Papers

Fourier optical processing underpins optical information manipulation, yet extending such operations to short wavelengths within compact platforms remains challenging. Here, we address this challenge by embedding reconfigurable Fourier-domain processing within IR-to-UV upconversion in a doubly resonant nonlinear metasurface. When coherently illuminated at the Fourier plane with an image-bearing signal and a spatially structured pump, the metasurface generates UV images via degenerate four-wave mixing. Crucially, the spatial-frequency content of these upconverted images is selectively shaped by the tailored spectrum of the pump. To boost the efficiency of this nonlinear process, the metasurface is designed to simultaneously support a toroidal dipole bound state in the continuum and a magnetic dipole resonance, providing spectrally aligned and independently enhanced field localization for signal and pump beams, respectively. Building on this architecture, we experimentally demonstrate directional and continuously tunable filtering at the upconverted UV wavelengths. These results establish nonlinear metasurfaces as a versatile platform for Fourier optics and reconfigurable all-optical image processing.

We extend our previously proposed image reconstruction method, which allows confocal microscopes to capture periodically moving objects at frequencies beyond their frame rates, to three-dimensional and two-dimensional wide-field imaging. This extension is achieved by implementing a synchronization scheme between a confocal laser scanning microscope and a function generator to ensure consistent initial phase alignment across image sequences acquired at different focal depths or fields of view. The method was demonstrated by visualizing the three-dimensional motion of silica particles attached to an aluminum bar oscillating at 100 Hz and the two-dimensional wide-field response of colloidal particles subjected to periodic pulsed excitation. Quantitative single-particle analysis confirmed that the reconstructed images accurately captured the underlying particle dynamics. The extended approach requires no additional specialized hardware and can be readily integrated with conventional confocal microscopes. Thus, it extends the applicability of confocal imaging to the fast dynamics of periodic processes in biological and soft-matter systems.

Inertial sensors can track object kinematics, however, unbounded drift from integrating noisy signals makes them impractical for MRI motion correction at millimeter resolution and minute-long scans. We introduce MR-Compass, which exploits the MRI system's static magnetic and gravitational fields to estimate 3-DOF orientation at 2 kHz directly, without integration, eliminating random-walk. The remaining 3-DOF translation is recovered via phase correlation from the MRI data. We experimentally validate the efficacy of the method retrospectively using a 3D radial koosh-ball sequence and prospectively using 2D EPI fMRI during large volunteer motions. MR-Compass followed by phase-correlation achieved a mean accuracy of 0.6$^o$ and 0.4 pixels across all experiments. Image quality improved when motion correction was applied in all volunteer scans for both retrospective and prospective correction cases. MR-Compass was effective in measuring head motion in the MRI scanner with high accuracy at unprecedented sample rates, and enabled both retrospective and prospective reconstruction to improve image quality by aligning the k-space data appropriately and by reducing the motion related artifacts.

The Weiner's theory (WT) is developed on the basis of the exactly solvable Schrödinger equation with trigonometric double-well potential (TDWP). The symmetric case of TDWP is considered. This modified version of WT (mWT) enables one to eliminate some severe approximations of the original Weiner's approach and to obtain more accurate results. An analytic formula is derived which provides the calculation of the proton transfer rate with the help of elements implemented in {\sl {Mathematica}}. We exemplify the application of mWT by calculating the proton transfer rate constant in the hydrogen bond of the proton-bound ammonia dimer cation ${\rm{N_2H_7^{+}}}$ (${\rm{H_3N\cdot\cdot\cdot H^{+} \cdot\cdot\cdot NH_3}}$). The parameters of the model for this object are extracted from available literature data on IR spectroscopy and quantum chemical calculations. The approach yields the transition from the Arrhenius-like exponential temperature dependence characteristic of thermal activation to that of quantum tunneling. Besides it is well suited for describing the phenomenon of vibrationally enhanced tunnelling.

