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The rapid rise and widespread use of short video applications (SVA) have significantly reshaped our understanding of social media, technology, and human social behavior. Particularly among young users, frequent social media usage and continuous swiping through endless video feeds have become pervasive phenomena. Using dual-process theory, this experimental study explored the psychological consequences of using SVA from two studies. Our findings contributed to the understanding of the cognitive implications of SVA use in young adults in the following ways. Watching short videos predicted less analytic thinking that could go beyond initially flawed intuitions in reasoning (Study 1). Additionally, the process of swiping through the short video feeds, rather than the video content itself, negatively influenced users' propensity to think analytically; and the decreased positive affect mediated the effect of TikTok usage on analytic thinking (Study 2). These results showed that users should be aware that the common daily leisure activity of using short video applications increases one's inherent tendency to fast and automatic thinking processing, which can impair their judgment and decision-making in other tasks, such as information discernment. This study indicated the negative implications of artificial intelligence agencies for young adults' thinking processing in psychological and social domains. [ABSTRACT FROM AUTHOR]

With the rapid development of mobile communication technology, short video applications, which combine the features of both social and multimedia applications, have become more and more popular. However, the transmission of short videos poses great challenges to the existing mobile networks. In this paper, mobile edge computing is adopted to provide content caching of short videos close to end users. To improve the quality of service, we take both the quality level of the video and the long-term wireless network transmission performance into consideration. The joint video quality selection and radio bearer control optimization problem is formulated as a Markov decision process, aiming at maximizing the long-term video quality profit and minimizing the cost of bearers and the penalty of latency. Deep reinforcement learning is used as the solution and the policy gradient based quality selection and radio bearer control method is proposed. The REINFORCE algorithm with baseline is used to train the policy network, and the episodic simulations are built to obtain the training samples. Different weight coefficients of the objective function are configured. Training results show that the proposed method can achieve the best accumulated value among all the comparison methods. When the weight coefficients are changed, the training processes can lead the policy networks to obtain proper trade-off between different objective factors. Moreover, the performance of the trained policy network is evaluated with different short video request arriving rates. Testing results show that the proposed method performs well when the arriving rates vary in a certain range.