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Your search keyword '"Zixian Dong"' showing total 6 results
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6 results on '"Zixian Dong"'

1. The triglyceride-glucose index is associated with atherosclerosis in patients with symptomatic coronary artery disease, regardless of diabetes mellitus and hyperlipidaemia

Author

Jiao Li, Zixian Dong, Hao Wu, Yue Liu, Yafang Chen, Si Li, Yufan Zhang, Xin Qi, and Liping Wei

Subjects
Triglyceride-glucose index, Risk factors, Coronary atherosclerosis, Carotid atherosclerosis, Diabetes mellitus, Hyperlipidaemia, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Abstract Background Diabetes and hyperlipidaemia are both risk factors for coronary artery disease, and both are associated with a high triglyceride-glucose index (TyG index). The TyG index has been presented as a marker of insulin resistance (IR). Its utility in predicting and detecting cardiovascular disease has been reported. However, few studies have found it to be a helpful marker of atherosclerosis in patients with symptomatic coronary artery disease (CAD). The purpose of this study was to demonstrate that the TyG index can serve as a valuable marker for predicting coronary and carotid atherosclerosis in symptomatic CAD patients, regardless of diabetes mellitus and hyperlipidaemia. Methods This study included 1516 patients with symptomatic CAD who underwent both coronary artery angiography and carotid Doppler ultrasound in the Department of Cardiology at Tianjin Union Medical Center from January 2016 to December 2022. The TyG index was determined using the Ln formula. The population was further grouped and analysed according to the presence or absence of diabetes and hyperlipidaemia. The Gensini score and carotid intima-media thickness were calculated or measured, and the patients were divided into four groups according to TyG index quartile to examine the relationship between the TyG index and coronary or carotid artery lesions in symptomatic CAD patients. Results In symptomatic CAD patients, the TyG index showed a significant positive correlation with both coronary lesions and carotid plaques. After adjusting for sex, age, smoking, BMI, hypertension, diabetes, and the use of antilipemic and antidiabetic agents, the risk of developing coronary lesions and carotid plaques increased across the baseline TyG index. Compared with the lowest quartile of the TyG index, the highest quartile (quartile 4) was associated with a greater incidence of coronary heart disease [OR = 2.55 (95% CI 1.61, 4.03)] and carotid atherosclerotic plaque [OR = 2.31 (95% CI 1.27, 4.20)] (P

Published
2023
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5. Multi-Ionic Hydrogel with Outstanding Heat-to-Electrical Performance for Low-Grade Heat Harvesting

Author

Yongli Zhou, Zixian Dong, Yongjie He, Wentao Zhu, Youlan Yuan, Haoran Zeng, Chen Li, Shanshan Chen, and Kuan Sun

Subjects
Organic Chemistry, General Chemistry, Biochemistry
Abstract

Ionic thermoelectric (i-TE) materials have attracted much attention due to their ability to generate ionic Seebeck coefficient of tens of millivolts per Kelvin. In this work, we demonstrate that the ionic thermopower can be enhanced by the introduction of multiple ions. The multi-ionic hydrogel possesses a record thermal-to-electrical energy conversion factor (TtoE factor) of 89.6 mV K

Published
2022

6. Photovoltaic inverter anomaly detection method based on LSTM serial depth autoencoder

Author

Wei Hu, Zixian Dong, Xingjie Huang, Yating Gao, Zhaoxia Zhang, and Jiaxin Hao

Subjects
History, Computer Science Applications, Education
Abstract

To ensure the safety of the massive growth of distributed photovoltaic grid-connected inverters and the security of backhaul data in the context of new power systems, research on anomaly detection has also been put on the agenda. The data of the photovoltaic grid-connected inverter has complex time dependence and uncertainty, and the data security problem is prone to occur in the process of data transmission, and the existing anomaly detection means do not comprehensively consider these factors, and cannot timely find the abnormal phenomena caused by the synthesis of data security and their faults, which leads to the tampering of power data and the increase of operation and maintenance costs. To solve the above problems, an anomaly detection method integrating a long short-term memory network (LSTM) and serial depth autoencoder (DAE) is proposed based on edge computing, characterized by the power and voltage of the device, the length, and the delay of the data. Firstly, the method performed time series transformation processing on the original training dataset through the sliding window. Then, the model was trained using LSTM and DAE. Finally, the decoding error of the test data was compared with the threshold obtained by the training. The example verification shows that the model has achieved good performance in the recall rate, accuracy rate and F1 score, and can effectively detect the equipment that produces its fault or data return abnormality in the photovoltaic inverter. So, the model has good industrial practical value.

Published
2023
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