We predict a supercurrent-driven Néel spin-orbit torque in a superconductor/$d$-wave altermagnet heterostructure, associated with the emergence of spin-triplet correlations. The effect can be understood as a consequence of the supercurrent-induced spin polarization, owing to the interplay between spin-orbit coupling and momentum-dependent spin splitting, as found, for example, in altermagnets. Remarkably, the supercurrent can be tuned by the Néel-vector direction, and the supercurrent-induced torque can both propel magnetic domain walls and reverse the Néel-vector orientation within a domain wall. These findings establish superconductor/altermagnet heterostructures as a versatile platform for the dissipationless control of the Néel vector, with potential applications in racetrack memory, dissipationless superconducting electronics, and unconventional computing.

This study analyses the potential of renewable energy to reduce inflationary pressures arising from energy imports in Turkiye. Annual data for the period 1980-2022 are used in the analysis. In this study, unit root properties are examined using the Zivot-Andrews and Lee-Strazicich tests, both of which explicitly account for structural breaks. Cointegration is investigated via the Johansen and Hatemi-J cointegration tests. Long-run coefficients are subsequently estimated using the DOLS and FMOLS estimators. The robustness of the empirical findings is further assessed using the ARDL approach. In addition, an interaction term is constructed to measure the impact of renewable energy in alleviating inflationary pressures arising from energy imports. The results show that energy imports and exchange rate have an increasing impact on inflation, while renewable energy and the interaction term have a decreasing impact. DOLS, FMOLS, and ARDL results support each other. Moreover, in both models, the impact of renewable energy in mitigating inflationary pressures stemming from energy imports is stronger than the direct disinflationary impact of renewable energy.

This work presents a variational physics-informed deep learning framework for phase-field modelling of brittle crack propagation in anisotropic media. Previous Deep Ritz Method (DRM) approaches have focused on second-order, isotropic phase-field fracture formulations. In contrast, the present work introduces, for the first time within a variational deep learning setting, a family of higher-order anisotropic phase-field models through a generalised crack density functional. The resulting fracture problem is solved by minimising the total energy using the DRM. The trial space is enriched with higher-order B-spline basis functions to represent higher-order gradients accurately and stably, thereby eliminating the need for conventional automatic differentiation. The methodology is assessed for isotropic, cubic, and orthotropic fracture surface energy densities. Numerical examples demonstrate direction-dependent crack growth in anisotropic cases, highlighting the capability of the method to accurately capture this behaviour.

Contrary to accepted turbulence folklore, which holds that no mathematical relation exists between the Navier-Stokes equations (NSEs) and the multifractal model (MFM) of Parisi and Frisch, we develop a theory that reconciles the MFM with Leray's weak solutions of Navier-Stokes analysis. From a combination of Euler invariant scaling and the NSEs set in a three-dimensional box of size $L$, we also derive the Paladin-Vulpiani inverse scale $η_{h,pav}$, which is related to the Reynolds number $\mathit{Re}$ by $Lη_{h,pav}^{-1} = \mathit{Re}^{1/(1+h)}$, and which acts as a mediator between the two theories. This is achieved by considering $L^{2m}$-norms of the velocity gradient to find a correspondence between $m$ and the local scaling exponent $h$ in the multifractal model. The parameter $m$ acts as if it were the sliding focus control on a telescope which allows us to zoom in and out on different structures. The range $1 \leqslant m \leqslant \infty$ is equivalent to $-2/3 \leqslant h_{min} \leqslant 1/3$, which lies precisely in the region where Bandak et al. (2022, 2024) have suggested that thermal noise makes the NSEs inadequate and generates spontaneous stochasticity. The implications of this are discussed.

Near-Earth asteroid (275677) 2000 RS11 was observed over 5 days in March 2014 with both the Arecibo (2380 MHz, 12.6 cm) and Goldstone (8560 MHz, 3.5 cm) planetary radar systems. The continuous-wave spectra and delay-Doppler images collected revealed a sub-km-sized object with a strongly bifurcated shape. We used these radar observations, in combination with 7 optical lightcurves collected in 2014 and one lightcurve from 2023, to create a comprehensive shape and spin-state model for RS11. We find a rotation period of P = (4.445+-0.001) hours around a pole of lambda = (225+-80) and beta = (-80+-9) relative to the plane of the ecliptic. The shape of RS11 is unusual in that it does not resemble many of the other near-Earth asteroids modelled with ground-based radar. Whilst RS11 consists of a largely spherical, smaller lobe attached to an elongated, larger lobe via a narrow neck, the smaller lobe is not aligned with the long axis of the larger lobe, but is closer to the larger lobe's shortest principal axis. In combination with a large concavity observed on the outer face of the larger lobe, this may point to an unusual formation or event in the object's past. We estimate that RS11 has an geometric albedo of (0.16+-0.06) and a radar albedo between 0.08 and 0.16. Analysis of its gravitational environment reveals that for standard S-type asteroid densities, we would not expect rotational instability and it is possible for RS11 to be a low tensile strength rubble-pile asteroid.

