Papers

We present a first-principles spectral mechanism for the emergence of nonextensive $q$-exponential dilution and power-law relaxation in non-ideal transport systems. By modeling an incompletely mixed reactor as a layered diffusion matrix with an absorbing boundary, we demonstrate that macroscopic power-law tails depend on the geometric interaction between the initial tracer placement and the domain's boundary configuration. For a one-dimensional system, an asymmetric, volumetrically distributed initial concentration profile projects onto the low-wavenumber eigenmodes, generating an emergent Gamma distribution of relaxation rates; at an infinitesimal boundary layer thickness ($\Delta z \to 0$), this profile yields the nonextensive $q$-exponential decay function exactly across the entire temporal domain with $q = 5/3$. Extended to $d$ dimensions under a highly localized, boundary-adjacent singular initial condition, the resulting scaling exponents and corresponding $q$ values depend explicitly on the spatial configuration of the absorbing boundaries. However, in the one-dimensional limit ($d=1$), these distinct initial states and boundary formulations intersect, rendering the $q=5/3$ exponent geometrically invariant. Our approach establishes a clear connection between linear diffusion transport and nonextensive statistical mechanics, showing how heavy-tailed transport can be derived from boundary geometry and spectral dimensionality.

Plasmas in space and astrophysical environments are frequently found in (quasi-)stationary states not in thermal equilibrium, indicated by velocity distribution functions (VDFs) featuring nonthermal characteristics, such as Lorentzian high-energy tails and kinetic (temperature and strahl/beam) anisotropies. For decades, in-situ observations of electrons and ions in the solar corona, solar wind or planetary magnetospheres have been modelled using so-called Kappa VDFs, whose standard form, however - depending on the considered VDF moment - imposes stringent constraints on the minimum value of the Kappa power-law parameter. Such limitations have posed difficulties on the derivation of a fully-consistent hydrodynamic formulation of suprathermal plasmas. To improve this situation, the recently proposed regularized Kappa VDF avoids strict constraints on the Kappa parameter by introducing an exponential, regularizing cut-off. In this work we assume that the plasma particles are described by regularized-Kappa VDFs that feature anisotropies not only in their thermal velocity spread parameters but also in their exponential cut-offs. The dielectric tensor components for waves propagating along a magnetic field are derived, in terms of new plasma dispersion functions. The resulting dispersion equations are solved with two complementary techniques, namely by using the analytical expressions derived here and by employing the ALPS code. This is done for physical parameters that give rise to electromagnetic and electrostatic instabilities. Both approaches show excellent agreement and the results demonstrate how an anisotropy in the regularizing cut-off of VDFs can serve, depending on an instability's nature, either as a stabilization factor or as an additional free energy source.

The recent development of small-scale quantum networks poses the question of which network-level mechanisms could enable quantum communication to operate at scale in a future Quantum Internet. The question can be answered within a well-established framework where a quantum network is mapped into a classical weighted graph, and communication reliability is assessed using methods from network theory. Previous work relied on this framework to determine sufficient conditions for viable network-wide communication only on special network topologies, such as regular lattices, and to devise communication protocols applicable only to small- and moderate-sized real networks. Here, we overcome both limitations by introducing a family of quantum communication protocols that scale to networks with hundreds of millions of nodes. We show that the competition between increasing communication distance and path redundancy determines their large-scale behavior, and that degree heterogeneity can provide sufficient redundancy to sustain communication in small-world networks. These conclusions are supported by a systematic analysis of both real network topologies and synthetic networks, and an analytical treatment in the limit of infinitely large random scale-free graphs.

Non-volatile phase-change material (PCM)-integrated metasurfaces offer a promising pathway toward next-generation solid-state reconfigurable free-space optics. However, their practical operation is currently bottlenecked by the highly non-uniform thermal profiles generated by the external heaters used to switch the PCM between its amorphous and crystalline states. The non-uniform heat profile in turn severely restricts the active switching area of the PCM integrated devices. In this work, we present an inverse-designed doped silicon resistive heater on a silicon-on-sapphire platform, featuring a spatially varying doping profile explicitly tailored to generate uniform heat. The new heater significantly improves spatial temperature uniformity, reducing the thermal gradient from 110 K in the case of typical conventional heater to merely 25 K in the case of the inverse-designed heater at a target temperature of ~1000 K. By meticulously optimizing the doping and annealing processes to suppress lateral dopant diffusion, we achieve a near-perfect spatial doping resolution capable of patterning two distinct doped Silicon filaments just 100 nm apart. We experimentally fabricate these devices and demonstrate their efficacy by successfully switching a large area (~18 x 14 micrometer square) of the wide-bandgap phase-change material (PCM) Sb2S3 using a compact heater geometry of size 26 x 26 micrometer square. We show that our inverse-designed heater achieves a 10-fold increase in active PCM switching area despite a reduction in the total heater footprint when compared to a conventional heater. Ultimately, this work provides a crucial steppingstone toward the development of non-volatile PCM-based reconfigurable free-space optics.

