This research develops a universal writing system for sign languages, enabling Deaf communities to record and edit stories in their own languages. The system combines handwritten simplicity with digital notation, capturing facial expression, body movement, and spatial structure to reflect the full linguistic richness and visual complexity of signed communication.

Using machine learning and Hidden Markov Models, this research analyzes the authorship of disputed New Testament letters. The results show that stylistic differences reflect the Apostle Paul’s versatile writing styles rather than forgery, demonstrating how modern computational tools can help recover long-standing historical truths.

This research uncovers how AI systems like GPT succeed at automatically grouping words—a task that traditionally required manual labeling. Using geometric tools such as convex hulls and Delaunay triangulation, the researcher developed an algorithm that replicates this capability, enabling powerful language models to be built with far fewer computational resources.