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Shading and drought are considered crucial abiotic stress factors that limit the normal growth of plants. Under natural conditions, the quality of Bletilla striata pseudobulbs (BP), a Chinese traditional medicinal crop, is often affected by the dual stresses of shading and drought. However, the relationship and mechanism of the interaction between the two stress factors in B. striata remain unclear. In this study, we examined the changes in photosynthetic properties and active ingredients of B. striata under shading (L), drought (W), and shading-drought dual stresses (LW). We aimed to explore the metabolite mechanism that led to these changes using GC-MS-based non-targeted metabolomics techniques. The results indicated a significant reduction in the polysaccharide content of BP under W and LW treatments compared to the control (CK). The total phenol content was significantly reduced under L treatment, while the total flavonoid content did not change significantly under the three stresses. The significant increase in militarine content under all three stresses implies that B. striata may modulate its biosynthesis in response to different environmental stresses. Transpiration rate and stomatal conductance were reduced, amino acid expression was up-regulated, and carbohydrate expression was down-regulated in B. striata under L treatment. The net photosynthesis rate, stomatal conductance, and transpiration rate exhibited significant reductions, and the tuber metabolic disorder marker Homocysteine increased and organic acid content as well under W treatment. The net photosynthetic rate, transpiration rate, stomatal conductance, and water use efficiency of B. striata were further reduced under LW compared with single stress, which is in agreement with the "trade-off theory". Pseudobulb metabolite changes, in combination with the results of the two single stresses, showed an up-regulation of amino acids and disaccharide compounds and a down-regulation of monosaccharide compounds. A support vector machine model (SVM) was used to screen 10 marker metabolites and accurately predict the changes in active ingredient content through an artificial neural network model (ANN). The results suggest that an appropriate stress environment can enhance the content of the target active ingredients based on cultivation goals. [ABSTRACT FROM AUTHOR]

[Display omitted] • A DNN model permanently records the multiple regeneration effects for real-time forecasting of drill bit operating status. • The forecasting accuracy, convergence speed and stability of this model are higher than other time-series forecasting models. • Accurately forecasting the chaotic behavior of the drill bit. • Increasing batch size and output window size decreases forecasting accuracy. • Appropriate input window size can improve forecasting accuracy. The multiple regenerative effects during the drilling process cause the drilling system to exhibit complex motions, even leading to chaotic behavior. Additionally, due to the inability to obtain real-time rock parameters, traditional drilling dynamics models and time-series forecasting models cannot accurately forecast the real-time operational status of the drilling system. In this paper, a deep neural network (DNN) model integrated with rock surface morphology model is proposed to forecast the real-time operational status of the drill bit. This new model can permanently record the changes in rock surface morphology and multiple regenerative effects during the drilling process, enhancing the perception of rock parameters and improving the forecasting accuracy of the drill bit's operational status. Experimental results show that the new model forecasts the drill bit's operational status with higher accuracy compared to other typical time-series forecasting models without integrating surface morphology model. Furthermore, this new model demonstrates superior convergence speed, stability, and generalization ability compared to other models. In addition, the influence of hyperparameters on the forecast accuracy of the new model has also been studied. These results play an important role in forecasting and preventing adverse vibrations such as stick slip, impact, reversal, and rebound of the drill bit. [ABSTRACT FROM AUTHOR]