Sugar metabolites not only act as the key compounds in tea plant response to stress but are also critical for tea quality formation during the post-harvest processing of tea leaves. However, the mechanisms by which sugar metabolites in post-harvest tea leaves respond to mechanical stress are unclear. In this study, we aimed to investigate the effects of mechanical stress on saccharide metabolites and related post-harvest tea genes. Withered (C15) and mechanically-stressed (V15) for 15 min Oolong tea leaves were used for metabolome and transcriptome sequencing analyses. We identified a total of 19 sugar metabolites, most of which increased in C15 and V15. A total of 69 genes related to sugar metabolism were identified using transcriptome analysis, most of which were down-regulated in C15 and V15. To further understand the relationship between the down-regulated genes and sugar metabolites, we analyzed the sucrose and starch, galactose, and glycolysis metabolic pathways, and found that several key genes of invertase (INV), α-amylase (AMY), β-amylase (BMY), aldose 1-epimerase (AEP), and α-galactosidase (AGAL) were down-regulated. This inhibited the hydrolysis of sugars and might have contributed to the enrichment of galactose and D-mannose in V15. Additionally, galactinol synthase (Gols), raffinose synthase (RS), hexokinase (HXK), 6-phosphofructokinase 1 (PFK-1), and pyruvate kinase (PK) genes were significantly upregulated in V15, promoting the accumulation of D-fructose-6-phosphate (D-Fru-6P), D-glucose-6-phosphate (D-glu-6P), and D-glucose. Transcriptome and metabolome association analysis showed that the glycolysis pathway was enhanced and the hydrolysis rate of sugars related to hemicellulose synthesis slowed in response to mechanical stress. In this study, we explored the role of sugar in the response of post-harvest tea leaves to mechanical stress by analyzing differences in the expression of sugar metabolites and related genes. Our results improve the understanding of post-harvest tea’s resistance to mechanical stress and the associated mechanism of sugar metabolism. The resulting treatment may be used to control the quality of Oolong tea.
Steering control for autonomous vehicles is used for more complex scenarios, such as nonlinear scenarios and varied vehicle speeds scenarios during the actual driving process. Model Predictive Control (MPC) is known as a feasible method for multi-constraints. However, a complicated mathematical model will lead to a great computational burden. To deal with this issue, a modified MPC-based adaptive steering strategy with nonlinear compensation by using Double Deep Q-learning Network Algorithm (DDQN) is proposed. Considering the real-time requirement for steering control, MPC is used as a basic controller to calculate the linear turning case. Then a DDQN algorithm is applied for compensating the errors caused by the nonlinear feature of the vehicle and the time-varying speed. Finally, numerical validations and experimental tests in a real vehicle are conducted to verify the effectiveness of the proposed control strategy. The traditional MPC and only the DDQN method are applied as the benchmark strategies. The comparison validation results under different speed and vehicle parameters indicate that the proposed method has superior performance in adapting to time-varying speeds and vehicle nonlinear features. And the experimental tests based on actual driving scenarios also validate the remarkable stability of the developed control method.
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