Persistent systematic errors in Earth system models (ESMs) arise from difficulties in representing the full diversity of subgrid, multiscale atmospheric convection and turbulence. Machine learning (ML) parameterizations trained on short high-resolution simulations show strong potential to reduce these errors. However, stable long-term atmospheric simulations with hybrid (physics + ML) ESMs remain difficult, as neural networks (NNs) trained offline often destabilize online runs. Training convection parameterizations directly on coarse-grained data is challenging, notably because scales cannot be cleanly separated. This issue is mitigated using data from superparameterized simulations, which provide clearer scale separation. Yet, transferring a parameterization from one ESM to another remains difficult due to distribution shifts that induce large inference errors. Here, we present a proof-of-concept where a ClimSim-trained, physics-informed NN convection parameterization is successfully transferred to ICON-A. The scheme is (a) trained on adjusted ClimSim data with subtracted radiative tendencies, and (b) integrated into ICON-A. The NN parameterization predicts its own error, enabling mixing with a conventional convection scheme when confidence is low, thus making the hybrid AI-physics model tunable with respect to observations and reanalysis through mixing parameters. This improves process understanding by constraining convective tendencies across column water vapor, lower-tropospheric stability, and geographical conditions, yielding interpretable regime behavior. In AMIP-style setups, several hybrid configurations outperform the default convection scheme (e.g., improved precipitation statistics). With additive input noise during training, both hybrid and pure-ML schemes lead to stable simulations and remain physically consistent for at least 20 years.

Subwavelength arrays of plasmonic nanoparticles allow us to control the behaviour of light at the nanoscale. Here, we develop an eigenmode analysis, employing a coupled electromagnetic dipole formalism, which permits us to isolate the contribution to the far-field radiation of each array mode. Specifically, we calculate the far-field radiation patterns by bulk, edge and corner out-of-plane eigenmodes in a finite 2D Su-Schrieffer-Heeger (SSH) array of plasmonic nanoparticles with out-of-plane dipolar resonances. The breaking of symmetries in multipartite unit cells is exploited to tailor the optical properties and far-field radiation of the resonant modes. We prove that the antisymmetric modes are darker and have higher Q-factors than their symmetric counterparts. Also, the out-of-plane nature of the dipolar resonances imposes that all bulk $Γ$-modes are dark, while corner and edge states need extra in-plane symmetries to cancel the far-field radiation; radiation patterns in turn become more complex and concentrated along the array plane with increasing array size.

On the basis of the extended classical elasticity theory, we propose universal semi-empirical analytical expressions for the energy and the equation of state for poly-crystalline solids. The validation of the relations has been made by means of first principle density functional theory simulations with the use of pseudo-potential approach and generalized gradient approximation for the exchange-correlation energy. The calculations performed for a large number of inorganic crystalline compounds with metal, covalent and ionic bonding (including diamond, Mg, sphalerite, B, magnesium carboboride, topaz, rocksalt, etc.) within the pressure range up to 300 GPa demonstrated an excellent agreement with the predictions of the analytical theory comparable in accuracy with Birch-Murnaghan approach.

This paper investigates the singularities at the vertex of multiply connected angular inhomogeneities for heat conduction and elastic deformation. With the aid of Eshelby's equivalent inclusion method (EIM), each inhomogeneity is simulated as an equivalent inclusion, exhibiting the same material properties as the matrix but containing a continuously distributed eigen-field with potential singularities at the vertices and edge lines. Specifically, the eigen-temperature-gradient (ETG) and eigenstrain are utilized to simulate material mismatch of thermal conductivity and stiffness, respectively. Using the separation of variables, the eigen-fields can be formulated in terms of distance to vertices and opening angles, and disturbed thermal/elastic fields are evaluated by domain integrals of Green's function multiplied by eigen-fields, which form Fredholm's integral equation of the second kind. The boundary value problem is reduced to solve for eigenvalues, which are used to determine the order of singularity. The present solution is versatile - by placing two identical inhomogeneities together, it recovers the classic solutions for a single wedge in a bimaterial media or infinite domain. The general and analytical formulae take full consideration of interactions of multiple inhomogeneities and reveal the effects of opening angles and material properties on the thermal and elastic singularities.

