In recent years, the advancement of multimodal large language models (MLLMs) has increasingly demonstrated their potential in medical data mining. However, the diversity and heterogeneity nature of medical images and radiology reports can pose significant challenges to the universality of data mining methods.

To address these challenges, a team led by Dr. Xin Zhang from the Institute of Medical Research, Northwestern Polytechnical University in Xi’an, China, systematically evaluated the performance of Gemini and GPT-series models across various medical tasks. Their findings validate the application potential of multimodal large models in the medical domain.

They proposed a program logic that can formally verify obstruction-freedom of practical implementations, as well as verify linearizability(a safety property), at the same time.

The 42-month project, which received nearly $1 million in funding, is a joint effort between the Texas Transportation Institute and the University of Texas at San Antonio (UTSA) exploring more efficient methods of anchoring steel reinforcement in bridge structure joints to enhance structural performance and accelerate construction timelines.

Birds are the undisputed champions of epic travel—but they are not the only long-haul fliers. A handful of bats are known to travel thousands of kilometers in continental migrations across North America, Europe, and Africa. The behavior is rare and difficult to observe, which is why long-distance bat migration has remained an enigma. Now, scientists from the Max Planck Institute of Animal Behavior (MPI-AB) have studied 71 common noctule bats on their spring migration across the European continent, providing a leap in understanding this mysterious behavior. Ultra-lightweight, intelligent sensors attached to bats uncovered a strategy used by the tiny mammals for travel: they surf the warm fronts of storms to fly further with less energy. The study is published in Science.