This work presents a fast, uncertainty-aware sequential data assimilation framework for estimating key aerodynamic states (e.g., instantaneous vorticity fields and aerodynamic loads) during severe gust encounters, where vortex-gust interactions strongly affect the flow dynamics. The framework comprises an ensemble Kalman filter (EnKF) designed to detect and reconstruct nearly impulsive flow disturbances with a wide range of strengths and orientations introduced at arbitrary times. The forecast and measurement update stages of the EnKF are composed of learned operators in a low-dimensional latent space obtained via a physics-augmented autoencoder. The forecast operator propagates undisturbed baseline dynamics but cannot predict random gust-induced deviations. The analysis stage therefore frequently assimilates surface pressure measurements to detect disturbance signals and initiate deviations from the nominal trajectory. The methodology is trained and tested on flowfield snapshots from high-fidelity simulations of two-dimensional airfoil-gust encounters and corresponding sparse pressure data. Because assimilation occurs entirely in the latent space, updates are computationally efficient and aerodynamic states can be continuously estimated from streaming pressure measurements. The latent state remains physically interpretable via decoding to the original high-dimensional flow. Eigenvalue decomposition of state and observation Gramians reveals the dominant correction directions required to capture the disturbance and quantifies how sensors inform state corrections during gust interaction. The framework also accounts for sensor failure: sensor-dropout experiments show that the EnKF adaptively reweights neighboring sensors to compensate for lost information, preserving estimation quality under degraded sensing.

Band structure analysis is central to understanding wave propagation in periodic media; however, it becomes challenging in open systems owing to energy leakage. Photonic crystal (PhC) slabs exemplify such systems, featuring periodicity in the $x$-$y$ plane and finite extent in the $z$-direction, and supporting diverse guided-mode resonances whose interactions give rise to phenomena such as bound states in the continuum (BICs), exceptional points (EPs), and circular polarisation states. Although numerical simulations can reveal these effects, effective non-Hermitian Hamiltonians are often employed to elucidate the underlying physical mechanisms. This approach, however, relies on manually selected resonant modes and may suffer from basis incompleteness. Here, a systematic first-principles approach is presented to derive the complex band structure. The minimal channels in the scattering matrix, either open or closed, are determined by the number of propagating bulk Bloch waves. The interactions between these waves fully reveal the complex band structure. For instance, two Bloch waves predict the leading-order imaginary frequency $ω''$ and identify accidental BICs, each associated with a dual Fabry--Pérot mode, whereas three waves reveal robust Friedrich--Wintgen and symmetry-protected BICs together with the associated linewidth behaviours. Orthogonally polarised waves are further incorporated to characterise far-field polarisation and EPs. When extended to a two-dimensional periodic structure, this framework accurately predicts $ω''$, encompasses all known BICs, and tracks their evolution with system parameters. Overall, this first-principles approach provides a unified foundation for studying complex band structure and facilitates the exploration of light confinement in periodic media.

The energy density generated by a vector current is characterized by a single parameter $a_{\mathcal{E}}$ bounded by unitarity to $-1/2 \leq a_{\mathcal{E}} \leq 1$, with extremal values saturated by free theories of different matter content. Through confinement, QCD transmutes fermionic matter into scalars, revealing a nontrivial flow between extremal correlators. We reconstruct this flow using perturbative QCD and chiral perturbation theory. The observable is accessible with currently available experimental data.

The quantum Zeno effect, which inhibits quantum state evolution via repeated weak measurements, significantly enhances the efficiency of interaction-free measurement (IFM). This fundamental mechanism facilitates high-efficiency counterfactual quantum communication, enabling information delivery without particle transmission through the channel. However, the transmission time of the counterfactual communication requires minutes for bit and suffers the bit error when transmitting an image. Applying the quantum Zeno effect, we experimentally demonstrate high-efficiency weak-trace-free counterfactual communication on a quantum photonic chip, achieving a transmission probability of 74.2 $\pm$ 1.6\% for bit 0 and 85.1 $\pm$ 1.3\% for bit 1. Furthermore, we successfully transmit our group's logo -- Quanta -- through counterfactual communication, and reduce the time cost from minutes to seconds for bit, with zero bit errors after information processing. Our study provides a promising approach for secure and efficient communication using integrated silicon quantum photonics.

