Researchers from Donghua University (Shanghai, China) have made a theoretical development in relational database normalization by investigating the computational complexity of prime attributes and keys specifically for relation schemes in second normal form (2NF). The study demonstrates that even when a relation scheme is known to be in 2NF, the prime attribute problem remains NP-complete, and the number of all keys can grow exponentially with the number of attributes and functional dependencies. The team also proposes a novel algorithm to find all keys in a 2NF relation scheme, deepening the theoretical understanding of 2NF normalization and providing practical references for database schema design.

Traditional server-based datacenters suffer from low resource utilization, insufficient hardware scalability, insufficient resource usage elasticity, and low fault tolerance. With the ever-increasing network speed and processing power in hardware controllers, physically disaggregating the coupled resources in a server has been a promising trend for building next-generation Datacenters, as it effectively addresses the limitations of server-based datacenters.

Non line of sight (NLOS) imaging achieves imaging of occluded objects by analyzing the reflection and scattering of relay surfaces. Deep learning methods are mainly used to restore two-dimensional hidden scenes and face challenges in reconstructing complex real-world scenes. To solve the problems, a research team led by Hao Liu and Mingliang Xu published their new study in Frontiers of Computer Science on 15 May 2026.

Existing research has focused on trajectory similarity computation under centralized data storage. However, in the real world, trajectories are often collected and held by multiple distinct entities that form a data federation. Due to commercial and legal restrictions, direct data sharing among these entities may be prohibited, which renders existing centralized methods incapable of supporting similarity computation over trajectory data federation.

A research team at The Hong Kong Polytechnic University (PolyU) has successfully developed Hong Kong’s first “AI Agent for Precision Diabetes Management – PIPE-AI” (AI Agent), designed specifically for Asian populations, together with a related disease risk prediction model. Leveraging artificial intelligence (AI) and large-scale local electronic health data, the system can more accurately predict the risk of deterioration of complications such as chronic kidney disease in patients with type 2 diabetes over the next 10 years, enabling healthcare professionals and patients to intervene early and improve disease management. The research findings have been published in the leading international journal npj Digital Medicine.