When we observe 3D objects, such as nanocrystals using a transmission electron microscope (TEM), the significant depth of the sample causes the contrast transfer function (CTF) to mix, resulting in complex and confused image. This paper predicts that a high-resolution projected image can be obtained by tilting the illumination and filtering it with a ring slit. This method produces images similar to those obtained using scanning transmission electron microscopy (STEM), which is not affected by the CTF problem. Potential applications are such as MOFs (metal-organic frameworks), perovskites and Solid-State Electrolyte (SSE) for Li-ion battery, where small ions or molecules play important role.

Physical human-robot interaction requires yielding transiently to contact yet recovering the commanded reference under sustained load. Finite-stiffness impedance control retains a static deflection there, while predictive alternatives typically optimize a nonlinear robot or impedance model online. Operational-space cancellation instead exposes a translational error double integrator with a fixed transition matrix and a configuration-scheduled input map, making interaction a predictive quantity rather than a property re-derived per configuration. We build on it a compact offset-free interaction-error MPC for torque-controlled manipulators: a force-domain random-walk state estimates persistent interaction and model error, and a 30-variable convex QP maps the correction through the current task inertia while constraining the applied joint torque. Conditional results establish impedance equivalence of the unconstrained passive feedback, offset-free regulation at feasible frozen configurations, and quadratic stabilizability of the scheduled backbone. In a 1kHz MuJoCo simulation of a 7-DOF Franka FR3, the estimator cuts steady-state error under a repeated 15N step from 2.77mm to 0.042mm when added to the otherwise identical 100Hz MPC. A stiffness-and-damping-calibrated impedance baseline attains 2.59mm but briefly saturates and needs 3.3x the peak positive joint power. Adding ideal measured-force cancellation to that baseline gives 1.39mm, so constant-load rejection is not unique to MPC; the sensorless controller still reaches 0.042mm in the moving task, a 65x reduction without force sensing and without the baseline's saturation or power cost. Demonstrated in simulation under a shared actuator budget, the contribution is an efficient operational-space realization complementing rather than replacing broader interaction-control architectures.

We investigate the mechanisms governing particle settling in turbidity currents using Eulerian$\unicode{x2013}$Lagrangian direct numerical simulations. The Eulerian carrier flow is driven either by solutal buoyancy or particle feedback, with the Lagrangian phase comprising passive tracers or inertial particles, respectively. The effective particle settling velocity is decomposed into a fluid velocity sampled at particle positions and a particle-fluid slip velocity. The Eulerian mean profiles of these velocities are obtained using a concentration-weighted average of the coarse-grained fields. The mean sampled fluid velocity is shown to be approximately equal to the ratio of the vertical turbulent flux of particles to their mean concentration and reflects biased sampling of upward turbulent fluctuations at particle positions, despite the zero Eulerian mean vertical fluid velocity. The passive-tracer cases show that the upward bias does not require particle inertia and arises from turbulent transport acting on concentration gradients, as it persists for inhomogeneous tracer seeding but disappears under uniform seeding. For the weakly inertial regime considered here, the upward bias dominates downward-directed biases associated with particle inertia. The mean slip velocity is well approximated by the terminal settling velocity predicted for a quiescent fluid. This is consistent with a leading-order balance between buoyancy and drag in the slope-normal direction. Modelling the sampled fluid velocity from the turbulent flux and using the slip-velocity approximation yield an Eulerian prediction for the settling velocity, in good agreement with the simulation data for the dilute, weakly inertial particles considered.

