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Purpose Kashin-Beck disease (KBD) is an osteoarthropathy affecting 2.5 million of 30 million residents of China's KBD-endemic regions. This study assesses the etiologic role of river water as a main source of drinking water, its impact on and interaction with KBD familial aggregation. Methods From 2006–2007, we conducted a population-based case-control study of familial KBD in Linyou County, Shaanxi Province, China. Study subjects included 212 case families (1,951 individuals) and 212 control families (1,897 individuals). Using conditional logistic regression to analyze case-control data and second-order generalized estimating equation (GEE2) to examine family data, we adjusted for age and gender in all marginal regression analyses. Results The odds ratio (OR) for river water intake's association with KBD was 5.97 (95% confidence interval [CI]), 3.32–10.72). Compared with relatives of controls, proband relatives had a higher risk of disease, with a conditional OR = 2.35 (95% CI, 1.05–5.24). When river water was controlled in the analyses, association parameters among first- and second-degree relatives were no longer statistically significant. Conclusion Collective river water intake among relatives was likely and predominantly responsible for familial KBD aggregation in southwestern China.

The total load on a crank bearing was calculated by performing a load analysis of the crank connecting rod mechanism. The Reynolds equation for hydrodynamic lubrication of the crank bearing was established at the Reynolds boundary condition and was then solved using the Holland method. From this the regular track of the bearing axis was obtained. As the crank bearing gradually wears the eccentricity ratio corresponding to the minimum oil film thickness increases gradually. The oil-bound film eventually breaks down, which allows friction and collision between the metal surfaces of the crank pin and the bearing. The rigid impact leads to excitation of high frequency vibrations at the natural frequencies of the connecting rod. The experiments show that the wear condition of the crank bearing can be identified correctly through the vibration signature at the natural frequencies of the connecting rod. The degree of wear can be predicted accurately through the energy content of the high frequency bands.

Support Vector Machines (SVM) is a new general machine-learning tool based on structural risk minimization principle. This characteristic is very signific ant for the fault diagnostics when the number of fault samples is limited. Considering that SVM theory is originally designed for a two-class classification, a hybrid SVM scheme is proposed for multi-fault classification of rotating machinery in our paper. Two SVM strategies, 1-v-1 (one versus one) and 1-v-r (one versus rest), are respectively adopted at different classification levels. At the parallel classification level, using 1-v-1 strategy, the fault features extracted by various signal analysis methods are transferred into the multiple parallel SVM and the local classification results are obtained. At the serial classification level, these local results values are fused by one serial SVM based on 1-v-r strategy. The hybrid SVM scheme introduced in our paper not only generalizes the performance of signal binary SVMs but improves the precision and reliability of the fault classification results. The actually testing results show the availability suitability of this new method.