Seismic resolution enhancement remains a critical challenge in exploration geophysics, particularly when processing field data characterized by limited bandwidth, strong noise, and insufficient labeled training samples. Existing deep learning methods typically rely on supervised learning with synthetic training data, leading to distribution mismatch and poor generalization on real seismic acquisitions. To address these limitations, we develop a physics-guided self-supervised diffusion model (PG-SSDM) that learns directly from field observations without requiring paired high-resolution labels. The proposed framework combines three key innovations. First, a self-supervised training strategy constructs learning targets by progressively filtering the observed data itself, eliminating the need for high-resolution ground truth through iterative refinement across multiple stages. Second, seismic convolution model is embedded as a hard physical constraint in both the training loss function and the reverse sampling process, ensuring that generated high-resolution outputs respect fundamental seismic wave propagation physics. Third, the probabilistic nature of diffusion models enables uncertainty quantification, providing spatial confidence maps that identify regions where resolution enhancement may be less reliable. We validate PG-SSDM on synthetic data under various noise conditions and on a 3D post-stack field dataset. Experimental results demonstrate that the proposed method effectively recovers thin layers and subtle structures, suppresses noise, preserves structural continuity, thereby significantly improving the resolution and interpretability of seismic data.

The evolution of an axisymmetric vortex column in a weakly compressible supercritical fluid is analysed. A thermal layer is imposed to radially stratify the fluid and uncover effects of the large fluid property variations across the pseudo-boiling line. A multi-dimensional flow solver based on a low-Mach approximation is employed. Using supercritical carbon dioxide as the fluid, we examine axisymmetric configurations at low Reynolds number with the vortex core hotter or colder than the surrounding fluid and for different thermodynamic pressures close to the critical pressure. Vorticity evolution depends strongly on the core temperature and ambient pressure, differing substantially from the classical Oseen solution during the thermal mixing process under highly varying fluid properties. Viscous effects dominate the vorticity evolution. Beyond diffusion, three additional viscous mechanisms are identified, which become significant across the pseudo-boiling line: (1) a vorticity stretching term, (2) an alignment of vorticity and viscosity/density gradients, and (3) a vorticity source due to the interaction between the fluid swirl and the viscosity and density gradients. The first two mechanisms alter existing vorticity, while the latter injects new vorticity. In fact, the third mechanism can generate reverse vorticity, locally increasing circulation and substantially modifying the temporal evolution of the vortex.

The advent of novel free-electron laser sources enabling time-resolved x-ray photoelectron spectroscopy (tr-XPS) provides a unique opportunity to monitor local chemical environments in real time by measuring sub-eV shifts in core-electron binding energies. These shifts reflect the interplay between electronic excitation and nuclear motion, an interplay that remains largely unexplored. In our combined theoretical and experimental study of fluoropyridine (C$_5$H$_4$FN), we investigate this link by monitoring the evolving chemical environment at the N and F atomic sites as the photoexcited $S_1$ state relaxes to the ground state via a conical intersection. We find that the F site responds primarily to vibrational relaxation, showing minimal sensitivity to the electronic excited state. In contrast, excitation to $S_1$ induces a measurable energy shift at the N site and significantly enhances its sensitivity to local vibrations within the ring. This behavior arises from a photoinduced redistribution of charge, which also increases the Coulomb interaction between the 1s electron at the N atom and the atomic partial charge at an adjacent C atom. This insight opens new avenues for exploring ultrafast dynamics and conical intersection pathways in more complex systems, from photostable DNA bases to light-harvesting materials.

We present a modular constrained-orbital-optimization framework for quantum chemistry. The formulation separates the correlated electronic-structure solver from the orbital optimizer: the solver supplies one- and two-particle reduced density matrices, while the molecular orbitals are updated on the orthonormality-constrained Stiefel manifold with an implicit steepest-descent algorithm. Because the orbital optimizer only requires reduced density matrices, MP2, CASCI, and DMRG can be treated within the same interface. For CASCI solvers, the approach is closely related to optimal-orbital full configuration interaction and CASSCF\cite{helgaker_MulticonfigurationalSelfConsistentField_2000a}, but uses a solver-independent constrained-optimization update rather than CAS-specific orbital-rotation equations. When conventional CASSCF orbital-rotation iterations converge to higher-energy local solutions, CO-CAS can recover lower-energy stationary solutions. We also introduce a modified direct inversion in the iterative subspace procedure to accelerate macro-iteration convergence and a dynamical-weighting scheme to improve state-averaged excited-state calculations. Applications to LiF, H$_2$O, and pyrazine show that orbital optimization lowers energies relative to fixed-orbital MP2, CASCI, and DMRG references while improving convergence and potential-energy-curve smoothness.