We present models of axion based on supersymmetric chiral gauge theories. In these models, the PQ symmetry is spontaneously broken by the non-perturbative dynamics of chiral gauge theory. Thanks to supersymmetry, IR dynamics of the models are calculable. We also present an example of a QCD axion model that is compatible with SU(5) grand unification. We find that in order to realize the gauge coupling unification with a certain precision, the GUT scale is the same with the PQ breaking scale, and the SUSY breaking scale is ${\cal O} (10^9)~{\rm GeV} $.

A key question about the QCD phase diagram is whether there is a critical point somewhere on the boundary between the hadronic and quark-gluon plasma phases, and if so where. Heavy-ion collisions offer a unique opportunity to search for signatures of such a critical point by analyzing event-by-event fluctuations in particle multiplicities. To draw meaningful conclusions from experimental data, a theoretical framework is needed to link QCD thermodynamics with the particle spectra and correlations observed in detectors. The Equation of State (EoS) of QCD near a critical point can be related to the universal Gibbs free energy of the 3D Ising model using four currently unknown non-universal mapping parameters whose values are determined by the microscopic details of QCD. We utilize the maximum entropy approach to freeze-out the fluctuations in order to make estimates for factorial cumulants of proton multiplicities, assuming thermal equilibrium, for a family of EoS with a 3D Ising-like critical point, varying the microscopic inputs that determine the strength and structure of the critical features. We quantify the effect of the non-universal mapping parameters, and the distance between the critical point and the freeze-out curve, on the factorial cumulants of proton multiplicities.

Let $\mathscr{M}$ be a conditional oriented matroid. We define a graded algebra $\widehat{\mathscr{VG}}_\mathscr{M}$ with vector space dimension given by the number of covectors in $\mathscr{M}$ which admits a distinguished filtration indexed by the poset $\mathscr{L}(\mathscr{M})$ of flats of $\mathscr{M}$. The subquotients of this filtration are isomorphic to graded Varchenko-Gelfand rings of contractions of $\mathscr{M}$, so we call $\widehat{\mathscr{VG}}_\mathscr{M}$ the {\em graded big Varchenko-Gelfand ring of $\mathscr{M}$.} We describe a no broken circuit type basis of $\widehat{\mathscr{VG}}_\mathscr{M}$ and study its equivariant structure under the action of $\mathrm{Aut}(\mathscr{M})$. Our key technique is the orbit harmonics deformation which encodes $\widehat{\mathscr{VG}}_\mathscr{M}$ (as well as the classical Varchenko-Gelfand ring) in terms of a locus of points.

Directional solidification of water-based solutions has emerged as a versatile technique for templating hierarchical porous materials. However, the underlying mechanisms of pattern formation remain incompletely understood. In this work, we present a detailed derivation and analysis of a quantitative phase-field model for simulating this nonequilibrium process. The phase-field model extends the thin-interface formulation of dilute binary alloy solidification with anti-trapping to incorporate the highly anisotropic energetic and kinetic properties of the partially faceted ice-water interface. This interface is faceted in the basal plane normal to the <0001> directions and atomically rough in other directions within the basal plane. On the basal plane, the model reproduces a linear or nonlinear kinetic relationship that can be linked to experimental measurements. In both cases, spontaneous parity breaking of the solidification front is observed, leading to the formation of partially faceted ice lamellae that drift laterally in one of the <0001> directions. We demonstrate that the drifting velocity is controlled by the kinetics on the basal plane and converges as the thickness of the diffuse solid-liquid interface decreases. Furthermore, we examine the effect of the form of the kinetic anisotropy, which is chosen here such that the inverse of the kinetic coefficient varies linearly from a finite value in the <0001> directions to zero in all other directions within the basal plane. Our results indicate that the drifting velocity of ice lamellae is not affected by the slope of this linear relation, and the radius and undercooling at the tip of an ice lamella converge at relatively small slope values. Consequently, the phase-field simulations remain quantitative with computationally tractable choices of both the interface thickness and the slope assumed in the form of the kinetic anisotropy.

Directional solidification of water-based solutions has emerged as a versatile technique to template hierarchical porous materials, but this nonequilibrium process remains incompletely understood. Here we use phase-field simulations to shed light on the mechanism that selects the growth direction of the lamellar ice structure that templates those materials. Our results show that this selection can be understood within the general framework of spontaneous parity breaking, yielding quantitative predictions for the tilt angle of lamellae with respect to the thermal axis. The results provide a theoretical basis to interpret a wide range of experimental observations.

