This study investigates the upward transport of waterborne pollutants from a lower container to an upper container through vertically falling water streams. While previous analyses have primarily focused on inclined channels, we extend the theoretical framework to consider vertical configurations. Two distinct cases are examined: (i) a vertical flow within a cylindrical tube, and (ii) a free-falling water jet. For the first case, we derive an analytical expression for the critical water flux required to prevent the upward migration of particles. In the second case, we establish a relationship among water flux, particle size, and vertical position along the stream, which determines the feasibility of upward particle transport. Our findings reveal a fundamental difference between the two configurations. In the tubular flow case, surface tension has negligible influence on particle motion. In contrast, in the free-fall scenario, upward particle transport is only possible in the presence of surface tension. Moreover, we demonstrate that for any given water flux, there exists a threshold height difference beyond which contamination of the upper container is not possible. Increasing the water flux further inhibits any upward transport of pollutants

The degradation of the hemi-wicking property of superhydrophilic high-energy surfaces due to contaminant adsorption from the ambient atmosphere is well documented. This degradation compromises the performance of such surfaces, thus affecting their efficacy in real-world applications where hemi-wicking is critical. In this work, the role of surface micro/nanostructure morphology of laser-textured metallic surfaces on superhydrophilicity degradation is studied. We explore intrinsic contact angle variations of superhydrophilic surfaces via adsorption of organics from the surroundings, which brings about the associated changes in surface chemistry. Furthermore, we explore condensation from humid air as a scalable and environment friendly methodology that can reinstate surface superhydrophilicity to a considerable extent (64% recovery of intrinsic wettability after three hours of condensation) due to the efficient removal of physisorbed contaminants from the surface texture features. The present results strengthen the argument that contact-line movement at fine scales can be used for de-pinning and removal of adsorbed organic molecules from contaminated surfaces.

The joint work of European target laboratories in the ChETEC-INFRA project is presented, to face the new experimental challenges of nuclear astrophysics. In particular, results are presented on innovative targets of 12,13C, 16O, and 19F that were produced, characterized, and, in some cases, tested under beam irradiation. STAR (Solid Targets for Astrophysics Research) is already acting to increase collaboration among laboratories, to achieve shared protocols for target production, and to offer a characterization service to the entire nuclear astrophysics community.

An understanding of how turbulent energy is partitioned between ions and electrons in weakly collisional plasmas is crucial for modelling many astrophysical systems. Using theory and simulations of a four-dimensional reduced model of low-beta gyrokinetics (the `Kinetic Reduced Electron Heating Model'), we investigate the dependence of collisionless heating processes on plasma beta and imbalance (normalised cross-helicity). These parameters are important because they control the helicity barrier, the formation of which divides the parameter space into two distinct regimes with remarkably different properties. In the first, at lower beta and/or imbalance, the absence of a helicity barrier allows the cascade of injected power to proceed to small (perpendicular) scales, but its slow cascade rate makes it susceptible to significant electron Landau damping, in some cases leading to a marked steepening of the magnetic spectra on scales above the ion Larmor radius. In the second, at higher beta and/or imbalance, the helicity barrier halts the cascade, confining electron Landau damping to scales above the steep `transition-range' spectral break, resulting in dominant ion heating. We formulate quantitative models of these processes that compare well to simulations in each regime, and combine them with results of previous studies to construct a simple formula for the electron-ion heating ratio as a function of beta and imbalance. This model predicts a `winner takes all' picture of low-beta plasma heating, where a small change in the fluctuations' properties at large scales (the imbalance) can cause a sudden switch between electron and ion heating.

Coupling a high-performance burning plasma core to a detached boundary solution is critical for realizing magnetic confinement fusion power. Predictive simulations of the edge and scrape-off layer are therefore essential and must self-consistently account for turbulence and the interplay between the plasma, neutral gas, and impurities. We present results on controlled full detachment in ASDEX Upgrade with an X-point radiator (XPR), obtained with the edge turbulence code GRILLIX. Two simulations with dense nitrogen radiation fronts, located 5 and 12 cm above the X-point (accounting for 80% of the input heating power), are discussed. In validations against density, temperature, and bolometry measurements, the simulations show good agreement and reproduce the detached divertor conditions observed in the experiment. Neutral gas is critical for achieving detachment and modulating the height of the XPR front, in agreement with previous SOLPS-ITER transport modeling and analytical power balance studies. In addition, the front structure is highly dynamic due to turbulence, consisting of ionizing and radiative mantles surrounding intermittent cold spots of recombining plasma. Near the detachment front, density and temperature fluctuation amplitudes exceed the background by more than 400%, compared to 40% in an attached reference case. In detached conditions, we observe an inward shift of the radial electric field well at the OMP, along with the breaking of poloidal symmetry in the electrostatic potential. The latter induces strong radial flows around the XPR, which may explain the ELM suppression observed in the H-mode XPR regime.

The continuous improvement in weather forecast skill over the past several decades is largely due to the increasing quantity of available satellite observations and their assimilation into operational forecast systems. Assimilating these observations requires observation operators in the form of radiative transfer models. Significant efforts have been dedicated to enhancing the computational efficiency of these models. Computational cost remains a bottleneck, and a large fraction of available data goes unused for assimilation. To address this, we used machine learning to build an efficient neural network based probabilistic emulator of the Community Radiative Transfer Model (CRTM), applied to the GOES Advanced Baseline Imager. The trained NN emulator predicts brightness temperatures output by CRTM and the corresponding error with respect to CRTM. RMSE of the predicted brightness temperature is 0.3 K averaged across all channels. For clear sky conditions, the RMSE is less than 0.1 K for 9 out of 10 infrared channels. The error predictions are generally reliable across a wide range of conditions. Explainable AI methods demonstrate that the trained emulator reproduces the relevant physics, increasing confidence that the model will perform well when presented with new data.

Two distinct measurement schemes have emerged for the new technique of two-dimensional terahertz spectroscopy (2DTS), complicating the literature. Here, we argue that the 'conventional' measurement scheme derived from nuclear magnetic resonance and its optical-frequency analogues should be favored over the 'alternative' measurement scheme implemented in the majority of 2DTS literature. It is shown that the conventional scheme avoids issues such as overlapping nonlinearities and facilitates physical interpretation of spectra, in contrast to the alternative scheme.

Three-dimensional (3D) refractive index (RI) tomography offers label-free, quantitative volumetric imaging but faces limitations due to optical aberrations, limited resolution, and the computational complexity inherent to existing approaches. To overcome these barriers, we propose Analytic Fourier Ptychotomography (AFP), a new computational microscopy technique that analytically reconstructs aberration-free, complex-valued 3D RI distributions without iterative optimization or axial scanning. AFP incorporates a new concept called the finite sample thickness (FST) prior, and analytically solves the inverse scattering problem through three sequential steps: complex-field reconstruction via the Kramers-Kronig relation, linear aberration correction using overlapping spectra, and analytic spectrum extension into the darkfield region. Unlike iterative reconstruction methods, AFP does not require parameter tuning or computationally intensive optimizations, which are often error-prone and non-generalizable. We experimentally demonstrate that AFP significantly enhances image quality and resolution under various aberration conditions across a range of applications. AFP corrected aberrations associated with 25 Zernike modes (with a maximal phase difference of 2.3$\pi$ and maximal Zernike coefficient value of 4), extended the synthetic numerical aperture from 0.41 to 0.99, and provided a two-fold resolution enhancement in all directions. AFP's simplicity and robustness make it an attractive imaging technology for quantitative 3D analysis in biological, microbial ecological, and medical studies.

Message passing techniques on networks encompasses a family of related methods that can be employed to ascertain many important properties of a network. It is widely considered to be the state of the art formulation for networked systems and advances in this method have a wide impact across multiple literatures. One property that message passing can yield is the size of the largest connected component in the network following bond percolation. In this paper, we introduce an alternative method of finding this value that differs from the standard approach. Like the canonical approach, our method is exact on trees and an approximation on arbitrary graphs. We show that our method lends itself to the description of a variety of generalisations of bond percolation such as sequential percolation and non-binary percolation and can yield information about the local environment of a node in percolation equilibrium that the traditional approach cannot.

The linear stability of a thermally stratified fluid layer between horizontal walls, where thermal particles are continuously injected at one boundary and extracted at the other - a system known as particulate Rayleigh-B\'enard (pRB) - is studied. For a fixed volumetric particle flux, reducing the injection velocity stabilizes the system when heavy particles are introduced from above, but destabilizes it when light particles are injected from below. For very light particles (bubbles), low injection velocities can shift the onset of convection to negative Rayleigh numbers, i.e. heating from above. Particles accumulate non-uniformly near the extraction wall and in regions of strong vertical flow, aligning with either wall-impinging or wall-detaching zones depending on whether injection is at sub- or super-terminal velocity.