Particle-in-cell (PIC) simulations are widely used for kinetic plasma modeling in energy applications, but their efficiency is often limited by repeated field solves on dense meshes. This work proposes a Latent Residual-Closure Fourier Neural Operator (LRC-FNO) for robust surrogate multi-field solving in PIC simulations. Rather than treating field prediction as a purely data-driven regression task, LRC-FNO formulates PIC field solving as a two-level residual-closure problem involving source compression and source-to-field operator mapping. An autoencoder extracts compact representations of particle-deposited source fields, while a Latent Closure Refiner recovers unresolved residual structures lost during compression. A Coarse-FNO Solver captures the dominant field response, and a Residual-Closure FNO restores full-resolution corrections. The method is tested on three benchmarks with increasing complexity: 1D linear Landau damping (LLD), 2D two-stream instability (TSI), and a 2D scrape-off layer (SOL) fusion plasma model. In LLD and TSI, LRC-FNO better preserves charge-to-potential mapping, potential-mode evolution, residual charge structures, and particle-field energy exchange during closed-loop PIC integration. In the SOL case, LRC-FNO achieves relative L2 errors of 0.0447 for the self-consistent potential and 0.0251 for the magnetic vector potential in single-step prediction. More importantly, when used as a neural initial guess with 20 iterative corrections, LRC-FNO maintains strong physical consistency in extrapolated closed-loop simulations, preserving charge and current density structures over a time range close to twice the training horizon. These results demonstrate that LRC-FNO can serve as both a fast surrogate field solver and a high-quality initialization strategy for iterative PIC field solvers.

Photonic reservoir computing offers a hardware-efficient route to processing temporal and sequential data, but delay-based implementations often rely heavily on temporal multiplexing, where long temporal masks are required to generate a sufficiently rich reservoir state. Here we show numerically that the spatial degrees of freedom of an active multicore fiber placed inside a delayed optical feedback loop can reduce this dependence on serial temporal encoding. The input signal is encoded by temporal and spatial masks, the pump distribution across the cores controls the reservoir operating point through the core-dependent effective gain and saturation energy, and the detected core intensities serve as readout features for a single trained linear layer. The system is modeled by linearly coupled nonlinear Schrödinger equations with saturable gain and solved using a split-step Fourier method. On the Mackey-Glass one-step-ahead prediction benchmark, a seven-core reservoir with equal temporal masks reduces the validation normalized root mean square error from 0.5956 for the single-core baseline to 0.0651 at a modulation rate of 40 GHz. At 1 GHz, spatial-only encoding reaches an error of 0.0323 using one temporal sample per symbol and no temporal mask. These results show that an active multicore fiber can provide both parallel readout channels and a tunable nonlinear transformation, offering a route to photonic reservoirs with reduced reliance on temporal multiplexing.

Kirigami, the art of cutting flat sheets into deployable 3D structures, has recently inspired a new class of parachutes which can deploy into a naturally stable inverted canopy. However, the dynamic mechanism, fluid forces, and geometrical parameters that grant this stability have not yet been clearly identified. In this paper, we use a novel Biot-Savart far-field boundary condition to perform prescribed acceleration and free-falling simulations in effectively infinite domains, tracking the descent of a parameterized kirigami parachute. The far-field velocity is reconstructed from the interior vorticity, resulting in less than 0.1% variation in the predicted dynamics as the domain size is doubled. We first show the linear forces drop 2-5 times as the parachute is deployed due to increased permeability, whereas the moments increase due the counterbalancing effect of the increased lever-arm. Next, we find that the kirigami parachute achieves stable flight for deployment heights as small as half its radius, quickly damping out applied perturbations. For smaller deployments, the parachute tumbles due to side-slip and rotational coupling, as in falling disks. These effectively unbounded simulations identify that deployments approximately equal to the radius offer high drag forces with strong dynamic stability, providing a simple design rule for deployable parachutes.

