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Prediction of state of health (SOH), a crucial aspect of battery management systems, necessitates accurate and reliable estimations for lithium-ion batteries. However, achieving high-precision SOH estimation using the deep extreme learning machine (DELM) in complex environments is challenging due to the instability caused by its random key parameters. To address this, we propose a novel approach that combines the improved bald eagle search (IBES) algorithm with DELM. By utilizing the IBES algorithm, we can extract highly relevant health indicators from the battery's parameter curve during charging and discharging, as well as the incremental capacity curve. These indicators serve as inputs to the constructed estimation model, which predicts SOH under different working conditions. The proposed method has been validated using publicly available experimental data, with an overall error in SOH estimation of less than 1%. In comparison to the existing model, the proposed method achieves RMSE, MAE, and MAPE values lower than 0.35%, 0.26%, and 0.21%, respectively. These findings demonstrate that the proposed method excels in terms of both the speed and accuracy of SOH estimation, showcasing its enhanced robustness and reliability. [ABSTRACT FROM AUTHOR]

The physical status of humans can be estimated by observing the appearance of the tongue coating, known as tongue diagnosis. The goals of this study were to reveal the relationship between tongue coating appearance and the oral microbiota in patients with gastric cancer and to open a novel research direction supporting tongue diagnosis. We used a tongue manifestation acquisition instrument to analyse the thickness of the tongue coating of patients with gastric cancer and that of healthy controls, and high-throughput sequencing was used to describe the microbial community of the tongue coating by sequencing the V2-V4 region of the 16S rDNA. The tongue coatings of 74 patients with gastric cancer were significantly thicker than those of 72 healthy controls (343.11 ± 198.22 versus 98.42 ± 48.25, P < 0.001); 51.35% of the patients were assessed as having thick tongue coatings, whereas all healthy controls were assessed as having thin tongue coatings. Thick tongue coatings presented lower microbial community diversity than thin tongue coatings. The tongue coating bacterial community is associated with the appearance of the tongue coating. The tongue coating may be a potential source for diagnosing gastric cancer, but its sensitivity needs to be further improved. [ABSTRACT FROM AUTHOR]