We consider the parameters of magnetoacoustic emission (MAE) in magnetites from the ores of the Urals and West Siberia. It has been shown that the differences in signals are related to various types of the domain structures of samples, whose fixity is determined by the formation conditions of magnetite and the effect of superimposed physicochemical processes. On the basis of field parameters, the magnetites have been divided into three types depending on the area of magnetic fields with MAE. These parameters can serve as the typomorphic features of magnetites of different genesis.
Being a matter of cognition, user interests should be apt to classification independent of the language of users, social network and content of interest itself. To prove it, we analyze a collection of English and Russian Twitter and Vkontakte community pages by interests of their followers. First, we create a model of Major Interests (MaIs) with the help of expert analysis and then classify a set of pages using machine learning algorithms (SVM, Neural Network, Naive Bayes, and some other). We take three interest domains that are typical of both English and Russian-speaking communities: football, rock music, vegetarianism. The results of classification show a greater correlation between Russian-Vkontakte and Russian-Twitter pages while English-Twitter pages appear to provide the highest score.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.