RANS approach is adopted to perform an investigation on aerodynamic characteristics of high speed trains in crosswind conditions. Both the flow structures and aerodynamic loads are analyzed in detail. Results reveal that abundant flow phenomena could be observed on the streamlined head and affected by the yaw angles of the incoming flow. Detached vortices can be found on the leeward side of train, which origin from the bottom of the streamlined head and develop along the train body and gets far away from the train body. The first car of the whole train owns the worst aerodynamic circumstance. As the yaw angle grows, the side force and the overturning moment of the first car gradually grow bigger, and the running circumstance of the train becomes worse.

Chinese to Slavic Mongolian Named Entity Translation in cross Chinese and Slavic Mongolian information processing has a very important significance. However, using the machine translation method directly cannot achieve satisfactory result. In order to solve the above problem, a novel approach was proposed to extract Chinese-Slavic Mongolian Named Entity pairs automatically. Only the Chinese named entities need to be identified, then extracting all of the candidate named entity pairs using sliding window method based on HMM word alignment result. Finally filtering all of the candidate named entity translation units based on Max Entropy Model integrated with five features, and choose the most probable aligned Slavic Mongolian NEs to the Chinese NEs. Experimental results show that this approach outperforms HMM model, achieves high quality of Chinese-Slavic Mongolian named entity pairs with relatively high precision, even though sometimes the word alignment result is partially correct.