We consider temporal decay estimates for global solutions of the Navier-Stokes equations with the Coriolis force. We show that under several conditions including the smallness of the initial data, the solution decays as fast as the corresponding linearized solutions, and its decay rate is higher than expected from the flow of the heat equation. The estimates are derived for all $L^p$-norms with $p\in[2, \infty].$

This manuscript assembles the full axisymmetric-with-swirl large-data program in a single self-contained master file. The paper fixes the lifted five-dimensional formulation, the extraction score, the coherent-versus-noncoherent branch structure, the geometric elimination of fragmented, vertically thinned, displaced-only, and off-axis thin-ring channels, and the local packet-window architecture for the residual axis-centered regime. The final analytic task is reduced to a localized proximal diffuse estimate on a finite packet window. We record the complete theorem stack and the exact local operator identities needed for that final verification, in a form suitable for direct journal submission and final checking.

We study the dynamics of $N$ photons in a Fock state, initially located inside one cavity, and coupled by an optical fiber to a second cavity. The entanglement of the photons is monitored by projective measurements, repeated with a fixed time step. This approach is applied to the formation of a photonic N00N state. We calculate the probability of the transition of $N$ photons from the left to the right cavity and the probability of the return of $N$ photons to the left cavity under repeated projective measurements. The entanglement is analyzed for the N00N state by its fidelity and its phase sensitivity, while for the entanglement between the states in the two cavities the entanglement entropy is calculated. In addition, we study the monitored evolution of photons in a single cavity, which are coupled to a single qubit, using the Jaynes-Cummings model. Photon entanglement is analyzed in terms of the entanglement entropy. In all these cases we find that entanglement is sensitive to the details of monitoring protocol, which can be used to control photon entanglement for specific applications.

Transcranial focused ultrasound (tFUS) offers noninvasive access to deep brain circuits but remains limited by skull-induced phase aberration, acoustic impedance mismatch, and poor volumetric control of intracranial pressure fields. Conventional phased-array and planar holographic strategies compensate aberrations electronically or computationally, yet do not resolve geometric and coupling inconsistencies imposed by subject-specific cranial morphology. We introduce personalized skull-conforming acoustic holograms that physically encode individualized wavefront corrections into a conformal acoustic interface. Within a subject-specific volumetric holography (SSVH) framework, cranial geometry and therapeutic constraints are embedded into a physics-based optimization pipeline for holographic phase synthesis. The resulting lens is integrated with a skull- and skin-conforming coupling layer that enhances impedance continuity, reduces reflection losses, and stabilizes spatial alignment, enabling simultaneous aberration mitigation and efficient transcranial transmission. Numerical simulations across multiple subjects and targets demonstrate consistent volumetric focusing and reliable target coverage while maintaining pressure fields within safety limits. Experimental validation using an ex vivo human skull confirms accurate fabrication, effective acoustic coupling, and faithful reconstruction of designed three-dimensional acoustic fields. By unifying wavefront engineering with anatomical conformity, this work establishes skull-conforming acoustic holography as a scalable strategy for high-fidelity, anatomically adaptive transcranial ultrasound targeting.

Authoring presentation slides involves navigating contextual constraints that shape how content is structured, adapted, and reused. While prior work frames constraints as limitations, little is known about how presenters actively reason about them. We conducted a formative study with ten presenters to examine how constraints emerge, are interpreted, and influence authoring decisions, leading to the Constraint-based Multi-session Presentation Authoring (CMPA) framework. CMPA treats time, audience, and communicative intent as key constraints shaping authoring. We instantiated CMPA in ReSlide, a research prototype for constraint-aware slide creation and reuse, and conducted two user studies on (1) single-session behaviors and (2) multi-session workflows. Compared to a baseline tool, ReSlide helped presenters treat constraints as active design drivers that guide narrative construction. The second study further shows how presenters flexibly reuse and adapt content across authoring cycles as constraints evolve. We then propose design implications for future constraint-aware presentation tools.

