Papers

We introduce the Asymmetric Chirped Electric field reconstruction (ACE) toolbox, a suite of algorithms enabling the reconstruction of a laser transverse distribution coupled to a chirped temporal profile. This suite includes the implementation of the reconstructed distribution in Particle In-Cell simulations in cylindrical and Cartesian geometry. Under the assumption of negligible spatio-temporal couplings, the ACE toolbox extends a previously established method for realistic simulations of the transverse distribution by adding spectral chirping to the modal decomposition of the electric field. The relevance of the ACE toolbox is demonstrated through the modelling of a Laser Wakefield Acceleration experiment where the produced electron bunch was optimized via spectral chirping of the laser pulse.

In this paper, we investigate the long-time asymptotics for the solution of the Cauchy problem of the defocusing Hirota equation on a finite-genus algebro-geometric background in the whole $(x,t)$-half-plane, whose method is mainly based on a Riemann-Hilbert (RH) formulation and Deift-Zhou nonlinear steepest descent method. The critical values of the phase function in the associated RH problem divide the space-time plane into four regions, in which the leading-order term is given by a phase-shifted finite-genus algebro-geometric solution. The subleading behavior depends on the region: the correction is of order $t^{-1/3}$ and is governed by a Painlevé-XXXIV model RH problem in the transition regions; the leading radiation is of order $t^{-1/2}$ in the Zakharov–Manakov region; and the error is $O(t^{-1})$ in the fast-decay region. These results can also be extended to other higher-order members of the AKNS hierarchy.

The dynamics of long rigid fibres transported by turbulent channel flow are investigated experimentally and numerically. Experiments use polystyrene fibres of three lengths, $\ell/h=0.25$, $0.5$ and $1$, with moderate inertia, $St^+\approx20$. Their settling velocity is comparable to the friction velocity, causing accumulation near the bottom wall. Measurements are compared systematically with simulations based on a rigid slender-body model. Statistics conditioned on the distance from the wall are used to characterise the effects of fibre length and confinement on translation, orientation and tumbling. Away from the wall, the experimental fibres lag the fluid, with no clear dependence of the velocity deficit on length. Near the wall, the shortest fibres move faster than the local mean flow, whereas longer fibres remain slower, indicating length-dependent sampling of near-wall turbulence. Confinement also strongly constrains orientation and rotation. Fibres close to the wall predominantly undergo "kayaking" motion, tumbling in planes approximately parallel to it. Orientation statistics collapse when wall distance is normalised by fibre length, identifying $y^+/\ell^+$ as the relevant geometrical variable. Where fibres can acquire a significant wall-normal orientation, "pole-vaulting" events produce a local tumbling-rate maximum at $y^+\approx\ell^+/2$. Its magnitude decreases approximately as $(\ell^+)^{-2}$, consistently with a dimensional estimate based on the near-wall velocity variation sampled along the fibre. Experiments and simulations agree well for orientation and tumbling but differ more for translational velocity. The discrepancies highlight the effect of settling, finite fibre thickness and finite slip Reynolds number that are not fully represented by the model.

Activities in aqueous electrolyte solutions, usually described by ionic activity and osmotic coefficients, are important properties for modeling many processes in industry and nature. Established activity models, such as those of Pitzer or Bromley, require fitting to experimental data for each electrolyte of interest and thus cannot predict properties for unstudied systems. While some predictive approaches exist, they are typically limited in scope and rely on additional ion-specific descriptors. In this work, we introduce a new hybrid model that combines the physics-based Bromley model with a matrix completion method (MCM) from machine learning. The MCM is employed to predict the electrolyte-specific parameters of the Bromley model, exploiting the fact that these parameters can be arranged in a matrix with cations and anions as rows and columns, respectively. Due to the lack of experimental data for many electrolytes, the initial parameter matrix is sparsely populated, making the prediction of the Bromley parameters for unstudied electrolytes a matrix completion problem. The hybrid model, Bromley-MCM, was trained end-to-end on experimental data for mean ionic activity coefficients and osmotic coefficients of aqueous solutions of 478 electrolytes at 298 K from the Dortmund Data Bank. As output, we obtain a completed matrix of Bromley parameters for 83 cations and 112 anions, enabling consistent prediction of concentration-dependent activities in aqueous solutions of 9,296 electrolytes at 298~K. This substantially extends the applicability of the Bromley model while maintaining high predictive accuracy, as demonstrated through evaluations on electrolytes excluded from model training.

