RESUMO
Lost circulation events during drilling operations are known for their abruptness and are difficult to control. Traditional diagnostic methods rely on qualitative indicators, such as mud pit volume changes or anomalous logging curve patterns. However, these methods are subjective and rely heavily on empirical knowledge, resulting in delayed or inaccurate predictions. To address this problem, there is an urgent need to develop efficient methods for a timely and accurate lost circulation prediction. In this study, a novel approach is proposed by combining principal component analysis (PCA) and empirical analysis to reduce the dimensionality of the model data. This dimensionality reduction helps to streamline the analysis process and improve prediction accuracy. The predictive model also incorporates an improved fruit fly optimization algorithm (IFOA) in conjunction with support vector machine (SVM) techniques. The actual instances of lost circulation serve as the evaluation criteria for this integrated method. To overcome the challenges associated with irregular population distribution within randomly generated individuals, a tent map strategy is introduced to ensure a more balanced and representative sample. In addition, the model addresses issues such as premature convergence and slow optimization rates by employing a sine-cosine search strategy. This strategy helps to achieve optimal results and speeds up the prediction process. The improved prediction model demonstrates exceptional performance, achieving accuracy, precision, recall, and F1 scores of 96.8, 97, 96, and 96%, respectively. These results indicate that the IFOA-SVM approach achieves the highest accuracy with a reduced number of iterations, proving to be an efficient and fast method for predicting the lost circulation events. Implementation of this methodology in drilling operations can lead to improved efficiency, reliability, and overall performance.
RESUMO
PURPOSE: The board application of black phosphorus quantum dots (BP-QDs) increases the risk of inhalation exposure in the manufacturing process. The aim of this study is to explore the toxic effect of BP-QDs on human bronchial epithelial cells (Beas-2B) and lung tissue of Balb/c mice. METHODS: The BP-QDs were characterized using transmission electron microscopy (TEM) and a Malvern laser particle size analyzer. Cell Counting Kit-8 (CCK-8) and TEM were used to detect cytotoxicity and organelle injury. Damage to the endoplasmic reticulum (ER) was detected by using the ER-Tracker molecular probe. Rates of apoptosis were detected by AnnexinV/PI staining. Phagocytic acid vesicles were detected using AO staining. Western blotting and immunohistochemistry were used to examine the molecular mechanisms. RESULTS: After treatment with different concentrations of BP-QDs for 24 h, the cell viability decreased, as well as activation of the ER stress and autophagy. Furthermore, the rate of apoptosis was increased. Inhibition of ER stress caused by 4-phenyl butyric acid (4-PBA) was shown to significantly inhibit both apoptosis and autophagy, suggesting that ER stress could be an upstream mediator of both autophagy and apoptosis. BP-QD-induced autophagy can also inhibit the occurrence of apoptosis using molecules related to autophagy including rapamycin (Rapa), 3-methyladenine (3-MA), and bafilomycin A1 (Bafi A1). In general, BP-QDs activate ER stress in Beas-2B cells, which further induces autophagy and apoptosis, and autophagy may be activated as a factor that protects against apoptosis. We also observed strong staining of related proteins of ER stress, autophagy, and apoptosis proteins in mouse lung tissue following intracheal instillation over the course of a week. CONCLUSION: BP-QD-induced ER stress facilitates autophagy and apoptosis in Beas-2B cells and autophagy may be activated as a protective factor against apoptosis. Under conditions of ER stress induced by BP-QDs, The interplay between autophagy and apoptosis determines cell fate.