In this work, phase singularities embedded in a wavepacket are shown to act as sources of atypical localized oscillations when the packet interacts with a linear system. We refer to these oscillations as \textit{chirpons}, since they arise as strong variations of the instantaneous frequency (chirp). A mathematical expression is then provided to describe \textit{chirpons}, and their behavior is explored through the interaction of a super-bandwidth wavepacket -- containing two singularities -- with a damped harmonic oscillator, a fundamental model for many physical systems. This interaction is analyzed theoretically, and the predictions are verified experimentally using a resonant electrical circuit as a realization of the oscillator. The results show that \textit{chirpons} evolve in a manner fundamentally different from standard Fourier oscillations, revealing features of linear systems that are otherwise inaccessible. This introduces a new approach to analyze and characterize system responses, with potential applications in high-resolution spectroscopy and signal sensing.

In the context of f(G) modified gravity, we address the cosmic application of the most generalized form of holographic dark energy (The European Physical Journal C, 77, (2017): 1-8) in this study, as well as a specific instance of it in the form of Barrow holographic dark energy (Physical Review D, 102(12), p.123525). Holographic dark energy and a well-known power law form of the scale factor a(t) are added to the f(G) model in order to achieve this. It is observed that a sufficient criterion for a realistic modified gravity model is satisfied by the reconstructed f(G). The reconstruction models are also tested under the four energy situations.

The basic physics for plasma core rocket engines was already completed in 1985. At the time successful containment of a fissioning uranium hexafluoride U(93%)F6 plasma was achieved for ~120 seconds using an argon vortex. Unfortunately, hydrodynamic confinement of a fissioning fuel in a gas core nuclear rocket without uranium loss is probably unachievable. This article proposes a plasma core that is magnetically contained and uses one of many possible stable force-free magnetic field configurations.

This paper presents a bio-inspired underwater whisker sensor for robust hydrodynamic disturbance detection and efficient signal analysis based on Physical Reservoir Computing (PRC). The design uses a tapered nylon spring with embedded accelerometers to achieve spatially distributed vibration sensing and frequency separation along the whisker. Towing-tank experiments and computational fluid dynamics simulations confirmed that the whisker effectively distinguishes vortex regimes across different fin angles and maintains Strouhal scaling with flow velocity, where higher speeds increase vibration intensity without affecting the dominant frequencies. Frequency-domain analysis, Shannon entropy, and machine learning further validated the sensing performance: vortex shedding frequencies were identified with less than 10\% error, entropy captured the transition from coherent vortex streets to turbulence, and logistic regression achieved 86.0\% classification accuracy with millisecond-level inference. These results demonstrate that structurally encoded whisker sensing provides a scalable and real-time solution for underwater perception, wake tracking, and turbulence-aware navigation in autonomous marine robots.

Road traffic accidents remain a major public health challenge worldwide, with urbanisation and population density identified as key factors influencing risk. This study analyses monthly accident data from 2009 to 2023 across 632 parliamentary constituencies in England, Wales, and Scotland, using an area-normalised approach based on population density. Segmented power law models consistently identified breakpoints separating sublinear rural from superlinear urban scaling behaviours. Seasonal variation in scaling exponents was pronounced in rural regions but less evident in urban ones. Fourier-based cross-spectral analysis of yearly cycles revealed systematic phase shifts: rural exponents lagged pre-exponential factors by 4.5 months, while urban exponents were 2.7 months out of phase, producing a 5.3 month shift between rural and urban exponents. These findings highlight the importance of pre-exponentials-defined as the expected density of accidents at unit population density-as comparable descaled metrics, revealing both long-term national declines and recurring seasonal peaks. Notably, the phase offsets suggest structurally distinct causes of rural and urban accident risk, with urban regions exhibiting increasing acceleration in accident scaling, potentially linked to growth in vehicle numbers, size, and weight. Residuals, modelled with the Type I Generalised Logistic Distribution (GLD), captured skewness and heterogeneity more effectively than normal assumptions. Geospatial mapping highlighted persistent urban hotspots alongside rural and coastal constituencies with systematically lower accident densities than predicted. Together, these findings advance understanding of how density and urbanisation shape accident risk and provide evidence to support more targeted road safety interventions and policy planning.

From atomic crystals to macroscopic material structures, twisted bilayer systems have emerged as a promising route to control wave phenomena. In few-layer van der Waals (vdW) materials, however, the intrinsically weak interlayer coupling typically demands fine control of small twist angles to reach magic-angle conditions. Here, we show that one-dimensional photonic crystal bilayers can overcome this limitation by accessing a regime of strong interlayer coupling -- comparable to intralayer coupling. This strong coupling enables flat-band formation over a broad angular range even at large twist angles. We experimentally realize this regime by stacking WS$_2$ gratings using a two-step lithography method, resulting in ultra-wide chiral flat-band cascades in magic-angle twisted bilayer gratings. Our work not only provides a platform for designing photonic applications with tunable localization but also explores a new regime of physics unattainable in conventional solid-state based moiré systems.

In the early days of laser spectroscopy Letokhov and Chebotayev proposed a scheme for measuring narrow spectral lines where the resolution is not restricted to Doppler effects because the molecules are entrained in a standing-wave light field. Now, such one-dimensional trapping in the intensity maxima of an intracavity field, slightly detuned from resonance, is experimentally demonstrated in the measurement of the very weak S(0) (2-0) quadrupole overtone transition in H$_2$ at 1189 nm. The trapping manifests as an extremely narrow absorption feature at the predicted zero-recoil position, a 70 kHz shift from the blue-recoil component observed in Lamb-dip spectroscopy. A quantitative analysis of the saturation and trapping conditions supports the findings.

The Exact Factorization (XF) of molecular wavefunctions can be viewed as an 'electronic wavepacket' framework for quantum dynamics. It is an appealing alternative to the conventional non-adiabatic dynamics, unfolding in the space of coupled electronic eigenstates. However, implementation of the non-linear XF equations for general systems presents a formidable challenge: the XF counterparts to the non-adiabatic coupling involve division by the nuclear probability density, which leads to severe numerical instabilities in the low-density regions of space. In case of the non-adiabatic dynamics the effect of coupling is relatively smooth, but this theoretical framework becomes impractical when numerous electronic states are involved. In this paper the origin of the XF-specific challenge is analyzed analytically. We demonstrate that the problem arises when the factorized wavefunction diverges, even without the explicit coupling of the Born-Huang electronic states used to describe the molecular wavefunction. Using a 'minimal' model of the photodissociation, we derive expressions for the XF dynamics and locate the source of the XF instability. We analyze the dependence of this instability on the nuclear wavefunction bifurcation in the stationary and moving frames of reference, the latter associated with the quantum trajectory ensemble describing the nuclear XF wavepacket in a compact form. We show that the near-singular behavior persists in the moving frame and in the atomic basis representation of the electronic wavefunction. This model and insight into the root of the XF implementation challenge will help to address the issue, leading to further development of the XF methods.

Conventional theoretical and computational approaches to fully coupled quantum molecular dynamics, i.e. when both the electrons and nuclei are treated as quantum-mechanical particles, are impractical for all but the smallest chemical systems. In this paper we describe the formalism and implementation of the Factorized Electron Nuclear Dynamics (FENDy) with effective complex potential [J. Chem. Theory Comput. 19 (2023), pp 1393-1408], which goes beyond the established framework of the Born-Oppenheimer approximation or Born-Huang expansion of the molecular wavefunction. This method is based on the exact factorization of the molecular wavefunction, with the nuclei evolving under a complex time-dependent potential which captures the key features of dynamics in the nuclear subspace. The complementary electronic component of the molecular wavefunction is normalized to one for all nuclear configurations. We implement and employ FENDy to model the dynamics of H$_2^+$ molecular ion under a femtosecond laser pulse. The electronic wavefunction is represented within the standard electronic structure bases without referencing the electronic eigenstates. The nuclear wavefunction is represented as a quantum-trajectory ensemble, which in principle circumvents the exponential scaling of the numerical cost with the system size. The challenging evaluation of the gradients on unstructured grids is performed by projection on auxiliary bases.

We present the first physical realization of in-pixel signal processing with integrated AI-based data filtering for particle tracking detectors. Building on prior work that demonstrated a physics-motivated edge-AI algorithm suitable for ASIC implementation, this work marks a significant milestone toward intelligent silicon trackers. Our prototype readout chip performs real-time data reduction at the sensor level while meeting stringent requirements on power, area, and latency. The chip is taped-out in 28nm TSMC CMOS bulk process, which has been shown to have sufficient radiation hardness for particle experiments. This development represents a key step toward enabling fully on-detector edge AI, with broad implications for data throughput and discovery potential in high-rate, high-radiation environments such as the High-Luminosity LHC.

We study the statistics of dynamical quantities associated with magnetic reconnection events embedded in a sea of strong background magnetohydrodynamic (MHD) turbulence using direct numerical simulations. We focus on the relationship of the reconnection properties to the statistics of global turbulent fields. For the first time, we show that the distribution in turbulence of reconnection rates (determined by upstream fields) is strongly correlated with the magnitude of the global turbulent magnetic field at the correlation scale. The average reconnection rates, and associated dissipation rates, during turbulence are thus much larger than predicted by using turbulent magnetic field fluctuation amplitudes at the dissipation or kinetic scales. Magnetic reconnection may therefore be playing a major role in energy dissipation in astrophysical and heliospheric turbulence.

Airy wavefunctions are associated with one of the simplest scenarios in wave mechanics: a quantum bouncing ball. In other words, they are the eigenstates of the time-independent Schrodinger equation with a linear potential. In the domain of optics, laser beams that are spatially shaped as Airy functions (`Airy beams') have been shown to exhibit a prominent lobe that follows a curved path, rather than propagating in a straight line, and which has self-healing properties in the presence of obstacles. Here, we observe the presence of Airy resonances in two-dimensional photonic crystals composed of a lattice of holes in a silicon slab. Analogously to electrons in a linear potential, these Airy resonances arise due to a linear spatial variation in the lattice constant of the holes. We map the electromagnetic description of the photonic crystal onto a 2D non-Hermitian Schrodinger equation with a linear potential, which we call a `superpotential'. The non-Hermiticity appears in the form of a complex effective mass due to out-of-plane radiation and fundamentally alters the collective optical response of the Airy resonances.

We present a systematic approach to optimise distributed acoustic sensing (DAS) fibre-optic cable layouts using global optimisation techniques. Our method represents cable geometries using splines, enabling efficient exploration of layouts while respecting physical deployment constraints. The use of evolutionary algorithms enables single and multi-objective optimisation, taking into account complex design constraints such as terrain, accesibility, exclusion zones and cable length, while allowing efficient parallelisation of the optimisation process. We demonstrate the approach on a real-world case study, optimising the layout of a DAS cable for monitoring slope stability in the Cuolm da Vi area of Switzerland. We adapt design criteria for seismic source location problems, and for ambient noise surface wave tomography, to account for the unique characteristics of DAS, such as directional sensitivity patterns. The results show significant potential for improvements in source location accuracy and surface wave tomographic resolution by optimising cable layouts, highlighting the potential of this approach for optimising DAS deployments in various geophysical applications.

This study presents a computational and theoretical framework inspired by thermodynamic principles to analyze the dynamics of economic inflation within adiabatic and non-adiabatic systems. In a framework referred to as developmental symmetry, inflation is formulated as a scalar field evolving through continuity equations, drawing an analogy with the Raychaudhuri equation in gravitational dynamics. The results show that adiabatic systems fail to reach equilibrium, while non-adiabatic systems can evolve toward stable states over time. The model successfully reproduces observed inflationary regimes-from hyperinflation to stable low-inflation phases-with characteristic transition periods of about a decade. These results indicate that production continuity and controlled monetary flow are crucial for achieving stability in complex economic systems, linking thermodynamic balance to macroeconomic equilibrium.

Accurate wellbore trajectory prediction is a paramount challenge in subsurface engineering, governed by complex interactions between the drilling assembly and heterogeneous geological formations. This research establishes a comprehensive, mathematically rigorous framework for trajectory prediction that moves beyond empirical modeling to a geomechanically-informed, data-driven surrogate this http URL study leverages Log ASCII Standard (LAS) and wellbore deviation (DEV) data from 14 wells in the Gulfaks oil field, treating petrophysical logs not merely as input features, but as proxies for the mechanical properties of the rock that fundamentally govern drilling dynamics. A key contribution of this work is the formal derivation of wellbore kinematic models, including the Average Angle method and Dogleg Severity, from the first principles of vector calculus and differential geometry, contextualizing them as robust numerical integration schemes. The core of the predictive model is a Gated Recurrent Unit (GRU) network, for which we provide a complete, step-by-step derivation of the forward propagation dynamics and the Backpropagation Through Time (BPTT) training algorithm. This detailed theoretical exposition, often omitted in applied studies, clarifies the mechanisms by which the network learns temporal dependencies. The methodology encompasses a theoretically justified data preprocessing pipeline, including feature normalization, uniform depth resampling, and sequence generation. Trajectory post-processing and error analysis are conducted using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R2).

Understanding molecular structure, dynamics, and reactivity requires bridging processes that occur across widely separated time scales. Conventional molecular dynamics simulations provide atomistic resolution, but their femtosecond time steps limit access to the slow conformational changes and relaxation processes that govern chemical function. Here, we introduce a deep generative modeling framework that accelerates sampling of molecular dynamics by four orders of magnitude while retaining physical realism. Applied to small organic molecules and peptides, the approach enables quantitative characterization of equilibrium ensembles and dynamical relaxation processes that were previously only accessible by costly brute-force simulation. Importantly, the method generalizes across chemical composition and system size, extrapolating to peptides larger than those used for training, and captures chemically meaningful transitions on extended time scales. By expanding the accessible range of molecular motions without sacrificing atomistic detail, this approach opens new opportunities for probing conformational landscapes, thermodynamics, and kinetics in systems central to chemistry and biophysics.

Thin-film lithium niobate (TFLN) has played a pivotal role in the advancement of integrated photonics, by supporting a diverse range of applications including nonlinear optics, electro-optics, and piezo-optomechanics. The effective realization and enhancement of these interactions rely heavily on the implementation of high quality photonic microresonators. The pursuit of novel resonator architectures with optimized properties thus represents a central research area in TFLN photonics. In this work, we design and fabricate TFLN Fabry-Perot microresonators, by placing a straight section of waveguide between a pair of tapered photonic crystal mirrors. The resonator features a high quality factor of 600k at 1530 nm and a compact length of 100 um. The functionality of the device is further demonstrated by integrating on-chip electrodes for high-frequency piezo-optomechanical modulation. Our device can serve as an appealing candidate for developing high-performance photonic components on the TFLN platform.

This volume contains contributions submitted to the 12th Low-Level RF Workshop which will be held in Newport News, Virginia, USA on October 12-16, 2025. This workshop continues the series of successful international workshops held in Newport News, USA (2001), Geneva, Switzerland (2005), Knoxville, USA (2007), Tsukuba, Japan (2009), Hamburg, Germany (2011), Tahoe City, USA (2013), Shanghai, China (2015), Barcelona, Spain (2017), Chicago, USA (2019), Brugg-Windisch, Switzerland (2022), and Gyeongju, South Korea (2023).

In plasmonics, nonlocal effects arise when the material response to optical excitations is strongly dependent on the spatial correlations of the excitation. It is well known that a classical free electron gas system supports local Drude volume plasmon waves. Whereas a compressible quantum electron gas system sustains hydrodynamic volume plasmons with nonlocal dispersion isotropic across all high-symmetry directions. Here, distinct from Drude and Hydrodynamic plasmon waves, we present the first observation of crystalline nonlocal volume plasmon waves. We use transmission-based momentum-resolved electron energy loss spectroscopy to measure the volume plasmon dispersion of silicon along all the fundamental symmetry axes, up to high momentum values ($q \sim 0.7$ reciprocal lattice units). We show that crystalline nonlocal plasmon waves have a prominent anisotropic dispersion with higher curvature along the light-mass ($\Gamma K$ \& $\Gamma L$) axes, compared to the heavy-mass ($\Gamma X$) axis. We unveil the origin of this phenomenon by experimentally extracting the anisotropic Fermi velocities of silicon. Our work highlights an exquisite nonlocality-induced anisotropy of volume plasmon waves, providing pathways for probing many-body quantum effects at extreme momenta.

Acousto-optic deflectors (AODs) are widely used across physics, microscopy, neuroscience, and laser engineering, providing fast, precise, and non-mechanical control of light. While conventional AODs naturally support multiplexing in one and two dimensions, no analogous device has existed for three-dimensional control, leaving a critical gap in rapid focus tuning and 3D beam shaping. Here we demonstrate a three-dimensional AOD system capable of multiplexed axial and lateral beam control with high speed and large dynamic range. We achieve this by combining a double-pass AOD with a diffraction grating in the Littrow configuration to realize a compact frequency-tunable lens with multiplexing capability. Our device enables axial scanning over more than twenty Rayleigh ranges with switching rates up to 100 kHz, while simultaneous multi-tone driving produces arbitrary multi-focal beam profiles. By integrating the axial module with lateral deflection, we generate reconfigurable 3D optical patterns. This approach establishes a broadly applicable platform for multiplexed 3D beam control, with potential applications from high-resolution microscopy and laser processing to scalable neutral-atom quantum technologies.

