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Your search keyword '"Dong, Ya-Xin"' showing total 4 results
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4 results on '"Dong, Ya-Xin"'

1. Ultrasound elastography predicts anticoagulation in lower extremity deep vein thrombosis.

Author

Zhang, Ao-Yi, Dong, Ya-Xin, Tan, Yan-Di, Dian-Shen, Heng-Sun, Nie, Shu-Ting, Shao, Yuan-Yuan, Feng-Xian, Hu, Wen-Shu, Li, Xin-YI, Tao-Xu, Li, An-Ni, Liang-Xu, and Chang-Zhou

Subjects
*VENOUS thrombosis, *ANTICOAGULANTS, *MULTIPLE regression analysis, *LOGISTIC regression analysis, *ULTRASONIC imaging
Abstract

OBJECTIVE: To investigate predictors of anticoagulation efficacy in deep venous thrombosis (DVT) by ultrasound elastography (UE). METHODS: The basic clinical, laboratory and ultrasound treatment data of fifty-eight patients with DVT were collected and analyzed. Then the results of ultrasound after 3-month anticoagulation treatment were compared among different groups. Multiple logistic regression analysis was used to identify independent risk factors that affected anticoagulation efficacy. The predictive efficacy of each independent risk factor was accessed by drawing operating characteristic (ROC) curves. RESULTS: According to the regression analysis, the elastic modulus (OR = 0.631, P = 0.001) and strain rate ratio (OR = 0.332, P = 0.006) were identified as independent risk factors for the effectiveness of anticoagulation therapy in patients with DVT. According to the ROC curves, elastic modulus and strain rate ratio could predict effective anticoagulation therapy for DVT, and the optimal threshold values were 22.10 kPa and 1.80 respectively. The corresponding AUC values were 0.879 and 0.854, with a sensitivity of 71.4% and 59.5%, a specificity of 93.7%, and a Youden index of 65.1% and 62.7%, respectively. CONCLUSIONS: The elastic modulus (≤22.10 kPa) or strain rate ratio (≤1.80) of the thrombus were independent predictors for the effectiveness of anticoagulation therapy. [ABSTRACT FROM AUTHOR]

Published
2024
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4. Research and design of distributed fault diagnosis system in nuclear power plant

Author

Liu Yong-kuo, Xie Chun-li, Dong Ya-xin, and Peng Minjun

Subjects
Artificial neural network, Computer science, Energy Engineering and Power Technology, Condition monitoring, Hardware_PERFORMANCEANDRELIABILITY, Sensor fusion, Fault (power engineering), Fault indicator, Reliability engineering, Stuck-at fault, Nuclear Energy and Engineering, Fault coverage, Fault model, Safety, Risk, Reliability and Quality, Waste Management and Disposal
Abstract

Nuclear power plant (NPP) is a complex system with abundant operation data and various fault types. Moreover, in most cases, change of system parameters and prompt of the alarm system can not necessarily tell us directly what types the fault belongs to or where it lies in. When multiple faults occur, there is no one-to-one relationship between the fault symptom and the fault itself. Furthermore, the degree and response rate of the fault are different, so some faults are gradual and some are sudden. It is thus clear that for different faults, we need to conduct combined diagnosis with a variety of diagnostic methods to ensure accuracy and instantaneity in NPP fault diagnosis. According to the characteristic of the distributed function of equipment and the digital instrument and control (I&C) system, we studied and designed the distributed condition monitoring and fault diagnosis system in NPP. Based on the “disassemble-synthesizing” diagnostic idea, this paper proposed an intelligent diagnosis method which applied the fuzzy neural network (FNN) in doing local diagnosis and multi-source information fusion technology in global diagnosis. The simulation result showed that this method can quickly and accurately complete the tasks of diagnosing different levels of the single fault and different types of multiple faults.

Published
2013

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