We study a diffuse-interface model for thermally driven phase separation in viscous incompressible mixtures. The system couples a convective Cahn-Hilliard equation for the order parameter with a Stokes subsystem for the velocity-pressure field and a heat equation for the temperature. Temperature enters the bulk free energy through a Landau-type coefficient, while the phase field feeds back on the flow through concentration-dependent density and viscosity, yielding a phenomenological temperature-coupled Cahn-Hilliard-Stokes-Heat system. We motivate the chemical potential by a temperature-dependent Landau free energy and derive a priori estimates for the regularized subproblems. On the analytical side, we prove local-in-time existence of weak solutions for a regularized coupled system. On the numerical side, we propose a fully discrete finite element scheme combining a convex-splitting time discretization for the Cahn-Hilliard equation with an implicit treatment of viscous and thermal diffusion terms and a an implicit Stokes solve. Under impermeable velocity boundary conditions, the Cahn-Hilliard substep conserves mass, in the purely diffusive isothermal case, the convex-splitting discretization is unconditionally energy-stable for the Cahn-Hilliard free energy. Numerical experiments in two dimensions illustrate thermally driven spinodal decomposition, wall-induced phase separation near cooled walls, and phase separation in narrow channels under imposed thermal gradients. The simulations show the qualitative influence of key nondimensional parameters (such as the mass and thermal Péclet numbers, the Cahn number, the density and viscosity ratios, and the gravitational parameter $G$) on pattern formation, interface motion, and flow structure, and confirm that the proposed framework is a robust tool for studying thermally driven phase separation in confined geometries.

We compute an asymptotic formula for a moment involving the spinor and the standard $L$-functions for holomorphic Siegel cusp forms of degree two and large weight $k$. Applications include simultaneous non-vanishing statements and lower bounds for second moments.

Unentangled wormlike micelle solutions relax stress through a dynamic interplay of reversible scission and intrachain relaxation involving a hierarchy of molecular timescales whose relationship to linear viscoelastic response remains incompletely resolved. A multiparticle mesoscopic Brownian dynamics framework has been developed in which persistent worms, represented by bead-spring chains with sticky ends, assemble to form wormlike micelles via reversible scission and fusion. Both linear and ring-like micelles are formed across the dilute and semidilute concentration regimes. Accurate predictions of dynamic properties are obtained through inclusion of hydrodynamic interactions using a RPY tensor. We identify and quantify characteristic timescales governing micellar dynamics, including bond lifetimes, self- and non-self-recombination times, breakage times of wormlike micelles of length $L$, relaxation times of various contributions to stress, and the longest relaxation time. The dependence of these timescales on sticker strength, concentration, micellar topology and hydrodynamic interactions is established. The presence of ring micelles is found to moderately prolong recombination and breakage processes, while hydrodynamic interactions are shown to affect some of the timescales by reducing sticker mobility. When appropriately scaled, the dependence on mean length of the non-self-recombination and micelle breakage times collapse onto master curves. Storage and loss moduli exhibit distinctive features in the intermediate-frequency regime that are absent in homopolymer solutions. A clear connection is made between micellar timescales and these signatures in the dynamic moduli at various characteristic frequencies, providing a direct link between microscopic dynamics and macroscopic rheology in unentangled wormlike micellar solutions, in dilute and semidilute concentration regimes.

