While the Boltzmann transport equation can accurately model transport problems with highly forward-peaked scattering, obtaining its solution can become arbitrarily slow due to near-unity spectral radius associated with source iteration. Standard acceleration techniques like diffusion synthetic acceleration and nonlinear diffusion acceleration obtain merely one order of magnitude speedups compared to source iteration due to slowly decaying error moments. Additionally, converging approximations to the Boltzmann equation like Fokker-Planck and Boltzmann Fokker Planck run into similar problems with slow convergence. In this paper we assess the feasibility of using Fourier neural operators to obtain AI-enhanced low order, and single-sweep solutions for the transport equation in slab geometry using a predictor-corrector framework.

Studies of the effects of the weak interaction in atomic systems provide tests of the Standard Model of particle physics, and explore physics scenarios beyond the Standard Model. In addition, these studies can offer valuable insights into low-energy nuclear physics. We provide an overview of the field of atomic parity violation, and discuss implications to nuclear and particle physics, and ongoing experimental efforts. Furthermore, we present our plans for precision measurements of the signatures of the weak interaction in atomic ytterbium.

During the Z-Pinch fusion process, electric current is injected into liquid metal from the plasma column, generating Lorentz forces that deform the liquid metal's free surface. Modeling this phenomenon is essential for assessing the feasibility of using liquid metal as an electrode wall in fusion devices. Traditionally, such problems, where liquid metal is exposed to electromagnetic forces, are modeled using magneto-hydrodynamic (MHD) formulation, which is more suitable for cases without external electric current penetration into liquid metals. MHD formulation typically models situations where liquid metal flows in the presence of an external magnetic field, with the initial magnetic field known and evolving over time via the magnetic induction equation. However, in Z-Pinch fusion devices, the electric current penetrates and traverses through the liquid metal, necessitating numerical calculations for the initial magnetic field. Additionally, the deformation of the liquid metal surface alters the current path's geometry and the resulting magnetic field, rendering traditional MHD formulations unsuitable. This work addresses this issue by directly solving Maxwell's equations, instead of the magnetic induction equation, in combination with Navier-Stokes equations, making it possible to predict the magnetic field even when the fluid is in motion. The Maxwell equations are solved in potential formulation alongside Navier-Stokes equations using a finite volume numerical method on a collocated grid arrangement. This proposed numerical framework successfully captures the deformation of the liquid metal's free surface due to the applied electric current.

Studies conducted on the experimental site of Lavalette (IRSTEA Montpellier) have shown variability in the observed agricultural yield, either attributable to spatial or temporal heterogeneities in water and nitrogen supply or to gradients of soil properties. The latter is addressed by performing a multi-depthg geophysical prospection that delivers maps of apparent electrical resistivity.

Photon transport through a diffusing slab can be described by the radiative transfer equation (RTE). When the slab is highly scattering and weakly absorbing, the RTE simplifies to the diffusion equation. In this paper, an inverse diffusion approximation (IDA) method is numerically developed to determine the optical properties (reduced scattering $\mu'_s$ and absorption coefficient $\mu_a$) of a homogeneous slab using simple reflectance and transmission measurements with a spectrometer. The reflectance and transmission of a diffusing slab with an arbitrary thickness can then be predicted by solving the forward problem by using the calculated $\mu'_s$ and $\mu_a$. Our method is validated both numerically, by directly comparing with Monte-Carlo simulations, and experimentally, by comparing with measurements on ZnO/PDMS and TiO$_2$/PDMS composite polymer films with varying thicknesses. The IDA method is also applied to distinguish between different types of tissue. Overall, our method could be used to guide the design of radiative cooling reflectors, or lighting and optical display diffusers for applications in medical imaging and other fields.

By adapting bus routes to users' requests, Demand-Responsive Transit (DRT) can serve low-demand areas more efficiently than conventional fixed-line buses. However, a main barrier to its adoption of DRT is its unpredictability, i.e., it is not possible to know a-priori how much time a certain trip will take, especially when no large prebooking is imposed. To remove this barrier, we propose a data-driven method that, based on few previously observed trips, quantifies the level of predictability of a DRT service. We simulate different scenarios in VISUM in two Italian cities. We find that, above reasonable levels of flexibility, DRT is more predictable than one would expect, as it is possible to build a model that is able to provide a time indication with more than 90% reliability. We show how our method can support the operators in dimensioning of the service to ensure sufficient predictability.

Core periphery structure represents a meso-scale structure in networks, characterized by a dense interconnection of core nodes and sparse connections among peripheral nodes. In this paper, we introduce an innovative approach for detecting core periphery structure, leveraging Artificial Ants. Core-periphery structures play a crucial role in elucidating network organization across various domains. The proposed approach, inspired by the foraging behavior of ants, employs artificial pheromone trails to iteratively construct and refine solutions, thereby eliminating the need for arbitrary partitions that often constrain traditional methods. Our method is applied to a diverse selection of real world networks including historical, literary, linguistic, sports, and animal social networks highlighting its adaptability and robustness. We systematically compare the performance of our approach against established core-periphery detection techniques, emphasizing differences in node classification between the core and periphery. Experimental results show that our method achieves superior flexibility and precision, offering marked improvements in the accuracy of core periphery structure detection.

High-energy large-scale particle colliders generate data at extraordinary rates. Developing real-time high-throughput data compression algorithms to reduce data volume and meet the bandwidth requirement for storage has become increasingly critical. Deep learning is a promising technology that can address this challenging topic. At the newly constructed sPHENIX experiment at the Relativistic Heavy Ion Collider, a Time Projection Chamber (TPC) serves as the main tracking detector, which records three-dimensional particle trajectories in a volume of a gas-filled cylinder. In terms of occupancy, the resulting data flow can be very sparse reaching $10^{-3}$ for proton-proton collisions. Such sparsity presents a challenge to conventional learning-free lossy compression algorithms, such as SZ, ZFP, and MGARD. In contrast, emerging deep learning-based models, particularly those utilizing convolutional neural networks for compression, have outperformed these conventional methods in terms of compression ratios and reconstruction accuracy. However, research on the efficacy of these deep learning models in handling sparse datasets, like those produced in particle colliders, remains limited. Furthermore, most deep learning models do not adapt their processing speeds to data sparsity, which affects efficiency. To address this issue, we propose a novel approach for TPC data compression via key-point identification facilitated by sparse convolution. Our proposed algorithm, BCAE-VS, achieves a $75\%$ improvement in reconstruction accuracy with a $10\%$ increase in compression ratio over the previous state-of-the-art model. Additionally, BCAE-VS manages to achieve these results with a model size over two orders of magnitude smaller. Lastly, we have experimentally verified that as sparsity increases, so does the model's throughput.

This study uses fully kinetic particle-in-cell (PIC) simulation to investigate the effects of anomalous diffusion on electron transport and the propulsive performance of magnetic nozzles (MN) typical of low-power cathode-less RF plasma thrusters. The analysis provides insights into how this non-collisional transport mechanism, driven by turbulence and wave-particle interactions, influences the formation of azimuthal electron currents and, consequently, impacts thrust generation, momentum/power balance, and propulsive efficiency metrics. A Bohm-type anomalous collisionality is applied to the case of a the 150 W class REGULUS-150-Xe thruster at low (30 W) and high (150 W) operating conditions. It is found that the enhanced cross-field transport of electrons inhibits the formation of the typical MN potential barrier, reducing the radial confinement. The downstream potential drop is reduced by up to 15%. Diamagnetic electron current is diminished in the absence of steep pressure gradients and the $E\times B$ current becomes purely paramagnetic. The MN efficiency is cut from circa 0.5 to 0.2 due to loss of electron thermal energy conversion and increased plume divergence. At the Bohm limit of $\omega_{ce}/16$, agreement to experimental thrust profiles of <20% is achieved in contrast to 48% overestimation at high-power in the classical case.

Path Integral Molecular Dynamics (PIMD) is a well established simulation technique to compute exact equilibrium properties for a quantum system using classical trajectories in an extended phase space. Standard PIMD simulations are numerically converged by systematically increasing the number of classical 'beads' or replicas used to represent each particle in the quantum system. Currently available scientific software for PIMD simulations leverage the massively parallel (with respect to number of beads) nature of the classical PIMD Hamiltonian. For particularly high-dimensional systems, contraction schemes designed to reduce the overall number of beads per particle required to achieve numerical convergence are also frequently employed. However, these implementations all rely on using the same number of beads to represent all atoms/particles, and become inefficient in systems with a large number of atoms where only a handful contribute significant quantum effects. Mixed time slicing (mixTS) offers an alternate path to efficient PIMD simulations by providing a framework where numerical convergence can be achieved with different numbers of beads for different types of atoms. Unfortunately, mixTS is not available in existing PIMD software. In this paper, we introduce MixPI for atomistic mixTS-PIMD simulations within the open-source software package CP2K. We demonstrate the use of MixPI in two different benchmark systems: we explore the use of mixTS in computing radial distributions functions for water, and in a more significant demonstration, for a solvated Co2+ ion represented as a classical Co3+ ion in water with an explicit, quantized 1024-bead electron localized on the metal ion.

For noncovalent interactions, it is generally assumed that CCSD(T) is nearly the exact solution within the 1-particle basis set. For the S66 noncovalent interactions benchmark, we present for the majority of species CCSDT and CCSDT(Q) corrections with a polarized double-zeta basis set. For hydrogen bonds, pure London complexes, and mixed-influence complexes, CCSD(T) benefits from error cancellation between (usually repulsive) higher-order triples, $T_3 - (T)$, and (almost universally attractive) connected quadruples, (Q). For $\pi$-stacking complexes, this cancellation starts breaking down and CCSD(T) overbinds; CCSD(T)$_\Lambda$ corrects the problem at the expense of London complexes. A fairly simple two-parameter model predicts CCSDT(Q)--CCSD(T) differences to 0.01 kcal/mol RMS, requiring no calculations that scale more steeply than $O(N^7)$.

Optically levitated dielectric nanoparticles have become valuable tools for precision sensing and quantum optomechanical experiments. To predict the dynamic properties of a particle trapped in an optical tweezer with high fidelity, a tool is needed to compute the particle's response to the given optical field accurately. Here, we utilise a numerical solution of the three-dimensional trapping light to accurately simulate optical tweezers and predict key optomechanical parameters. By controlling the numerical aperture and measuring the the particle's oscillation frequencies in the trap, we validate the accuracy of our method. We foresee broad applications of this method in the field of levitodynamics, where precise characterisation of optical tweezers is essential for estimating parameters ranging from motional frequencies to scattering responses of the particle with various dielectric properties.

We derive novel analytic tools for the Bass and SI models on networks for the spreading of innovations and epidemics on networks. We prove that the correlation between the nonadoption (noninfection) probabilities of $L \ge 2$ disjoint subsets of nodes $\{A_l\}_{l=1}^L$ is non-negative, find the necessary and sufficient condition that determines whether this correlation is positive or zero, and provide an upper bound for its magnitude. Using this result, we prove the funnel theorems, which provide lower and upper bounds for the difference between the non-adoption probability of a node and the product of its nonadoption probabilities on $L$ modified networks in which the node under consideration is only influenced by incoming edges from $A_l$ for $l=1, \dots, L$. The funnel theorems can be used, among other things, to explicitly compute the exact expected adoption/infection level on various types of networks, both with and without cycles.

We introduce a monolithically integrated terahertz optoelectronics (MITO) platform that uses quantum well (QW) structures to enable tunable terahertz signal generation and detection on a single chip. Through photomixing in QW PIN photodiodes, the MITO platform achieves terahertz generation and detection with substantially enhanced power efficiency and sensitivity over previous devices. By integrating semiconductor optical amplifiers, lasers, modulators, filters, demultiplexers, and other passive optical components with photomixers using commercially available photonic integrated circuit foundry processes, this platform supports the development of compact, scalable terahertz systems capable of high-speed data transfer, spectroscopy, and hyperspectral imaging. This advancement positions terahertz technology for widespread use, facilitating practical applications across remote sensing, communications, and medical diagnostics within portable devices.

