in which the plasma flow is super Alfv\'enic both upstream and downstream of the bow shock. Our findings help with understanding of the intense space weather impacts of such events.

For analytical description of the initial stage of jet generation in nonequilibrium inhomogeneous plasma in the magnetohydrodynamic approximation, possible generalizations of solutions of the nonlinear equation for the stream function are analyzed. The jet generation model is based on the mechanism of convective instability and the frozen-in condition of magnetic field lines and is characterized by a number of free parameters. The equation for the radial part of the stream function is satisfied by first-order Bessel functions. To satisfy all the conditions near the jet axis and on its periphery, the found solutions are smoothly joined at the boundary. The final analytical solution for the velocity field is applicable to arbitrary values of dimensionless coordinates. The poloidal velocity increases approximately exponentially, and the azimuthal velocity - according to a superexponential law. In this paper, the velocity field of the jet, which consists of seven sections, is calculated. The rotation of the jet turns out to be differential, and to obtain a solution in quadratures for the azimuthal velocity, one can use not only linear but also power dependences on the altitude. For the exponent n < 1, a noticeable increase in the azimuthal velocity with radius is observed immediately from the jet axis, and for n > 1, a region of relative calm is observed near the axis. The jet model is generalized to the case of an arbitrary dependence of the Brunt-V\"ais\"al\"a frequency on the altitude. The corresponding solutions are found for the radial and vertical velocity components. For the initial stage of development, the vertical and azimuthal components of the generated magnetic field of the jet were also found in the work.

CMOS-based microelectronics are limited to ~150{\deg}C and therefore not suitable for the extreme high temperatures in aerospace, energy, and space applications. While wide bandgap semiconductors can provide high-temperature logic, nonvolatile memory devices at high temperatures have been challenging. In this work, we develop a nonvolatile electrochemical memory cell that stores and retains analog and digital information at temperatures as high as 600 {\deg}C. Through correlative electron microscopy, we show that this high-temperature information retention is a result of composition phase separation between the oxidized and reduced forms of amorphous tantalum oxide. This result demonstrates a memory concept that is resilient at extreme temperatures and reveals phase separation as the principal mechanism that enables nonvolatile information storage in these electrochemical memory cells.

A new analytical model for the generation of axisymmetric tornado-type vortices has been developed. A solution to the nonlinear equation for the stream function in an unstable stratified atmosphere is obtained and analyzed within the framework of ideal hydrodynamics. The solution is sought by smooth connecting continuous solutions for the internal region ("eye"), the central region ("wall" with maximum velocities) and the external region of the tornado. Expressions describing radial dependences for the radial and vertical velocity components include combinations of Bessel functions. The vortex is spatially localized by radius and height. Convective instability of a stratified atmosphere leads to an increase in the radial and vertical components of velocities according to the hyperbolic sine law. A downward flow is observed near the tornado axis. The maximum speed of the upward flow is achieved at a certain radial distance at a certain height. Below this height, radial flows converge towards the central part of the tornado, and above this height there is an outflow from the "wall" to the axis and to the periphery. The radial structure of the azimuthal velocity is determined by the structure of the initial disturbance and can change with height. Maximum rotation is achieved in the tornado "wall" at a certain height. The increase in azimuthal velocity can occur according to a superexponential law. Possible structures of movements, scenarios for the development of a tornado and its dynamics are discussed.

Measuring the energy cascade rate in space plasmas is a challenging task for several reasons. This quantity is (i) inherently three-dimensional (ii) scale-dependent, (iii) anisotropic in the interplanetary plasma, and (iv) requires measurements of plasma parameters in at least four points. Here, we show how three of such problems have been addressed by applying the novel Lag Polyhedra Derivative Ensemble (LPDE) technique to the Magnetospheric Multiscale (MMS) mission in the Earth's magnetosheath.

Integer linear programming (ILP) is an elegant approach to solve linear optimization problems, naturally described using integer decision variables. Within the context of physics-inspired machine learning applied to chemistry, we demonstrate the relevance of an ILP formulation to select molecular training sets for predictions of size-extensive properties. We show that our algorithm outperforms existing unsupervised training set selection approaches, especially when predicting properties of molecules larger than those present in the training set. We argue that the reason for the improved performance is due to the selection that is based on the notion of local similarity (i.e., per-atom) and a unique ILP approach that finds optimal solutions efficiently. Altogether, this work provides a practical algorithm to improve the performance of physics-inspired machine learning models and offers insights into the conceptual differences with existing training set selection approaches.

We propose a technique that makes it possible to transform the intensity of a quasi-monochromatic single-photon wave packet, emitted by a radioactive M\"ossbauer gamma-ray source, into a sequence of short bursts with an arbitrary number of bursts, including a single burst. In addition, the technique allows one to individually and independently control, on demand, the moments of the burst appearance, as well as the peak intensity, duration and shape of each burst in the sequence. The technique is based on the transmission of M\"ossbauer (recoilless) photons through a resonantly absorbing medium, which is rapidly displaced at some moments of time relative to the source (or vice versa) along the photon propagation direction at a distance less than the photon wavelength, and returned to its original position. The burst durations can be comparable to the duration of the single-photon pulses produced by synchrotrons but have the controlled spectral-temporal characteristics. We show that the proposed technique can be implemented on the basis of currently available equipment with use of 14.4-keV recoilless photons, emitted by Co-57 source, and Fe-57 absorber, which opens up prospects for its applications in M\"ossbauer spectroscopy and x-ray quantum optics.

Amongst the available plasmonic nanostructures, nanoparticle-on-a-mirror (NPoM) cavities - consisting of metal nanoparticles separated from a metal mirror by a molecular-size monolayer - provide the ultimate light confinement in gaps even below 1 nm. A variation of the NPoM cavity is the nanoparticle-on-a-slit (NPoS) configuration, where the nanoparticle is placed on a functionalized narrow slit created on a metal plate so that there are two nanometric-scale gaps for plasmonic localization. Interestingly, the NPoS cavity can also perform as a dual dipole antenna to localize both infrared and visible light, which is useful in molecular optomechanics. For many applications, it is desirable to integrate such cavities on a chip and provide access to (and collection from) the hot spots via photonic integrated waveguides. In this work, we propose, design, and experimentally demonstrate the efficient integration of NPoS plasmonic cavities with dielectric waveguides on a silicon-based chip. To this end, we use silicon-nitride slot waveguides and show that both the fundamental TE and TM modes can be used to drive the cavity, making our device polarization-independent. We demonstrate our concept by performing surface-enhanced Raman spectroscopy of BPT molecules on a chip fabricated by standard silicon fabrication tools mixed with the deterministic positioning of gold nanospheres on the gap of a plasmonic dipole antenna.