We characterize the spatially resolved stellar population and emission-line properties of galaxies in the M51 group using the same methodology previously applied to the M101 group, aiming to understand how environmental processes shape galaxy properties across different groups. Properties are derived by applying the \textsc{AlStar} spectral fitting code to multi-band datacubes from the Javalambre Photometric Local Universe Survey (J-PLUS). We present spatially resolved maps of the main stellar population and emission-line properties for the M51 group galaxies. The interacting pair M51a/b displays clearly distinct properties: M51a shows prominent star-forming spiral arms, while its companion is essentially an early-type retired galaxy. M63 exhibits asymmetries in stellar age, dust attenuation, and H$_α$ equivalent width, consistent with outside-in quenching likely related to a past interaction. Relations between physical properties and stellar mass surface density ($Σ_\star$) were investigated. The age-$Σ_\star$ and nebular metallicity-$Σ_\star$ relations are flatter than those in the M101 group. In addition, all galaxies align with the resolved star-forming main sequence, except M51b, which shows the properties of a retired galaxy. Overall, the M51 group displays signatures of more advanced dynamical evolution than the M101 group. This is evidenced by flattened age and nebular metallicity gradients, enhanced dust content, and signs of environmental quenching in some members. In contrast, the less dynamically evolved M101 group largely preserves its inside-out formation signatures. While these results suggest that group mass and interactions influence galaxy evolution even in low-mass environments, the comparison of two systems remains limited by small-number statistics. This study highlights the potential of J-PLUS data for IFS-like analyses of nearby galaxies.

The flavor problem and the baryon asymmetry of the universe (BAU) are addressed simultaneously within a supersymmetric preon model. Standard Model fermions are three-body composites of preons confined at Lambda_cr ~ 10^14 GeV by a Maxwell-Chern-Simons interaction and a metacolor gauge symmetry SU(3)_mc. Gauge anomaly cancellation requires one spectator field chi and no other new fermions. Using four systematic numerical methods validated against the hydrogen atom, we reproduce the observed ratio m_e/m_u = 0.22 at metacolor string tension sigma*_mc/theta^2 = 2.11, and predict m_d > m_u with m_d/m_u ~ 2.3 (observed value of 2.0) from the Pauli principle applied to the psi_0^2 spin-color wavefunction. The neutrino is naturally massless at tree level by the same Pauli-principle argument; the spectator chi provides a Type I seesaw giving m_nu ~ Lambda_EW^2/Lambda_cr ~ 0.1 eV. The BAU is generated at Lambda_cr via the Callan-Harvey anomaly inflow mechanism: integrating out the massive charged preons induces a topological Chern-Simons term whose coefficient is fixed by the fermion/boson condensation asymmetry epsilon from intrinsic SUSY breaking. Matching the observed eta ~ 8.7x10^{-10} gives epsilon ~ 0.022, consistent with a one-loop origin. R-parity is derived dynamically from the composite structure, making the lightest superpartner absolutely stable.

In this paper we introduce a new model for the quantum-mechanical system of the hydrogen atom. We start with a four-dimensional Lorentzian quadratic space $(V,q)$ and let $C \subset V$ be the corresponding cone. The Hilbert space of our model, denoted by $H$, consists of $L^2$ functions on the cone, and observables are represented by operators in the algebra $D(C)$ of algebraic differential operators on $C$. We introduce a distinguished Schwartz subspace $H^{\infty}$ of $H$ that is naturally a $D(C)$-module. The Schrödinger operator in our system is represented by a Schrödinger family of operators in $D(C)$. We compute the spectrum of the Schrödinger family in the Schwartz space $H^{\infty}$ and show that it coincides with the spectrum in physics, and that solutions in $H^{\infty}$ correspond to the usual solutions in physics. The main differences from the standard model are as follows. First, we use the cone $C$ instead of $\mathbb{R}^3$ as our configuration space. As a result, the group of geometric symmetries of our configuration space is $O(q)\simeq O(3,1)$ rather than $O(3)\ltimes \mathbb{R}^3$. Second, we use only algebraic operators with no singularities. Third, we do not impose any specific boundary conditions on solutions of our equations; these are all encoded in the Schwartz space $H^{\infty}$.

