Codimension-two vortex solutions are important solitonic objects in both quantum field theory and gravity. In this paper, we construct a class of codimension-two Alice-vortex solutions in axio-dilaton gravity, in which monodromy around the vortex enacts the axion transformation $C_0 \mapsto -C_0$. In IIB supergravity, this furnishes a class of R7-brane backgrounds of the sort predicted by the Swampland Cobordism Conjecture. Such configurations generically carry an intrinsic dipole moment. We extract additional properties of such branes from scattering probes. These results provide further evidence that the worldvolume theory of an R7-brane is an 8D non-supersymmetric interacting quantum field theory.

Lattice gauge theories (LGTs) provide a powerful framework for studying confinement, topological order, and exotic quantum matter. In particular, the paradigmatic phenomenon of confinement, where dynamical matter is coupled to gauge fields and forms bound states, remains an open problem. In addition, LGTs can provide low-energy descriptions of quantum spin liquids, which is the focus of ongoing experimental research. However, the study of LGTs is often limited theoretically by their numerical complexity and experimentally in implementing challenging multi-body interactions, such as the plaquette terms crucial for the realization of many exotic phases of matter. Here we investigate a $(2+1)$D $\mathbb{Z}_2$ LGT coupled to hard-core bosonic matter featuring a global U(1) symmetry, and show that dynamical matter naturally induces sizable plaquette interactions even in the absence of explicit plaquette terms in the Hamiltonian. Using a combination of density matrix renormalization group simulations and neural quantum state calculations up to a system size of $20 \times 20$, we analyze the model across different fillings and electric field strengths. At small coupling strength, we find a large plaquette expectation value, independent of system size, for a wide range of fillings, which decreases in the presence of stronger electric fields. Furthermore, we observe signatures of a confinement-deconfinement transition at weak coupling strengths. Our results demonstrate that dynamical U(1) matter can induce complex multi-body interactions, suggesting a natural route to the realization of strong plaquette terms and paving the way for realizing a topological quantum spin liquid protected by a large gap.

Spinodal decomposition, a key mechanism to microstructure formation in materials, has long posed challenges for predictive modeling, due to the need for parameter-free approaches that accurately capture local energy landscapes. In this work, we propose an approach to predict spinodal behavior by introducing a disorder viscosity correction to bulk free energies computed from finite, small, representative cells. We approximate the energy penalty required to transition into a disordered state to enable the stabilization of locally concave bulk free energy regions - essential for interface formation - while suppressing long-range concentration fluctuations. This approximation circumvents the complexity of full ab initio parameterization of interfacial properties and is well-suited for high-throughput and machine-learning frameworks. Our approach captures the necessary physics underpinning spinodal kinetics, offering a scalable route to predict spinodal regions in compositionally complex and high-entropy materials.

One of the promising dark matter (DM) candidates is a keV scale sterile neutrino. In the early universe the observed relic of the sterile neutrino DM is generated via the \textit{Dodelson-Widrow} mechanism. However, this production scenario is severely constraint by various astrophysical observations. Many non-standard interactions between active ($ν_a$) and sterile ($ν_s$) neutrino have been proposed to evade these astrophysical bounds. Here, we study sterile neutrino in the context of a mass-varying scenario by coupling both active and sterile neutrino to a scalar field. This novel mechanism opens up a new parameter space that generates the observed DM relic and alleviates the \textit{Hubble tension}. We find that the resulting parameter space can be fully probed by future X-ray missions.

Diamond to graphite transformation is a complex kinetically driven process which has been studied under various conditions for its fundamental importance. We report the transformation of diamond embedded ceramic matrix composites during hypersonic impact. Diamond particles embedded in cubic boron nitride matrix provide a superhard composite that was subjected to high impact collisions of metal projectiles travelling at speeds reaching Mach 8.45. Our observations suggest that the energy absorption and fracture of the composite is primarily enabled via the phase change of diamond into graphite. Characterization of the impact-fractured composite shows transformed diamond particles and provides details of the shock-induced phase transformation and the nature of diamond-graphite interfaces formed during rapid phase change. The study provides new understanding of phase transformation of diamond under extreme conditions.