The turbulence characteristics of the scrape-off-layer (SOL) plasma in the W7-X stellarator are investigated using a gas-puff-imaging (GPI) diagnostic, newly installed and operated during the OP 2.1 campaign. The SOL plasma on W7-X features a chain of magnetic islands intersected by discrete divertor plates at five separate toroidal locations, forming a set of island divertors for heat and particle exhaust. GPI measures 2D emission fluctuations in the magnetic island plasma. A dedicated experiment was conducted to characterize the broadband turbulence and filament dynamics in the island divertor SOL in the standard magnetic configuration. This involves shifting the poloidal location of the magnetic island with respect to the GPI field of view. Overall, initial analyses confirm minimal intermittent type fluctuations, compared to tokamaks. A relationship between the radial profile of the connection length and the sheared poloidal flow structure is discussed. Turbulence statistical analyses reveal poloidal asymmetry of the fluctuation properties and a birth region of intermittent fluctuations indicated by skewness reversal. Their birth locations appear sensitive to plasma conditions. Finally, 2D cross-correlation analysis is presented to study turbulence propagation in the presence of multiple shear layers in the poloidal flows, showing a dominant poloidal motion and evidencing a shear effect.

We theoretically investigate and experimentally demonstrate that genuine bound states in the continuum (BICs) -- polarization-protected BICs -- can be completely localized within finite-size solid resonators. This bound mode is realized in a highly tunable mechanical system made of cylindrical granular crystals, where tunning the contact boundaries enables the in situ transition from the BICs to quasi-BICs in a controllable manner. Since a single-particle resonator can support BICs itself, these bound states can extend to form bound bands within periodic structures composed of such resonators. We experimentally demonstrate the emergence of a quasi-bound (flat) band in a finite chain with broken resonator symmetry, using a laser Doppler vibrometer. Remarkably, we show that all cylindrical resonators within the entire chain exhibit high-Q and dispersionless resonance.

The results of studies on the properties of ordinary and heavy water subjected to sharp mechanical impacts at acoustic repetition frequency are presented. Experimental evidence for the phenomenon of acoustically induced nuclear processes in water is provided, supported by direct measurements of radiation emission and the formation of new elements, which cannot be explained by chemical reactions. The complex influence of mechanical oscillations on changes in the concentrations of stable isotopes of elements such as Ti, B, Na, Mg, and Li in water is demonstrated. The cause of surface erosion of metal structures during cavitation in water is explained through the formation of fluorine and the subsequent creation of aggressive HF acid molecules. A mechanism for the occurrence of sonoluminescence under sharp acoustic impact on water is proposed.

More than 3 kV breakdown voltage was demonstrated in monolithic bidirectional GaN HEMTs for the first time having potential applications in 1200V or 1700V-class novel power converters. The on resistance of the fabricated transistors was ~20 ohm.mm or ~11 mili ohm.cm^2. Breakdown voltage was optimized by utilizing two field plates in either side of the transistor and optimizing their geometry. Shorter first field plate lengths (less than 2 micron) resulted in higher breakdown voltage and the possible reason for this was discussed. The transistors had a steep subthreshold swing of 92 mV / dec. The on/off ratio was greater than 10^5 and it was limited by the tool capacity. The fabricated 3 kV transistor was benchmarked against the state-of-the-art monolithic bidirectional GaN HEMTs in the performance matrices of breakdown voltage and on resistance, that showed crucial progress.

Training interatomic potentials for spin-polarized systems continues to be a difficult task for the molecular modeling community. In this note, a proof-of-concept, random initial spin committee approach is proposed for obtaining the ground state of spin-polarized systems with a controllable degree of accuracy. The approach is tested on two toy models of elemental sulfur where the exact optimal spin configuration can be known. Machine-learning potentials are trained on the resulting data, and increasingly accurate fits with respect to the ground state are achieved, marking a step towards machine-learning force fields for general bulk spin-polarized systems.

This study investigates the orbital decay and subsequent reentries of 12 Starlink satellites from 16 April to 15 May 2024. By examining Two-Line Element data, we observed a significant increase in orbital decay following the geomagnetic storm on 10 May 2024, consistent with expectations of increased thermospheric density. An unexpected increase in decay rates for 10 satellites was identified around 25 April 2024, while two lower-altitude satellites remained unaffected. Detailed analysis revealed that this enhanced decay rate prior to the storm was influenced by a spike in the O/N2 ratio and an increase in Extreme Ultra Violet (EUV) flux. Moreover, most of the satellites exhibited sharp decay during the early recovery phase of the geomagnetic storm. Based on the positions and local times of changes in decay rates, it is likely that the satellites were affected by various processes during elevated space weather activity, such as enhanced EUV flux, Joule heating, particle precipitation, and the equatorial neutral anomaly. This study highlights the complex role of preconditioning due to enhanced EUV flux and extreme space weather activity in the orbital dynamics of Low-Earth Orbit (LEO) satellites.

Many real-world complex systems are characterized by non-pairwise -- higher-order -- interactions among system's units, and can be effectively modeled as hypergraphs. Directed hypergraphs distinguish between source and target sets within each hyperedge, and allow to account for the directional flow of information between nodes. Here, we provide a framework to characterize the structural organization of directed higher-order networks at their microscale. First, we extract the fingerprint of a directed hypergraph, capturing the frequency of hyperedges with a certain source and target sizes, and use this information to compute differences in higher-order connectivity patterns among real-world systems. Then, we formulate reciprocity in hypergraphs, including exact, strong, and weak definitions, to measure to which extent hyperedges are reciprocated. Finally, we extend motif analysis to identify recurring interaction patterns and extract the building blocks of directed hypergraphs. We validate our framework on empirical datasets, including Bitcoin transactions, metabolic networks, and citation data, revealing structural principles behind the organization of real-world systems.

Seamlessly integrating stimulated Brillouin scattering (SBS) in a low-loss and mature photonic integration platform remains a complicated task. Virtually all current approaches fall short in simultaneously achieving strong SBS, low losses, and technological scalability. In this work we incorporate stong SBS into a standard silicon nitride platform by a simple deposition of a tellurium oxide layer, a commonly used material for acousto-optic modulators. In these heterogeneously integrated waveguides, we harness novel SBS interactions actuated by surface acoustic waves (SAWs) leading to more than two orders of magnitude gain enhancement. Three novel applications are demonstrated in this platform: (i) a silicon nitride Brillouin amplifier with 5 dB net optical gain, (ii) a compact intermodal stimulated Brillouin laser (SBL) capable of high purity radio frequency (RF) signal generation with 7 Hz intrinsic linewidth, and (iii) a widely tunable microwave photonic notch filter with ultra-narrow linewidth of 2.2 MHz enabled by Brillouin induced opacity. These advancements can unlock an array of new RF and optical technologies to be directly integrated in silicon nitride.

