Papers

The salinity of water and the slope of ice significantly influence the melt rate and surface morphology of ice, both highly relevant in the context of glacier and iceberg melting in oceanic environments. In this study, we conducted experiments on vertical and sloped ice blocks melting in quiescent saline water. Through the use of fringe projection profilometry, we measured the morphology of the ice's front face. In particular, we combine the spatio-temporal phase shifting and orthogonal sampling moire methods. The far field salinity in the experiments ranged from 0 g/kg to 35 g/kg, and angles were between -18{\deg} and 50{\deg}. The ice block sizes were 32 cm $\times$ 23 cm $\times$ 12 cm high, wide, and long respectively, leading to Ra = $\mathcal{O}(10^7)$. We identified and classify five surface morphologies and regimes arising from the flow regimes imposed by salinity and inclination, namely scalloped, channelized, top-melting, bottom-melting, and incurved. The channelized morphology consists of vertical channels carved along the ice surface, whose development originates from a Rayleigh–Bénard type instability, and which are enhanced by bubbles released from the melting ice and rising along the interface. The scalloped regime is characterize by a rough dimpled pattern commonly referred to as scallops. We observe that increasing the salinity leads to scallops that are smaller, shallower, and more uniform in size. Additionally, a salinity dependence of the melt rate is found, showing a non-monotonic behavior, while the inclination angle shows little influence on the overall melt rate.

Current operational air quality forecasts are computationally expensive, sensitive to errors in physics and emissions, and often neglect weather-related uncertainty. To address these limitations, we present AirFusion, a hybrid, diffusion-based framework that synergistically integrates knowledge from chemical transport models with real-world observational constraints to enable accurate and efficient probabilistic regional air quality prediction. We apply AirFusion to generate operational 6-day, 30-member ensemble forecasts of surface ozone across China, initialized with observations and driven by ensemble weather forecasts. AirFusion outperforms existing operational benchmarks, achieving substantially lower forecast errors against surface measurements, while also providing ensemble-based diagnostics that explicitly quantify the impacts of weather uncertainty on air quality predictability. Moreover, AirFusion can rapidly adapt to evolving emissions through fine-tuning with only one month of recent observations. These attributes establish AirFusion as a powerful and extensible framework for next-generation probabilistic air quality forecasting, with clear potential for application to other pollutants and regions.

The introduction of network science approaches into public transport research has seen great advances in the past 15 years. However, it has become apparent that monolayer networks are often not sufficient to model and analyse real-world systems in sufficient detail. In the last decade, the theory of multilayer networks has proven to be an invaluable tool in various disciplines, including transport. Multilayer networks consist of layers of networks that are coupled among themselves. This enables modelling of complex systems with heterogeneous elements and relations between them. Although there is a body of work in public transport research that uses multilayer networks, the related literature is scattered, lacking unified terminology and agreed-upon approaches. We posit that there is vast uncovered potential in using multilayer network approaches to public transport modelling, planning, and operations. We first present the basic formalisms of multilayer networks with a focus on how they (may) relate to public transport networks. We then provide a systematic review of the literature on multilayer networks in public transport research. We identify and taxonomise ways in which public transport systems are modelled as multilayer networks. Based on the survey and drawing from the state and history of network science in public transport research as well as multilayer approaches across other application domains, we propose a research agenda for multilayer public transport networks for the upcoming decade(s).

In this paper, the global $\chi^2$ function for the simultaneous fitting of correlated energy-dependent cross sections is constructed, where the correlations between the measured cross sections of different processes and/or at different center-of-mass energy points, as well as the contributions from the integrated luminosity measurement and the center-of-mass energy measurement, are taken into account.

Geometric incompatibility, the inability of a material's rest state to be realized in Euclidean space, underlies shape formation in natural and synthetic thin sheets. Classical Gauss and Mainardi-Codazzi-Peterson (MCP) incompatibilities explain many patterns in nature, but they do not exhaust the mechanisms that frustrate thin elastic sheets. We identify a new incompatibility that forbids smooth stretching-free configurations, even when the rest state of the elastic sheet locally satisfies the Gauss and MCP compatibility conditions. We demonstrate this principle in a model of surface growth with positive Gaussian curvature, where a geometric horizon forms, leading to the onset of frustration. Experiments, simulations, and theory show that the sheet responds by nucleating periodic d-cone-like dimples. We show that this obstruction to stretching-free configurations is topological, and we point to open questions concerning the origin of frustration.

