Software containerization technologies for resource-limited devices enable multi-tenant microcontrollers, which allow running multiple applications with different permission levels. However, current solutions lack run time configuration over various settings on container scheduling and container permissions to host resources. This limits the applicability of constrained containerization in dynamic and heterogeneous environments. This paper introduces TinyContainer, a lightweight software container management middleware designed for multi-tenant microcontrollers. TinyContainer provides per-container configurable scheduling and fine-grained access control to host resources through a metadata-driven approach, supporting multiple runtimes via a runtime abstraction layer. We analyze the performance of TinyContainer with a small WebAssembly runtime, CS4WAMR, and RIOT OS, a common RTOS. We report on experiments using popular IoT boards based on various Cortex-M microcontrollers. We show the endpoint system brought by TinyContainer allowing to regulate access of containers to host resources and provide host services to containers with an overhead of up to 4 ms per call. In particular, we showcase a TinyML use case, whereby containers retain data and model weights, while model inference is delegated to native host RTOS services.

A right group is a semigroup $(S,\cdot)$ in which, for every $a,b\in S$, there is a unique $x\in S$ such that $a\cdot x=b$. In this article, we develop the theory of heaps starting not from groups, but from right groups. We thus get a natural definition of right heap. It is even possible to develop part of the theory starting from a left quasigroup, which is the non-associative analogue of a right group. Our motivation for this study is the investigation of left non-degenerate set-theoretic solutions of the Yang--Baxter equation. Thus, we are led to an analogue of the skew left trusses introduced by T.~Brzeziński.

This paper provides partial identification and inference for treatment effects in nonparametric sample selection models with endogenous treatment and (weak) sample selection monotonicity. Outcomes are observed only for a non-randomly selected subsample and treatment is endogenous because of noncompliance with assignment. The proposed bounds for intensive margin treatment effects among compliers are sharp and tighter than those of Chen and Flores (2015). For inference, we develop semiparametrically efficient orthogonal moments and a debiased machine learning procedure that permits valid root-$n$ inference under high-dimensional covariates and/or flexible functional forms. Simulation results indicate good finite sample performance. Applications to Job Corps and the Oregon Health Insurance Experiment show that the method can deliver substantially tighter effect bounds and confidence intervals than existing alternatives.

The relative phase between two lasers in transmission of an optical cavity can be used to continuously measure its absolute length with sub-micron precision. The first laser is kept on resonance with the cavity, while a second laser is phase-locked to the first with a frequency separation equal to an integer multiple of the cavity's initial free spectral range. As the free spectral range frequency changes due to cavity length changes, the second laser de-tunes slightly from resonance and gains an additional phase offset in transmission of the cavity. The cavity length changes can be calibrated in terms of this phase offset. This technique is applied to a high-finesse optical cavity with a length of 123 meters, transforming it into a strainmeter with an effective sensitivity to transient seismic events of $10^{-10} \, - \, 10^{-9}$ m/m. We report absolute length changes associated with anthropogenic noise, a distant earthquake, and the diurnal and semidiurnal earth tides.

Covariance matrix adaptation evolution strategy (CMA-ES) is a state-of-the-art black-box optimization algorithm. In general, CMA-ES uses a portfolio of multiple stopping criteria to automatically determine when to stop the search. This mechanism aims to avoid unnecessary consumption of the function evaluation budget during stagnation. Stopping criteria play an important role in CMA-ES, particularly when restart strategies are employed. However, the effectiveness of stopping criteria in CMA-ES remains poorly understood. To address this issue, this paper investigates how the 11 stopping criteria in CMA-ES behave on the noiseless BBOB function set. The performance of the stopping criteria is quantitatively evaluated based on the optimal stopping point in terms of the number of function evaluations in a single run of CMA-ES. Our results show that, although which stopping criterion is triggered first depends significantly on the sample size $λ$ and the dimension $n$, \texttt{tolflatfitness} and \texttt{tolfun} are frequently the first criteria to be triggered among the portfolio of 11 stopping criteria. We also demonstrate that \texttt{tolfunhist} and the portfolio achieve the highest stopping accuracy in most cases. In addition, our results show that the \texttt{tolfun} and \texttt{tolfunhist} criteria are frequently triggered before CMA-ES reaches complete stagnation.