In this article, we present a method for computing accurate and scalable nuclear forces within the phaseless auxiliary-field quantum Monte Carlo (AFQMC) framework. Our approach leverages automatic differentiation of the energy functional to obtain nuclear gradients at a computational cost comparable to that of energy evaluation. The accuracy of the method is validated against finite difference calculations, showing excellent agreement. We then explore several machine learning (ML) strategies for learning noisy AFQMC data. These ML potentials are subsequently used to perform geometry optimizations and nudged elastic band (NEB) calculations, successfully identifying the transition state of the formamide-formimidic acid tautomerization. The resulting transition state geometry and barrier heights are in close agreement with coupled-cluster reference values. This work paves the way for highly accurate geometry optimization, molecular dynamics, or reaction path calculations.

Unbinned likelihood fits aim at maximizing the information one can extract from experimental data, yet their application in realistic statistical analyses is often hindered by the computational cost of profiling systematic uncertainties. Additionally, current machine learning-based inference methods are typically limited to estimating scalar parameters in a multidimensional space rather than full differential distributions. We propose a general framework for Simulation-Based Inference (SBI) that efficiently profiles nuisance parameters while measuring multivariate Distributions of Interest (DoI), defined as learnable invertible transformations of the feature space. We introduce Factorizable Normalizing Flows to model systematic variations as parametric deformations of a nominal density, preserving tractability without combinatorial explosion. Crucially, we develop an amortized training strategy that learns the conditional dependence of the DoI on nuisance parameters in a single optimization process, bypassing the need for repetitive training during the likelihood scan. This allows for the simultaneous extraction of the underlying distribution and the robust profiling of nuisances. The method is validated on a synthetic dataset emulating a high-energy physics measurement with multiple systematic sources, demonstrating its potential for unbinned, functional measurements in complex analyses.

Effective water resource management depends on accurate projections of flows in water channels. For projected climate data, use of different General Circulation Models (GCM) simulates contrasting results. This study shows selection of GCM for the latest generation CMIP6 for hydroclimate change impact studies. Envelope based method was used for the selection, which includes components based on machine learning techniques, allowing the selection of GCMs without the need for in-situ reference data. According to our knowledge, for the first time, such a comparison was performed for the CMIP6 Shared Socioeconomic Pathway (SSP) scenarios data. In addition, the effect of climate change under SSP scenarios was studied, along with the calculation of extreme indices. Finally, GCMs were compared to quantify spatiotemporal differences between CMIP5 and CMIP6 data. Results provide NorESM2 LM, FGOALS g3 as selected models for the Jhelum and Chenab River. Highly vulnerable regions under the effect of climate change were highlighted through spatial maps, which included parts of Punjab, Jammu, and Kashmir. Upon comparison of CMIP5 and CMIP6, no discernible difference was found between the RCP and SSP scenarios precipitation projections. In the future, more detailed statistical comparisons could further reinforce the proposition.

Moment equations offer a compelling alternative to the kinetic description of plasmas, gases, and liquids. Their simulation requires fewer degrees of freedom than phase space models, yet it can still incorporate kinetic effects to a certain extent. To derive moment equations, we use a parameterization of the distribution function using centered moments, as proposed by Burby. This yields moment equations for which the parameterized distribution function exactly solves the hyperbolic conservation law. Similarly, a particle model is derived based on a parametrization of the distribution function using phase space moments. Finally, we present the application of the method to the non-relativistic and relativistic Vlasov--Maxwell equations.

We introduce an absorption imaging technique for ultracold gases that suppresses interference fringes and coherence-induced artifacts by reducing the transverse spatial coherence of the imaging light. The method preserves the narrow spectral bandwidth required for resonant absorption imaging and is implemented as a modular extension to standard imaging setups using a rotating diffuser. We demonstrate tunability of the illumination light's coherence without modifying the imaging optics. Using this approach, we achieve reliable imaging of ultracold atomic clouds in micron-scale proximity to complex surfaces, where standing waves, edge diffraction, and speckle severely limit conventional absorption imaging.

