We develop and test an algorithmic approach for providing p-cycle survivable transport network designs. The basic approach is to first identify a set of primary p-cycles, then to search for improvements on those cycles through various operations to create a final set of cycles of high individual and collective efficiency, before finally placing one p-cycle at a time, iteratively, until all working capacity of the network is protected. We compare the solution quality of the algorithm to optimal designs obtained with ILP methods. The primary advantage of this algorithmic approach is that it entirely avoids the step of enumerating all cycles, which is a preliminary step in both ILP and heuristic solution methods based on preselection. This method proceeds initially with no more than S "primary" p-cycles, and in the worst case will enumerate no more than S2*N other candidate cycles during its execution, where S is the number of spans in the network and N is the number of nodes. We also find that the set of candidate cycles developed by the algorithm can themselves be used as a quite small but highly effective set of eligible cycles in an ILP design model. IndexTermsp-cycles, optical mesh network design, algorithmic and heuristic design, restoration and protection. 0-7803-81 18-1/03/$17.00 0 2003 IEEE

DC droop control strategy is usually used to improve load sharing in increasingly popular DC distribution networks. However, the conventional droop control strategy suffers from considerable terminal voltage drop and influences by line impedance. In this study, the authors propose a new robust droop control strategy to overcome these drawbacks. They first develop a mathematical model of the proposed robust controller and use the load terminal voltage as a feedback signal. They further treat the line impedance as part of the equivalent output impedance of individual power converters and minimise the inaccuracy of load sharing by regulating robust coefficients. They analyse the influence of the robust coefficients on the system stability. They have verified the robust control strategy with both power system computer-aided design/electromagnetic transients including DC simulation and control hardware in the loop semi-experimental method. It is shown that the robust concept improves the load sharing accuracy and suppressing the load voltage fluctuation.

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Endometrial cancer (EC), one of most common gynaecological malignant tumours, threatens the female health worldwide, especially in developed countries. 1 According to estimated data, more than 63 230 new EC cases and 11 350 EC deaths are projected to occur in the United States in 2018. 2 Current diagnoses for uterine corpus tumours mainly depend on clinical and histological features.However, 15%-20% of these tumours still have a high risk of recurrence and even further deterioration. Although some molecular AbstractAs endometrial cancer (EC) is a major threat to female health worldwide, the ability to provide an accurate diagnosis and prognosis of EC is promising to improve its treatment guidance. Since the discovery of miRNAs, it has been realized that miRNAs are associated with every cell function, including malignant transformation and metastasis. This study aimed to explore diagnostic and prognostic miRNA markers of EC.In this study, differential analysis and machine learning were performed, followed by correlation analysis of miRNA-mRNA based on the miRNA and mRNA expression data. Nine miRNAs were identified as diagnostic markers, and a diagnostic classifier was established to distinguish between EC and normal endometrium tissue with overall correct rates >95%. Five specific prognostic miRNA markers were selected to construct a prognostic model, which was confirmed more effective in identifying EC patients at high risk of mortality compared with the FIGO staging system. This study demonstrates that the expression patterns of miRNAs may hold promise for becoming diagnostic and prognostic biomarkers and novel therapeutic targets for EC. K E Y W O R D S diagnostic classifier, endometrial cancer, microRNA, molecular biomarker, prognostic model S U PP O RTI N G I N FO R M ATI O N Additional supporting information may be found online in the Supporting Information section. How to cite this article: Wang Q, Xu K, Tong Y, et al. Novel miRNA markers for the diagnosis and prognosis of endometrial cancer. J Cell Mol Med. 2020;24:4533-4546. https://doi.