Background: Angelicae Dahuricae Radix (ADR) is used as both a traditional Chinese medicine and a food ingredient in China and East Asian countries. ADR is generally sun-dried post-harvest but is sometimes sulfur-fumigated to prevent decay and rot. Although there are some studies on the effect of sulfur fumigation on ADR, they are not comprehensive. Methods: This study used HPLC fingerprinting, matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), in vitro anti-inflammatory assays, and metabolite analysis in blood based on UPLC-MS/MS to assess the impact of sulfur fumigation on the active ingredients of ADR. Results: There were significant decreases in specific coumarins and amino acids, particularly byakangelicol, oxypeucedanin, L-proline, and L-arginine, following sulfur fumigation. Among the 185 metabolites in blood, there were 30 different compounds, and oxypeucedanin was the most obvious component to decrease after sulfur fumigation. ADR showed anti-inflammatory activity regardless of sulfur fumigation. However, the effects on the production of cytokines in LPS-induced RAW264.7 cells were different. Conclusions: Chemometric analysis and in vitro anti-inflammatory studies suggested that byakangelicol and oxypeucedanin could serve as potential quality markers for identifying sulfur-fumigated ADR. These findings provide a chemical basis for comprehensive safety and functional evaluations of sulfur-fumigated ADR, supporting further research in this field.

Metabolic product accumulation exhibited variations among mulberry (Morus alba L.) leaves (MLs) at distinct growth stages, and this assessment was conducted using a combination of analytical techniques including high-performance liquid chromatography (HPLC), gas chromatography–mass spectrometry (GC-MS), and liquid chromatography–mass spectrometry (LC-MS). Multivariate analysis was applied to the data, and the findings were correlated with antioxidant activity and α-glucosidase inhibitory effects in vitro. Statistical analyses divided the 27 batches of MLs at different growth stages into three distinct groups. In vitro assays for antioxidant activity and α-glucosidase inhibition revealed that IC₅₀ values were highest at the Y23 stage, which corresponds to the ‘Frost Descends’ solar term. In summary, the results of this study indicate that MLs at different growth stages throughout the year can be categorized into three primary growth stages using traditional Chinese solar terms as reference points, based on the observed variations in metabolite content.

To ensure detection accuracy, the focus of the terahertz lens is required to be located on the surface of the workpiece in the process of terahertz imaging detection, and the outgoing optical axis of the lens is also required to be perpendicular to the surface of the workpiece. Therefore, when imaging curved samples, a 6-degree freedom manipulator is generally used to drive the terahertz lens, and the lens realizes non-destructive testing along the workpiece surface. The mechanical arm is required to drive the terahertz lens to move along the rotation curve in a two-dimensional plane when the mechanical arm is used to detect the power insulator of the rotating structure. This paper focuses on the path planning of the manipulator, that is, the curve is divided into several connected straight lines and arcs, and the manipulator moves continuously in each characteristic line segment. The D-H dynamic model of the manipulator was established to analyze the joint velocity and acceleration to ensure the stability of the moving process.

In order to improve the response speed and control accuracy of the tunneling machine control system, and to address the limitations of traditional algorithms that are prone to falling into local extremum and weak generalization ability during the solving process, a PSO-BP neural network PID adaptive control method for tunneling machines is proposed. The weights of the BP neural network are optimized by the global search ability of the PSO algorithm. The neural network outputs the fitness values of the PID parameters, and the initial weights of the neural network are updated. The PID parameters are adjusted in realtime. The results show that the unit Step response curve of the system reaches the stable state of the system after 0.85 s in the traditional PID control, while the PSO-BP neural network PID controller only needs 0.4 s to be stable, and the overshoot of the PSO-BP neural network PID controller is far less than that of the traditional PID controller. Therefore, the PSO BP neural network PID controller has good dynamic and static characteristics, which can maximize the generalization ability of the traditional BP neural network and ensure the Rate of convergence and learning ability.