Arctic sea ice performs a vital role in global climate and has paramount impacts on both polar ecosystems and coastal communities. In the last few years, multiple deep learning based pan-Arctic sea ice concentration (SIC) forecasting methods have emerged and showcased superior performance over physics-based dynamical models. However, previous methods forecast SIC at a fixed temporal granularity, e.g. sub-seasonal or seasonal, thus only leveraging inter-granularity information and overlooking the plentiful inter-granularity correlations. SIC at various temporal granularities exhibits cumulative effects and are naturally consistent, with short-term fluctuations potentially impacting long-term trends and long-term trends provides effective hints for facilitating short-term forecasts in Arctic sea ice. Therefore, in this study, we propose to cultivate temporal multi-granularity that naturally derived from Arctic sea ice reanalysis data and provide a unified perspective for modeling SIC via our Sea Ice Foundation Model. SIFM is delicately designed to leverage both intra-granularity and inter-granularity information for capturing granularity-consistent representations that promote forecasting skills. Our extensive experiments show that SIFM outperforms off-the-shelf deep learning models for their specific temporal granularity.

A previous work introduced pair space, which is spanned by the center of mass of a system and the relative positions (pair positions) of its constituent bodies. Here, I show that in the $N$-body Newtonian problem, a configuration that does not remain on a fixed line in space is a central configuration if and only if it conserves all pair angular momenta. For collinear systems, I obtain a set of equations for the ratios of the relative distances of the bodies, from which I derive some bounds on the minimal length of the line. For the non-collinear case I derive some geometrical relations, independent of the masses of the bodies. These are necessary conditions for a non-collinear configuration to be central. They generalize, to arbitrary $N$, a consequence of the Dziobek relation, which holds for $N=4$.

The precise measurement of parity-violating asymmetries in parity-violating electron scattering experiments is a powerful tool for probing new physics beyond the Standard Model. Achieving the expected precision requires both experimental and post-processing signal corrections. This includes using auxiliary detectors to distinguish the main signal from background signals and implementing post-measurement corrections, such as the Bayesian statistics method, to address uncontrolled factors during the experiments. Asymmetry values in the scattering of electrons off proton targets in QWeak and P2 and off electron targets in MOLLER are influenced by detector array configurations, beam polarization angles, and beam spin variations. The Bayesian framework refines full probabilistic models to account for all necessary factors, thereby extracting asymmetry values and the underlying physics under specified conditions. For the QWeak experiment, a reanalysis of the inelastic asymmetry measurement using the Bayesian method has yielded a closer fit to measured asymmetries, with uncertainties reduced by 40\% compared to the Monte Carlo minimization method. This approach was successfully applied to simulated data for the MOLLER experiment and is predicted to be similarly effective in P2.

Some of the most stringent constraints on physics beyond the Standard Model (BSM) arise from considerations of particle emission from astrophysical plasmas. However, many studies assume that particle production occurs in an isotropic plasma environment. This condition is rarely (if ever) met in astrophysical settings, for instance due to the ubiquitous presence of magnetic fields. In anisotropic plasmas, the equations of motion are not diagonal in the usual polarization basis of transverse and longitudinal modes, causing a mixing of these modes and breaking the degeneracy in the dispersion relation of the two transverse modes. This behavior is captured by a $3\times3$ mixing matrix $\pi^{IJ}$, determined by projecting the response tensor of the plasma $\Pi^{\mu\nu}$ into mode space, whose eigenvectors and eigenvalues are related to the normal modes and their dispersion relations. In this work, we provide a general formalism for determining the normal modes of propagation that are coupled to axions and dark photons in an anisotropic plasma. As a key part of this formalism, we present detailed derivations of $\Pi^{\mu\nu}$ for magnetized plasmas in the long-wavelength limit using the real-time formalism of finite-temperature field theory. We provide analytic approximations for the normal modes and their dispersion relations assuming various plasma conditions that are relevant to astrophysical environments. These approximations will allow for a systematic exploration of the effects of plasma anisotropy on BSM particle production.

We introduce a toluene-filled fiber platform for two-photon absorption measurements. By confining both the light and molecular sample inside the 5 $\mu$m hollow core of the fiber, we increase the distance over which the nonlinear light-matter interaction occurs. With only a 7.3 nL excitation volume, we measure classical two-photon absorption (C2PA) at an average laser power as low as 1.75 nW, which is a 45-fold improvement over a conventional free-space technique. We use this platform to attempt to measure entangled two-photon absorption (E2PA), a process with a limited operating regime due to a crossover in dominating processes from E2PA to C2PA as photon flux is increased. Recently, several teams of researchers have reported that E2PA cross sections are much smaller than previously claimed. As a result, the process dominates at photon fluxes so low that it is extremely difficult or impossible to measure using conventional free-space techniques. In this report, we implement the first E2PA measurement using a waveguide. We see no evidence of E2PA, and we set an upper bound on the cross section consistent with these recent reports.

Aberration correction is an important aspect of modern high-resolution scanning transmission electron microscopy. Most methods of aligning aberration correctors require specialized sample regions and are unsuitable for fine-tuning aberrations without interrupting on-going experiments. Here, we present an automated method of correcting first- and second-order aberrations called BEACON which uses Bayesian optimization of the normalized image variance to efficiently determine the optimal corrector settings. We demonstrate its use on gold nanoparticles and a hafnium dioxide thin film showing its versatility in nano- and atomic-scale experiments. BEACON can correct all first- and second-order aberrations simultaneously to achieve an initial alignment and first- and second-order aberrations independently for fine alignment. Ptychographic reconstructions are used to demonstrate an improvement in probe shape and a reduction in the target aberration.

Nearly 900 million people live in low-lying coastal zones around the world and bear the brunt of impacts from more frequent and severe hurricanes and storm surges. Oceanographers simulate ocean current circulation along the coasts to develop early warning systems that save lives and prevent loss and damage to property from coastal hazards. Traditionally, such simulations are conducted using coastal ocean circulation models such as the Regional Ocean Modeling System (ROMS), which usually runs on an HPC cluster with multiple CPU cores. However, the process is time-consuming and energy expensive. While coarse-grained ROMS simulations offer faster alternatives, they sacrifice detail and accuracy, particularly in complex coastal environments. Recent advances in deep learning and GPU architecture have enabled the development of faster AI (neural network) surrogates. This paper introduces an AI surrogate based on a 4D Swin Transformer to simulate coastal tidal wave propagation in an estuary for both hindcast and forecast (up to 12 days). Our approach not only accelerates simulations but also incorporates a physics-based constraint to detect and correct inaccurate results, ensuring reliability while minimizing manual intervention. We develop a fully GPU-accelerated workflow, optimizing the model training and inference pipeline on NVIDIA DGX-2 A100 GPUs. Our experiments demonstrate that our AI surrogate reduces the time cost of 12-day forecasting of traditional ROMS simulations from 9,908 seconds (on 512 CPU cores) to 22 seconds (on one A100 GPU), achieving over 450$\times$ speedup while maintaining high-quality simulation results. This work contributes to oceanographic modeling by offering a fast, accurate, and physically consistent alternative to traditional simulation models, particularly for real-time forecasting in rapid disaster response.