Superconducting devices have enabled breakthrough performance in quantum sensing and ultra-low-power computing applications. However, the need for a cryo-electronics platform that can interface superconducting electronics with Complementary Metal-Oxide-Semiconductor (CMOS) devices has become increasingly evident in many cutting-edge applications. In this work, we present a three-terminal superconducting switch fabricated using photolithography, which can directly interface with CMOS electronics. We investigate the switching characteristics of this micron-width wire-based cryotron (wTron) by varying its channel and gate dimensions and sweeping its terminal bias currents. The wTron exhibits an output impedance exceeding 1 k$\Omega$ and demonstrates reduced sensitivity to ambient magnetic noise, similar to its nanoscale predecessor, the nanocryotron. In addition, its micron-width wires have switching currents in the mA range, making wTrons well-suited for driving current-hungry resistive loads and highly capacitive CMOS loads. We demonstrate the capability of wTron to drive room-temperature CMOS electronics, including an LED and a MOSFET with a gate capacitance of 500 pF. Finally, we discuss design considerations for optimizing wTron device parameters to drive CMOS loads, such as MOSFETs, HEMTs, and electro-optic modulators. Our analysis shows that wTron will facilitate the interface between superconducting electronics and CMOS, thereby paving the way for the development of foundry-process-compatible cryo-electronics ecosystems to advance next-generation computing and quantum applications.

The disruption of protein structures by denaturants like urea is well studied, though its molecular mechanisms remain unclear. Using Molecular Dynamics (MD) simulations, we investigated how urea affects the structural stability of Bovine Serum Albumin (BSA) at concentrations from 0 M to 5 M. Our results reveal that urea induces a dehydration/rehydration cycle, characterized by displacement and partial replacement of water molecules in BSAs hydration shell. At low concentrations, urea reduces protein/water hydrogen bonds while enhancing protein-urea interactions. At higher concentrations, urea aggregation limits these interactions, promoting rehydration and changes in tertiary structure, while secondary structure remains largely intact. These findings provide insights into the mechanisms of protein denaturation and stability by urea.

Understanding pattern formation in crossing pedestrian flows is essential for analyzing and managing high-density crowd dynamics in urban environments. This study presents two complementary methodological approaches to detect and characterize stripe formations, an emergent structure observed when two pedestrian groups cross at various angles. First, we propose a matrix-based method that utilizes time-resolved trajectory data to determine the relative crossing order of pedestrians from opposing groups. By identifying points of minimal spatial separation between individuals and analyzing associated time differences, we construct a crossing matrix that captures the sequence and composition of stripes. Second, we introduce a geometric model based on elliptical approximations of pedestrian groups, enabling analytical prediction of two key macroscopic quantities: the number of stripes and the interaction time between groups. The model captures how these quantities vary with the crossing angle and shows strong agreement with experimental data. Further analysis reveals that group elongation during crossing correlates with the vertical cross-section of the elliptical shape. These methods provide effective tools for analyzing large-scale movement datasets, informing the design of public spaces, and calibrating mechanistic models. The study also presents hypotheses about pattern transitions in continuous pedestrian streams, suggesting promising directions for future research on collective motion under varying flow geometries and densities.

We present a review of the current experimental and theoretical understanding of electron transport in noble liquids. Special attention is given to recent measurements that coincide with the development of time projection chambers (TPCs) using liquid xenon and argon as detector media. To enable transparent benchmarking of simulations and to facilitate the comparison between early studies and modern TPC data, we introduce a new open-access database of electron mobility and diffusion measurements. In particular, we emphasize the transition to large-scale detector designs which incorporate extended drift distances alongside improved purity control and field uniformity. On the theoretical side, we contrast empirical transport models with ab initio approaches, highlighting our recent efforts to incorporate low-energy, liquid-specific scattering phenomena, including coherent scattering, polarization screening, and bulk potential modifications. While elastic transport has seen substantial theoretical progress, inelastic processes in liquids, including ionization, exciton formation and interband transitions, remain poorly understood due to the lack of experimental cross sections and validated models. We also discuss the applications and challenges of modeling scintillation, doped and mixture-liquid targets, and gas-liquid interface behavior, all of which are critical for the design and optimization of next-generation detectors.

We present an efficient method to optimize sensor placement for flow estimation using sensors with time-delay embedding in advection-dominated flows. Our solution allows identifying promising candidates for sensor positions using solely preliminary flow field measurements with non-time-resolved Particle Image Velocimetry (PIV), without introducing physical probes in the flow. Data-driven estimation in advection-dominated flows often exploits time-delay embedding to enrich the sensor information for the reconstruction, i.e. it uses the information embedded in probe time series to provide a more accurate estimation. Optimizing the probe position is the key to improving the accuracy of such estimation. Unfortunately, the cost of performing an online combinatorial search to identify the optimal sensor placement in experiments is often prohibitive. We leverage the principle that, in advection-dominated flows, rows of vectors from PIV fields embed similar information to that of probe time series located at the downstream end of the domain. We propose thus to optimize the sensor placement using the row data from non-time-resolved PIV measurements as a surrogate of the data a real probe would actually capture in time. This optimization is run offline and requires only one preliminary experiment with standard PIV. Once the optimal positions are identified, the probes can be installed and operated simultaneously with the PIV to perform the time-resolved field estimation. We show that the proposed method outperforms equidistant positioning or greedy optimization techniques available in the literature.

Characterizing conformational transitions in physical systems remains a fundamental challenge in the computational sciences. Traditional sampling methods like molecular dynamics (MD) or MCMC often struggle with the high-dimensional nature of molecular systems and the high energy barriers of transitions between stable states. While these transitions are rare events in simulation timescales, they often represent the most biologically significant processes - for example, the conformational change of an ion channel protein from its closed to open state, which controls cellular ion flow and is crucial for neural signaling. Such transitions in real systems may take milliseconds to seconds but could require months or years of continuous simulation to observe even once. We present a method that reformulates transition path generation as a continuous optimization problem solved through physics-informed neural networks (PINNs) inspired by string methods for minimum-energy path (MEP) generation. By representing transition paths as implicit neural functions and leveraging automatic differentiation with differentiable molecular dynamics force fields, our method enables the efficient discovery of physically realistic transition pathways without requiring expensive path sampling. We demonstrate our method's effectiveness on two proteins, including an explicitly hydrated bovine pancreatic trypsin inhibitor (BPTI) system with over 8,300 atoms.

Swirl-stabilized auto-igniting spray flames are essential for designing efficient and clean combustion systems. The present study performs large eddy simulations (LES) of the dilute auto-igniting methanol flame in a vitiated, hot coflow of varying swirl intensities. The six-dimensional Flamelet Generated Manifold (FGM) technique is used to solve the reactive flow accurately and economically. The swirl numbers (SN), i.e. 0.2, 0.6, 1.0, and 1.4, are used to assess their effect on auto-ignition and flame stability. At lower to moderate swirl numbers (SN =0.2, 0.6), the increase in swirl is found to increase the lift-off height. Beyond the critical swirl number (SN=0.6), the lift-off height drops. Also, the time-averaged flame structure transitions from a tubular-like flame into a uniformly distributed combustion region at these high swirl numbers. It also results in a more compact flame for the higher swirl numbers. These effects on flame dynamics are analyzed in detail using the mean gas-phase flow field distribution, particle statistics, and proper orthogonal decomposition (POD).

A crucial factor in the stability of high-pressure rocket-scale combustors is the temperature at which fuel is injected. This study investigates its effect on the stability of supercritical liquid oxygen (LOx)-methane combustion and highlights the impact of shear layer dynamics in cases with lower injection temperatures. The stability features of a rocket-scale combustor operating with multiple injector elements are investigated using a high-fidelity large eddy simulation (LES) framework. The numerical framework combines a flamelet-generated manifold (FGM) combustion model with complex real gas thermodynamics in a scale-resolving simulation setup. It reproduces the non-equilibrium transcritical injection and supercritical combustion characteristics of supercritical methane-oxygen flames. To ascertain the effect of injection temperature on flame and combustor stability, we perform several LES simulations at various methane injection temperatures and produce a stability map. Our analysis shows extremely unstable flame characteristics at lower fuel injection temperatures that are not seen under typical fuel injection circumstances. Below a specific methane injection temperature, LES captures a high-amplitude, self-sustaining instability. It is determined that the combustor becomes unstable below a specific stability boundary temperature. Detailed spectral and dynamic mode decomposition (DMD) analysis of the stable and unstable cases reveals the onset of longitudinal acoustic waves in the combustor. Our thorough investigation pinpoints the instability mechanism, emphasizing that the leading causes of this self-sustaining instability in the combustor are a reduced velocity ratio, fuel buildup, and fuel cut-off occurrences.

An incident wave at a temporal interface, created by an abrupt change in system parameters, generates time-refracted and time-reflected waves. We find topological characteristics associated with the temporal interface that separates distinct spatial topologies and report a novel bulk-boundary correspondence for the temporal interface. The vanishing of either time refraction or time reflection records a topological phase transition across the temporal interface, and the difference of bulk band topology predicts nontrivial braiding hidden in the time refraction and time reflection coefficients. These findings, which are insensitive to spatial boundary conditions and robust against disorder, are demonstrated in a synthetic frequency lattice with rich topological phases engendered by long-range couplings. Our work reveals the topological aspect of temporal interface and paves the way for using the time boundary effect to probe topological phase transitions and topological invariants.