Context. For decades, reproducing the orbital period distribution of non-magnetic Cataclysmic Variables (CVs) seemed to require a drastic decrease, usually termed disruption, of angular momentum loss through magnetic braking at the fully convective boundary, which argued for a change in the dynamo mechanism operating in fully and partially convective stars. However, recent studies showed that the magnetic braking prescription traditionally used in CV evolution theory is clearly outdated as saturation, that is, a weak period dependence for rapidly rotating stars, is not included. Aims. Here we test an updated version of a saturated magnetic braking prescription that has been developed to explain the spin-down of single stars in the context of CV evolution. This prescription contains a boosting and a disruption parameter that represent the change in the strength of magnetic braking at the fully convective boundary. Methods. We performed state of the art MESA simulations for CVs with the revised saturated magnetic braking prescription. Results. As in previous studies, we found that magnetic braking needs to be stronger in close binaries than in single stars and that, in contrast to what is observed in single stars, magnetic braking needs to be reduced at the fully convective boundary. However, in contrast to previous studies of CV evolution, only a moderate disruption by a factor of 2 - 3 is sufficient to explain key features of the CV orbital period distribution and the measured mass-radius relation for CV donors. Conclusions. The relatively small decrease of the efficiency of magnetic braking at the fully convective boundary might have implications for our understanding of dynamo models for fully and partially convective stars.

The extent of envelope stripping in the progenitor stars is directly reflected in the diversity of spectral features observed in stripped-envelope supernovae (SESNe). Through extensive spectral observation and analysis, we aim to clarify the statistical differences between the subclasses of SESNe. The Tsinghua Supernova group obtained 249 optical spectra of 62 SESNe during the years from 2010 to 2020, covering phases from $-$16 to over 190 days relative to maximum light. Most spectra were obtained during the photospheric phases after the supernova explosion. For each spectrum, the pseudo-equivalent widths (pEWs) and blueshift velocities of principal lines were measured. We further investigated the common spectral features by analysing their velocity and strength correlations across all subtypes. We identify the feature near 6200~Å in SNe Ib as H$\mathrmα$ through comparison with SNe IIb and Ic, which resolves inconsistent literature interpretations. Our finding reveals prevalent residual hydrogen in SNe Ib, further supporting a continuous stripping sequence from SNe IIb to Ib. We observe a trend in increasing velocity among different subtypes of stripped-envelope SNe, with SNe IIb exhibiting the lowest line velocities, followed by Ib, Ic, and Ic-BL. Typically, the O~I lines in SNe Ic/Ic-BL are stronger than those seen in SNe IIb/Ib. In nebular phases, the [Ca II] emission dominates over [O I] in SNe IIb/Ib while [O I] is stronger in SNe Ic, including the He-rich SN 2016coi. This spectral dichotomy implies that progenitors of SNe Ic (BL) have more massive CO cores and hence higher initial masses.

Magnetic white dwarfs (MWDs) are key to understanding the origin and evolution of magnetic fields in compact stars. While large spectroscopic surveys such as SDSS have greatly expanded the known sample, the potential of LAMOST has not yet been fully explored. Our aim is to identify and characterize isolated MWDs in the LAMOST DR10 database. We cross-matched LAMOST DR10 spectra with white dwarf candidates from Gaia EDR3 and with recent SDSS-based catalogs of MWDs. Zeeman splitting in Balmer and helium absorption lines was used as the primary diagnostic to identify magnetic fields and to estimate their strengths. Reference objects from SDSS catalogs were used to test the detectability of MWDs in LAMOST low-resolution spectra. We identified 63 isolated MWDs in LAMOST DR10, of which 32 are new discoveries. Surface magnetic field strengths were measured from Zeeman splitting, covering a range from a few MG up to several tens of MG. For previously known SDSS MWDs, our LAMOST-based field measurements show mostly agreement with published values. This work demonstrates the capability of LAMOST low-resolution spectroscopy to identify and characterize isolated MWDs. The newly discovered objects expand the known population and provide valuable targets for future high-resolution spectroscopic and polarimetric follow-up studies. Our results highlight the potential of combining LAMOST with Gaia and other large surveys to build a more complete census of MWDs.