As global climate change intensifies, accurate weather forecasting has become increasingly important, affecting agriculture, energy management, environmental protection, and daily life. This study introduces a hybrid model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to predict historical temperature data. CNNs are utilized for spatial feature extraction, while LSTMs handle temporal dependencies, resulting in significantly improved prediction accuracy and stability. By using Mean Absolute Error (MAE) as the loss function, the model demonstrates excellent performance in processing complex meteorological data, addressing challenges such as missing data and high-dimensionality. The results show a strong alignment between the prediction curve and test data, validating the model's potential in climate prediction. This study offers valuable insights for fields such as agriculture, energy management, and urban planning, and lays the groundwork for future applications in weather forecasting under the context of global climate change.

In the previous paper we have shown analytically that, if the drift function of the d-dimensional Langevin equation is the Langevin function with a properly chosen scale factor, then the evolution of the drift function is a martingale associated with the histories generated by the very Langevin equation. Moreover, we numerically demonstrated that those generated histories from a common initial data become asymptotically ballistic, whose orientations obey the classical canonical spin statistics under the external field corresponding to the initial data. In the present paper we provide with an analytical explanation of the latter numerical finding by introducing a martingale in the spin functional space. In a specific context the present result elucidates a new physical aspect of martingale theory.

Background: In Kreuz et al., J Neurosci Methods 381, 109703 (2022) two methods were proposed that perform latency correction, i.e., optimize the spike time alignment of sparse neuronal spike trains with well defined global spiking events. The first one based on direct shifts is fast but uses only partial latency information, while the other one makes use of the full information but relies on the computationally costly simulated annealing. Both methods reach their limits and can become unreliable when successive global events are not sufficiently separated or even overlap. New Method: Here we propose an iterative scheme that combines the advantages of the two original methods by using in each step as much of the latency information as possible and by employing a very fast extrapolation direct shift method instead of the much slower simulated annealing. Results: We illustrate the effectiveness and the improved performance, measured in terms of the relative shift error, of the new iterative scheme not only on simulated data with known ground truths but also on single-unit recordings from two medial superior olive neurons of a gerbil. Comparison with Existing Method(s): The iterative scheme outperforms the existing approaches on both the simulated and the experimental data. Due to its low computational demands, and in contrast to simulated annealing, it can also be applied to very large datasets. Conclusions: The new method generalizes and improves on the original method both in terms of accuracy and speed. Importantly, it is the only method that allows to disentangle global events with overlap.

A simple $\rm{H}_2\rm{O}$-related hydrogen bond model, modified from the Jaynes-Cummings model, is proposed and its various dynamic aspects are investigated theoretically. In this model, the formation and breaking processes of hydrogen bond are accompanied by the creation and annihilation of the thermal phonon of the medium. A number of simplifying assumptions about the dynamics of the molecules involved are used. Rotating wave approximation is applied under consideration of the strong-coupling condition. Dissipative dynamics under the Markovian approximation is obtained through solving the quantum master equation - Lindbladian. The probabilities of reaction channels involving hydrogen bond depending on the parameters of the external environment, are obtained. Differences between unitary and dissipative evolutions are disciussed. Consideration is given to the effect of all kinds of potential interactions and dissipations on evolution. Consideration is also given to the reverse processes (inflows) of dissipations. The results show that the magnitude changes of the interactions and dissipations have slight effect on the formation of hydrogen bond, but the variation of the reverse processes of dissipations significantly affect the formation of hydrogen bond. According to the findings, the dynamics of $\rm{H}_2\rm{O}$-related hydrogen bond model can be controlled by selectively choosing system parameters. The results will be used as a basis to extend the research to more complex chemical and biological model in the future.

Simulating transition dynamics between metastable states is a fundamental challenge in dynamical systems and stochastic processes with wide real-world applications in understanding protein folding, chemical reactions and neural activities. However, the computational challenge often lies on sampling exponentially many paths in which only a small fraction ends in the target metastable state due to existence of high energy barriers. To amortize the cost, we propose a data-driven approach to warm-up the simulation by learning nonlinear interpolations from local dynamics. Specifically, we infer a potential energy function from local dynamics data. To find plausible paths between two metastable states, we formulate a generalized flow matching framework that learns a vector field to sample propable paths between the two marginal densities under the learned energy function. Furthermore, we iteratively refine the model by assigning importance weights to the sampled paths and buffering more likely paths for training. We validate the effectiveness of the proposed method to sample probable paths on both synthetic and real-world molecular systems.

Semi-flexible polyelectrolytes are a group of biopolymers with a wide range of applications from drag reducing agents in turbulent flows to thickening agents in food and cosmetics. In this study, we investigate the rheology of aqueous solutions of xanthan gum as a canonical semi-flexible polyelectrolyte in steady shear and transient extensional flows via torsional rheometry and dripping-onto-substrate (DoS), respectively. The high molecular weight of the xanthan gum and the numerous charged groups on the side branches attached to the backbone allow the shear and extensional rheology of the xanthan gum solutions to be tuned over a wide range by changing the ionic strength of the solvent. In steady shear flow, increasing the xanthan gum concentration increases both the zero shear viscosity and the extent of shear-thinning of the solution. Conversely, increasing the ionic strength of the solvent by addition of sodium chloride (NaCl) decreases both the zero shear viscosity and the level of shear-thinning. In transient extensional flow, increasing the xanthan gum concentration changes the dynamics of the capillary thinning from an inelastic power-law (IP) response to an elastocapillary (EC) balance, from which an extensional relaxation time can be measured based on the rate of filament thinning. Increasing the NaCl concentration decreases the extensional relaxation time and the transient extensional viscosity of the viscoelastic solution. Based on the dynamics of capillary thinning observed in the DoS experiments, we provide a relationship for the smallest extensional relaxation time that can be measured using DoS. We suggest that the change in the dynamics of capillary thinning from an IP response to an EC response can be used as an easy and robust experimental method for identifying the rheologically effective overlap concentration of a semi-flexible polyelectrolyte solution, i.e., the critical concentration at which polymer molecules start to interact with each other to produce a viscoelastic strain-stiffening response (often perceived as "stringiness") in transient extensional flows such as those involved in dripping, dispensing and filling operations.

