The paper proposes an adoption of slope, elevation, speed and route distance preview to achieve optimal energy management of plug-in hybrid electric vehicles (PHEV). The approach is to identify route features from historical and realtime traffic data, in which information fusion model and traffic prediction model are used to improve the information accuracy. Then, dynamic programming combined with equivalent consumption minimization strategy is used to compute an optimal solution for real-time energy management. The solution is the reference for PHEV energy management control along the route. To improve the system's ability of handling changing situation, the study further explores predictive control model in the realtime control of the energy. A simulation is performed to model PHEV under above energy control strategy with route preview. The results show that the average fuel consumption of PHEV along the previewed route with MPC strategy can be reduced compared with optimal strategy and base control strategy. Index Terms-Energy management, model predictive control (MPC), optimal control, plug-in hybrid electric vehicle (PHEV) This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Recommended by Associate Editor Xiangyang Zhao.

Background. Oral squamous cell carcinoma (OSCC) severely affects the quality of life and the 5-year survival rate is low. Exploring the potential miRNA-mRNA regulatory network and analyzing hub genes and clinical data can provide a theoretical basis for further elucidating the pathogenesis of OSCC. Methods. The miRNA expression datasets of GSE113956 and GSE124566 and mRNA expression datasets of GSE31056, GSE37991 and GSE13601 were obtained from the Gene Expression Omnibus databases. The differentially expressed miRNAs (DEMs) and mRNAs (DEGs) were screened using GEO2R. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by DAVID database. The PPI network was established through STRING database and the hub genes were preliminarily screened out by Cytoscape software. After identifying the hub genes in the TCGA database, we predicted the potential DEM transcription factors, constructed a miRNA-mRNA regulatory network, and analyzed the relationship between the hub genes and clinical data. Results. A total of 28 DEMs and 764 DEGs were screened out, which were composed of 285 up-regulated genes and 479 down-regulated genes. Enrichment analysis showed that up-regulation of DEGs were mainly enriched in extracellular matrix organization and cancer-related pathway, while down-regulation of DEGs were mainly enriched in muscular system process and adrenaline signal transduction. After preliminary screening by PPI network and identification in TCGA, the up-regulated FN1, COL1A1, COL1A2, AURKA, CCNB1, CCNA2, SPP1, CDC6, and down-regulated ACTN2, TTN, IGF1, CAV3, MYL2, DMD, LDB3, CSRP3, ACTA1, PPARG were identified as hub genes. The miRNA-mRNA regulation network showed that hsa-miR-513b was the DEM with the most regulation, and COL1A1 was the DEG with the most regulation. In addition, CDC6, AURKA, CCNB1 and CCNA2 were related to overall survival and tumor differentiation. Conclusions. The regulatory relationship of hsa-miR-513b/ CDC6, CCNB1, CCNA2 and the regulatory relationship of hsa-miR-342-5p /AURKA were not only verified in the miRNA-mRNA regulatory network but also related to overall survival and tumor differentiation. These results indicated that they participated in the cellular regulatory process, and provided a molecular mechanism model for the study of pathogenesis.