Purpose In advanced computational MRI techniques, acquisition and reconstruction techniques are jointly designed. For reproducibility, it is therefore important to provide an open implementation of both. At the same time, any use in a clinical environment usually requires a close integration with the MRI scanner. Ensuring long-time reproducibility and maintenance then poses additional challenges. In this work, we aim to provide a fully integrated open-source framework that can meet these demands. Methods A software framework to develop pulse sequences is added to BART, an open-source toolbox for computational MRI. In addition, a vendor-specific driver sequence is developed that can be used to run the sequence on a clinical MRI scanner enabling online adjustment of all relevant sequence parameters. Using the Pulseq format, the exact same sequence can also be reproduced offline using a widely used vendor-neutral open-source standard. Using the Pulseq format, the exact same sequence can also be reproduced offline. As proof-of-concept, quantitative MRI methods for T1 and joint water/fat R2*, B0 mapping using radial FLASH and model-based reconstruction are implemented in the proposed framework. Consistency between online and offline acquisition is validated in phantom and in vivo experiments. Results Quantitative MRI methods highlighting specific challenges of acquisition and reconstruction were successfully implemented in BART. Acquisition parameters and FOV can be adapted online on a clinical MRI system. Quantitative parameter maps from model-based reconstruction agree for online and offline regenerated Pulseq acquisitions. Conclusion This work enables reproducibility of advanced computational MRI methods within a comprehensive end-to-end open-source framework.

The Jinping Neutrino Experiment (JNE), a next-generation neutrino observatory under construction at the China Jinping Underground Laboratory II (CJPL-II), requires high-precision waveform-based event reconstruction, imposing stringent demands on its readout electronics. To meet these requirements, we have developed a high-performance readout system featuring 1 GSa/s real-time sampling, 14-bit physical resolution with an effective number of bits (ENOB) of 10.6, a total data throughput of 64 Gbps, and a deterministic zero-delay clock distribution architecture. The new single-crate 64-channel system (PDS1500) was validated through bench tests and deployment on the upgraded JNE-1ton prototype detector. Its performance was further evaluated against a commercial reference system. The results demonstrate that all key metrics meet the JNE experimental requirements: zero data loss within a 1000 ns acquisition window, baseline noise reduced to one-third of the reference level, timing drift limited to 0.3 ns across power cycles, and an energy threshold as low as 0.1 MeV, enabling the detection of low-energy solar neutrinos. While the 14-bit physical resolution provides significantly higher waveform fidelity, the overall energy resolution in this test remains dominated by the intrinsic limitations of the JNE-1ton detector, as expected. Furthermore, the modular architecture provides the throughput and scalability required to support the full-scale 3000-channel JNE detector. These results collectively demonstrate that the newly developed electronics system fully satisfies the technical requirements of the future JNE experiment.

Fast prediction of the response of adhesive soft viscoelastic contacts represents a current challenge in soft robotics and for gripping and manipulation tasks. Determining the complete time-resolved force trajectory requires full numerical simulations, whose computational cost is strongly parameter-dependent, making them impractical for real-time application or design-optimization loops. In this work, we overcome this limitation by training a scalar-conditioned, stateful, sequence-to-sequence deep learning model to predict the full force evolution from a prescribed displacement history for both short- and long-range adhesion regimes. The data set spans four orders of magnitude in loading and unloading rates and includes varied dwell times, with the Tabor parameter ranging from $0.2$ to $3.2$. To enable learning across these heterogeneous time scales, we introduce a fixed-measurement-step (FMS) representation that converts variable-length trajectories into fixed-length sequences while preserving their physical-time information. Different architectures were trained, including long short-term memory (LSTM) networks, temporal convolutional neural (TCN) networks, and time-distributed dense layers with three different Tabor-conditioning mechanisms. The models were compared using global waveform and error metrics. We found that the best-performing model has an LSTM architecture with concatenated conditioning, which achieves a held-out mean-squared error of $5.0\times10^{-4}$, a median pull-off-force error of $\approx2.2\%$, and a median hysteresis error of $\approx1.1\%$. For the held-out protocols, the model predicts a complete force trajectory with a median inference time of $0.16$ s. The model is tested across unseen parameter combinations and against analytical limiting cases, providing a rapid surrogate for repeated numerical evaluations with potential use in control-oriented applications.

