SUTD researchers have developed a novel modeling toolkit that can predict the current of a new type of memory with excellent accuracy, bringing global research efforts one step closer towards better energy technologies.

The 12-month project, ‘Creator Labour: Screen Production Cultures and Transmedia Intersectionality in Yorkshire’, is backed by a grant of £40,000 from the Screen Industries Growth Network (SIGN) and sees the institution collaborate with researchers at the University of Sheffield.

A joint research group of the Institute of Statistical Mathematics, the University of Tokyo, and Tokyo University of Science has demonstrated that machine learning algorithms can be used to learn the compositional patterns of quasicrystals found so far and predict the chemical composition of new quasicrystals. Furthermore, by extracting the input- output rules inherent in the black box model of machine learning, they were able to uncover the laws of quasicrystal phase formation.

A paper published July 28 in Nature Reviews Physics highlights the growing adoption of autonomous discovery as a way to enhance data collection and analysis for research conducted at experimental facilities. Berkeley Lab's CAMERA organization has been at the forefront of this multi-year, multinational effort, which initially focused on synchrotron beamline research but is rapidly expanding into other scientific fields.

America Resilient proposed key ways to mitigate the degree of likely human suffering, loss of biodiversity, and disruptions to critical societal systems by building resilience and mitigating the effects of climate change in the United States.

Brain imaging and mathematical modeling reveal previously unreported mechanistic features of symptoms associated with major depressive disorder In a new study in the Journal of the American Medical Association Psychiatry led by Virginia Tech scientists Pearl Chiu and Brooks King-Casas of the Fralin Biomedical Research Institute at VTC.