The fluctuation theorem and detailed fluctuation theorem are formulated for classical systems whose governing equations can be written in Schrödinger-type equations and which possess either a unitary or an antiunitary time-reversal operator. The initial state vector is treated as a random variable drawn from a time-reversal-symmetric probability distribution, and a stochastic relative entropy defined from its probability density is used to formulate these theorems. The framework is applied to two collisionless plasma systems: the linear Vlasov-Poisson and linear gyrokinetic systems. For the linear Vlasov-Poisson system, the governing equations are recast into Schrödinger form, and Hamiltonian eigenvectors corresponding to Case-Van Kampen modes are derived to construct explicit solutions. The stochastic relative entropy is interpreted as entropy generation associated with Landau damping, in which electric-field energy is transferred from the lowest Hermite state to higher-order Hermite states acting as thermal reservoirs. For a specific class of initial distributions, a new analytical expression for the probability density function of the stochastic relative entropy is derived and validated numerically. For the linear gyrokinetic system in a uniform magnetic field, the governing equations are likewise transformed into Schrödinger form, and the corresponding time-reversal operators are identified. The state-vector space is constructed as a tensor product of species, perpendicular-velocity, and parallel-velocity spaces. The resulting state vectors decompose into two orthogonal components: one coupled to electromagnetic fluctuations and the other corresponding to ballistic modes. These results establish a nonequilibrium statistical-mechanical framework for collisionless plasma dynamics and provide useful examples for future quantum-computing applications to plasma simulations.

A quantum-enhanced approach for fast temperature diagnostics in ultrashort laser surface processing is introduced. The goal is to overcome limitations of existing methods, such as plasma emission, emissivity changes during ablation, and the need for time-consuming pump-probe measurements. The proposed method exploits polarization anisotropy in entangled photon pairs. The idler photon interacts with the laser-affected material surface, while its entangled counterpart is detected in a remote optical arm. Temperature-dependent changes in the complex refractive index modify the reflectance of p- and s-polarizations on the idler path, altering the coincidence-resolved polarization statistics of the signal photons. Using a Qiskit-based model incorporating experimental pump-probe reflectometry data, remote reconstruction of rapid thermal dynamics during femtosecond laser ablation is demonstrated. Although based on simulation, the model employs literature data to represent realistic material behavior. Due to the discrete nature of single-photon events, classical sliding-window analysis suffers from shot noise and temporal inertia. To overcome this limitation, a multilayer perceptron (MLP) regression network is applied to extract implicit anisotropy information from the photon bitstream. Compared with the classical approach, the neural-network method improves reconstruction robustness, reduces temperature noise, enhances the signal-to-noise ratio (SNR), and enables nanosecond-scale tracking of thermal dynamics. The results indicate that entangled-photon polarization anisotropy combined with machine-learning analysis is a promising approach for remote, background-rejected, high-speed temperature diagnostics in laser-matter interaction studies.

This study investigates Thermal-Large Eddy Simulations (T-LES) of anisothermal and turbulent channel flows under physical conditions representative of solar receivers. Solving the low-Mach number Navier-Stokes equations, T-LES results are evaluated a posteriori against Direct Numerical Simulation (DNS) data. We assess 12 subgrid-scale models. All models are based on the Anisotropic Minimum Dissipation (AMD) model. After computing a global error rate to evaluate all models, we select four for a detailed analysis regarding the effects of mesh resolution, numerical schemes, and model formulations. Results demonstrate that a two-layered mixed model combining the AMD/AMD-scalar with the Gradient model yields the best agreement with DNS.

Magneto-active polymers (MAPs) are field-responsive soft composites whose mechanical behaviour can be actively modified by external magnetic fields. Their ability to exhibit field-induced stiffening, magnetostriction, anisotropy and rate-dependent response makes them attractive for sensors, actuators, adaptive structures, vibration-control devices and soft robotic systems. This article presents a structured survey of constitutive modelling approaches for MAPs at finite strains. The review traces the development from early semi-empirical descriptions to thermodynamically consistent nonlinear continuum theories, with particular attention to isotropic and anisotropic magnetoelastic constitutive models, invariant-based and spectral representations, variational and polyconvex frameworks, microstructurally motivated models, dispersed-chain descriptions and magneto-viscoelastic theories. The principal constitutive variables, energy functions, coupling mechanisms and physical assumptions underlying the main models are discussed. The survey shows that constitutive modelling of MAPs has developed into a broad family of nonlinear, anisotropic and dissipative frameworks capable of describing versatile behaviours observed experimentally. Important challenges remain in resolving thermo-magneto-mechanical coupling and microstructure-sensitive effects, identification of parameters, validation of models as well as robust and efficient implementation on computers.