This work introduces a calibration framework for material parameter identification in isotropic hyperelastic constitutive models. The framework synergizes the Virtual Fields Method (VFM) to define an objective function with a Genetic Algorithm (GA) as the optimization method to facilitate automated calibration. The formulation of the objective function uses experimental displacement fields measured from Digital Image Correlation (DIC) synchronized with load cell data and can accommodate data from experiments involving homogeneous or inhomogeneous deformation fields. The framework places no restrictions on the target isotropic hyperelastic constitutive model, accommodating models with coupled dependencies on deformation invariants and specialized functional forms with a number of material parameters, and assesses material stability, eliminating sets of material parameters that potentially lead to non-physical behavior for the target hyperelastic constitutive model. To minimize the objective function, a GA is deployed as the optimization tool due to its ability to navigate the intricate landscape of material parameter space. The VFM-GA framework is evaluated by applying it to a hyperelastic constitutive model for compressible elastomeric foams. The evaluation process entails a number of tests that employ both homogeneous and inhomogeneous displacement fields collected from DIC experiments on open-cell foam specimens. The results outperform manual fitting, demonstrating the framework's robust and efficient capability to handle material parameter identification for complex hyperelastic constitutive models.

High-quality factor microresonators are an attractive platform for the study of nonlinear photonics, with diverse applications in communications, sensing, and quantum metrology. The characterization of loss mechanisms and nonlinear properties in a microresonator is a necessity for the development of photonic integrated circuits. Here, we demonstrate a high-quality chalcogenide ($Ge_{23}Sb_{7}S_{70}$) micro-racetrack resonator utilizing Euler curves. The racetrack geometry is studied to minimize loss at both the straight-curved waveguide junction and through the waveguide curve. The material absorption, intrinsic quality factor, and nonlinear index are extracted by a comprehensive model fit to laser wavelength resonance scans. The micro-racetrack resonator possesses an absorption loss of $0.43 dB/m$, an intrinsic quality factor of $4.5 \times 10^6$, and nonlinear index of $1.28 \times 10^{-18} m^2/W$, in a waveguide cross-section less than $1 {\mu}m^2$. Our results yield state-of-the-art nonlinear microresonators and establish $Ge_{23}Sb_{7}S_{70}$ as a low-loss PIC platform.

Arrays of hundreds or thousands of low temperature detectors have been deployed for many experiments, both bolometers for long wavelength applications and calorimeters for shorter wavelength applications. One challenge that is common to many of these arrays is the efficient use of focal plane area to achieve a large fill fraction of absorbers coupled to detectors. We are developing an integrated fabrication of soft X-ray transition edge sensors (TES) and microwave SQUID multiplexers ($\mu$MUX) with the goal of maximizing the fill fraction of the focal plane area on a scale of many thousand pixel detectors. We will utilize lithographically defined high density interconnects to circumvent limitations in existing solutions that use wirebonds or flip-chip bonds. Here we report the first demonstration of combining TES and $\mu$MUX processes into a single TES-System-on-a-Chip (TES-SoC) fabrication on a silicon wafer. The $\mu$MUX SQUIDs and TES electrothermal feedback circuits are microfabricated first and protected with passivating SiO$_2$, then the TES devices and TES-to-SQUID interconnects are fabricated, and finally the protective layer is removed before the fabrication of the microwave resonators. We show that the microwave SQUIDs are functional and have reasonable yield, and that we are able to read out the transition temperature of the connected TESs using those SQUIDs.

High Bandwidth Memory (HBM), as a key development trend in future memory chip technology, significantly enhances computer performance. At the same time, the thermal challenges arising from its stacked architecture have drawn considerable attention. Most existing studies on HBM thermal management are based on Fourier's law, neglecting the non-Fourier effects introduced by the micro/nanoscale structures within HBM. In this study, the Monte Carlo method (MC) is employed to solve the phonon Boltzmann transport equation (BTE) and investigate the impact of non-Fourier heat transport on the thermal behavior of HBM structures. The results reveal that non-Fourier heat transport leads to a junction temperature that is 59.8 K higher than that predicted by Fourier's law. Furthermore, it is found that the phonon transmittance at the chip interlayers has a severe impact on heat dissipation, with the temperature variation reaching up to 56.6 K. These findings provide more accurate thermal insights, which are critical for the optimized design of HBM systems.

In photonics, twisted bi-layer systems have demonstrated unprecedented control over light-matter interactions, primarily through the modulation of photonic band structures and the formation of Moiré patterns. Meanwhile, magnetic photonic crystals have served as cornerstone platforms for manipulating light propagation, facilitating key applications such as Faraday rotation-based isolators and non-reciprocal devices. Nevertheless, the synergistic integration of twist engineering and magneto-optical effects in bi-layer architectures remains unexplored. This work introduces twisted magnetic bi-layer photonic crystal slabs as a novel platform to unify these degrees of freedom. By continuously tuning the twist angle between two magneto-active photonic layers, the giant circular dichroism is observed, and the transmitted waves can be perfectly linearly polarized and rotated. These effects arise from the interplay between resonant properties of the Moiré cell and magnetization-dependent coupling of circularly polarized states. This work establishes a foundation for magnetic topological photonics, bridging twistronics and magneto-optics to unlock new mechanisms for dynamic light control in compact and reconfigurable devices.

In this work, important two-phase flow scalings are derived, which enable the quantification of grid-point and time-step requirements as functions of Re, We, and Ca numbers. The adequate grid resolution is determined in the inertia-dominated regime with the aid of high-fidelity simulations of stationary two-phase homogeneous isotropic turbulence by evaluating convergence of total interfacial area, size distribution, SMD, and curvature distribution. Although standards for DNS for single-phase turbulence flow exist, there is a lack of similar guidance in two-phase flows. Therefore, length scale ratios of the Kolmogorov-Hinze to the Kolmogorov scale of \eta_{KH}/\eta \sim We_{L}^{-3/5}Re_{L}^{3/4} in the inertia-dominated regime and the Kolmogorov-viscous to Kolmogorov scale of \eta_{KV}/\eta \sim Ca_{L}^{-1}Re_{L}^{3/4} for the viscous-dominated regime, are constructed. These scalings imply a computational cost increase like We_{L}^{12/5} and Ca_{L}^4, in the inertia-dominated and viscous-dominated regimes, respectively. A novel dimensionless number, coined as the ratio of interface scales (Ris), is proposed to aid in the classification of the turbulence regimes in the presence of an interface. Convergence of the total interfacial area, size distribution, SMD, and curvature distribution are observed for grid resolutions of k_{\max} \eta_{KH} \geq 60$ for second-order schemes. Furthermore, it is observed that this lower bound is the minimum required to capture intermittent events responsible for the increase of instantaneous total interfacial area. This criterion will be a valuable tool for determining grid resolution and time-step requirements a-priori for DNS of two-phase flows and for estimating the corresponding computational cost. This work provides guidelines and best practices for numerical simulations of two-phase flows, which will accelerate physics discovery and model development.

Precisely addressing single nanostructures inside dense ensembles remains a bottleneck for scalable photonic and quantum information devices. Here we demonstrate, through comprehensive finite element and variational Monte-Carlo modelling, that a reconfigurable three-dimensional array of needle shaped beams can selectively switch the quantum optical response of individual InAs nanorods embedded in GaAs. By tuning the local non resonant intensity pattern the exciton and biexciton energies were calculated, electromagnetically induced transparency (EIT) windows were examined, and correspondingly near-field diffraction carpets were dynamically reshaped. A single parameter the activation ratio between illuminated and dark nanorods provides continuous control over photoluminescence peak position (80 meV) and EIT bandwidth (six times). We further predict fully programmable Talbot self-imaging in nanorod arrays with sub-wavelength pitch. Importantly, the observed Talbot carpets enable spatially resolved identification of which nanorods were excited, offering a powerful diagnostic for verifying structured-light activation schemes. The concept offers a low crosstalk, wafer scale route toward reconfigurable quantum emitters, tunable diffractive optics and on-chip slow-light components.

We present a technique for binary spatial mode demultiplexing, using a double-clad fiber coupler as an optical mode sorter, for hypothesis testing for one or two point sources in an incident optical field. By directly coupling an optical field through a double-clad fiber coupler, we demultiplex the field into the fundamental mode and a superposition of higher-order modes. We use the ratio of multi-mode to single-mode power to distinguish between single and double point sources. In a tabletop demonstration of the technique, we demonstrate the capability to accurately identify the presence of two sources separated below the Rayleigh limit for relative brightnesses from 0 dB to -20 dB. For sources with less than 5 dB difference in their relative powers, our imaging protocol can correctly determine the presence of a second optical source even when the two sources have separations 50x smaller than the Rayleigh limit. These results highlight the potential of this technique as a simple tool for super-resolving classification of a pair of point emitters, especially in the context of astronomical imaging for binary systems.

The BeEST experiment is searching for sub-MeV sterile neutrinos by measuring nuclear recoil energies from the decay of $^7$Be implanted into superconducting tunnel junction (STJ) sensors. The recoil spectra are affected by interactions between the radioactive implants and the sensor materials. We are therefore developing aluminum-based STJs (Al-STJs) as an alternative to existing tantalum devices (Ta-STJs) to investigate how to separate material effects in the recoil spectrum from potential signatures of physics beyond the Standard Model. Three iterations of Al-STJs were fabricated. The first had electrode thicknesses similar to existing Ta-STJs. They had low responsivity and reduced resolution, but were used successfully to measure $^7$Be nuclear recoil spectra. The second iteration had STJs suspended on thin SiN membranes by backside etching. These devices had low leakage current, but also low yield. The final iteration was not backside etched, and the Al-STJs had thinner electrodes and thinner tunnel barriers to increase signal amplitudes. These devices achieved 2.96 eV FWHM energy resolution at 50 eV using a pulsed 355 nm (~3.5 eV) laser. These results establish Al-STJs as viable detectors for systematic material studies in the BeEST experiment.

Near-space, which covers altitudes from 20 to 100 kilometers, has been receiving more and more attention because of its special strategic value. Airships and high-altitude balloons are two common types of low-speed vehicles that operate in this region. They can be used for jobs like monitoring, communication, and remote sensing, but they need efficient propulsion systems to work well. Earlier, we proposed a new type of electric propulsion system that can ionize the surrounding air to create plasma and produce thrust for near-space vehicles. However, in past experiments, not enough was known about how certain parameters affect power absorption and electromagnetic behavior. Therefore, in this study, we used computer simulations to examine how gas pressure (200 to 1000 Pa), input power (200 to 600 W), frequency (13.56 to 52.24 MHz), and different gas types ($Ar$, $N_2$, $H_2$, $He$) influence inductively coupled plasma inside a quartz tube. We especially focused on comparing two antenna designs: one with a single turn and one with five turns. In all the simulations, the single-turn antenna consistently absorbed power better than the five-turns antenna. Higher frequencies significantly influence both plasma power absorption and magnetic field characteristics. The optimal power absorption occurs at a filling gas pressure of 400 Pa. When varying the input power, we observed an initial decrease followed by an increasing trend, which may be related to ionization mechanisms. In comparisons among different gas types, the inelastic collision mechanisms in molecular gases lead to a notable reduction in plasma power absorption efficiency. The results from this work will help guide the design of future experiments for this electric propulsion concept.

The role of graphene nanowindows in the nanohydroxyapatite bone scaffold preparation is important for the preparation of mechanically robust scaffolds and implementation in bone recovery. Here, we report a graphene-nanohydroxyapatite (G-nHAP) scaffold synthesized by the hydrothermal method, along with its formation mechanism and promising in vivo applications. The G-nHAP scaffold exhibits excellent mechanical strength comparable to that of cancellous bone. The nHAP was grown on 2D layers of graphene with nanowindows. The role of nanowindows was to attract electrostatically Ca2+, PO43+, and OH- precursors of nHAP, forming a layered structure of G-nHAP, in which nHAP nanorods of 23.8 nm in diameter and 84.7 nm in length were placed between graphene layers, as evidenced with molecular dynamic simulations. In vivo study showed mature and mineralized bone and osteoid after six weeks. This demonstrates the role of graphene nanowindows in the formation of nHAP scaffolds that are promising for future implementation in bone tissue regeneration.

Finite-amplitude low-frequency Alfvén waves are commonly found in plasma environments, such as space plasmas, and play a crucial role in ion heating. The nonlinear interaction between oblique Alfvén wave spectra and ions has been studied. As the number of wave modes increases, ions are more likely to exhibit chaotic motion and experience stochastic heating. The stochastic heating threshold in the parameter space can be characterized by a single parameter, the effective relative curvature radius $P_{eff.}$. The results show excellent agreement with the chaotic regions identified through test particle simulations. The anisotropic characteristics of stochastic heating are explained using a uniform solid angle distribution model. The stochastic heating rate $Q=\dot{T}$ is calculated, and its relationship with wave conditions is expressed as $Q/(\Omega_i m_i v_A^2) = H(\alpha) \tilde{v}^3 \tilde{B}_w^2 \tilde{\omega}_1$, where $\alpha$ is propagating angle, $\Omega_i$ is the gyrofrequency, $m_i$ is the ion mass, $v_A$ is the Alfvén speed, $\tilde{v}$ is the dimensionless speed, $\tilde{B}_w$ is the dimensionless wave amplitude, and $\tilde{\omega}_1$ is the lowest dimensionless wave frequency.

A novel anti-P-pseudo-Hermitian mechanical system that integrates piezoelectric actuators and sensors with non-reciprocal coupling into mechanical beams is proposed. This configuration enables the system to exhibit programmable exceptional points (EPs), which are critical for enhancing sensitivity in sensing applications. Our theoretical analysis, supported by numerical simulations and experimental validation, demonstrates the system's capability to detect minute mass variations and identify surface cracks with high precision. This advancement not only contributes to the field of non-Hermitian physics but also paves the way for the development of next-generation mechanical sensors leveraging EP physics.

Tomographic volumetric 3D printing offers layer free, rapid fabrication of objects with high design freedom, but is limited to relatively small curing volumes because of the optical constraints imposed by an assumed need for telecentricity. We present a method to virtually stitch multiple projections from different light sources to build a single workpiece. To avoid the built in requirement for telecentricity and thus the need for an index matching vat, projections are produced by a graphics processing unit accelerated raytracing solver. The method accounts for non ideal light propagation, including nontelecentricity, reflection, refraction, attenuation, as well as any output power mismatch among this http URL volumetric 3D printing offers layer free, rapid fabrication of objects with high design freedom, but is limited to relatively small curing volumes because of the optical constraints imposed by an assumed need for telecentricity. We present a method to virtually stitch multiple projections from different light sources to build a single workpiece. To avoid the built in requirement for telecentricity and thus the need for an index matching vat, projections are produced by a graphics processing unit accelerated raytracing solver. The method accounts for non ideal light propagation, including nontelecentricity, reflection, refraction, attenuation, as well as any output power mismatch among projectors.

Energetic electrons and holes generated from the decay of localized surface plasmons in metallic nanoparticles can be harnessed in nanoscale devices for photocatalysis, photovoltaics or sensing. In this work, we study the generation of such hot carriers in bimetallic Janus nanoparticles composed of Au, Ag and Cu using a recently developed atomistic modelling approach that combines a solution of the macroscopic Maxwell equation with large-scale quantum-mechanical tight-binding models. We first analyze spherical Janus nanoparticles whose unique hot-carrier spectrum can be associated with the spectra of the two hemispheres and the interface coupling and find that under solar illumination the Ag-Au system exhibits the highest hot-carrier generation rate. For dumbbell-shaped Janus nanoparticles, we observe a significant increase in hot-carrier generation with increasing neck size. This is caused by a dramatic enhancement of the electric field in the neck region. We also study the dependence of hot-carrier generation on the light polarization and find that the largest generation rates are obtained when the electric field is perpendicular to the interface between the two metals due to the maximal dipole coupling with the electric field. The insights from our study will guide the experimental design of efficient hot-carrier devices based on bimetallic Janus nanoparticles.

The term wavefront sensor refers to the entire class of devices capable of measuring the optical wavefront of the incoming beam. Although numerous solutions have been proposed so far, recent advances in structured light have opened new development possibilities through controlled modification of optical field amplitude and phase. We present an alternative approach to angle-based sensing, introducing optical vortices stable phase singularities within each subaperture of the Shack-Hartmann (S-H) architecture. Rather than changing the fundamental angle-based operating principle, it transforms the tracked quantity and its detection method. The presence of a singularity enables a dedicated tracking algorithm that outperforms conventional methods without increasing computational complexity. We evaluated its performance against the conventional S-H across a broad SNR range (from 2 to 22), corresponding to shot noise limited to high saturation regimes, demonstrating lower mean residual phase variance across all conditions. This work demonstrates that structured beam shaping can extend the capabilities of traditional S-H architectures without requiring fundamental redesign.

With increased glacial melting and the need to maintain sediment continuity for ecosystem health, sediment-laden flows through hydropower plants are becoming increasingly problematic, particularly due to erosion on runner blades and buckets. A widely used mitigation strategy is the use of filters to protect Pelton turbines. However, these filters lead to rapid sediment accumulation in reservoirs, which must be drained frequently to maintain storage capacity. The high cost of such drainage operations calls for longer intervals between them, without compromising runner integrity, as erosion-induced stress concentrations may cause bucket rupture. A better understanding of the causes of runner erosion under varying operational and sediment conditions is essential to allow more sediment to pass through the hydraulic machine safely. This study investigates how sediment-laden flow through the nozzle affects particle distribution in the jet for different Stokes numbers. Furthermore, it analyses how a realistic particle size and spatial distribution in the impacting jet compares to the assumption of a uniform particle distribution with particles of mean size when simulating bucket erosion in Pelton wheels. The results show that the particle distribution in the jet follows a similar axisymmetric shape for low Stokes numbers whereas at higher Stokes numbers it becomes asymmetric. Additionally, it is shown that imposing uniform particle distribution with particles of mean diameter under-predicts tip- and splitter erosion when simulating the erosion on the bucket but is captured by imposing the realistic size and spatial distribution.

