New research from experts in history, computer science and cognitive science shines fresh light on the French Revolution, showing how rhetorical and institutional innovations won acceptance for the ideas that built the French republic's foundation and inspired future democracies.
A new mathematical model can predict the effectiveness of microbiome therapies that manipulate the immune system through live bacteria and could help doctors choose the most appropriate treatment for people with inflammatory or allergic diseases, a study in eLife reveals.
Some Illinois researchers working on artificial muscles are seeing results even the fittest individuals would envy, designing muscles capable of lifting up to 12,600 times their own weight. Assistant professor of mechanical science and engineering Sameh Tawfick, Beckman postdoctoral fellow Caterina Lamuta, and Simon Messelot recently published a study on how to design super strong artificial muscles in the journal Smart Material and Structures. The new muscles are made from carbon fiber-reinforced siloxane rubber and have a coiled geometry.
Machine learning algorithms excel at finding complex patterns within big data, so researchers often use them to make predictions. Researchers are pushing the technology beyond finding correlations to help uncover hidden cause-effect relationships and drive scientific discoveries. At the University of South Florida, researchers are integrating machine learning techniques into their work studying proteins. One of their challenges has been a lack of methods to identify cause-effect relationships in data obtained from molecular dynamics simulations.
A Portland State University research team studying concussion has published an interactive diagram showing the many facets of mild traumatic brain injury (TBI) -- from sleep problems to mood disorders to the increased danger of dementia -- and how they connect with and affect each other.
Predicting and monitoring cardiovascular disease is often expensive and tenuous, involving high-tech equipment and intrusive procedures. However, a new method developed by researchers at USC Viterbi School of Engineering offers a better way. By coupling a machine learning model with a patient's pulse data, they are able to measure a key risk factor for cardiovascular diseases and arterial stiffness, using just a smart phone.
UCLA-led research finds that internet search terms and tweets related to sexual risk behaviors can predict when and where syphilis trends will occur.
US Army-funded researchers at the University of California in Los Angles have found a proverbial smoking gun signature of the long sought-after Majorana particle, and the find, they say, could block intruders on sensitive communication networks.
NUST MISIS scientists have finally found out why a material that could potentially become the basis for ultra-fast memory in new computers is formed. Professor Petr Karpov and Serguei Brazovskii, both researchers at NUST MISIS, have managed to develop a theory which explains the mechanism of the latent state formation in layered tantalum disulfide, one of the most promising materials for modern microelectronics.
Professor William Lee shows how the science of math can aid the profits of industry.