Electric vehicles (EVs) and internal-combustion-engine vehicles (ICEVs) differ fundamentally in their in-cabin acoustics, notably the attenuation or absence of engine-order content. Prior work reports associations between reduced engine sound, speed underestimation, and poorer speed maintenance; however, research on how EVs' new sound affects speed perception and control is scarce, and most newer studies focus on comfort and subjective pleasantness rather than speed perception. Addressing this gap, the present study uses a two-interval, two-alternative forced-choice (2AFC) paradigm to directly measure just-noticeable differences (JNDs) in speed under ICEV, EV, and silent conditions. Thirty participants performed a 2AFC task in which, on each trial, they viewed two first-person highway clips (reference vs. comparison) and indicated which appeared faster. Results from ANOVA and post-hoc tests indicate that at the 40 km/h reference speed participants showed no clear differences across sound conditions, whereas at 100 km/h there were marked differences in JND: mean values were 1.93 km/h (ICEV), 3.48 km/h (EV), and 5.15 km/h (silence). A psychoacoustic parameter analysis suggests that this effect is not explained by speed-dependent changes in loudness or sharpness; we interpret that RPM-related, clearly audible frequency shifts in ICEV provide the primary contributory cue. For EV NVH or artificial sound design, enhancing speed-contingent, trackable spectral cues while respecting comfort may help maintain drivers' ability to discriminate speed differences.

Full-field transmission X-ray microscopy (TXM) enables nondestructive three-dimensional imaging of thick and strongly absorbing materials with high spatial resolution. Such capabilities are essential for understanding structure-function relationships in hierarchical materials, with broad applications in biology, energy conversion, and energy storage. At high photon energies, however, TXM performance is limited by the reduced efficiency of diffractive optics and by the challenge of matching the numerical aperture (NA) of the illumination to that of the objective optics. Here we harness freeform diamond refractive optics to overcome a key illumination-efficiency bottleneck in high-energy TXM, demonstrating full-field nano-imaging at 20 keV with a half-period resolution of 72 nm. The optical system combines a custom-designed diamond refractive beam shaper that produces a uniform near-flat-top illumination at the sample with a 94% efficiency, a moving diffuser placed near the sample to increase the effective illumination NA and improve image quality and resolution, and high-precision aberration-corrected diamond compound refractive lenses as the objective optics. These results establish free-form diamond optics as a powerful route to efficient high-energy TXM, expanding full-field nano-imaging to complex, evolving materials systems and enabling in situ, operando, and tomographic studies under experimentally realistic conditions. Furthermore, it opens new avenues for innovation of joint X-ray optical-digital design for a new generation of high-energy X-ray nano-imaging.

Rubidium titanyl phosphate (RTP) is widely used in bulk Pockels cells because it combines a useful linear electro-optic response with low dielectric permittivity, high electrical resistivity, negligible piezoelectric ringing, and high optical-damage tolerance. Its use in integrated or guided-wave modulators has been limited by the absence of a mature thin-film RTP platform. Here we propose a dielectric-loaded guided-wave modulator in which a silicon nitride strip on an unetched bulk RTP crystal forms a near-surface optical mode that overlaps with side-electrode fields. The analysis focuses on full-vector optical modes, GSG electrostatic and RF response, tensor-aware electro-optic coupling, and device-level performance at 1064~nm.