We study ultralight scalar fields with quadratic couplings to Standard-Model fermions and derive strong constraints from white-dwarf mass-radius data. Such couplings source scalar profiles inside compact stars, shift fermion masses, and can produce a new ground state of matter. We analyze couplings to electrons and to nucleons, incorporating composition and finite-temperature effects in white dwarf structure and equations of state. We identify two robust observables: (i) forbidden gaps - ranges of radii with no stable configurations - and (ii) characteristic shape distortions that drive white dwarf masses toward the Chandrasekhar limit (electron couplings) or shift the maximum mass (nucleon couplings). Confronting these predictions with precise measurements for Sirius B and Procyon B, together with the global white dwarf population, excludes large regions of unexplored parameter space and extends earlier QCD-axion-specific bounds to a broader class of scalar theories. Our stellar constraints rely only on sourcing and do not assume the scalar constitutes dark matter; where mass reductions are small, precision laboratory searches remain competitive. White-dwarf astrophysics thus provides a powerful, largely assumption-minimal probe of ultralight, quadratically coupled scalars.

Turbulence remains one of the last unresolved problems of classical physics and a major bottleneck to accurate flow prediction in climate, aerospace, and energy systems. Industrial simulations therefore rely on averaged representations of turbulence, which often struggle to predict flows governed by multiple interacting mechanisms. We present a unified, data-driven turbulence modeling framework designed to learn robustly from sparse, indirect observations across diverse flow regimes. The framework embeds physical consistency into a flexible, frame-invariant closure, automatically selects representative training cases based on similarity of flow-feature distributions, and learns a single, unified model through a multi-objective ensemble strategy that balances competing objectives across flows and quantities of interest. The resulting unified foundation model adapts seamlessly across regimes without manual intervention. It outperforms existing turbulence models across a broad spectrum of canonical flows and maintains improved performance in complex three-dimensional configurations of industrial relevance, including a gas turbine diffuser, a generic car, and a generic aircraft. When application-specific accuracy is required, the framework further enables specialist models through additive fine-tuning on targeted flow datasets. The results demonstrate the feasibility of a deployable and generalized turbulence modeling approach that unifies multiple flow mechanisms within a single architecture for a broad range of natural and industrial flows.

In this paper, we present a computer-assisted approach for constructively proving the existence of traveling wave solutions of the suspension bridge equation on the infinite strip $Ω= \mathbb{R} \times (-d_2,d_2)$. Using a meticulous Fourier analysis, we derive a quantifiable approximate inverse $\mathbb{A}$ for the Jacobian $D\mathbb{F}(\bar{u})$ of the PDE at an approximate traveling wave solution $\bar{u}$. Such approximate objects are obtained thanks to Fourier coefficients sequences and operators, arising from Fourier series expansions on a rectangle $Ω_0 = (-d_1,d_1) \times (-d_2,d_2)$. In particular, the challenging exponential nonlinearity of the equation is tackled using a rigorous control of the aliasing error when computing related Fourier coefficients. This allows to establish a Newton-Kantorovich approach, from which the existence of a true traveling wave solution of the PDE can be proven in a vicinity of $\bar{u}$. We successfully apply such a methodology in the case of the suspension bridge equation and prove the existence of multiple traveling wave solutions on $Ω$. Finally, given a proven solution $\tilde{u}$, a Fourier series approximation on $Ω_0$ allows us to accurately enclose the spectrum of $D\mathbb{F}(\tilde{u})$. Such a tight control provides the number of negative eigenvalues, which in turns, allows to conclude about the orbital (in)stability of $\tilde{u}$.

Restricting individuals' access to some opportunities may steer their desire toward their substitutes, a phenomenon known as the forbidden fruit effect. We axiomatize a choice model named restriction-sensitive choice (RSC), which rationalizes the forbidden fruit effect and is compatible with the prominent psychological explanations: reactance theory and commodity theory. The model is identifiable from choice data, specifically from the observation of choice reversals caused by the removal of options. We conduct a normative analysis both in terms of the agent's freedom and welfare. We apply our model to shed light on two phenomena: the backfire effect of beliefs and the backlash of integration policies targeted towards minorities.

