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Abstract We use 3D fully kinetic simulations to study different turbulence modes and turbulence anisotropy of relativistic turbulence in magnetically dominated collisionless plasmas. We extend the method developed by J. Cho & A. Lazarian for decomposing nonrelativistic magnetohydrodynamic (MHD) turbulence into Alfvén, fast, and slow modes to the regime of collisionless plasmas. We find that Alfvén and slow modes are anisotropic, following the P. Goldreich & H. Sridhar scaling, while fast modes are isotropic. We observe a larger kinetic energy fraction of fast modes compared to that found in the nonrelativistic MHD turbulence, suggesting a stronger coupling of Alfvén and fast modes in relativistic magnetized turbulence in collisionless plasmas. We further examine the dynamic alignment and find a weaker scale dependence of the alignment angle than previously proposed. The dominant thermal fluctuations in the kinetic range can cause flattening of the turbulent velocity structure function and weakening of the turbulence anisotropy and dynamic alignment near the kinetic scales. 

Abstract Magnetic fields play a crucial role in various astrophysical processes within the intracluster medium, including heat conduction, cosmic-ray acceleration, and the generation of synchrotron radiation. However, measuring magnetic field strength is typically challenging due to the limited availability of Faraday rotation measure sources. To address the challenge, we propose a novel method that employs Convolutional Neural Networks (CNNs) alongside synchrotron emission observations to estimate magnetic field strengths in galaxy clusters. Our CNN model is trained on either magnetohydrodynamic (MHD) turbulence simulations or MHD galaxy cluster simulations, which incorporate complex dynamics such as cluster mergers and sloshing motions. The results demonstrate that CNNs can effectively estimate magnetic field strengths with mean-squared error of approximately 0.135µG², 0.044µG², and 0.02µG²forβ = 100, 200, and 500 conditions, respectively. Additionally, we have confirmed that our CNN model remains robust against noise and variations in viewing angles with sufficient training, ensuring reliable performance under a wide range of observational conditions. We compare the CNN approach with the traditional magnetic field strength estimate method that assumes equipartition between cosmic-ray electron energy and magnetic field energy. In contrast to the equipartition method, this CNN approach relies on the morphological feature of synchrotron images, offering a new perspective for complementing traditional estimates and enhancing our understanding of cosmic-ray acceleration mechanisms. 

Abstract This study implemented and evaluated a prediction-driven nurse staffing framework in a large adult emergency department. The framework leveraged a two-stage prediction model that forecasted patient volume and guided staffing decisions. Using a pre-post study design, we compared patient throughput (measured by door-to-evaluation time, active treatment time, boarding time, length of stay, and left-without-being-seen rate) and cost outcomes (measured as hourly nurse staffing costs) before and after implementation. The model achieved an RMSE of 11.261 and MAPE of 13.414% at the base stage, and an RMSE of 9.973 and MAPE of 12.126% at the surge stage. The framework reduced hourly staffing costs by $162.04 without negatively affecting throughput. Reducing one nurse per hour from the recommended level increased wait times by two minutes, with an additional 2.3-min increase when staffing dropped below 20% of recommendations. These findings highlight the potential of prediction-driven staffing to reduce costs while maintaining patient throughput. 

Antioxidant properties of inorganic nanoparticles in aqueous media are attracting growing interest due to their high surface reactivity. Materials such as cerium oxide, iron oxide, silver, and gold exhibit distinct radical-scavenging behaviors at the nanoscale, but reliable quantification remains challenging. Conventional assays developed for molecular antioxidants cannot be directly applied because probes such as 2,2-diphenyl-1-picrylhydrazyl (DPPH) require methanol–water mixtures and are unstable in aqueous nanoparticle suspensions, while other assays are affected by nanoparticle-induced absorption or fluorescence changes. Here we demonstrate strategies to correct these interferences by independently measuring nanoparticle optical properties after oxidation and customizing assay conditions to account for the dilute, per-particle concentrations of nanomaterials. Using a high-throughput 96-well format, four adapted assays revealed that silver, ceria, and iron oxide nanoparticles possess substantially higher antioxidant capacities than Trolox, while gold showed negligible activity. This optimized approach enables reproducible comparison of nanoparticle antioxidants and provides a platform for tailoring nanostructures with enhanced radical-scavenging properties. 

Abstract Measuring the 3D spatial distribution of magnetic fields in the interstellar medium and the intracluster medium is crucial yet challenging. The probing of the 3D magnetic field’s 3D distribution, including the field plane-of-sky orientation (ψ), the magnetic field’s inclination angle (γ) relative to the line of sight, and the magnetization (∼the inverse Alfvén Mach number $M_{\mathrm{A}}^{-1}$), at different distances from the observer makes the task even more formidable. However, the anisotropy and Faraday decorrelation effect in polarized synchrotron emission offer a unique solution. We show that due to the Faraday decorrelation, only regions up to a certain effective path length along the line of sight contribute to the statistical correlation of the measured polarization. The 3D spatial information can be consequently derived from synchrotron polarization derivatives (SPDs), which are calculated from the difference in synchrotron polarization across two wavelengths. We find that the 3D magnetic field can be estimated from the anisotropy observed in SPDs: the elongation direction of the SPD structures probesψ, and the degree of SPD anisotropy, along with its morphological curvature, provides insights into $M_{\mathrm{A}}^{-1}$andγ. To extract these anisotropic features and their correlation with the 3D magnetic field, we propose utilizing a machine learning approach, specifically the Vision Transformer (ViT) architecture, which was exemplified by the success of ChatGPT. We train the ViT using synthetic synchrotron observations generated from magnetohydrodynamic turbulence simulations in sub-Alfvénic and super-Alfvénic conditions. We show that ViT’s application to multiwavelength SPDs can successfully reconstruct the 3D magnetic fields’ 3D spatial distribution. 

