We investigate the directed flow of D and B mesons in the presence of electromagnetic fields incorporating finite electrical and chiral conductivities at LHC energies. The momentum evolution of heavy quarks in the quark-gluon plasma (QGP) is studied using Langevin dynamics, with their interactions with the medium described within the extended quasiparticle model (QPMp) framework. The electromagnetic fields are obtained from analytical solutions of Maxwell equations that account for both electrical and chiral conductivities. These conductivities modify the space-time evolution of the electromagnetic fields and influence the splitting of the directed flow between mesons and anti-mesons. However, the influence of chiral conductivity remains secondary to that of electrical conductivity and its impact on the directed flow is marginal. The results show that heavy mesons containing a charm quark develop a directed flow with a sign opposite to that of heavy mesons containing a bottom quark, with a smaller magnitude for the latter. The present study indicates that a simultaneous experimental measurement of v1 for heavy mesons containing both charm and bottom quarks would provide valuable insight into the electromagnetic field origin of v1 for heavy quarks.

Marcasite-type compounds have been proposed as promising hosts of exotic magnetic quantum states, yet experimental realizations in stoichiometric, disorder-free systems remain limited. Here, we report the high-pressure stabilization and magnetic characterization of MnSb$_2$, a marcasite-type compound that is thermodynamically metastable under ambient pressure. Single crystals were synthesized using a cubic multi-anvil press, and powder and single-crystal X-ray diffraction confirm the orthorhombic $Pnnm$ structure. These crystals are stable at ambient pressure for a long time up to between 450-500 K. Heat-capacity measurements reveal phase transitions at approximately 220 K and 118 K. Neutron diffraction uncovers an unconventional magnetic ground state below 220 K. Magnetic powder neutron diffraction refinements reveal possible multiple magnetic configurations that provide comparably acceptable fits to the experimental data. While most solutions are consistent with a spin-density-wave (SDW) description, helical models systematically yield inferior agreement factors. Across a broad range of models, the Mn ordered moment reaches a maximum value of approximately 2 $μ_B$ and remains predominantly collinear, with minimal canting along the $c$-axis. At 200 K, the magnetic propagation vector is $q$ = (0, 0.3975, 0.3783); upon cooling, the $b$ component increases toward 0.5, reflecting a temperature-dependent evolution of the modulation. The need for modification of the magnetic model between high and low temperatures further highlights the complex and strongly temperature-dependent nature of the magnetic order in this system. These results establish MnSb$_2$ as a pressure-stabilized marcasite magnet with a highly tunable, complex magnetic ground state and a compelling stoichiometric platform for exploring unconventional magnetic behavior, including potential altermagnetism.

Stochastic gradient methods are among the most widely used algorithms for large-scale optimization and machine learning. A key technique for improving the statistical efficiency and stability of these methods is the use of averaging schemes applied to the sequence of iterates generated during optimization. Starting from the classical work on stochastic approximation, averaging techniques such as Polyak--Ruppert averaging have been shown to achieve optimal asymptotic variance and improved convergence behavior. In recent years, averaging methods have gained renewed attention in machine learning applications, particularly in the training of deep neural networks and large-scale learning systems. Techniques such as tail averaging, exponential moving averages, and stochastic weight averaging have demonstrated strong empirical performance and improved generalization properties. This paper provides a survey of averaging techniques in stochastic gradient optimization. We review the theoretical foundations of averaged stochastic approximation, discuss modern developments in stochastic gradient methods, and examine applications of averaging in machine learning. In addition, we summarize recent results on the finite-sample behavior of averaging schemes and highlight several open problems and directions for future research.

The structure of maximal faces of the cone of completely positive matrices is still not well understood in higher dimensions, mainly due to the lack of a general characterization of extreme exposed rays of the copositive cone beyond small matrix orders. This paper contributes to the study of maximal faces of the cone of completely positive matrices by establishing sharper bounds on their dimensions than those currently available. For every odd dimension $n$, we prove that the exact lower bound on the dimensions of maximal faces of the cone of $n \times n$ completely positive matrices equals $n$. For even dimensions $n \geq 8$, we derive a new upper estimate for this lower bound and show that it lies between $n$ and $n+3$. These results substantially refine the previously known bounds.

