AIM To investigate the hepatitis C virus (HCV) infection in the tissues of carcinoma of extrahepatic bile duct and study their correlation. METHODS HCV NS5 protein and HCV RNA were detected by labeled streptavidin biotin (LSAB) method and in situ reverse transcription polymerase chain reaction (IS-RT-PCR) in sections of 51 cases of carcinoma of extrahepatic bile duct and 34 cases of control group (without malignant biliary disease). RESULTSIn 51 cases of carcinoma of extrahepatic bile duct, HCV NS5 protein was detected in 14 (27.5%), which was clearly stained in the cytoplasm of cancer cell but not in the nucleus or cell membrane. HCV RNA was detected in 18 (35.4%), which was located in the nucleus of cancer cell in 12 cases and in the cytoplasm in 6 cases. HCV NS5 protein and RNA coexistence was found in 2 cases. In 34 cases of control group, HCV RNA was detected in 2 (5.9%). HCV NS5 protein and RNA positive cells were found either scattered or in clusters. CONCLUSION The prevalence of hepatitis C viral infection in the tissues of carcinoma of extrahepatic bile duct was significantly higher than in control group (χ χ χ χ χ 2 =9.808, P=0.002). The findings suggest a correlation between HCV infection and carcinoma of extrahepatic bile duct, which is different from the traditional viewpoint. HCV infection might be involved in the development of carcinoma of extrahepatic bile duct.

Fault diagnosis can insure the power transformer safety and economic operation, and the data mining is the key technology of fault diagnosis for power transformer. In order to achieve the fast parallel fault diagnosis for power transformer, we need to put cloud computing technology into the smart grid. We give a parallel method of K-means based on MapReduce framework on the Hadoop distributed systems cluster to diagnose operation state of power transformer. Finally, through transformer fault diagnosis experimentations of massive DGA data, the results indicate closely linear speedup with an increasing number of node computers.

Gearbox affect the normal operation of the wind turbines, to study the fault diagnosis, support vector method was used. Parameters selection is very important and decides the fault diagnosis precision. In order to overcome the blindness of man-made choice of the parameters in least squares support vector machine (LSSVM) and improve the accuracy and efficiency of fault diagnosis, a method based on LSSVM trained by genetic algorithm was proposed. This method searches the optimized parameters in LSSVM by taking advantage of the genetic algorithms powerful global searching ability. The research is provided using this method on the fault diagnosis of wind turbine gearbox and compared with the diagnostic method of LSSVM. The experimental results show that the method achieves a higher diagnostic accuracy.

With the development of on-line monitoring technology of electric power equipment, and the accumulation of both on-line monitoring data and off-line testing data, the data available to fault diagnosis of power transformer is bound to be massive. How to utilize those massive data reasonably is the issue that eagerly needs us to study. Since the on-line monitoring technology is not totally mature, which resulting in incomplete, noisy, wrong characters for monitoring data, so processing the initial data by using rough set is necessary. Furthermore, when the issue scale becomes larger, the computing amount of association rule mining grows dramatically, and its easy to cause data expansion. So it needs to use attribute reduction algorithm of rough set theory. Taking the above two points into account, this paper proposes a fault diagnosis model for power transformer using association rule mining-based on rough set.

This paper analyzes the object-oriented modeling technology of IEC 61850 standard and the modeling steps of condition monitoring intelligent electronic devices (IED). Aiming at the problem of the existing configuration tools poor scalability for the IEC 61850 standard, a new design method of the IED configuration tool is brought up. Information model of transformer condition monitoring IED is established including logical device (LD), logical node (LN), data object (DO), data attribute (DA), dataset and report control block with the method mentioned above, and described with substation configuration description language.