Lean hydrogen flames are prone to thermo-diffusive instabilities due to preferential and differential diffusion effects, posing significant challenges for their modeling in computational fluid dynamics simulations. This work extends a tabulated-chemistry (TC) model that includes preferential and differential diffusion effects to a Reynolds-averaged Navier-Stokes (RANS) framework and assesses its performance for a lean premixed $\mathrm{H_2}$-air slot burner at two Reynolds numbers ($\mathrm{Re}=5500$ and $11000$) using direct numerical simulation (DNS) as a reference. The approach is based on transport equations for the progress variable and mixture fraction derived from the species mass transport equations considering mixture-averaged diffusion and Soret effect, and incorporates turbulence–chemistry interaction via a presumed probability density function (PDF) approach. RANS simulations including preferential-differential diffusion are able to correctly reproduce the DNS flame length, heat-release distribution, and the characteristic equivalence-ratio and super-adiabatic temperature branches of the slot flame. Comparisons with (i) a unity-Lewis-number variant and (ii) a model including thermo-diffusive effects only in the flamelet table show the impact of preferential and differential diffusion on the TC model at both the thermochemical and transport levels. Finally, the impact of the turbulence closures for turbulent diffusion, scalar dissipation rate, and Reynolds stresses is assessed. The results presented in this paper demonstrate the capability of the model to include preferential and differential diffusion effects in cost-effective RANS simulations of lean hydrogen flames.

In this paper, the random site percolation problem in a five-dimensional space for complex neighborhoods is studied. The efficient C++ code (with ordinary work division) of the classical Newman–Ziff algorithm is presented. The obtained speed-up of computations reduces 2170 years of single-core computations – necessary for obtaining the results presented in this paper – much below the typical half-decay time of the scientist. For complex neighborhoods, it is for neighborhoods composed with sites taken from several coordination zones (up to the seventh coordination zone), the 127 percolation thresholds are calculated (with 120 among them being estimated for the first time). For seven extended (compact) neighborhoods, the fractal dimensions are also calculated. The mean value of these fractal dimensions, averaged over these seven compact neighborhoods, is estimated as $\langle d_f\rangle\approx 3.5581(70)$. The percentage errors of the values obtained for the fractal dimensions for compact neighborhoods vary from 0.26% to 1.76% with respect to the theoretically predicted value based on scaling relations and the most recent estimates of critical exponents for five-dimensional space. The universality of the percolation threshold as dependent on the weighted coordination number $\zeta=\sum_i z_i r_i$ (where $z_i$ is the number of sites in the $i$-th coordination zone and $r_i$ is the Euclidean distance from the sites in the $i$-th coordination zone to the central site) is also verified. The latter manifests itself as the power law ($p_c\propto\zeta^{-g}$) with $g\approx 0.7913(43)$

Actuators which change shape in response to ambient pressure have great potential for use in extreme environments, from space to the deep ocean. Bi-layer actuators have so far not been explored to this end, despite their lightness and mechanical simplicity (there is no requirement for a separate power source or pump, for example) making them ideal for applications where transport and maintenance are difficult. In this paper, foam-based bilayer actuators powered by ambient pressure change have been constructed, modelled mathematically, characterised in terms of their actuating force, and built into deployable devices and metamaterial structures to showcase properties such as negative pressure expansivity. Actuators such as these are ideally suited for a range of applications, from deploying solar panels and sensors in space and extra-terrestrial atmospheres, to under-sea expandable structures and sensing.

Many real-world systems can be modelled as complex networks whose collective behaviour is governed by hidden interactions between nodes. Existing methods for inferring these interactions typically require controlled perturbations, time-resolved observations or multiple independent snapshots, all of which are often unavailable in practice. Here we show that class-based coupling strengths can be inferred from a single snapshot of node states when the system is observed close to a relative equilibrium. In this regime, all nodes share a common velocity, which can be absorbed into an effective class bias, transforming the inverse problem into a homogeneous linear system. The coefficients of this linear system are determined entirely by the observed local neighbourhoods and their coupling mechanism, enabling the application to arbitrary known coupling functions. We validate the approach on three different linear and nonlinear dynamical systems, recovering relative class-based couplings and, in special cases, absolute couplings. These results show that spatial heterogeneity can substitute for temporal sampling, enabling single-snapshot inference of hidden coupling strengths in networked dynamical systems.

