Intestinal microflora analysis was performed on 52 healthy elderly subjects of different ages and in different regions in Bama County, Guangxi, China. The participants were assigned to three groups depending on their age and location: longevous (group M; mean age = 98 years; n = 21); rural younger elderly (group S; mean age = 70 years; n = 18); and urban elderly (group C; mean age = 82 years; n = 13). Ten groups of bacteria were quantified using real-time PCR. Age-related differences were observed in the number of Clostridium coccoides-Eubacterium rectale--there were more in longevous participants. Region affected the numbers of Bacteroides--Prevotella and Clostridium perfringens subgroup, and longevous participants had significantly more of the two bacterial groups than urban elderly participants. Region-related effects were also observed for the relative abundance of E. coli, and rural elderly participants had a lower proportion. Both age and regional effects were observed in the amount of total bacteria, and longevous participants had higher numbers than urban elderly participants. A significantly higher proportion of lactobacilli was observed in rural younger elderly participants than urban elderly participants, but independent age or regional effects did not contribute to this difference. This study suggests that age and region can affect the intestinal microflora of elderly people.

No abstract

A linear magnetization model is built to replace the Jiles–Atherton model in order to describe the relationship between the magnetic field intensity and the magnetization intensity of the giant magnetostrictive vibrator (GMV). The systematic modeling of the GMV is composed of three aspects, i.e., the structural mechanic model, the magnetostrictive model, and the Jiles–Atherton model. The Jiles–Atherton model has five parameters to be defined; hence, its solution is so complex that it is not convenient in application. Therefore, the immune genetic algorithm (IGA) is applied in the identification of the five parameters of the Jiles–Atherton model and it showed a higher stability compared with the identification result of the differential evolution algorithm (DEA). The identification parameters of the two algorithms were employed, respectively, to calculate the excitation force and it was found that the relative error of IGA was evidently smaller than that of DEA, indicating that the former was more reliable than the latter. According to the identification results of IGA and based on the least square method (LSM), curve-fittings to the magnetic field intensity and magnetization intensity were conducted by using the linear function. And the linear magnetization model was built to replace the Jiles–Atherton model. Research results show that the linear model of the GMV can be established by combining the linear magnetization model with the structural mechanic model as well as the giant magnetostrictive model. The linear magnetization model, which has great engineering application value, can be applied in the open-loop control of the vibrator.

No abstract

Providing accurate and reliable railway regional environmental data is a key consideration in operation control and dynamic dispatching of high-speed train. However, there are problems of low reliability and high uncertainty in the single data processing of high-speed train operating area environment. Therefore, this paper proposes a novel multisource sensor data fusion method based on a three-level information fusion framework. Firstly, the feature of the same kind of sensor data is extracted by the Kalman Filter (KF) algorithm as the input of back propagation neural network (BPNN). Then input the sample site into the BPNN for training and recognition, the feature fusion of heterogeneous sensor data is carried out, the decision output of BPNN is obtained, the output results are normalized, and its output is used as the basic probability assignment of Dempster–Shafer (D-S) evidence theory and synthesis rules. Finally, the decision fusion of multisource data is realized by D-S evidence theory. The simulation results show that compared with the traditional single fusion algorithm, the algorithm improves the accuracy of the prediction of high-speed train operation environment and reduces the MAPE from 13.82% to 7.455%, and the RMSE from 0.77 to 0.69, and meanwhile, increases the R2 from 0.87 to 0.97.