The rapid integration of artificial intelligence (AI) into Internet of Things (IoT) and edge computing systems has intensified the need for robust, hardware-rooted trust mechanisms capable of ensuring device authenticity and AI model integrity under strict resource and security constraints. This survey reviews and synthesizes existing literature on hardware-rooted trust mechanisms for AI-enabled IoT systems. It systematically examines and compares representative trust anchor mechanisms, including Trusted Platform Module (TPM)-based measurement and attestation, silicon and FPGA-based Physical Unclonable Functions (PUFs), hybrid container-aware hardware roots of trust, and software-only security approaches. The analysis highlights how hardware-rooted solutions generally provide stronger protection against physical tampering and device cloning compared to software-only approaches, particularly in adversarial and physically exposed environments, while hybrid designs extend hardware trust into runtime and containerized environments commonly used in modern edge deployments. By evaluating trade-offs among security strength, scalability, cost, and deployment complexity, the study shows that PUF-based and hybrid trust anchors offer a promising balance for large-scale, AI-enabled IoT systems, whereas software-only trust mechanisms remain insufficient in adversarial and physically exposed settings. The presented comparison aims to clarify current design challenges and guide future development of trustworthy AI-enabled IoT platforms.

The transition of materials and devices to nanometer, atomic, and quantum scales makes thermal characterization increasingly challenging, driving the need for advanced nanoscale thermometry. Fluorescence nanothermometry has emerged as a powerful approach, enabling remote, spatially resolved temperature measurements with sub-micrometer-to-nanometer precision across applications in nanoelectronics, microfluidics, and biological systems. In these systems, temperature is inferred from variations in fluorescence observables, including spectral position, intensity, linewidth, and excited-state dynamics. This review provides a comprehensive and critical overview of fluorescence nanothermometry, covering fundamental mechanisms, material platforms, recent advances, and emerging applications. It further presents a critical evaluation of key challenges and discusses emerging strategies and future research directions toward achieving robust, real-time thermometry. It is anticipated that this review will stimulate further advances in material platforms and system design, accelerating the development of accurate, scalable, and application-ready nanoscale thermometers.

This paper is devoted to the analysis of the following nonlinear wave equation \[ u_{tt} - u_{xx} + (1 + qδ_0(x)) \sin u = 0, \] where $δ_0 = δ_0(x)$ is the Dirac delta function centered at the origin and $q \in \mathbb{R}$ is a constant. Equations of this form arise in the study of propagating solitons in the presence of a localized inhomogeneity. It is proved that the Cauchy problem for this equation is globally well-posed in the energy space $H^1_{\sin} \times L^2$. A complete characterization of stationary waves in the energy space, based on the parameter $q$, is also provided. Finally, a criterion to determine the stability or instability of the stationary waves, which depends upon the sign of the parameter $q$, is established.

We present SDO2IRIS$^2$: a visual transformer model that translates a combination of images of the chromosphere and transition region (TR), observed by AIA, and a line-of-sight magnetogram, provided by HMI, into temperature, line-of-sight velocity (v$_{los}$), velocity of the turbulent motions (v$_{turb}$), and electron density (n$_{e}$) in the chromosphere. Using the thermodynamic variables obtained from the inversion of the chromospheric lines Mg II h&k, observed by IRIS, as the target of the model, and the intensity images in the chromosphere and TR, and the photospheric magnetogram as the input, the predicted T and n$_{e}$ show a strong correlation ($\approx 0.80$) for $\approx$80% of the test inverted data, a moderate-to-strong correlation ($\approx0.63$) for 70% of the v$_{turb}$ of the target test inverted data, while for the $v_{los}$, the correlation is weak. Therefore, the predicted values by SDO2IRIS$^2$ may be used as an estimation of the thermodynamics in the chromosphere, either as a stand-alone result or as complementary information to other chromospheric data observed simultaneously. The execution time employed by SDO2IRIS$^2$ to obtain the thermodynamic values in the chromosphere is of the order of a few minutes, being $\le10$ minutes when using a CPU, and $\le5$ minutes when using a GPU. SDO2IRIS$^2$ opens a new avenue for the use of SDO data thanks to the inversions provided by IRIS observables.

