Recently, nomophobia (separation anxiety from mobile phone) has become a common phenomenon. The authors’ main purpose was to explore latent classes of solitude behaviors and how they are related to nomophobia. Chinese versions of the Nomophobia Scale and the Solitude Behaviour Scale were used in a sample of college students (351 female and 327 male). Latent class analysis, analysis of variance, and regression analysis were employed to classify solitude behaviors and explore the relationship between solitude and nomophobia. A six-class model best fit the data (BIC = 60086.49). Significant differences among the classes were found on nomophobia. Loneliness, social avoidance, and eccentricity significantly predicted nomophobia. Solitude behaviors of college students can be divided into six latent classes. The classes with a high response preference for solitude scored higher on nomophobia, especially the fear of losing an Internet connection. Not self-determined solitude and negative-solitude had a positive effect on nomophobia.

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Cellular networks have been widely deployed and are under ever-growing communication pressure. Detecting traffic hotspots or other essential characteristics of network traffic distributions can help to adjust the base station control strategies of networks to save energy. Generally, existing approaches detect these characteristics by data analysis techniques, making the detection process generally inefficient and not automatic. Moreover, those approaches are also difficult to describe the dynamic spatio-temporal evolution characteristics of network traffics. In this paper, we propose a novel modeling and analysis approach by applying the spatio-temporal model checking technique to the detection of network traffic characteristics. First, we model the spatio-temporal evolution process of cellular network traffic by closure space model. Second, we give the logical characterizations of detection requirements by suitable Spatio-Temporal Logic of Closure Space (STLCS) formulas. Third, we verify the spatio-temporal properties in the closure space model by model checking algorithms. The experiments are illustrated on the Milan network traffic dataset and indicate that our approach can automatically and effectively detect desirable spatio-temporal properties of cellular network traffic.

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