Background: We aimed to quantify hepatic vessel volumes across chronic liver disease stages and healthy controls using deep learning-based magnetic resonance imaging (MRI) analysis, and assess correlations with biomarkers for liver (dys)function and fibrosis/portal hypertension. Methods: We assessed retrospectively healthy controls, non-advanced and advanced chronic liver disease (ACLD) patients using a 3D U-Net model for hepatic vessel segmentation on portal venous phase gadoxetic acid-enhanced 3-T MRI. Total (TVVR), hepatic (HVVR), and intrahepatic portal vein-to-volume ratios (PVVR) were compared between groups and correlated with: albumin-bilirubin (ALBI) and model for end-stage liver disease-sodium (MELD-Na) score, and fibrosis/portal hypertension (Fibrosis-4 [FIB-4] score, liver stiffness measurement [LSM], hepatic venous pressure gradient [HVPG], platelet count [PLT], and spleen volume). Results: We included 197 subjects, aged 54.9 $\pm$ 13.8 years (mean $\pm$ standard deviation), 111 males (56.3\%): 35 healthy controls, 44 non-ACLD, and 118 ACLD patients. TVVR and HVVR were highest in controls (3.9; 2.1), intermediate in non-ACLD (2.8; 1.7), and lowest in ACLD patients (2.3; 1.0) ($p \leq 0.001$). PVVR was reduced in both non-ACLD and ACLD patients (both 1.2) compared to controls (1.7) ($p \leq 0.001$), but showed no difference between CLD groups ($p = 0.999$). HVVR significantly correlated indirectly with FIB-4, ALBI, MELD-Na, LSM, and spleen volume ($\rho$ ranging from -0.27 to -0.40), and directly with PLT ($\rho = 0.36$). TVVR and PVVR showed similar but weaker correlations. Conclusions: Deep learning-based hepatic vessel volumetry demonstrated differences between healthy liver and chronic liver disease stages and shows correlations with established markers of disease severity.

Graphene, and other members of the monolayer Xene family, represent an ideal materials platform for "valleytronics", the control of valley localized charge excitations. The absence of a gap in these semi-metals, however, precludes valley excitation by circularly polarized light pulses, sharply circumscribing the possibility of a lightwave valleytronics in these materials. Here we show that combining a deep ultraviolet linearly polarized light pulse with a THz envelope can induce highly valley polarized states in graphene. This dual frequency lightform operates by (i) the deep ultraviolet pulse activating a selection rule at the M saddle points and (ii) the THz pulse displacing the M point excitation to one of the low-energy K valleys. Employing both tight-binding and state-of-the-art time dependent density functional theory, we show that such a pulse results in a near perfect valley polarized excitation in graphene, thus providing a route via the saddle point to a lightwave valleytronics in the gapless Xene family.

Beam splitters are pivotal components in integrated microwave and photonic systems. However, conventional designs based on directional coupling or multi-mode interference often suffer from back scattering, frequency-dependent splitting ratios, and limited bandwidth. To overcome these limitations, here, we propose a new physical mechanism to achieve a broadband, robust, and tunable beam splitter by manipulating the mode coupling of the topological unidirectional surface magnetoplasmons (USMP) at the input and output waveguides. We show that the beam splitter not only exhibits strong robustness against obstacles but also achieves a broad bandwidth across nearly the entire USMP band with arbitrarily tunable and frequency-independent splitting ratios. Moreover, the operating band of the beam splitter can be actively tuned by adjusting the external magnetic field, while its robust and broadband characteristics are retained. Our results extend the research frontier of beam splitters and may have potential applications in integrated photonic devices and modern communication systems.

Many swimming bacteria naturally inhabit confined environments, yet how confinement influences their swimming behaviors remains unclear. Here, we combine experiments, continuum modeling and particle-based simulations to investigate near-surface bacterial swimming in dilute suspensions under varying confinement. Confinement reduces near-surface accumulation and facilitates bacterial escape. These effects are quantitatively captured by models incorporating the force quadrupole, a higher-order hydrodynamic singularity, that generates a rotational flow reorienting bacteria away from surfaces. Under strong confinement, bacterial trajectories straighten due to the balancing torques exerted by opposing surfaces. These findings highlight the role of hydrodynamic quadrupole interactions in near-surface bacterial motility, with implications for microbial ecology, infection control, and industrial applications.

Recent advances in nanophotonics have demonstrated that various optical resonances in nanostructures can achieve strong field confinement with substantially suppressed scattering. This study investigates the optical Kerr effect (OKE) enhancement in high-refractive-index metasurfaces featuring non-BIC trapped-mode resonances, using gallium phosphide (GaP) as a model material. Three-dimensional finite-difference time-domain simulations reveal a significant enhancement of the effective second-order refractive index, reaching values up to 700 times greater than bulk GaP. The numerical results show strong electromagnetic field localization at the trapped-mode resonance, characterized by a wide transmittance dip (low Q-factor) and near-unity reflectance. Remarkable stability is observed, with the OKE enhancement maintaining less than 5\% variation for moderate polarization angles and tolerating random nanohole rotations that model fabrication imperfections. Compared to bound states in the continuum (BIC), the non-BIC trapped mode demonstrates superior robustness to structural disorder while achieving comparable nonlinear enhancement. These findings suggest promising applications in reflective nonlinear optics, particularly for high harmonic generation and polarization-insensitive photonic devices.

Persistent systematic errors in Earth system models (ESMs) arise from difficulties in representing the full diversity of subgrid, multiscale atmospheric convection and turbulence. Machine learning (ML) parameterizations trained on short high-resolution simulations show strong potential to reduce these errors. However, stable long-term atmospheric simulations with hybrid (physics + ML) ESMs remain difficult, as neural networks (NNs) trained offline often destabilize online runs. Training convection parameterizations directly on coarse-grained data is challenging, notably because scales cannot be cleanly separated. This issue is mitigated using data from superparameterized simulations, which provide clearer scale separation. Yet, transferring a parameterization from one ESM to another remains difficult due to distribution shifts that induce large inference errors. Here, we present a proof-of-concept where a ClimSim-trained, physics-informed NN convection parameterization is successfully transferred to ICON-A. The scheme is (a) trained on adjusted ClimSim data with subtracted radiative tendencies, and (b) integrated into ICON-A. The NN parameterization predicts its own error, enabling mixing with a conventional convection scheme when confidence is low, thus making the hybrid AI-physics model tunable with respect to observations and reanalysis through mixing parameters. This improves process understanding by constraining convective tendencies across column water vapor, lower-tropospheric stability, and geographical conditions, yielding interpretable regime behavior. In AMIP-style setups, several hybrid configurations outperform the default convection scheme (e.g., improved precipitation statistics). With additive input noise during training, both hybrid and pure-ML schemes lead to stable simulations and remain physically consistent for at least 20 years.

Chiral responses in electromagnetic metasurfaces are typically categorized as extrinsic, resulting from asymmetric interactions between the structure and incident waves, and intrinsic, arising from three-dimensional symmetry breaking of the unit cell. However, most existing metasurface designs target only one type of chirality and lack a unified, continuously tunable platform for broader chiroptical control. To address this limitation, the designed kirigami-based flexible metasurface is proposed for dynamic, continuous modulation of chirality, which expands the control scope to both extrinsic and intrinsic chiral responses within a single, reconfigurable platform. Initially, the unfolded metasurface exhibits extrinsic chirality under oblique incidence. By introducing well-designed kirigami-based cuts and folds, the metasurface transitions from a planar and achiral configuration to a three-dimensional chiral geometry that breaks the mirror symmetry, thereby exhibiting tunable intrinsic chirality and asymmetric extrinsic chirality. As the folding angle increases, the resulting deformation enables continuous tuning of the chiral response, with circular dichroism and its asymmetry under oblique incidences progressively increasing and reaching pronounced levels across the X-band. Our work provides a lightweight, easy-fabricated, and mechanically reconfigurable metasurface, which offers strong potential for future development in adaptive photonic systems and advanced chiroptical technologies.

The effect of substituting a hydrogen atom by a chlorine atom or a methyl group by a trichloromethyl (CCl3) group at the stereogenic center of light-driven second generation molecular motors is calculated in order to assess the effect on rotational speed and the separation of the absorption peaks of the isomers. While experimental and theoretical studies have previously been carried out for fluorine substitution, this is the first study of chlorine substitution. Five well-characterized base molecules are studied and the trends are compared with the effect of fluorine substitution. The trichloromethyl substitution is found to accelerate the rotation more than a trifluoromethyl (CF3) substitution by reducing the life-time of the metastable state, due to larger steric hindrance in the metastable state than in the transition state for the thermal helix inversion (THI). A larger increase in the separation of the absorption peaks of the two isomers is also obtained. The Cl atom substitution, however, changes the energy landscape significantly, making the M isomer lower in energy than the P isomer, and raising the energy barrier for THI beyond that of the back transition, thus quenching the rotation.

Thin-film lithium niobate on sapphire provides an excellent platform for simultaneously confining acoustic and optical modes without suspended structures, enabling efficient acousto-optic modulation through strong piezoelectric coupling. Here, we identify the challenges in realizing the forward Brillouin interaction at visible wavelengths, and overcome the limitation by introducing a quasi-phase-matching scheme through periodic waveguide width modulation. We predict a complete inter-modal optical conversion over 1.1 mm using only 1 mW acoustic power. Our study paves the way for high-performance visible-wavelength acousto-optic devices on integrated platforms.

Large Sagnac interferometers in the form of active ring lasers have emerged as unique rotation sensors in the geosciences, where their sensitivity allows to detect geodetic and seismological signals. The passive laser gyroscope variant, however, is still at a stage of development, and adequate laser frequency stabilization techniques are explored to reach the desired levels of sensitivity and stability. Here, we present the first Hänsch-Couillaud locked passive laser gyroscope. We employ a lock-in scheme to overcome the flicker floor to achieve a sensitivity of \SI{3.1}{\nano\radian/\second}, corresponding to a fraction of \SI{7.7e-5}{} in the Earth's rotation rate.

This study analyzes how Europe can decarbonize its industrial sector while remaining competitive. Using the open-source model PyPSA-Eur, it examines key energy- and emission-intensive industries, including steel, cement, methanol, ammonia, and high-value chemicals. Two development paths are explored: a continued decline in industrial activity and a reindustrialization driven by competitiveness policies. The analysis assesses cost gaps between European green products and lower-cost imports, and evaluates strategies such as intra-European relocation, selective imports of green intermediates, and targeted subsidies. Results show that deep industrial decarbonization is technically feasible, led by electrification, but competitiveness depends strongly on policy choices. Imports of green intermediates can lower costs while preserving jobs and production, whereas broad subsidies are economically unsustainable. Effective policy should focus support on sectors like ammonia and steel finishing while maintaining current production levels.

We report a new method of two-pathway coherent control using three narrow-band cw laser sources, phase locked in an optical phase-lock loop, to maintain the high degree of optical coherence required for the coherent control process. In addition, we derive expressions for two-photon transition amplitudes and demonstrate their dependence on the polarization of the field components. This phase-locking technique expands the set of interactions to which coherent control techniques may be applied. It also allows for a constant low-frequency offset between the optical interactions, producing a continuous and constant phase ramp between the interactions, facilitating phase-sensitive detection of the modulating atomic signal. We illustrate this technique with two-photon vs.~one-photon excitation of a $\Delta F = 1$ component, and alternatively a $\Delta F = 0$ component, of the $6s \: ^2S_{1/2} \rightarrow 7s \: ^2S_{1/2}$ transition of atomic cesium.

Classical actuator disk theory, developed more than a century ago, provides an idealised description of turbine rotor performance. It treats a rotor as an infinitesimally-thin permeable disk and applies the governing flow equations over a streamtube encompassing the disk. A well-known limitation of the theory is its assumption of ideal flow downstream of the disk, which restricts its applicability to short downwind distances before turbulence and mixing processes governing the wake evolution take hold. As a result, the classical theory also leads to unphysical predictions for highly-loaded rotors. Turbulent axisymmetric wakes, by contrast, represent an extensively-studied canonical free shear flow with much of the progress and its applications to wind turbines limited to the far-wake dynamics. In this work, we introduce a generalised actuator disk theory based on a hybrid stream-tube and wake control volume, that seamlessly integrates classical actuator disk analysis with wake turbulence modelling at arbitrary distances from the rotor. The resulting model, while still idealised, can be used to predict variations in velocity, pressure, and cross-sectional flow area as function of position, both upstream and downstream of the rotor disk. Furthermore, by accounting for turbulent entrainment in the wake development, it provides more realistic predictions of thrust and power coefficients for highly-loaded disks.

Accurate and timely travel information is an asset for enhancing passenger travel experience during normal traffic, and for mitigating the discomforts during disruptions. With longer and more frequent disruptions as well as increasing ridership, traffic delays can incur substantial costs for passengers and other transport stakeholders, e.g., operators and infrastructure managers. Such costs can, however, be reduced thanks to effective travel information strategies during traffic disruptions. In this paper, we introduce an evaluation model to assess the value of travel information under different scenarios. Focusing on real-time travel information to train passengers, accessibility benefits are quantified in monetary terms based on historical delay distributions, timing of travel information (pre/on-trip) and ridership. Using a case study from the Swedish railways, the model is showcased and applied to a commuter line in Stockholm. The experimental results indicate individual valuations that are higher than references and savings at the system level of at least 23% of the delay costs. Further testing of the model, e.g., on larger-scale scenarios, and including transfer trips, is a possible direction for future works.

We examine more than a decade of quota policy at Unesp, analyzing Physics, Biology, and Pedagogy as representative programs of distinct assessment styles. Quotas show little impact in Physics, where the admission barrier is low, and in Pedagogy, where high pass rates make it difficult to differentiate students, but they reveal systematic differences in Biology. Focusing the analysis on Calculus I - an introductory course in Physics and other Science, Technology, Engineering, and Mathematics (STEM) programs - for which much larger statistics are available, a clear hierarchy emerges: students admitted through open competition perform best, those from public schools achieve intermediate results, and students from racial quotas perform worst. When students are divided directly by the admittance exam grade, the performance difference is even clearer. Statistical analysis also shows that, contrary to expectation, the probability of passing decreases as the number of attempts increases, indicating that initial educational gaps are difficult to overcome within higher education.

Superconducting radio frequency accelerating cavities made with different material layers, such as copper, Nb or Nb3Sn, are susceptible to thermoelectric effects due to differences in Seebeck coefficients between the metals. A temperature gradient across the surfaces can drive thermoelectric currents, which may impact the cavity performance. A layered Cu/Nb/Nb3Sn single cell cavity was tested with cryocoolers in 2022. Three heaters were mounted on the cavity surface at different locations and three single axis cryogenic fluxgate magnetometers were attached close to the cavity equator. A linear increase in the magnetic field was measured while increasing the heaters power. The cavity setup was analysed with COMSOL and the results showed a trend similar to that observed in the experiment. This contribution details the approach chosen for the simulation and some of the challenges encountered.

Symbolic regression (SR), the automated discovery of mathematical expressions from data, is a cornerstone of scientific inquiry. However, it is often hindered by the combinatorial explosion of the search space and a tendency to overfit. Popular methods, rooted in genetic programming, explore this space syntactically, often yielding overly complex, uninterpretable models. This paper introduces IdeaSearchFitter, a framework that employs Large Language Models (LLMs) as semantic operators within an evolutionary search. By generating candidate expressions guided by natural-language rationales, our method biases discovery towards models that are not only accurate but also conceptually coherent and interpretable. We demonstrate IdeaSearchFitter's efficacy across diverse challenges: it achieves competitive, noise-robust performance on the Feynman Symbolic Regression Database (FSReD), outperforming several strong baselines; discovers mechanistically aligned models with good accuracy-complexity trade-offs on real-world data; and derives compact, physically-motivated parametrizations for Parton Distribution Functions in a frontier high-energy physics application. IdeaSearchFitter is a specialized module within our broader iterated agent framework, IdeaSearch, which is publicly available at this https URL.

Perovskite-organic tandem solar cells (P-O TSCs) hold great promise for next-generation thin-film photovoltaics, with steadily improving power conversion efficiency (PCE). However, the development of optimal interconnecting layers (ICLs) remains one major challenge for further efficiency gains, and progress in understanding the improved long-term stability of P-O tandem configuration has been lagging. In this study, we experimentally investigate the enhanced stability of p-i-n P-O TSCs employing a simplified C60/atomic-layer-deposition (ALD) SnOx/PEDOT: PSS ICL without an additional charge recombination layer (CRL), which achieve an averaged efficiency of 25.12% and a hero efficiency of 25.5%. Our finding discovers that the recrystallization of C60, a widely used electron transport layer in perovskite photovoltaics, leads to the formation of grain boundaries during operation, which act as migration channels for the interdiffusion of halide and Ag ions. Critically, we demonstrate for the first time that the tandem device architecture, incorporating organic semiconductor layers, effectively suppresses the bi-directional ion diffusion and mitigates electrode corrosion. Thus, the P-O TSC establishes a mutual protection system: the organic layers stabilize the perovskite sub-cell by suppressing ion diffusion-induced degradation, and the perovskite layer shields the organic sub-cell from spectrally induced degradation. The simultaneous synergistic protection mechanism enables P-O TSCs to achieve exceptional long-term operational stability, retaining over 91% of their initial efficiency after 1000 hours of continuous metal-halide lamp illumination, and to exhibit minimal fatigue after 86 cycles (2067 hours) of long-term diurnal (12/12-hour) testing. These results demonstrate that tandem cells significantly outperform their single-junction counterparts in both efficiency and stability.