The variational quantum eigensolver (VQE) has emerged as one of the leading quantum algorithms for solving electronic structure problems on near-term noisy intermediate-scale quantum devices. However, its practical application to quantum chemistry remains challenging due to the limited coherence time, imperfect quantum gate fidelity, and the large number of measurements required, which together confine current electronic structure simulations to relatively small active spaces. In this work, we present an orbital-optimized VQE framework based on orbital compression, designed to improve the accuracy of electronic structure calculations while maintaining relatively small active spaces. Frozen natural orbitals (FNO) and split virtual orbitals (SVO) are first employed to construct compact active spaces for VQE simulations, leading to the FNO/SVO-VQE approach. Orbital optimization is then incorporated to further recover electron correlation effects, resulting in the FNO/SVO-OO-VQE methods. We apply the proposed method to simulate potential energy surfaces for molecular dissociation and the activation energy of formaldehyde decomposition. Numerical results demonstrate that both FNO-OO-VQE and SVO-OO-VQE improve the variational accuracy while substantially reducing measurement cost.

Practical performance of quantum sensors is often curtailed by uncontrolled environmental drift (bias-field instability, temperature fluctuations, mechanical vibration), background fields, and imperfect control pulses. This motivates developing physical mechanisms that intrinsically compensate for such perturbations while retaining high sensitivity to target fields. We introduce an interaction-protected magnetometry scheme where periodic driving steers the collective magnetization onto two long-lived, prethermal Floquet "orbit" axes well-separated on the Bloch sphere. Rapid toggling between these axes encodes target fields as a differential signal, whereas background fields appear as common-mode motion that is strongly rejected, achieving >1000-fold suppression while canceling prethermal transients. This enables accurate reconstruction of rapidly varying audio-band magnetic signals without predictive filtering or spectral tuning. We provide an experimental proof-of-principle using a dense ensemble of coupled nuclear spins, operated here as a broadband (0-1 kHz) magnetometer. The protocol is remarkably tolerant to imperfections, operating robustly across millions of pulses under pulse-angle (~10{\deg}) and pulse frequency (>1 kHz) errors, large bias-field drifts (>50 $\mathrm{\mu}$T), temperature variations over 150 K, and harsh mechanical vibrations. These results establish Floquet prethermalization as a resource for robust quantum sensors that combines broadband magnetic-field sensitivity with intrinsic immunity to diverse environmental and control perturbations, opening a path toward stable quantum metrology beyond controlled laboratory conditions.

The exotic physics emerging at singularities has long attracted intense theoretical and experimental attention. In non-Hermitian systems, exceptional points (EPs), unique spectral singularities, have given rise to a host of intriguing wave phenomena and enabled a broad range of promising applications across diverse physical platforms. Recently, considerable effort has been devoted to combining nonlinearity with exceptional points (EPs) to enable flexible control, overcome the limitations of linear EPs, discover previously unexplored singularities, and reveal novel physical phenomena and application potentials. In this review, we provide a detailed overview of the interplay between nonlinearity and EPs, highlighting key developments such as noise suppression for enhanced sensing, emerging mechanisms for chiral-like state transfer, the realization of optical isolators in nonlinear EP systems, applications including wireless energy transfer and frequency comb generation, among others. We also offer a perspective on future research directions and opportunities in this rapidly evolving field.

We construct a special class of three-dimensional (3D) equilibria with pressure anisotropy which showcase closed, nested toroidal magnetic surfaces that are strongly asymmetric in the toroidal direction by applying a sinusoidal perturbation to the axisymmetric Solov'ev equilibrium. They also exhibit distinct closed and nested current-density surfaces. For certain values of the free parameters involved, the perturbations lead to the formation of magnetic islands and stochastic areas in the outer plasma region, while well-defined magnetic surfaces persist in the inner region. In addition, it is demonstrated that the existence of closed and nested surfaces within the plasma region on which the magnetic field modulus is uniform (isomagnetic surfaces) is neither necessary nor sufficient for the existence of respective closed and nested magnetic surfaces.

