The aim of this study is to investigate the glucose metabolic status and its prognostic value in glioma. The Chinese Glioma Genome Atlas (CGGA), The Cancer Genome Atlas (TCGA), and GSE16011 datasets were used to develop the glucose-related signature. A cohort of 305 glioma samples with whole genome microarray expression data from the Chinese Glioma Genome Atlas database was included for discovery. TCGA and GSE16011 datasets were used for validation. Gene Set Enrichment Analysis (GSEA) and Cytoscape were used to explore the bioinformatic implication. GSEA revealed the biological process associated with the glucose-related signature. Cytoscape visualized the correlation analysis among the genes. We also collected the blood glucose information of patients with gliomas to analyze the association with tumor malignancy and patients' survival. In this study, we identified that glucose-related gene sets could distinguish the clinical and molecular features of gliomas, involved in the malignancy of gliomas. And then, we developed a glucose-related prognostic signature for patients with glioblastoma in the CGGA dataset, validated in other additional public datasets. GSEA illustrated that tumor with higher risk score of glucose-related signature could correlate with cell cycle phase. In addition, blood glucose concentration was associated with the malignancy of glioma and the survival of patients. These results might provide new view for the research of glioma malignancy and individual treatment. Our research provided important resources for future dissection of glucose metabolic role in glioma.

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The increasing penetration of renewable energy brings great challenges to the planning and operation of power systems. To deal with the fluctuation of renewable energy, the main focus of current research is on incorporating the detailed operation constraints into generation expansion planning (GEP) models. In most studies, the traditional objective function of GEP is to minimize the total cost (including the investment and operation cost). However, in power systems with high penetration of renewable energy, more attention has been paid to increasing the utilization of renewable energy and reducing the renewable energy curtailment. Different from the traditional objective function, this paper proposes a new objective function to maximize the accommodation of renewable energy during the planning horizon, taking into account short-term operation constraints and uncertainties from load and renewable energy sources. A power grid of one province in China is modified as a case study to verify the rationality and effectiveness of the proposed model. Numerical results show that the proposed GEP model could install more renewable power plants and improve the accommodation of renewable energy compared to the traditional GEP model.

A direct torque control (DTC) with a modified finite set model predictive strategy is proposed in this paper. The eight voltage space vectors of two-level inverters are taken as the finite control set and applied to the model predictive direct torque control of a permanent magnet synchronous motor (PMSM). The duty cycle of each voltage vector in the finite set can be estimated by a cost function, which is designed based on factors including the torque error, maximum torque per ampere (MTPA), and stator current constraints. Lyapunov control theory is introduced in the determination of the weight coefficients of the cost function to guarantee stability, and thus the optimal voltage vector reference value of the inverter is obtained. Compared with the conventional finite control set model predictive control (FCS-MPC) method, the torque ripple is reduced and the robustness of the system is clearly improved. Finally, the simulation and experimental results verify the effectiveness of the proposed control scheme.

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