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This study proposes a second digital divide measurement for university students in Guangdong, encompassing internet usage proficiency and internet-enabled learning levels. Drawing on information literacy theory, the dimensions of information awareness, acquisition, storage, retrieval, utilization, and ethical/security considerations are individually assessed. A total of 1038 valid responses were collected through a questionnaire survey, and data analysis was conducted using descriptive statistics and variance analysis. The internet usage and enabled learning levels of university students in Guangdong are found to be moderately above average. The second-level digital divide among university students in Guangdong is complex, resulting from the interactive effects of personal factors, growth environment, and university environment. Personal characteristics (gender, grade), growth environment (average living expenses, parental education, family location, growth location, family learning environment), and campus learning environment have a significant impact on both the level of internet usage and internet-enabled learning. Conversely, personal characteristics (disciplinary category), and school location exhibit no significant impact on internet-enabled learning levels.

The primary contributors to elevated line losses in low-voltage distribution networks are three-phase load imbalances and variations in load peak–valley differentials. The conventional manual phase sequence adjustment fails to capitalize on the temporal characteristics of the load, and the proliferation of smart homes has opened up new scheduling possibilities for managing the load. Consequently, this paper introduces a loss-reduction method for low-voltage distribution networks that leverages load-timing characteristics and adjustment capabilities. This method combines dynamic and static methods to reduce energy consumption from different time scales. To commence, this paper introduced a hierarchical fuzzy C-means algorithm (H-FCM), taking into account the distance and similarity of load curves. Subsequently, a phase sequence adjustment method, grounded in load-timing characteristics, was developed. The typical user load curve, derived from the classification of user loads, serves as the foundation for constructing a long-term commutation model, therefore mitigating the impact of load fluctuations on artificial commutation. Following this, this paper addressed the interruptible and transferable characteristics of various smart homes. This paper proposed a multi-objective transferable load (TL) optimal timing task adjustment model and a peak-shaving control strategy specifically designed for maximum sustainable power reduction of temperature-controlled loads (TCL). These strategies aim to achieve real-time load adjustment, correct static commutation errors, and reduce peak-to-valley differences. Finally, a simulation verification model was established in MATLAB (R2022a). The results show that the proposed method mainly solves the problems of three-phase imbalance and large load peak–valley difference in low-voltage distribution networks and reduces the line loss of low-voltage distribution networks through manual commutation and load adjustment. [ABSTRACT FROM AUTHOR]