Quantum Key Distribution (QKD) has emerged as a fundamentally secure approach to communication in the era of quantum computing, offering protection against threats posed to classical cryptographic schemes such as RSA and Diffie-Hellman. This thesis presents a comprehensive performance analysis of satellite-based QKD protocols, focusing on both prepare-and-measure and entanglement-based schemes under realistic atmospheric and operational conditions. The study begins by introducing the theoretical foundations of quantum communication, including qubits, entanglement, and quantum entropy, and motivates the need for satellite-based QKD to overcome the distance limitations of fiber-based systems. Subsequently, the thesis evaluates four prominent QKD protocols-BB84, B92, BBM92, and E91-using a circular beam propagation model that incorporates atmospheric effects such as diffraction, turbulence, attenuation, and pointing errors, along with environmental noise contributions for uplink and downlink. Comparative numerical simulations reveal that protocol performance is strongly influenced by channel asymmetries, beam propagation characteristics, and noise, providing guidance on optimal protocol selection for low Earth orbit (LEO) satellite links. The research further investigates high-dimensional (HD) QKD protocols, specifically HD-BB84 and HD-Extended B92, using the elliptic-beam approximation to account for turbulence-induced distortions for both uplink and downlink. Simulations under vary ing system dimensions, weather conditions, and zenith angles demonstrate that HD-BB84 achieves higher key rates, superior noise tolerance, and more favorable probability distributions of the key rate compared to HD-Extended B92, highlighting the advantages of high-dimensional encoding for robust satellite-based QKD.

In this article, the development and investigation of fast hadron shower simulation methods is presented. A test beam dataset has been recorded in 2018 at CERN with the AHCAL Technological Prototype of the CALICE Collaboration, where the calorimeter prototype was exposed to electron, muon, and negatively charged pion beams of various initial energies. The pion shower dataset, covering energies between 10 GeV and 200 GeV, has been used to develop a data-driven fast simulation algorithm of the AHCAL response to pion showers. The resulting shower model demonstrates excellent agreement with measured shower observables. In addition, a method for simulating pion showers at arbitrary energies is introduced, based upon interpolation between simulated showers at neighbouring beam energies.

Over any number field containing a root of unity of odd prime order, we construct a homogeneous space of $\mathrm{SL}_n$ with finite $2$-nilpotent geometric stabilizers, with a constant unramified algebraic Brauer group, which has no rational point but has local points in every completion of the ground field. This yields the first example of transcendental Brauer--Manin obstruction for homogeneous spaces of connected linear algebraic groups. Our method exploits a previous idea by Borovoi and Kunyavskii.

In the first half of this paper, we describe the homotopy groups of the equivariant automorphism group of Kirchberg algebras with isometrically shift-absorbing actions of compact groups in terms of equivariant KK-theory. This provides an equivariant version of Dadarlat's result. In the second half, we present a unified treatment of the equivariant Dadarlat-Pennig theory for strongly self-absorbing actions.

Ferroelectric nematic liquid crystals (FNLCs) are promising soft platforms for nonlinear optics, but quantitative determination of their second-order nonlinear optical coefficients has been hindered by limited alignment control. Here, polarization-resolved second-harmonic generation (SHG) measurements on a uniformly aligned dioxane-based FNLC, combined with Jones-matrix simulations, enable determination of all principal tensor components. The resulting tensor is consistent with the expected $C_{\infty v}$ and Kleinman symmetries, while the measured coefficients cannot be explained by a simple sum of molecular first hyperpolarizabilities. These results provide a quantitative basis for understanding nonlinear optical responses and guiding the design of FNLC-based nonlinear optical materials and devices.

Ultra-light dark matter fields induce nearly monochromatic signals in gravitational-wave detectors through their coupling to the Standard Model. Their spectral morphology exhibits features caused by sidereal modulation that, for frequencies below $\sim 30~$Hz, enable discrimination between spin-1 and spin-2 ultra-light dark matter signals, provided sufficient signal-to-noise ratio. In the context of LIGO--Virgo--KAGRA search techniques, we show that incorporating these spectral features can improve current excess-power constraints at low frequencies by up to $\sim36\%$. Additionally, we propose an optimised implementation of the cross-correlation statistics within the Band-Sampled-Data framework, enhancing the sensitivity of cross-correlation searches across nearly the entire frequency range, reaching up to $\sim42\%$ at low frequencies and $\sim35\%$ at high frequencies.

