MicroRNAs (miRs) have emerged as being important in cancer biology. miR‑191 is a conserved miRNA, which has been investigated in detail and is reported to be induced by hypoxia-inducible factor (HIF)‑1α and has an contributory action in the progression of breast, hepatic and pancreatic cancer. However, the effects of miR‑191 in the progression of lung cancer are a subject of debate. In the present study, it was found that the expression of miR-191 was significantly upregulated in non‑small cell lung cancer (NSCLC) cells in patients in vivo. However, the levels of miR‑191 remained unchanged in SK‑MES‑1, A549 and NCI‑H460 NSCLC cell lines, compared with the level in the normal HBE lung cell line, however, the levels were markedly upregulated in these NSCLC cell lines under conditions of chronic hypoxia. Subsequently, an miR‑191 mimic was transfected into the NSCLC cell lines to examine its effect on the progression of the NSCLC cells in vitro. The data obtained using MTT and Cell counting kit‑8 assays revealed that miR‑191 had no effect on the proliferation of the cells under normal condition, however, their proliferation was promoted under mild hypoxic conditions. In addition, the results of a Transwell migration assay showed that miR‑191 had a promoting effect on NSCLC cell migration under the conditions of chronic hypoxia. Furthermore, the TargetScan bioinformatics server and 3'-untranslated region luciferase reporter assay indicated that the transcription factor, nuclear factor 1α (NFIA) was a target of miR‑191. Subsequent western blot analysis showed that, in chronic‑hypoxia, the protein levels of NFIA and the tumor suppressor, CCAAT-enhancer-binding protein α, were sharply reduced in A549 cells. In conclusion, miR‑191 was induced by chronic hypoxia and promoted the proliferation and migration of NSCLC cells under chronic hypoxic conditions. This promotion may be associated with its targeting of NFIA. The present findings may provide a potential molecular target for the therapeutic treatment of NSCLC.

Aiming at the problem that it is difficult to accurately estimate the state of charge (SOC) of lithium‐ion batteries in the strongly nonlinear interval, a novel algorithm based on a fuzzy control strategy is proposed. It integrates extended Kalman filter (EKF) and ampere‐hour (Ah) integration accurately estimate the SOC of lithium‐ion batteries. First, the algorithm uses the advantage that the EKF algorithm has high estimation accuracy in the nonlinear interval and can solve the problem of the large error caused by the inaccurate initial value of the Ah integral algorithm. Then the fuzzy‐EKF‐Ah (F‐EKF‐Ah) is used to fuse the two algorithms of EKF and Ah integral. The fused algorithm can effectively solve the problems of the cumulative error caused by the sampling accuracy of the Ah integral algorithm and the large estimation error of the EKF algorithm in the strong nonlinear interval. Finally, the equivalent circuit model is used for analysis. The experimental results show that the improved algorithm can achieve high estimation accuracy under three experimental conditions.

Summary Lithium‐ion batteries are used in a wide range of applications due to their cleanliness and stability, and the health management of lithium‐ion batteries has become a necessity. The most important aspect of health management is the prediction of the remaining useful life (RUL) of the battery. Therefore, a RUL estimation model based on the aging factor of the charging process and an improved multi‐kernel relevance vector machine is proposed in order to achieve high accuracy estimation of the RUL of lithium‐ion batteries. First, eight aging features highly correlated with lithium‐ion batteries capacity degradation are extracted based on charging current, voltage, and temperature data, then, their correlation is proved using gray relation analysis. Secondly, the improved gray wolf constrained optimization algorithm is used to determine the kernel function combination coefficients of the multi‐kernel relevance vector machine, and the RUL prediction model of the improved multi‐kernel relevance vector machine is established. Finally, using the battery dataset from NASA, aging data of three datasets, 24°C, 43°C, and 4°C, with a total of 11 batteries, were selected for validation. The validation results show that the improved multi‐kernel relevance vector machine prediction model has higher prediction accuracy and more robust long‐term prediction capability, with RUL prediction error less than 10 cycles and MAE less than 0.05, both of which are better than that of the single‐kernel relevance vector machine model and other multi‐kernel relevance vector machine models.

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