We demonstrate that an effect phenomenologically analogous to circular dichroism can arise even for dielectric and isotropic chiral spherical particles. By analyzing the polarimetry of light scattered from a chiral, lossless microsphere illuminated with linearly polarized light, we show that the scattered light becomes nearly circularly polarized, exhibiting large, nonresonant values of the Stokes parameter $S_3$ for a broad range of visible frequencies. This phenomenon occurs only in the Mie regime, with the microsphere radius comparable to the wavelength, and provided that the scattered light is collected by a high-NA objective lens, including non-paraxial Fourier components. Altogether, our findings offer a theoretical framework and motivation for an experimental demonstration of a novel chiroptical effect with isolated dielectric particles, with potential applications in enantioselection and characterization of single microparticles, each and every one with its own chiral response.

For the construction of the ATLAS Inner Tracker strip detector, silicon strip sensor modules are glued directly onto carbon fibre support structures using a soft silicone gel. During tests at temperatures below \unit[-35]{$^{\circ}$C}, several of the sensors were found to crack due to a mismatch in coefficients of thermal expansion between polyimide circuit boards with copper metal layers (glued onto the sensor) and the silicon sensor itself. While module assembly procedures were developed to minimise variations between modules, cold tests showed a wide range of temperatures at which supposedly comparable modules failed. The observed variance (fracture temperatures between \unit[-35]{\textcelsius} and \unit[-70]{\textcelsius}) for supposedly comparable modules suggests an undetected variation between modules suspected to be intrinsic to the silicon wafer itself. Therefore, a test programme was developed to investigate the fracture stress of representative sensor wafer cutoffs. This paper presents results for the fracture stress of silicon sensors used in detector modules.

Hourly predictions are critical for issuing flood warnings as the flood peaks on the hourly scale can be distinctly higher than the corresponding daily ones. Currently a popular hourly data-driven prediction scheme is multi-time-scale long short-term memory (MTS-LSTM), yet such models face challenges in probabilistic forecasts or integrating observations when available. Diffusion artificial intelligence (AI) models represent a promising method to predict high-resolution information, e.g., hourly streamflow. Here we develop a denoising diffusion probabilistic model (h-Diffusion) for hourly streamflow prediction that conditions on either observed or simulated daily discharge from hydrologic models to generate hourly hydrographs. The model is benchmarked on the CAMELS hourly dataset against record-holding MTS-LSTM and multi-frequency LSTM (MF-LSTM) baselines. Results show that h-Diffusion outperforms baselines in terms of general performance and extreme metrics. Furthermore, the h-Diffusion model can utilize the inpainting technique and recent observations to accomplish data assimilation that largely improves flood forecasting performance. These advances can greatly reduce flood forecasting uncertainty and provide a unified probabilistic framework for downscaling, prediction, and data assimilation at the hourly scale, representing risks where daily models cannot.

A chaotic system is called ultra-chaos when its statistics have sensitivity dependence on initial condition and/or other small disturbances. In this paper, using two-dimensional turbulent Kolmogorov flow as an example, we illustrate that tiny variation of initial condition of Navier-Stokes equations can lead to huge differences not only in spatiotemporal trajectory but also in flow symmetry and its statistics. Here, in order to avoid the influence of artificial numerical noise, we apply ``clean numerical simulation'' (CNS) which can guarantee that the numerical noise can be reduced to such a desired low level that they are negligible in a time interval long enough for calculating statistics. This discovery highly suggests that the Navier-Stokes turbulence (i.e. turbulence governed by the Navier-Stokes equations) might be an ultra-chaos, say, small disturbances must be considered even from viewpoint of statistics. This however leads to a paradox in logic, since small disturbances, which are unavoidable in practice, are unfortunately neglected by the Navier-Stokes turbulence. Some fundamental characteristics of turbulence model are discussed and suggested in general meanings.

Uncertainty of the quantum electrodynamics theoretical prediction for the hyperfine splitting in the ground state of muonium is considered. It is compared with the respective discussion in the two most recent CODATA adjustments of the fundamental physical constants.

This work presents a nonlinear system identification framework for modeling the power extraction dynamics of wind turbines, including both freestream and waked conditions. The approach models turbine dynamics using data-driven power coefficient maps expressed as combinations of compact radial basis functions and polynomial bases, parameterized in terms of tip-speed ratio and upstream conditions. These surrogate models are embedded in a first-order dynamic system suitable for model-based control. Experimental validation is carried out in two wind tunnel configurations: a low-turbulence tandem setup and a high-turbulence wind farm scenario. In the tandem case, the identified model is integrated into an adapted K\omega^2 controller, resulting in improved tip-speed ratio tracking and power stability compared to BEM-based and steady-state models. In the wind farm scenario, the model captures the statistical behavior of the turbines despite unresolved turbulence. The proposed method enables interpretable, adaptive control across a range of operating conditions without relying on black-box learning strategies.

Terraforming Mars is an age old science fiction concept now worth revisiting through the lens of modern science and technology. This document serves as a summary of contemporary ideas about Mars terraforming, prepared for attendees of the 2025 Green Mars Workshop. It presents one illustrative story of how Mars might be transformed into a habitable world. The story is told in reverse, beginning with possible planetary endpoints and tracing backward to the steps required to reach them. Along the way, it highlights alternative approaches, critical unknowns and research priorities, and the near term applications and benefits of terraforming research for planetary science, climate engineering, and sustainable technologies on Earth.

To overcome the bottleneck of classical path planning schemes in solving NP problems and address the predicament faced by current mainstream quantum path planning frameworks in the Noisy Intermediate-Scale Quantum (NISQ) era, this study attempts to construct a quantum path planning solution based on parallel Quantum Approximate Optimization Algorithm (QAOA) architecture. Specifically, the grid path planning problem is mapped to the problem of finding the minimum quantum energy state. Two parallel QAOA circuits are built to simultaneously execute two solution processes, namely connectivity energy calculation and path energy calculation. A classical algorithm is employed to filter out unreasonable solutions of connectivity energy, and finally, the approximate optimal solution to the path planning problem is obtained by merging the calculation results of the two parallel circuits. The research findings indicate that by setting appropriate filter parameters, quantum states corresponding to position points with extremely low occurrence probabilities can be effectively filtered out, thereby increasing the probability of obtaining the target quantum state. Even when the circuit layer number p is only 1, the theoretical solution of the optimal path coding combination can still be found by leveraging the critical role of the filter. Compared with serial circuits, parallel circuits exhibit a significant advantage, as they can find the optimal feasible path coding combination with the highest probability.

Research into optical spiking neural networks (SNNs) has primarily focused on spiking devices, networks of excitable lasers or numerical modelling of large architectures, often overlooking key constraints such as limited optical power, crosstalk and footprint. We introduce SEPhIA, a photonic-electronic, multi-tiled SNN architecture emphasizing implementation feasibility and realistic scaling. SEPhIA leverages microring resonator modulators (MRMs) and multi-wavelength sources to achieve effective sub-one-laser-per-spiking neuron efficiency. We validate SEPhIA at both device and architecture levels by time-domain co-simulating excitable CMOS-MRR coupled circuits and by devising a physics-aware, trainable optoelectronic SNN model, with both approaches utilizing experimentally derived device parameters. The multi-layer optoelectronic SNN achieves classification accuracies over 90% on a four-class spike-encoded dataset, closely comparable to software models. A design space study further quantifies how photonic device parameters impact SNN performance under constrained signal-to-noise conditions. SEPhIA offers a scalable, expressive, physically grounded solution for neuromorphic photonic computing, capable of addressing spike-encoded tasks.

A simple way to calculate Rabi frequencies is outlined for interactions of atomic or nuclear multipole moments with laser fields that focuses on their relative geometry. The resulting expression takes the form of a dot product between the laser polarization and a vector spherical harmonic, thereby naturally connecting to the multipole's far-field spontaneous-emission pattern and providing a way to visualize the interaction. Since the vector spherical harmonics are not yet a standard tool in quantum science, their relevant properties are reviewed. This approach is illustrated in the calculation of a variety of beam effects, yielding both perturbative corrections and some nontrivial cases with non-vanishing coupling.

Close to Earth the solar wind is usually super-Alfvénic, i.e. the speed of the solar wind is much larger than the Alfvén speed. However, in the lower coronal regions, the solar wind is mostly sub-Alfvénic. With the Parker Solar Probe (PSP) crossing the boundary between the sub- and super-Alfvénic flow, Bandyopadhyay et al. (2022) performed a turbulence characterization of the sub-Alfvénic solar wind with initial data from encounters 8 and 9. In this study, we re-examine the turbulence properties such as turbulence amplitude, anisotropy of the magnetic field variance, intermittency and switchback strength extending with PSP data for encounters 8-19. The later orbits probe lower altitudes and experience sub-Alfvénic conditions more frequently providing a greater statistical coverage to contrast sub- and super-Alfvénic solar wind. Also, by isolating the intervals where the solar wind speed is approximately equal to the Alfvén speed, we explore the transition in more detail. We show that the amplitude of the normalized magnetic field fluctuation is smaller for the sub-Alfvénic samples. While solar wind turbulence in general is shown to be anisotropic, the sub-Alfvénic samples are more anisotropic than the super-Alfvénic samples, in general. Further, we show that the sub- and super-Alfvénic samples do not show much distinction in terms of intermittency strength. Finally, consistent with prior results, we find no evidence for polarity reversing > 90 degrees switchbacks in the sub-Alfvénic solar wind

We study group fairness in the context of feedback loops induced by meritocratic selection into programs that themselves confer additional advantage, like college admissions. We introduce a novel stylized inter-generational model for the setting and analyze it in situations where there are no underlying differences between two populations. We show that, when the benefit of the program (or the harm of not getting into it) is completely symmetric, disparities between the two populations will eventually dissipate. However, the time an accumulated advantage takes to dissipate could be significant, and increases substantially as a function of the relative importance of the program in conveying benefits. We also find that significant disparities can arise due to chance even from completely symmetric initial conditions, especially when populations are small. The introduction of even a slight asymmetry, where the group that has accumulated an advantage becomes slightly preferred, leads to a completely different outcome. In these instances, starting from completely symmetric initial conditions, disparities between groups arise stochastically and then persist over time, yielding a permanent advantage for one group. Our analysis precisely characterizes conditions under which disparities persist or diminish, with a particular focus on the role of the scarcity of available spots in the program and its effectiveness. We also present extensive simulations in a richer model that further support our theoretical results in the simpler, stylized model. Our findings are relevant for the design and implementation of algorithmic fairness interventions in similar selection processes.

Optically-active spin qubits have emerged as powerful quantum sensors capable of nanoscale magnetometry, yet conventional coherent sensing approaches are ultimately limited by the coherence time of the sensor, typically precluding detection in the sub-MHz regime. We present a broadly applicable fluorescence-encoding method that circumvents coherence-time constraints by transducing time-varying magnetic fields directly into modulated fluorescence signals. Using nitrogen-vacancy centers in diamond as a model system, we demonstrate shot-noise-limited sensitivity for AC magnetic fields spanning near-DC to MHz frequencies, with detection bandwidth tunable via optical excitation power. The technique captures complete spectral information in a single measurement, eliminating the need for point-by-point frequency scanning, and allows phase-sensitive multi-frequency detection with Hz-level resolution. This approach transforms quantum sensors into atomic-scale spectrum analyzers, with immediate applications for low-frequency RF communication, zero-field NMR, and bioelectronic sensing. Our approach is broadly applicable to the expanding class of optically-active spin qubits, including molecular systems and fluorescent proteins, opening new sensing regimes previously inaccessible to coherent techniques

We present a numerical framework for constructing a targeted digital twin (tDT) that directly models the dynamics of quantities of interest (QoIs) in a full digital twin (DT). The proposed approach employs memory-based flow map learning (FML) to develop a data-driven model of the QoIs using short bursts of trajectory data generated through repeated executions of the full DT. This renders the construction of the FML-based tDT an entirely offline computational process. During online simulation, the learned tDT can efficiently predict and analyze the long-term dynamics of the QoIs without requiring simulations of the full DT system, thereby achieving substantial computational savings. After introducing the general numerical procedure, we demonstrate the construction and predictive capability of the tDT in a computational fluid dynamics (CFD) example: two-dimensional incompressible flow past a cylinder. The QoIs in this problem are the hydrodynamic forces exerted on the cylinder. The resulting tDTs are compact dynamical systems that evolve these forces without explicit knowledge of the underlying flow field. Numerical results show that the tDTs yield accurate long-term predictions of the forces while entirely bypassing full flow simulations.

Magnetic particles underpin a broad range of technologies, from water purification and mineral processing to bioseparations and targeted drug delivery. The dynamics of magnetic particles in high-gradient magnetic fields-encompassing both their transport and eventual capture-arise from the coupled interplay of field-driven drift, fluid advection, and particle-field feedback. These processes remain poorly captured by existing models relying on empirical closures or discrete particle tracking. Here, we present a thermodynamically consistent continuum theory for collective magnetic particle transport and capture in high-gradient fields. The framework derives from a free-energy functional that couples magnetic energy, entropic mixing, and steric interactions, yielding a concentration-dependent susceptibility via homogenization theory. The resulting equations unify magnetism, mass transport, and momentum balances without ad hoc shut-off criteria, allowing field shielding, anisotropic deposition, and boundary-layer confinement to emerge naturally. Simulations predict canonical capture morphologies-axially aligned plumes, crescent-shaped deposits, and nonlinear shielding-across field strengths and flow regimes, consistent with trends reported in prior experimental and modeling studies. By organizing captured particle mass data into a dimensionless phase diagram based on the Mason number, we reveal three distinct regimes-thermodynamically controlled, transitional, and dynamically controlled. This perspective provides a predictive platform for in silico optimization and extension to three-dimensional geometries, and informing digital twin development for industrial-scale high-gradient magnetic separation processes.

Proteinaceous shells useful for various biomedical applications exhibit a wide range of anomalous structures that are fundamentally different from icosahedral viral capsids described by the Caspar-Klug paradigmatic model. Exploring the Protein Data Bank, we have identified nine different types of anomalous shells structurally close to flat octagonal quasicrystals. As we show, these numerous shells have cubic nets cut from short-period approximants of an octagonal tiling composed of square and rhombic tiles. The approximants and parent tiling are easily obtained within the Landau density wave approach, while the nonequilibrium assembly of them can be simulated using the pair potentials derived from critical density waves. Gluing a polyhedron net and mapping it onto a spherical surface induces tile distortions, and to reduce them, we introduce and minimize the effective elastic energy of the system. Thus, we return quasi-equivalence to previously equivalent tiles. Possible cubic faceting of the octagonal spherical tilings is discussed in terms of the topological charge distribution over the tiling vertices. The proposed structural models describe numerous proteinaceous shells including about half of the known symmetrical enzymes. Our results constitute a fundamental basis for further applications of identified octagonal assemblies and can help to discover and study similar systems in the future.

An attractive approach for stabilizing entangled many-body spin states is to employ engineered dissipation. Most existing proposals either target relatively simple collective spin states, or require numerous independent and complex dissipative processes. Here, we show a surprisingly versatile scheme for many-body reservoir engineering that relies solely on fully collective single-excitation decay, augmented with local Hamiltonian terms. Crucially, all these ingredients are readily available in cavity QED setups. Our method is based on splitting the spin system into groups of sub-ensembles, and provides an easily tunable setup for stabilizing a broad family of pure, highly entangled states with closed-form analytic descriptions. Our results have immediate application to multi-ensemble quantum metrology, enabling Heisenberg-limited sensing of field gradients and curvatures. Notably, the generated states have robustness against common-mode phase noise, and only require simple Ramsey-style measurements. The same setup also allows the stabilization of entangled states in a 1D chain of spin ensembles with symmetry-protected topological (SPT) order, and have a direct connection to the outputs of sequential unitary circuits. In particular, we present an efficient method for engineering the celebrated spin-1 Affleck-Kennedy-Lieb-Tasaki (AKLT) state.

We propose cascaded spontaneous four-wave mixing (SFWM) in microring resonators as a scalable and efficient approach for directly generating non-Gaussian states of light. Focusing on the well-understood "low-gain" regime, we demonstrate that triplet generation through cascaded SFWM can be achieved with high efficiency and favorable spectral characteristics using realistic microring sources in AlGaAs. The ability to achieve the generation of light in a single set of supermodes -- and the accessibility of the "high-gain" regime at realistic pump powers -- makes this source a promising candidate as a direct and deterministic source of non-Gaussian light for photonic quantum information processing.

We investigate the thermoelectric response of single-molecule junctions composed of acyclic cross-conjugated molecules, including dendralene analogues and related iso-poly(diacetylene) (iso-PDA) motifs, in which node-possessing repeat units are connected in series. Using many-body quantum transport theory, we show that increasing the number of repeat units leaves the fundamental gap essentially unchanged while giving rise to a split-node spectrum whose cumulative broadening dramatically enhances the thermopower. This form of quantum enhancement can exceed other interference-based mechanisms, such as the coalescence of nodes into a supernode, suggesting new opportunities for scalable quantum-interference-based materials. Although illustrated here with cross-conjugated systems, the underlying principles apply broadly to series-connected architectures hosting multiple interference nodes. Finally, we evaluate the scaling of the electronic figure of merit ZT and the maximum thermodynamic efficiency. Together, these results highlight the potential for split-node-based materials to realize quantum-enhanced thermoelectric response.