Energy transfer across scales is fundamental in fluid dynamics, linking large-scale flow motions to small-scale turbulent structures in engineering and natural environments. Triadic interactions among three wave components form complex networks across scales, challenging understanding and model reduction. We introduce Triadic Orthogonal Decomposition (TOD), a method that identifies coherent flow structures optimally capturing spectral momentum transfer, quantifies their coupling and energy exchange in an energy budget bispectrum, and reveals the regions where they interact. TOD distinguishes three components--a momentum recipient, donor, and catalyst--and recovers laws governing pairwise, six-triad, and global triad conservation. Applied to unsteady cylinder wake and wind turbine wake data, TOD reveals networks of triadic interactions with forward and backward energy transfer across frequencies and scales.

We review the main processes that drive the morphodynamics of dunes, i.e. their growth in height, migration and elongation, and emphasise the contribution of experiments to the understanding of these mechanisms. The main control parameters are the sediment flux $Q$ and the saturation length $L_{\rm sat}$ associated with the spatial relaxation of the flux towards the transport capacity. The other relevant quantities are essentially dimensionless: fluid response to a bed perturbation, dune geometry (orientation, aspect ratio), transport ratios under multi-directional wind regimes. We argue that laboratory experiments dealing with sedimentary bedforms in water flows are good analogues to study the morphodynamics of aeolian dunes at reduced length and time scales, as $L_{\rm sat}$ and $L_{\rm sat}^2/Q$ are expected to be smaller for subaqueous bedload. Besides, dune shape and dynamics are mainly governed by flow and boundary conditions, independent of the transport mode. We discuss different experimental set-ups and results, especially concerning dune pattern orientation and dune interaction. Under natural wind regimes in terrestrial deserts, we show the potential of field experiments in which the control of initial and boundary conditions allows for the quantification of all the relevant mechanisms involved in dune growth. We emphasise the general agreement between observations, measurements and theoretical predictions, which indicates a robust comprehension of the underlying processes. This understanding can serve as a foundation for further investigations, including the interpretation of dune landscapes and the resolution of inverse problems.

In this work, we theoretically study temporal interfaces between media with strong spatial dispersion and dielectrics. In particular, we consider a temporal discontinuity that transforms a wire medium sample, a metamaterial with resonant spatial dispersion, into a uniaxial dielectric. We show that this transition results in a transformation of the deeply subwavelength spatial spectrum of TEM waves propagating in the wire medium at a certain frequency into a spectrum of plane waves at new frequencies that are all higher than the initial one. The waves at different frequencies propagate in different directions. Their complex amplitudes and propagation directions are uniquely related to the amplitudes of the spatial harmonics of the fields which existed before the transition. We explain how to implement this transition. The revealed effect may result in a promising method of subwavelength imaging.

This work analytically compares the growth of Backward Stimulated Raman Scattering (B-SRS) induced by temporal laser pulses on the order of a few tens of laser cycles or a couple of picoseconds for nominally 10 um wavelength IR pulse with Gaussian versus the constant intensity profile assumed in the original theory, both delivering an equivalent energy. By evaluating the growth factors of these two pulse shapes, we demonstrate that the temporal structure of the laser pulse significantly influences the maximum growth rate of the instability. Specifically, while the growth rate for a square pulse increases linearly with time, the Gaussian pulse resultant growth rate tracks with the temporal intensity profile of the pulse. We utilize the newly calculated total maximum growth that a temporally varying pulse delivers above the B-SRS threshold and then determine the normalized intensity for a pulse that has same duration. and contains the same amount of total energy. We show that these energy equivalent square pulses yield the same total B-SRS growth as the Gaussian pulses.

The feasibility study of a new technique for thermal neutron detection using a Timepix3 camera (TPX3Cam) with custom-made optical add-ons operated in event-mode data acquisition is presented. The camera has a spatial resolution of ~ 16 um and a temporal resolution of 1.56 ns. Thermal neutrons react with 6 Lithium to produce a pair of 2.73 MeV tritium and 2.05 MeV alpha particles, which in turn interact with a thin layer of LYSO crystal to produce localized scintillation photons. These photons are directed by a pair of lenses to an image intensifier, before being recorded by the TPX3Cam. The results were reconstructed through a custom clustering algorithm utilizing the Time-of-Arrival (ToA) and geometric centre of gravity of the hits. Filtering parameters were found through data analysis to reduce the background of gamma and other charged particles. The efficiency of the converter is 4%, and the overall detection efficiency of the system including the lead shielding and polythene moderator is ~ 0.34%, all converted thermal neutrons can be seen by the TPX3Cam. The experiment used a weak thermal neutron source against a large background, the measured signal-to-noise ratio is 1/67.5. Under such high noise, thermal neutrons were successfully detected and predicted the reduced neutron rate, and matched the simulated rate of the thermal neutrons converted from the source. This result demonstrated the excellent sensitivity of the system.

The performance of the nonlinearly stable flux reconstruction (NSFR) schemes for resolving subsonic viscous turbulent free-shear flows is investigated. The schemes are extensively verified for the direct numerical simulation (DNS) of the Taylor-Green Vortex (TGV) problem. Several under-resolved simulations of the TGV problem are conducted to assess the performance of NSFR for large eddy simulation that is implicitly filtered and fully implicit (ILES). Increasing the flux reconstruction correction parameter ensures that NSFR is stable and accurate for ILES while allowing for larger explicit time-steps. The entropy-stable schemes implemented with sum-factorization for tensor and Hadamard products are shown to be more cost-effective than classical DG with over-integration. The choice of the two-point (TP) numerical flux does not impact the solution and the use of standard eddy-viscosity-based sub-grid scale models does not yield improvements for the problem considered. From the DNS results, the pressure dilatation-based dissipation rate for the nonlinearly stable schemes is consistent with literature when computed from the kinetic energy (KE) budget terms, while spurious oscillations are seen when the term is directly computed. The magnitude of these oscillations is significantly lower for a collocated scheme and are effectively eliminated with the addition of Roe upwind dissipation to the TP numerical flux. Therefore, these oscillations are believed to be associated with the treatment of the face terms in nonlinearly stable schemes. It is shown that oversampling the velocity field is necessary for obtaining accurate turbulent KE (TKE) spectra and eliminates an apparent pile-up of TKE at the smallest resolved scales. Lastly, the TKE spectra for a decaying homogeneous isotropic turbulence case are in good agreement with experiment measurements and computational results in the literature.

Fluids subject to both thermal and compositional variations can undergo doubly diffusive convection when these properties both affect the fluid density and diffuse at different rates. In natural doubly diffusive convection, the gradients of temperature and salinity are aligned with each other and orthogonal to gravity. The resulting buoyancy-driven flows are known to lead to the formation of a variety of patterns, including spatially localized states of convection surrounded by quiescent fluid, which are known as convectons. Localized pattern formation in natural doubly diffusive convection has been studied under a specific balance where the effects of temperature and salinity changes are opposite but of equal intensity on the fluid density. In this case, a steady conduction state exists and convectons bifurcate from it. The aforementioned buoyancy balance underpins our knowledge of this pattern formation but it is an ideal case that can hardly be met experimentally or in nature. This article addresses how localized pattern formation in natural doubly diffusive convection is affected by departures from the balanced case. In particular, the absence of a conduction state leads to the unfolding of the bifurcations to convectons. In thermally dominated regimes, the background flow promotes localized states with convection rolls attached to the end-walls, known as anticonvectons, and the existence of these states is found to be related to the emergence of convectons. The results presented here shed light on the convecton robustness against changes in the buoyancy ratio and extend the scope of our understanding of localized pattern formation in fluid systems.

Natural faults often contain a fluid-saturated, granular fault-gouge layer, whose failure and sliding processes play a central role in earthquake dynamics. Using a two-dimensional discrete element model coupled with fluid dynamics, we simulate a fluid-saturated granular layer, where fluid pressure is incrementally raised. At a critical fluid pressure level, the layer fails and begins to accelerate. When we gradually reduce fluid pressure, a distinct behavior emerges: slip-rate decreases linearly until the layer halts at a fluid pressure level below that required to initiate failure. During this pressure cycle the system exhibits (1) velocity-strengthening friction and (2) frictional hysteresis. These behaviors, well established in dry granular media, are shown to extend here to shear of dense fluid-saturated granular layers. Additionally, we observe a delay between fluid pressure increase and failure, associated with pre-failure dilative strain and "dilational-hardening". During the delay period, small, arrested slip events dilate the layer in preparation for full-scale failure. Our findings may explain (i) fault motion that continues even after fluid pressure returns to pre-injection levels, and (ii) delayed failure in fluid-injection experiments, and (iii) pre-failure arrested slip events observed prior to earthquakes.

Alkali-metal-noble-gas comagnetometers are precision probes well-suited for tests of fundamental physics and inertial rotation sensing, combining high sensitivity of the spin-exchange-relaxation free (SERF) magnetometers with inherent suppression of magnetic field noise. Past versions of the device utilizing continuous-wave optical pumping are sensitive to a single axis perpendicular to the plane spanned by the orthogonal pump and probe laser beams. These devices are susceptible to light shifts in the alkali atoms, and to power and beam pointing fluctuations of both the probe and pump lasers, the latter of which is a dominant source of $1/f$ noise. In this work, we model and implement a new approach to alkali-metal-noble-gas comagnetometers using pulsed optical pumping. After each pump laser pulse, an off-resonance probe beam measures the precession of noble-gas-spins-coupled alkali spins via optical rotation in the dark, thus eliminating effects from pump laser light shift and power fluctuations. Performing non-linear fitting on the sinusoidal transient signal with a proper phase enables separate and simultaneous measurement of signals along two orthogonal axes in the plane perpendicular to the pump beam. Effects from beam pointing fluctuations of the probe beam in the pump-probe plane is fundamentally eliminated, and signal response to pump beam pointing fluctuations is suppressed by compensation from noble-gas nuclear spins.

Scaling of the mean velocity profiles has been studied by many researchers, since it provides a template of universal dynamical patterns across a range of Reynolds numbers. Various normalization schemes have been shown in the past, some with a good degree of success. An alternative, universal scaling is presented, where an integrated velocity serves as a universal template for incompressible, adverse pressure-gradient and compressible flows.

Nonlinearity and non-Hermiticity, for example due to environmental gain-loss processes, are a common occurrence throughout numerous areas of science and lie at the root of many remarkable phenomena. For the latter, parity-time-reflection ($\mathcal{PT}$) symmetry has played an eminent role in understanding exceptional-point structures and phase transitions in these systems. Yet their interplay has remained by-and-large unexplored. We analyze models governed by the replicator equation of evolutionary game theory and related Lotka-Volterra systems of population dynamics. These are foundational nonlinear models that find widespread application and offer a broad platform for non-Hermitian theory beyond physics. In this context we study the emergence of exceptional points in two cases: (a) when the governing symmetry properties are tied to global properties of the models, and, in contrast, (b) when these symmetries emerge locally around stationary states--in which case the connection between the linear non-Hermitian model and an underlying nonlinear system becomes tenuous. We outline further that when the relevant symmetries are related to global properties, the location of exceptional points in the linearization around coexistence equilibria coincides with abrupt global changes in the stability of the nonlinear dynamics. Exceptional points may thus offer a new local characteristic for the understanding of these systems. Tri-trophic models of population ecology serve as test cases for higher-dimensional systems.

To explain reported solar interfacial-evaporation rates from porous materials beyond an apparent 100% efficiency using the thermal evaporation mechanism, many publications hypothesize that intermediate water inside porous materials have a reduced latent heat. Key supporting evidence is that water-only surfaces have lower dark evaporation rates than porous evaporators, with the ratio of the two rates taken as the latent heat reduction. Through simulations and experiments, we present benchmark evaporation rates of water and show that reported differences in natural evaporation are likely due to experimental error from recessed evaporating surfaces rather than from reduced latent heat. A few millimeters recession of the evaporating surface can drop evaporation rates over 50% due to a stagnant air layer, suggesting that the comparative experiments are prone to error and the latent heat reduction hypothesis cannot be substantiated. Our results indicate that new mechanistic directions need to be pursued to understand superthermal evaporation.

