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This study used hierarchical linear modeling (HLM) to investigate the differences in mobile game addiction tendencies among universities (i.e., double first-class universities, ordinary colleges and universities, and higher vocational colleges), and to examine the influencing factors of mobile game addiction tendency at the individual and university levels. The participants of this study were 4,024 college students who completed the Chinese Mobile Phone Game Addiction Scale, UCLA Loneliness Scale, and Positive and Negative Affect Scale. Loneliness (β = 0.052), positive emotions (β = −0.126), negative emotions (β = 0.232), and double first-class universities (γ = 0.368) significantly predicted mobile game addiction tendencies. A significant cross-level interaction between double first-class universities and other factors (i.e., positive emotions, negative emotions, and mobile game addiction) was observed. The novelty of this study is that it distinguishes the various effects of mobile phone addiction tendency at the individual and university levels.

This paper presents an enhanced efficiency 3-D convolution operator based on optimal field programmable gate array (FPGA) accelerator platform. The proposed system takes advantages of the intermediate data delay lines, implemented in an FPGA, to avoid loading repetition of the input feature maps. This 3-D convolution accelerator performs 268.07 giga operations per second at 100-MHz operation frequency, with 330-mW power consumption. We experimentally demonstrate the enhanced efficiency of the proposed convolution accelerator, in comparison with the conventional technologies. The proposed 3-D convolution accelerator may find interesting applications in neural networks and video processing.