Submesoscale fronts, occurring at intermediate scales between mesoscale eddies and boundary layer turbulence, play a crucial role in driving vertical transport from the ocean surface into the interior. Their dynamics involve complex interactions between submesoscale currents and turbulence. However, the mechanisms by which these multiscale processes combine to transport tracers such as pollutants or nutrients remain less well understood. This study uses large-eddy simulation to investigate passive tracer transport associated with submesoscale fronts. Intense turbulence develops during frontogenesis, leading to strong diapycnal tracer transport into the ocean interior. While part of this transport arises from the direct turbulent flux, represented by the covariance between turbulent velocity and tracer concentration fluctuations, a substantial portion is due to an advective diapycnal flux driven by the mean diapycnal velocity. The mean diapycnal velocity results from the evolving secondary circulation in the presence of turbulent density mixing. These findings reveal an underexplored diapycnal transport pathway in submesoscale frontal zones, with implications for improved representation of vertical exchange in ocean models.

This paper presents a non-dissipative, high-order, moving overset method for curvilinear grids to simulate unsteady compressible flows in complex geometries with moving components. Centered finite-difference schemes that are up to sixth-order accurate in the interior are used with a weak moving overset interface treatment. The novel aspects of the proposed approach compared to conventional overset methods are: (i) instead of overwriting all conservative or primitive variables at the interface (or fringe) points with the interpolated values, a characteristic decomposition is performed and only the incoming characteristic variables are imposed for inviscid flows, consistent with the hyperbolic character of the Euler equations; for viscous flows, the viscous fluxes are imposed in addition to the incoming characteristics variables, (ii) instead of using multiple layers of fringe points at the interface, the proposed approach ensures high-order accuracy and stability with a single layer, thus minimizing the parallel communication costs at each timestep, and (iii) the proposed approach ensures long time stability with non-dissipative schemes without introducing artificial dissipation explicitly (using numerical filters) or implicitly (using upwind schemes). The stability is demonstrated by an eigenvalue analysis of the time-dependent (semi-discrete) system matrix for moving grids, proving the eigenvalue spectra remains confined to the left half of the complex plane with grid motion. The proposed approach is validated over a range of canonical and practical unsteady flow problems involving moving grids: 1-D scalar advection, 2-D isentropic vortex convection, flow past rotating 2-D circular cylinder, pitching 2-D and 3-D airfoil/wing flow, and flow past 2-D and 3-D oscillating circular cylinder, demonstrating high-order accuracy and long time stability for inviscid/viscous flows.

Boron pebble aggregate was tested for the first time as a high-heat-flux granular plasma-facing material in a tokamak divertor. Exposures of up to $q_{\parallel} = \SI{80}{\MW\per\m\squared}$ incident heat flux were conducted in the DIII-D tokamak. Single protruding rods of pebble aggregate composed of sintered amorphous boron pebbles bound with carbon binder were mounted in the Divertor Material Evaluation System (DiMES) sample holders and exposed to L-mode lower single null (LSN) plasmas. Under these heat loads, significant boron dust emission from the boron spheres was observed, and this dust dominates the divertor boron ionization source. Only about half of the released boron was recovered locally as mm-sized particles; with the rest presumably lost mainly as dust into the plasma and vacuum chamber. Preliminary estimates suggest that the rate of surface recession of $\sim$1 cm/s in the pebble conglomerate within the plasma divertor is consistent with the recession rates observed in laser bench tests subjected to normal-incidence heat loads. Although core performance was not adversely affected by the high boron dust emission, future work will need to improve the boron pebble aggregate design to reduce boron dust emission at high heat loads.

Vortex stretching is a fundamental aspect of Navier-Stokes turbulence and is commonly understood in analogy to the stretching of infinitesimal material lines. However, the parallel alignment of material lines and vorticity cannot be maintained due to the role of viscosity in the directional realignment of vorticity. In Navier-Stokes turbulence, the result is relatively modest quantitative differences in the alignment and stretching rates of vorticity compared to material lines. In this study, the qualitative effect of viscous tilting of vorticity is demonstrated directly by surgically removing it from direct numerical simulations of isotropic turbulence. The result is a drastic change to the fundamental structure of the flow, including a substantial deviation from the -5/3 inertial range scaling of the energy spectrum brought about by the infiltration and prevalence of viscous effects beyond the smallest scales. These observations demonstrate that the viscous tilting of vorticity is an essential characteristic of fluid turbulence. By extension, the same may be said of the difference in orientation and stretching rates for vorticity and infinitesimal material lines.