Operating atom-interferometer gyroscopes outside a laboratory environment is challenging primarily owing to the instability of laser systems. To enhance the thermal stability of free-space laser systems, a compact laser system using fiber lasers and all-quartz-jointed optical modules was developed for a dual-atom-interferometer gyroscope. Millimeter-scale optical elements jointed on quartz plates with identical quartz supports, ensure laser power stability and facilitate component upgrades. The primary diode laser was locked to the modulation transfer spectrum of Rb atoms, and Raman lasers were phase-locked to the primary laser. Frequencies for repumping, blow-away, and detection lasers were adjusted with acousto-optic modulators. At room temperature, laser power fluctuation was under 1:1000, polarization extinction ratio exceeded 30 dB, frequency fluctuation was below 91 kHz, and phase noise reached to -100 dBc/Hz @ 1 kHz. The optical modules were tested at 5--50 $^{\circ}$C and applied to a dual-atom-interferometer gyroscope. The fringe contrast was tested over the temperature range. The proposed system paves the way for promoting field applications of atom-interferometer sensors.

Often only small amounts of sample are available for spectroscopic analytical determinations. This work investigates the enhancement of signal in columns packed with silica particles. We propose that silica particles cause the light to scatter through the column, effectively increasing optical path length. Packed columns are shown to be effective with fluorescence spectroscopy, but results were inconclusive with absorbance spectroscopy.

Background: Advances in artificial intelligence, particularly large language models (LLMs), have the potential to enhance technical expertise in magnetic resonance imaging (MRI), regardless of operator skill or geographic location. Methods: We assessed the accuracy of several LLMs in answering 570 technical MRI questions derived from a standardized review book. The questions spanned nine MRI topics, including Basic Principles, Image Production, and Safety. Closed-source models (e.g., OpenAI's o1 Preview, GPT-4o, GPT-4 Turbo, and Claude 3.5 Haiku) and open-source models (e.g., Phi 3.5 Mini, Llama 3.1, smolLM2) were tested. Models were queried using standardized prompts via the LangChain framework, and responses were graded against correct answers using an automated scoring protocol. Accuracy, defined as the proportion of correct answers, was the primary outcome. Results: The closed-source o1 Preview model achieved the highest accuracy (94%), exceeding the random-guess baseline (26.5%). GPT-4o and o1 Mini scored 88%, and GPT-4 Turbo and Claude 3.5 Haiku each scored 84%. Among open-source models, Phi 3.5 Mini performed well, achieving 78% accuracy, comparable to several closed-source models. Accuracy was highest in Basic Principles and Instrumentation categories but lower in Image Weighting and Contrast, History, and Artifacts and Corrections. Conclusions: LLMs exhibit high accuracy in addressing technical MRI questions, suggesting their potential to standardize and enhance MRI practice. These models may improve image quality and consistency across varied clinical environments. Further studies are needed to refine LLMs for clinical use and integrate them into MRI workflows.

The wake-induced transition on the suction surface of a T106A low-pressure turbine (LPT) blade is investigated through a series of implicit large eddy simulations, solving the two-dimensional (2D) compressible Navier-Stokes equations (NSE). The impact of the incoming Gaussian wake amplitude on the blade's profile loss and associated boundary layer parameters is examined, revealing a 50\% reduction in skin friction drag at the highest amplitude. The results indicate that increasing wake amplitude leads to delayed separation and earlier reattachment, resulting in reduced separated flow. The vorticity and enstrophy dynamics during the transition process under varying wake amplitudes reveal characteristic features of wake-induced transition, such as puffs, streaks, and turbulent spots. The periodic passing of wakes induces intermittent "calmed regions", which suppress flow separation and improve profile loss at low Reynolds numbers (Re), typically found in LPTs. The energy budget, accounting for both translational and rotational energy via the turbulent kinetic energy (TKE) and compressible enstrophy transport equation (CETE), respectively, shows trends with increasing wake amplitude. The relative contribution to TKE production and the roles of baroclinicity, compressibility, and viscous terms are explained.

We investigate the low Reynolds number hydrodynamics of a spherical swimmer with a predominantly hydrophobic surface, except for a hydrophilic active patch. This active patch covers a portion of the surface and exhibits chiral activity that varies as a function of $\theta$ and $\phi$. Our study considers two types of active patches: (i) a symmetric active patch (independent of $\phi$) and (ii) an arbitrary active patch (depends on both $\theta$ and $\phi$). The swimming velocity, rotation rate, and flow field of the swimmer are calculated analytically. The objective of this work is to find the optimal configurations for both patch models to maximize the swimmer's velocity and efficiency. Interestingly, the maximum velocity can be controlled by adjusting the hydrophobicity, patch configuration, and strength of the surface activity. We find that for the symmetric patch model, the swimmer's velocity is $U_{SP} = 1.414 U_s$, where $U_s$ is the velocity of a swimmer whose surface is fully covered with chiral activity as a reference. For the arbitrary patch model, the velocity is $U_{AP} = 1.45 U_s$, which is higher than that of the symmetric patch model. Our results indicate that swimmers with low hydrophobicity exhibit efficient swimming characteristics. Additionally, due to the incomplete coverage of the active patch, the Stokeslet and Rotlet terms appear in the flow field generated by the swimmer, which is a deviation compared to the case of a swimmer whose surface is fully covered with chiral activity. This study provides insights useful for designing synthetic active particles, which can be applied, for example, in targeted drug delivery, chemotaxis, and phototaxis.

Natural laminar flow airfoils are essential technologies designed to reduce drag and significantly enhance aerodynamic performance. A notable example is the SHM1 airfoil, created to meet the requirements of the small-business Honda jet. This airfoil has undergone extensive testing across various operational conditions, including low-speed wind tunnel tests and flight tests across a range of Reynolds numbers and free-stream Mach numbers, as detailed in "Natural-laminar-flow airfoil development for a lightweight business jet" by Fujino et al., J. Aircraft, 40(4), 2003. Additionally, investigations into drag-divergence behavior have been conducted using a transonic wind tunnel, with subsequent studies focusing on transonic shock boundary layer interactions through both experimental and numerical approaches. This study employs a series of numerical simulations to analyze the flow physics and aerodynamic performance across different free-stream Mach numbers in the subsonic and transonic regimes. This is achieved by examining computed instantaneous numerical Schlieren for various design conditions (such as low speed, climb, and cruise) and off-design scenarios (including transonic shock emergence, drag-divergence, and shock-induced separation). The dominant time scales, the time-averaged load distributions and boundary layer parameters are compared to provide a comprehensive overview of the SHM1's aerodynamics, establishing benchmark results for optimization of various flow separation and shock control techniques.

Here, we propose an isospectral reduction (IR) approach for the mapping of a trimer Su-Schrieffer-Heeger (SSH3) lattice into a simplified two-site model, whose coupling dynamics ingeniously results in a precise bulk-edge correspondence of the original lattice. The isospectrally-reduced model has inter-cell couplings with dynamic response to the eigenstate energy, allowing for the control of topological phase transition by energy. We relate the bulk property of the reduced model to the band topology of the SSH3 lattice, allowing for there distinct topological phases with different number of topological edge state pair. An acoustic SSH3 chain is fabricated for experimental demonstration. The said topological edge state pairs are measured. Our study takes a pivotal step toward the exploration of topology in multiple wave systems, opening up possibilities for advanced control of topological waves.

Introduction Speech is an integral component of human communication, requiring the coordinated efforts of various organs to produce sound (Titze & Alipour, 2006). The glottis region, a key player in voice production, assumes a crucial role in this intricate process. As air, emanating from the lungs in a confined space, interacts with the vocal folds (VFs) within the human body, it gives rise to the creation of voice (Alipour & Vigmostad, 2012). Understanding the mechanical intricacies of this process is very important. Studying VFs in vivo situations is hard work. However, the orientation, shape and size of VFs fibers have been extracted with synchrotron X-ray microtomography. (Bailly et al., 2018) The investigation of mechanical properties of both human and animal VFs has been carried out through various methodologies in the literature. The mechanical properties of VFs have been studied using the uniaxial extension test (Alipour & Vigmostad, 2012) assuming a linear behavior, while the nonlinearity and anisotropy of VFs has been determined using a multiscale method as in Miri et al. (2013). Pipette aspiration has also been used to extract in vivo elastic properties of VFs (Scheible et al., 2023). Mechanical behavior of VFs layers in tension, compression and shear has been studied. (Cochereau et al., 2020). Fluid-structure interaction (FSI) simulations provide a valuable tool to gain a deeper understanding of voice production (Ghorbani et al. 2022). These simulations allow us to model the dynamic interplay between the VFs and air. Our research focuses on investigating the mechanical properties of canine vocal folds and utilizing these findings in an FSI simulation. Through this simulation, we aim to unravel how these mechanical properties affect voice production.Methods To investigate the mechanical properties of canine VFs, an in vitro study was conducted involving 6 mixedbreed dogs. The samples were harvested from canine cadavers euthanized for reasons unrelated to this study. In the following, the VFs were harvested and tested upon 3-4 hours post-animal sacrifice. Experimental trials were carried out using the STM-1 device (SANTAM Co.), equipped with a 100 kg load cell. Seven uniaxial tensile tests were done on each sample, with displacement rates of 1, 5, 10, 20, 40, 60, and 120 mm/min. The very slow rate of 1 mm/min was chosen to assess only elastic properties eliminating viscosity effects. Various hyperelastic models were used to fit the experimental data. Subsequently, for each model, both the mean and standard deviation (SD) were determined for the hyperelastic model parameters and their residuals. For FSI analysis we used a simplified laryngeal model as a hollow cylinder with a diameter of 50 mm and a thickness of 3 mm. The overall length of the larynx was set at 100 mm. The VFs were modeled as a circular disc with a small elliptical fissure in the midst of the cylinder section. Boundary conditions were established based on pressure differentials, with the inlet gauge pressure set at 1200 Pa and the relative pressure at the outlet set to 0. To account for the turbulent nature of airflow within the larynx, we employed the K-epsilon method to solve the motion differential equations in a two-way fluid-structure interaction simulation using ANSYS FLUENT 2021. This approach enabled us to investigate how the acquired mechanical properties of canine vocal folds affect the FSI simulations during phonation, resulting in a more comprehensive understanding of their impact. To determine the vibrational frequency of VFs, we calculated the time it took to reach maximum displacement and then quadrupled this value to obtain the period of vibration.

To cope with environments with high levels of radiation, non-silicon semiconductors such as silicon carbide detectors are being proposed for instrumentation. 4H-SiC diodes for radiation detection have been fabricated in the IMB-CNM Clean Room, for which different strategies to define the electrical contact of the implants had been implemented, in an attempt to optimise the technology for, e.g., medical applications or low energy radiation detection, as the material choice can affect the sensitivity of the device. Among these technologies, it is included an epitaxially-grown graphene layer as part of the electrical contact. In this paper, a selection of four configurations of the IMB-CNM SiC diodes are characterised in terms of radiation detector response. Photodiode performance under 20 keV X-rays irradiation in the XALOC beam line at ALBA Synchrotron is presented. Over-responses in the range of 12-19% linked to the interaction of the radiation with the metallic layers are observed. A good uniformity response as well as a good linearity at 0~V bias is reported, even in the under-depleted devices. This work exemplifies the good performance of SiC detectors fabricated at IMB-CNM specifically for low-energy X ray characterization at high X-ray intensities.

The concept of `proximity-based cities' has gained attention as a new urban organizational model. Most prominently, the 15-minute city contends that cities can function more effectively, equitably and sustainably if essential, everyday services and key amenities are within a 15-minute walk or cycle. However, focusing solely on travel time risks overlooking disparities in service quality, as the proximity paradigm tends to emphasize the mere presence of an element in a location rather than bringing up more complex questions of identity, diversity, quality, value or relationships. Transitioning to value-based cities by considering more than just proximity can enhance local identity, resilience and urban democracy. Fostering bottom-up initiatives can create a culture of local care and value, while predominantly top-down governing strategies can lead to large inequalities. Balancing these approaches can maximize resilience, health and sustainability. This equilibrium has the potential to accompany sustainable growth, by encouraging the creation of innovative urban solutions and reducing inequalities.

