The Consortium for Enhancing Resilience and Catastrophe Modeling (CERCat)—a landmark partnership between Lehigh University and Rice University—convened at Rice University between Feb. 5-6, 2026, for its semi-annual meeting. This meeting featured comprehensive mid-year updates on six projects launched at the inaugural meeting of CERCat held at Lehigh University in August 2025 and served as a critical touchpoint to ensure CERCat’s research remains aligned with the evolving catastrophe modeling and resilience assessment needs of its industry partners.

Using machine learning, an electronic nose can “smell” early signs of ovarian cancer in the blood. The method is precise and, according to the LiU researchers behind the study, it could eventually be used to find many different cancers. The study is published in the scientific journal Advanced Intelligent Systems.

In a student-centered teaching project, undergraduates analyzed botanical elements in Taylor Swift music videos to activate prior knowledge and reinforce complex plant science concepts. The experience reports improved comprehension, high student satisfaction, and potential to counter “plant blindness” through popular culture.

A research team from Merck (China) has proposed Jigsaw-LightRAG, a novel method that improves how artificial intelligence systems manage and update knowledge graphs.  It decomposes a global knowledge graph (KG) into per-document subgraphs and leverages document lifecycle states (New, Modified, Persistent, Deleted) so that LLM-based extraction is invoked only for changed documents. The updated global KG is then reconstructed via code-level aggregation and strict string-matching deduplication without additional LLM token consumption. Experiments across public benchmarks (LongBench, PubMedQA, and QASPER) demonstrate that in localized corpus changing scenarios, LLM token consumption drops by orders of magnitude (with DELETE operations requiring 0 tokens), while KG structural integrity remains stable and question-answering performance stays on par with full-rebuild baselines.

A research team led by Professor Taesung Kim from the School of Mechanical Engineering has developed a technology that precisely controls the internal structure of semiconductors using heat, much like stamping out “bungeoppang” (fish-shaped pastry) in a mold. The team reported that this approach improves the performance of next-generation artificial intelligence (AI) hardware. With this technology, complex AI computations can be processed more quickly using significantly less electricity than before.