Embrace change with dynamic conservation models
Peer-Reviewed Publication
Updates every hour. Last Updated: 10-Jul-2025 12:10 ET (10-Jul-2025 16:10 GMT/UTC)
A recent article in the journal BioScience (https://doi.org/10.1093/biosci/biaf023), the journal of the American Institute of Biological Sciences, challenges conventional conservation wisdom, suggesting that protected areas such national parks and designated wilderness areas must embrace natural landscape dynamics rather than trying to preserve static conditions and landscape features.
A new national programme that aims to position Singapore at the forefront of advancements in RNA science and applications was officially launched today. This new initiative – named National Initiative for RNA Biology and Its Applications (NIRBA) – is supported by the National Research Foundation (NRF) with total funding of S$130 million (US$97 million) over seven years. NIRBA will engage scientists and clinicians from leading institutions like the National University of Singapore (NUS), Nanyang Technological University, Singapore (NTU Singapore), Agency for Science, Technology and Research (A*STAR), and Duke-NUS Medical School.
MIT scientists used light to control how a starfish egg cell jiggles and moves during its earliest stage of development. Their optical system could guide the design of synthetic, light-activated cells for wound healing or drug delivery.
Full Waveform Inversion (FWI) is capable of finely characterizing the velocity structure, anisotropy, viscoelasticity, and attenuation properties of subsurface media, which provides critical constraints for scientific problems such as understanding the Earth’s internal structure and material composition, earthquake preparation and occurrence, and plate motion and dynamic processes. In recent years, with advancements in high-performance computing platforms, improvements in numerical methods, and the cross-integration of multidisciplinary, FWI has demonstrated broad application prospects in deep underground structure exploration, resource and energy exploration, engineering geophysics, and even medical imaging. In this paper, we provide a comprehensive review and analysis of the development of the FWI method, addressing its current challenges, identifying key issues, future directions, and potential research areas in the theory, methodology, and application of high-resolution FWI imaging. The related findings were published in SCIENCE CHNIA: Earth Science, 68(2): 315‒342, 2025.
In a paper published in National Science Review, a research team from Institute of Automation, Chinese Academy of Sciences and Nanjing University present an overview of the historical developments in Generative Artificial Intelligence (Generative AI). They grouped the developments of Generative AI into four categories: 1) rule-based generative systems, 2) model-based generative algorithms, 3) deep generative methodologies, and 4) foundation models. They also described potential research directions aimed at better utilizing, understanding, and harnessing Generative AI technologies.