For most process systems, knowledge of the model structure is incomplete. This missing physics must then be learned from experimental data. Recently, a combination of universal differential equations and symbolic regression has become a popular tool to discover these missing physics. Universal differential equations employ neural networks to represent missing parts of the model structure, and symbolic regression aims to make these neural networks interpretable. These machine learning techniques require high-quality data to successfully recover the true model structure. To gather such informative data, a sequential experimental design technique is developed which is based on optimally discriminating between the plausible model structures suggested by symbolic regression. This technique is then applied to discovering the missing physics of a bioreactor.

New conditions for wave-wave interaction are considered. It is shown that using these conditions allows us to discover and describe new features of wave-wave interaction. Specifically, it is shown that an electromagnetic wave in a stationary periodic medium can excite a wave with a different frequency. It is shown that waves with different frequencies can effectively interact in such a medium. It is shown that these conditions can reveal new features of three-wave interaction in nonlinear media. The use of new conditions in quantum mechanics has revealed a number of new features in the dynamics of particles and their interactions with each other. In particular, new energy levels have been discovered that can exist even in stationary periodic potentials. An assessment is given of the level of additive and multiplicative noise that can suppress the interaction process

We develop a 3D aerothermodynamic simulator for the Orion reentry capsule at hypersonic speeds, a timely case study given its role in upcoming lunar missions. The large computational meshes required for these scenarios make traditional computational fluid dynamics impractical for full-mission performance prediction and control. In this work, we propose physics-enhanced 3D neural fields for predicting steady hypersonic flow around aerodynamic bodies. The model maps spatial coordinates and angle of attack to pressure, temperature, and velocity components. We enhance the base model with Fourier positional feature mappings, which allow it to capture the sharp discontinuities typical of hypersonic flows, and further constrain the solution by imposing no-slip and isothermal wall conditions. We compare our proposed approach to other surrogate alternatives, such as graph neural networks, and demonstrate its superior performance in capturing the steep gradients ubiquitous in this regime. Our formulation yields a continuous and computationally efficient aerothermodynamic surrogate that supports rapid exploration of operating conditions based on angle of attack variation under realistic flight profiles. While we focus on Orion, the proposed framework provides a general methodology for data-driven simulation in 3D hypersonic aerothermodynamics.

We solve numerically exactly a simple toy model to quantum general relativity or more properly to path integral on a curved space. We consider the thermal equilibrium of a quantum many body problem on the sphere, the surface of constant positive curvature. We use path integral Monte Carlo to measure the kinetic energy, the internal energy and the static structure of a bosons, fermions and anyons fluid at low temperatures on the sphere. For bosons we also measure the superfluid fraction and compare its behavior at the critical temperature with the universal jump predicted by Nelson and Kosterlitz in flat space in the thermodynamic limit at the superfluid phase transition. For fermions and anyons it is necessary to use the restricted path integral recipe in order to overcome the sign problem. Even if this recipe is exact for the non interacting fluid it reduces to just an approximation for an interacting system. And we make the example of the electron gas at low temperature. Snapshots of the many body path configuration during the evolution of the computer experiment show that the “speed” of the single particle path near the poles slows down as a consequence of the “hairy ball theorem” of Poincaré. The influence of curvature on the thermodynamic and structural properties of the many body fluid is also studied.

Trapped atomic ion qubits exhibit long coherence times and high fidelity qubit state preparation, manipulation and detection, making them well-suited for scalable quantum computing applications. Among several atomic species used in quantum computing and other application, singly-charged ions of barium stand out due to their long wavelength transitions and the presence of very long-lived metastable internal states. However, elemental barium is a highly reactive metal making it experimentally difficult to work with when making atomic beam sources. In this paper, we demonstrate a method of using resistively heated ovens loaded with a barium magnesium alloy (BaMg) as a source of barium for ion traps. This alloy is not very chemically reactive and does not oxidize in air. We found that a sample of BaMg in a resistively heated oven produced barium vapor pressures on the same order as a metallic barium sample prepared the same way. Two separate ovens, one with a sample of BaMg and one with metallic barium, were used as source for an ion trap. We observed reliable trapping of 138Ba+ ions both with the elemental barium source, and the BaMg source.

