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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.

Recent research on marine environment pollution (MEP) has primarily focused on legislative and market-based instruments rather than on understanding related knowledge, attitudes, and behaviors. Within this context, we used a survey of university students in China to investigate attitudes and behaviors related to MEP. Specifically, we employed a tri-component attitude model to analyze questionnaire data from 446 randomly selected students. Our results indicate that participants had a good knowledge of MEP. Furthermore, our data revealed the following three MEP-related attitudinal clusters: activists, supporters, and onlookers. Activists showed negative attitudes toward MEP with strong anti–MEP behaviors. Supporters also had negative attitudes toward MEP but performed less anti–MEP behaviors. Finally, onlookers exhibited indifferent attitudes with neutral MEP-related behaviors. Each of the three attitudinal clusters varied according to the demographic characteristics of the participants. The implications of these results on the reduction in MEP were discussed.

In this paper, we discuss the average path length for a class of scale-free modular networks with deterministic growth. To facilitate the analysis, we define the sum of distances from all nodes to the nearest hub nodes and the nearest peripheral nodes. For the unweighted network, we find that whether the scale-free modular network is single-hub or multiple-hub, the average path length grows logarithmically with the increase of nodes number. For the weighted network, we deduce that when the network iteration [Formula: see text] tends to infinity, the average weighted shortest path length is bounded, and the result is independent of the connection method of network.

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.