Elf5 expression is inversely correlated with EMT. Elf5 inhibits TGF-β-driven EMT via repressing SMAD3 phosphorylation in prostate cancer cells. In addition, Elf5 can be used as a biomarker of metastasis-free survival in patients with TGF-β-positive prostate cancer.
Numerous studies on wind power forecasting show that random errors found in the prediction results cause uncertainty in wind power prediction and cannot be solved effectively using conventional point prediction methods. In contrast, interval prediction is gaining increasing attention as an effective approach as it can describe the uncertainty of wind power. A wind power interval forecasting approach is proposed in this article. First, the original wind power series is decomposed into a series of subseries using variational mode decomposition (VMD); second, the prediction model is established through kernel extreme learning machine (KELM). Three indices are taken into account in a novel objective function, and the improved artificial bee colony algorithm (IABC) is used to search for the best wind power intervals. Finally, when compared with other competitive methods, the simulation results show that the proposed approach has much better performance.
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