We identify the precise thermodynamic roles of internal coupling in quantum thermal machines by analyzing the quantum Otto cycle across thermalized Gibbs-state limit cycles (GSLC) and finite-time non-equilibrating limit cycles (NELC). By systematically comparing the internally coupled system with standard and dressed-spectrum models, we resolve previous misconceptions regarding the physical origins of performance variations. First, we establish that the internally coupled system outperforms the standard system solely due to the Hamiltonian spectrum update, which intrinsically broadens the operating regimes. Second, we reveal that time-dependent efficiency and coefficient of performance (COP) are not caused by general quantum coherence, but specifically by eigenbasis mismatch arising from the non-commutativity of Hamiltonians across different strokes. The mismatch acts as quantum internal friction, reducing efficiency and COP and generating dissipative thermal modes. This results in the power-efficiency trade-off law, which we should find in realistic engines. However, we would not find the trade-off law when coherence exists but the eigenbasis mismatch does not exist, because there would be no friction, and hence the efficiency and COP would remain completely time-independent. Third, we isolate the distinct impact of coherence on power output. Validated by the global approach of the GKSL master equation, we demonstrate that coherence inherently reduces power by suppressing kinetic thermalization rates, regardless of eigenbasis mismatch.

Amorphous phase-change materials enable fast and reversible switching in optical and electronic devices, yet crystallization kinetics are still controlled primarily through empirical thermal protocols. Here we identify a microscopic picture governing crystallization in the prototypical phase-change material Ge2Sb2Te5, in which crystallization pathways are organized by the percolation of mobile atomic networks associated with $\beta$-relaxation. We show that this percolation transition distinguishes the dominance of diffusion-driven and diffusionless nucleation and growth during crystallization processes. We further demonstrate that frequency-selected ultrasonic excitation, applied in conjunction with heating, accelerates crystallization by enhancing percolation-mediated atomic dynamics. This acceleration is maximized near the $\beta$-relaxation frequency, consistent with resonant excitation of mobile atoms. Our results establish a direct link between glassy relaxation, atomic-scale percolation, and crystallization, and introduce a new route to modulating phase-change kinetics through targeted excitation of fundamental glassy dynamics.

A simple and efficient one-dimensional discrete Boltzmann method is developed for compressible flows with tunable specific heat ratios by incorporating extra degrees of freedom. To guarantee Galilean invariance in numerical simulations, a discrete velocity set is constructed with high spatial symmetry. Furthermore, an operator-splitting scheme is proposed to extend the one-dimensional kinetic formulation to simulations of one-, two-, and three-dimensional flow systems within a unified framework. The proposed model and numerical method are verified and validated against several benchmark problems, including the Sod shock tube, Lax shock tube, 2D Riemann problem, uniform translational flow, and acoustic wave propagation. The results demonstrate the accuracy, robustness, and flexibility of the present approach for compressible flow simulations.

Nonlinear propagation of ultrashort pulses in multi-mode waveguides, featuring complex spatial-temporal dynamics, provides new degrees of freedom in the fields of nonlinear optics and ultrafast lasers. Here, we demonstrate a new scheme of ultraviolet Cherenkov (dispersive-wave) radiation in a gas-filled capillary with unprecedently-high pulse energy, enabled by spatial-temporal dynamics. We found that mJ-level, 40-fs pulses, launched into a large-core capillary filled with high-pressure noble gas, would experience self-phase-modulation and self-steepening effects in this normal-dispersion waveguide, leading to high-intensity shock wave generation and asymmetric spectral broadening. Spatial-temporal dynamics, stemming from strong nonlinear inter-mode coupling, causes spatial shrink and temporal deceleration of the pulse which dramatically alter the capillary dispersion landscape. As a result, a phase-matching point can be created in the ultraviolet, giving rise to the radiation of multi-mode dispersive waves with 100-{\mu}J-level pulse energies and few-fs pulse widths. Our findings inspire new insights into multi-mode nonlinear optics, and the demonstrated high-energy ultraviolet light source with broadband tunability and compact set-up configuration, may find a few applications in time-resolved spectroscopy, ultrafast electronics and femtosecond chemistry.