Frequent Itemset Mining (FIM) is a foundational task in data analytics, but its candidate and conditional pattern spaces can grow rapidly, and maintaining support information becomes increasingly costly on dense datasets. These bottlenecks present a critical opportunity for quantum computing to redesign the way candidate representation and support verification are organized. Motivated by recent developments in quantum computing, we propose the \textit{QuantumFreqMine (QFM)} framework for FIM. QFM introduces three mechanisms: (1)~\textit{Bit-Vector Qubit Encoding}, (2)~\textit{Mining-Aware Candidate Superposition}, and (3)~\textit{Bit-Parallel Threshold Marking}. We provide a theoretical analysis in terms of time complexity, space comlexity, and logical resource usage. We implement QFM on IBM Qiskit and Amazon Braket. The experiments demonstrate that QFM outperforms representative baselines.

For dynamical systems in the plane, there can be many periodic attractors coexisting in a bounded region. They become easier to find in systems with small dissipation, which we call ``almost-conservative''. We ask what happens when there are many periodic attractors. That is the vague question we start with. For a test study, we chose the Hénon map with a tiny dissipation. We tuned the other parameter to yield a case with 50 attracting periodic orbits. They have a total of 4259 periodic points. We describe how these orbits can be organized into families. In addition to two low-period orbits, the remaining 48 orbits can be classified into three families, which we describe in detail.

The air--water interface governs the exchange of heat and gas between natural water bodies and their surrounding environment. Turbulence beneath the free surface imprints characteristic features: near-circular depressions (`dimples') and elongated indentations (`scars'). Recent studies have shown that these are linked to sub-surface flow features in a temporal sense. For instance, rapid increases in mean-square surface divergence due to upwelling events, precede dimple count surges. Yet the spatial structure of these connections remains unquantified. We employ spatial statistics to consider the spatial and temporal correlations between dimples and scars and two key velocity-derived fields, surface divergence $β=\partial_x u+\partial_y v$ and vertical vorticity $ω=\partial_x v-\partial_y u$. The dimples and scars are modelled as inhomogeneous Poisson point-processes, with intensity fields driven by the local variance of $β$ and $ω$. Parameters, including spatial support radius $r$ and time lag $τ$, are estimated by maximum likelihood against six DNS datasets, quantifying the spatial and temporal connection between dimples, scars, surface divergence and vorticity. Our results demonstrate a clear complementarity: Dimples show strong local connection to the vertical vorticity field but has weak spatial connection with surface divergence and a spatially ``global'' model is required for dimples to work as estimators of surface divergence; scars, in a similar but opposite manner, couple locally to surface divergence but globally to the vertical vorticity. The complementarity sheds new light on the way dimples and scars may be used to infer fluxes across the surface, e.g., in remote sensing contexts.

We study a family of genuinely exotic five-flavor molecular pentaquark states containing the five quark flavors $u,d,s,c,b$ that form experimentally accessible hadrons. We construct the meson-baryon interaction from the local hidden gauge symmetry combined with heavy-quark spin symmetry, following the chiral unitary description that reproduces the LHCb hidden-charm strange pentaquarks. Heavy-quark flavor symmetry allows us to obtain the $udsc\bar{b}$ sector by replacing the anti-charm quark in the meson with an anti-bottom quark while keeping the charm quark in the baryon. As a result, we obtain ten threshold-associated isoscalar poles with $J^P=1/2^-,3/2^-,5/2^-$ in the range $7.72$ to $7.96$ GeV. They are narrow and organized into heavy-quark spin multiplets with predicted near-degeneracies. There are four states that overlap with the earlier two-sector study in the literature to about $2$ MeV, which shows that the separate-sector treatment is recovered as a limit of this work. Moreover, we also identify two additional, more deeply bound $B_sΛ_c$ and $B_s^{*}Λ_c$ poles generated by strong inter-channel coupling because these poles are farther from their dominated thresholds. This is an interesting signal that can be searched for at LHCb in the $B_cΛ$ and $B_s^{*}Λ_c$ invariant mass spectra.

