This research uses natural language processing techniques to uncover evolutionary relationships between ancient proteins. By analyzing contextual patterns among amino acids, the new computational tool can identify connections between proteins that diverged billions of years ago, helping scientists reconstruct the history of early microbial life and Earth’s biological evolution.

This research introduces iCares, a smart wound-monitoring bandage designed to detect infection and inflammation before visible symptoms appear. Using biosensors, fluid sampling, and machine learning, the system provides real-time wound analysis, enabling earlier intervention, personalized treatment, reduced complications, and improved healing outcomes for patients with chronic wounds.

This research addresses exercise-related injuries by modeling individual physical capacity rather than relying on population averages. Using physiological and biomechanical data combined with machine learning, it aims to create personalized, dynamic thresholds for training. The goal is to prevent injury by aligning workload with real-time capacity, improving safety and long-term fitness outcomes.

Generative AI chatbots are predictive systems that generate human-like responses without true understanding. Using large datasets, they model word relationships similarly to weather forecasting. While effective, they can produce convincing inaccuracies, or “hallucinations.” This research emphasizes interpreting AI realistically—as probabilistic tools with limitations—rather than attributing human cognition to them.

This research examines how CEO personality influences environmental decoupling, where companies misalign environmental claims and actions. Using the Big Five framework and machine learning on CEO communications, it identifies traits linked to such behavior. Findings aim to improve corporate governance by helping stakeholders select leaders committed to genuine sustainability.

This research addresses the exclusion of minority and low-resource languages from modern language technologies. Using linked data principles, it builds interconnected, machine-readable linguistic resources for languages like Cree, Welsh, and Kurdish. The goal is to enable inclusive AI systems and future technologies that support global communication across diverse linguistic communities.

This research uses AI to detect subtle interactions between the Higgs boson and muons at the Large Hadron Collider. By refining large datasets, it aims to uncover how particles acquire mass at smaller scales. Confirming this interaction would deepen understanding of the Higgs field and fundamental physics.

This research shows that toxic behavior in online games is contagious, especially from teammates. Using machine learning and econometric analysis of 300,000 messages, it finds toxicity spreads socially rather than individually. The study suggests that effective interventions should target breaking transmission patterns rather than simply punishing players to improve online environments.

This research uses wearable data and AI to detect disease earlier by analyzing continuous health signals rather than isolated clinical snapshots. By personalizing models to individual baselines, the system identifies subtle changes linked to conditions like infections, heart issues, and mental health crises, enabling earlier intervention and potentially saving lives.

This research develops drones with soft robotic arms capable of safely grasping and transporting objects in challenging environments. By combining predictive modelling with visual feedback, it overcomes control challenges associated with soft materials. The work advances intelligent, adaptive aerial robotics for applications such as emergency delivery and hazardous environments.