Machine learning interatomic potentials (MLIPs) provide an effective approach for accurately and efficiently modeling atomic interactions, expanding the capabilities of atomistic simulations to complex systems. However, a priori feature selection leads to high complexity, which can be detrimental to both computational cost and generalization, resulting in a need for hyperparameter tuning. We demonstrate the benefits of active set algorithms for automated data-driven feature selection. The proposed methods are implemented within the Atomic Cluster Expansion (ACE) framework. Computational tests conducted on a variety of benchmark datasets indicate that sparse ACE models consistently enhance computational efficiency, generalization accuracy and interpretability over dense ACE models. An added benefit of the proposed algorithms is that they produce entire paths of models with varying cost/accuracy ratio.

A series of beam tests were conducted at the KEK AR test beamline in ordet to investigate the performance of a prototype detector for instrumentation in the low-E beamline at the CERN H2 line. For the silicon strip detector, the simultaneous readout of all strips of the X-Y detector was evaluated, in addition to the detection efficiency being assessed through coincidences with the trigger scintillators. The results demonstrated the successful acquisition of data with full strip readout and the reconstruction of the two-dimensional beam profile. Furthermore, we are contemplating the incorporation of GAGG crystals, an inorganic scintillator characterized by a high light yield ($\sim$$5 \times 10^{4}$ photons/MeV) and a brief time constant ($\sim$60 ns), within the newly developed time-of-flight detector. We assessed the resolution of time-of-flight based on the data, with the objective of achieving the required performance benchmarks through future improvements to the time-of-tlight detector.

Complex dynamics of silicon microring resonators loaded by delayed feedback elements enable high-speed photonic reservoir computing. Implementing feedback is especially challenging when the required delay should match the time scales of silicon's nonlinearities. To increase the computation speed and preclude any need for very long delay lines, we avoid relying on silicon's nonlinearity and merely employ either amplitude or phase modulation along with direct detection. By supplementing its memory with that of the electronic output layer, the proposed photonic reservoir composed of a Mach-Zehnder interferometer and a microring resonator is predicted to perform computations one order of magnitude faster than those based on silicon's nonlinearity with its speed only limited by the modulation/detection bandwidth. This reservoir performs accurately in NARMA-10, Mackey-Glass, and Santa-Fe prediction tasks, and enables signal equalization in optical communication systems.

Understanding nonlinear social contagion dynamics on dynamical networks, such as opinion formation, is crucial for gaining new insights into consensus and polarization. Similar to threshold-dependent complex contagions, the nonlinearity in adoption rates poses challenges for mean-field approximations. To address this theoretical gap, we focus on nonlinear binary-opinion dynamics on dynamical networks and analytically derive local configurations, specifically the distribution of opinions within any given focal individual's neighborhood. This exact local configuration of opinions, combined with network degree distributions, allows us to obtain exact solutions for consensus times and evolutionary trajectories. Our counterintuitive results reveal that neither biased assimilation (i.e., nonlinear adoption rates) nor preferences in local network rewiring -- such as in-group bias (preferring like-minded individuals) and the Matthew effect (preferring social hubs) -- can significantly slow down consensus. Among these three social factors, we find that biased assimilation is the most influential in accelerating consensus. Furthermore, our analytical method efficiently and precisely predicts the evolutionary trajectories of adoption curves arising from nonlinear contagion dynamics. Our work paves the way for enabling analytical predictions for general nonlinear contagion dynamics beyond opinion formation.

Classically, surface tension is seen as a force per unit length or as energy per unit area. The surface energy is calculated thermodynamically on the surface of a mathematical layer with no thickness. The surface energy concept is certainly practical and elegant, but it doesn't seem entirely satisfactory to us. In an earlier article 1 , we proposed a thought experiment in which surface tension is replaced with an equivalent force per unit area according to the principles of fluid mechanics. This theoretical tool can be used to rewrite the classical static equations in terms of surface stress gradient and propose a new way of calculating known phenomena such as meniscus, capillary tube, water drops, etc. In this article, we propose a new thought experiment assuming that beyond the classical interaction forces acting at short distance, molecules at liquid interfaces statistically reorganize according to a stationary process of creation/destruction at long distance. The process is such that the degree of statistical molecular organization decreases with a gradient from the surface to the bulk, where the molecules recover the natural disorder of Brownian motion. Assuming that this reorganization process follows an Avrami-type law, we will reinterpret the effects of surface tension in terms of energy per unit volume, which will allow us to retrieve the previous equations of force gradient per unit area.

The XAFS-DET work package of the European LEAPS-INNOV project is developing a high-purity Germanium detectors for synchrotron applications requiring spectroscopic-grade response. The detectors integrate three key features: (1) newly designed monolithic Germanium sensors optimised to mitigate charge-sharing events, (2) an improved cooling and mechanical design structure supported by thermal simulations, and (3) complete electronic chain featuring a low-noise CMOS technology-based preamplifier. enabling high X-ray count rate capability over a broad energy range (5-100 keV). This paper discusses the first integration and characterization of one of the two multi-element Ge detectors at the European Synchrotron Radiation Facility (ESRF). The integration phase included validating high-throughput front-End electronics, integrating them with the Ge sensor, and operating them at liquid nitrogen temperature, in addition to the experimental characterization, which consists of electronics noise study and spectroscopic performance evaluation.

Measuring the total electron emission yield of dielectric materials remains a challenging task. Indeed, the charge induced by irradiation and electron emission disturbs the measurement. It is therefore important to quantify this charge during the measurement. Using a Kelvin probe allows both the emission yield and the induced charge to be measured. However, this method requires the probe to be placed inside the vacuum chamber, which is often complicated or even impossible. We propose a complete redesign of this method to overcome this issue. A capacitive coupling now allows the potential probe to be placed outside the chamber, in ambient atmosphere. Beyond this major simplification in implementation, we have also introduced several improvements that simplify the measurement protocol and reduce the overall measurement time. The new method was first validated on a metallic sample (Cu), and subsequently applied to a polymer (Kapton).

The optical pump-probe technique is a common tool for investigation of ultrafast spin dynamics, which usually utilizes single-diode detection averaging the dynamics over the pumped area. Using ultrafast imaging technique, we show experimentally that a femtosecond laser pulse with a gradient distribution of intensity efficiently excites strongly inhomogeneous spin dynamics on spatial scales much smaller than the pump spot size. The mechanism responsible for the inhomogeneous distribution is based on temperature gradients and corresponds to a sign change of the torque derivative in different areas of the pump. We argue that the observed phenomenon is general for the systems with competitive magnetic anisotropies. Overlooking this effect in the majority of pump-probe experiments may result in a dramatic underestimation of the live time and amplitude of the laser-induced spin dynamics.

In superconducting linear accelerators (linacs), accurately monitoring beam dynamics is essential for minimizing beam losses and ensuring stable operations. However, destructive diagnostics must be avoided in superconducting sections to prevent the occurrence of particulates and outgassing, rendering direct measurements of the beam envelope particularly challenging. This study presents a non-destructive method that uses beam position monitors (BPMs) to estimate the transverse beam envelope based on measurements of the quadrupole moment of the beam distribution. Although this concept was originally proposed in the 1980s, its application, especially to hadron beams, has been limited because of low signal sensitivity and the accuracy constraints associated with conventional BPM geometries. To overcome these challenges, we employed $\cos{2\theta}$-type BPMs, which offer improved sensitivity to quadrupole components and are well-suited for low-$\beta$ heavy ion beams. This method was applied to the heavy ion beams in the superconducting RIKEN linac (SRILAC), for which data from eight BPMs were combined with transfer matrix calculations and supplemental wire scanner data. The resulting beam envelope estimates exhibited good agreement with conventional quadrupole scan results, demonstrating the feasibility of this technique for routine, non-destructive beam monitoring in superconducting accelerator sections.

Background: A growing number of cancer patients with brain metastases can benefit from stereotactic radiosurgery (SRS) thanks to recent advances in systemic therapies. With an increasing patient load, single-isocenter treatments on widely available C-arm linear accelerators are an attractive option. However, the planning of such treatments is challenging for multi-target cases due to the island blocking problem, which occurs when the multi-leaf collimator cannot conform to all targets simultaneously. Purpose: We propose a multi-target partitioning algorithm that mitigates excessive exposure of normal tissue caused by the island blocking problem. Methods: The algorithm divides (partitions) the set of targets into subsets to treat with separate arc passes, optimizing both subsets and collimator angles to minimize island blocking. The algorithm was incorporated into a fully automated treatment planning script and evaluated on 20 simulated patient cases, each with 10 brain metastases and 21 Gy prescriptions. It was also retrospectively evaluated on six clinical cases. Results: Partitioning significantly improved the gradient index, global efficiency index, and brain V12Gy compared to simultaneous treatment of all metastases. For example, the average gradient index improved from 5.9 to 3.3, global efficiency index from 0.32 to 0.46, and normal brain V12Gy from 49 cm3 to 26 cm3 between 3 and 9 arcs. The proposed algorithm outperformed baselines in utilizing a limited number of arcs. All target partitioning strategies increased the total number of monitor units (MUs). Conclusions: The dose gradient in single-isocenter VMAT plans can be substantially improved by treating a smaller subset of metastases at a time. This requires more MUs and arcs, implying a trade-off between delivery time and plan quality which can be explored using the algorithm proposed in this paper.

