BackgroundPrevious studies have shown that prolonged time spent on screen-based sedentary behavior was significantly associated with lower health status in children, independent of physical activity levels. The study aimed to explore the individual and environmental correlates of screen time (ST) among 8–19-year-old students in China.MethodsThe study surveyed ST using a self-administered questionnaire in Chinese students aged 8–19 years; 1063 participants were included in the final analysis. Individual and environmental correlates of ST were assessed using a mixed-effects model (for continuous outcome variables) and multiple logistic regression model (for binary outcome variables).ResultsProlonged ST was observed in 14.7% of boys and 8.9% of girls. Of the ST, weekend and mobile phone/tablet use represented 80% and 40%, respectively. A positive relationship was observed between media accessibility and ST in both boys and girls (p < 0.05), whereas the presence of parents/others while using screens was a negative factor for longer ST (p < 0.05). Among the assessed correlates, access to a television (TV) in students’ bedrooms was associated with prolonged total and weekend ST (p < 0.05 and p < 0.001, respectively). However, spending time on a mobile phone/tablet or a computer rather than viewing a TV, along with increased media accessibility, increased ST.ConclusionsThese results indicate that greater media accessibility was positively associated and the presence of parents/others was negatively associated with prolonged ST in both Chinese boys and girls. Development of new and effective strategies against prolonged ST are required, especially for small screen device-based ST on weekends.

This paper draws motivation from the fact that engineering optimizations were mostly carried out from scratch. In contrast, however, humans routinely take advantage of the knowledge from past experiences whenever a new task is met. Such a transfer learning process by leveraging knowledge from already completed tasks can be promising to significantly improve the performance of current state-of-the-art algorithms, particularly in solving expensive black-box problems. In light of the above, we propose a Cokriging based transfer optimization framework for the design of turbomachinery cascades, which is demonstrated by optimization to re-design the first-stage vane of GEE3. Specifically, when building Cokriging surrogate in such a transfer optimization context, the samples from already completed tasks are treated as low-fidelity (LF) data. The acquisition function of expected improvement is adopted to guide the search for high-fidelity (HF) data. In order to make full use of the “past experiences”, one of our efforts was drawn to designing selection strategies of initial HF samples. In addition, as the “past experiences” may do harm to the optimization of the target task, the correlation coefficients between source and target tasks in each optimization process were calculated to avoid “negative transfer”. The test results show that, by learning from the past, the transfer optimization framework can reduce the computational cost by much as 50%. More importantly, our proposed transfer learning strategy can effectively avoid “negative transfer” and thus always achieve better solutions than the compared state-of-the-art algorithms.

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