The Müller boundary integral equation for penetrable electromagnetic scattering is conventionally discretized using divergence-conforming basis functions, a restriction inherited from the PMCHWT framework. This paper demonstrates that this constraint can be bypassed. The double-gradient operator in the Müller formulation acts on the kernel difference $φ_a - φ_i$, so that the $\mathcal{O}(R^{-3})$ hypersingularity cancels identically, reducing the operators to weakly singular $\mathcal{O}(R^{-1})$ kernels. Exploiting this cancellation, we develop a nodal, high-order Galerkin formulation using $\mathrm{P}_2$ isoparametric shape functions on curved manifolds. The surface vector field is constructed via a metric-weighted orthonormal tangent frame. The singular integrals are evaluated by Sauter--Schwab quadrature, and a Morton-ordered Block Jacobi preconditioner is introduced. By capturing the dominant near-field interactions within geometrically clustered diagonal blocks, it yields robust, superlinear GMRES convergence under extreme material and geometric parameters. Validation against semi-analytical EBCM references confirms high-order spatial accuracy and optical-theorem satisfaction to high precision.

Data assimilation (DA) improves prediction of chaotic systems by combining model forecasts with sparse, noisy observations. Many DA methods are inherently probabilistic, but accurate probabilistic DA is often computationally expensive because it requires repeated high-resolution (HR) forecasts and large ensembles. In this study, we develop DiffSRDA, a probabilistic spatiotemporal super-resolution data assimilation framework based on denoising diffusion models, and evaluate it on an idealized barotropic ocean jet instability testbed. DiffSRDA is trained offline to generate short HR analysis windows conditioned on (i) a time series of low-resolution (LR) forecast frames and (ii) sparse HR observations. Repeated reverse diffusion sampling then produces an ensemble of HR analyses, providing both point estimates and uncertainty information. Despite relying only on low-cost LR forecasts, DiffSRDA achieves reconstruction quality close to that of an Ensemble Kalman Filter (EnKF) driven by HR forecasts, while improving over deterministic CNN-based SRDA baselines. The sampled ensemble also yields physically meaningful uncertainty patterns, with spread concentrated in dynamically active regions similarly to EnKF. A key practical result is that accurate base DiffSRDA cycling does not require long reverse chains: most of the full-chain accuracy is retained with only a few reverse steps, making diffusion-based SRDA practical for repeated cycling. Finally, by exploiting the score-based structure of diffusion sampling, we demonstrate training-free observation-consistency guidance for deployment-time sensor-layout shifts, enabling improved use of changed observation configurations without retraining. Overall, diffusion models provide a practical, uncertainty-aware, and computationally efficient approach for spatiotemporal SRDA in chaotic fluid flows.

This paper establishes a rigorous connection between regularized discrete-time reinforcement learning (RL) and continuous-time stochastic optimal control. Specifically, classical RL algorithms are typically solving a regularized discrete-time Bellman equation. We study the discretization error, namely, the gap between the optimal policy induced by the regularized discrete-time Bellman equation and the true optimal feedback control of the underlying continuous-time stochastic control problem. By deriving quantitative convergence rates for this gap, we provide a rigorous foundation for understanding the stability and implementation of exploratory RL policies in stochastic continuous-time environments.

The space of de Rham currents supported in finitely many points in a Lie group $G$ has the structure of a filtered differential graded Hopf algebra. The product is given by convolution of compactly supported currents, and the co-product dualizes to wedge product on differential forms. This space arises as the finitely supported sections functor $ Γ^{finite} $ applied to the bundle $ \mathcal{U}(G) $ of currents on $ G $ supported at a single (variable) point, and the differential Hopf algebra operations pull back via $ Γ^{finite} $ to bundle maps. Explicit formulas for these bundle maps are obtained, and we show in particular that the convolution product takes the form of a Hopf-algebraic smash product.