Monte Carlo (MC) simulations, particularly using FLUKA, are essential for replicating real-world scenarios across scientific and engineering fields. Despite the robustness and versatility, FLUKA faces significant limitations in automation and integration with external post-processing tools, leading to workflows with a steep learning curve, which are time-consuming and prone to human errors. Traditional methods involving the use of shell and Python scripts, MATLAB, and Microsoft Excel require extensive manual intervention and lack flexibility, adding complexity to evolving scenarios. This study explores the potential of Large Language Models (LLMs) and AI agents to address these limitations. AI agents, integrate natural language processing with autonomous reasoning for decision-making and adaptive planning, making them ideal for automation. We introduce AutoFLUKA, an AI agent application developed using the LangChain Python Framework to automate typical MC simulation workflows in FLUKA. AutoFLUKA can modify FLUKA input files, execute simulations, and efficiently process results for visualization, significantly reducing human labor and error. Our case studies demonstrate that AutoFLUKA can handle both generalized and domain-specific cases, such as Microdosimetry, with an streamlined automated workflow, showcasing its scalability and flexibility. The study also highlights the potential of Retrieval Augmentation Generation (RAG) tools to act as virtual assistants for FLUKA, further improving user experience, time and efficiency. In conclusion, AutoFLUKA represents a significant advancement in automating MC simulation workflows, offering a robust solution to the inherent limitations. This innovation not only saves time and resources but also opens new paradigms for research and development in high energy physics, medical physics, nuclear engineering space and environmental science.

Distinguishing between swarming and swimming, the two principal forms of bacterial movement, holds significant conceptual and clinical relevance. This is because bacteria that exhibit swarming capabilities often possess unique properties crucial to the pathogenesis of infectious diseases and may also have therapeutic potential. Here, we report a deep learning-based swarming classifier that rapidly and autonomously predicts swarming probability using a single blurry image. Compared with traditional video-based, manually-processed approaches, our method is particularly suited for high-throughput environments and provides objective, quantitative assessments of swarming probability. The swarming classifier demonstrated in our work was trained on Enterobacter sp. SM3 and showed good performance when blindly tested on new swarming (positive) and swimming (negative) test images of SM3, achieving a sensitivity of 97.44% and a specificity of 100%. Furthermore, this classifier demonstrated robust external generalization capabilities when applied to unseen bacterial species, such as Serratia marcescens DB10 and Citrobacter koseri H6. It blindly achieved a sensitivity of 97.92% and a specificity of 96.77% for DB10, and a sensitivity of 100% and a specificity of 97.22% for H6. This competitive performance indicates the potential to adapt our approach for diagnostic applications through portable devices or even smartphones. This adaptation would facilitate rapid, objective, on-site screening for bacterial swarming motility, potentially enhancing the early detection and treatment assessment of various diseases, including inflammatory bowel diseases (IBD) and urinary tract infections (UTI).

Graphene is a promising material for the development of applications in nanoelectronic devices, but the lack of a band gap necessitates the search for ways to tune its electronic properties. In addition to doping, defects, and nanoribbons, a more radical alternative is the development of 2D forms with structures that are in clear departure from the honeycomb lattice, such as graphynes, with the distinctive property of involving carbon atoms with both hybridizations sp and sp2. The density and details of how the acetylenic links are distributed allow for a variety of electronic signatures. Here we propose a graphyne system based on the recently synthesized biphenylene monolayer. We demonstrate that this system features highly localized states with a spin-polarized semiconducting configuration. We study its stability and show that the system's structural details directly influence its highly anisotropic electronic properties. Finally, we show that the symmetry of the frontier states can be further tuned by modulating the size of the acetylenic chains forming the system.

Efficient sampling in biomolecular simulations is critical for accurately capturing the complex dynamical behaviors of biological systems. Adaptive sampling techniques aim to improve efficiency by focusing computational resources on the most relevant regions of phase space. In this work, we present a framework for identifying the optimal sampling policy through metric driven ranking. Our approach systematically evaluates the policy ensemble and ranks the policies based on their ability to explore the conformational space effectively. Through a series of biomolecular simulation case studies, we demonstrate that choice of a different adaptive sampling policy at each round significantly outperforms single policy sampling, leading to faster convergence and improved sampling performance. This approach takes an ensemble of adaptive sampling policies and identifies the optimal policy for the next round based on current data. Beyond presenting this ensemble view of adaptive sampling, we also propose two sampling algorithms that approximate this ranking framework on the fly. The modularity of this framework allows incorporation of any adaptive sampling policy making it versatile and suitable as a comprehensive adaptive sampling scheme.

Data assimilation will be essential for the management and expansion of geological carbon storage operations. In traditional data assimilation approaches a fixed set of geological hyperparameters, such as mean and standard deviation of log-permeability, is often assumed. Such hyperparameters, however, may be highly uncertain in practical CO2 storage applications. In this study, we develop a hierarchical data assimilation framework for carbon storage that treats hyperparameters as uncertain variables characterized by hyperprior distributions. To deal with the computationally intractable likelihood function in hyperparameter estimation, we apply a likelihood-free (or simulation-based) inference algorithm, specifically sequential Monte Carlo-based approximate Bayesian computation (SMC-ABC), to draw independent posterior samples of hyperparameters given dynamic monitoring-well data. In the second step we use an ensemble smoother with multiple data assimilation (ESMDA) procedure to provide posterior realizations of grid-block permeability. To reduce computational costs, a 3D recurrent R-U-Net deep-learning surrogate model is applied for forward function evaluations. The accuracy of the surrogate model is established through comparisons to high-fidelity simulation results. A rejection sampling (RS) procedure for data assimilation is applied to provide reference posterior results. Detailed data assimilation results from SMC-ABC-ESMDA are compared to those from the reference RS method. These include marginal posterior distributions of hyperparameters, pairwise posterior samples, and history matching results for pressure and saturation at the monitoring location. Close agreement is achieved with 'converged' RS results, for two synthetic true models, in all quantities considered. Importantly, the SMC-ABC-ESMDA procedure provides speedup of 1-2 orders of magnitude relative to RS for the two cases.

Van der Waals (vdW) homo-/hetero-structures are ideal systems for studying interfacial tribological properties such as structural superlubricity. Previous studies concentrated on the mechanism of translational motion in vdW interfaces. However, detailed mechanisms and general properties of the rotational motion are barely explored. Here, we combine experiments and simulations to reveal the twisting dynamics of the MoS$_2$/graphite heterostructure. Unlike the translational friction falling into the superlubricity regime with no twist angle dependence, the dynamic rotational resistances highly depend on twist angles. Our results show that the periodic rotational resistance force originates from structural potential energy changes during the twisting. The structural potential energy of MoS$_2$/graphite heterostructure increases monotonically from0 to 30 degrees twist angles, and the estimated relative energy barrier is (1.43 +/- 0.36) x 10 J/m. The formation of Moir\'e superstructures in the graphene layer is the key to controlling the structural potential energy of the MoS$_2$/graphene heterostructure. Our results suggest that in twisting 2D heterostructures, even if the interface sliding friction is negligible, the evolving potential energy change results in a non-vanishing rotational resistance force. The structural change of the heterostructure can be an additional pathway for energy dissipation in the rotational motion, further enhancing the rotational friction force.