Recent papers by Ansari et al. (2024, Science Advances 10, eadn4650) and Richardson et al. (2025, arXiv eprint 2504.01455) have suggested that global warming of the Martian surface ('terraforming') by 35 K to sustain local habitats above the melting point of water could be achieved through the injection of engineered aerosols into the Martian atmosphere. Using the MarsWRF 3D Global Climate Model, we investigate how artificial warming of Mars through engineered aerosol release would affect the planetary water cycle and the distribution of the major surface ice reservoirs. Within our model framework, every 20 K of global warming induces a tenfold increase in atmospheric water vapour content due to sublimation of H2O ice from the North Polar Cap. This increases the potency of cloud radiative feedbacks which induces nighttime warming (~5-10 K) at low latitudes, but daytime cooling (up to 40 K) in the winter midlatitudes. Water is transferred from the edge of the North Polar Cap to the South Polar Cap and there is minor destabilisation of shallow northern midlatitude subsurface ice. As a result, seasonal sublimation of H2O ice from the South Pole has an increased impact on the global water cycle. These changes persist on Mars at least decades after loading of the atmosphere with engineered aerosols ceases. Our model is limited by the gaps in our knowledge of present-day Martian weather and climate, and of the microphysics and radiative properties of candidate warming agents. Much more data is therefore needed before warming Mars could become feasible.

A finite-deformation framework for gradient crystal plasticity is developed within a thermodynamically consistent setting grounded in Gurtin's power-conjugate formulation. The model introduces a flow rule that accounts explicitly for both energetic and dissipative microstress contributions. Numerical simulations are performed to investigate the response of single crystals subjected to passivation-type boundary constraints. The results reveal that constitutive laws capable of reproducing size-dependent strengthening at the onset of plastic flow simultaneously generate a pronounced, nearly elastic-type response when passivation is imposed. These findings establish a fundamental connection between gradient-induced yield strengthening and boundary-driven elevation of the mechanical response, highlighting the essential influence of dissipative gradient effects.

The generalized quantum master equation provides a powerful framework for non-Markovian dynamics of open quantum systems. However, the accurate and efficient evaluation of the memory kernel remains a challenge. In this work, we introduce a comprehensive tensorial extension to the Memory Kernel Coupling Theory (MKCT) to overcome this bottleneck. By elevating the original scalar formalism to a tensorial framework, the extended MKCT enables the calculation of general expectation values and cross-correlation functions. We demonstrate the numerical accuracy and efficiency of this method across multiple benchmark systems: capturing transient populations and coherences in the spin-boson model, resolving the excitonic absorption spectrum of the Fenna-Matthews-Olson complex, and simulating charge mobility in one-dimensional lattice models. These successful applications establish the tensorial MKCT as a highly efficient tool for investigating complex dynamics in open quantum systems.

Coherent Raman scattering provides highly sensitive vibrational analysis through nonlinear light-matter interactions. However, its application to metal interfaces has remained challenging because the intrinsically large non-resonant background (NRB) of metals overwhelms weak interfacial molecular vibrational signals, making direct Raman detection without plasmonic or electronic enhancement highly challenging. Here, we report a time-frequency hybrid coherent Raman spectroscopy approach that overcomes this limitation and enables sensitive detection of ångström-thick molecular systems even on atomically flat metal surfaces. Our method employs a time-frequency engineered detection scheme that combines femtosecond pump and Stokes pulses with a time-delayed, asymmetrically shaped picosecond probe pulse. By exploiting instantaneous temporal response of the metal NRB, this pulse configuration effectively filters out the dominant metal NRB in the time domain while retaining a controlled residual NRB that acts as an internal local oscillator, enabling strong interferometric amplification of weak interfacial vibrational signatures. This all-optical coherent enhancement strategy establishes a new route for direct, non-invasive Raman detection of interfacial molecular systems across a wide range of surfaces without requiring structure- and material-specific plasmonic and electronic enhancement mechanisms.