We propose an approximate mapping of a molecular Hamiltonian to a Hamiltonian of qubits, which allows for high accuracy quantum chemistry calculations of vertical excitation energies of some molecules. The mapping is based on separating of a very small active space of only two orbitals and on modeling the electronic excitations in the remaining orbitals by a set of qubits or, equivalently, by a set of oscillators. This approach is inspired by the Random Phase Approximation (RPA), in which the excitations of electron gas are described by bosonic degrees of freedom. As a result, the Hamiltonian of the molecule is reduced to that of a system-bath model. The "system" part of the Hamiltonian describes the two molecular orbitals -- the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) -- which are populated by two electrons. Two qubits are sufficient to encode the Hamiltonian of such a system. The "bath" consists of oscillators or, equivalently, of two level systems with each of them corresponding to an electron excitation from a doubly occupied orbital below the Fermi level to an empty orbital above the Fermi level. We hope that this mapping can inspire new approaches and algorithms aimed at calculating excitation energies of molecules on near term quantum computers.

The matrix element in the proton of the eikonal Odderon operator, with a helicity flip, has been shown to correspond to the dipole gluon Sivers function. We employ a three quark light-front model of the proton to determine the Sivers function at moderately small $x_0 \sim 0.1$ and transverse momentum $k_\perp \lesssim 1$~GeV. The model light-cone (LC) wave function predicts the properties of $x f_{1T}^{\perp g}(x,k_\perp)$ such as its overall magnitude, the position of its peak in $k_\perp$, and its behavior at small $k_\perp$. We then compute numerically the BFKL anomalous dimension characterizing the power-law tail at $k_\perp \gtrsim 1.5$~GeV of the gluon Sivers function at small LC momentum fractions, $α_s \log x_0/x = 1$: $x f_{1T}^{\perp g}(x,k_\perp) \sim k_\perp^{-3.3}$.

Suppose $\widehatθ_n$ is a strongly consistent estimator for $θ_0$ in some i.i.d. situation. Let $N_\varepsilon$ and $Q_\varepsilon$ be respectively the last $n$ and the total number of $n$ for which $\widehatθ_n$ is at least $\varepsilon$ away from $θ_0$. The limit distributions for ${\varepsilon}^2 N_\varepsilon$ and ${\varepsilon}^2 Q_\varepsilon$ as $\varepsilon$ goes to zero are obtained under natural and weak conditions. The theory covers both parametric and nonparametric cases, multi-dimensional parameters, and general distance functions. Our results are of probabilistic interest, and, on the statistical side, suggest ways in which competing estimators can be compared. In particular several new optimality properties for the maximum likelihood estimator sequence in parametric families are established. Another use of our results is ways of constructing sequential fixed-volume or shrinking-volume confidence sets, as well as sequential tests with power 1. The paper also includes limit distribution results for the last $n$ and the number of $n$ for which the supremum distance $\|F_n-F\|\ge\varepsilon$, where $F_n$ is the empirical distribution function. Yet other results are reached for $\varepsilon^{5/2} N_\varepsilon$ and $\varepsilon^{5/2} Q_\varepsilon$ in the context of nonparametric density estimation, referring to the last time and the number of times where $|f_n(x) f(x)|\ge\varepsilon$. Finally it is shown that our results extend to several non-i.i.d. situations.

While large-scale omni-models have demonstrated impressive capabilities across various modalities, their strong performance heavily relies on massive multimodal data and incurs substantial computational costs. This work introduces Speech-Omni-Lite, a cost-efficient framework for extending pre-trained Visual-Language (VL) backbones with speech understanding and generation capabilities, while fully preserving the backbones' vision-language performance. Specifically, the VL backbone is equipped with two lightweight, trainable plug-and-play modules, a speech projector and a speech token generator, while keeping the VL backbone fully frozen. To mitigate the scarcity of spoken QA corpora, a low-cost data construction strategy is proposed to generate Question-Text Answer-Text-Speech (QTATS) data from existing ASR speech-text pairs, facilitating effective speech generation training. Experimental results show that, even with only thousands of hours of speech training data, Speech-Omni-Lite achieves excellent spoken QA performance, which is comparable to omni-models trained on millions of hours of speech data. Furthermore, the learned speech modules exhibit strong transferability across VL backbones.

