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البنك المركزي السعودي : فتح باب التقديم لبرنامج الاقتصاديين السعوديين 19 aleqt.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from aleqt.com Daily Mail and Mail on Sunday newspapers.
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Webinario sobre Sistema de Protección Social en Paraguay senado.gov.py - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from senado.gov.py Daily Mail and Mail on Sunday newspapers.
Researchers from York University and the University of British Columbia have found social media use to be one of the factors related to the spread of COVID-19 within dozens of countries during the early stages of the pandemic. The researchers say this finding resembles other examples of social media misinformation ranging from the initial phase of vaccine rollout to the 2021 Capitol riot in the United States.
E-Mail The friendship paradox is the observation that the degrees of the neighbors of a node within any network will, on average, be greater than the degree of the node itself. In other words: your friends probably have more friends than you do. While the standard framing of the friendship paradox is essentially about averages, significant variations occur too. In the Journal of Complex Networks, Santa Fe Institute and University of Michigan researchers George Cantwell, Alec Kirkley, and Mark Newman address this by developing the mathematical theory of the friendship paradox. Some people have lots of friends, while others have only a few. Unless you have good reason to believe otherwise, it's reasonable to assume you have roughly an average number of friends.
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 resource-intensive processes to the cloud, requires additional power and time. In addition, during the transfer of information, the device is less protected from malware. The RUDN mathematician and his colleagues proposed an algorithm that allows reducing the offloading time and power consumption by 2.8 times. The algorithm also protects the devices for the offloading period with additional encryption.
In a new study published in Proceedings of the National Academy of Sciences (PNAS), a research team of the Institute of Complex Systems of the UB (UBICS) analysed the time evolution of real complex networks and developed a model in which the emergence of new nodes can be related to pre-existing nodes, similarly to the evolution of species in biology.