The equation of state (EOS) of cold dense matter is a central open problem in nuclear astrophysics. Its inference is hindered by the lack of \textit{ab initio} control above about twice nuclear saturation density, requiring extrapolation. Parametric schemes such as piecewise polytropes (PP) are efficient but restrictive, while nonparametric approaches like Gaussian processes (GP) allow more flexibility at the cost of larger prior volumes. We extend our hybrid EOS framework by replacing the high-density polytropic extension with a GP representation of the squared sound speed, anchored at low densities by the SLy crust EOS and a nuclear meta-model constrained by $χ$EFT and laboratory data. Using hierarchical Bayesian analysis, we jointly constrain the EOS and neutron star mass distribution with multi-messenger observations, including NICER radii, GW170817 and GW190425 tidal deformabilities, pulsar masses, and neutron skin experiments. We examine four scenarios defined by high-density extrapolation (PP vs.\ GP) and hotspot geometry in the NICER modeling of PSR~J0030$+$0451 (ST+PDT vs.\ PDT-U). GP extrapolations generally yield softer EOS posteriors with broader uncertainties. Hotspot assumptions also play an important role, shifting inferred mass--radius relations. Bayesian evidence strongly favors the ST+PDT geometry over PDT-U under both extrapolations, while GP is mildly preferred over PP. These results underscore the impact of observational modeling and EOS extrapolation on neutron star inferences, and show that a GP-based extension offers a robust way to quantify systematic uncertainties in high-density matter.

A recent direction in quantum computing for molecular electronic structure sees the use of quantum devices as configuration sampling machines integrated within high-performance computing (HPC) platforms. This appeals to the strengths of both the quantum and classical hardware; where state-sampling is classically hard, the quantum computer can provide computational advantage in the selection of high quality configuration subspaces, while the final molecular energies are evaluated by solving an interaction matrix on HPC and is therefore not corrupted by hardware noise. In this work, we present an algorithm that leverages stochastic Hamiltonian time evolution in Quantum-Selected Configuration Interaction (QSCI), with multireference perturbation theory capturing missed correlations outside the configuration subspace. The approach is validated through a hardware demonstration utilising 42 qubits of an IQM superconducting device to calculate the potential energy curve of the inorganic silane molecule, SiH4 using a 6-31G atomic orbital basis set, under a stretching of the Si-H bond length. We assess the resulting wavefunctions for compactness, a point on which QSCI has previously been criticised. At large separations, where static correlation dominates, we find a configuration space more than 200 times smaller than that obtained from a conventional SCI selection criterion yields comparable energies. We also compare against the best-in-class Heatbath Configuration Interaction algorithm and observe similar wavefunction compactness at convergence. This result is achieved with a configuration sampling scheme that uses the experimental orbital occupancies of a time-evolved quantum state to predict likely single and double excitations away from existing configurations to bias the subspace expansion procedure.

We define a sequence of invariants $\mathcal{Z}_{N}^ψ$ of tangles with flat $\mathfrak{sl}_{2}$ connections (i.e. hyperbolic structures) on their complements. These can be interpreted as a geometric twist of the Kashaev invariant or as a quantization of the $\operatorname{SL}_{2}(\mathbb{C})$ Chern-Simons invariant. To support the second interpretation we give a new description $\mathcal{I}^ψ$ of the Chern-Simons invariant of a tangle exterior. $\mathcal{Z}_{N}^ψ$ directly recovers $\mathcal{I}^ψ$ when $N = 1$. We build $\mathcal{Z}_{N}^ψ$ using modules over unrestricted quantum $\mathfrak{sl}_{2}$ at a root of unity and the holonomy $R$-matrices previously constructed by the author and Reshetikhin (arXiv:2509.02354). Unlike most previous constructions of geometric quantum invariants $\mathcal{Z}_{N}^ψ$ is defined without any phase ambiguity. It is natural to conjecture that $\mathcal{Z}_{N}^ψ$ is related to the quantization of Chern-Simons theory with complex, noncompact gauge group $\operatorname{SL}_{2}(\mathbb{C})$ and we discuss how to interpret our results in this context.

