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The therapeutic effect of apigenin (APG) on hyperlipidemia was investigated using network pharmacology combined with molecular docking strategy, and the potential targets of APG in the treatment of hyperlipidemia were explored. Genetic Ontology Biological Process (GOBP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment analysis of common targets were performed. Then, molecular docking was used to predict the binding mode of APG to the target. Finally, Sprague Dawley rats were used to establish a hyperlipidemia model. The expression levels of insulin (INS) and vascular endothelial growth factor A (VEGFA) mRNA in each group were detected by quantitative reverse transcription-polymerase chain reaction. Network pharmacological studies revealed that the role of APG in the treatment of hyperlipidemia was through the regulation of INS, VEGFA, tumor necrosis factor, epidermal growth factor receptor, matrix metalloprotein 9, and other targets, as well as through the regulation of the hypoxia-inducible factor 1 (HIF-1) signaling pathway, fluid shear stress, and atherosclerosis signaling pathways, vascular permeability; APG also participated in the regulation of glucose metabolism and lipid metabolism, and acted on vascular endothelial cells, and regulated vascular tone. Molecular docking showed that APG binds to the target with good efficiency. Experiments showed that after APG treatment, the expression levels of INS and VEGFA mRNA in the model group were significantly decreased (p < 0.01). In conclusion, APG has multiple targets and affects pathways involved in the treatment of hyperlipidemia by regulating the HIF-1 signaling pathway, fluid shear stress, and the atherosclerosis pathway.

With the rapid increase in vehicle ownership, exhaust pollution has become one of the important sources of air pollution in China. In this study, a MAHA METDH 6.3 exhaust gas test experimental platform was developed, and an emission model suitable for coupling with real-time road conditions was established based on large-scale vehicle emission test data. Based on traffic big data such as traffic volume, average vehicle speed, and vehicle model distribution, ArcGIS was used to select the road network information in the study area and combined with the emission model to realize the spatial distribution of line sources of vehicle emissions. Finally, based on the road network simulation model built by VISSIM, the emission changes caused by the two measures of trunk line optimization and new energy vehicle development were simulated and analyzed. The results show that the spatial distribution characteristics of vehicle exhaust pollutants in Zhangdian District are closely related to road type. Taking trunk line optimization measures and developing new energy vehicles have a certain reduction effect on vehicle emissions in Zhangdian District. This study lays a foundation for proposing targeted measures to reduce motor vehicle emissions based on big traffic operation data.

In order to improve the safety of autonomous vehicles during driving and the comfort of drivers and passengers, the longitudinal dynamics model of the vehicle is first established. Secondly, the longitudinal motion control strategy is designed considering the influence of vehicle driving safety and driver comfort. Based on this, a longitudinal motion controller based on model predictive control is established. The upper controller uses the model predictive control algorithm to calculate the expected acceleration, and the lower controller uses the vehicle inverse longitudinal dynamics model to convert the expected acceleration calculated by the upper controller into throttle opening and braking pressure. Finally, the effectiveness of the longitudinal motion controller is verified by MATLAB / Simulink under different working conditions. The simulation results show that the longitudinal motion controller designed in this paper improves the comfort of drivers and passengers under the premise of ensuring the safety of vehicles.