The Floquet exponents of periodic field lines are studied through the variations of the magnetic action on the magnetic axis, which is assumed to be elliptical. The near-axis formalism developed by Mercier, Solov'ev and Shafranov is combined with a Lagrangian approach. The on-axis Floquet exponent is shown to coincide with the on-axis rotational transform. A discrete solution suitable for numerical implementation is introduced, which gives the Floquet exponents as solutions to an eigenvalue problem. This discrete formalism expresses the exponents as the eigenvalues of a $6\times 6$ matrix.

The 2175Å bump is a prominent absorption feature at ultraviolet (UV) wavelengths in dust extinction and attenuation curves. Understanding the relative strength of this feature is important for making accurate dust corrections at both low- and high-redshift. This feature is postulated to arise from polycyclic aromatic hydrocarbon (PAH) dust grains; however, the carrier has not been definitively established. We present results on the correlation between the 2175Å feature and PAH abundances in a spatially-resolved manner for 15 local galaxies in the PHANGS-JWST survey that have NUV and mid-IR imaging data from Swift/UVOT and JWST/MIRI, respectively. We find a moderate positive correlation between the 2175Å feature strength and PAH abundance (Spearman’s coefficient, $0.3 \lesssim \rho \lesssim 0.5$), albeit with large intrinsic scatter. However, most of this trend can be attributed to a stronger negative correlation of both quantities with SFR surface density and specific-SFR (proxies of ionising radiation; $\rho\sim-0.6$). The latter trends are consistent with previous findings that both the 2175Å carrier and PAHs are small grains that are easily destroyed by UV photons, although the proxy for PAH abundance (based on photometry) could also be influenced by dust heating. When controlling for SFR surface density, we find weaker correlations between the 2175Å feature and PAH abundances ($\rho \lesssim 0.3$), disfavouring a direct link. However, analyses based on spectroscopic (instead of photometric) measurements of the 2175Å feature and PAH features are required to verify our findings. No significant trends with gas-phase metallicity or galactocentric radii are found for the 2175Å feature and PAHs; however, the metallicity range of our sample is limited ($8.40 \lt 12+\log[\mathrm{O/H}] \lt 8.65$). We provide prescriptions for the strength of the 2175Å feature and PAHs in local massive (metal-rich) galaxies with SFR surface density and specific-SFR; however, the former should be used with caution due to the fact that bump strengths measured from Swift/UVOT are expected to be underestimated.

Confinement quality in fusion plasma is influenced significantly by the presence of heavy impurities, which can lead to radiative heat loss and reduced confinement. This study explores the clustering of heavy impurity, i.e. tungsten in edge plasma, using high-resolution direct numerical simulations of the Hasegawa–Wakatani equations. We use the Stokes number to quantify the inertia of impurity particles. It is found that particle inertia will cause spatial intermittency in particle distribution and the formation of large-scale structures, i.e. the clustering of particles. The degrees of clustering are influenced by the Stokes number. To quantify these observations, we apply a modified Voronoi tessellation, which assigns specific volumes to impurity particles. By determining time changes of these volumes, we can calculate the impurity velocity divergence, which allows the clustering dynamics to be assessed. To quantify the clustering statistically, several approaches are applied, such as probability density function (PDF) of impurity velocity divergence and joint PDF of volume and divergence.

Collisionless shocks are complex non-linear structures that are not yet fully understood. In particular, the interaction between these shocks and the particles they accelerate remains elusive. Based on an instability analysis that relates the shock width to the spectrum of the accelerated particle and the shock density ratio, we find that the acceleration process could come to an end when the fraction of accelerated upstream particles reaches about 30%. Only unmagnetized shocks are considered.

