We propose an analytical approach based on the Frenet–Serret (FL) frame field, where an FL frame and the corresponding curvature and torsion are defined at each point along magnetic field lines, to investigate the evolution of magnetic tubes and their interaction with vortex tubes in magnetohydrodynamics. Within this framework, simplified expressions for the Lorentz force, its curl, the dynamics of flux tubes and helicity are derived. We further perform direct numerical simulations on the linkage between the magnetic and vortex tubes and investigate the effect of the initial angle $\theta$, ranging from $0^{\,\circ}$ to $45^{\,\circ}$, on their evolution. Our results show that magnetic tubes with non-zero curvature generate Lorentz forces, which in turn produce dipole vortices. These dipole vortices lead to the splitting of the magnetic tubes into smaller structures, releasing magnetic energy. Both magnetic and vortex tubes exhibit quasi-Lagrangian behaviour, maintaining similar shapes during initial evolution and consistent relative positions over time. A vortex tube with strength comparable to that of the magnetic tube, where the kinetic energy induced by the vortex tube is of the same order as the magnetic energy in the magnetic tube, can inhibit magnetic tube splitting by disrupting the formation of vortex dipoles. Additionally, minor variations in the angular configuration of the vortex tubes significantly influence their interaction with the magnetic field and the evolution of large-scale flow structures.

Machine learning (ML) models have been developed to identify randomised controlled trials (RCTs) to accelerate systematic reviews (SRs). However, their use has been limited due to concerns about their performance and practical benefits. We developed a high-recall ensemble learning model using Cochrane RCT data to enhance the identification of RCTs for rapid title and abstract screening in SRs and evaluated the model externally with our annotated RCT datasets. Additionally, we assessed the practical impact in terms of labour time savings and recall improvement under two scenarios: ML-assisted double screening (where ML and one reviewer screened all citations in parallel) and ML-assisted stepwise screening (where ML flagged all potential RCTs, and at least two reviewers subsequently filtered the flagged citations). Our model achieved twice the precision compared to the existing SVM model while maintaining a recall of 0.99 in both internal and external tests. In a practical evaluation with ML-assisted double screening, our model led to significant labour time savings (average 45.4%) and improved recall (average 0.998 compared to 0.919 for a single reviewer). In ML-assisted stepwise screening, the model performed similarly to standard manual screening but with average labour time savings of 74.4%. In conclusion, compared with existing methods, the proposed model can reduce workload while maintaining comparable recall when identifying RCTs during the title and abstract screening stages, thereby accelerating SRs. We propose practical recommendations to effectively apply ML-assisted manual screening when conducting SRs, depending on reviewer availability (ML-assisted double screening) or time constraints (ML-assisted stepwise screening).

Laser-driven inertial confinement fusion (ICF) diagnostics play a crucial role in understanding the complex physical processes governing ICF and enabling ignition. During the ICF process, the interaction between the high-power laser and ablation material leads to the formation of a plasma critical surface, which reflects a significant portion of the driving laser, reducing the efficiency of laser energy conversion into implosive kinetic energy. Effective diagnostic methods for the critical surface remain elusive. In this work, we propose a novel optical diagnostic approach to investigate the plasma critical surface. This method has been experimentally validated, providing new insights into the critical surface morphology and dynamics. This advancement represents a significant step forward in ICF diagnostic capabilities, with the potential to inform strategies for enhancing the uniformity of the driving laser and target surface, ultimately improving the efficiency of converting laser energy into implosion kinetic energy and enabling ignition.

A high-energy pulsed vacuum ultraviolet (VUV) solid-state laser at 177 nm with high peak power by the sixth harmonic of a neodymium-doped yttrium aluminum garnet (Nd:YAG) amplifier in a KBe2BO3F2 prism-coupled device was demonstrated. The ultraviolet (UV) pump laser is a 352 ps pulsed, spatial top-hat super-Gaussian beam at 355 nm. A high energy of a 7.12 mJ VUV laser at 177 nm is obtained with a pulse width of 255 ps, indicating a peak power of 28 MW, and the conversion efficiency is 9.42% from 355 to 177 nm. The measured results fitted well with the theoretical prediction. It is the highest pulse energy and highest peak power ever reported in the VUV range for any solid-state lasers. The high-energy, high-peak-power, and high-spatial-uniformity VUV laser is of great interest for ultra-fine machining and particle-size measurements using UV in-line Fraunhofer holography diagnostics.

In this paper, we study the existence of travelling wave solutions and the spreading speed for the solutions of an age-structured epidemic model with nonlocal diffusion. Our proofs make use of the comparison principles both to construct suitable sub/super-solutions and to prove the regularity of travelling wave solutions.

We numerically investigated the global linear instability and bifurcations in electro-thermo-convection (ETC) of a dielectric liquid confined in a two-dimensional (2-D) concentric annulus subjected to a strong unipolar injection. Seven kinds of solutions exist in this ETC system due to the complex bifurcations, i.e. saddle-node, subcritical and supercritical Hopf bifurcations. These bifurcation routes constitute at most four solution branches. Global linear instability analysis and energy analysis were conducted to explain the instability mechanism and transition of different solutions and to predict the local instability regions. The linearized lattice Boltzmann method (LLBM) for global linear instability analysis, first proposed by Pérez et al. (Theor. Comput. Fluid Dyn., vol. 31, 2017, pp. 643–664) to analyse incompressible flows, was extended here to solve the whole set of coupled linear equations, including the linear Navier–Stokes equations, the linear energy equation, Poisson's equation and the linear charge conservation equation. A multiscale analysis was also performed to recover the macroscopic linearized Navier–Stokes equations from the four different discrete lattice Boltzmann equations (LBEs). The LLBM was validated by calculating the linear critical value of 2-D natural convection; it has an error of 1.39% compared with the spectral method. Instability with global travelling wave behaviour is a unique behaviour in the annulus configuration electrothermohydrodynamic system, which may be caused by the baroclinity. Finally, the chaotic behaviour was quantitatively analysed through calculation of the fractal dimension and Lyapunov exponent.