Neural operators (NOs) provide a new paradigm for efficiently solving partial differential equations (PDEs), but their training depends on costly high-fidelity data from numerical solvers, limiting applications in complex systems. We propose a self-supervised neural operator (SNO) that generates accurate and diverse training data on the fly without numerical solvers. SNO consists of three parts: a physics-informed sampler (PI-sampler) based on Bayesian PINNs for efficient data generation, a function encoder (FE) for compact input-output representations, and an encoder-only Transformer for operator learning, mapping boundary/initial conditions, source terms, and geometries to PDE solutions. We validate SNO on 1D steady/unsteady nonlinear reaction-diffusion equations, a 2D nonlinear PDE with varying geometries, and vortex-induced vibration of a flexible cylinder in fluid dynamics. SNO achieves high accuracy in all cases, and lightweight finetuning (O(100) trainable variables) further improves predictions with only a few hundred steps. This work provides a new route toward pretrained foundation models as efficient PDE surrogates.

We study nearly parallel $\mathrm{G}_{2}$-structures with a three-torus symmetry via multi-moment map techniques. An effective three-torus action on a nearly parallel $\mathrm{G}_{2}$-manifold yields a multi-moment map. The torus acts freely on its regular level sets, so they are torus bundles over smooth three-dimensional manifolds. We show that the geometry of the base spaces is specified by two triples of closed two-forms related by a Riemannian metric. We then describe an inverse construction producing invariant nearly parallel $\mathrm{G}_{2}$-structures from three-dimensional data. We observe that locally this may produce examples with four-torus symmetry.

Mixed-precision inference techniques reduce the memory and computational demands of Large Language Models (LLMs) by applying hybrid precision formats to model weights, activations, and KV caches. However, existing systems struggle to (i) automatically generalize across diverse hardware architectures and precision formats, often requiring fragmented, hand-tuned kernels, and (ii) fully exploit available memory and compute resources, often causing performance bottlenecks. To address these problems, we propose TurboMind, a generalizable and efficient mixed-precision LLM inference engine of LMDeploy. TurboMind is built around two hardware-aware mixed-precision pipelines: A General Matrix Multiply (GEMM) pipeline that optimizes matrix operations through offline weight packing and online acceleration, and an attention pipeline that enables efficient attention computation with different Query, Key, and Value precision combinations. These pipelines are enabled by four key techniques: (i) Hardware-aware weight packing and (ii) adaptive head alignment for generalizability, and (iii) instruction-level parallelism and (iv) a KV memory loading pipeline for efficiency. We conduct comprehensive evaluations of LMDeploy powered by TurboMind across sixteen popular LLMs and four representative GPU architectures. Results demonstrate that LMDeploy achieves up to 61% lower serving latency (30% on average) and up to 156% higher throughput (58% on average) in mixed-precision workloads compared to existing mixed-precision frameworks, establishing consistent performance improvements across all tested configurations and hardware types. This work is open-sourced and publicly available at https://github.com/InternLM/lmdeploy.

We present a novel framework (TS+TT) to nest a Target Study (TS) within a Target Trial (TT) for evaluating the effects of interventions on disparities. The TS component grounds the measurement of disparity in ethical assumptions, based on the concept of allowability, and anchors it to an explicit population within calendar time. It specifies an enrollment plan of stratified sampling of eligible persons to yield a sample where social groups are distributionally similar on covariates deemed allowable for measuring disparity. Within this enrolled sample, the TT component specifies randomization of intervention strategies within each social group. Because social groups are similarly situated on allowable covariates at baseline, and because assigned intervention arms are exchangeable within social groups, TS+TT reflects a meaningful causal estimand for evaluating how interventions impact disparity. We describe the framework's key components, its emulation, and demonstrate its application to evaluate how hypothetical interventions on pulse oximeter bias affect disparities in treatment receipt in clinical care. We also extend semiparametric G-computation to accommodate continuous stochastic interventions and estimate counterfactual disparities in time-to-event outcomes. The TS+TT framework offers a versatile and policy-relevant approach for generating ethically informed causal evidence to reduce disparities and avoid exacerbating disparities.