Topological insulators have attracted numerous attentions recent years, where the Su-Schrieffer-Heeger (SSH) model is one of the most studied models. While the interacting version of it has been explored recently, the interplay between interactions and long-range hoppings merit further investigations. In this work, we uncover a rich phase diagram of the interacting SSH model with extended-range hoppings, in which it consists of several topological phases, two novel superconducting-like (SC-like) phases and five distinct charge-density-wave (CDW) phases. We substantiate that the SC-like and two CDW phases are direct consequences of imbalanced interactions and extended-range hoppings. We derive the order parameters (OPs) for each of the phases and verify them in large-system simulations, finding consistency with the entanglement entropy and the fidelity in capturing the phase transitions. In contrast to the non-interacting case where the favored hoppings are unidirectional in the topological phases, the derived OPs suggest non-unidirectional hoppings are possible under the influence of interactions.

In this paper, we develop an equivariant version of the classical theory of deformations of germs of complex spaces in the presence of complex Lie groups. As a tradition, the main result is the existence of equivariant semi-universal deformations of germs of complex spaces in the reductive case and the non-existence in general in the non-reductive case. The latter is justified by an explicit counterexample. In particular, it generalizes Grauert-Donin's existence theorem to the equivariant settings and Ferrer-Puerta-Slodowy's one to the case of reductive complex Lie groups. Several applications to deformations of pairs, rigidity and actions on Milnor fibers are given.

We present an optical-resolution photoacoustic microscopy (OR-PAM) system designed to overcome key limitations in conventional transducer integration within a compact microscopy configuration, while preserving high optical performance and improving acoustic detection efficiency. The system uses a reflective objective that reduces spatial constraints within the optical pathway, enabling the integration of a large-area PVDF transducer within the optical obscuration zone. The system performance was characterized through spatial resolution analysis, laser pulse energy measurement at the sample plane, and evaluation of photoacoustic signal dependence on laser pulse energy. For biological validation, OR-PAM imaging of sections from B16F10 tumors implanted in mice was performed and compared with optical microscopy and H&E stained histological sections. The results demonstrate strong spatial correlation between photoacoustic signal intensity and melanin rich regions, confirming label-free sensitivity to endogenous optical absorbers at 532 nm. This work establishes a compact OR-PAM imaging setup with improved optical-acoustic integration for high resolution biomedical imaging applications, with potential for future extension to multi-wavelength laser excitation.

Classical Euclidean instances for the Vehicle Routing Problem with Time Windows (VRPTW) have floating-point arc costs derived from node coordinates. This raises reproducibility and numerical consistency issues. Hence, a common practice is to scale and round data to integer values, yet the impact of these choices is poorly understood. This paper studies the effect of integer scaling by a factor P and compares three rounding schemes with distinct feasibility and optimality guarantees. We analyze how scaling and rounding influence empirical performance of two well-known VRPTW solvers: Hybrid Genetic Search (HGS) and OR-Tools. We evaluate the quality of solutions mapped back to the original floating-point instances. Our results show that scaling and rounding can significantly affect both solver performance and solution quality. Based on these findings, we provide practical recommendations for scaling and rounding in VRPTW benchmarks to improve efficiency, robustness and reproducibility.

The introduction of a semiconductor-semiconductor junction is an effective strategy to enhance the photocatalytic performance of perovskite nanocrystal-based systems. Herein, we optimized the synthesis of CsPbX3/Pb4S3X2 (X= Cl, Br, I) perovskites-chalcohalides heterostructures, whose band alignment can be tuned by halide composition. As a proof-of-concept, we evaluated the photooxidative coupling of p-substituted thiophenols at room temperature, under visible-light, air, and without sacrificial electron donor. Notably, CsPbBr3/Pb4S3Br2 achieved up to 94 % selectivity toward disulfide (p-OCH3 thiophenol with a turnover number of 14300) highlighting the crucial role of the type-II heterojunction to promote charge separation and efficient electron delocalization across the junction.

The hydrodynamic response of Brownian particles in liquids is fundamentally altered by inertial forces arising from unsteady momentum transport in the surrounding fluid. These forces are of two distinct types\,: the added mass and the history effect. While both are well understood in bulk and weakly-confined geometries, under deterministic driving, their respective behaviours under strong confinement and thermal fluctuations remain scarcely addressed, unclear and often entangled together. The goal of the present study is thus to fill this fundamental gap. The behaviours of the two distinct inertial contributions are quantitatively investigated in the vicinity of a flat, rigid wall, using a combination of broadrange thermal colloidal-probe atomic-force-microscopy experiments, advanced numerical simulations and theory. The separation of the added-mass and history-force contributions is achieved through their different frequency-scaling signatures within the measured high-resolution thermal spectra. Our results establish a complete picture of Brownian motion at interfaces, in the lubrication regime, with direct relevance to nanofluidics and interfacial biophysics.