Logic locking remains one of the most promising defenses against hardware piracy, yet current approaches often face challenges in scalability and design overhead. In this paper, we present TLGLock, a new design paradigm that leverages the structural expressiveness of Threshold Logic Gates (TLGs) and the energy efficiency of charge recycling to enforce key-dependent functionality at the gate level. By embedding the key into the gate's weighted logic and utilizing dynamic charge sharing, TLGLock provides a stateless and compact alternative to conventional locking techniques. We implement a complete synthesis-to-locking flow and evaluate it using ISCAS, ITC, and MCNC benchmarks. Results show that TLGLock achieves up to 30% area, 50% delay, and 20% power savings compared to latch-based locking schemes. In comparison with XOR and SFLL-HD methods, TLGLock offers up to 3x higher SAT attack resistance with significantly lower overhead. Furthermore, randomized key-weight experiments demonstrate that TLGLock can reach up to 100% output corruption under incorrect keys, enabling tunable security at minimal cost. These results position TLGLock as a scalable and resilient solution for secure hardware design.

While Large Language Models (LLMs) have substantially improved the functional correctness of code translation, the critical dimension of \textit{execution efficiency} remains overlooked. We present \textbf{\textsc{trace}}, the first benchmark to explicitly assess efficiency in LLM-translated code. \textsc{trace} includes 1,000 efficiency-critical tasks across C++, Java, and Python, each augmented with stress tests that reveal efficiency degradations often overlooked by small-scale tests. Using \textsc{trace}, we conduct an extensive evaluation of 28 representative LLMs and highlight several key insights: 1) Correctness is not a reliable proxy for efficiency: the correctness leader \textit{Claude-4-think} achieves only mid-level time efficiency, outperformed by smaller open-source LLMs such as \textit{Qwen2.5-Coder-14B-Instruct}. 2) Inefficiency is both prevalent and patterned: 23.5\% of correct translations exhibit pronounced inefficiency, distributed across algorithmic faults (11.9\%), language construct mismatches (66.4\%), and resource mismanagement (21.7\%). 3) Inference-time prompt strategies bring only modest improvements, suggesting that current LLMs lack intrinsic efficiency awareness. Together, our results establish efficiency as an essential dimension of code translation and position \textsc{trace} as a principled foundation for efficiency-oriented evaluation.

Exploring the mass modifications of $ϕ$ mesons in nuclei provides insights into the nature of strongly interacting matter. Specifically, $ϕ$ meson mass shifts can be related to the in-medium modification of the strange quark condensate. Therefore, the partial restoration of chiral symmetry can be studied by observing the mass shifts through the decay channels $ϕ\rightarrow e^+e^-$, and $ϕ\rightarrow K^+K^-$. In this paper, we examine the possibility of observing the $ϕ$ meson mass modifications of the $ϕ$ mesons in 30 GeV proton-nucleus (C, Cu, Pb) collisions, to be studied at the J-PARC E88 experiment, through the kaonic decay channel, with the off-shell Budapest Boltzmann-Uehling-Uhlenbeck (BuBUU) transport model. By applying different mean fields to the kaons, we examine their effects on the invariant mass spectra. Our simulations suggest that, although different mean fields for the kaons do affect the spectrum, there is a common observable effect primarily driven by the $ϕ$ mass shift. However, due to the threshold associated with the two kaons, the signal we observe is quite different from the one expected in dilepton spectra. Therefore, to meaningfully constrain the mass shift, it will be useful to include both kaon and dilepton channels in the analysis of the experimental data.

This study employs QCD sum rules to predict the masses and residues of spin-$\frac {3}{2} $ doubly heavy baryons including two heavy quarks (c and/or b) and one light quark, specifically focusing on $ Ξ_{cc}^*$, $ Ξ_{bc}^*$, $ Ξ_{bb}^*$, $ Ω_{cc}^*$, $ Ω_{bc}^*$ and $ Ω_{bb}^*$. Our study provides results for the ground state ($1S$), first orbital excitation ($1P$), and the first radial excitation ($2S$), within a consistent theoretical framework. In addition to mass spectra, we provide residue calculations as well. The calculated residues are essential for estimating the decay widths and branching ratios of these baryons at different decay channels. Our analysis incorporates nonperturbative QCD effects through operators up to dimension ten, leading to improved precision in the mass and residue calculations. These predictions offer crucial guidance for ongoing and future experimental searches, particularly in light of the current lack of empirical data for the ground and excited states, and provide a basis for comparison with future experimental data.

