This article presents a new dynamic motion stabilization approach to front-wheel drive in-wheel motor electric vehicles. The approach includes functions such as traction control system, electronic differential system, and electronic stability control. The presented electric vehicle was endowed with anti-skid performance in longitudinal accelerated start; smooth turning with less tire scrubbing; and safe driving experience in two-dimensional steering. The analysis of the presented system is given in numerical derivations. For practical verifications, this article employed a hands-on electric vehicle named Corsa-electric vehicle to carry out the tests. The presented approach contains an integrated scheme which can achieve the mentioned functions in a single microprocessor. The experimental results demonstrated the effectiveness and feasibility of the presented methodology.

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Due to the extensive social influence, public health emergency has attracted great attention in today's society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event's social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback) based on ACP simulation system which was successfully applied to the analysis of A (H1N1) Flu emergency.

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