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The quantity and complexity of environmental data show exponential growth in recent years. High-quality big data analysis is critical for performing a sophisticated characterization of the complex network of environmental pollution. Machine learning (ML) has been employed as a powerful tool for decoupling the complexities of environmental big data based on its remarkable fitting ability. Yet, due to the knowledge gap across different subjects, ML concepts and algorithms have not been well-popularized among researchers in environmental sustainability. In this context, we introduce a new research paradigm—“ChatGPT + ML + Environment”, providing an unprecedented chance for environmental researchers to reduce the difficulty of using ML models. For instance, each step involved in applying ML models to environmental sustainability, including data preparation, model selection and construction, model training and evaluation, and hyper-parameter optimization, can be easily performed with guidance from ChatGPT. We also discuss the challenges and limitations of using this research paradigm in the field of environmental sustainability. Furthermore, we highlight the importance of “secondary training” for future application of “ChatGPT + ML + Environment”.

With the application of lithium batteries more and more widely, in order to accurately estimate the state of charge (SoC) of the battery, this paper uses the iterated extended Kalman filter (IEKF) algorithm to estimate the SoC. The Levenberg–Marquardt (LM) method is used to optimize the error covariance matrix of IKEF. Based on the hybrid pulse power characteristics experiment, a second-order Thevenin model with variable parameters is established on the MATLAB platform. The experimental results show that the proposed model is effective under the constant current discharge condition, the Federal Urban Driving Schedule (FUDS) condition, and the Beijing dynamic stress test (BJDST) condition. The results show that the simulation error of the improved LM-IEKF algorithm is less than 2% under different working conditions, which is lower than that of the IKEF algorithm. The improved algorithm has a fast convergence speed to the true value, and it has a good estimation accuracy in the case of large changes in external input current. Additionally, the fluctuation of error is relatively stable, which proves the reliability of the algorithm.

Objective: To observe the treatment of severe preeclampsia in newborns with enoxaparin sodium combined with magnesium sulfate. Methods: A retrospective analysis was conducted on the clinical data of 80 patients with severe preeclampsia admitted to Hefei Second People's Hospital, China from January 2019 to December 2020. Treatment records showed that 40 cases received magnesium sulfate treatment (single group), and 40 cases received enoxaparin sodium combined with magnesium sulfate treatment (combination group). Levels of D-dimer, soluble fms-like tyrosine kinase 1 (sFlt-1), placental growth factor (PLGF), Apgar scores of newborns delivered before and after treatment were compared. Gestation weeks and incidence of adverse reactions were analyzed. Results: After treatment, levels of D-dimer, sfit-1 and adverse reactions in the combination group were significantly lower than those in the single group (P<0.05), and the level of PLGF, newborn Apgar score and length of gestation were significantly higher than those in the single group (P<0.05). Conclusion: Compared to magnesium sulfate alone, the combination of enoxaparin sodium and magnesium sulfate in the treatment of pregnant women with severe preeclampsia can more effectively regulate the cytokine level of patients, improve pregnancy outcome, and improve neonatal Apgar score. The incidence of adverse reactions is low, making it a safe and efficient treatment modality. [ABSTRACT FROM AUTHOR]

Lung nodule classification is crucial for the diagnosis and treatment of lung diseases. However, selecting appropriate metrics to evaluate classifier performance is challenging, due to the prevalence of negative samples over positive ones, resulting in imbalanced datasets. This imbalance often necessitates the augmentation of positive samples to train powerful models effectively. Furthermore, specific medical tasks require tailored augmentation methods, the effectiveness of which merits further exploration based on task objectives. This study conducted a detailed analysis of commonly used metrics in lung nodule detection, examining their characteristics and selecting suitable metrics based on this analysis and our experimental findings. The selected metrics were then applied to assessing different combinations of image augmentation techniques for nodule classification. Ultimately, the most effective metric was identified, leading to the determination of the most advantageous augmentation method combinations.