As online video platforms like YouTube and YouTube Kids continue to shape young children's daily media use, concerns about their exposure to inappropriate content persist. While these platforms implement various safeguards to protect young audiences, inappropriate videos continue to surface in search results and next-video recommendations, sometimes even at the top of the list. This study explores how metadata-derived features can be used to identify and address such content. Drawing on a ground-truth dataset of YouTube videos labeled for toddler appropriateness, we conduct a detailed feature analysis to uncover patterns linked to (in)appropriateness and train a classifier capable of predicting video appropriateness for young children. Building on these insights, we develop and evaluate score-based reranking strategies designed to reduce exposure to inappropriate videos while promoting age-appropriate content. Our findings show that metadata-informed reranking significantly improves the prioritization of suitable content, raising HitRate@1 of suitable videos from 14% to as high as 62%, but also reveal critical trade-offs: misclassified inappropriate videos, particularly when predicted with high confidence, may still appear in top-ranked positions. As such, detection and reranking methods like ours represent a first step that warrants further steps for safer recommendation environments for young children. This research provides a practical framework for improving recommendation outcomes and contributes to the broader conversation on designing safer, more transparent, and child-centered media systems.

Full Codebase available on the following repository - https://github.com/JoeydeW/KeepItPGorLetItGo

Energy communities are not yet fully self sufficient, mostly due to financial factors. Efforts are made to reduce these factors. Communities invest in community-owned assets, which provide more savings compared to individually-owned assets. Prosumers share their loads for better energy distribution, this can provide a significant impact. A good predictor for identifying the financial benefit for a community is the diversity of the consumption behaviors of the prosumers. However, an open question is how the diversity exactly affects the community costs. In this paper, we introduce Two-level K-means, an improvement on K-means, and use it on real consumption data to find energy profiles. We use the energy profiles to model communities, varying in diversity. Finally, we provide an analysis of the affects of diversity on costs. Results from the analysis show that an increased diversity factor can provide financial benefit. This is a result of residual demand being compensated by excess energy generated. However, the added financial benefit depends on the composition of the community.