The cardio-ankle vascular index (CAVI) has been widely accepted as a good indicator of arteriosclerosis. However, the lack of a reliable diagnostic criterion for CAVI hampers the proper clinical screening for arteriosclerosis using CAVI and impedes the prompt treatment of cardiovascular disease (CVD). There is an urgent need to determine a criterion for CAVI in arteriosclerosis prevention. We conducted a cross-sectional study to determine this criterion based on receiver operating characteristic (ROC) analyses in a Chinese population consisting of 328 participants. CAVI was measured in duplicate, and carotid ultrasound detection was performed in a quiet environment by well-trained physicians. After multivariate adjustment, CAVI was positively associated with the risk of carotid arteriosclerosis. Compared with participants in the lowest tertile of CAVI (5.15-7.40), those in the medium (7.41-8.65) and highest (8.66-13.60) tertiles had odds ratios (95% confidence interval) of 2.2 (1.0, 4.9) and 4.4 (1.5, 13.3), respectively, for developing carotid arteriosclerosis (P trend=0.007). The areas under the ROC curve (AUC) of the male, female and pooled populations were 0.789, 0.897 and 0.856, respectively. The cutoff point of CAVI≥8.0 resulted in the largest sensitivity and specificity. Furthermore, CAVI and age acted synergistically to increase the risk of carotid arteriosclerosis. CAVI≥8.0 may be an optimal cutoff point for carotid arteriosclerosis prediction. The older population with higher CAVI scores had a higher risk of carotid arteriosclerosis. Additional large prospective studies are needed to confirm our findings.
Ice accretion on wind turbine blades is one of the major faults affecting the operational safety and power generation efficiency of wind turbines. Current icing detection methods are based on either meteorological observing system or extra condition monitoring system. Compared with current methods, icing detection using the intrinsic supervisory control and data acquisition (SCADA) data of wind turbines has plenty of potential advantages, such as low cost, high stability, and early icing detection ability. However, there have not been deep investigations in this field at present. In this paper, a novel intelligent wind turbine blade icing detection method based on the wind turbine SCADA data is proposed. This method consists of three processes: SCADA data preprocessing, automatic feature extraction, and ensemble icing detection model construction. Specifically, deep autoencoders network is employed to learn multilevel fault features from the complex SCADA data adaptively. And the ensemble technique is utilized to make full use of all the extracted features from different hidden layers of the deep autoencoders network to build the ensemble icing detection model. The effectiveness of the proposed method is validated using the data collected from actual wind farms. The experimental results reveal that the proposed method is able to not only adaptively extract valuable fault features from the complex SCADA data, but also obtains higher detection accuracy and generalization capability compared with conventional machine learning models and individual deep learning model.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.