Electric vehicles (EVs) are key to sustainable mobility, yet their lithium-ion batteries (LIBs) degrade more rapidly under prolonged high states of charge (SOC). This can be mitigated by delaying full charging DFC until just before departure, which requires accurate prediction of user departure times. In this work, we propose Transformer-based real-time-to-event (TTE) model for accurate EV departure prediction. Our approach represents each day as a TTE sequence by discretizing time into grid-based tokens. Unlike previous methods primarily dependent on temporal dependency from historical patterns, our method leverages streaming contextual information to predict departures. Evaluation on a real-world study involving 93 users and passive smartphone data demonstrates that our method effectively captures irregular departure patterns within individual routines, outperforming baseline models. These results highlight the potential for practical deployment of the DFC algorithm and its contribution to sustainable transportation systems.

Swahili occupies a central place in African linguistic landscapes, yet it is significantly under-resourced in NLP, reflecting a mismatch between speaker population and data availability. We introduce Maneno Yetu, a dynamic and extensible corpus designed to address this gap. It is continuously updated with diverse sources such as news articles, blogs, literature, and educational content. This structure enables robust pretraining and fine-tuning for Swahili NLP. The evolving nature of the corpus allows for longitudinal linguistic analysis, providing a unique opportunity to track language change over time. It also serves as a foundation for creating niche, task-specific datasets in low-resource settings. Building on Maneno Yetu, we present the Swahili Language Foundational Model (SLFM), a transformer-based model trained to support core NLP tasks including tokenization, part-of-speech tagging, machine translation, and abusive language detection. Both the corpus and model are released publicly to support reproducible research and foster community-driven development in African language technologies.