The widespread use of digital devices among children and teenagers has raised concerns about overuse, particularly for early adolescents, who have unique developmental needs and engage with technology more frequently than other age groups. A challenge for designers and researchers interested in contributing solutions is a lack of synthesized design guidelines and characterization of the current state-of-the-art. In this paper, we present a systematic scoping review of academic literature and an analysis of 47 apps, providing a comprehensive characterization of existing tech-mediated solutions for early adolescents. Our review covers literature from two major databases (ACM DL and IEEE Xplore) spanning the past 10 years (2014-May 2024), following the scope of prior similar reviews. The app analysis includes Google Play and Apple App Store apps with features targeting tech overuse, excluding general-purpose apps (e.g., social media, games) and apps without a free trial version. Our findings highlight researchers' design recommendations for promoting tech disengagement in this demographic (e.g., supporting collaborative rule-setting and self-monitoring, maintaining privacy, addressing diverse user needs), while revealing that existing apps tend to prioritize restrictive measures, overlooking self-regulation and active parental engagement. Our findings also identify areas of agreement and potential misalignments between current digital interventions and prior research on target users' preferences.

Children's excessive use of technology is a growing concern, and despite taking various measures, parents often find it difficult to limit their children's device use. Limiting tech usage can be especially challenging with early adolescents as they start to develop a sense of autonomy. While numerous tech-based mediation solutions exist, in this paper, we aim to learn from early adolescents directly by having them contribute to co-design activities. Through a multi-session, group-based, online co-design study with 21 early adolescents (ages 11-14), we explore their perceptions towards tech overuse and what types of solutions they propose to help with disengagement. Findings from these co-design sessions contribute insights into how the participants conceptualized the problem of tech overuse, how they envisioned appropriate mediation strategies, and important design considerations. We also reflect on our study methods, which encouraged active participation from our participants and facilitated valuable contributions during the online co-design sessions.

Recently, protein complex prediction via computational approaches from PPI network data has gained significant popularity among bioinformatics researchers. Majority of these approaches are unsupervised. The quality measures they use to guide the mining process have some drawbacks in reflecting the properties of a protein complex presented in the PPI network data. In this paper, we propose a novel measure, named well-connectedness. It more accurately measures the similarity of interactions between a mined protein complex and a real protein complex. We have incorporated well-connectedness measure in two state-of-the-art overlapping protein complex prediction algorithms. Empirical analysis on four real world data sets has shown that, considering our measure enables to mine more meaningful protein complexes.