How much work does it cost for a propelled particle to stay localised near a stationary target, defying both thermal noise and a constant flow that would carry it away? We study the control of such a particle in finite time and find optimal protocols for time-dependent swim velocity and diffusivity, without feedback. Accuracy, quantified via the mean squared deviation from the target, and energetic cost turn out to be related by a trade-off, which complements the one between precision and cost known in stochastic thermodynamics. We show that accuracy better than a certain threshold requires active driving, which comes at a cost that increases with accuracy. The optimal protocols have discontinuous swim velocity and diffusivity, switching between a passive drift state with vanishing diffusivity and an active propulsion state. This study highlights how a time-dependent diffusivity enhances optimal control and sets benchmarks for cost and accuracy of artificial self-propelled particles navigating noisy environments.

Transitions between distinct dynamical regimes are ubiquitous in nonequilibrium systems. As a prototypical example, deposition growth is often accompanied by irreversible morphological instabilities. Forecasting such transitions from pre-transition configurations remains fundamentally challenging, as early precursors are weak, spatially heterogeneous, and masked by inherent fluctuations. Here, we investigate compact-to-dendritic transitions (CDTs) in a two-dimensional particle-based electrodeposition model and formulate a horizon-based early-warning task using trajectory-resolved transition points. We demonstrate that anticipating the CDT is intrinsically a spatiotemporal problem: neither static morphological descriptors nor temporal learning applied to predefined features alone yields reliable predictive signals. In contrast, end-to-end learning of jointly optimized spatial and temporal representations from growth images enables robust anticipation across a wide range of prediction horizons. Analysis of the learned latent dynamics reveals the emergence of a low-dimensional surrogate variable that tracks progressive morphological destabilization and undergoes reorganization near the transition. We further show that the learned spatiotemporal representation exhibits limited but systematic transferability across reaction-rate conditions, with predictive performance degrading as the inference condition departs from the training condition, consistent with changes in the latent-state dynamics. Overall, our results establish a general formulation for forecasting incipient instabilities in nonequilibrium interfacial growth, with implications for the predictive monitoring and control of pattern-forming driven systems.

Determining the three-dimensional structure of a protein from its amino-acid sequence remains a fundamental problem in biophysics. The discrete Frenet geometry of the C$_α$ backbone can be mapped, via a Hasimoto-type transform, onto a complex scalar field $ψ=κ\,e^{i\sumτ}$ satisfying a discrete nonlinear Schrödinger equation (DNLS), whose soliton solutions reproduce observed secondary-structure motifs. Whether this mapping, which provides an elegant geometric description of folded states, can be extended to a predictive framework for protein folding remains an open question. We derive an exact closed-form decomposition of the DNLS effective potential $V_{\text{eff}}=V_{\text{re}}+iV_{\text{im}}$ in terms of curvature ratios and torsion angles, validating the result to machine precision across 856 non-redundant proteins. Our analysis identifies three structural barriers to forward prediction: (i)~$V_{\text{im}}$ encodes chirality via the odd symmetry of $\sinτ$, accounting for ${\sim}31\%$ of the total information and implying a $2^N$ degeneracy if neglected; (ii)~$V_{\text{re}}$ is determined primarily (${\sim}95\%$) by local geometry, rendering it effectively sequence-agnostic; and (iii)~self-consistent field iterations fail to recover native structures (mean RMSD $= 13.1$\,Å) even with hydrogen-bond terms, yielding torsion correlations indistinguishable from zero. Constructively, we demonstrate that the residual of the DNLS dispersion relation serves as a geometric order parameter for $α$-helices (ROC AUC $= 0.72$), defining them as regions of maximal integrability. These findings establish that the Hasimoto map functions as a kinematic identity rather than a dynamical governing equation, presenting fundamental obstacles to its use as a predictive framework for protein folding.