Complex spatial patterns in biological systems often arise through self-organization without a central coordination, guided by local interactions and chemical signaling. In this study, we explore how motility-dependent chemical deposition and concentration-sensitive feedback can give rise to fractal-like networks, using a minimal agent-based model. Agents deposit chemicals only while moving, and their future motion is biased by local chemical gradients. This interaction generates a rich variety of self-organized structures resembling those seen in processes like early vasculogenesis and epithelial cell dispersal. We identify a diverse phase diagram governed by the rates of chemical deposition and decay, revealing transitions from uniform distributions to sparse and dense networks, and ultimately to full phase separation. At low chemical decay rates, agents form stable, system-spanning networks; further reduction leads to re-entry into a uniform state. A continuum model capturing the co-evolution of agent density and chemical fields confirms these transitions and reveals how linear stability criteria determine the observed phases. At low chemical concentrations, diffusion dominates and promotes fractal growth, while higher concentrations favor nucleation and compact clustering. These findings unify a range of biological phenomena - such as chemotaxis, tissue remodeling, and self-generated gradient navigation - within a simple, physically grounded framework. Our results also offer insights into designing artificial systems with emergent collective behavior, including robotic swarms or synthetic active matter.

This study investigates the dispersion and self-sorting dynamics of bidisperse particles, i.e., a mixture of two distinct particle sizes, during the evaporation of ethanol droplets on a heated substrate, focusing on the influence of surface wettability, Marangoni stresses, and relative particle density. To this end, numerical simulations are carried out using a two-stage numerical approach: the first stage simulates the gas-liquid flow along with the heat and vapor distribution, while the second stage models the particle behavior using Lagrangian particle tracking. The results reveal that for an ethanol droplet evaporating with a constant contact angle in the absence of thermocapillary Marangoni stresses, the flow induced by the receding motion of the contact line supersedes the capillary flow, moving the fluid from the contact line to the apex of the droplet. This flow moves the particles from the bulk of the droplet to the apex of the droplet and suppresses size-based self-sorting of the particles. However, in the presence of Marangoni stresses, a flow along the interface near the apex of the droplet promotes the self-sorting of particles based on their size, whereby smaller particles concentrate near the droplet apex and larger particles form an outer shell around them.

Photonic integrated devices are progressively evolving beyond passive components into fully programmable systems, notably driven by the progress in chalcogenide phase-change materials (PCMs) for non-volatile reconfigurable nanophotonics. However, the stochastic nature of their crystal grain formation results in strong spatial and temporal crystalline inhomogeneities. Here, we propose the concept of spatially-controlled planar Czochralski growth, a novel method for programming the quasi-monocrystalline growth of low-loss Sb2S3 PCM, leveraging the seeded directional and progressive crystallization within confined channels. This guided crystallization method is experimentally shown to circumvent the current limitations of conventional PCM-based nanophotonic devices, including a multilevel non-volatile optical phase-shifter exploiting a silicon nitride-based Mach-Zehnder interferometer, and a programmable metasurface with spectrally reconfigurable bound state in the continuum. Precisely controlling the growth of PCMs to ensure uniform crystalline properties across large areas is the cornerstone for the industrial development of non-volatile reconfigurable photonic integrated circuits.

Dissolution Dynamic Nuclear Polarisation (dDNP) is an experimental technique that increases the sensitivity of magnetic resonance experiments by more than a factor of $10^5$, permitting isotopically-labelled molecules to be transiently visible in MRI scans with their biochemical fates spatially resolvable over time following injection into a patient. dDNP requires a source of unpaired electrons to be in contact with the isotope-labelled nuclei, cooled to temperatures close to absolute zero, and spin-pumped into a given state by microwave irradiation. At present, these electrons are typically provided by chemical radicals which require removal by filtration prior to injection into humans. Alternative sources include UV irradiation, requiring storing samples in liquid nitrogen, or cobalt-60 gamma irradiation, which requires days and generates polarisation two to three orders of magnitude lower than chemical radicals. In this study, we present ultra-high dose rate electron beam irradiation as a novel alternative for generating non-persistent radicals in glycerol/alanine mixtures. These radicals are stable for months at room temperature, are present at concentrations dependent on irradiation dose, and generate comparable nuclear polarisation to the typically used trityl radicals (20%) through a novel mechanism. The process of their generation inherently sterilises samples, and they enable the imaging of alanine metabolism in vivo using dDNP. This new method of generating radicals for dDNP offers the potential to report on relevant biological processes while being translatable to the clinic.

This paper introduces a newly designed cavity tuner for superconducting radio-frequency (SRF) cavity. Aiming to overcome the drawbacks of traditional tuning systems, like the limited tuning range of piezoelectric tuner and the low-speed tuning of stepper-motor-based tuner, this novel tuner is crafted to improve SRF cavity performance and stability via efficient and accurate frequency tuning. The design encompasses several key elements. The cavity structure includes a commonly used 1.3 GHz single-cell superconducting cavity and a room-temperature coaxial tuner cavity. The coupling mechanism between the two cavities, along with the coupling window design, ensures effective energy transfer while minimizing losses. The mechanical tuning system, driven by electromagnetic coils, enables precise adjustments, and the cooling mechanisms for both cavities guarantee stable operation. Functioning by coupling an external resonant cavity to the superconducting one, this tuner can adjust frequencies through mechanical or electromagnetic methods. It realizes rapid tuning, with a speed much faster than traditional mechanical tuner, high-precision tuning down to the sub-mHz level, and a wide tuning range covering a broader frequency spectrum. Theoretical analysis and simulations verify that the tuner can remarkably enhance tuning speed, precision, and range. It also has distinct advantages such as a simplified structure, which reduces manufacturing and maintenance complexity, and enhanced reliability due to its non-contact tuning operation. In particle accelerators, this cavity tuner holds great potential. It represents a significant step forward in superconducting accelerator technology, offering a novel way to optimize the performance and stability of SRF cavity.

In this research, numerical analysis of nonlinear pulse propagation is carried out. This is done mainly by solving the nonlinear Schrodinger equation using the split step algorithm. In a nonlinear media, dispersive effects exist simultaneously with nonlinear effects. Refractive index dependence on intensity results in optical Kerr effect which causes narrowing of transmitted pulses by inducing self-phase modulation while second order group velocity dispersion causes the pulses to spread. In this project, group velocity dispersion is discussed followed by self-phase modulation. These individually detrimental effects are shown to combine beneficially for propagation of pulses here. Gaussian pulse is studied and propagated by using them as input in to the nonlinear Schrodinger equation. The split step algorithm is described in depth. Explanation of each step is included along with the relevant equations defining these steps.

Public goods game serves as a valuable paradigm for studying the challenges of collective cooperation in human and natural societies. Peer punishment is often considered as an effective incentive for promoting cooperation in such contexts. However, previous related studies have mostly ignored the positive feedback effect of collective contributions on individual payoffs. In this work, we explore global and local state-feedback, where the multiplication factor is positively correlated with the frequency of contributors in the entire population or within the game group, respectively. By using replicator dynamics in an infinite well-mixed population we reveal that state-based feedback plays a crucial role in alleviating the cooperative dilemma by enhancing and sustaining cooperation compared to the feedback-free case. Moreover, when the feedback strength is sufficiently strong or the baseline multiplication factor is sufficiently high, the system with local state-feedback provides full cooperation, hence supporting the ``think globally, act locally'' principle. Besides, we show that the second-order free-rider problem can be partially mitigated under certain conditions when the state-feedback is employed. Importantly, these results remain robust with respect to variations in punishment cost and fine.

Base-flow computations and stability analyses are performed for a hypersonic wind tunnel nozzle at a Mach number of 6. Isothermal and adiabatic wall boundary conditions are investigated, and moderate stagnation conditions are used to provide representative scenarios to study the transition in quiet hypersonic wind tunnel facilities. Under these conditions, the studied nozzle shows a small flow separation at the convergent inlet. Global stability analysis reveals that this recirculation bubble may trigger a classical three-dimensional stationary unstable global mode. Resolvent analysis reveals G\"ortler, first and second Mack modes affecting the divergent part of the nozzle, along with a Kelvin-Helmholtz instability induced by the bubble. The present study also highlights the key impact of perturbations located in the convergent inlet on the development of instabilities further downstream in the divergent outlet, helping understand the need and efficacy of a suction lip upstream of the nozzle throat to mitigate instabilities in the divergent nozzle. Detailed knowledge of all these mechanisms is essential for understanding flows in quiet hypersonic wind tunnel nozzles and, consequently, represents a key step toward the optimisation of such nozzles.