The data volumes generated by theWidefield ASKAP L-band Legacy All-sky Blind surveY atomic hydrogen (Hi) survey using the Australian Square Kilometre Array Pathfinder (ASKAP) necessitate greater automation and reliable automation in the task of source finding and cataloguing. To this end, we introduce and explore a novel deep learning framework for detecting low signal-to-noise ratio (SNR) Hi sources in an automated fashion. Specifically, our proposed method provides an automated process for separating true Hi detections from false positives when used in combination with the source finding application output candidate catalogues. Leveraging the spatial and depth capabilities of 3D convolutional neural networks, our method is specifically designed to recognize patterns and features in three-dimensional space, making it uniquely suited for rejecting false-positive sources in low SNR scenarios generated by conventional linear methods. As a result, our approach is significantly more accurate in source detection and results in considerably fewer false detections compared to previous linear statistics-based source finding algorithms. Performance tests using mock galaxies injected into real ASKAP data cubes reveal our method’s capability to achieve near-100% completeness and reliability at a relatively low integrated SNR $\sim3-5$. An at-scale version of this tool will greatly maximise the science output from the upcoming widefield Hi surveys.

We carry out timing and spectral studies of the Be/X-ray binary pulsar GX 304-1 using NuStar and XMM-Newton observations. We construct the long-term spin period evolution of the pulsar which changes from a long-term spin-up ($\sim1.3 \times 10^{-13}$ Hz s$^{-1}$) to a long-term spin-down ($\sim-3.4 \times 10^{-14}$ Hz s$^{-1}$) trend during a low luminosity state ($\sim10^{34-35}$ erg s$^{-1}$). A prolonged low luminosity regime ($L_X \sim 10^{34-35}$ erg s$^{-1}$) was detected during 2005–2010 and spanning nearly five years since 2018 December. The XMM-Newton and NuStar spectra can be described with a power law plus blackbody model having an estimated luminosity of $\sim2.5 \times 10^{33}$ and $\sim3.6 \times 10^{33}$ erg s$^{-1}$, respectively. The inferred radius of the blackbody emission is about 100–110 m which suggests a polar-cap origin of this component. From long-term ultraviolet observations of the companion star, an increase in the ultraviolet signatures is detected preceding the X-ray outbursts. The spectral energy distribution of the companion star is constructed which provides a clue of possible UV excess when X-ray outbursts were detected from the neutron star compared to the quiescent phase. We explore plausible mechanisms to explain the long-term spin-down and extended low luminosity manifestation in this pulsar. We find that sustained accretion from a cold disc may explain the prolonged low luminosity state of the pulsar since December 2018 but the pulsar was undergoing normal accretion during the low luminosity period spanning 2005–2010.

MagNetUS is a network of scientists and research groups that coordinates and advocates for fundamental magnetized plasma research in the USA. Its primary goal is to bring together a broad community of researchers and the experimental and numerical tools they use in order to facilitate the sharing of ideas, resources and common tasks. Discussed here are the motivation and goals for this network and details of its formation, history and structure. An overview of associated experimental facilities and numerical projects is provided, along with examples of scientific topics investigated therein. Finally, a vision for the future of the organization is given.

We present PCFTL (Probabilistic CounterFactual Temporal Logic), a new probabilistic temporal logic for the verification of Markov Decision Processes (MDP). PCFTL introduces operators for causal inference, allowing us to express interventional and counterfactual queries. Given a path formula ϕ, an interventional property is concerned with the satisfaction probability of ϕ if we apply a particular change I to the MDP (e.g., switching to a different policy); a counterfactual formula allows us to compute, given an observed MDP path τ, what the outcome of ϕ would have been had we applied I in the past and under the same random factors that led to observing τ. Our approach represents a departure from existing probabilistic temporal logics that do not support such counterfactual reasoning. From a syntactic viewpoint, we introduce a counterfactual operator that subsumes both interventional and counterfactual probabilities as well as the traditional probabilistic operator. This makes our logic strictly more expressive than PCTL⋆. The semantics of PCFTL rely on a structural causal model translation of the MDP, which provides a representation amenable to counterfactual inference. We evaluate PCFTL in the context of safe reinforcement learning using a benchmark of grid-world models.