Inter-level connections in science routinely require constructs that neither of the connected theories contains. Statistical mechanics requires assumptions such as the Stosszahlansatz to generate thermodynamic irreversibility - assumptions that Hamiltonian mechanics cannot provide. Quantum chemistry offers four incompatible analyses of chemical bonding for the same quantum state, none of which are selected by the Schrodinger dynamics. Molecular genetics has not converged on a stable definition of the gene despite decades of molecular detail. These are not isolated anomalies but instances of a common architectural pattern. The missing apparatus is the bridge theory: a third theoretical role that connects a dynamical theory to an observational theory through a many-to-one inter-level map. That map generates the contingent space - the set of dynamical states compatible with an observational description but not selected by it - whose geometry neither connected theory determines. Completing the bridge theory requires three conditions in order: a Partition that defines observational equivalence classes; a Magnitude that characterizes the geometry and scale of the contingent space; and a Closure that selects or weights its elements. The framework yields an objective distinction between closing and introducing rules, formalized by the Mirror Test, and supports a tripartite taxonomy of emergence. It explains why some inter-level disputes persist and what would be required to resolve them.

We studied two successive coronal mass ejections (CMEs) that erupted from the same active region (AR 12994) on 2022 April 15 and propagated toward Mercury. Using multi-view observations, we applied the revised cone model to determine the three-dimensional geometry and the early kinematics of the two CMEs. Our best fit parameters indicate large angular extents of 84 and 86 and propagation directions of 119.0 and 110.4 (measured from the Sun Earth line) for CME1 and CME2, respectively, while that of Mercury is 120.1. The derived axis inclinations are 28 for CME1 and 21 for CME2, consistent with the orientation of the erupting flux ropes in the source region. Height time analysis indicates approximately uniform motion speeds of 636 for CME1 and 696 for CME2, respectively. This paper provides valuable insights for predicting the impact of CMEs heading for Mercury as well as other solar planets in the future.

We study tunable shear thickening in active suspensions of non-Brownian, repulsive, frictional grains using particle-based simulation, finding that activity augments the rheology beyond the friction-mediated shear thickening paradigm. Specifically, increasing particle self-propulsion drives a viscosity-reducing `dethickening' of the system at large stress, where the material would otherwise be in a thickened, highly viscous state. Self-propulsion introduces additional isotropic dynamics to the particles, which compete with the flow-driven formation of frictional contacts. The degree of dethickening can thus be tuned by varying a suitably-defined dimensionless active stress that quantifies this competition. Recognising the parallels between self-propulsion and other contemporary routes to dethickening, we demonstrate that our data obey a recently proposed scaling framework, supporting a universal description of the tunable rheology of dense suspensions.

We construct a fractured structure, in the sense of Lurie, on the $\infty$-topos of condensed anima. This fractured structure allows us to better comprehend various properties of condensed anima - we use it to exhibit an explicit collection of jointly conservative points for condensed anima. To rule out further candidates for fractured structures, we analyze limits in the category of extremally disconnected spaces. In particular, we show that it does not admit all fibers, answering a question from Clausen.

Existing results on finite-time model predictive control (MPC) often rely on terminal equality constraint, switching inside one-step region, or terminal cost with short control horizon, leading to limited initial feasibility. This paper proposes an infinite-horizon Model Predictive Control (MPC) framework for the constrained finite-time stabilization of discrete-time systems, overcoming limitations found in existing finite-time MPC results. The proposed framework is built upon a terminal cost strategy, but expands it by replacing the short-horizon terminal cost with the sum of stage costs over an infinite control horizon. This design choice significantly enlarges the initial feasibility region and avoids the need for terminal equality constraints or switching strategies during implementation. It is proved that the proposed finite-time MPC guarantees finite-time stabilization performance once the state trajectory enters the predefined terminal set. The infinite-horizon finite-time MPC is shown to be equivalently implementable as a finite-horizon MPC with a terminal cost, thereby ensuring computational tractability. The proposed finite-time MPC is systematically extended and shown to be applicable to both constrained multi-input linear systems and a class of constrained nonlinear systems that are feedback linearizable.

We propose a novel strategy to probe feebly-interacting particles (FIPs) by exploiting the dense, confined circumstellar medium (CSM) surrounding core-collapse supernovae (CCSNe). FIPs produced in the proto-neutron star can deposit substantial visible energy into the CSM via decay prior to the shock breakout from the progenitor star. This energy injection heats and ionizes the CSM, establishing a FIP-induced photosphere that generates distinctive precursor blackbody emission. Using early-time observations of SN 2023ixf, we translate the non-detection of excessive precursor luminosity into stringent new constraints on MeV-scale dark photons as an exemplary model. Our results significantly extend existing CCSN bounds and exclude previously unexplored regions of parameter space. We further demonstrate that the FIP-induced dust sublimation offers robust diagnostics for future Galactic SNe, opening a new avenue to explore the dark sector.

