Abstract-This paper proposes a cascaded converter dedicated to long-distance HVDC infeed and asynchronous back-to-back interconnection of receiving grids. The cascaded converter is consisted of MMCs in series and parallel connection, meeting the high DC voltage and power demand of HVDC system. It realizes hierarchical feeding and asynchronous interconnection of receiving grids, optimizing the multi-infeed short circuit ratio and improving the flexibility of the receiving grids. The topology and operating characteristics of the cascaded converter are introduced in detail. The multi-infeed short-circuits ratio (MISCR) and the maximum power infeed of the cascaded converter based HVDC systems are analyzed. Various feasible operating modes with online switching strategies of the cascaded converter are studied to improve the operational flexibility of the system. The simulation results verify the effectiveness of the control strategy of the HVDC system embedding the cascaded converter. The DC faults clearing strategy and operating modes switching strategies are also validated.
Characterizing the spatial distributions of hydraulic conductivity of rock mass is important in geoscience and engineering disciplines. In this paper, the architecture of CNN is proposed to predict the spatial distributions of hydraulic conductivity based on limited geologic factors. The performance of CNN model is evaluated using the new data of hydraulic conductivity. A comparative study with the empirical method is performed to validate the reliability of CNN model. The effect of weathering and unloading on the spatial distributions of hydraulic conductivity is studied using the CNN model. The result shows that the hydraulic conductivity predicted by CNN model is within the error range of 5% compared to the Lugeon borehole tests. The predictive accuracy of the CNN method is higher than the estimations of the empirical relations. The spatial distributions of hydraulic conductivity versus depth can be divided into three stages. At first stage, the hydraulic conductivity is slightly reduced with the increasing of depth. Increasing to the depth range of 300-600 m (second stage), the hydraulic conductivity is slightly reduced as a function of lower weathering degree. At last stage, the hydraulic conductivity is not changed by the weathering, and converge to a constant with the depth increasing.
IntroductionMultiple sclerosis is an immune-mediated demyelinating disorder of the central nervous system. Because of the complexity of etiology, pathology, clinical manifestations, and the diversity of classification, the diagnosis of MS is very difficult. We found that McDonald Criteria is very strict and relies heavily on the evidence for DIS and DIT. Therefore, we hope to find a new method to supplement the evidence and improve the accuracy of MS diagnosis.ResultsWe finally selected GSE61240, GSE18781, and GSE185047 based on the GPL570 platform to build a diagnosis model. We initially selected 54 MS susceptibility locus genes identified by IMSGC and WTCCC2 as predictors for the model. After Random Forests and other series of screening, the logistic regression model was established with 4 genes as the final predictors. In external validation, the model showed high accuracy with an AUC of 0.96 and an accuracy of 86.30%. Finally, we established a nomogram and an online prediction tool to better display the diagnosis model.ConclusionThe diagnosis model based on microarray data in this study has a high degree of discrimination and calibration in the validation set, which is helpful for diagnosis in the absence of evidence for DIS and DIT. Only one SLE case was misdiagnosed as MS, indicating that the model has a high specificity (93.93%), which is useful for differential diagnosis. The significance of the study lies in proving that it is feasible to identify MS by peripheral blood RNA, and the further application of the model and be used as a supplement to McDonald Criteria still need to be trained with larger sample size.
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