We compare model solutions of a forced Kadomtsev-Petviashvili (fKP) equation with experimental observations of dust acoustic precursor solitons excited by a supersonically moving charged cylindrical object in a dusty plasma medium. The fKP equation is derived from a three-fluid-Poisson model of the dusty plasma using the reductive perturbation technique and numerically solved for parameters close to the experimental investigations of cylindrical precursor solitons. The fKP model solutions show excellent agreement with the experimental results in reproducing the prominent geometric features of the two-dimensional solitons and closely matching the quantitative values of their velocities, amplitudes, and temporal evolutions. Our findings suggest that the fKP equation can serve as a very realistic model to investigate the dynamics of precursor solitons and can be usefully employed in practical applications such as space debris detection and tracking techniques that are based on observing/predicting nonlinear plasma excitations induced by the debris in the ionosphere.

Frequency stabilisation of lasers to optical reference cavities is an established method to achieve state-of-the-art stability. The strengths of this method are the high discriminator coefficient of optical cavities, and the low-noise extraction of the stabilisation signal using modulation techniques. In this Letter we report beyond state-of-the-art performance on both of these fundamentals, unlocking $10^{-19}$ fractional frequency laser stabilisation. We employ a 68 cm long cavity to realise an optical storage time of 300 microseconds, achieving ultrahigh frequency discrimination. We develop a simple and robust scheme to actively cancel residual amplitude modulation (RAM) at the $10^{-7}$ level in an annealed-proton-exchanged lithium-niobate waveguide electro-optic-modulator (EOM).

Photonic reservoir computer (PRC) is a kind of real-time and adaptive recurrent neural network, where only weights in the readout layer require training. PRC is a promising tool to deal with the crucial issue of nonlinear equalization in optical fiber communications. Here we theoretically show a deep PRC for the nonlinear equalization of coherent signals with the format of 16- level quadrature amplitude modulation (16-QAM). The deep PRC consists of cascading injection-locked Fabry-Perot lasers with optical feedback. Both the in-phase component and the quadrature component of the 16-QAM signals are simultaneously injected into the deep PRC in parallel, based on the wavelength multiplexing of Fabry-Perot lasers. It is demonstrated that the deep PRC exhibits strong capability for the nonlinearity compensation of coherent signals. The Q factor is improved by more than 1 dB for 16-QAM signals with launch powers above 10 dBm, associated with a bit rate of 240 Gbps and a transmission distance of 50 km.

This note responds to Schwartz and Hutchison's Comment (arXiv:2403.06001) on our article (DOI:10.1002/adma.202309393). We think differences have arisen not in the experimental results themselves but in their interpretation: our more extensive experiments allowed us to distinguish between "true positive" and "false positive" results. We identify potential evidence of non-polaritonic effects in Schwartz and Hutchison's own work. We hope our work will encourage others to produce more systematic investigations of strong coupling.

Coalescence may not occur immediately when droplets impact a liquid film. Despite the prevalence of the high-temperature condition during the impact process in many applications, the effect of droplet temperature on droplet coalescence is rarely considered. In this study, we experimentally investigate the droplet coalescence during the impact of hot droplets on a liquid film by using color interferometry, high-speed imaging, and infrared imaging. We find that the coalescence of the hot droplet with the liquid film can be delayed which is mediated by the intervening gas layer between the droplet and the film. Compared with droplets at room temperature, the residence time of hot droplets can increase by more than two orders of magnitude. We find that the thickness of the gas layer increases with the droplet temperature, explaining that the thermal delay of coalescence is due to the thicker gas layer. During the hot droplet impact, the temperature gradient at the bottom of the droplet induces Maranogni flow, which can delay the drainage of the intervening gas layer. The results also show that as the Weber number increases, the residence time of the droplet decreases because of the thinner thickness of the gas layer.

Accurate dose calculation is crucial in radiotherapy, as tissue relative electron densities (RED) derived from CT scans play a vital role. This study investigated the impact of different CT-to-RED calibration curves on brain cancer treatment plans. Three calibration curves were compared: CIRS phantom-derived, Catphan phantom-derived, and the default curve in the Monaco Treatment Planning System. Ten volumetric modulated arc therapy (VMAT) plans were generated and recalculated using each curve. Dosimetric parameters for Planning Target Volume (PTV) and Organs at Risk (OARs) were analyzed. Results showed significant differences in PTV dose distribution between the CIRS-derived and default curves, while no significant differences were found between Catphan-derived and default curves. The CIRS-derived curve demonstrated superior performance in representing brain tissue electron densities. These findings emphasize the importance of using site-specific CT-to-RED calibration curves for accurate dose calculations in brain radiotherapy, potentially improving treatment safety and efficacy

The transparency of liquid scintillators or water is an important parameter for many detectors in particle and astroparticle physics. In this work, the Cavity Enhanced Long Light Path Attenuation Length Screening (CELLPALS) method for the determination of the attenuation length is presented for the first time. The method is based on an experimental setup similar to a Fabry-P\'erot interferometer but adding up multiple-reflected intensities of a modulated light source. CELLPALS was developed to measure the attenuation length of highly transparent liquids (> 10 m) with significantly lower uncertainties than with UV-Vis spectroscopy, which is a standard method for determining the attenuation length. In addition to the attenuation length, the group velocity of light in the sample can also be derived from the free spectral range of the cavity, which is not provided by any conventional method for determining the attenuation length. In this work, the CELLPALS method, its achievable precision and an experimental setup to demonstrate its feasibility are discussed. The attenuation lengths and group velocities of several transparent liquids were measured at wavelengths between 420 nm and 435 nm. In addition, the attenuation length and group velocity of linear alkylbenzene (LAB) samples after different purification stages and a purified LAB-based liquid scintillator were measured at a wavelength of 425 nm. The results confirmed the potential of CELLPALS to determine the attenuation length with an uncertainty of ~ 2 %. The group velocity of light in the sample can be determined with an uncertainty of ~ 0.03 %.

A simple closed-form formula for the period of a pendulum with finite amplitude is proposed. It reproduces the exact analytical forms both in the small and large amplitude limits, while in the mid-amplitude range maintains average error of 0.06% and maximum error of 0.17%. The accuracy should be sufficient for typical engineering applications. Its unique simplicity should be useful in a theoretical development that requires trackable mathematical framework or in an introductory physics course that aims to discuss a finite amplitude pendulum. A simple and formally exact solution of angular displacement for the full range of amplitudes is illustrated.

This study presents direct numerical simulation (DNS) of finite-size, interface-resolved ammonia and n-heptane droplets evaporating in decaying homogeneous isotropic turbulence. Simulations are conducted for each fuel in a dense spray region, where the liquid volume fraction exceeds $\mathcal{O}(10^{-2})$. The focus is on investigating the complex interactions between droplets, turbulence, and phase change, with emphasis on droplet-droplet interactions and their influence on the evaporation process. We provide state-of-the-art insights into the evaporation characteristics of two different fuels under turbulent conditions, aiming to provide a deeper understanding of their different evaporation rates in spray-combustion applications. To this end, the present study also explores how varying turbulence intensities affect the evaporation rates of each fuel, revealing differences in coalescence behavior and energy transfer from the liquid to the gaseous phase. These findings are crucial to the improvement of predictive CFD models and to the optimization of fuel injection in spray-combustion applications, especially under high-pressure conditions.

In this paper we introduce a new fix point iteration scheme for solving nonlinear electromagnetic scattering problems. The method is based on a spectral formulation of Maxwell's equations called the Bidirectional Pulse Propagation Equations. The scheme can be applied to a wide array of slab-like geometries, and for arbitrary material responses. We derive the scheme and investigated how it performs with respect to convergence and accuracy by applying it to the case of light scattering from a simple slab whose nonlinear material response is a sum a very fast electronic vibrational response, and a much slower molecular vibrational response.

Sequential tropical cyclone hazards--two tropical cyclones (TCs) making landfall in the same region within a short time--are becoming increasingly likely. This study investigates situational preparedness dynamics for six sequential TC events that affected seven states in the United States from 2020 to 2024. We find a combined effect of forecast wind speed and landfall sequence of a TC. Stronger forecast wind is always associated with higher preparedness levels. People tend to show a higher preparedness level for the second TC but are more sensitive to the increasing forecast wind speed of the first TC. We also find that the counties showing high preparedness levels for the first TC consistently show high preparedness levels for the subsequent one. Power outages induced by the first TC significantly increase preparedness for the subsequent TC (e.g., approximately 13% pct for mobility needs preparedness and 24% for structural reinforcement preparedness when increasing one-unit customers out). We identified spatial dependency in preparedness across counties. Power outage experiences in first TCs show statistically significant spillover effects on neighboring counties' preparedness levels for second TCs. Throughout sequential TCs, people with access and functional needs consistently show lower preparedness levels.

In this submission, we describe research goals, implementation, and timelines of the High Field Magnet Programme, hosted by CERN. The programme pursues accelerator-magnet R&D with low-temperature- and high-temperature superconductor technology with a main focus on the FCC-hh. Following a long tradition of magnet R&D for high-energy particle colliders, HFM R&D fosters important societal impact through synergies with other fields.