Water, a ubiquitous and fundamental substance, plays a critical role across a wide range of disciplines from physics and chemistry to biology and engineering. Despite theoretical predictions of several phases of two-dimensional (2D) ice confined between idealized hydrophobic walls, experimental validation has been limited to the square phase, whose structural origin remains controversial. Here, we propose a realistic nanoconfinement setup using wide-bandgap hexagonal boron nitride (h-BN) as the capping layer and Cu(111) as the substrate. This protocol enables scanning tunneling microscope (STM) to resolve the atomic-scale arrangement of water molecules beneath the h-BN layer, overcoming the limitations of conventional techniques. Simulated STM images unambiguously identify all ordered flat 2D ice phases, as well as coexisting phases, and effectively distinguish them from potential contaminants. These findings establish a robust framework for experiment to systematically probe the phase structures of 2D ice, opening an avenue for studying nanoconfined water under ambient conditions.

Altermagnets have recently garnered significant interest due to their vanishing net magnetic moment and non-relativistic momentum-dependent spin splitting. However, altermagnetic (AM) multiferroics especially triferroics remain scarce. We investigate the experimentally synthesized non-van der Waals CrSb as a model system to explore the effects of dimensionality and facet orientation on its ferroic properties. NiAs, MnP, wurtzite (WZ), zincblende (ZB), and rocksalt (RS) phases are considered. Using first-principles calculations, we predict the altermagnetism of CrSb in MnP phase which has comparable stability with experimental NiAs phase. Both NiAs- and MnP-phase (110) facets exhibit AM-ferroelastic (FC) biferroics, while the WZ-phase bulk and (001) facets host ferromagnetic (FM) or AM-ferroelectric (FE) biferroics. Notably, the WZ-phase (110) facets are identified as FM/AM-FE-FC triferroics, with moderate energy barriers of 0.129 and 0.363 eV atom-1 for FE and FC switching, respectively. Both FE and FC switching can reverse the AM spin splitting in antiferromagnetic (AFM) configurations while preserving the high spin polarization in FM states. The magnetic anisotropy is highly tunable, exhibiting either uniaxial or in-plane behavior depending on the phase, dimension, and facet. This work establishes a framework for designing AM multiferroics through polymorphic, dimensional, and facet engineering, offering promising avenues for multifunctional spintronic applications.

We present limits on spin-independent inelastic WIMP-nucleus scattering using the 737.1 kg $\cdot$ day dataset from the CDEX-1B experiment. Expected nuclear recoil spectra for various inelastic WIMP masses $m_\chi$ and mass splittings $\delta$ are calculated under the standard halo model. An accurate background model of CDEX-1B is constructed by simulating all major background sources. The model parameters are then determined through maximum likelihood estimation and Markov Chain Monte Carlo fitting. The resulting 90\% confidence level upper limits on the WIMP-nucleon cross section $\sigma_{\mathrm{n}}$ exclude certain DAMA/LIBRA allowed regions: the $\chi^2 < 4$ regions for $\delta < 30$ keV at $m_\chi = 250$ GeV and the $\chi^2 < 9$ region for $\delta < 50$ keV at $m_\chi = 500$ GeV. The method is applicable to other inelastic dark matter scenarios, and the upcoming CDEX-50 experiment is expected to improve sensitivity by four orders of magnitude.

We present a three-dimensional simulation study of silicon nanowire double quantum dots with leads, which extends beyond traditional effective-mass or quasi-1D and quasi-2D approaches typically applied to bulk or planar geometries. Using the Sentaurus QTX, we self-consistently couple a 3-D Poisson solver with two-dimensional Schrödinger solutions along the strongly confined width * thickness cross-sections normal to the transport direction, yielding bound-state energies and wavefunctions across the device cross-section. This method captures wavefunction evolution, subband formation, and valley splitting while retaining the full three-dimensional electrostatics of the device. Our results show that narrow quantum dots (width = 5 nm) provide strong confinement, enhanced valley splitting, and robust single-electron localization, whereas wider quantum dots (width = 20 nm) allow single-electron occupation at lower gate voltages but with shallower quantum wells and additional higher-order modes in the wavefunction. Importantly, the quantum dot width also plays a key role in tunnel coupling: smaller channel widths enhance wavefunction overlap across the barrier, resulting in higher tunnel coupling, whereas increasing the width reduces coupling, which eventually saturates once the width is approximately twice the plunger-gate length. By systematically varying the quantum dot width together with plunger- and barrier-gate lengths, we investigate their combined influence on the conduction-band profile and interdot coupling. Together, these simulations provide design guidelines for geometry-driven control of confinement, valley splitting, and tunnel coupling in silicon nanowire double quantum dots.

Forced response curves (FRCs) of nonlinear systems can exhibit complex behaviors, including hardening/softening behavior and bifurcations. Although topology optimization holds great potential for tuning these nonlinear dynamic responses, its use in high-dimensional systems is limited by the high cost of repeated response and sensitivity analyses. To address this challenge, we employ the spectral submanifolds (SSMs) reduction theory, which reformulates the periodic response as the equilibria of an associated reduced-order model (ROM). This enables efficient and analytic evaluation of both response amplitudes and their sensitivities. Based on the SSM-based ROM, we formulate optimization problems that optimize the peak amplitude, the hardening/softening behavior, and the distance between two saddle-node bifurcations for an FRC. The proposed method is applied to the design of nonlinear MEMS devices, achieving targeted performance optimization. This framework provides a practical and efficient strategy for incorporating nonlinear dynamic effects into the topology optimization of structures.

This study proposes a reversible phonon excitation strategy to dynamically modulate the thermal conductivity of boron arsenide (BAs), addressing the opposing thermal conductivity requirements in electronics and thermoelectrics. Using first-principles calculations and Boltzmann transport equation, we demonstrate that selective excitation of specific phonon modes enables active control over thermal transport. At an excitation multiplier of 25, the thermal conductivity of BAs can be enhanced by up to 2% or suppressed by up to 35% relative to its intrinsic value of 2235 W m^-1 K^-1. At a lower multiplier of 5, thermal conductivity can be increased by 2% or decreased by 11%. The modulation effect depends on excitation frequency, multiplier, and intrinsic phonon properties, with certain frequencies exhibiting opposite trends under different excitation intensities. Mechanistic analysis shows that at low excitation levels, phonon splitting suppresses Umklapp scattering, reducing the scattering rate, while at high levels, it enhances Normal scattering, increasing the scattering rate. This approach offers a dynamic and reversible route to tuning thermal conductivity, with applications in thermal management and thermoelectric energy conversion.

This paper presents a machine learning framework for electricity demand forecasting across diverse geographical regions using the gradient boosting algorithm XGBoost. The model integrates historical electricity demand and comprehensive weather and socioeconomic variables to predict normalized electricity demand profiles. To enable robust training and evaluation, we developed a large-scale dataset spanning multiple years and countries, applying a temporal data-splitting strategy that ensures benchmarking of out-of-sample performance. Our approach delivers accurate and scalable demand forecasts, providing valuable insights for energy system planners and policymakers as they navigate the challenges of the global energy transition.

The existence of cosmic fields made from yet unknown light bosons is predicted in many extensions to the Standard Model. They are especially of interest as possible constituents of dark matter. To detect such light and weakly interacting fields, atomic precision measurements offer one of the most sensitive platforms. In this work, we derive which atomic observables are sensitive to what kind of cosmic field couplings. For this we consider fields that couple either through scalar, pseudoscalar, vector, axial vector, or tensor couplings. We derive the corresponding non relativistic atomic potentials. Based on their symmetry properties, these can induce direct energy shifts or induce atomic electric dipole, magnetic dipole, electric quadrupole as well as nuclear Schiff and anapole moments.

We present the discovery of a fundamental composition law governing conjugate observables in the Random Permutation Sorting System (RPSS). The law links the discrete permutation count Np and the continuous elapsed time T through a functional relation connecting the characteristic function of timing distributions to the probability generating function of permutation counts. This framework enables entropy purification, transforming microarchitectural timing fluctuations into uniform randomness via geometric convergence. We establish convergence theorems with explicit bounds and validate the results experimentally, achieving Shannon entropy above 7.9998 bits per byte and chi-square uniformity across diverse platforms. The composition law provides a universal foundation for generating provably uniform randomness from general-purpose computation, securing cryptographic purity from emergent computational dynamics.

Large language models (LLMs) are increasingly deployed for climate-related applications, where understanding internal climatological knowledge is crucial for reliability and misinformation risk assessment. Despite growing adoption, the capacity of LLMs to recall climate normals from parametric knowledge remains largely uncharacterized. We investigate the capacity of contemporary LLMs to recall climate normals without external retrieval, focusing on a prototypical query: mean July 2-m air temperature 1991-2020 at specified locations. We construct a global grid of queries at 1° resolution land points, providing coordinates and location descriptors, and validate responses against ERA5 reanalysis. Results show that LLMs encode non-trivial climate structure, capturing latitudinal and topographic patterns, with root-mean-square errors of 3-6 °C and biases of $\pm$1 °C. However, spatially coherent errors remain, particularly in mountains and high latitudes. Performance degrades sharply above 1500 m, where RMSE reaches 5-13 °C compared to 2-4 °C at lower elevations. We find that including geographic context (country, city, region) reduces errors by 27% on average, with larger models being most sensitive to location descriptors. While models capture the global mean magnitude of observed warming between 1950-1974 and 2000-2024, they fail to reproduce spatial patterns of temperature change, which directly relate to assessing climate change. This limitation highlights that while LLMs may capture present-day climate distributions, they struggle to represent the regional and local expression of long-term shifts in temperature essential for understanding climate dynamics. Our evaluation framework provides a reproducible benchmark for quantifying parametric climate knowledge in LLMs and complements existing climate communication assessments.

Subwavelength arrays of plasmonic nanoparticles allow us to control the behaviour of light at the nanoscale. Here, we develop an eigenmode analysis, employing a coupled electromagnetic dipole formalism, which permits us to isolate the contribution to the far-field radiation of each array mode. Specifically, we calculate the far-field radiation patterns by bulk, edge and corner out-of-plane eigenmodes in a finite 2D Su-Schrieffer-Heeger (SSH) array of plasmonic nanoparticles with out-of-plane dipolar resonances. The breaking of symmetries in multipartite unit cells is exploited to tailor the optical properties and far-field radiation of the resonant modes. We prove that the antisymmetric modes are darker and have higher Q-factors than their symmetric counterparts. Also, the out-of-plane nature of the dipolar resonances imposes that all bulk $\Gamma$-modes are dark, while corner and edge states need extra in-plane symmetries to cancel the far-field radiation; radiation patterns in turn become more complex and concentrated along the array plane with increasing array size.

Perovskite oxides are promising for energy and quantum technologies, but wide-gap hosts such as NaAlO3 suffer from deep-UV absorption and limited carrier transport. Using first-principles GGA+U+SOC calculations, we investigate Eu3+-, Gd3+-, and Tb3+-doped NaAlO3 and evaluate their electronic, optical, elastic, and thermoelectric properties. Rare-earth substitution is thermodynamically favorable (formation energies 1.2-1.6 eV) and induces strong f-p hybridization, reducing the pristine band gap (about 6.2 eV) to about 3.1 eV for Tb. Spin-resolved band structures reveal Gd-driven half-metallicity, Eu-induced spin-selective metallicity, and Tb-stabilized p-type semiconducting behavior. The optical spectra show a red-shifted absorption edge (about 2.0-2.2 eV), a large static dielectric response (epsilon1(0) about 95 for Eu), and plasmonic resonances near 4 eV, enabling visible-light harvesting. Elastic analysis indicates mild lattice softening with preserved ductility (Pugh ratio B/G about 1.56-1.57). Thermoelectric performance is enhanced, with Seebeck coefficients greater than 210 uV/K for Eu and Tb and ZT about 0.45 at 500 K. These results identify rare-earth-doped NaAlO3 as a multifunctional perovskite platform for photovoltaics, photocatalysis, thermoelectrics, and spintronics.

The performance of silicon nano-devices at cryogenic temperatures is critical for quantum computing technologies. Through multi-valley Monte Carlo simulations of Si (110) systems, we reveal a fundamental shift in electron transport physics at low temperatures. Phonon scattering becomes negligible, and mobility is instead dictated by a competition between remote Coulomb scattering (RCS) at low carrier densities and surface roughness scattering (SRS) at high densities. This competition creates a distinct peak in electron mobility. Furthermore, we demonstrate a critical design trade-off for high-$\kappa$ dielectrics like $\mathrm{HfO_2}$: while enhancing gate control, they introduce strong remote phonon scattering, which can suppress mobility. These findings provide essential guidelines for the material selection and design of next-generation cryogenic nano-devices.

Understanding the relationship between many-body localization and spectra in non-Hermitian many-body systems is crucial. In a one-dimensional clean, long-range interaction-induced non-Hermitian many-body localization system, we have discovered the coexistence of static and dynamic spectral real-complex phase transitions, along with many-body ergodic-localized phase transitions. The phase diagrams of these two types of transitions show similar non-monotonic boundary trends but do not overlap, highlighting properties distinct from conventional disorder-induced non-Hermitian many-body localization. We also propose a potential experimental realization of this model in cold-atom systems. Our findings provide valuable insights for further understanding the relationship between non-Hermitian many-body localization and non-Hermitian spectra in long-range interacting systems.

We study the over-damped dynamics of individual one-dimensional elastic filaments subjected to a chiral active force which propels each point of the filament at a fixed angle relative to the tangent vector of the filament at that point. Such a model is a reasonable starting point for describing the behavior of polymers such as microtubules in gliding assay experiments. We derive sixth-order nonlinear coupled partial differential equations for the intrinsic properties of the filament, namely, its curvature and metric, and show that these equations are capable of supporting multiple different stationary solutions in a co-moving frame, i.e. that chiral active elastic filaments exhibit dynamic multi-stability in their shapes. A linear stability analysis of these solutions is carried out to determine which solutions are stable and a brief analysis of the time-dependent approach to stationary shape is considered. Finally, simulations are presented which confirm many of our predictions while also revealing additional complexity.

Excitation and control of antiferromagnetic magnon modes lie at the heart of coherent antiferromagnetic spintronics. Here, we propose a topological surface magnon-polariton as the potential means to excite antiferromagnetic magnons in the prototypical magnonic material hematite. We show that in an insulating canted antiferromagnet, where the magnons can strongly couple to photons using electrical on-chip layouts, a surface magnon-polariton mode exists in the gap of the bulk magnon-photon bands. The emergence of surface magnon-polariton mode is further attributed to the nontrivial topology of bulk magnon-photon bands. Magnon-photon coupling enhances the Berry curvature near the anticrossing points, leading to a topological bulk Chern band associated with the surface magnon-polaritons. Our work provides a general principle for the utilisation of optomagnetic properties in antiferromagnets, with an illustration of its feasibility in the particular case of hematite.

Atomic metasurfaces (AMs) provide a powerful nanophotonic platform for integrating topological effects into quantum many-body systems. In this Letter, we investigate the quantum optical and topological properties of a two-dimensional Kagome AM, going beyond the tight-binding approximation and incorporating all-to-all interactions. We reveal selective higher-order topological states with a unique dynamical ``chasing" behavior, protected by a generalized chiral symmetry and enabling efficient topological directional transfer. By introducing an impurity atom -- a giant atom -- coupled to all array atoms, we observe chiral emission patterns strongly dependent on the atomic polarization. This nonlocal coupling structure allows exploration of self-interference effects at subwavelength scales. Our findings establish AMs as a versatile platform for engineering tunable topological states and chiral quantum optical phenomena, with potential applications in customized light sources and photonic devices.

Mechanical systems provide a unique test bed for studying quantum phenomena at macroscopic length scales. However, realizing quantum states that feature quantum correlations among macroscopic mechanical objects remains an experimental challenge. Here, we propose a quantum system in which two micro-electromechanical oscillators interact through a chain of Rydberg atoms confined in optical tweezers. We demonstrate that the coherent dynamics of the system generate entanglement between the oscillators. Furthermore, we utilize the tunability of the radiative decay of the Rydberg atoms for dissipative entanglement generation. Our results highlight the potential to exploit the flexibility and tunability of Rydberg atom chains to generate nonclassical correlations between distant mechanical oscillators.

Despite competitive efficiency compared to Si solar cells and relevant stability at near room temperatures the rapid degradation at elevated temperatures remains the critical obstacle for exploitation of perovskite photovoltaics. In this work, a 4-(pyridin-4-yl)triphenylamine (TPA-Py) with pyridine anchor group was employed for inter-grain bulk modification of double-cation CsCH(NH2)2PbI3 perovskite absorbers to enhance thermal stability. Through coordination and dipole-dipole interactions, nitrogen-containing fragments (diphenylamine and pyridine) of TPA-Py passivate uncoordinated cations and improve the phase resilience of perovskite films against segregation. This resulted in a power conversion efficiency of 21.3% with a high open-circuit voltage of 1.14 V. Notable impact of self-assembled monolayer incorporated into the bulk of perovskite film manifested in huge improvement of thermal stability at 85°C (ISOS-D-2). TPA-Py modification improved extended the T80 lifetime to ~600 h compared to only 200 h for the reference under harsh heating stress in ambient conditions. In-depth analysis using photoinduced voltage transients and admittance spectroscopy after different stress periods revealed the screening of ion migration (0.45 eV) for devices with TPA-Py. This work offers an important understanding of the bulk modification of microcrystalline perovskite absorbers and guide for robust design of bulk and buried interfaces in highly efficient perovskite solar cells.