Neuromorphic electronics and spiking neural networks (SNNs) offer energy-efficient data processing, essential for real-time and edge-computing applications. In particular, interfacing and processing biological signals require devices that combine electronic performance with ionic sensitivity, which are capabilities uniquely provided by organic electrochemical transistors (OECTs). However, realizing a simple, fully integrated OECT-based neuron with rich dynamics and adaptability remains challenging. Most reported implementations rely on current-driven operation, which complicates large-scale integration and neuron-neuron coupling due to the need for precise matching of operating currents and bias voltages. Here we present a voltage-driven neuron circuit based on a multivibrator oscillator architecture, entirely fabricated from poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS). The neuron exhibits tunable adaptability through an additional control input, enabling switching between burst latency and length encoding modes. We further demonstrate a hardware-implemented two-neuron unit consisting of an inhibitory and a readout neuron, where readout activity is suppressed depending on the relative timing of the inhibitory input. Finally, we demonstrate that the fabrication process is compatible with polymer dendrite growth, enabling on-chip integration of synaptic elements on the same substrate. Owing to its structural simplicity and compatibility with a single, available material, this approach offers a scalable and accessible route toward integrated OECT-based SNNs.

We report on a novel type of dual-wavelength fiber laser with a structured-core design inside silica glass, forming a spatial separation of the several core areas doped with ytterbium and erbium ions. We have optimised the key parameters of the fiber core, such as the concentration of rare earth elements, and the optimal length of active fiber to operate simultaneously at two different wavelengths. Using the Modified Chemical Vapor Deposition method to obtain initial optical fiber preforms, and using the stack and draw technique, we have fabricated two types of active fibers, one with 7 and one with 19 rare-earth-doped rods (elements) forming the fiber core. We characterized the drawn fibers by investigating their structure by scanning electron microscopy, confirming the spatial separation of the elements within the core. Measuring absorption shows that concentration ratios Nt Yb: Nt Er were approximately 52: 48 for 7 core fibers and 56:44 for 19 core fibers. Lifetimes for both active fibers were 0.84 ms for Yb3+ ion and 10.30 ms for Er3+ ion. The performance of fiber lasers was determined, proving that fibers are capable of laser emission simultaneously at 1042 nm and 1550 nm. We have shown experimentally that the output power ratio between both lasing wavelengths can be controlled by the length of the fiber.

Plasma-based mid-infrared (MIR) laser generation has garnered significant interest owing to its advantage of high output power, continuous wavelength tunability, and ultrashort pulse durations. However, existing methodologies predominantly depend on high-intensity inputs at the hertz frequency level, with spectral energy concentrated near the central frequency, rendering them unsuitable for spectroscopic applications. This paper proposes and demonstrates a cascaded deceleration scheme that enables the generation of broadband MIR lasers with low energy inputs compatible with high-repetition-rate laser systems. By confining the input laser within a plasma channel, this approach preserves the laser intensity, which not only sustains the decelerating field strength but also enables the cumulative effect of deceleration across multiple distinct bubbles. Numerical simulations demonstrate that more than 30% of the 23 mJ input energy is converted into a broadband MIR output spanning wavelength from 0.58 to 6.86 {\mu}m, achieving peak powers on the order of gigawatts. The output exhibits unique time-frequency characteristics, defined by spectral sub-bands organized in a temporal sequence, wherein each sub-band comprises few-cycle pulses. Parametric analyses reveal that the spectral bandwidth broadens with increasing laser intensity, provided that the plasma density being adequate to ensure a sufficiently short deceleration length. This approach provides a practical, efficient route to broadband ultra-intense mid-infrared sources, promising for applications in Fourier transform spectroscopy and laser-induced electron diffraction.