The quality of an image observed through a scattering layer, such as a shower curtain, depends strongly on the relative position of the scattering layer between the object and the observer. This well-known phenomenon is commonly referred to as the shower curtain effect. When the scattering layer is placed close to the observer, the image is strongly degraded, whereas if it is located close to the object, the object may still be observed with relatively high resolution. Previous analyses of the shower curtain effect have primarily modeled the scattering layer as a section of a random medium. In this work, we present a new analysis in which the scattering layer is modeled instead as a rough interface, a description that arises naturally in many physical configurations. Within this framework, we derive explicit characterizations of both the image resolution and the signal-to-noise ratio, and determine how these quantities depend on the statistical properties of the rough interface and on its relative location between the object and the observer.

The Stokes-Mueller description of tissue polarimetry is conventionally interpreted through local observables such as birefringence, depolarization, and helicity preservation. We show that tissue structure generates topology in the polarization field itself. Polarization-resolved measurements of unstained human breast tissue reveal polarization vortices and, where the tissue architecture provides coupled azimuthal and radial variation, Neel-type polarization skyrmions. These structures are quantified by the vortex winding number and skyrmion charge, integer-valued topological invariants that vanish for homogeneous media and therefore provide zero background readouts of tissue heterogeneity. Malignant ductal carcinoma exhibits non-trivial polarization topology, whereas adjacent healthy tissue remains topologically trivial. The results establish topological invariants of the polarization field as physically interpretable observables of tissue organization and motivate topological polarimetry as a field-based approach to tissue characterization.

Kinesin is a molecular motor that transports intracellular cargoes along microtubules. Recent studies have quantified kinesin performance using various efficiencies within the framework of stochastic thermodynamics; however, quantitative comparisons remain difficult because different models and assumptions have been employed. As a result, it remains unclear which aspect of kinesin performance, if any, is thermodynamically optimized. Here, we systematically compare multiple thermodynamic efficiencies within a single kinesin-cargo model. To this end, we construct a thermodynamically consistent two-state kinesin-cargo model that retains both the discrete stepping of kinesin and its coupling to the cargo. Assuming a separation of time scales between the motor and the cargo, we derive analytical expressions for the thermodynamic efficiency, the information-thermodynamic efficiency, the thermodynamic uncertainty relation (TUR) efficiency, and the bipartite TUR efficiency, and compare them with numerical simulation results. We find that these efficiencies generally remain low, suggesting that kinesin is not optimized for maximizing the thermodynamic efficiencies considered here. Our results suggest that thermodynamic efficiencies alone may not fully characterize kinesin performance and motivate further investigation of complementary kinetic perspectives for assessing molecular motor function.

We design a reflective scatterer that operates only in the backscatter direction using modulation of the Kerker effect. This being a phenomena where the resonant modes of a scattering object produce total destructive interference in the backscatter direction. Our design consists of a dielectric shell and an interior metal element that provide the required dipolar contributions for Kerker interference with angular stability. By incorporating a PIN diode, we obtain a scatterer that exhibits strong amplitude modulation in the backscattering direction that is weaker at other bistatic angles. Backscatter modulation is a key technology to connect everyday objects but can be vulnerable to eavesdropping, the device presented in this work may help guard against such flaws.

We investigate deterministic coarsening dynamics in a spatially extended bistable gene toggle model with diffusive coupling. Unlike classical curvature-driven coarsening, where domain walls move continuously and annihilate gradually, the present system exhibits a qualitatively different mechanism. The domain walls remain pinned for long intervals and disappear abruptly through collective cascade events. The density of domain walls decays approximately as $\rho(t)\sim t^{-\delta}$, but the coarsening exhibits clear log-periodic oscillations superimposed on the power-law behavior. For all values of the promoter strength $\alpha$ considered, the measured exponent satisfies $\delta<0.5$, indicating a systematic deviation from the classical Allen–Cahn prediction $\delta=1/2$ for curvature-driven coarsening. We show that log-periodic oscillations are not controlled by the density of domain walls, but by the domains that disappear in each cascade. The average size of disappearing domains grows roughly linearly with cascade index, producing a constant geometric spacing of cascade times, consistent with discrete scale invariance.