Unravelling current complex food systems is relevant for their adjustment and redesign under the current changing climate conditions. Redesign may be necessitated by migration of people and changes of locations of major agri-food production. The redesign should be conducted synchronously with that of systems entangled with the food system, such as the socio-economic and cultural system. For such synchronous redesign a common methodological approach with a common set of methods is required. In the current article we suggest a common set of methods, and discuss how these methods find their basis in vastly different science fields, ranging from soft matter, biology, urban socio-economics, ecology, to machine learning. We address the various ways such methods have been applied in relatively small parts of the food systems and how they can be applied to larger parts of current and future food systems. The set of methods facilitates to identify the level of structuredness and randomness in complex systems. It helps to better predict upcoming transitions in complex systems, according critical points, and sudden instabilities. It facilitates in extracting information from a system, before, during and after the time that one makes an intervention, which in turn will help to decide which interventions are best to maintain or change functions of a complex system.

In this article, we introduce a minimization model via a non-convex transformed $\ell_p$ (TLp) penalty function with two parameters $a\in(0,\infty)$ and $p\in(0,1]$, where the case $p=1$ is known and was established by S. Zhang and J. Xin. Using the sparse convex-combination technique, we establish the exact and the stable sparse signal recovery based on the restricted isometry property (RIP). We apply a modified iteratively re-weighted least squares method and the difference of convex functions algorithm (DCA) to give the IRLSTLp algorithm for unconstrained TLp minimization and prove some convergence results. Finally, we conduct some numerical experiments to show the robustness of the IRLSTLp and the flexibility of the TLp minimization model. The novelty of these results lies in three aspects: (i) We introduce the concept of the relaxation degree RD$_P$ of a separable penalty function $P$ to quantitatively measure how closely $P$ approaches $\ell_0$, whose significance also lies in revealing the functional relationship of the parameters involved to keep a high performance of a multi-parameter minimization model. (ii) We introduce the TLp penalty, which includes two aforementioned adjustable parameters, offering more flexibility and stronger sparsity-promotion capability of the TLp minimization model, compared with the $\ell_p$ and the TL1 minimization models. (iii) The obtained RIP upper bound for signal recovery via TLp minimization can reduce, when $p\in(0,1]$ and as $a\to \infty$, to the sharp RIP bound obtained by R. Zhang and S. Li and, especially, can recover, when $p=1$, the well-known sharp bound $δ_{2s}<\frac{\sqrt{2}}{2}$.

We aim to develop a model-driven deep learning approach to age determination, by training neural networks on stellar evolutionary grids. Contrary to the usual data-driven deep learning approach of using prior age estimates as training data, our method has the potential for a wider and less biased range of application. The low computational cost of deep learning methods compared to bayesian isochrone-fitting allows for a broad analysis of large spectroscopic catalogues. We train multilayer perceptrons on different stellar evolutionary grids to map [M/H], MG, (GBP - GRP) to stellar age $τ$. We combine Gaia photometry and parallaxes, metallicities and $α$ elements from spectroscopic surveys and extinction maps, which are passed through the neural networks to estimate stellar ages. We apply our method to the LAMOST DR10, GALAH DR3 & DR4 and APOGEE DR17 spectroscopic surveys, for which we estimate the ages using the BaSTI tracks, along with other stellar evolutionary models. We leverage this novel technique to study, for the first time, differences in age estimates from several evolutionary grids applied on very large datasets. In addition, we date 13 open clusters and one globular cluster and find a median absolute deviation with literature ages of 0.20 Gyr. Along with the stellar ages catalogues from our estimates, we release NEST (Neural Estimator of Stellar Times), a python package to estimate stellar age based on this work, as well as a web interface. We show that, when using the same evolutionary grid, our method retrieves the same ages as a bayesian approach like SPInS, for only a fraction of the computational cost, with a 60,000 speedup factor for a typical star. This model-driven deep learning technique thus opens up the way for broad galactic archeology studies on the largest datasets available today and in the near future with upcoming surveys such as 4MOST.