This is the first of five papers comprising The Semantic Arrow of Time. The argument begins with a claim: computing's arrow of time is semantic, not thermodynamic. The direction in which meaning is preserved or destroyed across transactions is not a consequence of the second law but of design choices embedded in protocol architectures since Shannon's 1948 channel model. These choices encode the Forward-In-Time-Only (FITO) assumption – the commitment that causation is irreversible, acyclic, and globally monotonic. We trace this assumption from Eddington's 1927 coinage of "the arrow of time," through the Boltzmann–Loschmidt debate, to contemporary philosophy of physics: Price's time-symmetric ontology, Smolin's temporal naturalism, Rovelli's relational quantum mechanics, and Roberts's analysis of time-reversal symmetry. We show that fundamental physics is time-symmetric at the microscopic level, that the thermodynamic arrow emerges from boundary conditions rather than fundamental law, and that recent demonstrations of indefinite causal order confirm nature admits correlations with no well-defined temporal ordering. We then identify the category mistake (Ryle, 1949): computing inherited Newton's absolute background time – via Shannon, Lamport, and the impossibility theorems – and encoded it as a semantic primitive. The FITO assumption is not a law of nature but a design choice, and recognizing this dissolves apparent constraints that have shaped forty years of distributed systems theory. Subsequent papers develop the constructive alternative through link semantics, RDMA, transaction failures, and the Leibniz Bridge framework.

Motivated by the ATOMKI anomalies in 8Be and 4He transitions, we study X17-induced Lamb shifts and hyperfine splittings in muonic atoms with stable nuclei up to Z <= 15. The bound-state problem is solved within the Gaussian Expansion Method using a unified Hamiltonian that includes the standard electromagnetic baseline together with vector and pseudoscalar X17 exchange. The spin-independent Lamb shift is described by a coherent vector muon-nucleus interaction, while the spin-dependent hyperfine sector is built isotope by isotope from shell-model spin fractions. We find a clear complementarity between mediator hypotheses: the vector Lamb-shift signal grows toward heavier nuclei, the vector hyperfine scenario favors odd-N nuclei, and the pseudoscalar scenario favors odd-Z nuclei. Using a signal-to-precision ratio, we identify muonic deuterium, muonic helium-3 ion, and muonic helium-4 ion as the most promising near-term Lamb-shift probes among systems with existing precision benchmarks. For future spectroscopy, the largest vector Lamb-shift signal is predicted in muonic silicon-29, while the leading 1S hyperfine targets are silicon-29 for the vector scenario and phosphorus-31 for the pseudoscalar scenario. The main theoretical uncertainty comes from the Schmidt-model treatment of nuclear spin content.

Synthetic dimensions provide a powerful tool that uses comparatively simple structures to probe high-dimensional topological physics, in which edge states emerging at lattice boundaries are of great importance. However, the demonstration of lattice boundaries in synthetic dimensions is relatively nascent. In this work, we realize an integrated coupled ring system in a thin-film lithium niobate photonic platform that enables the simulation of one-dimensional frequency crystal lattice with sharp boundaries, attaining suppression for two coupling terms with a single auxiliary cavity. Their effect on tight-binding lattice dynamics was verified by acquiring discretized band structures of an N = 7 site lattice. The ability to create robust frequency-space boundaries is a key step toward the realization of topological systems that harness bulk-edge correspondence as well as optical information processing in a photonic chip.

We experimentally explore the effectiveness of a rectangular orifice synthetic jet actuator for wall-bounded vortex destruction. Vortex flows near a boundary often present unforeseen or undesired forcing on a neighboring surface due to the low pressure concentration within the vortex. Synthetic jets – primarily used for separation control, enhanced mixing, and induced turbulence – offer a unique strategy for vortex mitigation due to the unsteady flow at the region of the orifice disrupting the coherence of the oncoming flow. In a flat plate boundary layer, we test multiple jet orifice configurations, vortex lateral position relative to the orifice, and vortex sizes. We find that each jet was capable of reducing the incoming vortex rotational coherence up to 70%. This disruption led to pressure recovery within the vortex wake region. The velocity wake of the vortex was more persistent (most jets produced a wake of their own) though some cases were capable of accelerating the fluid while maintaining moderate rotation reduction and pressure recovery. These results indicate that synthetic jets have the potential to mitigate a near wall vortex structure, particularly in scenarios where the position and size of the vortex are known.