The monolithic integration of compact laser sources onto silicon remains a critical bottleneck for the scalable development of silicon photonics. In particular, the direct epitaxial growth of near-infrared vertical-cavity surface-emitting lasers (VCSELs) on silicon has long been hindered by the simultaneous requirements of low crystalline defect density in the active region and smooth interfaces in the distributed Bragg reflector (DBR) mirrors, both essential to achieving high cavity quality factors and low lasing thresholds. Here, we report the direct epitaxial integration of near-infrared VCSELs on silicon substrates using a two-step growth strategy specifically designed to suppress defect propagation while preserving mirror interface abruptness. This approach enables effective confinement of threading dislocations away from the active region and ensures high-reflectivity DBRs with excellent structural uniformity. Structural and optical characterizations reveal an epitaxial stack of high crystalline quality, leading to laser performance comparable to VCSELs commonly grown on GaAs substrates. We demonstrate, for the first time, directly grown VCSELs on silicon wafers exhibiting threshold currents of only a few milliamperes. These results establish a decisive proof of concept for the monolithic integration of good-performance VCSELs onto silicon platforms and open a viable pathway toward fully integrated, low-cost silicon-based photonic systems.

Neural quantum states offer expressive representations of quantum many-body wave functions, yet their practical accuracy can be limited by stochastic optimization rather than representational capacity. Here we identify a finite-sample instability, termed subspace trapping, in which physically important configurations become strongly underestimated, remain absent from successive sampling batches and receive insufficient gradient feedback. This self-reinforcing loss of sampled support can confine optimization to an effective subspace and produce apparently stationary states above the true ground state energy. To address this problem, we introduce annealed gradient descent (AGD), a sampling-aware update with annealing factor that temporarily increases the relative contribution of sampled low-probability configurations while limiting the dominance of high-probability ones. We establish the connection between finite-sample support loss and effective subspace optimization, and then evaluate the method across molecular systems, one and two-dimensional $J_1$-$J_2$ models. Annealed gradient descent suppresses metastable trapping, preserves physically relevant configurations and enables compact neural quantum states to attain chemical accuracy and competitive state-of-the-art performance. These results establish AGD as a lightweight complement to expressive neural architectures, improved sampling strategies for scalable quantum many-body optimization.

We address the existence, stability, and propagation dynamics of multipole solitons and vortex clusters in cubicquintic media subject to a harmonic trapping potential.We found that vortex clusters comprising N off centered vortices with alternating topological charges m equal to +(-)1, evenly distributed on a ring, can bifurcate from a multipole soliton for N less than or equal to 4 and from a second-order ring soliton for N greater than 4. Rigorous linear stability analysis, corroborated by direct numerical simulations, shows that upper branch vortex clusters with N equal to 2 and 4 remain stable over a wide range of the propagation constant. Thus, we reveal the formation mechanism of vortex clusters.

We present full-dimensional quantum simulations of $H_2^+$ dissociative ionization driven by strong orthogonal laser fields. We consider equal-frequency orthogonal components, which generate elliptical or circular polarization depending on their relative phase and amplitude, as well as orthogonal $800$- and $400$-nm two-color fields. These two-dimensional fields strongly modify the fragmentation dynamics. Most notably, we identify a high-energy peak in the proton kinetic-energy-release (KER) spectrum at approximately $4-5$ eV that is absent from the corresponding single-color, linearly polarized calculations. The yield of this peak can be coherently controlled by varying the relative carrier-envelope phase of the perpendicular field component. The perpendicular field also disrupts the clear electron-proton energy-sharing pattern observed in the main $3-3.5$ eV dissociation channel, indicating more complex multichannel dynamics. Time-dependent state projections and calculations initiated from individual excited states attribute the additional peak to laser-induced vibrational excitation of $H_2^+$. Furthermore, the perpendicular field rotates the fragment angular distributions, causing the most probable proton and electron emission directions to deviate substantially from the principal $z$ axis. These findings demonstrate that the spatial and temporal geometry of orthogonal laser fields provides an additional degree of freedom for controlling ultrafast electron-nuclear dynamics.