Let $(M^n, g)$ and $(X^m, h)$ be closed manifolds $m, n>2$, such that $(X, h)$ has constant positive scalar curvature. We consider the one parameter family of products $(M\times X, g+ε^2 h)$, $ε>0$. We prove that if either the scalar curvature of $g$, $s_g$, is constant or a certain dimensional constant $β=0$, there is some function $Φ:M\rightarrow \mathbb{R}$ that depends on $s_g$, the norm of the Ricci curvature of $g$ and the norm of the curvature tensor of $g$; such that if $ξ_0$ is a stable, isolated, critical point of $Φ$, then for each $K\in\mathbb{N}$, there is some $ε_0>0$ such that for every $ε\in (0,ε_0)$ the subcritical Yamabe equation $-ε^2Δ_g u+(1+{\bf{c}}ε^2 s_g)u=u^q$ has a positive $K-$peak solution, which concentrates around $ξ_0$. Here, ${\bf{c}}=\frac{N-2}{4(N-1)}$, $q=\frac{N+2}{N-2}$ and $N=n+m$. This provides solutions for the Yamabe equation on Riemannian products $(M\times X, g+ε^2 h)$ and covers some remaining cases of previous results which handle the case where $s_g$ has non-degenerate critical points and $β\neq0$.

Nonequilibrium chemistry is central to many astrophysical environments but remains a major computational bottleneck in simulations because solving the associated stiff ODE systems is expensive. Neural surrogates promise large speedups, yet existing studies rarely provide systematic comparisons of architectures or rigorous optimization toward both accuracy and efficiency. We introduce CODES, a principled framework for optimizing and benchmarking astrochemical surrogate models. Using CODES, we compare four neural surrogate architectures across four KROME-generated datasets spanning primordial and molecular-cloud chemistry with up to 287 reactions across 37 species. Dual-objective optimization reveals pronounced accuracy-efficiency trade-offs across architectures. Fully connected models achieve the highest accuracy and most reliable uncertainty estimates, while latent-evolution models show improved robustness under iterative prediction. Our results highlight the importance of systematic optimization and architectural comparison. The datasets, metrics, and benchmarking procedure are publicly released within CODES to enable reproducible surrogate benchmarking.

Accurate spatial prediction and rigorous uncertainty quantification are central to modern spatial epidemiology and environmental risk analysis. We introduce a statistically principled hybrid modelling framework that integrates the nonlinear, attention-based representation learning capabilities of a dynamic Graph Attention Network (GATv2) with a latent Gaussian spatial process from model-based geostatistics (MBG). This framework jointly captures relational dependence encoded in graph structures and continuous spatial dependence governed by physical proximity. We evaluate the proposed model via a controlled simulation study and an applied analysis of malaria prevalence data, comparing its predictive accuracy, calibration, and uncertainty quantification against classical geostatistical models and standalone GATv2 architectures. Our analyses show that GATv2 captures complex nonlinear interactions but fails to account for residual spatial autocorrelation, resulting in miscalibrated predictive distributions. Conversely, geostatistical models provide coherent uncertainty quantification through structured covariance functions yet are constrained by linear predictor assumptions and by their reliance on Euclidean distance to encode spatial structure. By integrating attention mechanisms and nonlinear features with an explicit probabilistic spatial random field, the hybrid model captured the relational dependence, consistently improved predictive accuracy, and provided more realistic uncertainty quantification in both simulation and applied settings. Overall, the findings demonstrate that the hybrid model constitutes a statistically coherent and empirically robust framework for modelling complex spatial and spatio-temporal processes in settings where both distance-based and structure-based dependencies operate.

Theoretical formation models and exoplanet detection surveys indicate that systems with multiple giant planets are common. We investigate how multiple super-thermal mass planets embedded in a circumstellar disk shape the dust distribution and examine the consequences for interpreting disk substructures and inferring planetary properties. We perform two-dimensional hydrodynamical simulations with a modified PLUTO code, treating dust as Lagrangian particles in a wide range of sizes. We analyze systems with two planets of different masses and orbital separations, comparing them to the single-planet scenario. We generate synthetic ALMA continuum maps using RADMC-3D and compute the relative impact velocities of dust particles to assess potential limitations to grain growth. Dust morphologies in multi-planet systems cannot be described as a simple superposition of single-planet gaps. Secular planetary perturbations can generate multiple dust traps and asymmetric structures, while also exciting significant eccentricities in dust particle orbits. As a consequence, the locations and widths of dust rings and gaps depend on the size of the particles, the masses of the planet, and the orbital configurations. Synthetic continuum images may hide gaps carved by multiple planets, thereby complicating the interpretation of observed substructures. In addition, eccentricities induced in dust orbits lead to stronger gas drag, reducing the Stokes number for a given particle size, and the enhanced relative velocities associated with eccentric orbits can further suppress grain growth, promoting fragmentation and replenishment of small dust grains.

