According to conceptual metaphor theory, individuals are thought to understand or express abstract concepts by using referents in the physical world—right and left for moral and immoral, for example. In this research, we used a modified Stroop paradigm to explore how abstract moral concepts are metaphorically translated onto physical referents in Chinese culture using the Chinese language. We presented Chinese characters related to moral and immoral abstract concepts in either non-distorted or distorted positions or rotated to the right or to (...) the left. When we asked participants to identify the Chinese characters, they more quickly and accurately identified morally positive characters if they were oriented upright or turned to the right and more quickly and accurately identified immoral characters when the characters were distorted or rotated left. These results support the idea that physical cues are used in metaphorically encoding social abstractions and moral norms and provided cross-cultural validation for conceptual metaphor theory, which would predict our results. (shrink)

Lithium-ion batteries have been widely used as energy storage systems and in electric vehicles due to their desirable balance of both energy and power densities as well as continual falling price. Accurate estimation of the state-of-charge of a battery pack is important in managing the health and safety of battery packs. This paper proposes a compact radial basis function neural model to estimate the state-of-charge of lithium battery packs. Firstly, a suitable input set strongly correlated with the package SOC is (...) identified from directly measured voltage, current, and temperature signals by a fast recursive algorithm. Secondly, a RBF neural model for battery pack SOC estimation is constructed using the FRA strategy to prune redundant hidden layer neurons. Then, the particle swarm optimization algorithm is used to optimize the kernel parameters. Finally, a conventional RBF neural network model, an improved RBF neural model using the two stage method, and a least squares support vector machine model are also used to estimate the battery SOC as a comparative study. Simulation results show that generalization error of SOC estimation using the novel RBF neural network model is less than half of that using other methods. Furthermore, the model training time is much less than the LSSVM method and the improved RBF neural model using the two-stage method. (shrink)