This article investigates the intrinsic link between skewness and statistical intermittency in velocity and temperature increments within homogeneous isotropic turbulence. The theoretical framework builds upon the author's previously established closure schemes for the von Karman-Howarth and Corrsin equations. A transition Taylor-scale Reynolds number is first estimated via a formal bifurcation analysis of the closed von Karman-Howarth equation. A central thesis of this work is that while the nonlinearity of the Navier-Stokes equations is fundamentally responsible for intermittency, it is insufficient on its own to recover the Kolmogorov scaling law. We demonstrate that the non-observability of bifurcation modes constitutes the missing conceptual link: the concomitant effect of nonlinearity and non-observability not only determines the Kolmogorov scaling and drives an intermittency that grows monotonically with the Taylor-scale Reynolds number, but also enables the analytical determination of the internal structure functions of velocity and temperature differences, along with their corresponding PDFs and statistics. By invoking Fisher's principle (1922) for statistical description, we show that the entire statistics of increments can be analytically derived through a decomposition into bifurcation modes governed by quasi-probability distribution functions (quasi-PDFs). These provide the formal mathematical basis to also represent local energy backscatter. Notably, the analysis recovers the Kolmogorov law – specifically the scaling of the velocity standard deviation ratio as R_lambda^(1/2) – as a consequence of non-observability. Our analysis reveals that bifurcation modes exhibit amplitudes whose third statistical moment scales as R_lambda^(-3). The results show excellent agreement with benchmark numerical and experimental data in the literature.

By measuring the thermal effects resulting from stimulated Raman excitations, stimulated Raman photothermal (SRP) microscope offers a new access to spatially and temporally resolved Raman signatures across various sample types. Unlike stimulated Raman scattering (SRS) microscopy, SRP is highly compatible with noisy ultrafast laser sources, allowing the use of high peak power, low repetition rate optical parametric amplifier (OPA) lasers to boost sensitivity and imaging speed. Here, we report a single pulse SRP (spSRP) microscope system in which SRP signals are induced by individual pairs of laser pulses generated by an OPA laser. Extensive pulse chirping is used to maximize SRS excitation rate and to minimize photodamage. The single-pixel limit of detection (LOD) of spSRP on dimethyl sulfoxide (DMSO) reaches 890 {\mu}M, which is a ~44-fold improvement over SRS microscope. Versatile applications to fungi, cells, and tissues are demonstrated. Live cell spSRP imaging was carried out at speed of 10 frames per second. Particularly, the spSRP system enables direct visualization of cholesterol-rich domains, co-localized with caveolin immunofluorescence, in the plasma membrane of HeLa cells.

Accurate determination of the complex effective permittivity is fundamental to optical material engineering, but it remains a critical metrology challenge for heterogeneous systems. In polymer blends and optical composites, scattering and nonlinear dielectric effects severely distort spectral signatures, causing conventional linear unmixing and data-driven approaches to fail. Here, we present an inverse reconstruction framework that retrieves the broadband complex permittivity and constituent composition of strongly scattering mixtures from a single infrared extinction spectrum. The method integrates scattering theory, Lorentz oscillator modeling, and a generalized set of nonlinear effective medium approximations to identify component spectra, estimate volume fractions, and, crucially, diagnose the underlying microstructure. The reconstruction algorithm demonstrates robust performance across synthetic two- and multi-component polymer blends, rigorously testing the efficacy of inverted, logarithmic, and cubic mixing regimes. By comparing the statistical causality and fitting quality of these competing EMAs, the framework uniquely provides a non-destructive optical diagnosis of the blend's dominant interaction topology (e.g., co-continuous vs. stratified/series). The reconstructed permittivity spectra are dispersion-consistent and reveal physically interpretable optical properties across the full IR range. This framework establishes a new paradigm for inverse metrology in photonics, providing a necessary physics-grounded foundation for the quantitative characterization and rational design of nonlinear optical composites. Specifically, by providing scattering-immune effective permittivity for forward modeling and delivering a physics-based diagnosis of the underlying microstructure, the framework enables engineers to reliably link fabrication parameters to the intended optical function.