We solve a long-standing open problem, formulated by Krasner in the 1950's, in the context of Polish (i.e. separable complete) ultrametric spaces by providing a characterization of their isometry groups using suitable forms of generalized wreath products of full permutation groups. Since our solution is developed in the finer context of topological (Polish) groups, it also solves a problem of Gao and Kechris from 2003. Furthermore, we provide an exact correspondence between the isometry groups of Polish ultrametric spaces belonging to some natural subclasses and various kinds of generalized wreath products proposed in the literature by Hall, Holland, and Malicki.

Materials for extreme environments require high strength yet ductile to tolerate catastrophic damage. Face-centered cubic (FCC) metals are typically ductile under stress, but single-crystal FCC iridium exhibits intrinsically brittle, limiting its wider applications. Great efforts on theoretical studies have attributed this to non-planar dislocation cores or impurities, while direct experimental evidence has remained elusive. Here we report that high-density, sessile Frank dislocation loops with zero-net Burgers vectors are the primary cause of the brittleness, identified through atomic-resolution scanning transmission electron microscopy. Through first-principles calculations, supported by discrete dislocation dynamics simulations, we reveal that these loops form via an energetically favorable transformation from mixed perfect dislocations under stress, a process unique to iridium among other FCC metals. The immobile loops act as potent barriers, drastically increasing yield strength and work hardening by impeding dislocation glide and consuming mobile dislocations. These decisive results not only deepen the understanding of the iridium brittleness, but also describe the existence of a new embrittlement mechanism inherent to the FCC lattice and not previously described in the literature. The latter may enable novel routes for property tuning across a broad class of materials, which is of paramount importance to metallurgical technology

We have studied how the transport of charge and heat as well as associated observables become influenced by a weak-momentum anisotropy arising due to the asymptotic expansion of baryon asymmetric matter in the initial stages of heavy ion collisions. This study facilitates the understanding of the local equilibrium property of the medium through the Knudsen number, and explores the correlation between the heat flow and the charge flow through the Lorenz number in the Wiedemann-Franz law for an anisotropic hot QCD medium at finite baryon asymmetry. We have determined the electrical and the thermal conductivities by solving the relativistic Boltzmann transport equation in the relaxation time approximation within the kinetic theory approach. The interactions among partons are appended through their distribution functions within the quasiparticle model of the hot QCD medium at finite temperature, anisotropy and baryon asymmetry. We have observed a decrease in both electrical and thermal conductivities in the presence of expansion-induced anisotropy for baryonless scenario as well as for baryon asymmetric scenario. Conversely, these conductivities are found to be larger in the baryon asymmetric matter as compared to their counterparts in the baryonless matter. The impact of anisotropy on the baryon asymmetric matter is as conspicuous as on the baryonless matter. The above results are attributed to the squeezing of the distribution function due to the momentum anisotropy generated by the asymptotic expansion of baryon asymmetric matter and the dispersion relations of partons in the presence of anisotropy. Additionally, the aforesaid observables are also modulated by the expansion-induced anisotropy in the baryon asymmetric medium, indicating new predictions for the equilibrium characteristic and the relative behavior between the heat and charge flow for the said medium.

Patient safety training is essential for preparing healthcare professionals to identify, investigate, and prevent adverse events. However, conventional simulation-based approaches often require substantial faculty time, physical resources, and standardized facilitation. This paper presents a prototype AI-powered simulation platform designed to support more scalable patient safety training through root cause analysis (RCA). The system provides a Unity-based 3D simulation environment, which allows trainees to investigate an ICU adverse event by interviewing five virtual team members represented as AI-powered avatars. Each avatar is driven by a large language model (LLM) agent with role-specific knowledge and variable states of mind. Moreover, emotional text-to-speech and AI-supported facial and body animation enable more realistic and immersive interactions. After completing the simulation, trainees submit a written RCA report and receive rubric-guided formative and summative feedback automatically generated by an LLM-based assessment component. The prototype is built to support patient safety training for healthcare professionals, focusing on skills in communication, investigation, thinking, and analysis, with low recurring instructional burden. We describe the design of the platform, its core technical components, and an RCA case based on a published ICU scenario. This work demonstrates the feasibility of integrating generative AI into immersive simulation for scalable patient safety education.

