news, journals and articles from all over the world.
Researchers at the University of New Hampshire used SDSC’s Comet supercomputer to validate a model using a machine learning technique called Dynamic Time Lag Regression (DTLR) to help predict the solar wind arrival near the Earth’s orbit from physical parameters of the Sun.
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Research from the McKelvey School of Engineering at Washington University in St. Louis has shown that understanding brain activity as a network instead of readings from an EEG allow for more accurate and efficient detection of seizures in real-time.
Read moreResearchers at Binghamton University, State University of New York are using machine learning to track the coronavirus and predict where it might surge next.
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NIBIB-funded researchers at Stanford University have created an artificial neural network that analyzes lung CT scans to provide information about lung cancer severity that can guide treatment options.
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Argonne scientists Michael Bishof, Maria Chan, Marco Govini, Alessandro Lovato, Bogdan Nicolae and Stefan Wild have received funding for their research as part of DOE’s Early Career Research Program.
Read moreAt a glance:
• Scientists develop AI-based tool to predict adverse drug events
• Such events are responsible for some 2 million U.S. hospitalizations per year
• The free, open-source system could enable safer drug design, optimize drug safety
Understanding the source and network of signals as the brain functions is a central goal of brain research. Now, Carnegie Mellon engineers have created a system for high-density EEG imaging of the origin and path of normal and abnormal brain signals.
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This new agreement will dramatically improve and reduce the computational expense of fluid dynamics models. Both partners aim to improve the design and durability of engine components.
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ISPOR—The Professional Society for Health Economics and Outcomes Research (HEOR) announced that its HEOR Short Course Program is now being offered virtually with 9 upcoming short courses in June and July.
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In order to identify materials that can improve storage technologies for fuel cells and batteries, you need to be able to visualize the actual three-dimensional structure of a particular material up close and in context. Researchers from the University of Delaware’s Catalysis Center for Energy Innovation (CCEI) have done just that, developing new techniques for characterizing complex materials.
Read moreUsing advanced machine learning, drones could be used to detect dangerous “butterfly” landmines in remote regions of post-conflict countries, according to research from Binghamton University, State University at New York.
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