This paper provides an overview of the current status of ultrafast and ultra-intense lasers with peak powers exceeding 100 TW and examines the research activities in high-energy-density physics within China. Currently, 10 high-intensity lasers with powers over 100 TW are operational, and about 10 additional lasers are being constructed at various institutes and universities. These facilities operate either independently or are combined with one another, thereby offering substantial support for both Chinese and international research and development efforts in high-energy-density physics.

Eotetranychus kankitus is an important pest on several agricultural crops, and its resistance to pesticides has promoted the exploration of biological control strategies. Beauveria bassiana and Neoseiulus barkeri have been identified as potential agents for suppressing spider mites. This study aimed to investigate the pathogenicity of B. bassiana on E. kankitus and its compatibility with N. barkeri. Results showed that among the five tested strains of B. bassiana, Bb025 exhibited the highest level of pathogenicity on E. kankitus. Higher application rates (1 × 108 conidia/mL) of Bb025 led to a higher mortality rate of E. kankitus (90.402%), but also resulted in a 15.036% mortality of N. barkeri. Furthermore, preference response tests indicated that both E. kankitus and N. barkeri actively avoided plants sprayed with Bb025 compared to the control group that was sprayed with Tween-80. In a no-choice test, we observed that N. barkeri actively attacked Bb025-treated E. kankitus with no adverse effect on its predatory capacities. Furthermore, N. barkeri laid more eggs when fed on Bb025-treated E. kankitus compared to Tween-80-treated E. kankitus, but the subsequent generation of surviving individuals fed on Bb025-treated E. kankitus was reduced. These findings demonstrate that the Bb025 strain of B. bassiana is highly virulent against E. kankitus while causing less harm to N. barkeri. Consequently, a promising strategy for controlling E. kankitus could involve the sequential utilisation of Bb025 and N. barkeri at appropriate intervals.

The effect of the polarizations of two counter-propagating relativistic laser pulses interacting with subwavelength thin solid-density foil is investigated. Three-dimensional particle-in-cell simulations and analytical modelling show that the interaction and resulting transverse instability depend strongly on the polarization directions as well as the intensity distribution of the resultant light field in the foil. The left- and right-handed circularly polarized laser pair with the same phase at the common focal spot in the ultrathin foil leads to the strongest distortion of the foil. The fastest growing mode and maximum growth rate depend mainly on the laser intensity. For all polarization and phase-difference combinations, the instability is weakest when the two laser pulses are exactly out of phase at the common focusing point in the foil.

We incorporate the liquidity trap and private behavioral preferences into a New Keynesian dynamic stochastic general equilibrium model to analyze fiscal multipliers. The results indicate that the influence of the liquidity trap on fiscal policy is driven by a combination of the interest rate transmission effect and the precautionary savings effect, showing a notable amplification of multipliers based on estimates from U.S. data. Furthermore, we examine two types of private behavioral preferences: habit formation and investor confidence. Habit formation significantly boosts short-term government spending multipliers while exhibiting diverse impacts on different types of taxation. Compared to superficial habits, deep habits result in flatter multiplier curves. Investor confidence, being highly sensitive to output fluctuations, enhances both spending and tax multipliers over the medium to long term. Additionally, the investor confidence channel slightly amplifies the expansionary effect of the liquidity trap on multipliers, contrasting with the impact of habit formation.

The Asian corn borer, Ostrinia furnacalis (Guenée), emerges as a significant threat to maize cultivation, inflicting substantial damage upon the crops. Particularly, its larval stage represents a critical point characterised by significant economic consequences on maize yield. To manage the infestation of this pest effectively, timely and precise identification of its larval stages is required. Currently, the absence of techniques capable of addressing this urgent need poses a formidable challenge to agricultural practitioners. To mitigate this issue, the current study aims to establish models conducive to the identification of larval stages. Furthermore, this study aims to devise predictive models for estimating larval weights, thereby enhancing the precision and efficacy of pest management strategies. For this, 9 classification and 11 regression models were established using four feature datasets based on the following features geometry, colour, and texture. Effectiveness of the models was determined by comparing metrics such as accuracy, precision, recall, F1-score, coefficient of determination, root mean squared error, mean absolute error, and mean absolute percentage error. Furthermore, Shapley Additive exPlanations analysis was employed to analyse the importance of features. Our results revealed that for instar identification, the DecisionTreeClassifier model exhibited the best performance with an accuracy of 84%. For larval weight, the SupportVectorRegressor model performed best with R2 of 0.9742. Overall, these findings present a novel and accurate approach to identify instar and predict the weight of O. furnacalis larvae, offering valuable insights for the implementation of management strategies against this key pest.

