In the present study, the anti-diabetic effects of a traditional Chinese medicinal formula extract, TongGuanWan, were investigated in type 2 diabetic animals. It was orally administered to C57BL/KsJ-db/db mice once a day for 4 weeks at the doses of 62, 125, and 250 mg/kg body weight. TongGuanWan significantly lowered the blood glucose and glycosylated haemoglobin levels as well as improved the glucose tolerance in db/db mice. The serum triglyceride levels in the db/db mice were significantly decreased, whereas the high-density lipoprotein cholesterol levels were significantly increased, after treatment with this herbal formula. TongGuanWan also markedly decreased the animals' body weights compared to those of the control db/db group but did not alter food intake. The effects of TongGuanWan were compared to those of the drug rosiglitazone. In addition, five main constituents of TongGuanWan, mangiferin, berberine, cinnamic aldehyde, timosaponin BII, and timosaponin AIII, were quantified using high performance liquid chromatography coupled with a diode array and an evaporative light scattering detector (HPLC-DAD-ELSD). These results suggest that TongGuanWan may be useful for the treatment of type 2 diabetes.

Gearboxes are extensively used in various areas including aircraft, mining, manufacturing, and agriculture, etc. The breakdowns of the gearbox are mostly caused by the gear failures. It is therefore crucial for engineers and researchers to monitor the gear conditions in time in order to prevent the malfunctions of the plants. In this paper, a condition monitoring and faults identification technique for rotating machineries based on independent component analysis (ICA) and fuzzy k-nearest neighbor (FKNN) is described. In the diagnosis process, the ICA was initially employed to separate characteristic vibration signal and interference vibration signal from the parallel time series obtained from multi-channel accelerometers mounted on different positions of the gearbox. The wavelet transform (WT) and autoregressive (AR) model method then were performed as the feature extraction technique to attain the original feature vector of the characteristic signal. Meanwhile, the ICA was used again to reduce the dimensionality of the original feature vector. Hence, the useless information in the feature vector could be removed. Finally, the FKNN algorithm was implemented in the pattern recognition process to identify the conditions of the gears of interest. The experimental results suggest that the sensitive fault features can be extracted efficiently after the ICA processing, and the proposed diagnostic system is effective for the gear multi-faults diagnosis, including the gear crack failure, pitting failure, gear tooth broken, compound fault of wear and spalling, etc. In addition, the proposed method can achieve higher performance than that without ICA processing with respect to the classification rate.

In the condition monitoring and fault diagnosis, useful information about the incipient fault features in the measured signal is always corrupted by noise. Fortunately, the Kalman filtering technique can filter the noise effectively, and the impending system fault can be revealed to prevent the system from malfunction. This paper has discussed recent progress of the Kalman filters for the condition monitoring and fault diagnosis. A case study on the rolling bearing condition monitoring and fault diagnosis using Kalman filter and support vector machine (SVM) has been presented. The analysis result showed that the integration of the Kalman filter and SVM was feasible and reliable for the rolling bearing condition monitoring and fault diagnosis and the fault detection rate was over 96.5%.

Using over 3 500 first P arrival times recorded by nine digital seismic stations from Hainan Digital Seismic Network during 1999~2005, a 3-D P-wave velocity model of the crust under Hainan Island and adjacent regions has been determined. The results show that the pattern of velocity anomalies in the shallower upper crust is somewhat associated with the surface geological tectonics in the region. A relative low-velocity anomaly appears north of the Wangwu-Wenjiao fault zone and a relative high-velocity anomaly appears south of the Wangwu-Wenjiao fault zone, corresponding to the depressed areas in north Hainan Island, where many volcanoes are frequently active and geothermal values are relatively higher, and the uplifted and stable regions in central and south of the Hainan Island. In the middle and lower crust velocities are relatively lower in east Hainan than those in west Hainan, possibly suggesting the existence of the upwelling of hot materials from the mantle in east Hainan. The pattern of velocity anomalies also indicates that NW faults, i.e., the Puqian-Qinglan fault, may be shallower, while the E-W Wangwu-Wenjiao fault may be deeper, which perhaps extends down to Moho depth or deeper.