In this work, we introduce new batching algorithms to effectively handle large contractions encountered in coupled-cluster singles and doubles (CCSD) implementations in Python on the Video Random Access Memory (VRAM) of graphical processing units (GPUs), thereby improving performance. Specifically, we benchmark the performance of the CuPy and PyTorch libraries on a single NVIDIA Hopper (H100) and the Grace Hopper (GH200) architectures. We begin by optimizing the particle-particle ladder bottleneck contraction in CCSD using an asymmetric and dynamic splitting recipe, and then move toward a generic tensor contraction protocol that enables tensor contractions to be performed almost exclusively on GPUs. We benchmark our new, fully generic GPU-accelerated coupled-cluster implementations for various molecular systems and basis-set sizes, using both the CuPy and PyTorch libraries. While PyTorch outperforms CuPy on H100 by approximately 20%, both perform similarly on the GH200 architecture. Compared to our initial GPU implementation [J. Chem. Theory Comput. 2024, 20, 3, 1130–1142], we achieve a 10-fold speedup. In molecular CCSD calculations, we report additional speedups between 3 and 16 for a single CCSD iteration using Cholesky-decomposed electron repulsion integrals compared to our original GPU-CPU hybrid implementation.

Data-driven discovery of governing equations is well developed for deterministic systems but much less so for stochastic ones. We present a sparse-symbolic algorithm that recovers both the drift $b(x)$ and the diffusion $a(x)$ of a stochastic differential equation from a single discretely sampled trajectory. It replaces the time-indexed test functions of weak-form SINDy with state-dependent spatial kernels. Fixed-width time-window weighting induces a persistent endogeneity bias: for the Ornstein-Uhlenbeck process it erases the drift coefficient entirely, whatever the window shape. Spatial kernels avoid this through a martingale strong-law argument, linked to the estimating-function theory of Bibby and Sorensen. We prove consistency of the drift and diffusion estimators for the Euler-Maruyama coefficients, a $T^{-1/2}$ central limit theorem for the drift, a finite-step correction that links drift and diffusion estimation, and a correction for additive Gaussian observation noise. The theorems assume ergodicity of the Euler-Maruyama chain, which holds for the OU and multiplicative systems but not for the cubic double well, so the double-well results are empirical. The method is validated on three benchmark SDEs (Ornstein-Uhlenbeck, a double well, and multiplicative diffusion), on eight further systems identified from single trajectories with elastic-net-stabilized sparse regression, and in a support-recovery experiment. Exact support recovery from one trajectory needs a long record (about $T \gtrsim 200$ on the double well) and is not guaranteed for shorter records or weakly identified systems.

Receiver functions (RFs) are widely used to image crustal and upper-mantle structure, and their variation with backazimuth and epicentral distance contains key information about layering and azimuthal anisotropy. In practice, however, RFs are contaminated by nuisance effects from unknown earthquake source signatures and seismic noise, which obstruct reliable crustal imaging. Sparse RF coverage across backazimuths and epicentral distances also leads to biased anisotropy estimates. We address these challenges using conditional diffusion models, conditioned on backazimuth, epicentral distance, and station coordinates, to produce high-quality virtual radial and transverse RFs. RFs from earthquakes with similar backazimuths and epicentral distances share consistent crustal responses but differ in nuisance effects, allowing the model to suppress the latter. Our framework generates virtual RFs within gaps in backazimuth and epicentral distance coverage, enhancing the interpretation of crustal anisotropy and layering. On synthetic RFs with realistic non-Gaussian noise, virtual RFs correlate more strongly with the true RFs than traditional linear or phase-weighted stacking. Applied to the Cascadia Subduction Zone, virtual radial RFs sharply image scattered S-waves from the dipping slab, with enhanced phase clarity and backazimuthal coverage relative to previous work. In southern California, anisotropy parameters inferred from virtual RFs are spatially coherent and consistent with regional fault geometry. Our approach leverages all available RFs, regardless of quality, to increase spatial coverage and support robust, automated RF analysis.