The evolutionary process that led to the emergence of modern human behaviors during the Middle Stone Age in Africa remains enigmatic. While various hypotheses have been proposed, we offer a new perspective that integrates the variability selection hypothesis (VSH) with the evolution of cooperation among human groups. The VSH suggests that human adaptability to fluctuating environments was a primary force driving the development of key evolutionary traits. However, the mechanisms by which environmental variability (EV) influenced human evolution, particularly the emergence of large-scale and complex cooperative behaviors, are not yet fully understood. To explore the connection between intensified EV and the evolution of intergroup cooperation, we analyzed three stochastic models of EV: (i) Regional Variability (RV), where resource-rich areas shift while overall resource levels remain stable; (ii) Universal Variability (UV), where overall resource levels fluctuate but resource-rich areas remain stable; and (iii) Combined Variability (CV), where both resource-rich areas shift and overall resource levels fluctuate. Our results show that RV strongly promotes cooperation, while UV has a comparatively weaker effect. Additionally, our findings indicate that the coevolution of cooperation and network structures is crucial for EVs to effectively promote cooperation. This study proposes a novel causal link between EV and the evolution of cooperation, potentially setting a new direction for both theoretical and empirical research in this field.

AI models are essential in science and engineering, but recent advances are pushing the limits of traditional digital hardware. To address these limitations, physical neural networks (PNNs), which use physical substrates for computation, have gained increasing attention. However, developing effective training methods for PNNs remains a significant challenge. Current approaches, regardless of offline and online training, suffer from significant accuracy loss. Offline training is hindered by imprecise modeling, while online training yields device-specific models that can't be transferred to other devices due to manufacturing variances. Both methods face challenges from perturbations after deployment, such as thermal drift or alignment errors, which make trained models invalid and require retraining. Here, we address the challenges with both offline and online training through a novel technique called Sharpness-Aware Training (SAT), where we innovatively leverage the geometry of the loss landscape to tackle the problems in training physical systems. SAT enables accurate training using efficient backpropagation algorithms, even with imprecise models. PNNs trained by SAT offline even outperform those trained online, despite modeling and fabrication errors. SAT also overcomes online training limitations by enabling reliable transfer of models between devices. Finally, SAT is highly resilient to perturbations after deployment, allowing PNNs to continuously operate accurately under perturbations without retraining. We demonstrate SAT across three types of PNNs, showing it is universally applicable, regardless of whether the models are explicitly known. This work offers a transformative, efficient approach to training PNNs, addressing critical challenges in analog computing and enabling real-world deployment.

This study introduces a dual-band plasmonic absorber designed for simultaneous sensing applications in the near-infrared (NIR) and mid-infrared (MIR) regions. The absorber, composed of silver nanostructures on a metal plate with a dielectric spacer, exhibits a combination of localized and gap surface plasmon resonances, resulting in two distinct absorption peaks in theoretical analysis based on the FDTD method. Numerical simulations also validate the sensor's high refractive index sensitivity, enabling the detection of biomolecules, proteins, viruses, and various solutes in aqueous solutions. The absorber demonstrates significant resonance shifts, making it a promising candidate for environmental monitoring, medical diagnostics, and chemical sensing.

The opening or closing mechanism of a voltage-gated ion channel is triggered by the potential difference crossing the cell membrane in the nervous system. Based on this picture, we model the ion channel as a nanoscale two-terminal ionic tunneling junction. External time-varying voltage exerting on the two-terminal ionic tunneling junction mimics the stimulation of neurons from the outside. By deriving the quantum Langevin equation from quantum mechanics, the ion channel current is obtained by the quantum tunneling of ions controlled by the time-varying voltage. The time-varying voltage induces an effective magnetic flux which causes quantum coherence in ion tunnelings and leads to sideband effects in the ion channel current dynamics. The sideband effects in the ionic current dynamics manifest a multi-crossing hysteresis in the I-V curve, which is the memory dynamics responding to the variation of the external voltage. Such memory dynamics is defined as the active quantum memory with respect to the time-varying stimuli. We can quantitatively describe how active quantum memory is generated and changed. We find that the number of the non-zero cross points in the I-V curve hysteresis and the oscillation of the differential conductance are the characteristics for quantitatively describing the active quantum memory. We also explore the temperature dependence of the active quantum memory in such a system. The discovery of this active quantum memory characteristics provides a new understanding about the underlying mechanism of ion channel dynamics.

Resistive memories are outstanding electron devices that have displayed a large potential in a plethora of applications such as nonvolatile data storage, neuromorphic computing, hardware cryptography, etc. Their fabrication control and performance have been notably improved in the last few years to cope with the requirements of massive industrial production. However, the most important hurdle to progress in their development is the so-called cycle-to-cycle variability, which is inherently rooted in the resistive switching mechanism behind the operational principle of these devices. In order to achieve the whole picture, variability must be assessed from different viewpoints going from the experimental characterization to the adequation of modeling and simulation techniques. Herein, special emphasis is put on the modeling part because the accurate representation of the phenomenon is critical for circuit designers. In this respect, a number of approaches are used to the date: stochastic, behavioral, mesoscopic..., each of them covering particular aspects of the electron and ion transport mechanisms occurring within the switching material. These subjects are dealt with in this review, with the aim of presenting the most recent advancements in the treatment of variability in resistive memories.

Understanding bubble behaviour under ultrasound excitation is key for applications like industrial cleaning and biomedical treatments. Our previous work demonstrated that ultrasound-induced shape instabilities in microbubbles generate periodic jets capable of puncturing cells and enabling targeted drug delivery (Cattaneo et al., 2024). This study investigates the physics of these instabilities in a controlled setup involving individual micrometric air bubbles on rigid substrates. Using a dual-view imaging system combining visible light and phase-contrast x-ray radiation, we capture high-speed, high-resolution recordings on bubble shape dynamics. Four distinct response regimes are identified: spherical oscillations, harmonic meniscus waves, half-harmonic axisymmetric shape mode, and mixed shape mode. The half-harmonic modes arise from Faraday instability, with pressure thresholds aligning well with classical interface stability theory adapted for rigid substrates. Simulations using a 3D boundary element method closely match experimental observations, validating this approach for predicting bubble dynamics. The shape mode spectrum shows degeneracy and a continuous range of mode degrees, consistent with our theoretical predictions. Shape modes achieve higher amplitudes than volumetric modes, making them more effective at converting acoustic energy into kinetic energy. These modes generate substrate-directed jets when a lobe folds inward with sufficient acceleration. This process recurs with each cycle of the shape mode and has the potential to damage the substrate. These jets represent a new class of bubble jets, distinct from classical single inertial jets that form under high-pressure gradients during bubble collapse, as they are cyclic, driven by an interfacial instability, and occur at much lower acoustic intensities.

A theory based on the superposition principle is developed to uncover the basic physics of the wave behavior in a finite grating of N unit cells. The theory reveals that bound states in the continuum (BICs) of infinite quality factor (Q-factor) can be supported by such grating when the perfect reflection is introduced at its boundaries. If geometrical perturbations are introduced in the structure, the dark BICs transit to bright quasi-BICs of finite Q-factor, whose spectral behaviors are nearly the same as that of quasi-BICs supported by infinite gratings. When the boundaries are replaced with metallic mirrors of high reflectivity, the Q-factor of the resonant mode is reduced to be finite; however, it can be much larger than that in the corresponding nanostructure of open boundaries and can be tuned in a large range by varying the number of unit cells or boundary conditions.

Acoustic microfluidic is an important technology in particle manipulations in biomedical analyses and detections. However, the particle-movement manipulations achieved by the standing surface acoustic wave is suitable for particles in a thin layer of fluids, however it is difficult to manipulate particles in deeper solutions due to the energy loss of surface acoustic waves. The traditional standing bulk wave method can realize the particle manipulation in deep solutions, but it cannot work properly for particle manipulation within a long distance due to the energy loss. In this work, the topological rainbow defect-state trapping is realized, the results show that an effect of point accumulation of acoustic pressure in the waveguide path exists, the position of maximum acoustic pressure can be adjusted flexibly by changing the frequency of the incident acoustic wave, based on which, long-distance movement and capture manipulations of particles in deep solution have been realized. The phenomenon presented in this work can provide a reliable method for manipulations of continuous long-distance particle movement and capture to meet the demand of multiple processing steps in biochemical analyses and detections. The experiment verification results will be presented in the near future.

In inertial confinement fusion (ICF), affected by non-steady ablation and various physical mechanisms, we extend the classical buoyancy-drag (BD) model into an ablative version for evaluating and controlling nonlinear ablative Rayleigh-Taylor instability (ARTI) in real space. The application of our ablative BD model in the nonlinear phase lies in a single adjustable coefficient influenced by initial perturbations, linear growth rate and terminal velocity. After validating the effectiveness and sensitivity of this model through simulations, we propose a strategy to shift the dominant mode away from the "most dangerous mode", which depends on initial perturbations. Our findings suggest that the "most dangerous mode" may clarify gain differences among targets of similar qualities and provide guidance for target manufacturing and pulse optimization in proximity to the ignition cliff.

Wall-modeled large-eddy simulation (WMLES) is widely recognized as a useful method for simulation of turbulent flows at high Reynolds numbers. Nevertheless, a continual issue in different wall models is the shift of the mean velocity profile from the wall-model/RANS (Reynolds-averaged Navier-Stokes) region to the LES region. This phenomenon, referred to as logarithmic layer mismatch (LLM), occurs in both wall shear stress models and hybrid RANS/LES models. Many efforts have been made to explain and resolve this mismatch, including decreasing the high correlation between the wall shear stress and the velocity at the matching layer, modifying the subgrid-scale (SGS) eddy viscosity, and adding a stochastic forcing. It is widely believed that the inclusion of the resolved Reynolds shear stress (or the convection term) is essential to elliminate the LLM, as it prevents the overseimation of the modeled Reynolds shear stress and promotes the generation of the small-scale flow structures in the near-wall region. In this work, by comparing three different SGS eddy viscosity models, we demonstrate that ensuring the total shear stress conservation (TSSC) conservation is key to resolving the LLM. Under the TSSC framework, the effect of the convection term on LLM can be quantitatively assessed. Furthermore, a modified SGS eddy viscosity modfication model that adheres to the TSSC constraint is tested at different Reynolds numbers ($Re_\tau=1000, 2000, 4200$). Our results demonstrate the robust performance of the present model in predicting skin friction and low-order turbulence statistics, even under a relatively low grid resolution ($\Delta x^+, \Delta z^+ \lesssim 500$, $2\leq \Delta_x/\Delta_{y,mat} \leq 4$, where $\Delta_{y,mat}$ is the wall-normal grid spacing in the wall-model region).

An analysis methodology is developed for the time-of-flight (TOF) signals recorded by two or more collinear neutron detectors located at different distances from a pulsed neutron source. It is based on taking central moments of the TOF signals and relating these to a set of co-moments of the distribution of production times and velocities of neutrons emitted towards the detectors. Given n detectors, we can obtain all such co-moments of order n-1 and lower. Co-moments contain information on the time-varying behaviour of the neutron source. A physical interpretation is provided for several co-moments of interest.

This article explores how a submerged elastic plate, clamped at one edge, interacts with water waves. Submerged elastic plates have been considered as potentially effective design elements in the development of wave energy harvesters but their behavior in a wave field remains largely unexplored, especially experimentally. Positioned at a fixed depth in a wave tank, the flexible plate demonstrates significant wave reflection capabilities, a characteristic absent in rigid plates of identical dimensions. The experiments thus reveal that plate motion is crucial for wave reflection. Sufficiently steep waves are shown to induce a change in the mean position of the plate, with the trailing edge reaching the free surface in some cases. This configuration change is found to be particularly efficient to break water waves. These findings contribute to understanding the potential of elastic plates for wave energy harvesting and wave attenuation scenarios.

Rapid identification of microparticles in liquid is an important problem in environmental and biomedical applications such as for microplastic detection in water sources and physiological fluids. Existing spectro-scopic techniques are usually slow and not compatible with flow-through systems. Here we analyze single microparticles in the 14 - 20 micrometer range using a combination of two electronic sensors in the same microfluidic system: a microwave capacitive sensor and a resistive pulse sensor. Together, this integrated sen-sor system yields the effective electrical permittivity of the analyte particles. To simplify data analysis, 3D electrode arrangements were used instead of planar electrodes, so that the generated signal is unaffected by the height of the particle in the microfluidic channel. With this platform, we were able to distinguish between polystyrene (PS) and polyethylene (PE) microparticles. We showcase the sensitivity and speed of this tech-nique and discuss the implications for the future application of microwave cytometry technology in the en-vironmental and biomedical fields.