The natural gradient is central in neural quantum states optimizations but it is limited by the cost of computing and inverting the quantum geometric tensor, the quantum analogue of the Fisher information matrix. We introduce a block-diagonal quantum geometric tensor that partitions the metric by network layers, analogous to block-structured Fisher methods such as K-FAC. This layer-wise approximation preserves essential curvature while removing noisy cross-layer correlations, improving conditioning and scalability. Experiments on Heisenberg and frustrated $J_1$-$J_2$ models show faster convergence, lower energy, and improved stability.

We report the first capture and time-of-flight spectrometry of highly charged ions produced following antiproton annihilations in a Penning-Malmberg trap. At the AEgIS experiment, we employed a multi-step nested-trap technique to isolate ions from antiproton annihilations with ultra-low-density helium and argon gas. The capture and identification of highly-charged argon ions in charge-states up to $Ar^{5+}$ demonstrates a new method for in-trap synthesis. This work establishes a clear path towards the direct capture and mass spectrometry of cold nuclear annihilation fragments, which will enable a complementary tool for exploring the neutron to proton density ratio at the extreme nuclear periphery.

The stochastic dynamics of tracers arising from hydrodynamic fluctuations in a driven electrolyte is studied using a self-consistent field theory framework in all dimensions. A plethora of scaling behaviour including two distinct regimes of anomalous diffusion is found, and the crossovers between them are characterized in terms of the different tuning parameters. A short-time ballistic regime is found to be accessible beyond two dimensions, whereas a long-time diffusive regime is found to be present only at four dimensions and above. The results showcase how long-ranged hydrodynamic interactions can dominate the dynamics of non-equilibrium steady-states in ionic suspensions and produce strong fluctuations despite the presence of Debye screening.

We study coherence distillation under time-translation-invariant operations: given many copies of a quantum state containing coherence in the energy eigenbasis, the aim is to produce a purer coherent state while respecting the time-translation symmetry. This symmetry ensures that the output remains synchronized with the input and that the process can be realized by energy-conserving unitaries coupling the system to a reservoir initially in an energy eigenstate, thereby modeling thermal operations supplemented by a work reservoir or battery. For qubit systems, we determine the optimal asymptotic fidelity and show that it is governed by the purity of coherence, a measure of asymmetry derived from the right logarithmic derivative (RLD) Fisher information. In particular, we find that the lowest achievable infidelity (one minus fidelity) scales as $1/N$ times the reciprocal of the purity of coherence of each input qubit, where $N$ is the number of copies, giving this quantity a clear operational meaning. We additionally study many other interesting aspects of the coherence distillation problem for qubits, including computing higher-order corrections to the lowest achievable infidelity up to $O(1/N^3)$, and expressing the optimal channel as a boundary value problem that can be solved numerically.

Dynamic systems have a fundamental relevance in the description of physical phenomena. The search for more accurate and faster numerical integration methods for the resolution of such systems is, therefore, an important topic of research. The present work introduces a new approach for the numerical integration of dynamic systems. We propose an association of numerical integration methods (integrators) in order to optimize the performance. The standard we apply is the balance of the duo : precision obtained x running time. The numerical integration methods we have chosen, for this particular instance of association, were the Runge-Kutta of fourth order and seventheighth order. The algorithm was implemented in C++ language. The results showed an improvement in accuracy over the lower grade numerical integrator (actually, we have achieved, basically, the precision of the top integrator) with a processing time performance closer to the one of the lower grade integrator. Similar results can be obtained for other pairs of numerical integration methods.

Turbulence enhances the wall shear stress in boundary layers, significantly increasing the drag on streamlined bodies. Other flow features such as freestream pressure gradients and streamwise boundary layer growth also strongly influence the skin friction. In this paper, an angular momentum integral (AMI) equation is introduced to quantify these effects by representing them as torques that alter the shape of the mean velocity profile. This approach uniquely isolates the skin friction of a Blasius boundary layer in a single term that depends only on the Reynolds number, so that other torques are interpreted as augmentations relative to the laminar case having the same Reynolds number. The AMI equation for external flows shares this key property with the so-called FIK relation for internal flows [Fukagata et al. 2002, Phys. Fluids, \textbf{14}, L73-L76]. Without a geometrically imposed boundary layer thickness, the length scale in the Reynolds number for the AMI equation may be chosen freely. After a brief demonstration using Falkner-Skan boundary layers, the AMI equation is applied as a diagnostic tool on four transitional and turbulent boundary layer DNS datasets. Regions of negative wall-normal velocity are shown to play a key role in limiting the peak skin friction during the late stages of transition, and the relative strengths of terms in the AMI equation become independent of the transition mechanism a very short distance into the fully-turbulent regime. The AMI equation establishes an intuitive, extensible framework for interpreting the impact of turbulence and flow control strategies on boundary layer skin friction.

Neuronal circuits arise as axons and dendrites extend, navigate, and connect to target cells. Axonal growth, in particular, integrates deterministic guidance from substrate mechanics and geometry with stochastic fluctuations generated by signaling, molecular detection, cytoskeletal assembly, and growth cone dynamics. A comprehensive quantitative description of this process remains incomplete. We review stochastic models in which Langevin dynamics and the associates Fokker-Planck equation capture axonal motion and turning under combined biases and noise. Paired with experiments, these models yield key parameters, including effective diffusion (motility) coefficients, speed and angle distributions, mean-square displacement, and mechanical measures of cell-substrate coupling, thereby linking single-cell biophysics and intercellular interactions to collective growth statistics and network formation. We further couple the Fokker-Planck description to a mechanochemical actin-myosin-clutch model and perform a linear stability analysis of the resulting dynamics. Routh--Hurwitz criteria identify regimes of steady extension, damped oscillations, and Hopf bifurcations that generate sustained limit cycles. Together, these results clarify the mechanisms that govern axonal guidance and connectivity and inform the design of engineered substrates and neuroprosthetic scaffolds aimed at enhancing nerve repair and regeneration.

Voltage-sensitive fluorescence imaging is widely used to image action potential waves in the heart. However, while the electrical waves trigger mechanical contraction, imaging needs to be performed with pharmacologically contraction-inhibited hearts, limiting studies of the coupling between cardiac electrophysiology and tissue mechanics. Here, we introduce a high-resolution multi-camera optical mapping system with which we image action potential waves at high resolutions across the entire ventricular surface of the beating and strongly deforming heart. We imaged intact isolated rabbit hearts inside a soccer-ball shaped imaging chamber facilitating even illumination and panoramic imaging. Using 12 high-speed cameras, ratiometric voltage-sensitive imaging, and three-dimensional (3D) multi-view motion tracking, we reconstructed the entire 3D deforming ventricular surface and performed corresponding voltage-sensitive measurements during sinus rhythm, paced rhythm, and ventricular fibrillation. Our imaging setup defines a new state-of-the-art in the field and can be used to study the heart's electromechanical physiology during health and disease at unprecedented resolutions. For instance, we measured electrical activation times and observed mechanical strain waves following electrical activation fronts during pacing, observed electromechanical vortices during ventricular fibrillation, and measured action potential duration and contractile changes in response to pharmacological blockage of potassium ion channels.

Transcranial ultrasound stimulation (TUS) is an emerging technology for non-invasive brain stimulation. The International Transcranial Ultrasonic Stimulation Safety and Standards consortium (ITRUSST) has established consensus on considerations for nonsignificant biophysical risk of TUS, drawing upon the literature and established regulations for biomedical devices. Here, we assume the application of TUS to individuals without contraindications, compromised thermoregulation, vascular vulnerabilities, or administered ultrasound contrast agents. In this context, we present a concise yet comprehensive set of levels for nonsignificant risks of TUS application. For mechanical effects, it is non-significant risk if the mechanical index (MI) or the mechanical index for transcranial application (MItc) does not exceed 1.9. For thermal effects, it is non-significant risk if any of the following three levels are met: the peak temperature rise does not exceed 2°C or the peak absolute temperature does not exceed 39°C, assuming a baseline temperature of 37°C, the thermal dose does not exceed 2 CEM43 in brain tissue, 16 CEM43 in bone tissue, and 21 CEM43 in skin tissue, or specific values of the thermal index (TI) for a given exposure time. This report reflects a consensus expert opinion and can inform, but not replace, regulatory guidelines or official international standards. Similarly, this consensus can inform, but not replace, ethical evaluation, which weighs the total burden, risks, and benefits of the proposed action. The stated levels are not safety limits per se, and further data is needed to establish the threshold for significant risk. We review literature relevant to our considerations and discuss limitations and future developments of our approach.

This paper builds upon the work of Dougherty and Heckelman (2020) by determining the frequency that 13 voting systems violate Arrow's social choice criteria with up to six alternatives. These results determine which of the 13 voting systems, is the fairest based on their probabilistic likelihood of violating Arrow's social choice criteria. The voting systems considered are: Plurality, Borda, Dowdall, Top Two, Hare, Coombs, Baldwin, Copeland, Anti-Plurality, Nanson, Ranked Pairs, Pairwise Majority, and Minimax. Elections with up to 10,000 voters and between three and six alternatives are simulated using both Impartial Culture and Impartial Anonymous Culture. These simulations show that Pairwise Majority is the least likely to jointly violate Arrow's criteria. As the number of alternatives increases, the joint-violation frequencies increase for each voting method. For all systems except Pairwise Majority, the joint-violation frequencies in elections with at least 30 voters and four alternatives are greater than 98%.

The Navier Stokes equations (NSEs) are partial differential equations (PDEs) to describe the nonlinear convective motion of fluids and they are computationally expensive to simulate because of their high nonlinearity and variables being fully coupled. Reduced-order models (ROMs) are simpler models for evolving the flows by capturing only the dominant behaviors of a system and can be used to design controllers for high-dimensional systems. However it is challenging to guarantee the stability of these models either globally or locally. Ensuring the stability of ROMs can improve the interpretability of the behavior of the dynamics and help develop effective system control strategies. For quadratically nonlinear systems that represent many fluid flows, the Schlegel and Noack trapping theorem (JFM, 2015) can be used to check if ROMs are globally stable (long-term bounded). This theorem was subsequently incorporated into system identification techniques that determine models directly from data. In this work, we relax the quadratically energy-preserving constraints in this theorem, and then promote local stability in data-driven models of quadratically nonlinear dynamics. First, we prove a theorem outlining sufficient conditions to ensure local stability in linear-quadratic systems and provide an estimate of the stability radius. Second, we incorporate this theorem into system identification methods and produce a-priori locally stable data-driven models. Several examples are presented to demonstrate the effectiveness and accuracy of the proposed algorithm.

Life is an exergonic chemical reaction. Many individual reactions in metabolism entail slightly endergonic steps that are coupled to free energy release, typically as ATP hydrolysis, in order to go forward. ATP is almost always supplied by the rotor-stator ATP synthase, which harnesses chemiosmotic ion gradients. Because the ATP synthase is a protein, it arose after the ribosome did. What was the energy currency of metabolism before the origin of the ATP synthase and how (and why) did ATP come to be the universal energy currency? About 27% of a cell's energy budget is consumed as GTP during translation. The universality of GTP-dependence in ribosome function indicates that GTP was the ancestral energy currency of protein synthesis. The use of GTP in translation and ATP in small molecule synthesis are conserved across all lineages, representing energetic compartments that arose in the last universal common ancestor, LUCA. And what came before GTP? Recent findings indicate that the energy supporting the origin of LUCA's metabolism stemmed from H2-dependent CO2 reduction along routes that strongly resemble the reactions and transition metal catalysts of the acetyl-CoA pathway.

We investigate trends in the data-error scaling laws of machine learning (ML) models trained on discrete combinatorial spaces that are prone-to-mutation, such as proteins or organic small molecules. We trained and evaluated kernel ridge regression machines using variable amounts of computational and experimental training data. Our synthetic datasets comprised i) two naïve functions based on many-body theory; ii) binding energy estimates between a protein and a mutagenised peptide; and iii) solvation energies of two 6-heavy atom structural graphs, while the experimental dataset consisted of a full deep mutational scan of the binding protein GB1. In contrast to typical data-error scaling laws, our results showed discontinuous monotonic phase transitions during learning, observed as rapid drops in the test error at particular thresholds of training data. We observed two learning regimes, which we call saturated and asymptotic decay, and found that they are conditioned by the level of complexity (i.e. number of mutations) enclosed in the training set. We show that during training on this class of problems, the predictions were clustered by the ML models employed in the calibration plots. Furthermore, we present an alternative strategy to normalize learning curves (LCs) and introduce the concept of mutant-based shuffling. This work has implications for machine learning on mutagenisable discrete spaces such as chemical properties or protein phenotype prediction, and improves basic understanding of concepts in statistical learning theory.

The relaxation time of several second generation molecular motors is analysed by calculating the minimum energy path between the metastable and stable states and evaluating the transition rate within harmonic transition state theory based on energetics obtained from density functional theory. Comparison with published experimental data shows remarkably good agreement and demonstrates the predictive capability of the theoretical approach. While previous measurements by Feringa and coworkers [Chem.\,Eur.\,J.\,(2017) 23, 6643] have shown that a replacement of the stereogenic hydrogen by a fluorine atom increases the relaxation time because of destabilization of the transition state for the thermal helix inversion, we find that a replacement of CH$_3$ by a CF$_3$ group at the same site shortens the relaxation time because of elevated energy of the metastable state without a significant shift in the transition state energy. Since these two fluorine substitutions have an opposite effect on the relaxation time, the two combined can provide a way to fine tune the rotational speed of a molecular motor.

Analyzing stop-and-go waves at the scale of miles and hours of data is an emerging challenge in traffic research. The past 5 years have seen an explosion in the availability of large-scale traffic data containing traffic waves and complex congestion patterns, making existing approaches unsuitable for repeatable and scalable analysis of traffic waves in these data. This paper makes a first step towards addressing this challenge by introducing an automatic and scalable stop-and-go wave identification method capable of capturing wave generation, propagation, dissipation, as well as bifurcation and merging, which have previously been observed only very rarely. Using a concise and simple critical-speed based definition of a stop-and-go wave, the proposed method identifies all wave boundaries that encompass spatio-temporal points where vehicle speed is below a chosen critical speed. The method is built upon a graph representation of the spatio-temporal points associated with stop-and-go waves, specifically wave front (start) points and wave tail (end) points, and approaches the solution as a graph component identification problem. It enables the measurement of wave properties at scale. The method is implemented in Python and demonstrated on a large-scale dataset, I-24 MOTION INCEPTION. Our results show insights on the complexity of traffic waves. Traffic waves can bifurcate and merge at a scale that has never been observed or described before. The clustering analysis of all the identified wave components reveals the different topological structures of traffic waves. We explored that the wave merge or bifurcation points can be explained by spatial features. The gallery of all the identified wave topologies is demonstrated at this https URL.

Research on 2D materials has achieved significant milestones and fuelled a rapidly growing industrial sector. This progress, however, is accompanied by challenges in reproducibility, arising from the atomic thinness, fragility, and environmental sensitivity of these materials. Subtle variations in methods or materials can lead to drastically different outcomes, undermining reliability and slowing down both scientific and technological advances. At the same time, academic publishing and funding systems continue to place greater value on novelty than on efforts to improve reproducibility. This Expert Recommendation outlines concrete, actions researchers can take to improve reproducibility in 2D materials science. We introduce two tools - STEP (Standardised Template for Experimental Procedures) and ReChart (Reproducibility Charter) - designed to support rigorous documentation and transparent sharing of protocols, failure modes, and raw data. To illustrate the application of STEP, we provide three detailed examples covering key processes in 2D materials research: graphene growth by chemical vapour deposition (CVD) on copper foil, wet transfer of CVD graphene, and dry assembly of van der Waals heterostructures. We offer practical recommendations that spans the full research process and show how researchers can engage constructively with stakeholders across academia, funding, publishing, and industry to create a stronger basis for reproducibility, transparency and trust in the field.

Although conceptual assessment tests are commonly administered at the beginning and end of a semester, this pre-post approach has inherent limitations. Specifically, education researchers and instructors have limited ability to observe the progression of student conceptual understanding throughout the course. Furthermore, instructors are limited in the usefulness of the feedback they can give to the students involved. To address these challenges, we propose an alternative approach that leverages computerized adaptive testing (CAT) and increasing the frequency of CAT-based assessments during the course, while reducing the test length per administration, thus keeping or decreasing the total number of test items administered throughout the course. The feasibility of this idea depends on how far the test length per administration can be reduced without compromising the test accuracy and precision. Specifically, the overall test length is desired to be shorter than when the full assessment is administered as a pretest and subsequent post-test. To achieve this goal, we developed a CAT algorithm that we call Chain-CAT. This algorithm sequentially links the results of each CAT administration using collateral information. We developed the Chain-CAT algorithm using the items of the Force Concept Inventory (FCI) and analyzed the efficiency by numerical simulations. We found that collateral information significantly improved the test efficiency, and the overall test length could be shorter than the pre-post method. Without constraints for item balancing and exposure control, simulation results indicated that the efficiency of Chain-CAT is comparable to that of the pre-post method even if the length of each CAT administration is only 5 items and the CAT is administered 9 times throughout the semester. (To continue, see text.)

A ring laser is defined by its perimeter, which directly enters the conversion factor between measured Sagnac frequency and the actual rotation rate. Large ring lasers employed in geodesy and fundamental physics require stability of the perimeter at or below the parts-per-billion level. We present two complementary approaches to actively control the perimeter length of such ring lasers, reaching a relative length stability of $4\times 10^{-10}$. One of these approaches is based on a phase detection between the beat of two resonances of different longitudinal mode index and a stable local oscillator. The other approach employs a highly stable wavelength meter to measure the absolute frequency of the laser light. These methods can readily be implemented and bring the stability of heterolithic devices on par with monolithic designs.