Stanley's theorem establishes that for any finite graph $G$, the number $h_G(s)$ of magic labelings with magic sum $s$ can be expressed as a sum of two polynomials in $s$. However, determining the precise form of $h_G(s)$ is generally challenging. This paper aims to compute $h_G(s)$ and its generating function for pseudo-line graphs and pseudo-cycle graphs, thereby extending the earlier work of Bóna et al.\cite{Bona-1,Bona}.

We propose metal-organic frameworks (MOFs) as a versatile platform for realizing the nonlinear Hall effect. We develop an analytical down-folding scheme that maps a broad class of MOFs onto a universal effective low-energy model. As representative examples, we analyze two $C_3$-symmetric frameworks: Cu-dicyanoanthracene and triphenyl-metal monolayer, demonstrating how their low-energy bands can be efficiently captured by a star-lattice geometry. First-principles calculations corroborate this mapping and show that both Fermi levels lie close to symmetry-protected Dirac points. Spin-orbit coupling or inversion-symmetry breaking gaps these Dirac cones, generating Berry-curvature hotspots near the Fermi level. Supported with symmetry-based indicators, these MOFs thus suggest themselves for strain and substrate engineering as well as doping to achieve a finite nonlinear Hall response. We formulate a synthesis-oriented strategy that implements the Dirac gap directly within the framework architecture without externally applied strain. Our results establish MOFs as a broadly designable platform for engineering Berry-curvature physics and nonlinear Hall transport.

In this paper, we study a hyperbolic-parabolic coupled system arising in nonlinear three-dimensional thermoelasticity. We establish the global well-posedness and asymptotic behavior of solutions. Our main result shows that, a thermoelastic body asymptotically converges to an equilibrium state with a uniform temperature distribution for every initial data, determined by energy conservation. The proof of the global well-posedness is divided into some steps. To begin with, we introduce an approximate problem and derive its solvability. Next, we establish a time-independent upper bound for the temperature via Moser iteration technique. Together with an estimate of gradient of entropy, we use a functional involving the Fisher information of the temperature, which enables us to handle a delicate Gronwall-type inequality, to obtain required estimates of the higher-order derivatives. Further, we prove the strict positivity of temperature by applying Moser iteration again on the negative part of the logarithm of the temperature, followed by a uniqueness argument for the weak solution. Finally, we define a dynamical system on a proper functional phase space and analyze the $ω$-limit set for every initial data. This work provides a complete proof of the global well-posedness and the long-time behavior in the nonlinear three-dimensional thermoelasticity system.

We develop and compare several analytical approximations for the polaron problem in finite-width, non-parabolic conduction bands. The main focus of the work is an extension of the Feynman variational method to a tight-binding lattice, where the effective-mass approximation is no longer applicable. The resulting variational formulation is not restricted to a specific phonon dispersion or electron-phonon interaction and provides a uniform description across weak-, intermediate-, and strong-coupling regimes. We revisit and generalize other analytical approaches traditionally formulated for continuum polarons, including canonical transformations and self-consistent Wigner-Brillouin-type approximations. For lattice polarons, these methods exhibit qualitative features absent in the continuum case, such as a nontrivial connection between weak- and strong-coupling limits. We show that an improved Wigner-Brillouin scheme yields a momentum-dependent polaron self-energy free of resonances and in good agreement with numerically exact results over the whole range of momenta within the Brillouin zone. All methods are applied to the Holstein model and are benchmarked against numerically exact calculations, including Diagrammatic Monte Carlo (both our calculations and preceding works), exact diagonalization, and density-matrix renormalization-group results. The analytical approaches are extended to polarons with Rashba-type spin-orbit coupling, providing a stringent test of their applicability in systems with nontrivial band structure. Our results demonstrate that the modified Feynman variational method yields ground-state energies and dispersions with accuracy comparable to, and in many cases exceeding, that of other established analytical approaches. The developed framework offers a versatile and reliable analytical description of lattice polarons beyond the continuum approximation.