We establish estimates for short character sums to prime power moduli evaluated at binary quadratic forms. This complements estimates established by Heath-Brown for such character sums to squarefree moduli. Our approach uses $p$-adic analysis. More precisely, we use tools from the $p$-adic theory of exponential sums, as initiated by Milićević.

To examine associations between screen time and anxiety, depression, behavior or conduct problems, and ADHD among children and adolescents during the pandemic, and to assess whether physical activity, sleep duration, and bedtime regularity mediate these associations. Data from 50231 US children and adolescents aged 6 to 17 years in the 2020 to 2021 National Survey of Childrens Health were analyzed. Exact natural effect models and structural equation modeling assessed mediation by physical activity, short sleep duration, and irregular bedtime. We found that daily screen time equal or more than 4 hours was linked to higher risks of anxiety (aOR = 1.45, 95% CI 1.32, 1.58), depression (aOR = 1.65, 95% CI 1.41, 1.93), behavior or conduct problems (aOR = 1.17, 95% CI 1.05, 1.30), and ADHD (aOR = 1.21, 95% CI 1.11, 1.33). Physical activity accounted for 30.2% to 39.3% of the association, irregular bedtime for 18.2% to 25.7%, and short sleep duration for 2.77% to 7.34%. Excessive screen time was associated with poorer mental health and ADHD, partly explained by reduced physical activity, irregular bedtime, and insufficient sleep. Interventions should promote physical activity, regular sleep routines, and adequate sleep duration to effectively mitigate mental health issues and ADHD among children and adolescents.

We introduce crystal polaritons, hybrid excitations formed when the collective excitations of a periodic quantum-emitter array strongly couple to the resonant Bloch modes of a metasurface. This realizes a cavity-QED platform in which periodic light and periodic matter are treated on the same footing, allowing strong collective light-matter coupling in an extended, lossy, and dispersive nanophotonic structure. To describe this regime, we develop a reciprocal-space few-mode quantization based on macroscopic quantum electrodynamics, which maps the metasurface resonances seen by the emitter array onto a cavity-QED Hamiltonian at each in-plane momentum. We show that both plasmonic surface-lattice resonances and dielectric bound states in the continuum can enter the strong-coupling regime with a single emitter per unit cell. As a consequence of the resonant nonlinearities of the resulting crystal polaritons, the platform enables quantum light generation with efficiencies orders of magnitude higher than those achieved in conventional nonlinear metasurfaces.

We investigate the fractional time description of a generalized quantum light-matter system modeled by a time-dependent Jaynes-Cummings (JC) interaction, with different coupling types: constant, linear, exponential, and sinusoidal. Two formulations of the time fractional Schrödinger equation (TFSE) are examined, with a focus on their impact on population inversion and entanglement. Our findings highlight that the introduction of fractional order introduces memory effects, associated with damped oscillations and asymptotic decay. Furthermore, we find that the time-dependent couplings, combined with distinct fractional formulations, influence how these effects occur, ultimately resulting in high or low entanglement. A key finding of our work is that, under sinusoidal coupling, non-periodic dynamics is preserved for both formulations of the TFSE; however, within a certain range, the fractional order can act as a control mechanism for the non-periodic evolution.

High-pressure polyhydrides are leading contenders for room temperature superconductivity. The next frontier lies in stabilizing them at ambient pressure, which would allow their practical applications. In this first-principles computational study, we investigate the potential for record-low pressure stabilization of binary superhydrides within the RbH$_{12}$ system including lattice quantum anharmonic effects in the calculations. We identify five competing phases for the pressure range between 0 and 100 GPa. Incorporating anharmonic and quantum effects on ion dynamics, we find the $Immm$ and $P6_3/mmc$ phases to be the most probable, potentially metastable even at pressures as low as 10 GPa. Notably, all phases exhibit metallic properties, with critical temperatures between 46 and 111 K within the pressure range they are dynamically stable. These findings have the potential to inspire future experimental exploration of high-temperature superconductivity at low pressures in Rb-H binary compounds.