We study structure formation in alternative cosmological models constrained by background observations, including $Λ$CDM, wCDM, the Chevallier-Polarski-Linder parametrisation and a flexible Chebyshev expansion of the dark energy equation of state. The models are constrained using baryon acoustic oscillations, cosmic microwave background, cosmic chronometers and strong lensing measurements. Using the best-fitting parameters, we generate cosmology-dependent initial conditions and perform N-body simulations to analyse the matter power spectrum, halo mass function and halo density profiles. Although all models remain broadly consistent with $Λ$CDM at the background level, differences in the physical matter density $Ω_{0m}h^2$ and in the expansion history $H(z)$ lead to distinct growth histories that are amplified by non-linear evolution. We find a clear hierarchy in the power spectrum amplitude and in $σ_8$, with the Chebyshev and CPL models exhibiting enhanced small-scale power, earlier halo formation at $z\gtrsim2$ and a migration of excess toward higher masses at late times. The wCDM model displays milder and partially compensating effects driven by its different expansion history. When expressed in terms of the scaled radius $r/R_{200c}$, halo density profiles show a high degree of universality across cosmologies, indicating that internal halo structure is largely governed by the same gravitational dynamics. These results demonstrate that even modest background-level variations in $w(z)$ can translate into coherent non-linear signatures, highlighting the constraining power of large-scale structure observables in extended dark energy models.

The explosion of big social data has created a scalability trap for traditional qualitative research, as manual coding remains labor-intensive and conventional topic models often suffer from semantic thinning and a lack of domain awareness. This paper introduces Textual Hybrid Embedding based Topic Analysis (THETA), a novel computational paradigm and open-source tool designed to bridge the gap between massive data scale and rich theoretical depth. THETA moves beyond frequency-based statistics by implementing Domain-Adaptive Fine-tuning (DAFT) via LoRA on foundation embedding models, which effectively optimizes semantic vector structures within specific social contexts to capture latent meanings. To ensure epistemological rigor, we encapsulate this process into an AI Scientist Agent framework, comprising Data Steward, Modeling Analyst, and Domain Expert agents, to simulate the human-in-the-loop expert judgment and constant comparison processes central to grounded theory. Departing from purely computational models, this framework enables agents to iteratively evaluate algorithmic clusters, perform cross-topic semantic alignment, and refine raw outputs into logically consistent theoretical categories. To validate the effectiveness of THETA, we conducted experiments across six domains, including financial regulation and public health. Our results demonstrate that THETA significantly outperforms traditional models, such as LDA, ETM, and CTM, in capturing domain-specific interpretive constructs while maintaining superior coherence. By providing an interactive analysis platform, THETA democratizes advanced natural language processing for social scientists and ensures the trustworthiness and reproducibility of research findings. Code is available at https://github.com/CodeSoul-co/THETA.

In 2010, Shiffman and Zelditch proved a central limit theorem (CLT) for smooth statistics of Gaussian random zeros in codimension one over compact Kähler manifolds. They raised the question of whether this result admits a two-fold generalization -- to arbitrary codimensions and to both smooth and numerical statistics -- which has remained open since then. In this paper we resolve this long-standing problem. We establish a universal CLT that holds for both types of statistics arising from several independent Gaussian sections, thereby fully extending the Shiffman--Zelditch theorem. The proof builds on a new geometric framework that lifts the probabilistic tools of Wiener chaos and Feynman diagrams from scalar processes to random currents on complex manifolds, providing a robust mechanism for analyzing fluctuations in random complex geometry beyond the classical codimension-one setting.

We model stochastic choice as environment-dependent switching among a small library of deterministic decision rules. A Random Rule Model generates menu-level choice probabilities via named, interpretable rules weighted by observable menu characteristics. Identification has a two-step structure: within-feature decisive-side variation identifies relative rule weights; cross-feature richness identifies the gate. Applied to binary lottery choices, the estimated weights concentrate on a small subset of rules and shift systematically with complexity and dispersion asymmetry. The model closes nearly all of the prediction gap to a flexible neural-network benchmark, while remaining interpretable, restrictive under permutation diagnostics, and portable to an independent dataset.