The spin periods of magnetars and X-ray dim isolated neutron stars (XDINS) cluster within a remarkably narrow range. Using the current sample of 30 magnetars with measured periods (ranging from 0.33 to 11.78 s) and 8 XDINS (ranging from 3.45 to 12.76 s), we utilize the point-likelihood technique to constrain the birth and final periods of these sources, assuming a steady-state population. Employing a general braking law characterized by a constant braking index $n$, we find that for $n > 2$ the final (cut-off) period of magnetars is constrained to $P_f \simeq 11.8 - 12.0$ s and XDINS to $P_f \simeq 12.8 - 14.9$ s, at the 95 per cent confidence level, while the birth periods remains largely unconstrained for dipole spin-down ($n=3$) as in earlier work. The slight increase in the upper cutoff from $\sim$12 to $\sim$15 s over two decades of discoveries of new sources, yielding a threefold increase in the known magnetar population, and the extension of the minimum period to $\sim 0.33$ s strongly support a physical origin for this clustering. We discuss this result in the context of magnetic-field-decay models and fallback-disc torque-equilibrium scenarios. The combined magnetar and XDINS sample (38 sources) yields the tightest constraints on $P_f\simeq 12.8-12.9$ s, for $n=3$, suggesting possible evolutionary connections between these populations and pointing toward a common physical mechanism that terminates the observable phase of these neutron stars at periods near 14 s.

Microchannel heat sinks (MCHS) are widely used for thermal management in high-power electronics due to their ability to dissipate large heat fluxes with minimal coolant consumption. While numerous strategies - such as geometric modifications, surface disruptions, and enhanced coolant formulations - have been explored to improve heat transfer, many of these approaches increase hydraulic resistance and pumping power requirements. Recent studies have shown that slip/no-slip wall patterns can enhance flow rates and convective heat removal without additional energy input, and that patterned microstructures can induce secondary swirling motions known to promote mixing and heat transfer. Motivated by these findings, we investigate a slip/no-slip pattern specifically designed to generate swirl flow inside a straight microchannel. Building upon prior work on passive chaotic advection and boundary-condition engineering, we assess the hydrodynamic and thermal performance of this patterned configuration under conditions relevant to laminar microchannel cooling. Our results demonstrate that appropriately arranged slip/no-slip regions can induce swirl without geometric perturbations or increased pumping power, ultimately improving heat transfer efficiency at fixed volumetric flow rate. This study highlights the potential of boundary-condition patterning as a simple, energy-neutral strategy for enhancing the performance of microfluidic heat-transfer devices.

Modern storage systems predominantly use flash-based SSDs as a cache layer due to their favorable performance and cost efficiency. However, in tiny-object workloads, existing flash cache designs still suffer from high write amplification. Even when deploying advanced log-structured flash devices (e.g., Zoned Namespace SSDs and Flexible Data Placement SSDs) with low device-level write amplification, application-level write amplification still dominates. This work proposes Nemo, which enhances set-associative cache design by increasing hash collision probability to improve set fill rate, thereby reducing application-level write amplification. To satisfy caching requirements, including high memory efficiency and low miss ratio, we introduce a bloom filter-based indexing mechanism that significantly reduces memory overhead, and adopt a hybrid hotness tracking to achieve low miss ratio without losing memory efficiency. Experimental results show that Nemo simultaneously achieves three key objectives for flash cache: low write amplification, high memory efficiency, and low miss ratio.

It has been speculated that the CP symmetry of 4D SU(3) Yang-Mills theory at $θ=π$ is spontaneously broken in the confined phase, and it is recovered precisely at the deconfining temperature. The direct simulation of the theory at $θ=π$ is, however, difficult due to the sign problem. We therefore simulate the theory with an imaginary theta parameter and perform analytic continuation to the real theta to explore the phase diagram. We implement the stout smearing technique in the hybrid Monte Carlo simulation to recover the topological property of the gauge field. The smearing-time dependence of the observable is investigated using the reweighting method with respect to the smearing step parameters, and a clear scaling behavior is observed. The order parameter of the CP symmetry is then computed in the scaling region to detect symmetry breaking. We report preliminary results on the expected CP breaking and restoration temperature.