The Bethe-Salpeter equation (BSE) accurately describes low-energy optical spectra in materials with strong excitonic effects, but its high computational cost limits the number of electron-hole transitions that can be included. Neglecting high-energy transitions leads to an underestimation of the real part of the dielectric function, often producing a spurious plasmon peak in the BSE electron energy loss spectrum (EELS). The recently introduced BSE+ method addresses this by combining a four-point BSE-like equation for the irreducible polarisability with a two-point Dyson equation that includes the high-energy transitions at the Random Phase Approximation (RPA) level. Here, we present a detailed account of the method, extend it to two-dimensional materials, and apply it to a set of transition metal dichalcogenide monolayers. BSE+ preserves the excitonic features of the BSE at low energies while reproducing the plasmonic structure of the RPA at higher energies, yielding good agreement with experimental EELS data across the full energy range and strongly suppressing the spurious plasmon. BSE+ converges much faster than BSE with respect to the electron-hole basis size, at a comparable computational cost, and is implemented in the GPAW code.

Despite growing concerns over energy security, infrastructure planning and modelling for emerging green fuel supply chains often neglect risks from supply disruptions. Using a stochastic optimisation model of EU hydrogen imports, we show that 'naive' infrastructure planning results in welfare losses of 12 % (24 billion EUR) compared to risk-aware planning that anticipates supply disruptions. Despite requiring higher upfront investments, anticipatory planning achieves welfare levels close to those of an idealised system without disruptions, but entails a markedly different infrastructure configuration. Two complementary resilience strategies emerge: diversification across import corridors and strategic over-investment. This leads to increased intra-European transport capacity, a broader set of import pipelines, and investments in costly shipping terminals for hydrogen carriers. Our results show that incorporating supply risk considerations into infrastructure planning helps prevent the structural vulnerabilities seen in fossil fuel systems when designing future hydrogen supply chains.

Human interaction is continuous, multimodal, and full-duplex by nature. Although recent omni models have made substantial progress in unified speech, vision, and text modeling, combining seamless real-time interaction with complex reasoning and tool use remains challenging. We present DuplexOmni, a method for real-time multimodal full-duplex interaction. DuplexOmni separates model capability into an interaction layer and a thinking layer, which collaborate asynchronously in parallel. The interaction layer is implemented by the DuplexOmni model, an end-to-end system that processes streaming audio and video inputs while generating text and speech responses in real time. The thinking layer is a pluggable module that provides complex reasoning and tool-use capabilities. To support this method, we further develop a Writer-Director pipeline for constructing continuous-interaction training data. Experiments show that DuplexOmni achieves strong performance on multiple public benchmarks and exhibits natural full-duplex interaction ability.

We construct here global in time mild solutions to the forced fractional Navier-Stokes-Boussinesq system in some critical Fourier-Herz spaces which will be based on Lorentz norms in the time and the frequency variables. For the initial data and for the external force we will consider Fourier-Besov spaces that will also be based on Lorentz spaces. Moreover, we will show, by performing a separate study of the velocity field and the temperature, that it is possible to consider one of the largest functional space -in the Fourier-Besov-Lorentz framework-for the initial velocity field.

Large Language Models (LLMs) are increasingly used in AI-based software engineering, but their limitations in complex task execution and multi-tool coordination have driven growing interest in the Model Context Protocol (MCP). Existing research has mainly focused on MCP's technical design, with limited empirical evidence on how it is adopted and used in enterprise practice, particularly with regard to deployment challenges, operational risks, and practitioner expectations. To address this gap, we conducted semi-structured interviews with 20 practitioners from eight companies in the Internet and financial sectors. The findings show that MCP is valued for supporting cross-system collaboration, task decoupling, and knowledge reuse in LLM-based workflows, but its adoption remains constrained by ecosystem fragmentation, cross-component coordination difficulties, and unresolved problems in distributed state management and fault diagnosis. Participants also expressed strong demand for better standardization, lower adoption barriers through low-code or plugin-based approaches, and more systematic operational support. These results provide early empirical evidence on enterprise MCP practice and offer practical implications for improving MCP's standardization, usability, and deployment readiness in real-world software engineering environments.