This work presents a semi-analytical framework for analyzing spatially discrete traveling-wave modulated (SDTWM) loop networks, which exhibit cavity-like behavior and support discrete spatiotemporal modes. We introduce a computationally efficient method, based on the Interpath Relation, to analyze periodic networks using a single unit cell. This allows characterization of driven systems with single-tone, multi-tone, and multi-conductor loop configurations. The framework captures both multi-modal and multi-frequency harmonic interactions, and is extended to compute the spatial Green's functions of such loop networks using analytic array scanning. The analysis of example designs, such as an electrically small antenna and a non-magnetic circulator, is presented. These examples confirm that the proposed approach is computationally efficient and offers physical insight, making it well-suited for the optimization of multifunctional and nonreciprocal SDTWM electromagnetic systems.

Most statistical and machine learning methods for directed interactions focus on pairwise effects among variables. Even existing cyclic models represent feedback primarily through node-level dependencies, making large-scale recurrent organization difficult to estimate and compare. This limitation is particularly acute in biological and neural systems, where interactions are highly recurrent and involve many overlapping cycles. We introduce a variational framework for statistical inference on cyclic interactions. Directed interactions are represented as edge flows on a simplicial complex and evolved under an energy-minimizing dynamical system. The resulting dynamics separate transient interaction components from persistent harmonic flows, yielding a low-dimensional cycle space that captures stable recurrent organization. Rather than enumerating individual cycles, the proposed framework represents cyclic interactions as elements of a Hilbert space, enabling projection, averaging, comparison, and population-level statistical inference. We establish theoretical properties of the harmonic projection, including characterization of the cycle space, variance reduction, and population inference. Simulations demonstrate substantially improved recovery of cyclic structure in dense recurrent systems compared with existing directed-interaction methods. Applied to resting-state fMRI from 400 human subjects, the framework reveals reproducible large-scale cyclic organization that is not detectable through edgewise averaging. These results provide a scalable statistical framework for studying recurrent interactions in high-dimensional dynamical systems.

The interaction between turbulent flows and bubbles is a complex phenomenon ubiquitous in natural and industrial settings. In this work, we experimentally investigate, from a fundamental perspective, the interaction between a rising bubble and a vortex ring in counterflow. Using time-resolved three-dimensional Lagrangian Particle Tracking (4D-LPT) coupled with shadowgraphy, we obtain simultaneous measurements of the bubble motion and the surrounding liquid flow. This approach enables detailed observation of bubble dynamics, deformation, and eventual breakup, as well as the fluid motion. We examine several flow configurations by varying the vortex circulation and the Weber number while maintaining a comparable vortex-to-bubble size ratio. Based on these measurements, we classify the interaction events into three categories according to their impact on bubble dynamics and vortex stability over time. Through experiments, we address for the first time the three-dimensional effects of these interactions, which had not been considered in previous studies. The analysed experiments comprise: Case I, corresponding to a weak interaction in which neither the bubble nor the vortex is significantly affected; Case II, where the bubble is captured and advected by the vortex, leading to a strong distortion of the vortex due to the presence of the bubble within its core; and Case III, involving a stronger vortex capable of capturing the bubble and breaking it into two fragments without a severe loss of energy in the vortex core. The analysis of these results provides insight into the bubble breakup process and the mechanisms responsible for the destabilisation of the vortex ring.

We study charge transport in a magnetized relativistic plasma using kinetic theory within the relaxation-time approximation. By exactly solving the linearized Boltzmann equation in a uniform magnetic field, we obtain an analytic solution for the distribution function in terms of Bessel functions. Using this solution, we compute the full set of retarded current-current correlators and verify the Ward identities. In the hydrodynamic limit, we extract the charge diffusion modes, demonstrating that the transverse diffusion coefficient is strongly suppressed by the magnetic field, scaling as $1/B_0^2$ in the strong-field regime, while the longitudinal diffusion remains unaffected. Furthermore, we analyze the non-hydrodynamic branch cuts in the complex frequency plane, determining their kinematic thresholds and identifying the underlying wave-particle interactions as longitudinal Landau damping and transverse cyclotron damping.