In rare events experiments, such as those devoted to the direct search of dark matter, a precise knowledge of the environmental gamma and neutron backgrounds is crucial for reaching the design experiment sensitivity. The neutron component is often poorly known due to the lack of a scalable detector technology for the precise measurement of low-flux neutron spectra. Gd$_3$Al$_2$Ga$_3$O$_{12}$ (GAGG) is a newly developed, high-density scintillating crystal with a high gadolinium content, which could allow to exploit the high $(n,\gamma)$ cross section of $^{155}$Gd and $^{157}$Gd for neutron measurements in underground environments. GAGG crystals feature a high scintillation light yield, good timing performance, and the capability of particle identification via pulse-shape discrimination. In a low-background environment, the distinctive signature produced by neutron capture on gadolinium, namely a $\beta/\gamma$ cascade releasing up to 9 MeV of total energy, and the efficient particle identification provided by GAGG could yield a background-free neutron capture signal. In this work, we present the characterization of a first GAGG detector prototype in terms of particle discrimination performance, intrinsic radioactive contamination, and neutron response.

We compare the enhancement of high-order harmonic generation in argon using 800--400 and 800--266~nm laser fields with their optimized single-color counterparts. We observe that the two-color fields generally outperform the single-color 800~nm field by factors of 2 to 3800, depending on the photon energy and generation scheme. From this comparison, we determine which scheme is optimal for each photon energy. We also observe that the divergence of HHG produced by the 800--266~nm laser fields is smaller than that of the other single- and two-color driving fields, perhaps suggesting that the 800--266~nm fields optimize the recombination probability of the so-called ``short" electron trajectories.

Radio-frequency (RF) sensing enables long-range, high-resolution detection for applications such as radar and wireless communication. RF photonic sensing mitigates the bandwidth limitations and high transmission losses of electronic systems by transducing the detected RF signals into broadband optical carriers. However, these sensing systems remain limited by detector noise and Nyquist rate sampling with analog-to-digital converters, particularly under low-power and high-data rate conditions. To overcome these limitations, we introduce the micro-ring perceptron (MiRP) sensor, a physics-inspired AI framework that integrates the micro-ring (MiR) dynamics-based analog processor with a machine-learning-driven digital backend. By embedding the nonlinear optical dynamics of MiRs into an end-to-end architecture, MiRP sensing maps the input signal into a learned feature space for the subsequent digital neural network. The trick is to encode the entire temporal structure of the incoming signal into each output sample in order to enable effectively sub-Nyquist sampling without loss of task-relevant information. Evaluations of three target classification datasets demonstrate the performance advantages of MiRP sensing. For example, on MNIST, MiRP detection achieves $94\pm0.1$\% accuracy at $1/49$ the Nyquist rate at the input RF signal of $1$~ pW, compared to $11\pm0.4$\% for the conventional RF detection method. Thus, our sensor framework provides a robust and efficient solution for the detection of low-power and high-speed signals in real-world sensing applications.

We introduce an array-scalable, magnon-based detector (MOSAIC) to search for the spin-dependent interactions of electron-coupled axion dark matter. These axions can excite single magnons in magnetic targets, such as the yttrium iron garnet (YIG) spheres used here, which are subsequently sensed by the detector. For MOSAIC, this sensing is implemented by coupling the magnons in the YIG spheres to magnetic-field-resilient single-electron charge-qubits, whose state is then interrogated with a quantum non-demolition measurement. Using standard superconducting fabrication techniques, MOSAIC can integrate many YIG sphere-qubit sensors, forming a large detector array. We outline the detector design and operation, and determine its sensitivity to axion dark matter. We find that a detector built with available technology will exceed the sensitivity of previous ferromagnetic haloscopes, and provides a platform where further improvements in performance would search for electron-coupled axion dark matter in unexplored parameter space.

High-speed, non-volatile tunability is critical for advancing reconfigurable photonic devices used in neuromorphic information processing, sensing, and communication. Despite significant progress in developing phase change and ferroelectric materials, achieving highly efficient, reversible, rapid switching of optical properties has remained a challenge. Recently, sliding ferroelectricity has been discovered in 2D semiconductors, which also host strong excitonic effects. Here, we demonstrate that these materials enable nanosecond ferroelectric switching in the complex refractive index, largely impacting their linear optical responses. The maximum index modulation reaches about 4, resulting in a relative reflectance change exceeding 85%. Both on and off switching occurs within 2.5 nanoseconds, with switching energy at femtojoule levels. The switching mechanism is driven by tuning the excitonic peak splitting of a rhombohedral molybdenum disulfide bilayer in an engineered Coulomb screening environment. This new switching mechanism establishes a new direction for developing high-speed, non-volatile optical memories and highly efficient, compact reconfigurable photonic devices. Additionally, the demonstrated imaging technique offers a rapid method to characterize domains and domain walls in 2D semiconductors with rhombohedral stacking.

We propose a formal framework for understanding and unifying the concept of observers across physics, computer science, philosophy, and related fields. Building on cybernetic feedback models, we introduce an operational definition of minimal observers, explore their role in shaping foundational concepts, and identify what remains unspecified in their absence. Drawing upon insights from quantum gravity, digital physics, second-order cybernetics, and recent ruliological and pregeometric approaches, we argue that observers serve as indispensable reference points for measurement, reference frames, and the emergence of meaning. We show how this formalism sheds new light on debates related to consciousness, quantum measurement, and computational boundaries; by way of theorems on observer equivalences and complexity measures. This perspective opens new avenues for investigating how complexity and structure arise in both natural and artificial systems.

Context: Dimethyl sulfide (DMS; CH$_3$SCH$_3$) is an organosulfur compound that has been suggested as a potential biosignature in exoplanetary atmospheres. In addition to its tentative detections toward the sub-Neptune planet K2-18b, DMS has been detected in the coma of the 67/P comet and toward the galactic center molecular cloud G+0.693-0.027. However, its formation routes have not been characterized yet. Aims: In this work, we have investigated three gas-phase reactions (CH$_3$SH + CH$_3$OH$_2^+$, CH$_3$OH + CH$_3$SH$_2^+$, and the CH$_3$ + CH$_3$S radiative association), aiming at characterizing DMS formation routes in shocked molecular clouds and star-forming regions. Methods: We have performed dedicated quantum and kinetics calculations to evaluate the reaction rate coefficients as a function of temperature to be included in astrochemical models. Results: Among the investigated processes, the reaction between methanethiol (CH$_3$SH) and protonated methanol (CH$_3$OH$_2^+$)(possibly followed by a gentle proton transfer to ammonia) is a compelling candidate to explain the formation of DMS in the galactic center molecular cloud G+0.693-0.027. The CH$_3$ + CH$_3$S radiative association does not seem to be a very efficient process, with the exclusion of cold clouds, provided that the thiomethoxy radical (CH$_3$S) is available. This work does not deal directly with the possible formation of DMS in the atmosphere of exoplanets. However, it clearly indicates that there are efficient abiotic formation routes of this interesting species.

Classically simulating quantum systems is challenging, as even noiseless $n$-qubit quantum states scale as $2^n$. The complexity of noisy quantum systems is even greater, requiring $2^n \times 2^n$-dimensional density matrices. Various approximations reduce density matrix overhead, including quantum trajectory-based methods, which instead use an ensemble of $m \ll 2^n$ noisy states. While this method is dramatically more efficient, current implementations use unoptimized sampling, redundant state preparation, and single-shot data collection. In this manuscript, we present the Pre-Trajectory Sampling technique, increasing the efficiency and utility of trajectory simulations by tailoring error types, batching sampling without redundant computation, and collecting error information. We demonstrate the effectiveness of our method with both a mature statevector simulation of a 35-qubit quantum error-correction code and a preliminary tensor network simulation of 85 qubits, yielding speedups of up to $10^6$x and $16$x, as well as generating massive datasets of one trillion and one million shots, respectively.

Current infrared sensing devices are based on costly materials with relatively few viable alternatives known. To identify promising candidate materials for infrared photodetection, we have developed a high-throughput screening methodology based on high-accuracy r$^2$SCAN and HSE calculations in density functional theory. Using this method, we identify ten already synthesized materials between the inverse perovskite family, barium silver pnictide family, the alkaline pnictide family, and ZnSnAs$_2$ as top candidates. Among these, ZnSnAs$_2$ emerges as the most promising candidate due to its experimentally verified band gap of 0.74 eV at 0 K, and its cost-effective synthesis through Bridgman growth. BaAgP also shows potential with an HSE-calculated band gap of 0.64 eV, although further experimental validation is required. Lastly, we discover an additional material, Ca$_3$BiP, which has not been previously synthesized, but exhibits a promising optical spectra and a band gap of 0.56 eV. The method applied in this work is sufficiently general to screen wider bandgap materials in high-throughput and now extended to narrow-band gap materials.