We report inelastic neutron scattering (INS) measurements on the magnetically frustrated $S=\frac12$ sawtooth-chain compound atacamite Cu$_2$Cl(OH)$_3$ featuring inequivalent Cu(1) and Cu(2) sites. Transverse to the sawtooth chains, INS reveals two dispersive spin-wave modes and a gap of at least 0.75 meV. This behavior is rationalized within a zigzag-chain model of Cu(2) spins in an effective magnetic field of Cu(1) spins. The model is compatible with first-principles calculations and accounts for INS dispersions within linear spin-wave theory calculations. Our results reveal a unique case of an effective separation of energy scales between two differently oriented one-dimensional chains, with the zigzag-chain model being essential to fully characterize atacamite's low-energy magnetism.

Designing efficient experiments under practical constraints is critical in both scientific research and industrial practice. Focusing on minimizing the average variance of the parameter estimates, A-optimal designs show advantages in screening factors and reducing prediction errors. Compared with other criteria, however, algorithms and software for generating A-optimal designs are scarce. In this paper, we characterize A-optimal designs under generalized linear models theoretically and develop efficient algorithms for identifying them. When a predetermined finite set of experimental settings is given, we derive analytic solutions or establish necessary and sufficient conditions for obtaining A-optimal approximate allocations. We show that a lift-one algorithm based on our formulae outperforms commonly used algorithms for finding A-optimal allocations. When continuous factors or design regions get involved, we develop a ForLion algorithm that is guaranteed to find A-optimal designs with mixed factors. Numerical studies show that our algorithms can find highly efficient designs with reduced numbers of distinct experimental settings, which may save both experimental time and cost significantly. Along with a rounding-off algorithm that converts approximate allocations to exact ones, we demonstrate that stratified samplers based on A-optimal allocations may provide more accurate parameter estimates than commonly used samplers.

This investigation employed microwave whispering gallery mode (WGM) analysis to characterize the dielectric properties of a cylindrical, single-crystal sample of calcium tungstate (CaWO$_4$). Through investigation of quasi-transverse\hyp{}magnetic and quasi-transverse\hyp{}electric mode families, we can assess loss mechanisms and relative permittivity from room temperature down to cryogenic conditions. We report the biaxial permittivity values of $ε_{||} = 9.029 \pm 0.009$ and $ε_{\perp} = 10.761 \pm 0.01$ at $295$ K, and $ε_{||} = 8.794 \pm 0.009$ and $ε_{\perp} = 10.440 \pm 0.01$ at $4$ K. Components are denoted with respect to the c\hyp{}axis of the crystal unit cell. The parallel component agrees well with the published literature at MHz frequencies; however, the perpendicular component is $4.8$\% lower. The WGM technique offers greater precision, with accuracy limited primarily by the uncertainty in the crystal's dimensions. WGMs also serve as sensitive probes of lattice dynamics, enabling monitoring of temperature-dependent loss mechanisms. At room temperature, the measured loss tangents were $\tanδ_{||}^{295,\mathrm{K}} = (4.1 \pm 1.4) \times 10^{-5}$ and $\tanδ_{\perp}^{295,\mathrm{K}} = (3.64 \pm 0.92) \times 10^{-5}$. Upon cooling to 4 K, the loss tangents improved by approximately two orders of magnitude, reaching $\tanδ_{||}^{4,\mathrm{K}} = (1.56 \pm 0.52) \times 10^{-7}$ and $\tanδ_{\perp}^{4,\mathrm{K}} = (2.05 \pm 0.79) \times 10^{-7}$. These cryogenic values are higher than those reported in prior studies, likely due to a magnetic loss channel associated with an unidentified paramagnetic spin ensemble. These findings have implications for the use of CaWO$_4$ in applications such as spin-based quantum systems and cryogenic bolometry, highlighting the potential of WGMs for novel sensing applications.