Projected subgradient descent (PSD) has gained popularity for solving robust Markov decision processes (RMDPs) because it applies to a broader class of uncertainty sets than traditional dynamic programming. Existing work claims that RMDPs with a general compact uncertainty set satisfy the subgradient dominance property, under which exact PSD converges to an $\varepsilon$-optimal policy in a polynomial number of updates (e.g., Wang et al., 2023). We show that these claims are incorrect. Even when the uncertainty set has cardinality two, the RMDP objective is not subgradient-dominant and can admit suboptimal strict local minima. Moreover, we prove that finding an $\varepsilon$-optimal policy can be NP-hard even in settings where subgradients are efficiently computable: (i) finite transition uncertainty sets and (ii) $sa$-rectangular finite transition uncertainty sets with finite cost uncertainty sets. Finally, we identify two conditions under which RMDPs do satisfy subgradient dominance: when, for each policy, either the worst-case transition kernel or the worst-case action-value function is unique.

This article presents a novel mathematical formalism for advanced manifold--metric pairs, enhancing the frameworks of geometry and topology. We construct various D-dimensional manifolds and their associated metric spaces using functional methods, with a focus on integrating concepts from mathematical physics, field theory, topology, algebra, probability, and statistics. Our methodology employs rigorous mathematical construction proofs and logical foundations to develop generalized manifold--metric pairs, including homogeneous and isotropic expanding manifolds, as well as probabilistic and entropic variants. Key results include the establishment of metrizability for topological manifolds via the Urysohn Metrization Theorem, the formulation of higher-rank tensor metrics, and the exploration of complex and quaternionic codomains with applications to cosmological models like the expanding spacetime. By combining spacetime generalized sets with information-theoretic and probabilistic approaches, we achieve a unified framework that advances the understanding of manifold--metric interactions and their physical implications.

We present a dynamical solution to the dark matter-baryon coincidence problem based on the neutron portal operator connecting the visible and dark sector asymmetries. This framework is motivated by the possibility that a strongly supercooled dark confinement phase transition accounts for the nano-Hz stochastic gravitational wave signal observed by pulsar timing arrays, while also generating the dark matter and baryon asymmetry in the Universe. We show that the GeV-scale mass of asymmetric dark matter can be naturally correlated with the (multi-)TeV scale cut-off for the neutron portal through its ultraviolet completion. The dark sector is governed by an approximate fixed point and confines once the heavy portal states are integrated out, dynamically generating a scale of $\mathcal{O} ({\rm GeV})$. We analyze both tree and loop-level ultraviolet completions and demonstrate how the resulting confinement scale is linked to the effective neutron portal scale. We also discuss cosmological constraints and experimental prospects in beam dump searches and colliders for probing the neutron portal.

We extend the concept of a partial group action to non-associative algebras in a variety \(\mathcal{V}(I)\), solve the globalization problem within \(\mathcal{V}(I)\) and examine its universal property. It is achieved using what we call the ``$Λ$-construction'', which we also apply to deal with covariant representations in the associative and Lie algebra settings, considering related categories and constructing an adjoint pair of functors between them. We also show that the $Λ$-construction behaves well with semidirect products of Lie algebras.

This work presents a non-equilibrium framework for thermodynamicized black holes, inspired by the entropy-functional interpretation of emergent gravity and by residue-based methods in black hole thermodynamics. The main idea is to unify three components: an entropy functional principle for selecting physical on-shell backgrounds, a Euclidean and contour-based description of the horizon temperature through simple pole singularities, and a topological residue classification of multi-horizon black hole configurations. On this basis, the paper introduces a quasi-stationary non-equilibrium partition functional in which irreversible entropy production appears as an additional contribution to the singular action. The formalism reproduces the standard equilibrium relations in the adiabatic limit, while also extending them to dynamically driven black-hole systems with matter, charge, and rotational fluxes. The framework is then applied to Kerr Newman type black holes in constant curvature f(R) gravity, where the equilibrium entropy remains weighted by the derivative of f at the background curvature, while non-equilibrium corrections arise from flux-induced deformations of the effective thermodynamic action. The analysis further shows that the outer and inner horizons carry opposite topological orientations, so the non-extremal Kerr Newman family stays in the topological class W = 0 unless a horizon bifurcation or merger changes the singularity structure. Finally, several function plots are provided to illustrate the behavior of equilibrium and non-equilibrium free energy, the Kerr Newman temperature curve, and the entropy production law.