We are interested in the relative conditioning of the problem $y_0\mapsto \mathrm{e}^{tA}y_0$, i.e., the relative conditioning of the action of the matrix exponential $\mathrm{e}% ^{tA}$ on a vector with respect to perturbations of this vector. The present paper is a qualitative study of the long-time behavior of this conditioning. In other words, we are interested in studying the propagation to the solution $y(t)$ of perturbations of the initial value for a linear ordinary differential equation $y^\prime(t)=Ay(t)$, by measuring these perturbations with relative errors. We introduce three condition numbers: the first considers a specific initial value and a specific direction of perturbation; the second considers a specific initial value and the worst case by varying the direction of perturbation; and the third considers the worst case by varying both the initial value and the direction of perturbation. The long-time behaviors of these three condition numbers are studied.

We investigate the propagation of initial value perturbations along the solution of a linear ordinary differential equation \( y'(t) = Ay(t) \). This propagation is analyzed using the relative error rather than the absolute error. Our focus is on the long-term behavior of this relative error, which differs significantly from that of the absolute error. The present paper is a practical sequel to the theoretical papers \cite{M1,M2} on the long-time behavior of the relative error: it includes applicative examples and important issues not addressed in \cite{M1,M2}. In addition, the present paper shows that understanding the long-term behavior provides insights into the growth of the relative error over all times, not just at large times. Therefore, it represents a crucial and fundamental aspect of the conditioning of linear ordinary differential equations, with applications in, for example, non-normal dynamics.

Low-temperature spin dynamics can become trapped in long-lived patterns shaped by the geometry of the interaction network. Here we introduce Chladni states: spin configurations obtained by binarizing the eigenmodes of the interaction Laplacian. These graph-spectral patterns organize the metastable configurations reached by Ising systems under non-ergodic relaxation. The resulting Topological Mode Decomposition provides a compact way to monitor and reconstruct frozen spin configurations in ferromagnets, frustrated antiferromagnets, and spin glasses.

In this work, we address the exact D-optimal experimental design problem by proposing an efficient algorithm that rapidly identifies the support of its continuous relaxation. Our method leverages a column generation framework to solve such a continuous relaxation, where each restricted master problem is tackled using a Primal-Dual Interior-Point-based Semidefinite Programming solver. This enables fast and reliable detection of the design's support. The identified support is subsequently used to construct a feasible exact design that is provably close to optimal. We show that, for large-scale instances in which the number of regression points exceeds by far the number of experiments, our approach achieves superior performance compared to existing branch-and-bound-based algorithms in both computational efficiency and solution quality.

One of the forthcoming major challenges in particle physics is the experimental determination of the Higgs trilinear self-coupling. While efforts have largely focused on on-shell double- and single-Higgs production in proton-proton collisions, off-shell Higgs production has also been proposed as a valuable complementary probe. In this article, we design a hybrid neural simulation-based inference (NSBI) approach to construct a likelihood of the Higgs signal incorporating modifications from the Standard Model effective field theory (SMEFT), relevant background processes, and quantum interference effects. It leverages the training efficiency of matrix-element-enhanced techniques, which are vital for robust SMEFT applications, while also incorporating the practical advantages of classification-based methods for effective background estimates. We demonstrate that our NSBI approach achieves sensitivity close to the theoretical optimum and provide expected constraints for the high-luminosity upgrade of the Large Hadron Collider. While we primarily concentrate on the Higgs trilinear self-coupling, we also consider constraints on other SMEFT operators that affect off-shell Higgs production.

Non-Hermitian quantum systems exhibit various interesting and inter-connected spectral, topological, and boundary-sensitive features. By introducing conditional boundary conditions (CBCs) for non-Hermitian quantum systems, we explore a winding-control mechanism that selectively collapses specific periodic boundary condition (PBC) loop-type spectra onto their open boundary condition (OBC) counterparts, guided by their specific winding numbers, together with a composite reconstruction of the Brillouin zone (BZ) and generalized Brillouin zone (GBZ). The corresponding eigenstates also manifest nontrivial skin effects or extended behaviors arising from the interplay between BZ and GBZ structures. Intuitively, the winding-control mechanism is tied to the residual imaginary velocity originating from the corresponding Fermi sea, establishing the CBCs as the transition boundaries between different non-Hermitian topology of spectral windings. Furthermore, we can generalize our control by incorporating similarity transformations and holomorphic mappings with the boundary controls. We demonstrate the winding control numerically within various models, which enriches our knowledge of non-Hermitian physics across the spectrum, topology, and bulk-boundary correspondence.