We investigate the stability of continuous-time quantum walks (CTQW) across cycle, complete, star, Erdős-Rényi, small-world, and scale-free topologies under energy-based intrinsic decoherence, node-based Haken-Strobl noise, and edge-based quantum stochastic walk (QSW) decoherence. Defining stability as the preservation of quantum properties, we characterize it using node probabilities, $\ell_1$-norm of coherence, fidelity, quantum-classical distance, and von Neumann entropy. Our results show that intrinsic decoherence preserves coherence longest while QSW causes rapid decay. Stability rankings vary and depend on the decoherence types, network structure, and properties of node where the walker is initialized specifically in heterogeneous networks. Dense connected network like complete and heterogenous networks, for instance, star, and scale-free are stable under Haken-Strobl noise but become uniquely fragile under QSW when initialized on high degree nodes. However, these same networks, due to their inherent localization, exhibit lower coherence in the noiseless regime, highlighting a fundamental trade-off between localization and coherence. Furthermore, the centrality of the initialization node has a pronounced impact on relaxation time and stability measures, underscoring the critical role of local topological features in quantum dynamics.

Approximate computing offers promising energy efficiency benefits for error-tolerant applications, but discovering optimal approximations requires extensive design space exploration (DSE). Predicting the accuracy of circuits composed of approximate components without performing complete synthesis remains a challenging problem. Current machine learning approaches used to automate this task require retraining for each new circuit configuration, making them computationally expensive and time-consuming. This paper presents ApproxGNN, a construction methodology for a pre-trained graph neural network model predicting QoR and HW cost of approximate accelerators employing approximate adders from a library. This approach is applicable in DSE for assignment of approximate components to operations in accelerator. Our approach introduces novel component feature extraction based on learned embeddings rather than traditional error metrics, enabling improved transferability to unseen circuits. ApproxGNN models can be trained with a small number of approximate components, supports transfer to multiple prediction tasks, utilizes precomputed embeddings for efficiency, and significantly improves accuracy of the prediction of approximation error. On a set of image convolutional filters, our experimental results demonstrate that the proposed embeddings improve prediction accuracy (mean square error) by 50% compared to conventional methods. Furthermore, the overall prediction accuracy is 30% better than statistical machine learning approaches without fine-tuning and 54% better with fast finetuning.

This paper measures the effects of temperature and precipitation shocks on Mexican inflation using a regional panel. To measure the long-term inflationary effects of climate shocks, we estimate a panel autoregressive distributed lag model (panel ARDL) of the quarterly variation of the price index against the population-weighted temperature and precipitation deviations from their historical norm, computed using the 30-year moving average. In addition, we measure the short-term effects of climate shocks by estimating impulse response functions using panel local projections. The result indicates that, in the short term, the climate variables have no statistical effect on Mexican inflation. However, in the long term, only precipitation norms have a statistical effect, and the temperature norms have no statistical impact. Higher than normal precipitation has a positive and statistically significant effect on Mexican inflation for all items.

The evolution of natural languages poses a riddle to any theoretical perspective based on efficiency considerations. If languages are already optimally effective means of organization and communication of thought, why do they change? And if they are driven to become optimally effective in the future, why do they change so slowly, and why do they diversify, rather than converge towards an optimum? We look here at the hypothesis that disorder, rather than efficiency, may play a dominant role. Most traditional approaches to study diachronic language dynamics emphasize lexical data, but it would seem that a crucial contribution to the effectiveness of a thought-coding device is given by its core generative structure, i.e., its syntax. Based on the reduction of syntax to a set of binary syntactic parameters, we introduce here a model of natural language change in which diachronic dynamics stem from disordered interactions between/among parameters, even in the idealized limit of identical external inputs. We show in which region of phase space such dynamics show the glassy features that are observed in natural language across time. In particular, binary syntactic vectors remain trapped in glassy metastable (ie, tendentially stable) states when the degree of asymmetry in the disordered interactions is below a critical value, consistent with studies of spin glasses with asymmetric interactions. We further show that an added Hopfield-type memory term, would indeed, if strong enough, stabilize syntactic configurations, but losing their multiplicity. Finally, using a notion of linguistic distance in syntactic state space we show that a phylogenetic signal may remain among related languages, despite their gradually divergent syntax, exactly as recently pointed out for real-world languages. These statistical results appear to generalize beyond the 94 parameters across 58 languages used here.