Optical metasurfaces composed of metallic or dielectric scatterers (meta-atoms) promise a powerful way of tailoring light-matter interactions. Phase-change materials (PCMs) are prime candidates for non-volatile resonance tuning of metasurfaces based on a refractive index change. Precise resonance control can be achieved by locally applying laser pulses to crystallize a PCM, modifying the dielectric surrounding of meta-atoms. However, the complex crystallization kinetics of PCMs in the vicinity of metallic meta-atoms have not been studied yet. Here, we experimentally investigate metallic dimer antennas on top of the PCM Ge3Sb2Te6 and address these nanoantennas with laser pulses. Our study reveals inhomogeneous crystallization caused by the absorption and heat conduction of the metallic nanoantennas. A self-consistent multiphysics model, including electromagnetic, thermal, and phase-transition processes, is employed to simulate the crystallization and understand the resulting resonance shift of the antennas. This model enables the optimization of the laser parameters and the geometry of the meta-atoms to achieve an optimal crystallization pattern and resonance shift. Our work paves the way towards complex antenna geometries optimized for local addressing of PCMs to achieve sophisticated crystallization patterns, enabling on-demand programming of individual nanoantennas within metasurfaces.

Laser-driven ion acceleration from nanostructured targets offers a promising route to compact, high-energy ion sources. In this work, we demonstrate through particle-in-cell simulations that rectangular nanoring targets significantly enhance energy absorption and increase the cutoff energy of laser-accelerated ions. The nanoring geometry enables strong field confinement within its hollow core when optimally oriented relative to the laser polarization, leading to hotter electron populations and more robust sheath acceleration. These results demonstrate that rectangular nanorings offer a versatile platform for controlling laser-plasma interactions at solid densities and advancing compact, high-repetition-rate particle sources.

The Earth's lower mantle hosts a subtle but pervasive quantum phenomenon: the pressure-induced spin crossover of iron in its dominant minerals, bridgmanite and ferropericlase. In this transition, iron ions gradually shift from high-spin to low-spin electronic states without structural change, altering their volume, compressibility, and elastic properties. Although long recognized experimentally and theoretically, its geophysical significance has only recently become clear through the integration of mineral physics and three-dimensional seismic imaging. The spin crossover reduces bulk modulus and P-wave velocities while leaving S-wave speeds largely unaffected, producing a distinctive decoupling between P- and S-wave anomalies. This signature is now observed in global tomography and reconciles seismic observations with realistic mantle temperatures and compositions. Rather than forming a sharp boundary, the crossover extends across most of the lower mantle, acting as a diffuse yet essential control on seismic structure. This work highlights how quantum-scale electronic transitions influence planetary-scale dynamics and interpretations of Earth's deep interior.

The Out-of-Time-Order Correlator (OTOC) is a standard algebraic diagnostic of quantum information scrambling, but it offers limited direct geometric intuition. In this note, we propose a Bohmian, trajectory-based framework for constructing a geometric diagnostic of scrambling-related sensitivity using Lagrangian Descriptors (LDs). To avoid the uncertainty-principle obstruction to assigning independent initial position and momentum within a single wave function, we evaluate Bohmian dynamics over a two-dimensional preparation space of localized Gaussian wavepackets labeled by their initial center and momentum kick. For the inverted harmonic oscillator, this construction is analytically tractable: the wavepacket-center dynamics and their dependence on preparation parameters can be written explicitly. In particular, away from the equilibrium origin, the exponential growth of the associated preparation-space stability matrix yields an $\mathcal{O}(e^{\omega T})$ bound on the sensitivity of the wavepacket-center LDs, motivating a semiclassical comparison with sensitivity structures associated with OTOC growth. In this sense, the LD provides a geometric indicator of scrambling-related sensitivity. We conclude by discussing how this preparation-space picture suggests a program for future work regarding the distinct microcanonical regimes previously reported for the inverted harmonic oscillator.