We study a variant of the Haldane honeycomb model that has non-reciprocal hoppings between the next-nearest neighbours. The system on a torus hosts time-reversal symmetry protected exceptional rings(ER) in the spectrum. The ERs act as Berry-curvature flux tubes i.e the Berry curvature is non-zero only inside the ERs. The system on a cylinder having zig-zag boundaries (and transverse momentum $k_x$) hosts edge-states that have zero group velocity at $k_x=π$ and are therefore `non-chiral'. The edge states undergo a bifurcation transition at an exceptional point(EP)in the BZ and delocalise into the bulk. As the non-reciprocity is increased, the bulk states that are approaching each other are converted into pairs of EPs due to non-Hermiticity. As the non-reciprocity is further increased, there is a `Russian doll'-like nested proliferation of pairs of EPs, leading to an EP-cascade. The proliferation of EPs takes place only at specific values of the non-hermiticity parameter, leading to a step-like structure in the EP-pair density when plotted as a function of non-Hermiticity. Further, using wave packet dynamics, we find a tunable regime where the non-chiral edge states can be dynamically stabilised for large timescales. The `self-acceleration' term in the equations of motion tends to diffuse the wave packets into the bulk, thus making them `ephemeral edge states'. But we find that for small non-hermiticity, the edge localisation is stabilised until late times for sufficiently wider wave packets. Thus, we have brought forth an intriguing phenomenology of the exceptional phase of the non-reciprocal Haldane model, which may bear direct relevance for systems such as disordered Kitaev honeycomb model, wherein such ERs have been predicted.

We present the first constraints on the overlaps between an Axion-like particle (ALP) and the $π^0$ and $η$ mesons from the analysis of the distortions to the $\langle K|\overline{s}γ^μu | π\rangle$ form factors. We demonstrate that these distortions can be tightly constrained by combining data from $τ^-\to π^0 K^-ν$ and $K^+\to π^0\ell^+ν$ decays, and go on to map the constraints to the ALP-meson overlaps. We establish that, in general, the ALP-meson and meson-ALP overlaps are different due to the presence of ALP-quark derivative couplings in the UV Lagrangian, and need to be treated separately. Using lattice results and BaBar, Belle, and NA48/2 data, we obtain exclusion limits on the overlaps and give projections for Belle II. Our techniques are independent of the branching ratios of the ALP, hence, robust against ALP decay channel assumptions. For masses of the ALP below 1 GeV, the bounds on the effective scale of the ALP physics extend to $\mathcal{O}$(TeV) for restricted regions of the parameter space for the ALP-$π$ and $π$-ALP overlaps. On the other hand, these bounds persist for extended regions of the parameter space for ALP-$η$ and $η$-ALP overlaps.

Double white dwarfs (DWDs) are by far the most common compact binaries in the Milky Way, are important low-frequency gravitational-wave sources, and in some cases merge to become Type Ia supernovae. So far, no DWD has been identified solely through relativistic Doppler beaming, even though the beaming amplitude directly relates to the radial velocity semi-amplitude. In this work, we initiate a comprehensive binary population synthesis using SeBa and incorporate the resulting binaries into a tripartite Galaxy model. Our proof-of-concept simulations demonstrate that the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) can reliably recover relatively bright ($r \lesssim20~$mag) unequal-mass binaries in compact orbits with P $\approx$ 10-600 minutes with moderate to high inclinations. We find that LSST can detect at least 287 short-period DWDs, of which 47 are LISA-detectable gravitational wave sources. LSST lightcurves allow us to readily determine the period and fully characterize the orbit, in contrast with the challenges of orbit determination for DWDs in spectroscopic searches. The formation of unequal mass, short-period DWDs strongly depends on the assumptions regarding the mass-transfer phases during binary population synthesis, and the total number and characteristics of Doppler-beamed DWD systems observed in LSST will provide new tests of models of stellar binary evolution. Here, we lay the foundation for the comprehensive integration of synthetic Galactic binary population into realistic LSST survey simulations, thereby enabling quantitative forecasts of the number and characteristics of any binary sub-population during the LSST era.