Physics sets the information contained in the signals that cells sense. But cells are active, not passive, sensors. They shape and reshape both the environment and themselves. These active behaviors allow cells to amplify, redistribute, share, and prioritize sensory information, often surpassing or obviating passive physical limits. Here, we review recent results on active sensing. After describing classic and more recent limits to sensory precision, we focus on four ways that cells implement active sensing: coordinating with other cells, reshaping their environment, dynamically updating themselves, and discriminating signals. We conclude with potential future directions.

In the paraxial domain, a difference-frequency generation (DFG) process provides a versatile platform for exploring non-Hermitian dynamics in an all-classical interaction. This letter aims to show the existence of topologically non-trivial optical analogues of anti-Skyrmionic states in an 'anti' parity-time (anti-PT) symmetry-broken dynamical interaction in a DFG process. A practical DFG set-up, which is constituted by a periodically-poled lithium tantalate (PPLT) crystal and pumped by a visible (green) pump laser, is considered for ascertaining the non-Hermitian dynamics. In the anti-PT symmetry broken phase, a negatively phase-mismatched interaction gives rise to a topologically non-trivial pseudo-magnetization texture that could support anti-skyrmion-like quasi-particles in signal and idler modes. The analysis also reveals the existence of anti-meron like pseudo-magnetization texture for a phase-matched DFG.

Newly formed ions in $E \times B$ devices are rapidly accelerated by strong radial electric fields and execute large cycloidal orbits in the presence of an axial magnetic field. At locations where these orbits intersect, ions originating from different birth radii arrive with substantially different velocities, producing a non-Maxwellian velocity distribution with a large velocity variance. Through Coulomb collisions and collective interactions, this distribution relaxes toward a drifting Maxwellian in the rotating frame. Here, we present the first optical emission spectroscopy (OES) measurements of the line-of-sight-convolved ion-velocity distribution, from which an apparent ion temperature is determined, in Avalanche Energy's centrifugal mirror machine. High-resolution $H_α$ spectra obtained along five chordal lines-of-sight spanning the plasma radius are analyzed using two complementary models representing limiting cases of the ion dynamics: a collisionless cycloidal model based on the ion-velocity distribution arising from deterministic single-particle orbits, and a rotating Gaussian model based on collisions and collective processes that fully randomize the cycloidal motion into a drifting Maxwellian in the rotating frame. Combined, these approaches bracket the possible degree of velocity-space relaxation and provide a stringent test of the inferred ion energies. Both models reproduce the measured spectra relatively well and yield density-weighted apparent ion temperatures of $1.40\pm0.43$ keV for the rotating Gaussian model and $1.55\pm0.24$ keV for the cycloidal model. These results provide direct spectroscopic evidence that strong $E \times B$ rotation in a device only a few centimeters in size can generate ion populations with keV energy spreads.

Highly accurate stability boundary values for the ADER-DG method are obtained for arbitrary degrees $N$ of basis polynomials. In the linear case, stability is violated precisely when one of the matrix eigenvalues reaches $λ= -1$, regardless of the phase $θ$. A rigorous mathematical framework for the stability is developed. The stability condition is significantly simplified, reducing it to the problem of calculating the roots of polynomials in the Courant number $\mathrm{CFL}$. The maximum of the Courant numbers $\mathrm{CFL}_{\rm max}(N)$ are calculated. These results are new and very convenient for practical use. A comparison of the obtained results with existing results reveals differences that may be significant for the selection of calculation parameters, especially for high degrees $N$. It is shown that widely used existing estimates $\mathrm{CFL}_{\rm max}(N) \propto 1/(2N+1)$ are overestimated. An interesting qualitative asymptotic $\mathrm{CFL}_{\rm max}(N) \propto (N+1)^{2}$ is obtained. A rigorous direct proof of the approximation is presented. Approximation orders $p = N+1$ for arbitrary degrees $N$ are rigorously derived. A set of numerical experiments is carried out to apply the ADER-DG method to solving both a linear advection equation and an Euler system of equations. The results obtained in these calculations confirm the theoretical results well. In particular, an excess of the Courant number over the $\mathrm{CFL}_{\rm max}(N)$ by even 1% in the linear case immediately leads to significant instability of the numerical solution. The obtained estimates of the boundary Courant number in the nonlinear case are somewhat underestimated -- by no more than 5%, which is due to the diffusivity and stability of the approximate Riemann solver. Empirical convergence orders are obtained, which are in good agreement with the theoretical results.