Many fields of physics use quantum Monte Carlo techniques, but struggle to estimate dynamic spectra via the analytic continuation of imaginary-time quantum Monte Carlo data. One of the most ubiquitous approaches to analytic continuation is the maximum entropy method (MEM). We supply a dual Newton optimization algorithm to be used within the MEM and provide analytic bounds for the algorithm's error. The MEM is typically used with Bryan's controversial algorithm [Rothkopf, "Bryan's Maximum Entropy Method" Data 5.3 (2020)]. We present new theoretical issues that are not yet in the literature. Our algorithm has all the theoretical benefits of Bryan's algorithm without these theoretical issues. We compare the MEM with Bryan's optimization to the MEM with our dual Newton optimization on test problems from lattice quantum chromodynamics and plasma physics. These comparisons show that in the presence of noise the dual Newton algorithm produces better estimates and error bars; this indicates the limits of Bryan's algorithm's applicability. We use the MEM to investigate authentic quantum Monte Carlo data for the uniform electron gas at warm dense matter conditions and further substantiate the roton-type feature in the dispersion relation.

With improvements in data resolution and quality, researchers can now represent complex systems as signed, weighted, and directed networks. In this article, we introduce a framework for measuring net and indirect effects without simplifying these information-rich networks. It captures both direct and indirect interactions, the effect of the whole network on a node, and conversely, the effect of a node on the entire network, while accommodating the complexity of signed, weighted, and directed links. Our taxonomy unifies and extends existing approaches and measures from network science, computational social science, and ecological networks. We demonstrate its value in ecological systems, where net and indirect effects are critical yet difficult to quantify. Using generalized Lotka-Volterra dynamics, we find a strong correlation between negative net effects and species extinction. We further apply the framework to a real-world social network, where it identifies informative rankings that illuminate influence propagation and power dynamics.

We present a fully stabilized 1-GHz Yb-fiber laser frequency comb built on silica substrates, utilizing "optical cubes" to house all optical components, ensuring long-term stability and practical operation. Both the femtosecond laser and f-to-2f interferometer are constructed to silica bricks, with a compact footprint of 290 mm * 250 mm, and a total weight of 1.8 kg. This system provides a stable repetition rate, offset frequency, and a supercontinuum spanning 460-1560 nm without requiring amplification. The carrier-envelop offset frequency exhibits exceptional in-loop stability, with a fractional frequency instability of 3.07* 10^(-18) at a 1 second averaging time, improving to 2.12*10^(-20) at a 10,000 second, maintaining uninterrupted operation for over 60 hours. This work demonstrates a high-performance GHz fiber-based frequency comb, paving the way for applications beyond laboratory environments, including dual-comb spectroscopy, astronomical spectrograph calibration, and portable optical clocks.

This study addresses a challenge of parametrizing a resolution function of the neutron beam from the neutron time of flight facility n_TOF at CERN. A difficulty stems from a fact that a resolution function exhibits rather strong variations in shape, over approximately 10 orders of magnitude in neutron energy. In order to avoid a need for a manual identification of the appropriate analytical forms - hindering past attempts at its parametrization - we take advantage of the versatile machine learning techniques. In particular, we parametrize it by training a multilayer feedforward neural network, relying on a key idea that such networks act as the universal approximators. The proof of concept is presented for a resolution function for the first experimental area of the n_TOF facility, from the third phase of its operation. We propose an optimal network structure for a resolution function in question, which is also expected to be optimal or near-optimal for other experimental areas and for different phases of n_TOF operation. In order to reconstruct several resolution function forms in common use from a single parametrized form, we provide a practical tool in the form of a specialized C++ class encapsulating the computationally efficient procedures suited to the task. Specifically, the class allows an application of a user-specified temporal spread of a primary proton beam (from a neutron production process at n_TOF) to a desired resolution function form.

Models of network diffusion typically rely on the Laplacian matrix, capturing interactions via direct connections. Beyond direct interactions, information in many systems can also flow via indirect pathways, where influence typically diminishes over distance. In this work, we analyze diffusion dynamics incorporating such indirect connections using the $d$-path Laplacian framework. We introduce a parameter, the indirect influence, based on the change in the second smallest eigenvalue of the generalized path Laplacian, to quantify the impact of these pathways on diffusion timescales relative to direct-only models. Using perturbation theory and mean-field approximations, we derive analytical expressions for the indirect influence in terms of structural properties of random networks. Theoretical predictions align well with numerical simulations, providing a phase diagram for when indirect influence becomes significant. We also identify a structural phase transition governed by the emergence of $d$-paths and derive the critical connection probability above which they dramatically alter diffusion. This study provides a quantitative understanding of how indirect pathways shape network dynamics and reveals their collective structural onset.

Since Berry's pioneering 1984 work, the separation of geometric and dynamic contributions in the {\it phase} of an evolving wave has become fundamental in physics, underpinning diverse phenomena in quantum mechanics, optics, and condensed matter. Here we extend this geometric-dynamic decomposition from the wave-evolution phase to a distinct class of wave scattering problems, where observables (such as frequency, momentum, or position) experience shifts in their expectation values between the input and output wave states. We describe this class of problems using a unitary scattering matrix and the associated generalized Wigner-Smith operator (GWSO), which involves gradients of the scattering matrix with respect to conjugate variables (time, position, or momentum, respectively). We show that both the GWSO and the resulting expectation-values shifts admit gauge-invariant decompositions into dynamic and geometric parts, related respectively to gradients of the eigenvalues and eigenvectors of the scattering matrix. We illustrate this general theory through a series of examples, including frequency shifts in polarized-light transmission through a time-varying waveplate (linked to the Pancharatnam-Berry phase), momentum shifts at spatially varying metasurfaces, optical forces, beam shifts upon reflection at a dielectric interface, and Wigner time delays in 1D scattering. This unifying framework illuminates the interplay between geometry and dynamics in wave scattering and can be applied to a broad range of physical systems.

Space-time varying media with moving interfaces unlock new ways to manipulate electromagnetic waves. Yet, analytical solutions have been mostly limited to interfaces moving at constant velocity or constant proper acceleration. Here, we present exact scattering solutions for an arbitrarily accelerating interface, derived directly in the laboratory frame through a suitable change of variables. We show that acceleration introduces rich effects that do not occur with uniform motion, including transitions between multiple velocity regimes, multiple scattering events and generalized frequency chirping. We also solve the inverse problem of designing an interface trajectory that produces a desired chirping profile, demonstrating how tailored acceleration can synthesize complex frequency modulations. These results provide a fundamental framework to understand and control wave interactions with accelerated boundaries, opening pathways for advanced applications in space-time signal processing and dynamic pulse shaping.

We present the implementation of relativistic coupled cluster quadratic response theory (QR-CC), following our development of relativistic equation of motion coupled cluster quadratic response theory (QR-EOMCC) [X. Yuan et al., J. Chem. Theory Comput. 2023, 19, 9248]. These codes, which can be used in combination with relativistic (2- and 4-component based) as well as non-relativistic Hamiltonians, are capable of treating both static and dynamic perturbations for electric and magnetic operators. We have employed this new implementation to revisit the calculation of static and frequency-dependent first hyperpolarizabilities of hydrogen halides (HX, X=F-Ts) and the Verdet constant of heavy noble gas atoms (Xe, Rn, Og) and of selected hydrogen halides (HF to HI), in order to investigate the differences and similarities of QR-CC and the more approximate QR-EOMCC. Furthermore, we have determined the relative importance of scalar relativistic effects and spin-orbit coupling to these properties, through a comparison of different Hamiltonians, and extended our calculations to superheavy element species (HTs for hyperpolarizabilities, Og for the Verdet constant). Our results show that as one moves towards the bottom of the periodic table, QR-EOMCC can yield rather different results (hyperpolarizabilities) or perform rather similarly (Verdet constant) to QR-CC. These results underscore the importance of further characterizing the performance of QR-EOMCC for heavy element systems.

The Fast Beam Condition Monitor (FBCM) is a standalone luminometer for the High Luminosity LHC (HL-LHC) program of the CMS Experiment at CERN. The detector is under development and features a new, radiation-hard, front-end application-specific integrated circuit (ASIC) designed for beam monitoring applications. The achieved timing resolution of a few nanoseconds enables the measurement of both the luminosity and the beam-induced background. The ASIC, called FBCM23, features six channels with adjustable shaping times, enabling in-field fine-tuning. Each ASIC channel outputs a single binary asynchronous signal encoding time of arrival and time over threshold information. The FBCM is based on silicon-pad sensors, with two sensor designs presently being considered. This paper presents the results of tests of the FBCM detector prototype using both types of silicon sensors with hadron, muon, and electron beams. Irradiated FBCM23 ASICs and silicon-pad sensors were also tested to simulate the expected conditions near the end of the detector's lifetime in the HL-LHC radiation environment. Based on test results, direct bonding between the sensor and ASIC was chosen, and an optimal bias voltage and ASIC threshold for FBCM operation were proposed. The current design of the front-end test board was validated following the beam test and is now being used for the first front-end module, which is expected to be produced in summer 2025. These results represent a major step forward in validating the FBCM concept, first version of the firmware and establishing a reliable design path for the final detector.

An optical centrifuge is a laser pulse which enables controlled rotational excitation of molecules. Centrifuged molecules rotating with well-defined angular frequencies are ideal candidates to probe many-body quantum systems at the nanoscale. Because the interaction with the quantum environment increases the effective moment of inertia of the embedded molecular probes, the required rotational acceleration of the optical centrifuge must be lowered to accommodate adiabatic spinning. We demonstrate a new design of an optical centrifuge, based on the method of spectral focusing, which enables extremely low, down to zero, rotational accelerations. We discuss the potential of using such constant frequency centrifuge to investigate molecular rotation inside helium nanodroplets.

Recent advances in continuum embedding models have enabled the incorporation of solvent and electrolyte effects into density functional theory (DFT) simulations of material surfaces, significantly benefiting electrochemistry, catalysis, and other applications. To extend the simulation of diverse systems and properties, the implementation of continuum embedding models into the Environ library adopts a modular programming paradigm, offering a flexible interface for communication with various DFT programs. The speed and scalability of the current implementation rely on a smooth definition of the key physical properties of the atomistic system, in particular of its electronic density. This has hindered the coupling of Environ with all-electron simulation packages, as the sharp electron density peaks near atomic nuclei are difficult to represent on regular grids. In this work, we introduce a novel smoothing scheme that transforms atom-centered electron densities into a regular grid representation while preserving the accuracy of electrostatic calculations. This approach enables a minimal and generic interface, facilitating seamless interoperability between Environ and all-electron DFT programs. We demonstrate this development through the coupling of Environ with the FHI-aims package and present benchmark simulations that validate the proposed method.

An open-source database encompassing 457 experimental and numerical entries from 32 machines among solid-walled tokamaks, stellarators and linear plasma devices is assembled. From there, we derive physics-informed multi-machine scaling laws predictive of fuel and impurity puffing rates sufficient to access plasma detachment -- leading candidate for a reactor-relevant solution to the open issue of plasma-wall interaction. Validation of our laws in up to 40 plasmas in low- and high-confinement mode also featuring advanced configurations demonstrates accuracy within a factor 1.5 in up to 50% of the instances, and within a factor 2 on average. Divertor volume alone is found to correlate to fuelling. The addition of plasma opaqueness leads to the empirically-calibrated law $\Gamma_{\text{D}}\propto [n_{\text{sep}}\times a\times(S_{\text{div}}/V_{\text{div}})^{-1.5}]^{1.05}$ valid across all toroidal devices. Its simplification to $\Gamma_{\text{D}}^{\text{HDL}}\propto 0.43\times a^{1.58}\times\lambda_q^{-0.89}$ avoids a dependence on $n_{\text{sep}}$ and bears intrinsic significance provided by the H/L density limit and the power fall-off length. Non-linearities of the impurity dynamics manifest in the seeding rate -- which the present work identifies both a generalised formula for, and the Greenwald-Eich-Scarabosio simplification $\Gamma_{\text{Z}}^{\text{GES}}\propto a^{1.51}\times\lambda_q^{-0.27}$. Similar relationships are defined for stellarators, closely following the mainstream but without providing data for validation -- which should stimulate further investigations. In the era of nuclear fusion reactor design, these results find immediate applicability in informing reactor fuel cycle design and edge plasma modelling. More generally, this study demonstrates that physics-based 0D laws able to relate detachment to the engineering actuators exist.

Modern X-ray detector systems urgently require compact, efficient, and fast data compression schemes to handle the transmission of big data from pixel arrays, enabling frame rates in the MHz regime. In this work, a data compression ASIC that implements a streaming fixed-length lossy compression scheme is introduced and analyzed, proving the feasibility and benefits of on-chip compression. The compression scheme utilizes a vector matrix product logic, which performs a number of floating-point multiplications, additions, and accumulations. The logic is verified, synthesized, and shown to fit in the area resource available for the X-ray detector under study, which comprises 192 x 168 pixels each of 12-bit width, and having a total area of 20 mm x 20 mm, about 2 mm x 20 mm of which are available for the digital logic. Several system architectures, precisions, and compression ratios ranging from 100 to 250 were analyzed to pave the way for on-chip fixed-length compression (e.g., principal component analysis, singular value decomposition) and data reduction (e.g., azimuthal integration) for X-ray and electron detectors.

We present an analysis of the coherent structures in Langmuir turbulence, a state of the ocean surface boundary layer driven by the interactions between water waves and wind-induced shear, via a resolvent framework. Langmuir turbulence is characterised by multiscale vortical structures, notably counter-rotating roll pairs known as Langmuir circulations. While classic linear stability analyses of the Craik-Leibovich equations have revealed key instability mechanisms underlying Langmuir circulations, the vortical rolls characteristic of Langmuir turbulence, the present work incorporates the turbulent mean state and varying eddy viscosity using data from large-eddy simulation (LES) to investigate the turbulence dynamics of fully developed Langmuir turbulence. Scale-dependent resolvent analyses reveal a new formation mechanism of two-dimensional circulating rolls and three-dimensional turbulent coherent vortices through linear amplification of sustained harmonic forcing. Moreover, the integrated energy spectra predicted by the principal resolvent modes in response to broadband harmonic forcing capture the dominant spanwise length scales that are consistent with the LES data. These results demonstrate the feasibility of resolvent analyses in capturing key features of multiscale turbulence-wave interactions in the statistical stationary state of Langmuir turbulence.

Foundation models for materials modeling are advancing quickly, but their training remains expensive, often placing state-of-the-art methods out of reach for many research groups. We introduce Nequix, a compact E(3)-equivariant potential that pairs a simplified NequIP design with modern training practices, including equivariant root-mean-square layer normalization and the Muon optimizer, to retain accuracy while substantially reducing compute requirements. Nequix has 700K parameters and was trained in 100 A100 GPU-hours. On the Matbench-Discovery and MDR Phonon benchmarks, Nequix ranks third overall while requiring a 20 times lower training cost than most other methods, and it delivers two orders of magnitude faster inference speed than the current top-ranked model. We release model weights and fully reproducible codebase at this https URL.

OpenCAL is a low cost CAL based printing and post processing platform developed using commercial off the shelf (COTS) components and standard rapid prototyping tools. The system incorporates recent advancements in VAM, including Optical Scattering Tomography (OST), and is designed with modularity to support future technological integration. The post processing systems developed include standardized procedures for solvent based removal of uncured resin and a centrifugal cleaning module for enhanced material recovery. To support the continued advancement of this technology, multiple community engagement pathways have been established to foster the collaborative development of the OpenCAL ecosystem. A primary deployment environment for OpenCAL is academic makerspaces, where the system is specifically designed to leverage the rapid prototyping tools typically available in these facilities. OpenCAL not only introduces advanced volumetric additive manufacturing capabilities to makerspaces but also serves as a platform for hands on education in emerging manufacturing technologies, photopolymer science, and computational imaging. By lowering the barriers to entry for volumetric printing research, OpenCAL enables a broader range of students, from undergraduate to graduate levels, to engage directly in experimental research, contribute to the refinement of open source CAL technologies, and participate in interdisciplinary projects spanning materials science, mechanical engineering, and computer science. In doing so, OpenCAL has the potential to significantly expand the technical capabilities of academic makerspaces, transforming them into hubs for next generation manufacturing innovation and research driven learning.

We develop a numerical approach based on the sinc basis set for first-principles electronic structure calculations in one-dimensional systems. The method exploits the inherent accuracy and non-local character of the sinc functions to handle long-range couplings effectively. As a proof of principle, we provide a computational study of a molecule in an optical cavity, with our code directly calculating the emergence of the energy level splitting in the spectrum.

This letter presents a kinetic closure of the filtered Boltzmann-BGK equation, paving the way towards an alternative description of turbulence. The closure naturally incorporates the turbulent subfilter stress tensor without the need for explicit modeling, unlike in Navier-Stokes models. In contrast, it accounts for the subfilter turbulent diffusion in the nonconserved moments by generalizing the BGK collision operator. The model requires neither scale separation nor a Smagorinsky-type ansatz for the subfilter stress tensor. The Chapman-Enskog analysis shows that its hydrodynamic limit converges exactly to the filtered Navier-Stokes equations, with velocity gradients isolating subfilter contributions. Validations through lattice Boltzmann simulations of the Taylor-Green vortex and the turbulent mixing layer demonstrate improved stability and reduced dissipation, benchmarked against the Smagorinsky model.

