Objective
This study investigated the relationship between risk-taking behavior and academic self-efficacy and problematic internet use in adolescent university students and whether problematic internet use varies according to the variables of gender, possession of a computer and living with the family.Methods
The study was performed with 556 students from the Karadeniz Technical University Fatih Faculty of Education in Turkey. The Problematic Internet Use Scale, the Adolescent Risk-Taking Questionnaire, the Academic Self-Efficacy Scale and Personal Information Collection Form were used in the collection of data.Results
Pearson correlation analysis revealed a significant positive correlation between problematic internet use and risk-taking behavior (r = .37, p < .01) and a significant negative correlation between problematic internet use and academic self-efficacy (r = − .12, p < .05). Multiple regression analysis revealed that risk-taking behavior and academic self-efficacy accounted for 14% of total problematic internet use variance (F(2,553) = 46.11, p < .05). The individual contribution to the model of risk-taking (β = .37) and academic self-efficacy (β = − .09) is significant. Our results also showed that university adolescents' problematic internet use scores vary by gender (t = − 4.90, p < .05) and possession or otherwise of a computer (t = 3.10, p < .05), but not on the basis of whether they live with their families (t = − .13, p > .05).Conclusions
Risk-taking behavior and academic self-efficacy emerged as significant predictors of problematic internet use. 相似文献Multiprocessor open shop makes a generalization to classical open shop by allowing parallel machines for the same task. Scheduling of this shop environment to minimize the makespan is a strongly NP-Hard problem. Despite its wide application areas in industry, the research in the field is still limited. In this paper, the proportionate case is considered where a task requires a fixed processing time independent of the job identity. A novel highly efficient solution representation is developed for the problem. An ant colony optimization model based on this representation is proposed with makespan minimization objective. It carries out a random exploration of the solution space and allows to search for good solution characteristics in a less time-consuming way. The algorithm performs full exploitation of search knowledge, and it successfully incorporates problem knowledge. To increase solution quality, a local exploration approach analogous to a local search, is further employed on the solution constructed. The proposed algorithm is tested over 100 benchmark instances from the literature. It outperforms the current state-of-the-art algorithm both in terms of solution quality and computational time.
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