This study employs direct numerical simulations to examine the effects of varying backpressure conditions on the turbulent atomisation of impinging liquid jets. Using the incompressible Navier–Stokes equations, and a volume-of-fluid approach enhanced by adaptive mesh refinement and an isoface-based interface reconstruction algorithm, we analyse spray characteristics in the environments with ambient gas densities ranging from 1 to 40 times the atmospheric pressure under five different backpressure scenarios. We investigate the behaviour of turbulent jets, incorporate realistic orifice geometries and identify significant variations in the atomisation patterns depending on backpressure. Two distinct atomisation types emerge, namely jet-sheet-ligament-droplet at lower backpressures and jet-sheet-fragment-droplet at higher ones, alongside a transition from dilute to dense spray patterns. This variation affects the droplet size distribution and spray dynamics, with increased backpressure reducing the spray's spreading angle and breakup length, while increasing the droplet size variation. Furthermore, these conditions promote distributions that induce rapid, nonlinear wavy motion in liquid sheets. Topological analysis of the atomisation field using velocity-gradient tensor invariants reveals significant variations in topology volume fractions across different regions. Downstream, the droplet Sauter mean diameter increases and then stabilises, reflecting the continuous breakup and coalescence processes, notably under higher backpressures. This research underscores the substantial impact of backpressure on impinging-jet atomisation and provides essential insights for nozzle design to optimise droplet distributions.

Cognitive diagnostic models (CDMs) are latent variable models developed to infer latent skills, knowledge, or personalities that underlie responses to educational, psychological, and social science tests and measures. Recent research focused on theory and methods for using sparse latent class models (SLCMs) in an exploratory fashion to infer the latent processes and structure underlying responses. We report new theoretical results about sufficient conditions for generic identifiability of SLCM parameters. An important contribution for practice is that our new generic identifiability conditions are more likely to be satisfied in empirical applications than existing conditions that ensure strict identifiability. Learning the underlying latent structure can be formulated as a variable selection problem. We develop a new Bayesian variable selection algorithm that explicitly enforces generic identifiability conditions and monotonicity of item response functions to ensure valid posterior inference. We present Monte Carlo simulation results to support accurate inferences and discuss the implications of our findings for future SLCM research and educational testing.

This work investigates the spatio-temporal evolution of coherent structures in the wake of a generic high-speed train, based on a three-dimensional database from large eddy simulation. Spectral proper orthogonal decomposition (SPOD) is used to extract energy spectra and energy ranked empirical modes for both symmetric and antisymmetric components of the fluctuating flow field. The spectrum of the symmetric component shows overall higher energy and more pronounced low-rank behaviour compared with the antisymmetric one. The most dominant symmetric mode features periodic vortex shedding in the near wake, and wave-like structures with constant streamwise wavenumber in the far wake. The mode bispectrum further reveals the dominant role of self-interaction of the symmetric component, leading to first harmonic and subharmonic triads of the fundamental frequency, with remarkable deformation of the mean field. Then, the stability of the three-dimensional wake flow is analysed based on two-dimensional local linear stability analysis combined with a non-parallelism approximation approach. Temporal stability analysis is first performed for both the near-wake and the far-wake regions, showing a more unstable condition in the near-wake region. The absolute frequency of the near-wake eigenmode is determined based on spatio-temporal analysis, then tracked along the streamwise direction to find out the global mode growth rate and frequency, which indicate a marginally stable global mode oscillating at a frequency very close to the most dominant SPOD mode. The global mode wavemaker is then located, and the structural sensitivity is calculated based on the direct and adjoint modes derived from a local spatial analysis, with the maximum value localized within the recirculation region close to the train tail. Finally, the global mode shape is computed by tracking the most spatially unstable eigenmode in the far wake, and the alignment with the SPOD mode is computed as a function of streamwise location. By combining data-driven and theoretical approaches, the mechanisms of coherent structures in complex wake flows are well identified and isolated.