We report the outcome of evaluations of the skill of long-range forecasts from the ocean wave model component of the Navy's global coupled modeling system. Specifically, the model output is taken from a single member of the ensemble system, and we evaluate the skill of predicting seven model "wave height" parameters, computed from: energy in four frequency bands, energy in all bands combined, swell energy, and wind sea energy. The model is evaluated using two methods of "ground truth". The first is a new instrument for measuring wave spectra from space, Surface Waves Investigation and Monitoring (SWIM). The second is analyses from the same model. We propose a new method of band-wise bias correction of the observational dataset, using in situ wave observations, with the numerical wave model used as an intermediary.

The paper demonstrates the design and execution of a low-cost optical spectrometer that employs a webcam, diffraction grating & Python (a free, open-source programming language). The device's design prioritized economy and usability, with a black box casing to reduce stray light and increase measurement accuracy. A diffraction grating made from a DVD was used to split light into its constituent wavelengths, which were then collected by the camera. The calibration procedure used a RED TIDE USB650 Fiber Optic Spectrometer to set calibration values for various wavelength ranges, which ensured that the spectrometer's results closely matched those derived from the former, a high-cost industry-standard model. Spectrums of several light sources, such as red, green, blue, yellow, white, magenta, orange, and UV LEDs, as well as a green laser, were studied and compared. The results showed a high level of precision, with minimal divergence from industry-standard spectrometer measurements. This comparison was carried out utilizing Origin software, which allowed for extensive analysis and display of the spectrum data. In addition, the spectrometer captured data in real time, allowing users to watch live spectrum changes and ensure instant accessibility of results. Despite its simplicity and low cost, the spectrometer provides significant value for instructional and practical applications, making it a valuable tool in cost-constrained situations.

Topological invariants have proved useful for analyzing emergent function as they characterize a property of the entire system, and are insensitive to local details, disorder, and noise. They support boundary states, which reduce the system response to a lower dimensional space and, in 2D systems, offer a mechanism for the emergence of global cycles within a large phase space. Topological invariants have been heavily studied in quantum electronic systems and have been observed in other classical platforms such as mechanical lattices. However, this framework largely describes equilibrium systems within an ordered crystalline lattice, whereas biological systems are often strongly non-equilibrium with stochastic components. We review recent developments in topological states in discrete stochastic models in 1D and 2D systems, and initial progress in identifying testable signature of topological states in molecular systems and ecology. These models further provide simple principles for targeted dynamics in synthetic systems and in the engineering of reconfigurable materials. Lastly, we describe novel theoretical properties of these systems such as the necessity for non-Hermiticity in permitting edge states, as well as new analytical tools to reveal these properties. The emerging developments shed light on fundamental principles for non-equilibrium systems and topological protection enabling robust biological function.

Wearable sensors and IoT/IoMT platforms enable continuous, real-time monitoring, but objective digital markers for eating disorders are limited. In this study, we examined whether actimetry and machine learning (ML) could provide objective criteria for food addiction (FA) and symptom counts (SC). In 78 participants (mean age 22.1 +/- 9.5 y; 73.1% women), one week of non-dominant wrist actimetry and psychometric data (YFAS, DEBQ, ZSDS) were collected. The time series were segmented into daytime activity and nighttime rest, and statistical and entropy descriptors (FuzzyEn, DistEn, SVDEn, PermEn, PhaseEn; 256 features) were calculated. The mean Matthews correlation coefficient (MCC) was used as the primary metric in a K-nearest neighbors (KNN) pipeline with five-fold stratified cross-validation (one hundred repetitions; 500 evaluations); SHAP was used to assist in interpretation. For binary FA, activity-segment features performed best (MCC = 0.78 +/- 0.02; Accuracy ~ 95.3% +/- 0.5; Sensitivity ~ 0.77 +/- 0.03; Specificity ~ 0.98 +/- 0.004), exceeding OaS (Objective and Subjective Features) (MCC = 0.69 +/- 0.03) and rest-only (MCC = 0.50 +/- 0.03). For SC (four classes), OaS slightly surpassed actimetry (MCC = 0.40 +/- 0.01 vs 0.38 +/- 0.01; Accuracy ~ 58.1% vs 56.9%). Emotional and restrained eating were correlated with actimetric features. These findings support wrist-worn actimetry as a digital biomarker of FA that complements questionnaires and may facilitate privacy-preserving clinical translation.

The Standard Model predicts a long-range force mediated by a pair of neutrinos, known as ``the neutrino force". It scales as $G_F^2/r^5$, where $G_F$ is the Fermi constant. However, as $r \lesssim \sqrt{G_F}$, the four-Fermi theory breaks down and the neutrino force no longer has the $1/r^5$ scaling. For the first time, we derive a complete expression for the neutrino force that is valid at all distances. For $r \gg \sqrt{G_F}$, the result reduces to the known $G_F^2/r^5$; for $r \ll \sqrt{G_F}$, it scales as $1/r$. We explore the implications of this result for atomic parity violation (APV) experiments. A key feature of the neutrino force is that it is a long-range effect compared to the atomic length scale. Thus, in general, it cannot be simply treated as a correction to the tree-level $Z$-exchange diagram without considering the atomic wavefunctions. We calculate the effects in muonium and positronium, finding that the neutrino force contributes about 4\% and 16\%, respectively, compared to the leading $ Z$ exchange. This indicates a significant impact on APV, with important implications for detecting the neutrino force and measuring the weak mixing angle in APV experiments.

Accurate weather forecasts are important for disaster prevention, agricultural planning, etc. Traditional numerical weather prediction (NWP) methods offer physically interpretable high-accuracy predictions but are computationally expensive and fail to fully leverage rapidly growing historical data. In recent years, deep learning models have made significant progress in weather forecasting, but challenges remain, such as balancing global and regional high-resolution forecasts, excessive smoothing in extreme event predictions, and insufficient dynamic system modeling. To address these issues, this paper proposes a global-regional nested weather forecasting framework (OneForecast) based on graph neural networks. By combining a dynamic system perspective with multi-grid theory, we construct a multi-scale graph structure and densify the target region to capture local high-frequency features. We introduce an adaptive messaging mechanism, using dynamic gating units to deeply integrate node and edge features for more accurate extreme event forecasting. For high-resolution regional forecasts, we propose a neural nested grid method to mitigate boundary information loss. Experimental results show that OneForecast performs excellently across global to regional scales and short-term to long-term forecasts, especially in extreme event predictions. Codes link this https URL.

The Lee-Huang-Yang (LHY) energy correction at the edge of the mean-field stability regime is known to give rise to beyond mean-field structures in a wide variety of systems. In this work, we analytically derive the LHY energy for two-, three- and four-component one-dimensional bosonic short-range interacting mixtures across the mean-field stability regime. For varying intercomponent attraction in the two-component setting, quantitative deviations from the original LHY treatment emerge being imprinted in the droplet saturation density and width. On the other hand, for repulsive interactions an unseen early onset of phase-separation occurs for both homonuclear and heteronuclear mixtures. Closed LHY expressions for the fully-symmetric three- and four-component mixtures, as well as for mixtures comprised of two identical components coupled to a third independent component are provided and found to host a plethora of mixed droplet states. Our results are expected to inspire future investigations in multicomponent systems for unveiling exotic self-bound states of matter and unravel their nonequilibrium quantum dynamics.

The linear stability of a lipid membrane under a DC electric field, applied perpendicularly to the interface, is investigated in the electrokinetic framework, taking into account the dynamics of the Debye layers formed near the membrane. The perturbed charge in the Debye layers redistributes and generates destabilizing Maxwell stress on the membrane, which outweighs the stabilizing contribution from the electrical body force, leading to a net destabilizing effect. The instability is suppressed as the difference in the electrolyte concentration of the solutions separated by the membrane increases, due to a weakened base state electric field near the membrane. This result contrasts with the destabilizing effect predicted using the leaky dielectric model in cases of asymmetric conductivity. We attribute this difference to the varying assumptions about the perturbation amplitude relative to the Debye length, which result in different regimes of validity for the linear stability analysis within these two frameworks.

Josephson junctions manufactured to tight tolerances are necessary components for superconducting quantum computing. Developing precise manufacturing techniques for Josephson junctions requires an understanding of their make-up and robust feedback metrics against which to optimise. Here we consider complementary techniques assessing what conclusions they allow us to draw about the barriers in junctions. Monte-Carlo simulations of barriers show that standard deviations of 15-20% of the total barrier thickness are compatible with our experimental data. Electrical breakdown allows us to probe the weakest points in barriers. Narrowing the distribution of this breakdown provides a promising feedback mechanism for barrier optimisation. Grouping junctions by breakdown voltage allows us to identify sub-ensembles of junctions with different median resistance. Transmission electron microscopy can be used to find average barrier thickness, although we highlight challenges forming robust conclusions on the distribution of thicknesses in a barrier from these experiments.

Neutral atoms emitted from liquid metal ion sources are an often-overlooked source of contamination and damage in focused ion beam microscopy. Beyond ions and single atoms, these sources also emit atom clusters. While most studies have investigated charged clusters, here we demonstrate that neutral clusters are also emitted. These neutral clusters bypass the electrostatic beam blanking system, allowing them to impinge on samples even when the ion beam is blanked. We investigate this phenomenon using thin (<20 nm) freestanding membranes of hexagonal boron nitride, silicon, and silicon nitride as targets. Randomly dispersed nanopores that form upon neutral cluster exposure are revealed. The average nanopore diameter is ~2 nm with a narrow size distribution, suggesting that the atom clusters emitted from the source have a preferred size. Various electron microscopy techniques are used to characterize the nanopores, including high-resolution transmission electron microscopy, multislice ptychography, and electron energy-loss spectroscopy. Finally, we show how electron irradiation in the transmission electron microscope can be used to both remove any amorphous material that may clog the pores and to controllably grow the pores to specific sizes. Tunable nanopores such as these are interesting for nanofluidic applications involving size-selective membranes.

Two-dimensional non-Hermitian photonic lattices with asymmetric couplings offer rich possibilities for controlling wave localization, through the emergence of the non-Hermitian skin effect at lattice corners or sides. Incorporating optical nonlinearity fundamentally alters these boundary-localization characteristics. Here we show that in a two-dimensional Hatano-Nelson lattice with Kerr nonlinearity, the interplay between self-trapping and directional propagation leads to position dependent amplitude thresholds. Single-site excitations having above a critical amplitude become confined to their initial position, with lower thresholds near the position where the linear eigenmodes are localized and higher thresholds within the lattice's bulk. Additionally, we study the differences of this dynamical interplay, for wider initial excitations, between the focusing and defocusing Kerr-nonlinearity regimes. Lastly, we identify skin soliton solutions in a variety of two-dimensional lattice geometries featuring coupling asymmetry.

Most solar desalination efforts are photothermal: they evaporate water with ``black'' materials that absorb as much sunlight as possible. Such ``brine-boiling'' methods are limited by the high thermal mass of water, i.e., its capacity to store and release heat. Here, we study the light-enhanced evaporation by a hard, white, aluminum nitride wick, and propose a route to selectively target salt-water bonds instead of bulk heating via deep-UV interactions. Through experiments and analyses that isolate the effects of light absorption and heating in aluminum nitride, we provide experimental evidence of a light-driven, spectrum-selective path to non-photothermal saltwater evaporation. Leverage of these light-matter interactions in white ceramic wicks may achieve low-cost, low-energy desalination, reduce the heat island effects of traditional solar technologies, and contribute to future cooling technologies where drought is also a concern.

Reactive metals generally undergo hydrogen evolution rather than forming direct hydrides when reacting with acids. However, an alternative pathway that involves the direct insertion of atomic hydrogen into the metal lattice, analogous to hydrogen embrittlement (HE), can induce extreme localized stresses on the order of gigapascals (GPa). This mechanochemical process significantly promotes the formation of metal hydrides under high-stress conditions. Inspired by this mechanism, we synthesized at least 19 high-purity metal hydrides, including MgH2, ScH2, YH2, LaH2, LaH2.3, CeH2, SmH2, LuH2, TiH2, ZrH1.6, ZrH2, HfH1.7, HfH2, VH0.8, VH2, NbH, NbH2, Ta2H, and TaH, using bulk metal foils and sulfuric or oleic acid as hydrogen sources. Through high-pressure technique conducted at the GPa level using a diamond anvil cell (DAC), we introduce the pressure concepts Delta-Ph and Delta-Peq to elucidate the synthesis and stabilization mechanisms. Quantitative analysis across all 19 hydrides reveals that the criterion abs(Delta-Peq) greater than Delta-Pph is essential for successful acid-mediated hydride formation; conversely, when abs(Delta-Peq) less than Delta-Pph, hydrogen-induced brittle fracture occurs. This principle not only enables the synthesis of challenging hydrides such as LiH, but also explains failure mechanisms in metals like Fe. Ultimately, this work repurposes HE from a failure mechanism into a controlled tool for hydride synthesis.

Countless biological processes are fueled by energy-rich molecules like ATP and GTP that supply energy with extreme efficiency. However, designing similar energy-delivery schemes from the bottom up, essential for the development of powered nanostructures and other {\it de novo} machinery, presents a significant challenge: how can an energy-rich structure be stable in solution yet still deliver this energy at precisely the right time? In this paper, we present a purely physical mechanism that solves this challenge, facilitating energy transfer akin to ATP hydrolysis, yet occurring between synthetic nanostructures without any biochemical interactions. This targeted energy delivery is achieved by exploiting a differentiable state-based model to balance the energy profiles that govern the structural transitions in the two nanostructures, creating a combined relaxation pathway with minimal barriers that facilitates energy delivery. We verify the effectiveness and robustness of this mechanism through Molecular Dynamics simulations, demonstrating that a bath of the high-energy structures can systematically and repeatedly drive the target structure out of equilibrium, enabling it to perform tasks. As the mechanism operates only through explicit physical forces without any biochemistry or internal state variables, our results present generic and far-reaching design principles, setting the stage for the next generation of synthetic nanomachines.

For the simple system of a point-like particle confined to a straight line, I compile, initially in a concise table, the structural elements of quantum mechanics and contrast them with those of classical (statistical) mechanics. Despite many similarities, there are the well-known fundamental differences, resulting from the algebraic non-commutativity in the quantal structure. The latter was discovered by Werner Heisenberg (1901-1976) in June 1925 on the small island of Helgoland in the North Sea, as a consequence of understanding atomic spectral data within a matrix scheme consistent with energy conservation. I discuss the differences and exemplify their quantifications by the variance and entropic indeterminacy inequalities, by (pseudo-)classical bounds on quantum canonical partition functions, and by the correlation inequalities of John Bell (1928-1990) and others.

Non-Hermitian physics has unveiled a realm of exotic phenomena absent in Hermitian systems, with the non-Hermitian skin effect (NHSE) showcasing boundary-localized eigenstates driven by non-reciprocal interactions. Here, we introduce a new class of non-Hermitian systems exhibiting pure decay modes-eigenstates with pure, smooth exponential decay, devoid of the oscillatory wave patterns typical of traditional NHSE. Modeled as directed graphs with non-reciprocal hopping, these systems reveal quantized decay charges, defined as the sum of decay constants along edges at each node, offering a novel topological invariant. We derive universal conditions for these modes, enabling versatile configurations from one-dimensional rings, directed graphs with complicated connectivity, to higher-dimensional lattices. Experimental validation using microwave resonant circuits confirms the predicted pure decay profiles. This discovery paves the way for potential applications in photonics, signal processing, and beyond, harnessing the unique topological properties of non-Hermitian networks

In quantum optics, the postselection amplitude of a nondegenerate parametric down-conversion (PDC) process is linked to a beamsplitter (BS) via partial time reversal, up to a normalization coefficient which is related to the parametric gain [Proc. Natl. Acad. Sci. USA 117, 33107 (2020)]. A special example where the gain is low is reminiscent of Klyshko's advanced-wave picture in quantum imaging. Here, we propose and prove a generalized duality for multiple spatial paths connecting a quantum nonlinear interference setup consisting of nondegenerate PDCs and linear optical systems to a linear one, where the PDCs are directly replaced by hypothetical wavelength-shifting BSs. This replacement preserves the geometry of the original setup, and cascaded PDCs become optical cavities whose calculation involves the Redheffer star product. Additional terms in the normalization coefficient are related to the contribution of looping photons inside the cavities. Then, we discuss the case of coherent state input and postselection for $Q$-function calculation. This theorem will be helpful in the development of quantum photonic devices beyond the low-gain limit.

We introduce MORPH, a shape-agnostic, autoregressive foundation model for partial differential equations (PDEs). MORPH is built on a convolutional vision transformer backbone that seamlessly handles heterogeneous spatiotemporal datasets of varying data dimensionality (1D--3D) at different resolutions, multiple fields with mixed scalar and vector components. The architecture combines (i) component-wise convolution, which jointly processes scalar and vector channels to capture local interactions, (ii) inter-field cross-attention, which models and selectively propagates information between different physical fields, (iii) axial attentions, which factorizes full spatiotemporal self-attention along individual spatial and temporal axes to reduce computational burden while retaining expressivity. We pretrain multiple model variants on a diverse collection of heterogeneous PDE datasets and evaluate transfer to a range of downstream prediction tasks. Using both full-model fine-tuning and parameter-efficient low-rank adapters (LoRA), MORPH outperforms models trained from scratch in both zero-shot and full-shot generalization. Across extensive evaluations, MORPH matches or surpasses strong baselines and recent state-of-the-art models. Collectively, these capabilities present a flexible and powerful backbone for learning from heterogeneous and multimodal nature of scientific observations, charting a path toward scalable and data-efficient scientific machine learning. The source code, datasets, and models are publicly available at this https URL.