In this study, we numerically evaluate the learning performance of a vortex spin-torque oscillator (VSTO) with a modified free layer, called a modified VSTO (m-VSTO), in which an additional layer (AL) of smaller radius is stacked on the free layer, for physical reservoir computing. The vortex-core dynamics are computed using the Thiele equation incorporating the potential deformation induced by the AL. We identify the edge of chaos from the maximal Lyapunov exponent and quantify the short-term memory capacity (STMC) as well as the information processing capacity (IPC) in a time-multiplexed reservoir scheme. We find that the m-VSTO exhibits finite STMC and IPC in a low-current and low-field regime below the threshold current of the conventional VSTO, and can achieve up to approximately twice the IPC with about one quarter of the power consumption. The pulse-width dependence of the IPC can be further interpreted by combining an analytical estimate of the transient time with Lyapunov-exponent data. Longer pulse widths promote stronger recovery-induced contraction toward the groove-trapped orbit over a wide range of subthreshold currents. In contrast, this difference in contraction rate becomes less pronounced near the threshold current, where the transient time increases rapidly. Consequently, the IPC is enhanced in a stable regime with a negative Lyapunov exponent rather than exactly at the edge of chaos. These results suggest that engineering the potential landscape and pulse-width-dependent recovery dynamics enables low-power spintronic physical reservoirs.

Bayesian optimization (BO) is widely used to accelerate physics and materials research, where objective function evaluations are computationally or experimentally expensive. While many BO frameworks focus on algorithmic efficiency, practical usability and portability are equally critical for sustained use in real research environments. PHYSBO is a Bayesian optimization library designed to address these needs by enabling optimization over user-defined candidate pools and by supporting domain-specific problem settings. This paper presents the major updates introduced in PHYSBO versions 2 and 3, with a focus on improvements in usability, portability, and practical deployment rather than on new optimization algorithms. In PHYSBO version 2, the software license was changed from GPL to MPL to improve compatibility with a wider range of research and software ecosystems. Building on this revision, PHYSBO version 3 introduces a set of implementation-oriented updates aimed at improving usability and portability, without modifying the core optimization algorithms. These updates include improvements in computational performance and scalability, extended support for multi-objective optimization, the introduction of range-based policies for continuous-variable optimization, the removal of environment-dependent components such as tightly coupled Cython modules, and compatibility with NumPy 2. These improvements reduce the technical and organizational burden on users, enabling PHYSBO to be deployed across diverse computing environments and research workflows. By emphasizing portability and ease of integration while maintaining sufficient performance, PHYSBO version 3 is positioned as a sustainable research infrastructure for Bayesian optimization in physics and materials science.

Multifunctional wound dressings with antibacterial and antioxidant properties hold significant promise for treating chronic wounds; however, achieving a balance of these characteristics while maintaining biocompatibility is challenging. To enhance this balance, this study focuses on the design and development of 3D-printed chitosan-matrix composite scaffolds, which are incorporated with varying amounts of cerium oxide nanoparticles (0, 1, 3, 5, and 7 wt%) and subsequently coated with a vancomycin-loaded alginate layer. The structure, antibiotic drug delivery kinetics, biodegradation, swelling, biocompatibility, antibacterial, antioxidant, and cell migration behaviors of the fabricated dressings were evaluated in-vitro. The findings reveal that all of the formulations demonstrated a robust antibacterial effect against S. aureus bacterial strains in disk diffusion tests. Furthermore, the dressings containing cerium oxide nanoparticles exhibited proper antioxidant capabilities, with over 78.1% reactive oxygen species (ROS) scavenging efficiency achieved with 7% cerium oxide nanoparticles. The sample containing 5% cerium oxide nanoparticles was identified as the optimal formulation, characterized by the most favorable cell biocompatibility, an ROS scavenging ability of over 73.4%, and the potential to close the wound bed within 24 h. This study highlights that these dressings are promising for managing chronic wounds by preventing infection and oxidative stress in a correct therapeutic sequence.