Layered two-dimensional (2D) materials, with their atomic-scale thickness and tunable electronic, optical, and mechanical properties, open many promising pathways to significantly advance modern electronics. The field effect caused by a strong electric field, typically of MV/cm level, applied perpendicular to the material layers, is a highly effective method for controlling these properties. Field effect allows the regulation of the electron flow in transistor channels, improves the photodetector efficiency and spectral range, and facilitates the exploration of novel exotic quantum phenomena in 2D materials. However, existing approaches to induce the field effect in 2D materials utilize circuit-based electrical gating methods fundamentally limited to microwave response rates. Device-compatible ultrafast, sub-picosecond control needed for modern technology and basic science applications still remains a challenge. In this study, we demonstrate such an ultrafast field effect in atomically thin MoS2, an archetypal 2D semiconductor, embedded in a hybrid 3D-2D terahertz nanoantenna structure. This nanoantenna efficiently converts an incident terahertz electric field into the vertical ultrafast gating field in MoS2 while simultaneously enhancing it to the required MV/cm level. We observe the terahertz field effect optically as coherent terahertz-induced Stark shift of characteristic exciton resonances in MoS2. Our results enable novel developments in technology and the fundamental science of 2D materials, where the terahertz field effect is crucial.

We present a Bayesian inversion-based digital twin that employs acoustic pressure data from seafloor sensors, along with 3D coupled acoustic-gravity wave equations, to infer earthquake-induced spatiotemporal seafloor motion in real time and forecast tsunami propagation toward coastlines for early warning with quantified uncertainties. Our target is the Cascadia subduction zone, with one billion parameters. Computing the posterior mean alone would require 50 years on a 512 GPU machine. Instead, exploiting the shift invariance of the parameter-to-observable map and devising novel parallel algorithms, we induce a fast offline-online decomposition. The offline component requires just one adjoint wave propagation per sensor; using MFEM, we scale this part of the computation to the full El Capitan system (43,520 GPUs) with 92% weak parallel efficiency. Moreover, given real-time data, the online component exactly solves the Bayesian inverse and forecasting problems in 0.2 seconds on a modest GPU system, a ten-billion-fold speedup.

The conversion of $\mathrm{CO_2}$ into useful products such as methanol is a key strategy for abating climate change and our dependence on fossil fuels. Developing new catalysts for this process is costly and time-consuming and can thus benefit from computational exploration of possible active sites. However, this is complicated by the complexity of the materials and reaction networks. Here, we present a workflow for exploring transition states of elementary reaction steps at inverse catalysts, which is based on the training of a neural network-based machine learning interatomic potential. We focus on the crucial formate intermediate and its formation over nanoclusters of indium oxide supported on Cu(111). The speedup compared to an approach purely based on density functional theory allows us to probe a wide variety of active sites found at nanoclusters of different sizes and stoichiometries. Analysis of the obtained set of transition state geometries reveals different structure--activity trends at the edge or interior of the nanoclusters. Furthermore, the identified geometries allow for the breaking of linear scaling relations, which could be a key underlying reason for the excellent catalytic performance of inverse catalysts observed in experiments.

A major question in the field of femtosecond laser-induced demagnetization is whereto the angular momentum lost by the electrons is transferred. Recent ultrafast electron diffraction measurements [Tauchert \textit{et al.}, Nature {\bf 602}, 73 (2022)] suggest that this angular momentum is transferred to the rotational motion of atoms on a sub-picosecond timescale, but a theory confirmation of this proposition has yet to be given. Here we investigate the coupled electron-nuclear dynamics during ultrafast demagnetization of L1$_0$ FePt, using Ehrenfest nuclear dynamics simulations combined with the time-dependent density functional theory (TDDFT) framework. We demonstrate that atomic rotations appear, i.e., the generation of phonons carrying finite angular momentum following ultrafast demagnetization. We further show that both ultrafast demagnetization and the generation of phonons with angular momentum arise from symmetry constraints imposed by the spin-orbit coupling, thus providing insight in spin-phonon interaction at ultrafast timescales.

Physics-Informed Neural Networks (PINNs) have shown promise in solving partial differential equations (PDEs), including the frequency-domain Helmholtz equation. However, standard training of PINNs using gradient descent (GD) suffers from slow convergence and instability, particularly for high-frequency wavefields. For scattered acoustic wavefield simulation based on Helmholtz equation, we derive a hybrid optimization framework that accelerates training convergence by embedding a least-squares (LS) solver directly into the GD loss function. This formulation enables optimal updates for the linear output layer. Our method is applicable with or without perfectly matched layers (PML), and we provide practical tensor-based implementations for both scenarios. Numerical experiments on benchmark velocity models demonstrate that our approach achieves faster convergence, higher accuracy, and improved stability compared to conventional PINN training. In particular, our results show that the LS-enhanced method converges rapidly even in cases where standard GD-based training fails. The LS solver operates on a small normal matrix, ensuring minimal computational overhead and making the method scalable for large-scale wavefield simulations.

The goal of many applications in energy and transport sectors is to control turbulent flows. However, because of chaotic dynamics and high dimensionality, the control of turbulent flows is exceedingly difficult. Model-free reinforcement learning (RL) methods can discover optimal control policies by interacting with the environment, but they require full state information, which is often unavailable in experimental settings. We propose a data-assimilated model-based RL (DA-MBRL) framework for systems with partial observability and noisy measurements. Our framework employs a control-aware Echo State Network for data-driven prediction of the dynamics, and integrates data assimilation with an Ensemble Kalman Filter for real-time state estimation. An off-policy actor-critic algorithm is employed to learn optimal control strategies from state estimates. The framework is tested on the Kuramoto-Sivashinsky equation, demonstrating its effectiveness in stabilizing a spatiotemporally chaotic flow from noisy and partial measurements.

Safety assessment of patients with one-dimensionally structured passive implants, like cranial plates or stents, exposed to low or medium frequency magnetic fields, like those generated in magnetic resonance imaging or magnetic hyperthermia, can be challenging, because of the different length scales of the implant and the human body. Most of the methods used to estimate the heating induced near such implants neglect the presence of the metallic materials within the body, modeling the metal as thermal seeds. To overcome this limitation, a novel numerical approach that solves three-dimensional and one-dimensional coupled problems is proposed. This method leads to improved results by modelling the thermal diffusion through the highly conductive metallic implants. A comparison of the proposed method predictions with measurements performed on a cranial plate exposed to the magnetic field generated by a gradient coil system for magnetic resonance imaging is presented, showing an improved accuracy up to 25 % with respect to the method based on thermal seeds. The proposed method is finally applied to a magnetic hyperthermia case study in which a patient with a cranial plate is exposed to the magnetic field generated by a collar-type magnetic hyperthermia applicator for neck tumour treatment, predicting a temperature increase in proximity of the implant that is 10 % lower than the one overestimated by relying on thermal seeds.

Josephson junctions (JJs) are the key element of many devices operating at cryogenic temperatures. Development of time-efficient wafer-scale JJ characterization for process optimization and control of JJ fabrication is essential. Such statistical characterization has to rely on room temperature techniques since cryogenic measurements typically used for JJs are too time consuming and unsuitable for wafer-scale characterization. In this work, we show that from room temperature capacitance and current-voltage measurements, with proper data analysis, we can independently obtain useful parameters of the JJs on wafer-scale, like oxide thickness, tunnel coefficient, and interfacial defect densities. Moreover, based on detailed analysis of current vs voltage characteristics, different charge transport mechanisms across the junctions can be distinguished. We exemplary demonstrate the worth of these methods by studying junctions fabricated on 200 mm wafers with an industrially scale-able concept based on subtractive processing using only CMOS compatible tools. From these studies, we find that our subtractive fabrication approach yields junctions with quite homogeneous average oxide thickness across the full wafers, with a spread of less then 3$\,$%. The analysis also revealed a variation of the tunnel coefficient with oxide thickness, pointing to a stoichiometry gradient across the junctions' oxide width. Moreover, we estimated relatively low interfacial defect densities in the range of 70 - 5000$\,$defects/cm$^2$ for our junctions and established that the density increased with decreasing oxide thickness, indicating that the wet etching process applied in the JJs fabrication for oxide thickness control leads to formation of interfacial trap state

Solid-state ion conductors hold promise as next generation battery materials. To realize their full potential, an understanding of atomic-scale ion conduction mechanisms is needed, including ionic and electronic degrees of freedom. Molecular simulations can create such an understanding, however, including a description of electronic structure necessitates computationally expensive methods that limit their application to small scales. We examine an alternative approach, in which neural network models are used to efficiently sample ionic configurations and dynamics at ab initio accuracy. Then, these configurations are used to determine electronic properties in a post-processing step. We demonstrate this approach by modeling the superionic phase of AgI, in which cation diffusion is coupled to rotational motion of local electron density on the surrounding iodide ions, termed electronic paddlewheels. The neural network potential can capture the many-body effects of electronic paddlewheels on ionic dynamics, but classical force field models cannot. Through an analysis rooted the generalized Langevin equation framework, we find that electronic paddlewheels have a significant impact on the time-dependent friction experienced by a mobile cation. Our approach will enable investigations of electronic fluctuations in materials on large length and time scales, and ultimately the control of ion dynamics through electronic paddlewheels.

