This study aims to examine online learning effects regarding self-efficacy, generalized anxiety, and fear of COVID-19 on three distinct online learning satisfaction levels (low, moderate, and high) among university students. A cross-sectional survey was utilized for data collection between June 2020 and August 2020 to assess students' online self-efficacy, general anxiety, fear of COVID-19, and online learning satisfaction. The descriptive data analysis demonstrated a fundamental understanding of the gathered data results. Meanwhile, discriminant data analysis was employed to explore different online learning satisfaction levels following various study factors. The correlational analysis implied online learning self-efficacy to be significantly and positively associated with online learning satisfaction while general anxiety and fear of COVID-19 were significantly and negatively related to online learning satisfaction. The discriminant analysis revealed the emergence of three online learning satisfaction levels from online self-efficacy, general anxiety, and fear of COVID-19. This study theoretically justified the essentiality of online learning self-efficacy towards online learning satisfaction. High online learning satisfaction levels occurred with high online self-efficacy, moderate general anxiety, and low fear of COVID-19. Two discriminant functions (academic engagement and fear) were subsequently evolved. Academic engagement corresponded to online self-efficacy and general anxiety while fear was associated with COVID-19. In this vein, online learning self-efficacy and moderate general anxiety led to high online learning satisfaction. The fear of COVID-19 also required alleviation towards online learning satisfaction. For example, academicians and policymakers needed to focus on developing online self-efficacy and reducing the fear of COVID-19 for high online learning satisfaction.

Heterogeneous networks are rapidly emerging as one of the key enablers of beyond fifth-generation (5G) wireless networks. It is gradually becoming clear to the network operators that existing cellular networks may not be able to support the traffic demands of the future. Thus, there is an upsurge in the interest of efficiently deploying small-cell networks for accommodating a growing number of user equipment (UEs). This work further extends the state-of-the-art by proposing an optimization framework for reducing the power consumption of small-cell base stations (BSs). Specifically, a novel algorithm has been proposed which dynamically switches off the redundant small-cell BSs based on the traffic demands of the network. Due to the dynamicity of the formulated problem, a new UE admission control policy has been presented when the problem becomes infeasible to solve. To validate the effectiveness of the proposed solution, the simulation results are compared with conventional techniques. It is shown that the proposed power control solution outperforms the conventional approaches both in terms of accommodating more UEs and reducing power consumption.

No abstract

No abstract

No abstract