Eukaryotic DNA replication must remain robust under thermal, chemical, and genotoxic stress despite large fluctuations in replication dynamics. Here, we develop a lattice-based stochastic Monte Carlo framework for whole-genome replication in Saccharomyces cerevisiae at single base-pair resolution, incorporating probabilistic origin firing, replication fork-speed distributions, and a time-dependent limiting factor that governs the availability of cellular replication resources. The model is benchmarked quantitatively against experimental replication profiles before being applied to stress conditions, and reproduces diverse replication stress responses using only two effective parameters. Importantly, the analysis reveals that replication fork-speed heterogeneity underlies the emergence of Erlang-distributed S-phase durations and rare, anomalously prolonged replication events observed experimentally in Escherichia coli and human cell lines, while predicting similar behavior in S. cerevisiae. The framework further predicts non-monotonic thermal behavior, power-law scaling under hydroxyurea stress, and total replication-time dynamics under diverse genotoxic conditions.

Conjugated carbon rings are fundamental skeletons of organic functional materials, and their selective formation is of paramount importance in molecular materials engineering. However, steering the formation and 2D crystallization of conjugated carbon rings on the surface with high chemo- and stereoselectivities remains a great challenge. Here, we report a highly chemoselective [1+1+1+1] cycloaddition of isocyanides on the Au(111) surface, which affords the stereospecific tetraaza[4]radialene products and further enables their long-range-ordered 2D crystallization via enantioselective molecular recognition. Using the progressive annealing method, we found that at room temperature, isocyanides undergo a coordination reaction with Au adatoms to form two-fold symmetric isocyanide-Au-isocyanide complexes. In contrast, gradually increasing the annealing temperature induces the transformation of these complexes and subsequent covalent polymerization, leading to the selective generation of tetraaza[4]radialenes with homotactic configurations. The tetraaza[4]radialenes further assemble into 2D homochiral molecular crystals through enantioselective molecular recognition driven by multiple C-H – Cl hydrogen-bonding interactions. By combining scanning tunneling microscopy/spectroscopy and non-contact atomic force microscopy, we determined the atomic structure and molecular orbitals of tetraaza[4]radialene, confirming that its four-membered ring adopts a planar geometry with a localized lowest unoccupied molecular orbital. Density functional theory calculations suggest that the [1+1+1+1] cycloaddition process involves stepwise formation of C-C bonds and its high selectivity arises from the spatial steric hindrance. Our findings provide new insights into the selective formation of conjugated rings on surfaces and have implications for engineering 2D homochiral molecular crystallization.

We present a Gaussian-process (GP) surrogate model for the normal-incidence reflectance spectrum of one-dimensional GaAs/Al$_{0.3}$Ga$_{0.7}$ distributed Bragg reflectors (DBRs). A Latin-hypercube dataset of 1500 transfer-matrix-method (TMM) simulations is used to train and evaluate the model. Principal component analysis reduces the spectral output to 26 components; one GP is fitted per component. On a held-out test set the GP achieves RMSE=0.085 and $R^2=0.276$, while a Random Forest baseline reaches RMSE=0.065 and $R^2=0.572$. GP inference is 4.4 ms per spectrum compared with ~308 ms for TMM, yielding a ~70x speedup. Uncertainty calibration shows that the GP 95% prediction band covers 98.9% of test residuals. These results establish a rapid surrogate for DBR design-space exploration.

The universal solution of the Korteweg-de Vries equation (KdV) introduced by Gurevich and Pitaevskii in order to describe the onset of dispersive shock waves is known to also obey the self-similar reduction of the next member in the KdV hierarchy. We show that, if this common solution obeys some lower order partial differential equation, its differential order must be one, and we provide its local representation as a converging Laurent series depending on both space and time.

Objective: To determine whether free-breathing golden-angle radial sparse parallel (GRASP) magnetic resonance imaging (MRI) can represent respiratory-induced organ motion in patients with liver malignancies undergoing stereotactic body radiation therapy (SBRT). Methods: A retrospective analysis of 54 patients undergoing liver SBRT was conducted. Four-dimensional computed tomography (4D-CT), the gold standard for motion assessment, was used to characterize liver tumor motion. Image fusion was performed between free-breathing GRASP MRI and each respiratory phase of 4D-CT using an in-house registration program, with fusion quality quantified by maximum cross-correlation coefficient (MCC). Validation involved two blinded radiation oncologists: one repeated image fusion using the Eclipse-built-in module, while the other evaluated clinical relevance on a five-point scale. Results: The 50% respiratory phase of 4D-CT achieved the highest fusion quality with GRASP MRI, showing no significant differences compared to the 30% (P = 0.106), 40% (P = 0.632), and 60% (P = 0.792) phases. In contrast, fusion quality declined significantly beyond the mid-respiratory window (30%-60%), with poor fusion at the 0%, 10%, 20%, 80%, and 90% phases (P < 0.001). Validation by radiation oncologists corroborated these findings, with the 50% phase achieving the highest score. Subjective scores remained above 4 for phases 30%-70%, while scores for the remaining phases fell below 4. Conclusion: Free-breathing GRASP MRI cannot independently represent organ motion across all respiratory phases; it accurately characterizes motion only within the mid-respiratory phases (30%-60%), with optimal performance at the 50% phase. When used as a delineation standard in liver SBRT, GRASP MRI should be combined with 4D-CT or dynamic imaging modalities to ensure comprehensive motion assessment and accurate target volume definition.

