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p-Index From 2021 - 2026
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This Author published in this journals
All Journal Emerging Science Journal Makara Human Behavior Studies in Asia
Liu, Cong
Unknown Affiliation
Environmental Science

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Parents' role in teens' personal photo sharing: a moderated mediation model incorporating privacy concerns and network size Liu, Cong; Lwin, May; Ang, Rebecca
Makara Human Behavior Studies in Asia Vol. 23, No. 2
Publisher : UI Scholars Hub

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Abstract

Disclosure of personal photographs on social networking sites could lead to potential harm to adolescent users. This study aims to understand parents’ role in adolescents’ visual information disclosure on Facebook as well as the mediating role of privacy concern and moderating role of Facebook network size. A total of 351 secondary school students who use Facebook participated in the study (49.6% boys; mean age M = 13.98, SD = 0.94). Results showed a significant mediating effect of privacy concern on the relationship between parental mediation and visual disclosure (b= -0.07, Boot SE = 0.02, 95% CI [-0.116, -0.028]). Furthermore, the effect of parental mediation on privacy concern was shown to be moderated by the level of network size (b = 0.15, SE = 0.05, t = 3.04, p < 0.01). Findings contribute to a better understanding of visual disclosure drivers, particularly of the underlying mechanisms of the protective effect of parental mediation. Practical suggestions for parents are discussed.
Modeling and Performance Optimization for Complex Workflow in IoT Lu, Ting; Li, Huiling; Zeng, Qingtian; Duan, Hua; Liu, Cong
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-02

Abstract

This study addresses the growing challenge of time scheduling in Internet of Things (IoT) workflows, where efficiency in time utilization and resource profitability is increasingly constrained by uncertainty. Real-world workflows are characterized by non-deterministic activity execution and resource preparation times, yet existing research often neglects these fundamental dynamics when modeling IoT-based processes. To bridge this gap, we propose a comprehensive modeling and performance optimization framework that explicitly incorporates uncertainty. Methodologically, the framework introduces two distinct types of places to represent activities and resources, with resource properties capturing reusability and preparation processes abstracted as specialized activities. For workflow activities, timing functions are defined to model minimum and maximum execution times, enabling the computation of earliest and latest start times and the identification of critical activities driving overall workflow duration. To mitigate resource conflicts during execution, three alternative resolution strategies are developed and systematically evaluated. Results demonstrate that the proposed approach effectively identifies optimal scheduling strategies under uncertainty, enhancing both temporal efficiency and resource utilization. A workflow case study illustrates the applicability of the framework, offering methodological and practical insights for designing resilient IoT workflow scheduling systems in complex, real-world environments.
Co-Authors Ang, Rebecca Duan, Hua Li, Huiling Lwin, May Ting Lu Zeng, Qingtian