Thin wall treatments modify high-speed boundary-layer instability through the pressure they admit or absorb at the wall. This paper develops a unified admittance formulation for such effects on trapped compressible Rayleigh modes. For a simple rigid-wall eigenpair, we prove the spectral sensitivity law \[ c(A)=c_0+KA+\mathcal O(|A|^2), \qquad δσ=α\Imag(KA)+\mathcal O(|A|^2), \] where \(A\) is the wall admittance and \(K\) is an explicit functional of the rigid-wall eigenfunction. The formula separates wall physics from outer-mode physics and yields a phase criterion for stabilisation. Matched asymptotics show that viscous and thermal wall layers, blind-pore coatings and shallow non-separating roughness all reduce to this same boundary condition, with additive leading admittances. Mach-4.5 computations validate the sensitivity coefficient and demonstrate porous damping, viscous-wall damping and sign-changing reactive roughness effects.

Simulating strongly correlated molecular systems on near-term quantum hardware remains challenging due to modern hardware's limited quantum volume and moderate-fidelity qubits. One potential way to circumvent this challenge is through bootstrap embedding (BE). Bootstrap embedding breaks molecules into smaller fragments that are then embedded into the "bath" of other fragments in an iterative way. Bootstrap embedding is appealing for quantum simulation because fragmenting the system reduces the qubit requirements for any given fragment. In this work, we develop a quantum bootstrap embedding (QBE) workflow that uses variational quantum eigensolver (VQE) fragment solvers and study the algorithmic choices that determine the overall VQE-QBE algorithm's success. To improve efficiency, we introduce FastAdaptVQE, a sparse matrix-accelerated form of the adaptive variational quantum eigensolver (ADAPT-VQE) that replaces symbolic commutator evaluation with direct statevector linear algebra, and MatrixFreeAdaptVQE, a matrix-free extension that removes the sparse-matrix memory bottleneck that appears when treating larger fragments. We also modify the ADAPT-VQE operator selection step by replacing the purely greedy choice with a look-ahead strategy. Benchmarks on $H_4$ and $F_2$ reach chemical accuracy, within 1 kcal/mol of bootstrap embedding results using a full configuration interaction (FCI) solver. These results show that combining QBE with VQE can accurately calculate energies of molecular systems. This research lays the foundation for extending energy calculations to larger molecular systems and quantum materials on near-term quantum hardware.

The Wigner representation for quantum mechanics of particles is generalized to Bose fields. The standard Hilbert space quantization becomes, via the Weyl transform, a quantization method that consists of adding a Gaussian zeropoint field distribution to the vacuum. I comment on the possible advantages of the method in order to study quantum fields in curved spaces. I study a unified formulation of non-relativistic quantum electrodynamics in the Weyl-Wigner formalism, in terms of (classical-like) c-numbers.

We investigate the effective four-dimensional cosmology induced by unimodular gravity in $D=4+d$ dimensions, where the internal extra-dimensional volume is encoded in a scalar degree of freedom. After dimensional reduction, we show that the resulting FLRW equations admit a natural autonomous formulation whose phase-space structure differs qualitatively from that of general relativity. In the vacuum sector, the reduced system exhibits a continuous family of finite equilibrium points, $λ=dH$, together with well-defined asymptotic Poincaré directions. In the matter sector, we focus on the five-dimensional case $d=1$ and use the reduced Bianchi relation as the consistency condition that links the ordinary matter component to the internal-volume degree of freedom. The system is then closed by adopting the minimal higher-dimensional conservation prescription, according to which matter is diluted by both the external volume and the internal-volume modulus. This leads to a reduced matter--geometry dynamics with isolated critical points and a globally organized compactified flow. Numerical examples illustrate how the internal-volume degree of freedom affects the background evolution and the global phase-space structure. The comparison with $Λ$CDM is used only as a benchmark, while a full observational analysis and more general matter--geometry exchange prescriptions are left for future work.