The ALICE experiment will upgrade the innermost three layers of its vertexing detector, the Inner Tracking System (ITS), during the next LHC Long Shutdown (LS3) with a novel, bent, ultra-light MAPS-based tracker. Six wafer-scale sensor chips will be bent into three cylinders, held in place only by carbon foam, leaving no material except for the silicon die in most of the ALICE central barrel acceptance. Two prototype ASICs, approximately $25.9\,\mathrm{cm}$ in length, called MOSS (MOnolithic Stitched Sensor) and MOST (MOnolithic Stitched sensor with Timing), have been produced. These two chips follow complementary approaches to evaluate the use of stitched CMOS sensors for the first time in an HEP experiment. This article gives an overview of powering tests, functional studies, pixel matrix characterization, and in-beam tests of both test structures. The overall yield of MOSS is measured to be approximately $76\,\%$ per region (1/80th of a chip). This number takes into account powering, as well as functional aspects such as digital and analog pulsing. Two major failure modes have been identified and understood: short in the power grid of the chip and readout issues, that can be clearly attributed to the readout architecture design. Disregarding these issues, the overall yield increases to about $98\,\%$ per region. Furthermore, it is shown that MOSS can operate with $>99\,\%$ efficiency and $<10^{-1}\,\mathrm{hits/pixel/s}$ fake-hit rate up to $4\,\mathrm{kGy}$ TID and $4\times 10^{12}\,\mathrm{1~MeV~n_{eq}~cm^{-2}}$ NIEL. The MOST prototype successfully demonstrated use of power gating, which allows for disconnected parts of the pixel matrix from the power grid in case of shorts. MOSS and MOST successfully proved that designing stitched wafer-scale sensors is feasible and deliver valuable input for the design of the final ITS3 ASIC.

How microorganisms respond to and interact with their environment can vary significantly from individual to individual, which can have important microbiological and ecological implications. However, most microscopy techniques can only observe motile microorganisms for short times because of their limited fields of view. Using Lagrangian tracking, a single microorganism can be followed in 3D, potentially indefinitely, allowing to decipher individual phenotypical traits. Current Lagrangian tracking methods use the fluorescence signal emitted by the microorganism as feedback to keep it in focus. However, over long times, epifluorescent imaging can induce photobleaching and photodamage, and importantly, not all microorganisms can easily be made fluorescent. Additionally, traditional algorithms used in feedback loops to determine microorganism position are prone to errors, especially in optically complex media. Here, we present a faster, more reliable, and versatile Lagrangian tracking method that uses deep learning to determine the 3D position of the microorganism. This new method demonstrates enhanced accuracy and speed in tracking fluorescent bacteria with fluorescence microscopy also in optically complex media. Furthermore, we track bacteria with other microscopy modalities, such as brightfield microscopy – for example, this enables us to track magnetotactic bacteria, which cannot be made fluorescent without degrading their magnetotactic properties. These novel capabilities allow to extract previously inaccessible quantitative information, significantly advancing the study of microorganism behavior – and thus opening new avenues for research in complex biological and ecological systems.

We investigate the transport and adsorption of solutes within graded porous filters characterised by a spatially varying microstructure. While classical homogenisation theory typically assumes periodic media, we employ the method of multiple scales to derive an effective macroscopic model for “near-periodic” geometries where the porosity varies slowly over the longitudinal coordinate. A key novelty of this work is the departure from the standard solenoidal constraint; instead, we introduce a modified incompressibility condition derived from non-equilibrium thermodynamics that accounts for the coupling between the solute concentration and the solvent velocity. This leads to a generalised Darcy-scale description where the fluid velocity field is non-solenoidal within the porous domain. Through asymptotic analysis, we determine the leading-order concentration profiles and quantify first-order corrections that capture the interplay between the porosity gradient and the mixture composition. We evaluate filter performance across several metrics, including outflux concentration and total adsorption rate, under both fixed-flow and fixed-pressure-drop operating conditions. Our results demonstrate that the porosity gradient and the coupling parameter significantly influence the filtration efficiency, particularly as the medium approaches the clogging limit. The analysis reveals that the optimal filter design is highly sensitive to the chosen performance metric, highlighting the necessity of physically consistent boundary conditions and mixture dynamics in the design of high-efficiency graded filters.