Abstract Understanding the role of turbulence in shaping the interstellar medium (ISM) is crucial for studying star formation, molecular cloud evolution, and cosmic-ray propagation. Central to this is the measurement of the sonic Mach number (M_s), which quantifies the ratio of turbulent velocity to the sound speed. In this work, we introduce a convolutional-neural-network-(CNN)-based approach for estimatingM_sdirectly from spectroscopic observations. The approach leverages the physical correlation between increasingM_sand the shock-induced small-scale fluctuations that alter the morphological features in intensity, velocity centroid, and velocity channel maps. These maps, derived from 3D magnetohydrodynamic turbulence simulations, serve as inputs for the CNN training. By learning the relationship between these structural features and the underlying turbulence properties, CNNs can predictM_sunder various conditions, including different magnetic fields and levels of observational noise. The median uncertainty of the CNN-predictedM_sranges from 0.5 to 1.5 depending on the noise level. While intensity maps offer lower uncertainty, channel maps have the advantage of predicting the 3DM_sdistribution, which is crucial in estimating 3D magnetic field strength. Our results demonstrate that machine-learning-based tools can effectively characterize complex turbulence properties in the ISM. 

Abstract The dynamics of star-forming gas can be affected by many physical processes, such as turbulence, gravity, supernova explosions, and magnetic fields. In this paper, we investigate several nearby star-forming regions (Orion, Upper Sco, Taurus, and Perseus) for kinematic imprints of these influences on the newly formed stars. Using Gaia DR3 astrometry and APOGEE DR17 radial velocities, we compute first-order velocity structure functions (VSFs) of young stars in galactic Cartesian coordinates in both 6D (3D positions and 3D velocities) and 4D (3D positions and each 1D velocity) to identify signatures of turbulence and anisotropic motion. We also construct 3D and 1D radial velocity profiles to identify coherent expansion trends, and compare stellar proper motions to plane-of-sky magnetic field orientations in Taurus and Perseus. We find that the VSFs are mildly anisotropic, with slightly different amplitudes, slopes, or features in different directions in several groups, but in general, they are all consistent with Larson’s Relation at intermediate length scales, especially in less compact groups. In several cases, the VSFs exhibit features suggestive of local energy injection from supernovae. Radial velocity profiles reveal clear anisotropic expansion in multiple groups, with the most extreme cases corresponding to those with the most anisotropic VSFs. In Perseus, we find that the motions of young stars are preferentially perpendicular to the local magnetic field. We find multiple, overlapping causes in each group for the observed kinematics. Our findings support that young stars remember more than just the turbulent state of their natal clouds. 

Abstract Magnetic fields and turbulence are fundamental to the evolutions of galaxies, yet their precise measurement and analysis present significant challenges. The recently developed Velocity Gradient Technique (VGT), which capitalizes on the anisotropy inherent in magnetohydrodynamic (MHD) turbulence, represents a new method for mapping magnetic fields in galaxies using spectroscopic observations. Most validations of VGT thus far have relied upon idealized MHD turbulence simulations, however, which lack the more complex dynamics found in galaxies and galaxy mergers. In this study, we scrutinize VGT using an AREPO-based cosmological galaxy merger simulation, testing its effectiveness across pre-merger, merging, and post-merger stages. We examine the underlying assumptions of VGT and probe the statistics of gas density, velocity, and magnetic fields over time. We find that the velocity fluctuations are indeed anisotropic at each stage, being larger in the direction perpendicular to the local magnetic field, as required by VGT. We find additionally that galaxy mergers substantially intensify the velocity and density fluctuations and amplify the magnetic fields at all scales. The observed scaling of the velocity fluctuations shows a steeper trend thanr^1/2between 0.6 and 3 kpc and a shallower trend at larger scales. The scaling of the magnetic field and density fluctuations at scales ≲1.0 kpc also predominantly aligns withr^1/2. Finally, we compare results from VGT to those derived from polarization-like mock magnetic field measurements, finding consistent and statistically significant global agreement in all cases. 

Abstract Synchrotron observation serves as a tool for studying magnetic fields in the interstellar medium and intracluster medium, yet its ability to unveil three-dimensional (3D) magnetic fields, meaning probing the field’s plane-of-the-sky (POS) orientation, inclination angle relative to the line of sight, and magnetization from one observational data, remains largely underexplored. Inspired by the latest insights into anisotropic magnetohydrodynamic (MHD) turbulence, we found that synchrotron emission’s intensity structures inherently reflect this anisotropy, providing crucial information to aid in 3D magnetic field studies: (i) the structure’s elongation gives the magnetic field’s POS orientation and (ii) the structure’s anisotropy degree and topology reveal the inclination angle and magnetization. Capitalizing on this foundation, we integrate a machine learning approach—convolutional neural network (CNN)—to extract this latent information, thereby facilitating the exploration of 3D magnetic fields. The model is trained on synthetic synchrotron emission maps, derived from 3D MHD turbulence simulations encompassing a range of sub-Alfvénic to super-Alfvénic conditions. We show that the CNN is physically interpretable and the CNN is capable of obtaining the POS orientation, inclination angle, and magnetization. Additionally, we test the CNN against the noise effect and the missing low-spatial frequency. We show that this CNN-based approach maintains a high degree of robustness even when only high-spatial frequencies are maintained. This renders the method particularly suitable for application to interferometric data lacking single-dish measurements. We applied this trained CNN to the synchrotron observations of a diffuse region. The CNN-predicted POS magnetic field orientation shows a statistical agreement with that derived from synchrotron polarization.