Calculations of the two-loop electron self-energy for the $1S$ Lamb shift are reported, performed to all orders in the nuclear binding strength parameter $Z\alpha$ (where $Z$ is the nuclear charge number and $\alpha$ is the fine structure constant). Our approach allows calculations to be extended to nuclear charges lower than previously possible and improves the numerical accuracy by more than an order of magnitude. Extrapolation of our all-order results to hydrogen yields a result twice as precise as the previously accepted value [E. Tiesinga et al. Rev. Mod. Phys. 93, 025010 (2021)], differing from it by 2.8 standard deviations. The resulting shift in the theoretical prediction for the $1S$-$2S$ transition frequency in hydrogen decreases the value of the Rydberg constant by one standard deviation.

Enhancing light emission from perovskite nanocrystal (NC) films is essential in light-emitting devices, as their conventional stacks often restrict the escape of emitted light. This work addresses this challenge by employing a TiO$_2$ grating to enhance light extraction and shape the emission of CsPbBr$_3$ nanocrystal films. Angle-resolved photoluminescence (PL) demonstrated a tenfold increase in emission intensity by coupling the Bloch resonances of the grating with the spontaneous emission of the perovskite NCs. Fluorescence lifetime imaging microscopy (FLIM) provided micrometer-resolution mapping of both PL intensity and lifetime across a large area, revealing a decrease in PL lifetime from 8.2 ns for NC films on glass to 6.1 ns on the TiO$_2$ grating. Back focal plane (BFP) spectroscopy confirmed how the Bloch resonances transformed the unpolarized, spatially incoherent emission of NCs into polarized and directed light. These findings provide further insights into the interactions between dielectric nanostructures and perovskite NC films, offering possible pathways for designing better performing perovskite optoelectronic devices.

We present a polarizable embedding quantum mechanics/molecular mechanics (QM/MM) framework for ground- and excited-state Complete Active Space Self-Consistent Field (CASSCF) calculations on molecules within complex environments, such as biological systems. These environments are modeled using the AMOEBA polarizable force field. This approach is implemented by integrating the OpenMMPol library with the CFour quantum chemistry software suite. The implementation supports both single-point energy evaluations and geometry optimizations, facilitated by the availability of analytical gradients. We demonstrate the methodology by applying it to two distinct photoreceptors, exploring the impact of the protein environment on the structural and photophysical properties of their embedded chromophores.

Forecasting the geomagnetic effects of solar coronal mass ejections (CMEs) is currently an unsolved problem. CMEs, responsible for the largest values of the north-south component of the interplanetary magnetic field, are the key driver of intense and extreme geomagnetic activity. Observations of southward interplanetary magnetic fields are currently only accessible through in situ measurements by spacecraft in the solar wind. On 10-12 May 2024, the strongest geomagnetic storm since 2003 took place, caused by five interacting CMEs. We clarify the relationship between the CMEs, their solar source regions, and the resulting signatures at the Sun-Earth L1 point observed by the ACE spacecraft at 1.00 AU. The STEREO-A spacecraft was situated at 0.956 AU and 12.6{\deg} west of Earth during the event, serving as a fortuitous sub-L1 monitor providing interplanetary magnetic field measurements of the solar wind. We demonstrate an extension of the prediction lead time, as the shock was observed 2.57 hours earlier at STEREO-A than at L1, consistent with the measured shock speed at L1, 710 km/s, and the radial distance of 0.04 AU. By deriving the geomagnetic indices based on the STEREO-A beacon data, we show that the strength of the geomagnetic storm would have been decently forecasted, with the modeled minimum SYM-H=-478.5 nT, underestimating the observed minimum by only 8%. Our study sets an unprecedented benchmark for future mission design using upstream monitoring for space weather prediction.

The 6th International Symposium on Space Sailing (ISSS 2023) took place on June 5-9, 2023 at the New York City College of Technology, the City University of New York. Since its inauguration in Herrsching (Germany, 2007), the ISSS has been held in New York (USA, 2010), Glasgow (UK, 2013), Kyoto (Japan, 2017) and Aachen (Germany, 2019). During the five-day symposium, participants from 14 countries gathered to discuss recent advances in space sailing, investigating new concepts and designs, describing innovative hardware and enabling technologies, strategies for dynamics and control, and providing updates on testing results for systems under development and future mission applications. As part of the 18 sessions, almost 50 oral presentations were held and, subsequently, 17 papers were submitted for review and publication. This paper aims to give an overview of all the cutting-edge technologies, detailed analysis and promising results shared with the scientific community as part of the event. Following the noteworthy deployment of the world's first solar sail IKAROS in 2010, missions like NanoSail-D2 (2011) and LightSail-2 (2019) have showcased the potential of solar sailing technology through successful demonstrations. Besides highlighting advancements in present and future programs, the symposium was an opportunity to reflect on objectives, design and test results from research centers and universities, as well as illustrate applications for interstellar travel, evaluate degrading performance and suggest alternative solutions for known limitations. The following Symposium is scheduled for early summer 2025 and will be hosted by TU Delft.

In previous works, we analysed the internal shear layers excited by a viscous forcing (longitudinal libration) in a spherical shell geometry (He et al., J. Fluid Mech. 939, A3, 2022; 974, A3, 2023). We now consider the stronger inviscid forcing corresponding to the vertical oscillation of the inner core. We limit our analysis to two-dimensional geometries but examine three different configurations: freely-propagating wave beams in an unbounded domain and two wave patterns (a periodic orbit and an attractor) in a cylindrical shell geometry. The asymptotic structures of the internal shear layers are assumed to follow the similarity solution of Moore & Saffman (Phil. Trans. R. Soc. Lond. A, 264 (1156), 1969, 597-634) in the small viscous limit. The two undefined parameters of the similarity solution (singularity strength and amplitude) are derived by asymptotically matching the similarity solution with the inviscid solution. For each case, the derivation of the latter is achieved either through separation of variables combined with analytical continuation or the method of characteristics. Global inviscid solutions, when obtained, closely match numerical solutions for small Ekman numbers far from the critical lines, while viscous asymptotic solutions show excellent performance near those lines. The amplitude scalings of the internal shear layers excited by an inviscid forcing are found to be divergent as the Ekman number E decreases, specifically O(E-1/6) for the critical point singularity and O(E-1/3) for attractors, in contrast to the convergent scalings found for a viscous forcing.

How a cloud ensemble responds to external forcing is a puzzle in tropical convection research. Convectively coupled gravity waves (CCGWs) in a finite domain have controllable wavelengths, providing a convenient simulation setup for studying the cloud ensemble. A multiscale analysis shows that the growth of CCGWs in a finite-domain involves not only the amplitude growth of individual clouds but also the synchronization of convective lifecycles. To understand the synchronization mechanism, we build a microscopic model with many clouds. For each cloud, the microscopic model simulates the evolution of equivalent potential temperature $\theta_e$ in the boundary layer, which is reduced by convective transport and radiative cooling and increased by surface heating. At the shallow convection stage, the $\theta_e$ grows until reaching an upper threshold where the convective inhibition energy is eliminated, and the system transitions to the deep convection stage. At the deep convection stage, the $\theta_e$ drops until reaching a lower threshold where the convective available potential energy is exhausted, and the system transitions to the shallow convection stage. The wave influences $\theta_e$ with the boundary layer convergent flow and adjusts the phase of the convective lifecycle. Numerical simulations of the microscopic model show that when the period of convection and wave equals, the wave gradually synchronizes convection. Theoretical analysis shows that the microscopic synchronization appears as the macroscopic resonant growth of the cloud ensemble. In the resonant state, the averaged $\theta_e$ and vertical velocity in the boundary layer are in phase, agreeing with the cloud-permitting simulation.

In this contribution, we compute the $^1$H nuclear magnetic resonance (NMR) relaxation rate of liquid water at ambient conditions. We are using structural and dynamical information from Coupled Cluster Molecular Dynamics (CCMD) trajectories generated at CCSD(T) electronic structure accuracy while considering also nuclear quantum effects in addition to consulting information from X-ray and neutron scattering experiments. Our analysis is based on a recently presented computational framework for determining the frequency-dependent NMR dipole-dipole relaxation rate of spin $1/2$ nuclei from Molecular Dynamics (MD) simulations, which allows for an effective disentanglement of its structural and dynamical contributions, and is including a correction for finite-size effects inherent to MD simulations with periodic boundary conditions. A close to perfect agreement with experimental relaxation data is achieved if structural and dynamical informations from CCMD trajectories are considered including a re-balancing of the rotational and translational dynamics, according to the product of the self-diffusion coefficient and the reorientational correlation time of the H-H vector $D_0\times\tau_\mathrm{HH}$. The simulations show that this balance is significantly altered when nuclear quantum effects are taken into account. Our analysis suggests that the intermolecular and intramolecular contribution to the $^1$H NMR relaxation rate of liquid water are almost similar in magnitude, unlike to what was predicted earlier from classical MD simulations.

Wolfram's hypergraph dynamics should replace outmoded models in physics. This should even more so be the case if experimental evidence for the theory is found (which I believe is probable). However, due to the breadth and depth of the theory, it may be difficult to produce experimental evidence which falsifies it. Some of Wolfram's personal work relating to his physics project is philosophical, and so mechanics of particular phenomena in the natural world can become a triviality or an aside. In other words, the general theory "casts a wide net", and it is the philosophical topics I will challenge. I find that Wolfram must adopt a radical epistemology through his so-called Observer Theory because there is no clear notion of Truth. Tegmark believes in an objective Truth, but I find its relation to the observer untenable, and the proof of his Mathematical Universe Hypothesis (MUH) is gematria. I argue both Wolfram and Tegmark conflate the inherent potential of mathematical truths with their instantiation or actuality in reality, making a similar error to the "so-called" Pythagoreans rebuked by Aristotle. Nonetheless, I believe that combinatorial structures of the kind used in the physics project (abstract rewriting, directed acyclic graphs) will be the future of physics as we know it.

We present a single-fluid approach for the simulation of partially-ionized plasmas (PIPs) which is designed to capture the non-ideal effects introduced by neutrals while remaining close in computational efficiency to single-fluid MHD. This is achieved using a model which treats the entire partially-ionized plasma as a single mixture, which renders internal ionization/recombination source terms unnecessary as both the charged and neutral species are part of the mixture's conservative system. Instead, the effects of ionization and the differing physics of the species are encapsulated as material properties of the mixture. Furthermore, the differing dynamics between the charged and neutral species is captured using a relative-velocity quantity, which impacts the bulk behavior of the mixture in a manner similar to the treatment of the ion-electron relative-velocity as current in MHD. Unlike fully-ionized plasmas, the species composition of a PIP changes rapidly with its thermodynamic state. This is captured through a look-up table referred to as the tabulated equation of state (TabEoS), which is constructed prior to runtime using empirical physicochemical databases and efficiently provides the ionization fraction and other material properties of the PIP specific to the thermodynamic state of each computational cell. Crucially, the use of TabEoS also allows our approach to self-consistently capture the non-linear feedback cycle between the PIP's macroscopic behavior and the microscopic physics of its internal particles, which is neglected in many fluid simulations of plasmas today.

The interaction of light with photosynthetic proteins is an extremely efficient process and has been thoroughly investigated. However, exploring light-matter interactions in hybrid nano-solid-photosynthetic proteins is a relatively new and existing field of research. The properties of these hybrid materials significantly influence the energy levels, non-radiative energy transfer, absorption, and fluorescence of the photosynthetic proteins upon interaction with light. There is special interest in levering these light-matter interactions for applications such as photo-sensing and converting light energy to electricity. The development of efficient devices requires the formation of a junction for oriented attachment, facilitating efficient energy and electronic transfer between the solids and the proteins. This review will outline the major advancements in solid-state photosynthetic protein devices, elucidate the underlying mechanism, and assess electron transfer efficiency. Furthermore, it will explore and analyze the effect of plasmons on the enhancement of absorption, fluorescence, and photocurrent in hybrid devices.