The present work is devoted to the proper application of the schlieren imaging technique, combined with inverse Abel transformation, to record the flow field induced by atmospheric pressure cold plasmas, in a quantitative manner. Theoretical principles, along with meaningful diagrams, design of the schlieren setup, and involved calibration process, are given in detail. It is demonstrated that erroneous conclusions may be reached if Abel transformation is omitted. The concept is employed to study flow field parameters around a surface dielectric barrier discharge, operating in ambient air and having a cylindrical configuration, rather than a typical planar design. Maximum density and lowest temperature equal to 1.24 kg m -3 and 285.4 K, respectively, and lowest density and maximum temperature equal to 1.11 kg m -3 and 318.8 K, respectively, downstream of the driven electrode and close to the surface are identified with respect to the unperturbed air where density equals 1.21 kg m -3 and temperature equals 291.5 $K$. The temperature and density patterns unveil intense perturbations at shorter distances from the surface and lower amplitudes of the sinusoidal (10 kHz) voltage that sustains the discharge. Electrohydrodynamic effects seem to dominate over thermal mechanisms in governing the flow field. The importance of the quantitative schlieren technique, as a non-invasive one, in the study of dielectric barrier discharges is justified by the interest that they attract in numerous demanding cases of plasma-assisted flow field control (propulsion, actuators, etc.).

A three-camera Event-Based Vision Sensor (EVS) system is employed to perform three-dimensional measurements of bubble motion, morphology, and bubble{\DH}bubble interactions. The multi-EVS configuration mitigates the absence of direct depth information inherent to binary event-based imaging while preserving key advantages, including high temporal resolution, low latency, and reduced data throughput. The experimental configuration consisted of an octagonal tank equipped with a controlled particle release mechanism and an air diffuser. Camera synchronization and pulsed LED illumination were achieved using a dedicated signal generator and driver circuitry, while calibration was performed using pulsed-illumination recordings of a target acquired at multiple depths. A comprehensive, inhouse computational framework was developed to process the event data for three-dimensional motion trajectory reconstruction. The validation of the developed tracking framework followed a rigorous multi-stage pipeline to ensure reconstruction fidelity. The framework was initially benchmarked against synthetic rendering cases of increasing kinematic complexity to evaluate 3D trajectory reconstruction accuracy under controlled conditions, achieving sub-millimeter global accuracy with root-mean-square error (RMSE) values ranging from 0.015 to 0.36 mm. Following numerical validation, physical baseline experiments were conducted using precisely manufactured particle releases through both a gated chamber and a single-particle claw opening mechanism. Subsequently, dynamic bubble plumes generated via multiple inlets across various compressed air flow rates were evaluated. The EVS framework successfully resolved dense bubble-bubble interactions, producing smooth, physically consistent three-dimensional trajectories across all tested conditions. Quantitative results showed strong agreement, yielding an overall velocity consistency exceeding 97% between conventional centroid tracking and independent velocity estimates derived from continuous-illumination event streaks. Overall, this methodology demonstrates a robust framework for high-speed volumetric tracking and bubble flow measurement. However, event oversaturation remains a key hardware limitation in ultra-dense regimes, as oversaturation in a single camera can cause system desynchronization.

Chiral connectedness describes chiral structures that transform smoothly into their mirror images while never being achiral. The phenomenon is illustrated with a two-parameter family of complex vector fields E representing paraxial optical Gaussian beams. E is chiral everywhere in the parameter plane except the origin. During a circuit of the origin, E reverses chirality twice without being achiral. Different aspects of chirality are associated with the vector and scalar nature of E; natural measures of each, and their combinations, change sign at parameter values where E is chiral. The analysis is simpler for planar fields E, but their chiralities are essentially the same when the subtleties of three dimensions are included.