We investigate the spectral energy dynamics of turbulent flows with variable viscosity using direct numerical simulation of homogeneous isotropic turbulence of generalised Newtonian fluids described by the Carreau constitutive model, covering both shear-thinning and shear-thickening regimes. The spectral evolution equations for the variable viscosity Navier-Stokes system show that the viscous term becomes nonlinear and gives rise to a convolution product in spectral space, formally analogous to that of the convective term. Unlike the constant viscosity case, where it acts as a purely local dissipation mechanism, the variable viscosity term carries both conservative (transfer) and non-conservative (dissipation) contributions entangled in the convolution product. We present novel computations of the viscous \mtm coupling $\hat{V}(\k, \kP)$, which does not satisfy a detailed conservation property analogous to that of the convective term. The viscous coupling maps reveal two distinct spectral regions: a sign-definite non-conservative region near $\kP \approx \bm{0}$, and a transfer-like dipole near $\kP \approx \k$ in shear-thickening fluids. The dipole satisfies the approximate antisymmetry $\hat{V}(\k, \kP) \approx -\hat{V}(\kP, \k)$, which is the defining signature of a conservative energy transfer. This demonstrates that energy transfer across scales, a role traditionally attributed exclusively to the convective nonlinearity, can arise from any nonlinear term in the momentum equation. The viscous energy transfer participates in the forward cascade alongside the convective transfer, eventually taking over the latter in the dissipation range. Its presence is connected to the emergence of power-law spectral decay replacing the classical exponential cutoff in shear-thickening fluids.

Scaling extreme ultraviolet (EUV) source power for next-generation lithography demands higher conversion efficiency (CE) at reduced per-pulse energies. We demonstrated a 40% CE enhancement by simultaneous dual-beam irradiation of a planar Sn target with a 2090-nm, 20-ns Ho:YAG laser. Single-beam irradiation at 40 mJ yielded an EUV CE of 2.6%; splitting the same total energy equally into two beams of 20 mJ each - at identical peak intensity - raised the EUV CE to 3.6%, which was the highest reported for 2-{\mu}m-driven laser-produced plasma sources. The EUV source size (60-70 {\mu}m) and energetic-ion spectra were nearly identical across both configurations, confirming comparable plasma conditions. Because the scheme requires only passive beam splitting and scales readily to three or more beams, it offers a practical route toward multi-kW-class, energy-efficient EUV sources for high-NA and hyper-NA lithography.

The ALICE experiment will replace its three innermost tracking layers with the Inner Tracking System 3 (ITS3) during LHC Long Shutdown 3. This upgrade introduces the first fully cylindrical, wafer-scale silicon vertex detector, utilising Monolithic Active Pixel Sensors (MAPS) fabricated in a 65nm CMOS process. By thinning sensors to 50$\mu$m and bending them to radii as small as 19mm, the design achieves a self-supporting structure that eliminates traditional support material. Wafer-scale stitching enables 27cm-long seamless sensors with integrated power and signal distribution, removing the need for flexible printed circuits within the active volume. These innovations, combined with a move from water to air cooling, reduce the material budget to less than 0.09%X$_0$ per layer. The R&D program has been validated through full-scale prototypes (MOSS, MOST), which demonstrated stitching feasibility, high yield, and radiation hardness. Engineering models confirmed the feasibility of air-convection cooling, indicating effective thermal management and structural stability. This contribution summarises the key advances in stitched sensor development, mechanical integration, and the path toward the final qualification model.

