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Objective In the actual metro construction, it is often necessary to lay turnouts on large-slope sections due to planning and construction constraints, land conservation or topographical limitations. However, it is found through practical application that the large-slope turnout structure leads to poor train stability and ride comfort during operation, thus an investigation on the factors influencing vehicle passing performance through large-slope metro turnouts is required. Method Based on multi-body dynamics theory, using simulation analysis software and taking the No.9 turnout installed on a 30‰ slope as research object, a coupled vehicle-turnout multi-body dynamics model is established. The dynamics performance indicators (including wheel-rail interaction, safety indicators, and stability indicators) of single vehicle passing through large-slope turnout at different operating speeds and friction coefficients are compared，aiming to analyze the impact of various factors on the vehicle passing performance through large-slope metro turnouts. Result & Conclusion It is revealed through simulation research that the impact of operating speed on wheel-rail interaction, stability indicators, and safety indicators is independent of the slope direction during vehicle passing turnouts. When a vehicle passes through turnouts on uphill and downhill slopes, the influence trends of operating speed on the dynamics indicators are consistent, with a more significant impact when going downhill; the friction coefficient greatly affects the wheel-rail dynamic interaction and the vehicle lateral acceleration. As the friction coefficient increases, the wheel-rail interaction force decreases, the carbody vertical acceleration shows no significant change, and the lateral acceleration increases significantly, with a more pronounced effect when going downhill. Therefore, certain strengthening and protective measures should be taken when vehicle going downhill.

This paper conducts the research on the attention sates based on ERP analysis when the subjects are under the quiet, flute and zither background music. The amplitude and latency of P300 are analyzed. The results show that there are greater P300 amplitudes and smaller P300 latencies of CZ, PZ, OZ and CP3 in music background than those in quiet background. The PCA and ICA achieve to select the effective data components and the head model is reconstructed. The active degree of brain areas are analyzed by using the source location methods. The result shows that the brain’s excitement is very obvious under the Bach's flute background. The study also indicates that some background music might help to improve the attention.

With the purpose of finding the cancer-related genes from the high-dimension DNA microarray data for the classification of tumors, an evolutionary hypernetwork model is proposed in this paper. The evolutionary hypernetwork is a kind of cognitive learning model inspired by the biological networks and its learning process is very suitable for mining gene-gene interactions. In this paper, the gene selection is based on a signal-to-noise ratio method, and the selected normalized genes are processed by the hypernetwork through the learning and the classification phases. Empirical studies on the acute leukemia dataset and the colon cancer dataset demonstrate that the proposed hypernetwork classifier is high comparable with other state-of-the-art approaches in terms of classification rate of learned results. [ABSTRACT FROM AUTHOR]