We solve ‘half’ the problem of finding three-dimensional quasisymmetric magnetic fields that do not necessarily satisfy magnetohydrostatic force balance. This involves determining which hidden symmetries are admissible as quasisymmetries, and then showing explicitly how to construct quasisymmetric magnetic fields given an admissible symmetry. The admissibility conditions take the form of a system of overdetermined nonlinear partial differential equations involving second derivatives of the symmetry's infinitesimal generator.

Asymptotic giant branch (AGB) stars play a significant role in our understanding of the origin of the elements. They contribute to the abundances of C, N, and approximately 50% of the abundances of the elements heavier than iron. An aspect often neglected in studies of AGB stars is the impact of a stellar companion on AGB stellar evolution and nucleosynthesis. In this study, we update the stellar abundances of AGB stars in the binary population synthesis code binary_c and calibrate our treatment of the third dredge-up using observations of Galactic carbon stars. We model stellar populations of low- to intermediate-mass stars at solar-metallicity and examine the stellar wind contributions to C, N, O, Sr, Ba, and Pb yields at binary fractions between 0 and 1. For a stellar population with a binary fraction of 0.7, we find $\sim$20–25% less C and s-process elements ejected than from a population composed of only single stars, and we find little change in the N and O yields. We also compare our models with observed abundances from Ba stars and find our models can reproduce most Ba star abundances, but our population estimates a higher frequency of Ba stars with a surface [Ce/Y] > $+0.2\,$dex. Our models also predict the rare existence of Ba stars with masses $ \gt 10\,\textrm{M}_{\odot}$.

Throughout all the domains of life, and even among the co-existing viruses, RNA molecules play key roles in regulating the rates, duration, and intensity of the expression of genetic information. RNA acts at many different levels in playing these roles. Trans-acting regulatory RNAs can modulate the lifetime and translational efficiency of transcripts with which they pair to achieve speedy and highly specific recognition using only a few components. Cis-acting recognition elements, covalent modifications, and changes to the termini of RNA molecules encode signals that impact transcript lifetime, translation efficiency, and other functional aspects. RNA can provide an allosteric function to signal state changes through the binding of small ligands or interactions with other macromolecules. In either cis or trans, RNA can act in conjunction with multi-enzyme assemblies that function in RNA turnover, processing and surveillance for faulty transcripts. These enzymatic machineries have likely evolved independently in diverse life forms but nonetheless share analogous functional roles, implicating the biological importance of cooperative assemblies to meet the exact demands of RNA metabolism. Underpinning all the RNA-mediated processes are two key aspects: specificity, which avoids misrecognition, and speedy action, which confers timely responses to signals. How these processes work and how aberrant RNA species are recognised and responded to by the degradative machines are intriguing puzzles. We review the biophysical basis for these processes. Kinetics of assembly and multivalency of interacting components provide windows of opportunity for recognition and action that are required for the key regulatory events. The thermodynamic irreversibility of RNA-mediated regulation is one emergent feature of biological systems that may help to account for the apparent specificity and optimal rates.

Quantum learning models hold the potential to bring computational advantages over the classical realm. As powerful quantum servers become available on the cloud, ensuring the protection of clients’ private data becomes crucial. By incorporating quantum homomorphic encryption schemes, we present a general framework that enables quantum delegated and federated learning with a computation-theoretical data privacy guarantee. We show that learning and inference under this framework feature substantially lower communication complexity compared with schemes based on blind quantum computing. In addition, in the proposed quantum federated learning scenario, there is less computational burden on local quantum devices from the client side, since the server can operate on encrypted quantum data without extracting any information. We further prove that certain quantum speedups in supervised learning carry over to private delegated learning scenarios employing quantum kernel methods. Our results provide a valuable guide toward privacy-guaranteed quantum learning on the cloud, which may benefit future studies and security-related applications.