The temperature-redshift relation (cosmic thermometer) is rediscussed in the framework of gravitationally induced photon production in the expanding universe. It is found that the same extended temperature law can be derived based on three independent methods, namely: (i) through a simple heuristic approach, (ii) based on macroscopic irreversible thermodynamics with photon creation, and (iii) using a kinetic relativistic Boltzmann equation incorporating gravitational photon creation. All these methods yield the same answer. The modified kinetic treatment reveals not only that the Planckian blackbody spectrum endowed with the adiabatic creation is preserved in the course of the expansion but also that CMB anisotropies and distortions can be treated properly. The new cosmic thermometer suggests a crucial test to the standard cosmic concordance model in the thermal sector. It is also argued that the present results may open a new route for scrutinising the H0 Supernovae-CMB tension challenging the LambdaCDM model.

Einstein field equations allow cosmological dynamics to depart from the Friedmann-Lemaitre-Robertson-Walker (FLRW) idealisation in several physically different ways. Matter may become spatially inhomogeneous, the local expansion scalar may vary across a hypersurface, the expansion may acquire anisotropic components through shear, and the free gravitational field may be encoded in nonzero Weyl curvature. The key question is not only how far a model is from FLRW, but which geometric mechanism is responsible. A single departure from FLRW number cannot distinguish these mechanisms. This paper introduces a compact geometric diagnostic framework that keeps them separate while using standard quantities in general relativity. The framework is observer-explicit and domain-explicit, intended as a practical tool for comparing analytic and numerical solution families rather than as a new invariant classification of spacetime. Buchert's kinematical backreaction is retained as a derived explanatory quantity rather than a separate axis, since it is already fixed by the expansion-variance and shear contributions. A single curvature normalisation is used for all Weyl diagnostics. The method is tested on six benchmarks, namely FLRW, Bianchi-I, Kasner, Lemaitre-Tolman-Bondi dust, scalar-perturbed FLRW, and tensor-perturbed FLRW. These benchmarks occupy distinct regions of the diagnostic space, and the magnetic Weyl contribution appears only in the tensor case. The classification remains stable under changes of perturbation amplitude, spatial resolution, averaging domain, constraint reliability, and a leading-order observer tilt. The curvature expressions for the exact benchmarks are verified symbolically against metric-derived Weyl invariants, and the supporting computer code, numerical results, tables, and figures are publicly available.

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 ($μ$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 $μ$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 $μ$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 $ωτ$ rather than on $ω$ and $τ$ 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.

High performance computers produce extreme-scale data sets that require sampling or compression if they are to be used to their full potential. Existing data compression techniques typically exploit features such as sparsity in the data, homogeneity in the data, or {\it a priori} knowledge of what subsets of data are of most interest. Fluid dynamics data in general do not exhibit these features and so are attractive test beds for generic compression techniques that are objective, robust, and tuneable with respect to information lost due to compression. Presented here is a method based on tensor networks, specifically matrix product states or tensor trains, that meets these requirements. The method is demonstrated for compression in one-dimension and is extensible to higher dimensionality. Lossless compression is demonstrated for random Fourier series for sufficiently high bond dimension of the tensor network, with the memory required to store the tensor network scaling directly proportional to the bond dimension. The lossy compression exhibited at lower bond dimension can be well within the relative error of many fluid simulations. The compression algorithm is tested for the time evolution of Burger's equation with excellent results. We additionally demonstrate the capability to perform computations in the compressed form through a tensor network periodic convolution that can be orders of magnitude faster than using fast Fourier transforms and the convolution theorem. In addition to being an attractive method for working with data sets generated by existing computers, the tensor network methods utilised are directly translatable to the emerging paradigm of quantum computing.

While renewable energy sources are the fastest-growing electricity generation technology globally, their competitiveness is still the subject of controversy. This paper presents an electricity system model for investment and dispatch to determine the cost-optimal shares of renewable energy sources. We compute and analyse renewable generation shares in market equilibrium for Germany and Texas, using annual data for 2015 to 2024, and in five-year intervals for 2030 to 2050. Furthermore, we identify the key drivers of the renewable competitiveness and quantify their contribution through parameter variations. Our results show that renewable generation achieves considerable market shares even without subsidies. In Germany, the increase in renewable generation is primarily driven by CO2 pricing, complemented by declining investment costs for renewable technologies. In Texas, solar PV is part of the cost-optimal system, even in the absence of CO2 pricing and despite low natural gas prices.