We study experimentally a a three-dimensional reduced model of a sail shape performing pitching oscillations around a mean incidence angle ($\alpha_{m}$) with respect to an incoming flow in a hydrodynamic channel at a constant velocity where the Reynolds number based on the mean chord of the sail is Re$_{c} = \rho U_{\infty} c / \mu = 11900$. The problem is inspired by the "pumping" maneuver used by windsurf athletes. At the start of a race or in light winds, to get or keep the board in foiling mode, for example after a tack change, athletes use intermittent propulsion by "pumping" the sail, i.e. periodically changing the angle of incidence of the sail relative to the wind. The flapping or pitching parameters and position of the sail according to the flow (incidence angle) influence the aerodynamic forces acting on the sail by destabilising the flow and generating unsteady forces. We experimentally characterise the aerodynamic forces of the sail. We compare the sailing ($C_{drive}, \ C_{drift}$) and aerodynamic ($C_{drag}, \ C_{lift}$) coefficients between a static and an oscillating sail for different flapping parameters and different mean incidence angles of the sail and angles of attack of the boat. Thanks to the use of "pumping", we observe that it is possible to generate a drive force greater than the one generated without oscillation. Furthermore, "pumping" increases the range of mean incidence angle in which the drive force is positive. However, this increase inevitably comes with an increase in drift force, which is often detrimental. These data can be used to improve the Velocity Prediction Programme (VPP) associated with windsurfing and to help athletes optimise their "pumping".

The interaction between a particle beam and the accelerating mode of a radiofrequency (RF) cavity cause beam loading, representing the beam-induced cavity fields. Beam loading leads to amplitude and phase errors in the cavity fields and reduces the beam quality, especially in accelerators with large beam currents, wideband RF cavities, or circular machines where particles stay for multiple turns. Insight into the principle of beam loading is helpful to understand the beam measurement results and propose efficient compensation methods in low-level RF systems. In this work, the beam loading effects are studied with the equivalent circuit model of standing wave cavities. Analytical results of beam-induced cavity voltages are derived for both a single bunch and a bunch train using the phasor Laplace transform method. The results are general for wideband cavities with a bandwidth that may cover multiple harmonics of the bunch repetition frequency. Based on the analysis, control methods in form of feedforward and feedback are proposed to compensate for the beam loading. Simulation studies are carried out to validate these control methods with a cavity simulator including both the RF drive and beam loading. The analysis and control methods are also applicable to the beam in a circular accelerator with coupled-bunch instabilities, which are discussed in the last part of this paper. This work also acts as a supplementary material to another work of the author, in which the beam loading effects are analyzed only for narrow-band cavities with only one beam harmonic appearing in the cavity bandwidth.

Acoustic borehole images provide high-resolution borehole-wall structure, but large-scale interpretation remains difficult because dense expert annotations are rarely available and subsurface information is intrinsically multimodal. The challenge is developing weakly supervised methods combining two-dimensional image texture with depth-aligned one-dimensional well-logs. Here, we introduce a weakly supervised multimodal segmentation framework that refines threshold-guided pseudo-labels through learned models. This preserves the annotation-free character of classical thresholding and clustering workflows while extending them with denoising, confidence-aware pseudo-supervision, and physically structured fusion. We establish that threshold-guided learned refinement provides the most robust improvement over raw thresholding, denoised thresholding, and latent clustering baselines. Multimodal performance depends strongly on fusion strategy: direct concatenation provides limited gains, whereas depth-aware cross-attention, gated fusion, and confidence-aware modulation substantially improve agreement with the weak supervisory reference. The strongest model, confidence-gated depth-aware cross-attention (CG-DCA), consistently outperforms threshold-based, image-only, and earlier multimodal baselines. Targeted ablations show its advantage depends specifically on confidence-aware fusion and structured local depth interaction rather than model complexity alone. Cross-well analyses confirm this performance is broadly stable. These results establish a practical, scalable framework for annotation-free segmentation, showing multimodal improvement is maximized when auxiliary logs are incorporated selectively and depth-aware.