Two-dimensional (2D) kagome materials have become a hot research topic in the current scientific community due to their unique electronic structural properties, and the design of novel 2D kagome materials represents a significant exploration direction in this field. In this study, by employing the "1+3" design strategy, surface passivation and charge balance strategies, we successfully designed a novel family of 2D kagome material $B_{18}S_{8}$, $B_{18}S_{8}H_{2}$, $B_{18}S_{6}X_{2}$ (X=Cl,Br,I). Electronic structure analysis revealed that although $B_{18}S_{8}$ exhibits excellent kagome band characteristics, its Dirac cone is located approximately 1 eV above the Fermi level, making it difficult to utilize. However, by surface hydrogen passivation, the Dirac cone can be effectively adjusted to the Fermi level. Further research found that introducing halogen atoms to replace surface sulfur atoms can similarly adjust the position of the Dirac cone to the Fermi level. The Fermi velocities near the Dirac cone for these five materials reach as high as 2.69 to 3.07*$10^5$ m/s. Additionally, spin-orbit coupling can open a bandgap of approximately 20 to 55 meV at the Dirac cone. Our research not only provides an outstanding example for the design of 2D boron-based kagome materials but also fully demonstrates the immense potential of such materials in the electronics field.

The dichromatic number of a digraph $D$, denoted by $\vecχ(D)$, is the smallest number of colours required to colour the vertices of $D$ such that each colour class induces an acyclic digraph. A conjecture of Erdős and Neumann-Lara states that there exists a function $f(k)$ such that for every graph $G$ with $χ(G) \geq f(k)$ there is an orientation of $G$ such that the resulting digraph $D$ satisfies $\vecχ(D) \geq k$. We prove the list version of this conjecture: if $G$ has large list chromatic number then there is an orientation of $G$ such that the resulting digraph has large list dichromatic number. The main tool in our result is the following theorem, which is an extension of an analogous result of Alon for the chromatic number: every graph of minimum degree $d$ admits an orientation such that the resulting digraph has list dichromatic number of order at least $\ln d$.

Non-local models of stellar convection can account for mixing effects in regions adjacent to convectively unstable layers and for changes to the mean temperature structure caused by free, buoyancy driven convection. The physical completeness of such models, however, depends on how third order correlations, which characterize the non-local transport processes, are expressed in terms of second order correlations and the stellar mean structure. Physical arguments and 3D hydrodynamical simulations were used to develop and test new closure relations for the skewness of the vertical velocity and temperature fields and third order cross-correlations to improve the predictive capabilities of non-local models of convection used in stellar astrophysics and in other disciplines such as meteorology. The structural form of the closure correlations was developed by a series of physical arguments and their accuracy was evaluated through self-consistency tests based on 3D hydrodynamical simulations for the Sun and a DA type white dwarf. The new closure relations derived for the skewness of vertical velocity and temperature fields provided improvements of up to an order of magnitude compared to previous models. This allows releasing the full potential of closure relations for the vertical velocity and temperature cross-correlations previously proposed in meteorology as well as the construction of new, more reliable models for the third order moments of vertical velocity and temperature in non-local models of turbulent convection. The new models for the skewness and third order cross-correlations of vertical velocity and temperature permit the construction of non-local models of turbulent convection which remove, among others, several major short-comings of three equation non-local convection models that are based on the downgradient approximation.

Using JWST/MIRI observations, we report the detection of CO$_2$ ice in the dusty torus of the planetary nebula NGC 6302, an environment generally considered hostile to fragile molecular species and ices due to intense UV irradiation. This detection accompanies cold (20-50 K) gas-phase CO$_2$ along the same sightlines. The ice absorption profile exhibits a double-peak profile, a characteristic of pure, crystalline CO$_2$ ice. The CO$_2$ gas-to-ice ratio is more than an order of magnitude higher than in young stellar objects, pointing to distinct ice formation or processing mechanisms in evolved stellar environments. This discovery demonstrates that the dusty torus provides sufficient shielding to harbour ice chemistry, and that ice-mediated surface reactions must be incorporated into chemical models of planetary nebulae.