Topology provides a powerful language for describing global invariants in physical systems, yet optical topology has been explored predominantly with fully coherent light. Recent studies have shown that incoherent light can host topological structures mediated by coherence singularities; however, a general framework for their construction and control has been lacking. Here, we introduce an incoherent Milnor polynomial, which establishes a theoretical framework for real-space incoherent topological structured light, in which topology and statistical coherence emerge as independent and jointly addressable degrees of freedom. This framework overcomes a fundamental limitation of coherent topological structured light, enabling arbitrary intensity engineering without altering the underlying topological configuration. Experimentally, we realize incoherent Hopf-linked and trefoil-knotted coherence singularities with programmable statistical coherence. We further demonstrate a robust optical information-encoding scheme inspired by Rubik's-cube-like rotations, where statistical coherence determines far-field intensity patterns associated with the cube's initial states, and topological structures govern controlled rotations acting as encryption keys. Our results advance incoherent topological structured light from a physical curiosity to a programmable photonic platform, opening new avenues for optical information encoding, statistical photonics, and coherence-engineered functionalities beyond coherent optical topology.

This paper presents a novel frequency-robust perfectly matched layer (PML) boundary integral equation (BIE) method for solving two-dimensional electromagnetic scattering problems involving periodic arrays of obstacles. In periodic scattering problems, standard BIE formulations based on the quasi-periodic Green's function require the evaluation of lattice sums or challenging Sommerfeld-type integrals, which diverge at Rayleigh–Wood (RW) anomalies. An alternative is to use BIE formulations based on the Helmholtz free-space Green's function, but these are defined on unbounded unit-cell boundaries and therefore require suitable truncation strategies, such as the Windowed Green Function (WGF) method. Although such approaches avoid the use of expensive quasi-periodic Green's functions, they also suffer from breakdowns at RW anomalies unless an appropriate mode correction is incorporated. Similarly, the direct application of PML-BIE techniques to periodic structures experiences comparable difficulties near RW anomalies due to the destruction of exponential convergence near RW anomalies for fixed PML parameters. To overcome this challenge, we propose a modified PML-BIE method that combines the PML technique with a finite-mode correction, ensuring both high accuracy and robustness at and around RW-anomalies. Convergence of the PML-truncated boundary integral operators is proved and several numerical examples are presented to validate the efficiency and performance of the proposed method.

While previous versions of the Ai2 Climate Emulator (ACE) have been trained with CO$_2$ as a forcing, they are only accurate within a narrow range of scenarios, for example climate over the last 80 years forced by observed sea surface temperature (SST), sea ice, and CO$_2$ (AMIP), or equilibrium or near-equilibrium climates with CO$_2$ concentrations ranging from 1x to 4x that of the present day. Attempting to simulate climate forced by AMIP SST perturbed by +4 K or the response to an abrupt quadrupling of CO$_2$, results in unphysical behavior. We attribute this to these models being trained on datasets where the SST and CO$_2$ are correlated, limiting their ability to accurately learn their separate effects. In this study we introduce a new class of "random-CO$_2$" reference simulations where the SST and CO$_2$ are prescribed to vary independently. Trained on a balance of AMIP, equilibrium-climate, and random-CO$_2$ data, and including a total energy conservation constraint for improved interpretability, we present a more data-efficient model that not only accurately emulates its reference model in scenarios in which previous models excelled, but also scenarios like AMIP +4 K and slab-ocean-coupled abrupt 4xCO$_2$ where they did not. Limitations are that it has simplified or prescribed representations of other Earth system components like the ocean, land, and sea ice; does not expose other known climate drivers as forcings; and relies solely on physics-based model output for training data, inheriting the biases relative to observations thereof. Each of these represent opportunities for future work.

Weyl fermions in inversion-symmetric Weyl semimetals occur in pairs of opposite chirality, leading to symmetric optical responses under circularly-polarised light and a vanishing net photocurrent. Here, we show that tailored two-colour light fields break this symmetry and enable selective excitation of individual Weyl nodes. The interference between a circularly-polarised $\omega$ field and a phase-locked linearly-polarised $2\omega$ field generates a chirality-dependent redistribution of carriers in momentum space, resulting in a nonzero controllable photocurrent. We demonstrate that both the magnitude and sign of the photocurrent can be tuned via the relative phase and field strength of the two colours, and identify an optimal regime in which chiral selectivity is maximised. Our results establish a general route to optically-controlled chiral charge dynamics in Weyl semimetals using polarisation-structured light.