In hot arid and semi-arid regions, substantial irrigation water is lost through surface evaporation under intense solar irradiation and high temperatures, limiting freshwater sustainability and crop productivity. Superhydrophobic Sand (SHS) mulch, a plastic-free, bio-inspired technology, has been proposed as a dry diffusion barrier to suppress evaporative losses. Here, we combine controlled column experiments with heat and mass transfer modeling to quantify how SHS thickness and soil properties govern evaporation under fixed irradiation. Relative to unmulched controls, a 5 mm SHS layer reduced evaporative flux by 65$\%$ in fine sand and 63$\%$ in coarse sand, while a 10 mm layer reduced flux by 83$\%$ and 70$\%$, respectively. Notably, soil-type trends reversed after mulching: although unmulched fine sand exhibited 37.5$\%$ higher evaporation than coarse sand, application of a 10 mm SHS layer reduced fine-sand evaporation to 40$\%$ below that of coarse sand. To explain this counterintuitive behavior, we developed a coupled heat and vapor transport model incorporating soil thermophysical properties and diffusion through the porous mulch layer. The model accurately predicted steady-state temperature profiles and evaporation rates for both mulched and unmulched systems. Our results show that SHS mulch shifts evaporation from a surface-temperature-controlled regime to a diffusion-limited regime governed by mulch thickness and soil thermal conductivity. This mechanistic understanding clarifies the performance of SHS and supports its potential to enhance irrigation efficiency in arid agricultural and landscaping applications.

High frequency pedestrian motion forecasting when interacting with autonomous vehicles (AVs) can be enhanced through the use of behavioural frameworks, such as discrete choice models, that can explicitly account for correlation among similar movement alternatives. We formulate the pedestrian next step choice as a spatial discrete choice defined by a grid of speed adjustment and heading change. Using naturalistic pedestrian-AV encounters from nuScenes and Argoverse 2 (1 sec decision interval), we estimate a multinomial logit baseline and four spatial generalized extreme value (GEV) specifications (SCL, GSCL, SCNL, and GSCNL). We then compare them to a residual neural network logit (ResLogit) model that learns cross alternative effects while retaining an interpretable linear utility component. Across the evaluated data, spatial GEV structures yield only marginal improvements over multinomial logit, whereas ResLogit achieves a substantially better fit and produces behaviourally coherent errors concentrated among neighbouring grid cells. The results suggest that in dense, high frequency spatial choice sets, learning based residual corrections can capture proximity induced correlation more effectively than analyst specified GEV nesting structures, while maintaining interpretability.

Preparation of nonclassical light with special quantum properties is essential for quantum technologies. High-harmonic generation (HHG) is a process which not only enables the creation of attosecond pulses but also has the potential to generate light with intricate quantum properties. In a recent experiment [PRX Quantum 5. 040319], nonclassical inter-harmonic correlations have been measured from a HHG source between low-order harmonics. In this work, we theoretically investigate entanglement between different harmonics within an effective, phenomenological quantum optical model. This model implements a significant degree of simplification regarding the processes within the target material, treating the material through susceptibilities, as it is usual in quantum optics. Such an approach yields a general description of HHG in the few-harmonic generation regime, permitting the implications that can be derived within it to hold broadly within the domain of validity. We find that entanglement is produced as a result of the often neglected back-action. We can qualitatively reproduce experimentally measured nonclassicalities, which suggests that intermodal entanglement can, to an extent, be considered a universal phenomenon associated with HHG, rather than a result of using specific material targets.

Aims. We investigated plasma turbulence in the context of solar wind. We concentrated on properties of ideal second-order magneto-hydrodynamic (MHD) and Hall MHD invariants. Methods. We studied the results of a two-dimensional hybrid simulation of decaying plasma turbulence with an initial large cross helicity and a negligible magnetic helicity. We investigated the evolution of the combined energy and the cross, kinetic, mixed, and magnetic helicities. For the combined energy and the cross, kinetic, and mixed helicities, we analysed the corresponding Kármán-Howarth-Monin (KHM) equation in the hybrid (kinetic proton and fluid electron) approximation. Results. The KHM analysis shows that the combined energy decays at large scales. At intermediate scales, this energy cascades (from large to small scales) via the MHD non-linearity and this cascade partly continues via Hall coupling to sub-ion scales. The cascading combined energy is transferred (dissipated) to the internal energy at small scales via the resistive dissipation and the pressure-strain effect. The Hall term couples the cross helicity with the kinetic one, suggesting that the coupled invariant, referred to here as the mixed helicity, is a relevant turbulence quantity. However, when analysed using the KHM equations, the kinetic and mixed helicities exhibit very dissimilar behaviours to that of the combined energy. On the other hand, the cross helicity, in analogy to the energy, decays at large scales, cascades from large to small scales via the MHD+Hall non-linearity, and is dissipated at small scales via the resistive dissipation and the cross-helicity equivalent of the pressure-strain effect. In contrast to the combined energy, the Hall term is important for the cross helicity over a wide range of scales. The magnetic helicity is scantily generated through the resistive term and does not exhibit any cascade.

