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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]

Cinnamaldehyde is an ideal feed additive with good immune enhancement and anti-inflammatory regulation effects. However, the anti-inflammatory regulation mechanism in fat greenling (Hexagrammos otakii, H. otakii) remains unclear. The nine targets of cinnamaldehyde were gathered in identified by the Traditional Chinese Medicine Systems Pharmacology database and Uniprot database, and 1,320 intestinal inflammation disease (IIF)-related proteins were screened from DrugBank, Online Mendelian Inheritance in Man (OMIM), Genecards, and Pharmacogenetics and Pharmacogenomics Knowledge Base (PHARMGKB) Databases. According to the Gene Ontology enrichment results and Kyoto Encyclopedia of Genes and Genomes pathway results, cinnamaldehyde may regulated the responses to bacteria, lipopolysaccharide, an inflammatory cytokine, and external stimuli via the nuclear factor kappa-B (NFκB) signaling pathway within on inflammatory network. In addition, the protein–protein interaction analysis assisted in obtaining the closely related inflammatory regulatory proteins, including the C5a anaphylatoxin chemotactic receptor 1 (C5aR1), transcription factor p65 (RELA), prostaglandin G/H synthase 2 (PTGS2), and toll-like receptor 4 (TLR4), which were confirmed as the bottleneck nodes of the network to be more deeply verified via the molecular docking. Moreover, a cinnamaldehyde feeding model was established for evaluating the anti-inflammatory effect of cinnamaldehyde in vivo. According to the current findings implied that cinnamaldehyde may play a protective role against IIF H. otakii by reducing inflammation through the C5 complement (C5)/C5aR1/interleukin-6 (IL-6) and TLR4/NFκB/PTGS2 pathway. The study focused on investigating the action mechanism of cinnamaldehyde on IIF through combining pharmacology and experimental verification in vivo, which provided a fresh perspective on the promoting effect of cinnamaldehyde on IIF in fish. [ABSTRACT FROM AUTHOR]