We study the ground-state and low-lying metastable phases of repulsive binary Bose-Einstein condensates confined in twisted, spin-dependent periodic optical lattices. For balanced mixtures, weak intercomponent interactions yield a fourfold momentum-space symmetry dictated by the lattice geometry. Increasing the coupling strength leads to the emergence of additional momentum peaks that combine with the lattice-induced structure to produce an eightfold rotationally symmetric pattern, signaling quasicrystalline order. At intermediate interactions, global phase separation suppresses this quasicrystalline state; however, at stronger coupling, local phase separation gives rise to a long-lived metastable phase in which the eightfold symmetry is restored. In this regime, a secondary ring of dominant momentum peaks appears at smaller wave vectors, indicating longer-wavelength density modulations and a crossover from lattice-dominated to interaction-driven quasicrystalline order. In contrast, imbalanced mixtures form partially miscible density clusters with eightfold-symmetric aperiodic patterns only at intermediate coupling, while stronger interactions drive global phase separation and permanently destroy quasicrystalline order. Real-time simulations demonstrate that these aperiodic structures are dynamically stable and experimentally accessible. Our results show that quasicrystalline order can emerge in binary condensates without explicitly aperiodic lattices and reveal population balance as a key ingredient for stabilizing quantum quasicrystals.

Context. Warm Jupiters are excellent case studies for the investigation of giant planet internal structures and formation theories. However, the sample of long-period transiting giants is still small today for a better understanding of this population. Aims. Starting from a single transit found in the Transiting Exoplanet Survey Satellite (TESS) data, we confirm the planetary nature of the signal and measure its orbital parameters, mass, and radius. We put this system in the context of long-period giant transiting planets and analyzed the viability to sustain atmospheric or dynamical follow-up. Methods. We carried out a spectroscopic follow-up using FEROS and PLATOSpec to obtain precise radial velocities. We added a photometric follow-up with HATPI and Observatoire Moana to obtain a more precise estimate of the orbital period. We derived the orbital and physical parameters through a joint analysis of this data. Results. We report the discovery and characterization of TIC65910228b, a transiting warm Jupiter with a mass of $4.554 \pm 0.255$ $M_J$ and a radius of $1.088 \pm 0.061$ $R_J$, orbiting an evolved F-type star every $\sim 180.52$ days in an eccentric orbit ($e = 0.25 \pm 0.04$). Conclusions. This planet joins a still under-explored population of long-period ($P > 100$) massive ($M_p > 4$ $M_J$) transiting giant planets, being one of the few with a mild eccentricity. This target is a nice example of the potential of single-transit events to populate this region of the parameter space.

We investigate how the H$_2$ ortho-to-para ratio (OPR) and dueterium fractionation in star-forming regions are affected by nuclear spin conversion (NSC) on dust grains. Particular focus is placed on the rotational energy difference between ortho-H$_2$ (o-H$_2$) and para-H$_2$ (p-H$_2$) on grain surfaces. While the ground state of o-H$_2$ has a higher rotational energy than that of p-H$_2$ by 170.5 K in the gas phase, this energy difference is expected to become smaller on solid surfaces, where interactions between the surface and adsorbed H$_2$ molecules affect their rotational motion. A previous study by Furuya et al. (2019) developed a rigorous formulation of the rate for the temporal variation of the H$_2$ OPR via the NSC on grains, assuming that adsorbed o-H$_2$ has higher rotational energy than adsorbed p-H$_2$ by 170.5 K, as in the gas phase. In this work, we relax the assumption and re-evaluate the rate, varying the rotational energy difference between their ground states. The re-evaluated rate is incorporated into a gas-ice astrochemical model to study the evolution of the H$_2$ OPR and the deuterium fractionation in prestellar cores and the outer, cold regions of protostellar envelopes. The inclusion of the NSC on grains reduces the timescale of the H2 OPR evolution and thus the deuterium fractionation, at densities of >10$^4$ cm$^{-3}$ and temperatures of <14-16 K (depending on the rotational energy difference), when the ionization rate of H$_2$ is 10$^{-17}$ s$^{-1}$.

A cornerstone of advanced materials design is establishing a framework for assembling nanoparticle superstructures with tailored symmetries. A longstanding challenge has been assembling diamond-like superstructures for photonic devices. Traditionally, such open superstructures require functionalized nanoparticles with directional or anisotropic interactions, reminiscent of valence bonding in a diamond. Here, we present a robust strategy for assembling valence-free nanoparticles into a broad array of cubic superstructures. By grafting nanoparticles with oppositely charged, end-functionalized water-soluble polymers of adjustable molecular weight, we gain control over electrostatic interactions and conformational constraints. This unified approach yields lattices analogous to rock salt, CsCl, zinc-blende, diamond, and the rare simple cubic phase, with tunable lattice constants. Theoretical models and simulations elucidate the underlying interactions, providing a framework for engineering valence-free nanoparticle superlattices.