The effect of columnar grain boundaries on the fracture toughness was investigated using micro-cantilever fracture testing with a bridge notch, and a unique hard coating consisting of two distinct microstructures: one with columnar grains and another with an epitaxial layer. The bridge-failure sequence qualitatively demonstrated the lower fracture toughness at the columnar-grained structure. Quantitatively, the load drops measured at bridge-failure also revealed a significant decrease in fracture toughness due to grain boundaries. Specifically, the fracture toughness decreased by around 30%, from 4.1 +/- 0.4 MPa m1/2 for epitaxial microstructure to 3.0 +/- 0.3 MPa m1/2 for columnar-grained structure. The fracture toughness of columnar-grained structure is 3.0 +/- 0.2 MPa m1/2 perpendicular to the growth direction higher than 2.7 +/- 0.1 MPa m1/2 along it. These findings suggest that future optimization of hard coatings should focus on grain boundary toughening, and the present toolbox proposes suitable techniques for such microstructure optimization.

This paper presents a generalized flux-corrected transport (FCT) algorithm, which is shown to be total variation diminishing under some conditions. The new algorithm has improved properties from the standpoint of use and analysis. Results show that the new FCT algorithm performs better than the older FCT algorithms and is comparable with other modern methods. This reformulation will also allow the FCT to be used effectively with exact or approximate Riemann solvers and as an implicit algorithm. This paper was originally submitted to the Journal of Computational Physics in 1990. It got lost in review. One reviewer loved the paper and suggested it be published immediately (he also died while it was in review). Another reviewer savaged the paper being from the FCT camp. The journal also went through several changes in management. Ultimately I declined to continue pursuing the paper as I had one infant child at the time and another on the way in 1995. Now 30 years on I am going to put this online.

The coalescence of liquid drops is a fundamental process that remains incompletely understood, particularly in the intermediate regimes where capillary, viscous, and inertial forces are comparable. Here, we experimentally investigate the dynamics of drop-to-drop coalescence during the transition between viscous and inertial regimes using high-speed imaging. Our results reveal that the liquid bridge between droplets shows power-law growth with exponents between 1/2 and 1 during drop coalescence. We propose a novel scaling approach using a dimensionless crossover function that smoothly transitions between viscous and inertial limits. This simple approach, inspired by previous work on drop impact, successfully collapses the experimental data for a wide range of liquid viscosities and coalescence times onto a single master curve. We further compare our results with recent theoretical models and demonstrate how our approach complements and extends current understanding in the crossover of drop coalescence. This study contributes to both the fundamental physics of drop coalescence and its practical applications in various industrial processes.

Bound states in the continuum (BICs) exhibit significant electric field confinement capabilities and have recently been employed to enhance nonlinear optics response at the nanoscale. In this study, we achieve substantial enhancement of third-harmonic generation (THG) and degenerate four-wave mixing (dFWM) by implementing Brillouin zone folding-induced BICs (BZF-BICs) in an air-hole type nonlinear metasurface. By introducing gap perturbations within the metasurface, guided modes below the light line can be folded into the light cone, resulting in three resonant modes: guided resonances (GRs), $\Gamma$-BICs, and BZF-BICs. Through the eigenvalue analysis and multipole decompositions, we establish their excitation conditions. With their resonantly enhanced local field, we successfully boost both THG and dFWM under $x$- and $y$- polarizations within the same metasurfaces. The simulated results indicate that the BZF-BICs provide the most significant enhancement of third-order nonlinear optical responses, with the output power of THG to 10$^{-4}$ W and dFWM output power of 10$^{-2}$ W under a moderate input power density of 1 MW/cm$^{2}$. These findings demonstrate that the BZF-BICs can offer an effective pathway for chip-scale nonlinear optical applications.

Recently, deep-learning weather forecasting models have surpassed traditional numerical models in terms of the accuracy of meteorological variables. However, there is considerable potential for improvements in precipitation forecasts, especially for heavy precipitation events. To address this deficiency, we propose Leadsee-Precip, a global deep learning model to generate precipitation from meteorological circulation fields. The model utilizes an information balance scheme to tackle the challenges of predicting heavy precipitation caused by the long-tail distribution of precipitation data. Additionally, more accurate satellite and radar-based precipitation retrievals are used as training targets. Compared to artificial intelligence global weather models, the heavy precipitation from Leadsee-Precip is more consistent with observations and shows competitive performance against global numerical weather prediction models. Leadsee-Precip can be integrated with any global circulation model to generate precipitation forecasts. But the deviations between the predicted and the ground-truth circulation fields may lead to a weakened precipitation forecast, which could potentially be mitigated by further fine-tuning based on the predicted circulation fields.

This paper introduces the discrete-velocity-direction model (DVDM) in conjunction with the hyperbolic quadrature method of moments (HyQMOM) to develop a multidimensional spatial-temporal approximation of the BGK equation, termed DVD-HyQMOM. Serving as a multidimensional extension of HyQMOM, DVD-HyQMOM model achieves higher accuracy than other DVDM submodels, especially with an increased number of abscissas. The efficiency and effectiveness of this model are demonstrated through various numerical tests.

Recent advances in optical technology have significantly enhanced the resolution of imaging of living cells, achieving nanometer-scale precision. However, the crowded three-dimensional environment within cells presents a challenge for measuring the spatio-temporal dynamics of cellular components. One solution to this issue is expansion microscopy, which cannot be used for living cells. Here, we present a method for flattening live cells to a thickness of down to 200 nanometers by confining them between two surface-treated transparent plates. The anti-fouling coating on the surfaces restricts the cells to a quasi-two-dimensional space by exerting osmotic control and preventing surface adhesion. This technique increases the distance between cellular components, thereby enabling high-resolution imaging of their spatio-temporal dynamics. The viability and phenotype of various cell types are demonstrated to be unaltered upon release from flat-cell confinement. The flat cell imaging method is a robust and straightforward technique, making it a practical choice for optical microscopy.

Absorption cross-sections for the 5th (6 $\leftarrow$ 0) and 6th (7 $\leftarrow$ 0) OH overtones for gas-phase methanol, ethanol, and isopropanol were measured using a slow flow cell and Incoherent Broadband Cavity-Enhanced Absorption Spectroscopy (IBBCEAS). Measurements were performed in two wavelength regions, 447-457 nm, and 508-518 nm, using two different instruments. The experimental results are consistent with previous computational predictions of the excitation energies for these transitions. Treating the OH stretch as a local mode allowed for calculation of the fundamental vibrational frequency ($\omega_e$), anharmonicity constant ($\omega_e x_e$), and the vertical dissociation energy (VDE) for each alcohol studied. The fundamental vibrational frequency is $3848 \pm 18 \, \text{cm}^{-1}$, $3807 \pm 55 \,\text{cm}^{-1}$, and $3813 \pm 63 \, \text{cm}^{-1}$ for methanol, ethanol, and isopropanol, respectively. The anharmonicity constant was measured to be $84.8 \pm 2.1 \, \text{cm}^{-1}$, $80.2 \pm 5.9 \, \text{cm}^{-1}$, and $84.4 \pm 6.8 \, \text{cm}^{-1}$ for methanol, ethanol, and isopropanol, respectively. The OH vertical dissociation energy was measured to be $499.4 \pm 18.4$ kJ/mol, $518.0 \pm 56.7$ kJ/mol, and $492.7 \pm 59.9$ kJ/mol. The spectroscopically measured values are compared to thermodynamically measured OH bond dissociation energies. The observed differences in previous measurements of the bond dissociation energies compared to the values reported herein can be explained due to the difference between vertical dissociation energies and bond dissociation energies. If the OH overtone stretching mode is excited in methanol to either the 5th or 6th overtone, the bimolecular reaction between methanol and O$_2$ becomes thermodynamically feasible and could contribute to formation of methoxy and HO$_2$ radicals under the proper combination of pressure and temperature.

This paper investigates the energy fluxes for the 6D kinetic Vlasov system. We introduce a novel method for calculating particle and energy flows within this framework which allows for the determination of energy and particle fluxes, as well as the Poynting flux, directly from the system's moments such as kinetic energy density, momentum transfer tensor. The fluxes computed using the new method exhibit fewer gyrooscillations. This approach also enables the identification of both the gyrokinetic $\vec{E} \times \vec{B}$ heat flux and additional non-gyrokinetic contributions, while simultaneously reducing inherent gyrooscillations in the energy and particle fluxes. Our semi-Lagrangian solver for the 6D kinetic Vlasov system, features a highly efficient scheme to address the $\vec v \times \vec B$ acceleration from the strong background magnetic field allows for the simulation of plasma waves and turbulence with frequencies extending beyond the cyclotron frequency, independent of gradient strength or fluctuation levels. The solver has been rigorously tested in the low-frequency regime for dispersion relations and energy fluxes in both linear and nonlinear scenarios.

The invention of X-ray interferometers has led to advanced phase-sensing devices that are invaluable in various applications. These include the precise measurement of universal constants, e.g. the Avogadro number, of lattice parameters of perfect crystals, and phase-contrast imaging, which resolves details that standard absorption imaging cannot capture. However, the sensitivity and robustness of conventional X-ray interferometers are constrained by factors, such as fabrication precision, beam quality, and, importantly, noise originating from external sources or the sample itself. In this work, we demonstrate a novel X-ray interferometric method of phase measurement with enhanced immunity to various types of noise, by extending, for the first time, the concept of the SU(1,1) interferometer into the X-ray regime. We use a monolithic silicon perfect crystal device with two thin lamellae to generate correlated photon pairs via spontaneous parametric down-conversion (SPDC). Arrival time coincidence and sum-energy filtration allow a high-precision separation of the correlated photon pairs, which carry the phase information from orders-of-magnitude larger uncorrelated photonic noise. The novel SPDC-based interferometric method presented here is anticipated to exhibit enhanced immunity to vibrations as well as to mechanical and photonic noise, compared to conventional X-ray interferometers. Therefore, this SU(1,1) X-ray interferometer should pave the way to unprecedented precision in phase measurements, with transformative implications for a wide range of applications.

We present metastable qubits in trapped ions as potential erasure qubits for which most fundamental algorithm errors can be converted into erasures. We first implement an erasure conversion scheme which enables us to detect $\sim$94% of spontaneous Raman scattering errors and nearly all errors from qubit decay. Second, we perform a two-ion geometric phase gate with a SPAM-corrected fidelity of 98.56% using far-detuned (-43 THz) Raman beams. Subtracting runs where erasures were detected, this fidelity becomes 99.14%. We present a pathway for improved gate efficiency and reduced overhead from erasure conversion.

Many living and physical systems such as cell aggregates, tissues or bacterial colonies behave as unconventional systems of particles that are strongly constrained by volume exclusion and shape interactions. Understanding how these constraints lead to macroscopic self-organized structures is a fundamental question in e.g. developmental biology. To this end, various types of computational models have been developed. Here, we introduce a new framework based on optimal transport theory to model particle systems with arbitrary dynamical shapes and deformability properties. Our method builds upon the pioneering work of Brenier on incompressible fluids and its recent applications to materials science. It lets us specify the shapes and volumes of individual cells and supports a wide range of interaction mechanisms, while automatically taking care of the volume exclusion constraint at an affordable numerical cost. We showcase the versatility of this approach by reproducing several classical systems in computational biology. Our Python code is freely available at \url{https://iceshot.readthedocs.io/}.

Recent observations from the Juno spacecraft during its transit over flux tubes of the Galilean moons have identified sharp enhancements of particle fluxes at discrete energies. These banded structures have been suspected to originate from a bounce resonance between particles and standing Alfven waves generated by the moon-magnetospheric interaction. Here, we show that predictions from the above hypothesis are inconsistent with the observations, and propose an alternative interpretation that the banded structures are remote signals of particle absorption at the moons. In this scenario, whether a particle would encounter the moon before reaching Juno depends on the number of bounce cycles it experiences within a fixed section of drift motion determined by moon-spacecraft longitudinal separation. Therefore, the absorption bands are expected to appear at discrete, equally-spaced velocities consistent with the observations. This finding improves our understanding of moon-plasma interactions and provides a potential way to evaluate the Jovian magnetospheric models.