The use of temporal correlations in scattered photons to probe the microscopic dynamics of ultracold quantum gases has emerged as a powerful, minimally destructive approach for in situ analysis. Here, we demonstrate that temporal coherence spectroscopy can quantitatively characterize atomic motion in a magnetic trap, despite the perturbative effects of the probing light. By measuring the first-order correlation function g (1) ($\tau$ ) of light scattered by a 87 Rb cloud confined in a quadrupole trap, we identify radiation-pressure-induced acceleration and heating as the origin of the apparent discrepancy between coherence spectra and temperatures inferred from time-of-flight measurements. A simple dynamical model incorporating these effects restores agreement between theory and experiment, establishing coherence spectroscopy as a reliable in situ probe of velocity distributions in trapped atomic ensembles. Our results pave the way for time-resolved studies of nonequilibrium dynamics and thermalization processes in confined cold gases, complementing conventional destructive imaging techniques.

We observe pump-induced double anticrossings whose gap sizes and center-frequency shifts depend strongly on pump power, indicating a nonlinear mechanism. The anticrossings vanish at high magnetic fields, where energy conservation suppresses three-magnon splitting, thereby identifying the underlying process as three-magnon scattering. We attribute the double anticrossings to nondegenerate three-magnon splitting, which generates two magnon populations at distinct frequencies. Each population forms a standing-wave mode and couples independently to the Kittel mode, giving rise to two effective coupling channels. These results demonstrate that nonlinear magnon interactions can dynamically generate multiple coupling channels within a single system.

Small neutrino detectors are a ton level detectors which can be placed very close to the core of Nuclear Power Plant. In some detectors liquid scintilling (LS) material is used as the detecting material. The antineutrinos from the reactor core fall on the liquid scintillator of the detector where they deposit energy via Inverse Beta Decay process (IBD). The energy absorbed by liquid scintillator is re-emitted in the form of scintillation. These photons then travel through the scintillating material and hit the Silicon Photo Multipliers (SiPMs) which are installed on the inner surface of detector's spherical copper shell. These SiPMs absorb the photons to give a charge output signal. The energy and radius reconstruction is done using the information of charge collected by the SiPMs. Due to factors like large photo-coverage with large photon detection efficiency, small spherical detector size and low temperature operation, small size LS detectors can achieve an unprecedented energy resolution. In this paper, we have used a template-dependent method exploiting simulated data to reconstruct the event radius and energy. This method uses response functions generated using radioactive source calibration data and the charge information to reconstruct the energy and the radius of the event by constructing maximizing a likelihood function. This methodology is applicable to all similar size spherical neutrino detector experiments.

We study a focusing Majda–McLaughlin–Tabak type equation undergoing finite-time wave collapse. This singularity terminates the classical smooth solution and opens a post-blowup regime where infinitely many solutions may exist. To probe this nonunique regime, we regularize the dynamics either by viscous diffusion or by nonlinear saturation and study the corresponding vanishing-regularization limits. Both regularizations prevent blowup at fixed parameter and recover the same inviscid collapse as the parameter vanishes. Before collapse, they converge to the same smooth inviscid solution. After collapse, however, their limits differ. The viscous approximation undergoes anomalous mass dissipation whereas the saturating approximation remains conservative. Moreover, neither regularization selects a unique post-blowup solution. Vanishing perturbations of the regularization parameter or of the initial condition survive the singular limit and generate finite post-blowup uncertainty. This places collapsing wave turbulence in the setting of spontaneous stochasticity, where the inviscid limit is better described in terms probability law on inviscid solutions rather than deterministically. Scale-by-scale fluctuation budgets identify collapse events as localized sources of uncertainty production. While spontaneous stochasticity is usually associated with fluid turbulence, these results provide numerical evidence that it can be applied to a broader class of systems, including dispersive media in which experiments could be conducted.