Currently explored rechargeable aqueous zinc-ion battery (RAZIB) cathode materials, such as $α$-MnO$_{2}$, suffer from severe capacity fade when cycling at rates appropriate for grid-scale operation. Mn dissolution has been previously identified as the cause of $α$-MnO$_{2}$ cathode degradation during RAZIB cycling, with conflicting evidence being found in support of the proposed Jahn-Teller effect-assisted charge disproportionation reaction as the mechanism behind Mn dissolution. In order to unveil the Mn dissolution mechanism in MnO$_{2}$ cathode cells under RAZIB operation conditions, the energetic feasibility for Mn vacancy formation was probed in both charged (MnO$_{2}$) and discharged (ZnMn$_{2}$O$_{4}$) phases of $α$ and $λ$ polymorphs of MnO$_{2}$ using density functional theory. The formation of a Mn vacancy, and consequently the dissolution of Mn as Mn$^{2+}_{(aq)}$, was found to be thermodynamically feasible for the $α$-ZnMn$_{2}$O$_{4}$ phase due to the energetically unfavourable Zn bent coordination formed during the Zn$^{2+}$ intercalation process, indicating that Mn dissolution is promoted by an unstable Zn coordination environment. The theoretical calculations were then corroborated by operando $^{1}$H nuclear magnetic resonance experiments which captured the Mn dissolution occurring throughout the RAZIB discharge, with subsequent electrochemical deposition of the Mn atoms on the electrode during charge. The combined computational and experimental analysis reveals the critical role of defect energetics and coordination environment in driving active material dissolution, and consequently capacity fade, with the proposed mechanism also relevant for understanding cathode degradation in other intercalating ion battery chemistries.

Post-starburst (PSB) galaxies, having recently experienced a starburst followed by rapid quenching, are excellent laboratories to probe physical mechanisms that drive starbursts and shutting down of star formation. Integral-field spectroscopy reveals the galaxies' spatially-resolved properties, where observed directional patterns can be linked to the galaxies' past evolution. We measure the resolved star-formation histories (SFHs), stellar metallicity evolution and dust properties of three local PSBs from the MaNGA survey, down to $0.5$" resolution ($\sim0.3\,$kpc) using a hierarchical Bayesian model. Local parameters were constrained simultaneously with parameters describing spatial trends. We found that all three galaxies first experienced an outer, weaker and slower quenching starburst, followed by a central, stronger and faster quenching starburst that peaked $\sim 1\,$Gyr after the first. The central starbursts induced a significantly stronger rise in stellar metallicity compared to the outer starbursts. These results are consistent with the effects of a recent gas-rich (wet) merger, where the first pericentre passage triggered starbursts in the outer regions, while the later coalescence triggers a stronger centralised starburst. We find non-axisymmetric features in the maps of burst mass fraction and dust attenuation in all galaxies, which could be caused by tidal effects during the recent merger. Comparisons with literature binary merger simulations suggests that the galaxies' rapid quenching was driven by gas consumption and the stabilisation against gas gravitational collapse by a growing spheroid, while AGN feedback was not necessarily a primary cause.

Energy flows among coupled subsystems are essential for ultrafast dynamics and high-speed technologies. In magnetic materials, spin fluctuations -- magnons -- mediate these flows in ultrafast magnetism. Yet momentum-resolved access to low-energy magnons governing the microscopic dynamics has been lacking. Using time-resolved resonant diffuse scattering alongside complementary time-resolved X-ray techniques and quantum-kinetic simulations, we unveil the hierarchical energy pathways among correlated systems in the photoexcited antiferromagnet CuO. Above-bandgap excitation triggers near-instantaneous spin disorder, generating non-thermal magnons throughout reciprocal space within femtoseconds. Real-time momentum-resolved tracking reveals picosecond magnon quasi-thermalization, followed by nanosecond recovery via momentum-selective magnon-phonon scattering. The quasiparticle dispersion mismatch creates recovery bottlenecks that control non-equilibrium lifetimes. This microscopic framework transcends phenomenological models and generalizes across materials, establishing design principles for ultrafast control of material properties.