Interactions of isolated quantum many-body systems typically scramble local information into the entire system and make it unrecoverable. Ergodicity-breaking systems possess the potential to exhibit fundamentally different information scrambling dynamics beyond this paradigm. For many-body localized systems with strong ergodicity breaking, local transport vanishes and information scrambles logarithmically slowly. Whereas in Rydberg atom arrays, local qubit flips induce dynamical retardation on surrounding qubits through the Rydberg blockade effect, giving rise to quantum many-body scars that weakly break ergodicity, and resulting in the predicted unconventional quantum information spreading behaviours. Here, we present the first measurements of out-of-time-ordered correlators and Holevo information in a Rydberg atom array, enabling us to precisely track quantum information scrambling and transport dynamics. By leveraging these tools, we observe a novel spatio-temporal collapse-and-revival behaviour of quantum information, which differs from both typical chaotic and many-body localized systems. Our experiment sheds light on the unique information dynamics in many-body systems with kinetic constraints, and demonstrates an effective digital-analogue approach to coherently reverse time evolution and steer information propagation in near-term quantum devices.

We examined the origin of 3He abundance enhancement in 23 high-energy (25-50 MeV) solar proton events that coincide with 3He-rich periods detected by ACE ULEIS in 1997-2021. In seven events, 3He enhancement was due to 3He leftover from preceding events or independent 3He events occurring during proton events. One event is the most likely impulsive (3He-rich), and another is unclear. Reaccelerated remnant flare material was the most probable cause of 3He enhancements in the remaining 14 proton events. Imaging observations showed coronal jets in the parent active regions in six of these 14 events. Remarkably, the highest 3He/4He occurred in events with jets, implying their contribution to 3He enhancement.

We present an experimental platform for linear-optical quantum information processing. Our setup utilizes multiphoton generation using a high-quality single-photon source, which is demultiplexed across multiple spatial channels, a custom-designed, programmable, low-loss photonic chip, and paired with high-efficiency single-photon detectors. We demonstrate the platform's capability in producing heralded arbitrary two-qubit dual-rail encoded states, a crucial building block for large-scale photonic quantum computers. The programmable chip was fully characterized through a calibration process that allowed us to create a numerical model accounting for fabrication imperfections and measurement errors. As a result, using on-chip quantum state tomography (QST), we achieved high-fidelity quantum state preparation, with a fidelity of 98.5\% specifically for the Bell state.

Microfluidics has revolutionized control over small volumes through the use of physical barriers. However, the rigidity of these barriers limits flexibility in applications. We present an optofluidic toolbox that leverages structured light and photothermal conversion to create dynamic, reconfigurable fluidic boundaries. This system enables precise manipulation of fluids and particles by generating 3D thermal landscapes with high spatial control. Our approach replicates the functions of traditional barriers while additionally allowing real-time reconfiguration for complex tasks, such as individual particle steering and size-based sorting in heterogeneous mixtures. These results highlight the platform's potential for adaptive and multifunctional microfluidic systems in applications such as chemical synthesis, lab-on-chip devices, and microbiology, seamlessly integrating with existing setups due to its flexibility and minimal operation requirements.

The electronic transport characteristics of two-dimensional (2D) systems have widespread application prospects in the fabrication of multifunctional nanodevices. However, the current research for basic transport phenomena, such as anomalous valley Hall effect (AVHE) and piezoelectric response, is limited to discrete discussion. Here, we theoretically propose a valley-piezoelectricity coupling strategy beyond the existing paradigm to realize AVHE and layer Hall effect (LHE) in ferrovalley (FV) systems, and its essential principle can be extended to general valleytronic materials. Through first-principles calculations, we demonstrate that the large polarized electric field of 2.8*106 (1.67*107) V/m can be induced by 0.1% uniaxial strain in FV 2H-LaHF (1T-LaHF) monolayers. In addition, the microscopic mechanism of interlayer antiferromagnetic (AFM) state of 2H-LaHF bilayer is uncovered by the spin Hamiltonian and super-superexchange (SSE) interaction. Our findings pave the way for new explorations of valley Hall-related effect involving piezoelectricity.

Acoustic haptic technology adds touch sensations to human-machine interfaces by integrating piezoelectric actuators onto touchscreens. Traditional piezoelectric haptic technologies use opaque lead-containing ceramics that are both toxic and visible. We have developed a highly transparent lead-free piezoelectric haptic device using potassium sodium niobate (KNN) and transparent conductive oxide thin films. The KNN film, grown on glass, exhibits a pure perovskite phase and a dense microstructure. This device achieves up to 80% transmittance, surpassing lead zirconate titanate (PZT) thin films. It generates an acoustic resonance at 16.5 kHz and produces a peak-to-peak displacement of 1.0 um at 28 V unipolar, making it suitable for surface rendering applications. This demonstrates the potential of transparent lead-free piezoelectric actuators as an effective alternative to conventional PZT haptic actuators.

We report on the masses and hyperfine structure of ground and isomeric states in $^{114,116,118,120}$Ag isotopes, measured with the phase-imaging ion-cyclotron-resonance technique (PI-ICR) with the JYFLTRAP mass spectrometer and the collinear laser spectroscopy beamline at the Ion Guide Isotope Separator On-Line (IGISOL) facility, Jyv\"askyl\"a, Finland. We measured the masses and excitation energies, electromagnetic moments, and charge radii, and firmly established the nuclear spins of the long-lived states. A new isomer was discovered in $^{118}$Ag and the half-lives of $^{118}$Ag long-lived states were reevaluated. We unambiguously pinned down the level ordering of all long-lived states, placing the inversion of the $I = 0^-$ and $I = 4^+$ states at $A = 118$ $(N = 71)$. Lastly, we compared the electromagnetic moments of each state to empirical single-particle moments to identify the dominant configuration where possible.

Desert locusts are notorious agriculture pests prompting billions in losses and global food scarcity concerns. With billions of these locusts invading agrarian lands, this is no longer a thing of the past. This study taps into the existing doppler weather radar (DWR) infrastructure which was originally deployed for meteorological applications. This study demonstrates a systematic approach to distinctly identify and track concentrations of desert locust swarms in near real time using single polarization radars. Findings reveal the potential to establish early warning systems with lead times of around 7 hours and spatial coverage of approximately 100 kilometers. Embracing these technological advancements are crucial to safeguard agricultural landscapes and upload global food security.

