The key to spotting dyslexia early could be AI-powered handwriting analysis
Peer-Reviewed Publication
Updates every hour. Last Updated: 15-May-2025 00:09 ET (15-May-2025 04:09 GMT/UTC)
A new University at Buffalo-led study outlines how artificial intelligence-powered handwriting analysis may serve as an early detection tool for dyslexia and dysgraphia among young children.
A new study achieves unprecedented accuracy in modelling extreme cosmic events like black hole and neutron star collisions by calculating the fifth post-Minkowskian (5PM) order, crucial for interpreting gravitational wave data from current and future observatories.
The research reveals the surprising appearance of Calabi-Yau three-fold periods – complex geometric structures from string theory and algebraic geometry – within calculations of radiated energy and recoil, suggesting a deep connection between abstract mathematics and astrophysical phenomena.
Utilising over 300,000 core hours of high-performance computing, an international team demonstrated the power of advanced computational methods in solving complex equations governing black hole interactions, paving the way for more accurate gravitational wave templates and insights into galaxy formation.
The Exposome Moonshot Forum will take place May 12th to 15th in the heart of Washington, DC. This highly participatory, impact-driven, multi-stakeholder forum will consider issues and opportunities surrounding data protection, AI integrations, and multi-national representation to build an effective and ethically informed launchpad for the Human Exposome. The Human Exposome, a counterpart to the Human Genome Project, uses precision analytics, predictive environmental data, and biometrics to understand the impact of lived environment on individual health profiles and outcomes. This once-in-a-generation endeavor will revolutionize the way we understand and address public health challenges, offering highly precise health profiles that consider every individual’s lived experience and local context.