Temporal editing patterns on Wikipedia provide a computational lens on cultural dynamics across linguistic communities. We analyse a decade of editorial activity (2001–2010) in eleven Wikipedia language editions and apply hierarchical clustering to two representations of collective behaviour: a static one, based on the thematic composition of each edition, and a temporal one, based on its weekly activity cycle within each thematic category. For the temporal representation we compare two normalisations: one retains only the shape of the weekly cycle, and the other combines it with thematic composition. The linguistic communities exhibit distinct circadian and weekly editing rhythms. Some groupings are recovered by both representations and by both activity metrics (edits and editors), most notably Russian and Chinese, whereas others, such as German and French, emerge only from temporal activity rhythms. Temporal information thus reveals structure that thematic composition alone does not, which points to time as a relevant and often neglected dimension in cross-cultural analyses of digital trace data, and in studies that use online platforms as proxies for collective cultural behaviour.

A 17th-century oil painting by an unknown artist, once owned by the Tayler family and now in the collection of Trinity College, Cambridge, is currently identified as a portrait of a young Isaac Barrow. The sitter is shown pointing to a proposition in Barrow's 1655 edition of Euclid's Elements, but the portrait bears little resemblance to other depictions of Barrow. Moreover, Barrow is unlikely to have posed with that book, which appeared in print eight months after he had left England on a four-year tour of the Continent. Plausible alternatives are that the portrait is of Francis Willughby or Isaac Newton, both of whom resembled the man pictured and may be characterized as disciples of Barrow. If the Tayler were Newton's portrait, it could shed light on the patronage that allowed him to rise from undergraduate servant ("subsizar") to Lucasian Professor of Mathematics in only five and half years.

Turbulent convection is ubiquitous in fluid systems. In particular, multi-physical convection problems involve mass, heat, and particle transfer. When the particles are charged and driven by a high-voltage electric field, both buoyancy and electric forces contribute to driving and maintaining the convection. In this work, we perform numerical analysis using a high-fidelity Fourier-Chebyshev spectral solver. We further derive the dynamical systems governing the kinetic energy, the enstrophy, the potential energy, and the electric energy analytically. Using the simulated data, we apply a long short-term memory recurrent neural network to predict the chaotic time series of domain-average energy terms. Finally, we perform a data-driven modal decomposition to show the coherent structures that contain energy and enstrophy in 2D turbulent convection.

The heating of the solar wind is a key to understand its dynamics and acceleration process. The observed radial decrease of proton temperature in the solar wind is slow compared to the adiabatic prediction and it is thought to be caused by turbulent dissipation. To generate the observed 1/R decrease, the dissipation rate has to reach a specific level which varies in turn with temperature, wind speed, and heliocentric distance. We want to prove that MHD turbulent simulations can lead to the 1/R profile. We consider here the slow solar wind, characterized by a quasi-2D spectral anisotropy. We use the EBM (expanding box model) equations, which incorporate into 3D MHD equations the expansion due to the mean radial wind, allowing to follow the plasma evolution between 0.2 and 1 AU. We vary the initial parameters which are: Mach number, expansion parameter, plasma beta, and properties of the energy spectrum as the spectral range and slope. Assuming turbulence starts at 0.2 AU with a Mach number equal to unity, with a 3D spectrum mainly perpendicular to the mean field, we find radial temperature profiles close to 1/R in average. This is done at the price of limiting the initial spectral extent, corresponding to the small number of modes in the inertial range available, due to the modest Reynolds number reachable with high Mach numbers.