This study aims to explore how gamification strategies and serendipity influence addictive behaviors in short video applications. Based on the Dual Process Model, the study examined two pathways affecting short video addiction: automated and controlled processes. Flow experience represents the automated process, while willpower resource depletion represents the controlled process. The findings reveal that both gamification and serendipity significantly enhance flow experience, and serendipity reduces willpower resource depletion. Flow experience promotes short video addiction, while willpower resource depletion serves as an inhibitory factor. Additionally, the study found that flow experience both mediates the relationship between gamification and short video addiction, serendipity and short video addiction. While willpower resource depletion just mediates the relationship between serendipity and short video addiction. This study contributes to understanding the complex psychological mechanisms of short video addiction and provides new insights and practical guidance for the healthy development of short video platforms.

Layout hotspot detection is one of the most important issues for the reliability enhancement of integrated circuits. Machine learning-based hotspot detectors have shown their advantages of efficiency and generalization compared with computationally intensive lithography process simulation. However, most machine learning-based hotspot detectors only accept layout clips of fixed size as input with the potential defect whose location is restricted at the center of each clip. Therefore, they cannot be used directly for multiple hotspots detection in a large area, which occurs frequently in real design cases. In this article, we build a new end-to-end hotspot detector based on a fully convolutional network, which has the flexibility of detecting a various number of hotspots in a layout of any size at one time. Moreover, we also develop a transfer learning scheme matching our proposed detector network, which can reduce the requirement of sample number when setting up a new model for a more advanced technology node. The experimental results demonstrate our proposed hotspot detector outstanding among state-of-the-art works and the transfer learning scheme is effective.