This study constrains the cosmological parameters within the scope of a spherically symmetric FLRW cosmological model, the role of a nonlinear spinor field in the universe's evolution. To test this approach, we incorporate the recent Cosmic chronometers, Supernova, and Sloan Digital Sky Survey data. It is found that if spherical coordinates give the FLRW model, the energy-momentum tensor (EMT) of the spinor field possesses nontrivial non-diagonal components. These non-diagonal components of EMT neither depend on the spinor field nonlinearity nor the value of the parameter $k$ defining the type of curvature of the FLRW model. In this context, we construct a dark energy model and perform an MCMC simulation to obtain the best-fit values of the parameters. The results are well comparable to the present Hubble parameter and deceleration parameter, indicating the accelerated expansion of the universe.

Scalar mixing fronts develop at the interface of agitated fluids of different solute concentrations. In such fronts, scalar fluctuations form at both microscopic and macroscopic scales, due to stretching-enhanced molecular diffusion and hydrodynamic dispersion respectively. While these two elementary processes are well understood separately, predicting how their coupling governs the evolution of concentration statistics within dispersing fronts remains a challenge. Here, we propose a theoretical framework to describe scalar fluctuations in fronts mixed by smooth chaotic flows. We find that the transfer of energy between the macroscopic and microscopic scalar fluctuation scales operates at a characteristic length scale $s_i$, for which dispersion and stretching-enhanced diffusion are of equal strength. This leads to a closed expression for the concentration variance, which captures the results of direct numerical simulations with no fitting parameters, for a broad range of Péclet numbers. These findings open a new avenue for predicting both conservative and reactive mixing in smooth chaotic flows such as porous media or microfluidic flows.

Gas injection in the context of the three-phase flow in porous media appears in applications such as Enhanced Oil Recovery, aquifer remediation, and carbon capture, utilization, and storage (CCUS). In general, this technique suffers from a difficulty related to excessive gas mobility, which can be circumvented by using foam. This study addresses the non-linear system of differential equations describing the three-phase foam flow based on Corey relative permeability functions. A major obstacle is an umbilic point, where the characteristic wave velocities for different families coincide, complicating the identification of stable wave structures. We developed a methodology to solve the Riemann problem describing the three-phase foam displacement in the case when the gas viscosity exceeds that of oil and water. To allow the analysis, we assume foam in local equilibrium (or maximum foam texture), resulting in a constant mobility reduction factor (MRF). These simplifications allowed the classification of possible solutions for the injection of foamed gas and water mixtures under a wide range of initial conditions within the framework of non-classical Conservation Law Theory. As a relevant industrial application of the proposed solution, we investigate the conditions resulting in oil bank formation. Besides improving the general physical understanding of foam flow in a porous medium, this analysis can be applied to calibrate numerical simulators and perform uncertainty quantification. Our analytical estimates were validated through numerical simulations.

This paper offers a commentary on the use of notions of statistical significance in choice modelling. We review the reasons for uncertainty in parameter estimates, provide a precise discussion on the computation of measures of uncertainty and confidence intervals, and discuss the use of statistical tests. We argue that, as in many other areas of science, there is an over-reliance on 95\% confidence levels, and misunderstandings of the meaning of significance. We also observe a lack of precision in the reporting of measures of uncertainty in many studies, especially when using $p$-values and even more so with \emph{star} measures. The paper also stresses the importance of considering behavioural or policy significance in addition to statistical significance. Finally, we stress a number of points that are specific to choice modelling and which require special attention, notably in relation to derived measures such as willingness-to-pay, the treatment of random heterogeneity, and the use of repeated choice data.