An oriented graph $H$ is Turánable (resp. tileable) if there exist $n_0 \in \mathbb{N}$ such that every semi-regular near-tournament on $n \ge n_0$ vertices contains a copy of $H$ (resp. a perfect $H$-tiling). We disprove a conjectured characterization of Turánable oriented graphs by DeBiasio, Han, Lo, Molla, Piga, and Treglown, show that there are Turánable oriented graphs which are not tileable, and provide a new example of tileable oriented graph.

Let $\mathcal{ORD}_{n}$ be the semigroup consisting of all oriented and order-decreasing full transformations on the finite chain $X_{n}=\{ 1<\cdots<n \}$, and for $1\leq r\leq n-1$, let $$\mathcal{ORD}(n,r) =\{α\in \mathcal{ORD}_{n}\, :\, \lvert \textrm{im}(α)\rvert \leq r\}.$$ In this paper, we determine the cardinality of $\mathcal{ORD}(n,r)$ and the number of nilpotent elements of $\mathcal{ORD}(n,r)$, we find a minimal generating set and the rank of $\mathcal{ORD}(n,r)$, and moreover, we characterize all maximal subsemigroups of $\mathcal{ORD}(n,r)$ for each $3\leq r\leq n-1$.

We explore a functionalist approach to emotion by employing an ansatz -- an initial set of assumptions -- that a hypothetical concept generation model incorporates unproven but biologically plausible traits. From these traits, we mathematically construct a theoretical reinforcement learning framework grounded in functionalist principles and examine how the resulting utility function aligns with emotional valence in biological systems. Our focus is on structuring the functionalist perspective through a conceptual network, particularly emphasizing the construction of the utility function, not to provide an exhaustive explanation of emotions. The primary emphasis is not of planning or action execution, but such factors are addressed when pertinent. Finally, we apply the framework to psychological phenomena such as humor, psychopathy, and advertising, demonstrating its breadth of explanatory power.

In this paper, we provide a comprehensive cross-country validation study of compositional mortality modeling and forecasting methods. Thus, we consider two one-to-one transformations: the cumulative distribution function and the centered log-ratio transformation in compositional data analysis. Between the two transformations, the cumulative distribution function provides a scale-free way to visualize the gender gap and cross-country heterogeneity in the probability of dying by sex and country. Drawing on age-specific period life-table death counts from 24 countries in the Human Mortality Database (2025), we assess and compare the point and interval forecast accuracy of the two transformations, using the same forecasting method. Enhancing the forecast accuracy of period life-table death counts is of significant value to demographers, who rely on such forecasts to estimate survival probabilities and life expectancy, and to actuaries, who use them to price annuities across various entry ages and maturities.

Let $U$ be a smooth and connected curve over an algebraically closed field of positive characteristic, with smooth compactification $X$. We generalize classical Geometric Class Field theory to provide a classification of fppf $G$-torsors over $U$ in terms of isogenies of generalized Jacobians, for any finite abelian group scheme $G$. We then apply this classification to give a novel description of the abelianized Nori fundamental group scheme of $U$ in terms of the Serre--Oort fundamental groups of generalized Jacobians of $X$; when $U=X$ is projective, we recover a well known description of the abelianized fundamental group scheme of $X$ as the projective limit of all torsion subgroup schemes of its Jacobian.

In the present work, we examine the production of $Ξ(1530)$ in the $K^- p \to K^{+} Ξ(1530)^{-}$ and $K^- p \to K^{0} Ξ(1530)^{0}$ reactions utilizing an effective Lagrangian approach. To accurately fit the cross sections for both processes, we include nine $Λ$ and $Σ$ hyperons and their resonances in both $s$- and $u$-channel processes. Considering the discrepancy of the measured cross sections for $K^- p \to K^+ Ξ(1530)^-$ within the range $\sqrt{s}=[2.087, 2.168]\ \mathrm{GeV}$, we employ two distinct fitting strategies: a uniform weighting scheme (model A) and a different weighting approach (model B). A comparative analysis suggests that model A yields a superior global agreement with experimental data compared to model B. Beyond fitting the cross sections, we also estimate the individual contributions from various intermediate states. Our results reveal that the cross section arising from the $Σ(1193)$ intermediate process is dominant. Furthermore, we predict different cross sections for $K^- p\to K^+Ξ(1530)^-$ and $K^- p\to K^0Ξ(1530)^0$ at several representative center-of-mass energies, providing testable predictions for forthcoming J-PARC experiments.