Cutting-edge technologies addressing climate, healthcare, and digital authenticity are spotlighted in the report co-published by the World Economic Forum and Frontiers. Pooling the expertise of hundreds of leading scientists from around the world, the report identifies early-stage technologies with the potential to become fully developed and adopted within the next five years. 

In their research on enhancing LoRA-based fine-tuning for LLMs, the research team introduced an Enhanced Matrix Decomposition for single-task scenarios and a routing mechanism for multi-task learning, improving flexibility and performance without increasing computational complexity.

Time-consuming testing and computer simulations are bottlenecks in the design of new materials. A thesis from the University of Gothenburg aims to develop an AI model that can efficiently determine the durability and strength of woven composite materials.

Engineers at the University of Maine have developed a method to more accurately predict the strength of lightweight 3D-printed objects. This research, conducted at UMaine’s Advanced Structures and Composites Center (ASCC), will enable designers to create more robust and reliable printed components that can apply to a wide range of sectors, including aerospace, automotive and medical device manufacturing.

Research team offers a comprehensive review of cognitive strategy-enhanced persuasive dialogue agents (CogAgent). They formalized cognitive strategies from cognitive psychology, summarized research, and analyzed benchmarks and evaluation metrics to advance the field.

Research team proposed POGs, providing containers with individual log configuration, storage, and view to enhance isolation, security, and efficiency with negligible performance overhead.

Research team proposed MDIDCN, a dual-channel network model for predicting miRNA-drug interactions (MDIs). Using TCN and BiLSTM, their model achieved high accuracy.