The accurate prediction of atmospheric wind speed in near space is of importance for both middle and upper atmospheric scientific research and engineering applications. In order to improve the accuracy of short-term wind speed predictions in near space, this paper proposes a multi-step hybrid prediction method based on the combination of variational modal decomposition (VMD), particle swarm optimization (PSO) and long short-term memory neural networks (LSTM). This paper uses the measurement of wind speed in the height range of 80–88 km at the Kunming site (25.6° N, 103.8° E) for wind speed prediction experiments. The results show that the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of multi–step wind predictions are less than 6 m/s and 15%, respectively. Furthermore, the proposed VMD–PSO–LSTM method is compared with the traditional seasonal difference autoregressive sliding average model (SARIMA) to investigate its performance. Our analysis shows that the percentage improvement of prediction performance compared to the traditional time series prediction model can reach at most 85.21% and 83.75% in RMSE and MAPE, respectively, which means that the VMD–PSO–LSTM model has better accuracy in the multi-step prediction of the wind speed.
(1) Background: Ovarian cancer (OV) has the high mortality rate among gynecological cancers worldwide. Inefficient early diagnosis and prognostic prediction of OV leads to poor survival in most patients. OV is associated with ferroptosis, an iron-dependent form of cell death. Ferroptosis, believed to be regulated by long non-coding RNAs (lncRNAs), may have potential applications in anti-cancer treatments. In this study, we aimed to identify ferroptosis-related lncRNA signatures and develop a novel model for predicting OV prognosis. (2) Methods: We downloaded data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression, and Gene Expression Omnibus (GEO) databases. Prognostic lncRNAs were screened by least absolute shrinkage and selection operator (LASSO)-Cox regression analysis, and a prognostic model was constructed. The model’s predictive ability was evaluated by Kaplan–Meier (KM) survival analysis and receiver operating characteristic (ROC) curves. The expression levels of these lncRNAs included in the model were examined in normal and OV cell lines using quantitative reverse transcriptase polymerase chain reaction. (3) Results: We constructed an 18 lncRNA prognostic prediction model for OV based on ferroptosis-related lncRNAs from TCGA patient samples. This model was validated using TCGA and GEO patient samples. KM analysis showed that the prognostic model was able to significantly distinguish between high- and low-risk groups, corresponding to worse and better prognoses. Based on the ROC curves, our model shows stronger prediction precision compared with other traditional clinical factors. Immune cell infiltration, immune checkpoint expression levels, and Tumor Immune Dysfunction and Exclusion analyses are also insightful for OV immunotherapy. (4) Conclusions: The prognostic model constructed in this study has potential for improving our understanding of ferroptosis-related lncRNAs and providing a new tool for prognosis and immune response prediction in patients with OV.
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