Understanding how swine farms are interconnected, directly and indirectly, is essential to characterizing infectious disease transmission. This study aimed to describe the connectivity of swine farms across 11 network types, including vehicle movements (i.e., trucks and trailers), animal movements, and distance-based farm-to-farm contacts, to identify links among production types and farms likely to be consistently characterized as super-spreaders. Truck and trailer movement networks were the most densely connected, particularly for feed transport, showing connectivity levels between 98.7% and 99.7% higher than those of pig movement and distance-based networks. These networks also exhibited the highest degree and frequency of connections between farms, while the aggregated truck network, which included all truck types, showed the greatest potential to act as a bridge connecting farms. Finisher farms were highly interconnected with other farm types across all networks. Sow farms were frequently reached by other farm types, especially through feed truck movements, representing up to 8.7% of these links. We demonstrated that in vehicle movements and proximity networks, finisher farms played a major role as super-spreaders. When comparing the top 50 farms ranked by super-spreader score in each network, vehicle-based networks showed the highest similarity, with up to 89% of top-ranked farms shared between vehicle networks. In contrast, pig movement and distance-based networks identified largely distinct sets of top-ranked farms, sharing at most 4% and 8%, respectively, with other contact networks. Overall, each network exhibited a distinct connectivity structure, resulting in different sets of high-risk farms, particularly regarding potential transmission to breeding farms. These findings support the integration of multiple transmission pathways into disease surveillance.

Large language models (LLMs) increasingly interact with one another in multi-agent systems, from simulations of human discourse to influence operations and fully LLM-driven social platforms. These interactions give rise to new regimes of opinion propagation that are not yet well understood. We investigate whether classical opinion dynamics models, which have long been used to explain how interactions shape collective beliefs in human societies, can capture the behavior of LLM networks. Specifically, we quantify the relative contributions of classical opinion-dynamics mechanisms, including neighbor influence, stubbornness, homophily, and bias, to the evolution of LLM opinions. Among the mechanisms considered, a bias term capturing regression toward a model- and topic-dependent opinion emerges as a significant explanatory component, with its inclusion reducing cumulative estimated mean opinion error by up to $88\%$. We additionally observe similar trends across the evaluated model families, discussion topics, and network structures.

This paper introduces a comprehensive two-dimensional analytical model of a toroidal magnetic ring with circular cross-section under sinusoidal excitation. Applying Maxwell's equations in local polar coordinates within a complex permeability, the model derives analytical expressions for the internal magnetic field, magnetic flux, complex impedance, and total losses. It rigorously separates the contributions of eddy current losses, hysteresis losses, and winding losses, while explicitly incorporating the skin effect in the conductive core via Bessel functions. An expression for the apparent permeability is also provided, enabling the nonlinear core behavior to be mapped onto simplified linear material models. The resulting analytical model offers a computationally efficient and accurate foundation for standardized magnetic material characterization, such as Brockhaus and Iwatsu ring measurements, as a powerful alternative to 2D and 3D finite element analysis.

The IIT Madras nano-satellite aims to investigate the science of energetic particle precipitation from the inner Van Allen radiation belt into the upper ionosphere as a potential precursor to earthquakes. Precursors in the form of low frequency electromagnetic waves can appear several hours before an earthquake. These waves, captured near the ionosphere magnetosphere transition region, propagate along geomagnetic field lines as Alfven waves and interact resonantly with trapped energetic particles in the radiation belt, causing their precipitation. Such precipitation can be observed by satellites as energetic particle bursts occurring a few hours prior to the earthquake. A numerical study of Alfven wave energetic proton interactions in the inner Van Allen belt is presented here to investigate the energetic proton precipitation and make predictions to support the scientific objective of the IITM satellite mission. A kinetic model of the energetic trapped proton population in the inner belt is developed, yielding a steady-state distribution that reproduces the observed density profile. The Finite Difference Time Domain method is employed to simulate both narrowband seismic event specific emissions and broadband background noise representing magnetohydrodynamic Alfven wave activity in the inner radiation belt. The studies of interactions of narrow-band Alfven wave packets with the energetic protons in the belt reveals that a sharp cyclotron resonance condition arises at a low Alfven frequency 10 Hz, causing substantial precipitation of high energy protons 125 MeV from their stable mirror orbits. This precipitation can be clearly distinguished from background noisy interactions. Based on these results, we predict the optimal satellite orbital altitude for detecting such energetic proton bursts.