The GW approximation represents the state-of-the-art ab-initio method for computing excited-state properties. Its execution requires control over a larger number of (often interdependent) parameters, and therefore its application in high-throughput studies is hindered by the intricate and time-consuming convergence process across a multi-dimensional parameter space. To address these challenges, here we develop a fully-automated open-source workflow for G$_0$W$_0$ calculations within the AiiDA-VASP plugin architecture. The workflow is based on an efficient estimation of the errors on the quasi-particle (QP) energies due to basis-set truncation and the pseudo-potential norm violation, which allows a reduction of the dimensionality of the parameter space and avoids the need for multi-dimensional convergence searches. Protocol validation is conducted through a systematic comparison against established experimental and state-of-the-art GW data. To demonstrate the effectiveness of the approach, we construct a database of QP energies for a diverse dataset of over 320 bulk structures. The openly accessible workflow and resulting dataset can serve as a valuable resource and reference for conducting accurate data-driven research.

The availability of precise and accurate simulation is a limiting factor for interpreting and forecasting data in many fields of science and engineering. Often, one or more distinct simulation software applications are developed, each with a relative advantage in accuracy or speed. The quality of insights extracted from the data stands to increase if the accuracy of faster, more economical simulation could be improved to parity or near parity with more resource-intensive but accurate simulation. We present Fast Perfekt, a machine-learned regression that employs residual neural networks to refine the output of fast simulations. A deterministic morphing model is trained using a unique schedule that makes use of the ensemble loss function MMD, with the option of an additional pair-based loss function such as the MSE. We explore this methodology in the context of an abstract analytical model and in terms of a realistic particle physics application featuring jet properties in hadron collisions at the CERN Large Hadron Collider. The refinement makes maximum use of domain knowledge, and introduces minimal computational overhead to production.

With the oncologist acting as the ``game leader'', we employ a Stackelberg game-theoretic model involving multiple populations to study prostate cancer. We refine the drug dosing schedule using an empirical Bayes feed-forward analysis, based on clinical data that reflects each patient's prostate-specific drug response. Our methodology aims for a quantitative grasp of the parameter landscape of this adaptive multi-population model, focusing on arresting the growth of drug-resistant prostate cancer by promoting competition across drug-sensitive cancer cell populations. Our findings indicate that not only is it is feasible to considerably extend cancer suppression duration through careful optimization, but even transform metastatic prostate cancer into a chronic condition instead of an acute one for most patients, with supporting clinical and analytical evidence.

RNA molecules exhibit various biological functions intrinsically dependent on their diverse ecosystem of highly flexible structures. This flexibility arises from complex hydrogen-bonding networks defined by canonical and non-canonical base pairs that require protonation events to stabilize or perturb these interactions. Constant pH molecular dynamics (CpHMD) methods provide a reliable framework to explore the conformational and protonation space of dynamic structures and for robust calculations of pH-dependent properties, such as the pK$_\mathrm{a}$ of titrable sites. Despite growing biological evidence concerning pH regulation of certain motifs and in biotechnological applications, pH-sensitive in silico methods have rarely been applied to nucleic acids. In this work, we extended the stochastic titration CpHMD method to include RNA parameters from the standard $\chi$OL3 AMBER force field and highlighted its capability to depict titration events of nucleotides in single-stranded RNAs. We validated the method using trimers and pentamers with a single central titrable site while integrating a well-tempered metadynamics approach into the st-CpHMD methodology (CpH-MetaD) using PLUMED. This approach enhanced the convergence of the conformational landscape and enabled more efficient sampling of protonation-conformation coupling. Our pK$_\mathrm{a}$ estimates agree with experimental data, validating the method's ability to reproduce electrostatic changes around a titrable nucleobase in single-stranded RNA. These findings provided molecular insight into intramolecular phenomena, such as nucleobase stacking and phosphate interactions, that dictate the experimentally observed pK$_\mathrm{a}$ shifts between different strands. Overall, this work validates both the st-CpHMD and the metadynamics integration as reliable tools for studying biologically relevant RNA systems.

Recently, contrastive learning (CL), a technique most prominently used in natural language and computer vision, has been used to train informative representation spaces for galaxy spectra and images in a self-supervised manner. Following this idea, we implement CL for stars in the Milky Way, for which recent astronomical surveys have produced a huge amount of heterogeneous data. Specifically, we investigate Gaia XP coefficients and RVS spectra. Thus, the methods presented in this work lay the foundation for aggregating the knowledge implicitly contained in the multimodal data to enable downstream tasks like cross-modal generation or fused stellar parameter estimation. We find that CL results in a highly structured representation space that exhibits explicit physical meaning. Evaluating Using this representation space to perform cross-modal generation and stellar label regression results in excellent performance with high-quality generated samples as well as accurate and precise label predictions.

Describing the dynamics of strong-laser driven open quantum systems is a very challenging task that requires the solution of highly involved equations of motion. While machine learning techniques are being applied with some success to simulate the time evolution of individual quantum states, their use to approximate time-dependent operators (that can evolve various states) remains largely unexplored. In this work, we develop driven neural quantum propagators (NQP), a universal neural network framework that solves driven-dissipative quantum dynamics by approximating propagators rather than wavefunctions or density matrices. NQP can handle arbitrary initial quantum states, adapt to various external fields, and simulate long-time dynamics, even when trained on far shorter time windows. Furthermore, by appropriately configuring the external fields, our trained NQP can be transferred to systems governed by different Hamiltonians. We demonstrate the effectiveness of our approach by studying the spin-boson and the three-state transition Gamma models.

Determination of molecular energetics and properties is one of the core challenges in the near-term quantum computing. To this end, hybrid quantum-classical algorithms are preferred for Noisy Intermediate Scale Quantum (NISQ) architectures. The Projective Quantum Eigensolver (PQE) is one such algorithms that optimizes the parameters of the chemistry-inspired unitary coupled cluster (UCC) ansatz using a conventional coupled cluster-like residual minimization. Such a strategy involves the projection of the Schrodinger equation on to linearly independent basis towards the parameter optimization, restricting the ansatz is solely defined in terms of the excitation operators. This warrants the inclusion of high-rank operators for strongly correlated systems, leading to increased utilization of quantum resources. In this manuscript, we develop a methodology for determining the generalized operators in terms of a closed form residual equations in the PQE framework that can be efficiently implemented in a quantum computer with manageable quantum resources. Such a strategy requires the removal of the underlying redundancy in high-rank excited determinants, generated due to the presence of the generalized operators in the ansatz, by projecting them on to an internally contracted lower dimensional manifold. With the application on several molecular systems, we have demonstrated our ansatz achieves similar accuracy to the (disentangled) UCC with singles, doubles and triples (SDT) ansatz, while utilizing an order of magnitude fewer quantum gates. Furthermore, when simulated under stochastic Gaussian noise or depolarizing hardware noise, our method shows significantly improved noise resilience compared to the other members of PQE family and the state-of-the-art variational quantum eigensolver.