The interlayer quasi-chemical-bonding (QCB) interactions of two-dimensional (2D) layered materials promote the research field of interlayer-engineering and cause interlayer charge density redistributions (ICDRs). The ICDRs have been reported experimentally and theoretically, which show different redistributions, e.g., accumulation, depletion, or a more complicated behavior. The underlying mechanism for the different ICDRs remain to be elucidated. In the current work, via a systematic theoretical study of the ICDRs of transition metal dichalcogenides with different number of d-electrons filling (d^0 TiS2, d^1 NbS2, and d^2 MoS2) in T and H phases, we reveal three mechanisms based on the coexistence of different types of interlayer QCB interactions. Mechanism (1) is from a competition between two types of interlayer interactions: namely, the interlayer interaction between fully occupied energy levels (in short: o-o interaction) depletes electrons in the overlap region while that between occupied and empty levels (o-e interaction) promotes electron accumulation; and the competition between them leads to that the d^0 TiS2 tends to electron accumulation in T phase than in H phase. Mechanism (2), the interlayer interaction between half-filled levels (h-h interaction) promotes the electron accumulation of d^1 NbS2. Mechanism (3), the interlayer interaction of multiple filled-levels of d^2 MoS2 (namely, the multi-level o-o interaction) leads to a more complicated ICDR. The current study provides a unified understanding to the different ICDRs of van der Waals materials and paves the way for further exploration of their electronic properties and applications.

Generative AI and agentic tools are reshaping agile software development, yet many engineering curricula still teach agile methods and AI competencies separately and largely lecture-based. This paper presents a project-based AI Engineering curriculum designed to prepare students for AI-driven agile development by integrating agile practices and AI-enabled engineering throughout the program. We contribute (1) the curriculum concept and guiding principles, (2) a case study of interdisciplinary, AI-enabled agile student projects, and (3) early evidence from a mixed-methods evaluation. In our case study, second-semester bachelor students work in teams over seven two-week sprints on a realistic software product. AI tools are embedded into everyday agile engineering tasks - requirements clarification, backlog refinement, architectural reasoning, coding support, testing, and documentation - paired with reflection on human responsibility and quality. Initial results indicate that the integrated approach supports hands-on competence development in AI-assisted engineering. Key observations highlight the need for agile teaching adaptations due to rapid tool evolution, the critical role of oral verification to ensure foundational learning. We close with lessons learned and recommendations for educators designing agile project-based curricula in the age of AI.

We investigate and define in this paper, in the context of the correspondence between anti-de Sitter $3$-space and $(1,1)$-conformal metrics, the analogs of $\cW$-volume, Epstein surfaces, and Liouville action. These notions were well-studied in the correspondence between $3d$-hyperbolic manifolds and $2d$ conformal metrics. We apply our construction to positive curves in flag manifolds equipped with a positive structure to obtain invariants of these curves that are finite in the case of piecewise circles.

Automated scientific discovery aims to improve scientific understanding through machine learning. A central approach in this field is symbolic regression, which uses genetic programming or sparse regression to learn interpretable mathematical expressions to explain observed data. Conventionally, the focus of symbolic regression is on identifying ordinary differential equations. The general view is that noise only complicates the recovery of deterministic dynamics. However, explicitly learning a symbolic function of the noise component in stochastic differential equations enhances modelling capacity, increases knowledge gain and enables generative sampling. We introduce a method for symbolic discovery of stochastic differential equations based on genetic programming, jointly optimizing drift and diffusion functions via the maximum likelihood estimate. Our results demonstrate accurate recovery of governing equations, efficient scaling to higher-dimensional systems, robustness to sparsely sampled problems and generalization to stochastic partial differential equations. This work extends symbolic regression toward interpretable discovery of stochastic dynamical systems, contributing to the automation of science in a noisy and dynamic world.