The Smith Hat tile is the first known aperiodic monotile, having been discovered in 2023. The simple structure, constructed using only 8 kites, is unique and well motivated for analysis within percolation theory. The primary goal of this paper is to discover the critical threshold $p_c$ in both site and bond Bernoulli structures using Monte Carlo simulation for the Smith hat tile(1,$\sqrt3$). Our findings are site and bond values of $p_c^s = 0.822725 \pm 0.000044$ and $p_c^b = 0.798161 \pm 0.000044$ for edge percolation and $0.544247 \pm 0.000101$ for site percolation on the dual graph.

With the evolution of multiple-input multiple-output (MIMO) technology toward extremely large (XL) MIMO systems comprising hundreds of, or more, antennas, this work investigates scalable and fronthaul-efficient reception design for the uplink of cell-free (CF) XL-MIMO systems. In such systems, the uplink signals transmitted by mobile user equipments (UEs) are jointly decoded at a central processing unit (CPU) connected to distributed access points (APs) via finite-capacity fronthaul links. We address the joint optimization of linear transform matrices, used by the APs to reduce the signal dimension and fronthaul load, and fronthaul compression strategies to maximize the uplink sumrate. A fractional programming (FP)-based iterative algorithm is first developed, followed by a reduced-complexity variant, termed accelerated FP (A-FP), along with its decentralized implementation whose fronthaul overhead remains independent of the number of AP antennas. Numerical results show that the proposed A-FP scheme significantly reduces computational complexity compared to FP implemented with general-purpose solvers, while substantially outperforming scalable baseline schemes that rely solely on local channel state information.

In this paper, we study the stochastic homogenization for a class of symmetric random walks in random conductance model, whose one-step transition probability from $x$ to $y$ is proportional to $|x-y|^{-d-2}$. As the associated jumping kernel fails to be $L^2$-integrable yet admits a finite $α$-th moment for all $α\in (0,2)$, we refer to the corresponding process $(X^\w_t)_{t\ge0}$ as a long-range random walk with critical jump index. In this critical regime, the scaled process $\bigl(k^{-1}X_{k^2(\log k)^{-1}t}\bigr)_{t\ge 0}$, whose scaling order is different from the diffusive scaling and the $α$-stable scaling, converges to a Brownian motion. Besides characterizing the limiting Brownian motion, we will give a convergence rate for associated scaled resolvents, which obeys the order $(\log k)^{-\frac{1}{2}+\frac{1}{2(d-2)}+\varepsilon}$ with any $\varepsilon>0$ for all $d>3$.

We develop tools which use common fusion systems building techniques in order to compute higher limits over the centric orbit category. We apply these tools in order to study both the Diaz-Park sharpness conjecture as well as the weaker cohomological sharpness conjecture which predicts vanishing of higher limits only for the cohomology Mackey functors . Our approach leads to proving cohomological sharpness (but not sharpness) for all saturated fusion systems over p-groups of either maximal nihlpotency or of rank 2 and all polynomial, Henke-Shpectorov and van Beek fusion systems. This list includes all but 2 of the cases for which cohomological sharpness was previously known as well as most currently known families of exotic fusion systems. For the polynomial, Henke-Shpectorov and 6 of the van Beek fusion systems, sharpness is also approximated by proving vanishing of all but the first higher limits of any Mackey functor. The distinction our approach makes between sharpness and cohomological sharpness is somewhat surprising and interesting by itself. Our approach draws a new connection between cohomological sharpness and fusion system building techniques. We believe that this connection will lead to a better understanding of both fusion systems and Mackey functors over them.