The radiation chemistry and physics of solid N2O have been increasingly studied due to its potential presence on the surfaces of cold, outer Solar System bodies. However, to date, no study has investigated systematically the influence of temperature on this chemistry and physics. In this present study, crystalline N2O ices were irradiated using 2 keV electrons at five different temperatures in the 20-60 K range and the radiolytic dissociation of the molecular solid (as well as the radiolytic formation of seven product molecules) was quantified through the G-value. Our results indicate that temperature does indeed play a role in the radiolytic destruction of crystalline N2O, with higher temperatures being associated with higher destruction G-values. The formation G-values of NO, NO2, N2O2, N2O3, N2O4, N2O5, and O3 were also noted to vary with temperature, with each product molecule exhibiting a distinct trend. The applications of our experimental results to further understanding solid-phase radiation chemistry in the outer Solar System are discussed.

We introduce a graph renormalization procedure based on the coarse-grained Laplacian, which generates reduced-complexity representations for characteristic scales identified through the spectral gap. This method retains both diffusion probabilities and large-scale topological structures, while reducing redundant information, facilitating the analysis of large graphs by decreasing the number of vertices. Applied to graphs derived from EEG recordings of human brain activity, our approach reveals macroscopic properties emerging from neuronal interactions, such as collective behavior in the form of coordinated neuronal activity. Additionally, it shows dynamic reorganization of brain activity across scales, with more generalized patterns during rest and more specialized and scale-invariant activity in the occipital lobe during attention-focused tasks.

This study evaluates metrics for tasks such as classification, regression, clustering, correlation analysis, statistical tests, segmentation, and image-to-image (I2I) translation. Metrics were compared across Python libraries, R packages, and Matlab functions to assess their consistency and highlight discrepancies. The findings underscore the need for a unified roadmap to standardize metrics, ensuring reliable and reproducible ML evaluations across platforms. This study examined a wide range of evaluation metrics across various tasks and found only some to be consistent across platforms, such as (i) Accuracy, Balanced Accuracy, Cohens Kappa, F-beta Score, MCC, Geometric Mean, AUC, and Log Loss in binary classification; (ii) Accuracy, Cohens Kappa, and F-beta Score in multi-class classification; (iii) MAE, MSE, RMSE, MAPE, Explained Variance, Median AE, MSLE, and Huber in regression; (iv) Davies-Bouldin Index and Calinski-Harabasz Index in clustering; (v) Pearson, Spearman, Kendall's Tau, Mutual Information, Distance Correlation, Percbend, Shepherd, and Partial Correlation in correlation analysis; (vi) Paired t-test, Chi-Square Test, ANOVA, Kruskal-Wallis Test, Shapiro-Wilk Test, Welchs t-test, and Bartlett's test in statistical tests; (vii) Accuracy, Precision, and Recall in 2D segmentation; (viii) Accuracy in 3D segmentation; (ix) MAE, MSE, RMSE, and R-Squared in 2D-I2I translation; and (x) MAE, MSE, and RMSE in 3D-I2I translation. Given observation of discrepancies in a number of metrics (e.g. precision, recall and F1 score in binary classification, WCSS in clustering, multiple statistical tests, and IoU in segmentation, amongst multiple metrics), this study concludes that ML evaluation metrics require standardization and recommends that future research use consistent metrics for different tasks to effectively compare ML techniques and solutions.

The polarization of light is critical in various applications, including quantum communication, where the photon polarization encoding a qubit can undergo uncontrolled changes when transmitted through optical fibers. Bends in the fiber, internal and external stresses, and environmental factors cause these polarization changes, which lead to errors and therein limit the range of quantum communication. To prevent this, we present a fast and automated method for polarization compensation using liquid crystals. This approach combines polarimetry based on a rotating quarter-waveplate with high-speed control of the liquid-crystal cell, offering high-fidelity compensation suitable for diverse applications. Our method directly solves for compensation parameters, avoiding reliance on stochastic approaches or cryptographic metrics. Experimental results demonstrate that our method achieves over 99% fidelity within an average of fewer than six iterations, with further fine-tuning to reach above 99.5% fidelity, providing a robust solution for maintaining precise polarization states in optical systems.

Quantum computing offers promising new avenues for tackling the long-standing challenge of simulating the quantum dynamics of complex chemical systems, particularly open quantum systems coupled to external baths. However, simulating such non-unitary dynamics on quantum computers is challenging since quantum circuits are specifically designed to carry out unitary transformations. Furthermore, chemical systems are often strongly coupled to the surrounding environment, rendering the dynamics non-Markovian and beyond the scope of Markovian quantum master equations like Lindblad or Redfield. In this work, we introduce a quantum algorithm designed to simulate non-Markovian dynamics of open quantum systems. Our approach enables the implementation of arbitrary quantum master equations on noisy intermediate-scale quantum (NISQ) computers. We illustrate the method as applied in conjunction with the numerically exact hierarchical equations of motion (HEOM) method. The effectiveness of the resulting quantum HEOM algorithm (qHEOM) is demonstrated as applied to simulations of the non-Lindbladian electronic energy and charge transfer dynamics in models of the carotenoid-porphyrin-C60 molecular triad dissolved in tetrahydrofuran and the Fenna-Matthews-Olson complex.

An extreme yet reconfigurable nonlinear response to a single photon by a photonic system is crucial for realizing a universal two-photon gate, an elementary building block for photonic quantum computing. Yet such a response, characterized by the photon blockade effect, has only been achieved in atomic systems or solid states ones that are difficult to scale up. Here we demonstrate electrically tunable partial photon blockade in dipolar waveguide polaritons on a semiconductor chip, measured via photon-correlations. Remarkably, these "dipolar photons" display a two-orders-of-magnitude stronger nonlinearity compared to unpolarized polaritons, with an extracted dipolar blockade radius up to more than 4 $\mu$m, significantly larger than the optical wavelength, and comparable to that of atomic Rydberg polaritons. Furthermore, we show that the dipolar interaction can be electrically switched and locally configured by simply tuning the gate voltage. Finally we show that with a simple modification of the design, a full photon blockade is expected, setting a new route towards scalable, reconfigurable, chip-integrated quantum photonic circuits with strong two-photon nonlinearities.

Dynamic spectroscopies in Scanning Probe Microscopy (SPM) are critical for probing material properties, such as force interactions, mechanical properties, polarization switching, and electrochemical reactions and ionic dynamics. However, the practical implementation of these measurements is constrained by the need to balance imaging time and data quality. Signal to noise requirements favor long acquisition times and high frequencies to improve signal fidelity. However, these are limited on the low end by contact resonant frequency and photodiode sensitivity, and on the high end by the time needed to acquire high-resolution spectra, or the propensity for samples degradation under high field excitation over long times. The interdependence of key parameters such as instrument settings, acquisition times, and sampling rates makes manual tuning labor-intensive and highly dependent on user expertise, often yielding operator-dependent results. These limitations are prominent in techniques like Dual Amplitude Resonance Tracking (DART) in Piezoresponse Force Microscopy (PFM) that utilize multiple concurrent feedback loops for topography and resonance frequency tracking. Here, a reward-driven workflow is proposed that automates the tuning process, adapting experimental conditions in real time to optimize data quality. This approach significantly reduces the complexity and time required for manual adjustments and can be extended to other SPM spectroscopic methods, enhancing overall efficiency and reproducibility.

We derive explicit expressions for the expected adoption and infection level in the Bass and SI models, respectively, on sparse Erd\H{o}s-R\'enyi networks and on $d$-regular networks. These expressions are soloutions of first-order ordinary differential equations, which are fairly easy to analyze. To prove that these expressions are exact, we show that the effect of cycles vanishes as the network size goes to infinity.

We study the normal and lateral components of the Casimir-Lifshitz (CL) force between a nanoparticle and 1D graphene grating deposited on a fused silica slab. For this purpose, the scattering matrix approach together with the Fourier modal method augmented with local basis functions are used. We find that, by covering a fused silica slab by a graphene grating, the spectrum of the normal CL force at small frequencies is increased by about 100% for a grating filling fraction of 0.5, and even more when the slab is completely covered. The typically employed additive approximation (the weighted average of the force with and without the graphene coating) cannot provide any information on the lateral CL force, and, as we show, cannot provide accurate estimation for the normal CL force. When the nanoparticle is laterally shifted ($x_A$), the normal CL force is modulated and remains attractive. On the contrary, the lateral CL force changes sign twice in each period, showing a series of alternating stable and unstable lateral equilibrium positions, occurring in the graphene strips and of the grating slits regions, respectively. Finally, we show that the lateral shift effect is sensitive to the geometric factor $d/D$ ($d$ is the separation distance, and $D$ is the grating period). We identify two clear regions: a region ($d/D<1.0$) where the lateral shift significantly affects the CL energy, and a region ($d/D \geq 1.0$) where this effect is negligible, with a crossover at $d\approx D$. Our predictions can have relevant implications to experiments and applications of the CL normal and lateral forces acting on nanoparticles interacting with structured objects at the nano/micro scale, and are also directly valid for atoms close to these nanostructures.

Efficient and accurate prediction of material properties is critical for advancing materials design and applications. The rapid-evolution of large language models (LLMs) presents a new opportunity for material property predictions, complementing experimental measurements and multi-scale computational methods. We focus on predicting the elastic constant tensor, as a case study, and develop domain-specific LLMs for predicting elastic constants and for materials discovery. The proposed ElaTBot LLM enables simultaneous prediction of elastic constant tensors, bulk modulus at finite temperatures, and the generation of new materials with targeted properties. Moreover, the capabilities of ElaTBot are further enhanced by integrating with general LLMs (GPT-4o) and Retrieval-Augmented Generation (RAG) for prediction. A specialized variant, ElaTBot-DFT, designed for 0 K elastic constant tensor prediction, reduces the prediction errors by 33.1% compared with domain-specific, material science LLMs (Darwin) trained on the same dataset. This natural language-based approach lowers the barriers to computational materials science and highlights the broader potential of LLMs for material property predictions and inverse design.

This work aims to provide a computational model that can describe the complex behaviour of refractory industrial components under working conditions. Special attention is given to the asymmetric tension-compression behaviour and its evolution in the full range of working temperatures. The model accounts for inelastic flow in compression and brittle fracture behaviour in tension by leveraging the continuum-mechanics theory of plasticity and phase-field fracture damage. The model is implemented in the Finite Element open-source platform FEniCS and is used to analyze the fracture phenomenon in the refractory plate used in ladle slide gate systems to control the liquid steel flow from the ladle to the tundish.

We elaborate and validate a generalization of the renowned transition-path-sampling algorithm for a paradigmatic model of active particles, namely the Run-and-Tumble particles. Notwithstanding the non-equilibrium character of these particles, we show how the consequent lack of the microscopical reversibility property, which is usually required by transition-path sampling, can be circumvented by identifying reasonable backward dynamics with a well-defined path-probability density. Our method is then applied to characterize the structure and kinetics of rare transition pathways undergone by Run-and-Tumble particles having to cross a potential barrier in order to find a target.

Photonic quantum information processing in metropolitan quantum networks lays the foundation for cloud quantum computing [1, 2], secure communication [3, 4], and the realization of a global quantum internet [5, 6]. This paradigm shift requires on-demand and high-rate generation of flying qubits and their quantum state teleportation over long distances [7]. Despite the last decade has witnessed an impressive progress in the performances of deterministic photon sources [8-11], the exploitation of distinct quantum emitters to implement all-photonic quantum teleportation among distant parties has remained elusive. Here, we overcome this challenge by using dissimilar quantum dots whose electronic and optical properties are engineered by light-matter interaction [12], multi-axial strain [13] and magnetic fields [14] so as to make them suitable for the teleportation of polarization qubits. This is demonstrated in a hybrid quantum network harnessing both fiber connections and 270 m free-space optical link connecting two buildings of the University campus in the center of Rome. The protocol exploits GPS-assisted synchronization, ultra-fast single photon detectors as well as stabilization systems that compensate for atmospheric turbulence. The achieved teleportation state fidelity reaches up to 82+-1%, above the classical limit by more than 10 standard deviations. Our field demonstration of all-photonic quantum teleportation opens a new route to implement solid-state based quantum relays and builds the foundation for practical quantum networks.

