Selection of rice varieties and application of amendments are effective measures to ensure food safety. Here we report that in the non-Cd area, the grain quality of all rice varieties met the Chinese National Grain Safety Standards (CNGSS). In the high-Cd area, rice varieties showed significant different bioaccumulation of Cd with lower rice yields than those in non-Cd area with the average decrease of 31.1%. There was a negative correlation between grain Cd content and yields. A total of 19 rice varieties were selected as low Cd accumulating rice varieties and their Cd content met CNGSS in the low-Cd area. Six of them met CNGSS in the high-Cd area. The application of amendments significantly reduced Cd content in rice grains by 1.0–84.7% with an average of 52.6% and 13 of varieties met CNGSS. The amendments reduced available Cd content in soils by 1.1–75.8% but had no significant effects on rice yields. Therefore, the current study implied that proper agronomic management with selection of rice varieties and soil amendments was essential in controlling Cd accumulation in rice grains.

This study investigated the ability of artificial neural networks (ANNs) to resolve the nonlinear dynamics inherent in the behavior of complex fluid flows, which often exhibit multifaceted characteristics that challenge traditional analytical or numerical methods. By employing flow profile pairs that are generated through high-fidelity numerical simulations, encompassing both the one-dimensional benchmark problems and the more intricate three-dimensional boundary layer transition problem, this research convincingly demonstrates that neural networks possess a remarkable capacity to effectively capture the discontinuities and the subtle wave characteristics that occur at small scales within complex fluid flows, thereby showcasing their robustness in handling intricate fluid dynamics phenomena. Furthermore, even in the context of challenging three-dimensional problems, this study reveals that the average velocity profiles can be predicted with a high degree of accuracy, utilizing a limited number of input profiles during the training phase, which underscores the efficiency and efficacy of the model in understanding complex systems. The findings of this study significantly underscore the immense potential that artificial neural networks, along with deep learning methodologies, hold in advancing our comprehension of the fundamental physics that govern complex fluid dynamics systems, while concurrently demonstrating their applicability across a variety of flow scenarios and their capacity to yield insightful revelations regarding the nonlinear relationships that exist among diverse flow parameters, thus paving the way for future research in this critical area of study.

Micro-vortex generators (MVGs) are widely utilized as passive devices to control flow separation in supersonic boundary layers by generating ring-like vortices that mitigate shock-induced effects. This study employs large eddy simulation (LES) to investigate the flow structures in a supersonic boundary layer (Mach 2.5, Re = 5760) controlled by two MVGs installed in tandem, with spacings varying from 11.75 h to 18.75 h (h = MVG height), alongside a single-MVG reference case. A fifth-order WENO scheme and third-order TVD Runge–Kutta method were used to solve the unfiltered Navier–Stokes equations, with the Liutex method applied to visualize vortex structures. Results reveal that tandem MVGs produce complex vortex interactions, with spanwise and streamwise vortices merging extensively, leading to a significant reduction in vortex intensity due to mutual cancellation. A momentum deficit forms behind the second MVG, weakening that from the first, while the boundary layer energy thickness doubles compared to the single-MVG case, indicating increased energy loss. Streamwise vorticity distributions and instantaneous streamlines highlight intensified interactions with closer spacings, yet this complexity diminishes overall flow control effectiveness. Contrary to expectations, the tandem configuration does not enhance boundary layer control but instead weakens it, as evidenced by reduced vortex strength and amplified energy dissipation. These findings underscore a critical trade-off in tandem MVG deployment, suggesting that while vortex interactions enrich flow complexity, they may compromise the intended control benefits in supersonic flows, with implications for optimizing MVG arrangements in practical applications.

Modal analysis on micro-vortex generator (MVG)-controlled supersonic flow at different Mach numbers is performed in this paper. The purpose of this investigation is to clarify the different properties of streamwise and ring-like vortical modes, and the effects of different Mach numbers on these modes, to further understand the vortical structures as they travel from MVG down to the shock wave/boundary-layer interaction (SWBLI) region. To this end, a high order and high resolution large eddy simulation (LES) was carried out, which identified the vortical structures behind the MVG and in the shock wave/boundary-layer interaction (SWBLI) region in the supersonic ramp flow with flow speeds of three different Mach numbers 1.5, 2.0, and 2.5. The proper orthogonal decomposition (POD) then was adopted to investigate the modes of the fluctuation flow field. It emerged that the streamwise and ring-like vortical modes were disparate in energy distribution, structural order, frequency and amplitude. Furthermore, it showed that as the Mach number increased, the energy of the streamwise modes increased while the opposite was true for ring-like modes; and the streamwise modal structures were altered more significantly than the ring-like modes, and the frequency of each mode scarcely varied. It was also found that the streamwise vortices absorbed energy from the ring-like vortices while they traveled from the MVG down to the SWBLI region, but the dominant frequency of each mode rarely changed during this process.