Rapid and reliable assessment of the intrinsic reactivity of amorphous aluminosilicates is critical for their application in alkali-activated materials (AAMs) and blended cements. Although physics-informed glass-structure descriptors have demonstrated strong structure-reactivity relationships for predominantly amorphous systems, their extension to heterogeneous precursors with mixed crystalline-amorphous phases has been limited. Here, quantitative X-ray diffraction combined with bulk compositional analysis was used to reconstruct the effective amorphous compositions of five fly ashes (FAs) and three ground granulated blast-furnace slags (GGBSs). These compositions served as inputs for molecular dynamics simulations employing a melt-and-quench approach to generate atomic-scale structural models of the glassy phases. Based on these structures, the previously introduced descriptors, i.e., average metal oxygen dissociation energy and average metal oxygen bond strength, were refined to cover a broader compositional space spanning SiO2-Al2O3-TiO2-Fe2O3-CaO-MgO-MnO-Na2O-K2O. The refined descriptors exhibit strong inverse correlations with multiple independent reactivity indicators, including cumulative heat release from isothermal calorimetry, bound water content from thermogravimetric analysis, and compressive strength, for both single precursors and binary FA-GGBS blends activated with NaOH. These results demonstrate that physics-informed glass-structure descriptors can be extended from ideal amorphous systems to heterogeneous mixed-phase precursors and capture relative intrinsic reactivity trends in alkaline solutions. The proposed framework provides a transferable, structure-informed basis for comparative assessment of precursor reactivity that complements experimental testing and may inform precursor screening and mix designs for AAM and blended cement systems.

Bartnik's quasi-local mass is a functional on Bartnik data $(\mathbb S^2,γ,H,P,ω^\perp)$, consisting of a metric $γ$, scalar functions $H$ and $P$, and a 1-form $ω^\perp$ on the $2$-sphere $\mathbb S^2$. We construct initial data $(M,g,K)$ for the Einstein equations with boundary $Σ\cong\mathbb S^2$, and boundary conditions for $g$ and $K$ determined by Bartnik data with $H,P$ constant and $ω^\perp\equiv0$. Furthermore this initial data agrees with spherically symmetric initial data for a Schwarzschild spacetime outside of a compact set with controlled mass. As an application, we obtain estimates for the Bartnik mass for such Bartnik data, outside of the time-symmetric setting. We also construct initial data on the cylinder $\mathbb S^2\times[0,1]$ connecting this same class of Bartnik data to time-symmetric data so that estimates for the Bartnik mass outside of time-symmetry can be obtained from prior estimates for time-symmetric data.

We present a rigorous and comprehensive classification of the asymptotic behavior of time-quasiperiodic Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) equations under the assumption of Hermitian jump operators. Our main contributions are twofold: first, we establish a criterion for the uniqueness of steady states. The criterion is formulated in terms of the algebra generated by the GKSL generators and provides a necessary and sufficient condition when the generators are analytic functions of time. We demonstrate the utility of our criterion through prototypical examples, including quantum many-body spin chains. Second, we extend the concept of strong symmetry for time-dependent GKSL equations by introducing two distinct forms, strong symmetry in the Schrödinger picture and that in the interaction picture, and completely classify the asymptotic dynamics with them. More concretely, we rigorously uncover that the strong symmetry in the interaction picture is responsible for non-trivial time-dependent steady states, such as coherent oscillations, whereas that in the Schrödinger picture controls the existence of time-independent steady states. This classification not only encompasses established mechanisms underlying non-trivial oscillatory steady states, such as strong dynamical symmetry and Floquet dynamical symmetry, but also reveals symmetry-predicted, time-dependent asymptotic dynamics in a novel class of open quantum systems. Our framework thus provides a rigorous foundation for controlling dissipative quantum systems in a time-dependent manner.