We propose a numerical algorithm that integrates quantum two-level systems (TLSs) into the finite-difference time-domain (FDTD) framework for simulating quantum emitters in arbitrary 3D photonic environments. Conventional methods struggle with these systems due to their semi-classical nature and spurious self-interactions that arise when a TLS is driven by its own radiation field. We address these issues by determining the correct electric field for driving the TLS, as well as the current source used in FDTD for modeling photon emission. Our method, focusing on single-excitation states, employs a total field-incident field (TF-IF) technique to eliminate self-interactions, enabling precise simulations of photon emission and scattering. The algorithm also successfully models complex phenomena such as resonant energy transfer, superradiance, and vacuum Rabi splitting. This powerful computational tool is expected to substantially advance research in nanophotonics, quantum physics, and beyond.

Understanding the origins of complexity is a fundamental challenge with implications for biological and technological systems. Network theory emerges as a powerful tool to model complex systems. Networks are an intuitive framework to represent inter-dependencies among many system components, facilitating the study of both local and global properties. However, it is unclear whether we can define a universal theoretical framework for evolving networks. While basic growth mechanisms, like preferential attachment, recapitulate common properties such as the power-law degree distribution, they fall short in capturing other system-specific properties. Tinkering, on the other hand, has shown to be very successful in generating modular or nested structures "for-free", highlighting the role of internal, non-adaptive mechanisms in the evolution of complexity. Different network extensions, like hypergraphs, have been recently developed to integrate exogenous factors in evolutionary models, as pairwise interactions are insufficient to capture environmentally-mediated species associations. As we confront global societal and climatic challenges, the study of network and hypergraphs provides valuable insights, emphasizing the importance of scientific exploration in understanding and managing complexity.

Collisionless tearing instability with a power-law distribution function in a relativistic pair plasma with a guide field is studied. When the current sheet is supported by plasma pressure, the tearing mode is suppressed as the particle spectrum hardens. In the force-free limit, the instability growth rate becomes independent of the particle spectrum. We apply these results to relativistic MHD turbulence, where magnetic energy greatly exceeds plasma rest energy, and derive an expression for the transverse size of turbulent eddies unstable to tearing mode. We also establish the critical plasma magnetization parameter above which charge starvation prevents the tearing instability. These results might be useful for developing more accurate models of particle acceleration in relativistic astrophysical sources.

Quantum information scrambling, which describes the propagation and effective loss of local information, is crucial for understanding the dynamics of quantum many-body systems. In general, a typical interacting system would thermalize under time evolution, leading to the emergence of ergodicity and linear lightcones of information scrambling. Whereas, for a many-body localized system, strong disorders give rise to an extensive number of conserved quantities that prevent the system from thermalization, resulting in full ergodicity breaking and a logarithmic lightcone for information spreading. Here, we report the experimental observation of anomalous information scrambling in an atomic tweezer array. Working in the Rydberg blockade regime, where van der Waals interaction dominates, we observe a suppressed linear lightcone of information spreading characterized by out-of-time-order correlators for the initial N\'eel state, accompanied by persistent oscillations within the lightcone. Such an anomalous dynamics differs from both generic thermal and many-body localized scenarios. It originates from weak ergodicity breaking and is the characteristic feature for quantum many-body scars. The high-quality single-atom manipulations and coherent constraint dynamics, augmented by the effective protocol for time-reversed evolution we demonstrate, establish a versatile hybrid analog-digital simulation approach to explore diverse exotic non-equilibrium dynamics with atomic tweezer arrays.

Thermal tides are atmospheric tides caused by variations in day-night insolation, similar to gravitational tides but with key differences. While both result in delayed mass redistribution, energy dissipation, and angular momentum exchanges between the planet and its host star, thermal tides can drive a planet's dynamics away from the rotational equilibrium states predicted by classical tidal theory. In this work, we present a novel closed-form solution for the thermotidal response of rocky planets. This general solution is derived from first principles, assuming either dry or moist adiabatic temperature profiles for the planet's atmosphere, and can be readily applied to study the long-term evolution of exoplanets in the habitable zones of their host stars. Despite relying on a small number of parameters, the model successfully captures the key features of the thermotidal torque predicted by General Circulation Models (GCMs). It also accurately predicts Earth's current semidiurnal thermotidal response and provides new insights into the evolution of the length of day during the Precambrian era.

We numerically investigate the diffusive behavior of active Brownian particles in a two-dimensional confined channel filled with soft obstacles, whose softness is controlled by a parameter $K$. Here, active particles are subjected to external bias $F$. Particle diffusion is influenced by entropic barriers that arise due to variations in the shape of the chosen channel geometry. We observed that the interplay between obstacle softness, entropic barriers, and external bias leads to striking transport characteristics of the active particles. For instance, with increasing $F$, the non-linear mobility exhibits non-monotonic behavior, and effective diffusion is greatly enhanced, showing multiple peaks in the presence of soft obstacles. Further, as a function of $K$ and $F$, particles exhibit various diffusive behaviors, e.g., normal diffusion - where the role of obstacles is insignificant, subdiffusion or superdiffusion - where the particles are partially trapped by the obstacles, and particles are ultimately caged by the obstacles. These findings help understand the physical situations wherein active agents diffuse in crowded environments.

Deep learning models are defined in terms of a large number of hyperparameters, such as network architectures and optimiser settings. These hyperparameters must be determined separately from the model parameters such as network weights, and are often fixed by ad-hoc methods or by manual inspection of the results. An algorithmic, objective determination of hyperparameters demands the introduction of dedicated target metrics, different from those adopted for the model training. Here we present a new approach to the automated determination of hyperparameters in deep learning models based on statistical estimators constructed from an ensemble of models sampling the underlying probability distribution in model space. This strategy requires the simultaneous parallel training of up to several hundreds of models and can be effectively implemented by deploying hardware accelerators such as GPUs. As a proof-of-concept, we apply this method to the determination of the partonic substructure of the proton within the NNPDF framework and demonstrate the robustness of the resultant model uncertainty estimates. The new GPU-optimised NNPDF code results in a speed-up of up to two orders of magnitude, a stabilisation of the memory requirements, and a reduction in energy consumption of up to 90% as compared to sequential CPU-based model training. While focusing on proton structure, our method is fully general and is applicable to any deep learning problem relying on hyperparameter optimisation for an ensemble of models.