We report the discovery of strongly absorbed [OI]63um in a sample of 12 DSFGs at 4.2<z<5.8 selected from the SPT survey. This is the first systematic survey of the [OI]63um fine-structure line at z>4. Using ALMA Bands 9 and 10, we obtain spatially and spectrally resolved observations that probe the interstellar medium on sub-kpc scales. Despite reaching sensitivities 10-100x deeper than most previous studies, we detect [OI]63um in emission in only 2 sources at low significance, with the remaining galaxies yielding stringent non-detections over the full velocity range covered by robust detections of other far-infrared lines, including [CII] and [NII]205um. We identify several compact (0.05-0.2") regions having [OI]63um absorption against the far-infrared dust continuum, some of which are possibly reaching below rest-frame CMB radiation level. We also detect narrow, spatially localised [OI]63um emission "escape channels" preferentially detected in regions with weak or absent dust continuum emission. We predict that similar absorption effects may appear in the [CII] line, particularly when concentrating on the regions with the densest foreground material along the line of sight. The [OI]63um line appears to be originate from a mix of compact, high optical depth [OI]63um emitting regions and sub-thermally excited, oxygen-rich molecular clouds dispersed throughout high-redshift starbursts that are capable of absorbing the ground-state line emission. Combined with a comparison to cosmological radiation hydrodynamical simulations, this supports the interpretation that regions with higher gas and dust column densities may lead to weakening an intrinsically strong [OI]63um line emission. We argue that the high [OI]63um optical depth is the dominant effect causing the strong absorption, limiting the diagnostic power of this line to trace regions of massive star formation in high-redshift DSFGs.

Chalcogenide perovskites have emerged as promising absorber materials for next-generation photovoltaic devices, yet their experimental realization remains limited by competing phases, structural polymorphism, and synthetic challenges. Here, we present a fully data-driven and experimentally grounded screening and ranking framework to assess the stability and experimental feasibility of chalcogenide perovskites, integrating interpretable analytical descriptors, machine-learning models, and sustainability metrics. Using a curated experimental dataset of halide and chalcogenide compounds, we derive a new tolerance factor via the SISSO (sure independence screening and sparsifying operator) algorithm that more accurately distinguishes perovskite-forming compositions than established tolerance-factor-based screening criteria. This descriptor is combined with generative crystal structure prediction, composition-based bandgap estimation, and machine-learning-based feasibility assessment to systematically explore a wide chemical space of hypothetical chalcogenide perovskites. The resulting candidates are further evaluated using sustainability indicators, enabling multi-objective ranking tailored to both single-junction and tandem photovoltaic architectures. Beyond identifying several promising and previously unexplored chalcogenide perovskites, this work demonstrates a transferable screening strategy for chemically constrained materials spaces that balances optoelectronic performance, experimental viability, and long-term sustainability.

Context. Stellar ages are typically very difficult to estimate for field stars. New empirical methods, based on abundance ratios of chemical elements, are emerging and need to be calibrated. Aims. Our main aim is to contribute to revealing relations between [C/N] and [Y/Mg] ratios and stellar ages by determining astroseismic ages and non-local thermodynamic equilibrium (NLTE) abundances, and accounting for stellar evolutionary stages and birth places in the Galaxy. Methods. We searched for solar pulsations in a sample of 1250 bright F, G, and K giants using data from the TESS space telescope and determined asteroseismic ages using the BASTA and PARAM codes. For the [Y/Mg] relations with age, we determined abundances accounting for deviations from the local thermodynamic equilibrium. For the [C/N] relations with age, we separated stars according to their evolutionary stages. Results. We determined asteroseismic ages for 218 giants and derived [Y/Mg] and [C/N] relations with age for subsamples of stars in three regions of the Galactic thin disc and the thick disc. Conclusions. The [Y/Mg]-age relation exhibits a clear radial dependence across the Galactic disc, with a steeper trend in the outer disc, progressively flatter relations towards the inner disc, and a very flat trend in the thick disc. NLTE abundances of Mg and especially of Y have to be used in order to obtain a more precise stellar age evaluation from [Y/Mg] ratios. When using [C/N] abundance ratios as stellar age indicators, evolutionary stages of stars have to be taken into account.

Be/X-ray binaries are the most numerous group of high-mass X-ray binaries. Their long-term optical and infrared variability reflects the evolution of the circumstellar disk around the luminous companion. This variability manifests photometrically as an excess of flux that increases with wavelength and spectroscopically as line emission. The disk is also expected to generate linear polarization. We present a systematic study of the optical long-term polarimetric variability of Be/X-ray binaries on data collected over 10 years. Our aim is to characterize the polarimetric properties of these systems and to probe the structure of their circumstellar disks. We have been monitoring Be/X-ray binaries visible from the Northern hemisphere with the RoboPol polarimeter. Optical polarimetric variability is a common trait in Be/X-ray binaries. The variability can be attributed to the Be star's circumstellar disk. Our polarization analysis confirms previous claims based on spectroscopic data that the circumstellar disks in BeXBs are, on average, smaller and denser than those in Be stars in non-binary systems. Our data also confirms the presence of highly distorted disks before giant X-ray outbursts, although this result is still affected by the lack of simultaneous and well-sampled observations during major X-ray outbursts.