This paper investigates a quasilinear parabolic system arising in thermoviscoelasticity of Kelvin-Voigt type with temperature-dependent viscosity and coupled terms. The system, given by \begin{equation*} \begin{cases} u_{tt}=\nabla\cdot\big(γ(Θ)\nabla u_t\big)+aΔu-\nabla\cdot f(Θ), & x \in Ω,\ t > 0, Θ_t=ΔΘ+γ(Θ)|\nabla u_t|^2-f(Θ)\nabla u_t, & x \in Ω,\ t > 0, u=0,\quad\frac{\partialΘ}{\partialν}=0, & x \in \partialΩ,\ t > 0, u(x,0)=u_0(x),\; u_t(x,0)=u_{0t}(x),\;Θ(x,0)=Θ_0(x), & x \in Ω, \end{cases} \end{equation*} models heat generation by acoustic waves in solid materials and can be derived as a scalar simplification of more complex piezoelectric-thermoviscoelastic model. Under the assumptions that $u_0\in H_0^1(Ω)$, $u_{0t}\in L^2(Ω)$, $Θ_0\in L^1(Ω)$ with $Θ_0\geqslant0$ a.e.~in $Ω$, that $γ,f\in C^0([0,\infty))$ satisfy $f(0)=0$, and that there exist constants $k_γ,K_γ,K_f>0$ and $0<α<\frac{N+2}{2N}$ such that $$k_γ\leqslantγ(ξ)\leqslant K_γ\quad\text{and}\quad |f(ξ)|\leqslant K_f(1+ξ)^α\qquad\forall~ξ\geqslant0,$$ we establish the global existence of weak solutions for arbitrarily large initial data in bounded domains $Ω\subset\mathbb{R}^N$ ($N\geqslant1$). The result extends recent one-dimensional finding \cite{WinklerZAMP} to the multi-dimensional setting without requiring any smallness condition on the data.

This study presents investigations of Yb-doped $β$-Ga$_2$O$_3$, an ultrawide bandgap semiconductor with potential use in future power and optoelectronic devices operating in high-radiation environments. The research has focused on the problem of structural damage caused by the implantation of Yb-ions into three differently oriented crystals and the optical response of created systems. The (001), (010), and (-201)-oriented $β$-Ga$_2$O$_3$ crystals were implanted with three different fluences of 150 keV Yb ions and examined using a variety of experimental techniques: high-resolution X-ray diffraction (HRXRD), Rutherford backscattering spectrometry in channeling mode (RBS/c), Raman and photoluminescence (PL) spectroscopies, to provide comprehensive information about studied systems. Furthermore, the RBS/c studies were supported by Monte Carlo simulations. The results show distinctions between differently oriented crystals. In particular, (010)-oriented crystals are characterized by the lowest concentration of extended defects and the presence of compressive stress. In contrast, samples with the other two orientations exhibit tensile stress and significantly higher levels of extended defects. Interestingly, the PL spectra of (010)-oriented $β$-Ga$_2$O$_3$ show the lowest emission from Yb$^{3+}$ ions, suggesting that specific types of extended defects, whose formation is more favorable in the other two orientations than in (010), enhance Yb$^{3+}$ luminescence instead of suppressing it.

We analyze superselection sectors, non-invertible symmetries and selection rules for RG flows triggered via perturbations of a UV two-dimensional conformal field theory (CFT$_2$). To this end we describe a method whose input is the local data, and whose output is the set of submodels of the modular invariant completions. We explain how this output set provides a classification of superselection sectors (DHR categories and Q-systems) and of topological defect lines, leading to a unified and potentially complete approach to selection rules for RG flows. This method is applied to scenarios in which the UV is a coset or a parafermion model. For these CFT$_2$ we explicitly find all submodels of the diagonal modular invariants. Our results gives selection rules that unify several known facts about such RG flows, while also allowing us to find new aspects.

Benchmarking presence-only passive reconnaissance in smart-grid communications is challenging because the adversary is receive-only, yet nearby observers can still alter propagation through additional shadowing and multipath that reshapes channel coherence. Public smart-grid cybersecurity datasets largely target active protocol- or measurement-layer attacks and rarely provide propagation-driven observables with tiered topology context, which limits reproducible evaluation under strictly passive threat models. This paper introduces an IEEE-inspired, literature-anchored benchmark dataset generator for passive reconnaissance over a tiered Home Area Network (HAN), Neighborhood Area Network (NAN), and Wide Area Network (WAN) communication graph with heterogeneous wireless and wireline links. Node-level time series are produced through a physically consistent channel-to-metrics mapping where channel state information (CSI) is represented via measurement-realistic amplitude and phase proxies that drive inferred signal-to-noise ratio (SNR), packet error behavior, and delay dynamics. Passive attacks are modeled only as windowed excess attenuation and coherence degradation with increased channel innovation, so reliability and latency deviations emerge through the same causal mapping without labels or feature shortcuts. The release provides split-independent realizations with burn-in removal, strictly causal temporal descriptors, adjacency-weighted neighbor aggregates and deviation features, and federated-ready per-node train, validation, and test partitions with train-only normalization metadata. Baseline federated experiments highlight technology-dependent detectability and enable standardized benchmarking of graph-temporal and federated detectors for passive reconnaissance.