Magnetoelectric (ME) phenomena are commonly driven by spin-lattice coupling. Here we demonstrate a different route based on frustrated quantum spin trimers that intrinsically intertwine magnetic moments and electric dipoles. Using molecular design principles, we realize a weakly coupled lattice of equilateral $S=1/2$ spin trimers in the organic radical crystal TNN$\cdot$CH$_3$CN. In this material, correlated electronic fluctuations within each trimer generate electric dipoles, while geometrically frustrated intertrimer interactions organize them into collective ME states. Magnetization, thermodynamic, and dielectric measurements reveal multiple magnetic-field-induced phases, including the $1/3$-magnetization plateau marked by pronounced dielectric anomalies. Effective low-energy theories and numerical simulations show that these phenomena are driven by electronically generated trimer dipoles whose collective order is stabilized by frustration relief of the intertrimer interactions, establishing a direct connection between geometric frustration and emergent magnetoelectricity. Our results identify quantum spin trimers as multifunctional building blocks, providing a bottom-up route for designing correlated ME materials from electronically active quantum spin clusters.

Social Learning is a decentralized decision-making paradigm in which spatially dispersed agents collect streaming observations regulated by one of a finite number of models (the hypotheses). The agents are interested in assigning probability scores (the beliefs) to the possible hypotheses. To this end, the agents exchange their beliefs according to a certain communication graph. It has been shown that, under reasonable conditions on the identifiability of the decision model and the network connectivity, each agent ultimately places all the belief mass on the true hypothesis governing the data. However, several questions remain unanswered regarding the evaluation of the social learning performance. One recently adopted performance metric is the rejection rate, i.e., the rate at which the beliefs about the erroneous hypotheses vanish. One contribution of this work is to establish that the rejection rate leads to several paradoxes, which make it unsuitable as a valid performance measure. We then focus on studying the error probability measure. For a binary Gaussian problem, we derive an analytical formula characterizing the ratio between the individual agents' probabilities and the optimal Bayesian probability. The formula shows that this ratio is expressed by the product of two terms quantifying the effect of the network connectivity and the role of the prior information. As a result, an irreducible gap emerges between the decentralized and the centralized error probabilities, which is agent-dependent and does not disappear asymptotically.

We establish sharp, uniform-in-time strong error estimates for a nonlinearity-explicit tamed finite element method (FEM) applied to a class of superlinear stochastic partial differential equations (SPDEs) driven by multiplicative noise, including the stochastic Allen--Cahn equation with a moderately thick interface. This tamed-FEM was first introduced in [Z. Liu and J. Shen, arXiv:2502.19117] to ensure long-time unconditional stability and to preserve the Lyapunov structure of this class of SPDEs. We further prove that the scheme is exponentially ergodic and derive the convergence rate between the exact invariant measure and its numerical counterpart in the Wasserstein-2 distance. Finally, we present numerical experiments that verify the ergodicity as well as the sharpness and time-independence of the strong convergence rates for this tamed-FEM.

Benchmarking fundamental stellar properties is essential for calibrating evolutionary models and establishing empirical relationships between mass, radius, luminosity and related stellar properties. We determine the current ages and initial equatorial velocities at the Zero-Age Main Sequence (ZAMS) for a well-characterized sample of 108 detached main-sequence eclipsing binaries. Evolutionary tracks from the ZAMS to the present are calculated by accounting for tidal interactions, including meridional circulation, and angular momentum loss via stellar winds, under the assumption of circular orbits. System ages are derived by identifying the evolutionary stage at which the calculated radii of both stellar components best match the observed values. While initial velocities for currently synchronized systems cannot be uniquely constrained, as a wide range of initial states naturally converges to synchronisation over time, we are able to determine unique solutions for ZAMS spin velocities in systems that remain asynchronous today. Our models successfully reproduce observed present-day equatorial velocities with a precision of 1\%. Two systems, HD~71636 and V396~Cas, were found to have primaries with initially retrograde spin at ZAMS. We also find an increasing dispersion of spin velocities with age. Our results demonstrate that tidal and evolutionary effects in binary systems actively counteract rotational deceleration from stellar winds, effectively preventing the substantial spin-down that typically characterizes the evolution of isolated single stars.