We present a general and efficient approach to compute phase-resolved multidimensional spectra of anharmonic molecular polaritons, based on a semiclassical evolution of the molecular Hamiltonian and cavity field in the large-$\mathcal{N}$ limit of many molecules coupled to a confined photonic mode. By systematically expanding the response in both amplitudes and phases of the input fields, our method enables a transparent and computationally simple construction of phase-cycled two-dimensional single- and double-quantum polariton spectra from the underlying nonlinear signal components. Here, phase cycling acts as an analogue of phase matching with oblique pulses, allowing for the isolation of the contributing nonlinear pathways in Liouville space. We specialize to vibrational polaritons and benchmark the method through direct comparison with experimentally measured single-quantum spectra, providing an explanation for the longstanding puzzle of the polariton bleach effect observed at short waiting times. Further, we show how the imprint of various types of anharmonicities on the double-excitation manifold can be directly probed and analyzed through double-quantum coherence spectroscopy. Taken together, our results establish a practical and powerful framework for the modeling and interpretation of nonlinear spectroscopic experiments on strongly coupled light-matter platforms and for guiding the design of cavity-enhanced molecular platforms.

Let $n=hw$, where $h$ and $w$ are integers with $h,w \ge 2$. We determine the set of possible intersection numbers of two $n \times n$ latin squares having the additional `Sudoku' constraint based on a $w \times h$ grid of $h \times w$ boxes.

This paper studies 43-class malware type classification on MalNet-Image Tiny, a public benchmark derived from Android APK files. The goal is to assess whether a compact image classifier benefits from four components evaluated in a controlled ablation: a feature pyramid network (FPN) for scale variation induced by resizing binaries of different lengths, ImageNet pretraining, lightweight augmentation through Mixup and TrivialAugment, and schedule-free AdamW optimization. All experiments use a ResNet18 backbone and the provided train/validation/test split. Reproducing the benchmark-style configuration yields macro-F1 (F1_macro) of 0.6510, consistent with the reported baseline of approximately 0.65. Replacing the optimizer with schedule-free AdamW and using unweighted cross-entropy increases F1_macro to 0.6535 in 10 epochs, compared with 96 epochs for the reproduced baseline. The best configuration combines pretraining, Mixup, TrivialAugment, and FPN, reaching F1_macro=0.6927, P_macro=0.7707, AUC_macro=0.9556, and L_test=0.8536. The ablation indicates that the largest gains in F1_macro arise from pretraining and augmentation, whereas FPN mainly improves P_macro, AUC_macro, and L_test in the strongest configuration.

In this paper, we investigate the cosmic expansion scenarios within the framework of $f(Q,T)$ gravity by using the affine equation of state (EoS) parameter. Specifically, we consider the linear form $f(Q,T)=Q+βT$, where $β$ is a free model parameter. We use Bayesian statistical methods, specifically the $χ^2$ minimization technique to constrain the model parameters using Cosmic Chronometer (CC), Pantheon+SH0ES and DESI BAO data. We further analyze the characteristics of the derived cosmological model. A comprehensive study of energy density, pressure, equation of state parameter and cosmographic parameters are carried out to understand the evolution of the Universe in this model. The determination of present age of the universe for this model is within $1σ$ with Planck results.

Scientific posters are one of the most common forms of scholarly communication and contain early-stage insights with potential to accelerate scientific discovery. We investigated where posters are shared, to what extent their sharing aligns with the FAIR principles, and how commonly they are reused. We identified 86 platforms hosting posters, with many not assigning persistent identifiers. A total of 150k posters are shared as of 2024 on the 43 platforms where we were able to count, which is relatively low. Looking in more detail at posters shared on Zenodo and Figshare, we found that repositories are not always supporting structured metadata critical for poster discovery, like conference information, and that researchers are not providing such metadata even if they are supported. We also observed that while there is some engagement with posters in terms of views and downloads, citing posters is not yet a common practice. Our recommendations are for the scientific community to encourage poster sharing and reuse and establish clear guidelines to make posters FAIR.