Magnetotelluric (MT) inversion is a very useful technique to image the subsurface electrical resistivity structures. It is used for mineral exploration, geothermal studies, groundwater assessment, and lithospheric investigations. In this work, we proposed a physics-informed machine learning framework for 1D MT inversion based on implicit neural representations (INR). Our approach models the subsurface resistivity as a continuous function of depth using a coordinate-based neural network. This method does not require fixed discretization or layered models. The neural network is trained directly on a differentiable MT forward-model loss based on Wait's recursive impedance formulation. This setup allows inversion to occur in a physics-consistent optimization framework. The implicit regularization avoids the need for manual tuning of external regularization. We have tested this method on synthetic conductor models and real MT data. The results showed its ability to recover geologically relevant resistivity structures over various depths and thicknesses. Through different initializations, we can compute an ensemble of plausible models to estimate model uncertainty. These results suggest that implicit neural representations provide a flexible framework for geophysical inversion, with even greater potential in higher-dimensional MT problems and joint inversion applications.

This study focuses on the complex coupling between discharge and flow properties in a ns-pulsed He micrometer scale atmospheric pressure plasma jet ($\mu$APPJ). This is investigated by integrating electrical measurements, schlieren photography, ICCD imaging, and space-resolved Optical Emission Spectroscopy (OES) with Computational Fluid Dynamics (CFD) simulations. In the flow rate range QV=0.1-1 slm, a critical threshold emerges at 0.3 slm, where the discharge consumes the highest energy overall, achieving maximum propagation length and remarkable collimation. Below 0.3 slm, insufficient momentum renders the jet susceptible to buoyancy and air entrainment, leading to shorter effluents, while higher flow rates enhance shear layer instabilities. CFD simulations reproduce the schlieren flow profiles to quantify the axial helium mass fraction (YHe) confirming a stable helium-rich core at 0.3 slm (YHe=90%), not seen in other flow rates. Furthermore, lower and higher flow rates promote stochastic branching which is more pronounced at QV>0.3 slm. Numerous lateral branches are clearly distinguished and quantified via single-shot ICCD imaging for the first time in a He $\mu$APPJ. The increase of voltage amplitude (VP) in the range 4-9.5 kV, amplifies their activity in the effluent tip at 0.3 slm. At VP=9.5 kV, their number increases for QV<0.3 slm compared to QV=0.3 slm, while for QV>0.3 slm they occur much closer to the nozzle exit and intensify farther downstream. Timeresolved imaging reveals a distinct peak in ionization wave velocities (up to $\approx$600 km/s at 0.3 slm/9.5 kV) just after the nozzle exit, followed by a progressive decay which becomes more abrupt at the higher flow rates. This correlates spatially with a surge and subsequent axial drop in N2 + (FNS) emission intensity, indicating Penning ionization as a key mechanism behind this acceleration. Average gas temperature estimations (TGas$\approx$350 K) suggest that localised thermal expansion could contribute to the instabilities observed, possibly combined with a sudden rise in the average electrohydrodynamic force at the nozzle exit for QV$\ne$0.3 slm. Finally, the device geometry also plays a decisive role in internal vortex formation, especially at higher flow rates, affecting effluent stability. These results provide a unique framework for optimizing $\mu$APPJs for high-precision applications such as in analytical chemistry and surface processing.

In this work, we show that the Fredholm integral equations underlying the distribution of relaxation times (DRT), the distribution of capacitive times (DCT), and related frameworks share a common mathematical structure, namely that of a Mellin convolution. This comes from the fact that all standard immittance (impedance or admittance) kernels depend on the product $\omega\tau$ rather than on $\omega$ and $\tau$ independently. Exploiting this structure, we derive an exact algebraic inversion formula in Mellin space that converts the deconvolution problem into a closed-form relation between the Mellin transform of the measured immittance and that of the unknown distribution function. The framework is validated analytically on a set of examples including the constant phase element (CPE), the Davidson-Cole (DC) model, and the finite-length Warburg model with blocking boundary conditions. It is also validated numerically using the fast Mellin transform via the fast Fourier transform algorithm for both the CPE and the DC model, including their DRT and DCT recovery under clean and noisy conditions. The approach unifies the impedance- and admittance-based inversions under a single spectral framework, and provides a new approach for the characterization of electrochemical systems from immittance data.