We propose a numerical method for kinetic plasma simulation in which the phase-space distribution function is represented by a low-rank tensor network with an adaptive level of compression. The Vlasov-Poisson system is advanced using Strang splitting, and each substep is treated spectrally in the corresponding variable. By expressing both the distribution function and the Fourier transform as tensor network objects (state and operator representations), spectral transforms are applied directly in compressed form, enabling time stepping without reconstructing the full phase-space grid. The self-consistent electric field is also computed within the tensor formalism. The charge density is obtained by contracting over velocity degrees of freedom and extracting the zero Fourier mode, which provides the source term for a spectral Poisson solver. We validate the approach on standard benchmarks, including Landau damping and the two-stream instability. Finally, we systematically study how compression parameters, including truncation tolerances and internal ranks (bond dimensions), affect momentum and energy conservation, positivity behavior, robustness to filamentation, and computational cost.

Diffusion-driven instability is a fundamental mechanism underlying pattern formation in spatially extended systems. In almost all existing works, diffusion across the links of the underlying network is modeled through scalar weights, possibly complemented by cross-diffusion terms that are homogeneous across links. In this work, we investigate the emergence of Turing patterns on Matrix Weighted Networks (MWNs), a recently introduced framework in which each edge is associated with a matrix weight. Focusing on the class of coherent MWNs, we provide a novel characterization of coherence in terms of node-dependent orthonormal matrices, showing that link transformations can be written as relative rotations between nodes. This representation allows us to deal with coherent MWNs of any size and to introduce an orthonormal change of variables capable to reduce diffusion on a coherent MWN to diffusion on a standard weighted network with scalar weights. Building on this, we extend the classical Turing instability analysis to MWNs and derive the conditions under which a homogeneous equilibrium of the local dynamics loses stability due to matrix-weighted diffusion. Our results show how network topology, scalar weights, and inter-node transformations jointly shape pattern formation, and provide a constructive framework to analyze and design Turing patterns on matrix-weighted and higher-order networked systems.

The transition to global synchronization in coupled dynamical systems is governed by the interplay between coupling strength and structural topology. Although abrupt, first-order-like synchronization transitions have been extensively reported in heterogeneous networks, it is unclear whether comparable accelerated onset behavior can emerge purely from coordination geometry in spatially homogeneous, regular lattices. In this study, we investigate large-scale ($N=10^5$) stochastic Stuart-Landau oscillator networks defined on regular lattices with controlled coordination number. Using topological data analysis (TDA), simplicial-complex characterization, and optimal-transport-based geometric diagnostics, we identify a coordination-controlled crossover in synchronization onset dynamics at approximately $z_{c} \approx 7$ within the class of regular lattices considered. Low-coordination lattices ($z < z_{c}$) exhibit persistent $H_2$ topological features in the dynamical amplitude field that correlate with delayed coherence and surface-limited propagation. In contrast, higher-coordination lattices ($z > z_{c}$) display rapid fragmentation of these features, reduced interface roughness, and predominantly positive Ricci curvature. This is consistent with enhanced path redundancy and improved transport efficiency. In this regime, the global order parameter exhibits accelerated exponential-like growth during the onset stage. Throughout this work, abrupt synchronization refers specifically to this exponential onset behavior rather than to thermodynamic first-order hysteresis. Our results demonstrate that increasing coordination density induces a qualitative reorganization of higher-order topological structure that strongly correlates with synchronization efficiency in regular lattice systems.

We present a novel structure-preserving framework for solving the Vlasov-Poisson-Landau system of equations using a particle in cell (PIC) discretization combined with discrete gradient time integrators. The Vlasov-Poisson-Landau system is an accurate model for studying hot plasma dynamics at a kinetic scale where small-angle Coulomb collisions dominate. Our scheme guarantees conservation of mass, momentum and energy as well as preservation of the monotonicity of entropy production in both the time-continuous and discrete systems. We employ the conservative integrator for both the Hamiltonian Vlasov-Poisson equations and the dissipative Landau equation using the PETSc library (www.mcs.anl.gov/petsc) to showcase structure-preserving properties.