RUDN University mathematician and his colleagues from France and Hungary developed an algorithm for parallel computing, which allows solving applied problems, such as electrodynamics or hydrodynamics. The gain in time is up to 50%.
Credit: Ben Wigler/CSHL, 2021
You would not be surprised to see an elephant in the savanna or a plate in your kitchen. Based on your prior experiences and knowledge, you know that is where elephants and plates are often to be found. If you saw a mysterious object in your kitchen, how would you figure out what it was? You would rely on your expectations or prior knowledge. Should a computer approach the problem in the same way? The answer may surprise you. Cold Spring Harbor Laboratory Professor Partha Mitra described how he views problems like these in a Perspective in
E-Mail
IMAGE: RUDN mathematician and his colleagues from China, Egypt, Saudi Arabia, United Kingdom, and Qatar have developed an algorithm allowing the distribution of computing tasks between the IoT devices and the. view more
Credit: RUDN University
RUDN mathematician and his colleagues from China, Egypt, Saudi Arabia, United Kingdom, and Qatar have developed an algorithm allowing the distribution of computing tasks between the IoT devices and the cloud in an optimal way. As a result, the power and time costs are reduced by about three times. The study was published in the
Big Data.
With the development of technologies and devices, Internet of Things (IoT) applications require more and more computing power. The amount of data that the IoT devices need to process can be so large that it is reasonable to migrate computing to the cloud. Cloud computing provides flexible data processing and storage capabilities. But Computation offloading, meaning transferring of the
E-Mail
IMAGE: Cassandra Spracklen is an assistant professor of biostatistics and epidemiology in the UMass Amherst School of Public Health and Health Sciences. view more
Credit: UMass Amherst
By ensuring ethnic diversity in a largescale genetic study, an international team of researchers, including a University of Massachusetts Amherst genetic epidemiologist, has identified more regions of the genome linked to type 2 diabetes-related traits.
The findings, published May 31 in
Nature Genetics, broaden the understanding of the biological basis of type 2 diabetes and demonstrate that expanding research into different ancestries yields better results. Ultimately the goal is to improve patient care worldwide by identifying genetic targets to treat the chronic metabolic disorder. Type 2 diabetes affects and sometimes debilitates more than 460 million adults worldwide, according to the International Diabetes Federation. About 1.5 million deaths were directly caused by diab