Integrated photonic circuits play a crucial role in implementing quantum information processing in the noisy intermediate-scale quantum (NISQ) era. Variational learning is a promising avenue that leverages classical optimization techniques to enhance quantum advantages on NISQ devices. However, most variational algorithms are circuit-model-based and encounter challenges when implemented on integrated photonic circuits, because they involve explicit decomposition of large quantum circuits into sequences of basic entangled gates, leading to an exponential decay of success probability due to the non-deterministic nature of photonic entangling gates. Here, we present a variational learning approach for designing quantum photonic circuits, which directly incorporates post-selection and elementary photonic elements into the training process. The complicated circuit is treated as a single nonlinear logical operator, and a unified design is discovered for it through variational learning. Engineering an integrated photonic chip with automated control, we adjust and optimize the internal parameters of the chip in real time for task-specific cost functions. We utilize a simple case of designing photonic circuits for a single ancilla CNOT gate with improved success rate to illustrate how our proposed approach works, and then apply the approach in the first demonstration of quantum stochastic simulation using integrated photonics.

We investigate a one-dimensional tight-binding lattice with asymmetrical couplings and various type of nonlinearities to study nonlinear non-Hermitian skin effect. Our focus is on the exploration of nonlinear skin modes through a fixed-point perspective. Nonlinearities are shown to have no impact on the spectral region in the semi-infinite system; however, they induce considerable changes when boundaries are present. The spectrum under open boundary conditions is found not to be a subset of the corresponding spectrum under the semi-infinite boundary conditions. We identify distinctive features of nonlinear skin modes, such as power-energy dependence, degeneracy, and power-energy discontinuity. Furthermore, we demonstrate that a family of localized modes that are neither skin nor scale-free localized modes is formed with the introduction of a coupling impurity. Additionally, we show that an impurity can induce discrete dark and anti-dark solitons.

We present a high-precision solution of Dirac equation by numerically solving the minmax two-center Dirac equation with the finite element method (FEM). The minmax FEM provide a highly accurate benchmark result for systems with light or heavy atomic nuclear charge $Z$. A result is shown for the molecular ion ${\rm H}_2^+$ and the heavy quasi-molecular ion ${\rm Th}_2^{179+}$, with estimated fractional uncertainties of $\sim 10^{-23}$ and $\sim 10^{-21}$, respectively. The result of the minmax-FEM high-precision of the solution of the two-center Dirac equation, allows solid control over the required accuracy level and is promising for the application and extension of our method.

While traditional game models often simplify interactions among agents as static, real-world social relationships are inherently dynamic, influenced by both immediate payoffs and alternative information. Motivated by this fact, we introduce a coevolutionary multiplex network model that incorporates the concepts of a relationship threshold and a recommendation mechanism to explore how the strength of relationships among agents interacts with their strategy choices within the framework of weak prisoner's dilemma games. In the relationship layer, the relationship strength between agents varies based on interaction outcomes. In return, the strategy choice of agents in the game layer is influenced by both payoffs and relationship indices, and agents can interact with distant agents through a recommendation mechanism. Simulation of various network topologies reveals that a higher average degree supports cooperation, although increased randomness in interactions may inhibit its formation. Interestingly, a higher threshold value of interaction quality is detrimental, while the applied recommendation protocol can improve global cooperation. The best results are obtained when the relative weight of payoff is minimal and the individual fitness is dominated by the relationship indices gained from the quality of links to neighbors. As a consequence, the changes in the distribution of relationship indices are closely correlated with overall levels of cooperation.

In the astrophysics community it is common practice to model collisionless dust, entrained in a gas flow, as a pressureless fluid. However a pressureless fluid is fundamentally different from a collisionless fluid - the latter of which generically possess a non-zero anisotropic pressure or stress tensor. In this paper we derive a fluid model for collisionless dust, entrained in a turbulent gas, starting from the equations describing the motion of individual dust grains. We adopt a covariant formulation of our model to allow for the geometry and coordinate systems prevalent in astrophysics, and provide a closure valid for the accretion disc context. We show that the continuum mechanics properties of a dust fluid corresponds to a higher-dimensional anisotropic Maxwell fluid, after the extra dimensions are averaged out, with a dynamically important rheological stress tensor. This higher-dimensional treatment has the advantage of keeping the dust velocity and velocity of the fluid seen, and their respective moments, on the same footing. This results in a simplification of the constitutive relation describing the evolution of the dust Rheological stress.

Artificial soft matter systems have appeared as important tools to harness mechanical motion for microscale manipulation. Typically, this motion is driven either by the external fields or by mutual interaction between the colloids. In the latter scenario, dynamics arise from non-reciprocal interaction among colloids within a chemical environment. In contrast, we eliminate the need for a chemical environment by utilizing a large area of optical illumination to generate thermal fields. The resulting optothermal interactions introduce non-reciprocity to the system, enabling active motion of the colloidal structure. Our approach involves two types of colloids: passive and thermally active. The thermally active colloids contain absorbing elements that capture energy from the incident optical beam, creating localized thermal fields around them. In a suspension of these colloids, the thermal gradients generated drive nearby particles through attractive thermo-osmotic forces. We investigate the resulting dynamics, which lead to various swimming modes, including active propulsion and chiral motion. We have also experimentally validated certain simulated results. By exploring the interplay between optical forces, thermal effects, and particle interactions, we aim to gain insights into controlling colloidal behavior in non-equilibrium systems. This research has significant implications for directed self-assembly, microfluidic manipulation, and the study of active matter.

It is well-established that the home advantage (HA), the phenomenon that on average the local team performs better than the visiting team, exists in many sports. In response to the COVID-19 outbreak, spectators were banned from football stadiums, which we leverage as a natural experiment to examine the impact of stadium spectators on HA. Using data from the first division of the German Bundesliga for seasons 2016/17 to 2023/24, we are the first to focus on a longer time horizon and consider not only the first but all three seasons subject to spectator regulations as well as two subsequent seasons without. We confirm previous studies regarding the disappearance of the HA in the last nine matches of season 2019/20. This drop materialised almost entirely through a reduction of home goals. The HA in season 2020/21 (with spectator ban during most matches) was very close to the pre-COVID-19 season 2018/19, indicating that teams became accustomed to the absence of spectators. For season 2021/22, with varying spectator regulations, we detect a U-shaped relationship between HA and the stadium utilisation rate, where HA increases considerably for matches with medium stadium utilisation which is associated with a larger difference in running distance between the home and away teams.

Transitions between solid-like and fluid-like states in living tissues have been found in steps of embryonic development and in stages of disease progression. Our current understanding of these transitions has been guided by experimental and theoretical investigations focused on how motion becomes arrested with increased mechanical coupling between cells, typically as a function of packing density or cell cohesiveness. However, cells actively respond to externally applied forces by contracting after a time delay, so it is possible that at some packing densities or levels of cell cohesiveness, mechanical coupling stimulates cell motion instead of suppressing it. Here we report our findings that at low densities and within multiple ranges of cell cohesiveness, cell migration speeds increase with these measures of mechanical coupling. Our observations run counter to our intuition that cell motion will be suppressed by increasingly packing or sticking cells together and may provide new insight into biological processes involving motion in dense cell populations.

Entanglement is a fundamental pillar of quantum mechanics. Probing quantum entanglement and testing Bell inequality with muons can be a significant leap forward, as muon is arguably the only massive elementary particle that can be manipulated and detected over a wide range of energies, e.g., from approximately 0.3 to $10^2$ GeV, corresponding to velocities from 0.94 to nearly the speed of light. In this work, we present a realistic proposal and a comprehensive study of quantum entanglement in a state composed of different-flavor fermions in muon-electron scattering. The polarization density matrix for the muon-electron system is derived using a kinematic approach within the relativistic quantum field theory framework. Entanglement in the resulting muon-electron qubit system and the violation of Bell inequalities can be observed with a high event rate. This paves the way for performing quantum tomography with muons.

In this work, we present a novel method called the complex frequency fingerprint (CFF) to detect the complex frequency Green's function, $G(\omega\in\mathbb{C})$, in a driven-dissipative system. By utilizing the CFF, we can measure the complex frequency density of states (DOS) and local DOS (LDOS), providing unique insights into the characterization of non-Hermitian systems. By applying our method to systems exhibiting the non-Hermitian skin effect (NHSE), we demonstrate how to use our theory to detect both the non-Hermitian eigenvalues and eigenstates. This offers a distinctive and reliable approach to identifying the presence or absence of NHSE in experimental settings.

Implementing arbitrary unitary transformations is crucial for applications in quantum computing, signal processing, and machine learning. Unitaries govern quantum state evolution, enabling reversible transformations critical in quantum tasks like cryptography and simulation and playing key roles in classical domains such as dimensionality reduction and signal compression. Integrated optical waveguide arrays have emerged as a promising platform for these transformations, offering scalability for both quantum and classical systems. However, scalable and efficient methods for implementing arbitrary unitaries remain challenging. Here, we present a theoretical framework for realizing arbitrary unitary matrices through programmable waveguide arrays (PWAs). We provide a mathematical proof demonstrating that cascaded PWAs can implement any unitary matrix within practical constraints, along with a numerical optimization method for customized PWA designs. Our results establish PWAs as a universal and scalable architecture for quantum photonic computing, effectively bridging quantum and classical applications, and positioning PWAs as an enabling technology for advancements in quantum simulation, machine learning, secure communication, and signal processing.

Using the strong dispersive coupling to a high-cooperativity cavity, we demonstrate fast and non-destructive number-resolved detection of atoms in optical tweezers. We observe individual atom-atom collisions, quantum state jumps, and atom loss events with a time resolution of $100\ \mu$s through continuous measurement of cavity transmission. Using adaptive feedback control in combination with the non-destructive measurements, we further prepare a single atom with $92(2)\%$ probability.

The implementation of a monolithic fiber-optically coupled CMOS-based TemCam-XF416 camera into our ultra-high vacuum (UHV) ultrafast reflection high-energy electron diffraction setup is reported. A combination of a pumpable gate valve and a self-built cooling collar allows UHV conditions to be reached without the need to remove the heat-sensitive device. The water-cooled collar is mounted to the camera housing and prevents heating of the detector upon bake-out of the UHV chamber. The TemCam provides an one order of magnitude higher spatial resolution than the previously used microchannel plate (MCP) based detector (Burle Chevron 3040FM) which enables a 30% higher resolution in reciprocal space. The low background intensity and the 4$\times$ lager dynamic range enables analysis of the diffuse intensity of the diffraction pattern like Kikuchi lines and bands. A key advantage over the previous MCP detector is the complete absence of the blooming effect, which enables the quantitative spot profile analysis of the diffraction spots. The inherent light sensitivity in an optical pump experiment can be overcome by using photons with h{\nu} < 1.12 eV, i.e., the indirect band gap of silicon, or by shielding any stray light.

Understanding and predicting the structure and evolution of coronal mass ejections (CMEs) in the heliosphere remains one of the most sought-after goals in heliophysics and space weather research. A powerful tool for improving current knowledge and capabilities consists of multi-spacecraft observations of the same event, which take place when two or more spacecraft fortuitously find themselves in the path of a single CME. Multi-probe events can not only supply useful data to evaluate the large-scale of CMEs from 1D in-situ trajectories, but also provide additional constraints and validation opportunities for CME propagation models. In this work, we analyse and simulate the coronal and heliospheric evolution of a slow, streamer-blowout CME that erupted on 23 September 2021 and was encountered in situ by four spacecraft approximately equally distributed in heliocentric distance between 0.4 and 1 au. We employ the Open Solar Physics Rapid Ensemble Information (OSPREI) modelling suite in ensemble mode to predict the CME arrival and structure in a hindcast fashion and to compute the "best-fit" solutions at the different spacecraft individually and together. We find that the spread in the predicted quantities increases with heliocentric distance, suggesting that there may be a maximum (angular and radial) separation between an inner and an outer probe beyond which estimates of the in-situ magnetic field orientation (parameterised by flux rope model geometry) increasingly diverge. We discuss the importance of these exceptional observations and the results of our investigation in the context of advancing our understanding of CME structure and evolution as well as improving space weather forecasts.