Spin defects in semiconductors are widely investigated for various applications in quantum sensing. Conventional host materials such as diamond and hexagonal boron nitride (hBN) provide bulk or low-dimensional platforms for optically addressable spin systems, but often lack the structural properties needed for chemical sensing. Here, we introduce a new class of quantum sensors based on naturally occurring spin defects in boron nitride nanotubes (BNNTs), which combine high surface area with omnidirectional spin control, key features for enhanced sensing performance. First, we present strong evidence that these defects are carbon-related, akin to recently identified centers in hBN, and demonstrate coherent spin control over ensembles embedded within dense, microscale BNNTs networks. Using dynamical decoupling, we enhance spin coherence times by a factor exceeding 300x and implement high-resolution detection of radiofrequency signals. By integrating the BNNT mesh sensor into a microfluidic platform we demonstrate chemical sensing of paramagnetic ions in solution, with detectable concentrations reaching levels nearly 1000 times lower than previously demonstrated using comparable hBN-based systems. This highly porous and flexible architecture positions BNNTs as a powerful new host material for quantum sensing.

The flows of tissues of epithelial cells often involve T1 transitions. These neighbour exchanges are irreversible rearrangements crossing an energy barrier. Here, by an exact geometric construction, I determine this energy barrier for general, isolated T1 transitions dominated by line tensions. I show how deviations from regular cell packing reduce this energy barrier, but find that line tension fluctuations increase it on average. By another exact construction, I show how nonlinear tensions in vertex models of epithelial tissues also resist T1 transitions. My results thus form the basis for coarse-grained descriptions of cell neighbour exchanges in continuum models of epithelia.

We propose an online learning framework for forecasting nonlinear spatio-temporal signals (fields). The method integrates (i) dimensionality reduction, here, a simple proper orthogonal decomposition (POD) projection; (ii) a generalized autoregressive model to forecast reduced dynamics, here, a reservoir computer; (iii) online adaptation to update the reservoir computer (the model), here, ensemble sequential data assimilation.We demonstrate the framework on a wake past a cylinder governed by the Navier-Stokes equations, exploring the assimilation of full flow fields (projected onto POD modes) and sparse sensors. Three scenarios are examined: a na\"ive physical state estimation; a two-fold estimation of physical and reservoir states; and a three-fold estimation that also adjusts the model parameters. The two-fold strategy significantly improves ensemble convergence and reduces reconstruction error compared to the na\"ive approach. The three-fold approach enables robust online training of partially-trained reservoir computers, overcoming limitations of a priori training. By unifying data-driven reduced order modelling with Bayesian data assimilation, this work opens new opportunities for scalable online model learning for nonlinear time series forecasting.

Rashba spin-orbit coupling significantly modifies the electronic band structure in two-dimensional (2D) van der Waals (vdW) heterobilayers, which may enhance their thermoelectric (TE) properties. In this study, we use first-principles calculations and Boltzmann transport theory to explore the strain effect on the TE performance of the 2D vdW heterobilayer MoTe$_{2}$/PtS$_{2}$. A strong Rashba spin-splitting is observed in the valence band, resulting in an increase in the Seebeck coefficient for p-type. The lattice thermal conductivity of MoTe$_{2}$/PtS$_{2}$ is remarkably low about of 0.6 Wm$^{-1}$K$^{-1}$ at $T = 300$ K due to large anharmonic scattering. Furthermore, biaxial strain enhances the power factor (PF) by introducing band convergence. At a strain of 2\%, the optimal PF for the n-type material reaches 170 $\mu$W/cmK$^{2}$, indicating approximately 84.78\% increase compared to the unstrained state (92 $\mu$W/cmK$^{2}$). Given the low lattice thermal conductivity, the optimized figure of merit $ZT$ achieves up to 0.88 at 900 K for n-type. Our findings indicate that MoTe$_{2}$/PtS$_{2}$ is a highly promising candidate for 2D heterobilayer TE materials, owing to its strong Rashba splitting and significant anharmonicity.

Lipid bilayer membranes are fundamental biological structures that serve as cellular boundaries, mediating transport, signaling, and maintaining structural integrity. This study introduces a novel mathematical model for open membranes immersed in Stokes flows, accounting for membrane elasticity, line tension at the open edge, and fluid-membrane interactions. The model is derived from an energy functional that incorporates Helfrich bending energy and a line energy associated with the open edge. By balancing dissipation in both the bulk fluid and the membrane surface, following the maximal dissipation principle, we derive the governing equations within an energy variational framework. Assuming axisymmetry and employing a boundary integral reduction, we transform the 3D problem into an effectively 1D problem, for which we develop a finite element-based numerical method to solve the resulting moving boundary problem. Several numerical examples are provided to validate the model and compare the results with existing studies.

Accurate wave height prediction is critical for maritime safety and coastal resilience, yet conventional physics-based models and traditional machine learning methods face challenges in computational efficiency and nonlinear dynamics modeling. This study introduces Chronos, the first implementation of a large language model (LLM)-powered temporal architecture (Chronos) optimized for wave forecasting. Through advanced temporal pattern recognition applied to historical wave data from three strategically chosen marine zones in the Northwest Pacific basin, our framework achieves multimodal improvements: (1) 14.3% reduction in training time with 2.5x faster inference speed compared to PatchTST baselines, achieving 0.575 mean absolute scaled error (MASE) units; (2) superior short-term forecasting (1-24h) across comprehensive metrics; (3) sustained predictive leadership in extended-range forecasts (1-120h); and (4) demonstrated zero-shot capability maintaining median performance (rank 4/12) against specialized operational models. This LLM-enhanced temporal modeling paradigm establishes a new standard in wave prediction, offering both computationally efficient solutions and a transferable framework for complex geophysical systems modeling.

We address the question whether the magneto-rotational instability (MRI) can operate in the near-surface shear layer (NSSL) of the Sun and how it affects the interaction with the dynamo process. Using hydromagnetic mean-field simulations of $\alpha\Omega$-type dynamos in rotating shearing-periodic boxes, we show that for negative shear, the MRI can operate above a certain critical shear parameter. This parameter scales inversely with the equipartition magnetic field strength above which $\alpha$ quenching set in. Like the usual $\Omega$ effect, the MRI produces toroidal magnetic field, but in our Cartesian cases it is found to reduce the resulting magnetic field strength and thus to suppress the dynamo process. In view of the application to the solar NSSL, we conclude that the turbulent magnetic diffusivity may be too large for the MRI to be excited and that therefore only the standard $\Omega$ effect is expected to operate.

We present Crystal Host-Guided Generation (CHGGen), a diffusion-based framework for crystal structure prediction. Unconditional generation with diffusion models demonstrates limited efficacy in identifying symmetric crystals as the unit cell size increases. CHGGen addresses this limitation through conditional generation with the inpainting method, which optimizes a fraction of atomic positions within a predefined and symmetrized host structure. We demonstrate the method on the ZnS-P$_2$S$_5$ and Li-Si chemical systems, where the inpainting method generates a higher fraction of symmetric structures than unconditional generation. The practical significance of CHGGen extends to enabling the structural modification of crystal structures, particularly for systems with partial occupancy, surface absorption and defects. The inpainting method also allows for seamless integration with other generative models, providing a versatile framework for accelerating materials discovery.

Disordered stealthy hyperuniform (SHU) packings are an emerging class of exotic amorphous two-phase materials endowed with novel physical properties. Such packings of identical spheres have been created from SHU point patterns via a modified collective-coordinate optimization scheme that includes a soft-core repulsion, besides the standard `stealthy' pair potential. Using the distributions of minimum pair distances and nearest-neighbor distances, we find that when the stealthiness parameter $\chi$ is lower than 0.5, the maximal values of $\phi$, denoted by $\phi_{\max}$, decrease to zero on average as the particle number $N$ increases if there are no soft-core repulsions. By contrast, the inclusion of soft-core repulsions results in very large $\phi_{\max}$ independent of $N$, reaching up to $\phi_{\max}=1.0, 0.86, 0.63$ in the zero-$\chi$ limit and decreasing to $\phi_{\max}=1.0, 0.67, 0.47$ at $\chi=0.45$ for $d=1,2,3$, respectively. We obtain explicit formulas for $\phi_{\max}$ as functions of $\chi$ and $N$ for a given $d$. For $d=2,3$, our soft-core SHU packings for small $\chi$ become configurationally very close to the jammed hard-particle packings created by fast compression algorithms, as measured by the pair statistics. As $\chi$ increases beyond $0.20$, the packings form fewer contacts and linear polymer-like chains. The resulting structure factors $S(k)$ and pair correlation functions $g_2(r)$ reveal that soft-core repulsions significantly alter the short- and intermediate-range correlations in the SHU ground states. We also compute the spectral density $\tilde{\chi}_{_V}(k)$, which can be used to estimate various physical properties (e.g., electromagnetic properties, fluid permeability, and mean survival time) of SHU two-phase dispersions. Our results offer a new route for discovering novel disordered hyperuniform two-phase materials with unprecedentedly high density.