Abstract:In the present work, we quantify the irregularity of different European languages belonging to four linguistic families (Romance, Germanic, Uralic and Slavic) and an artificial language (Esperanto). We modified a well-known method to calculate the approximate and sample entropy of written texts. We find differences in the degree of irregularity between the families and our method, which is based on the search of regularities in a sequence of symbols, and consistently distinguishes between natural and synthetic randomized texts. Moreover, we extended our study to the case where multiple scales are accounted for, such as the multiscale entropy analysis. Our results revealed that real texts have non-trivial structure compared to the ones obtained from randomization procedures.

Our aim is to illustrate how the thermodynamics-based concept of entropy has spread across different areas of knowledge by analyzing the distribution of papers, citations and the use of words related to entropy in the predefined Scopus categories. To achieve this, we analyze the Scopus papers database related to entropy research during the last 20 years, collecting 750 K research papers which directly contain or mention the word entropy. First, some well-recognized works which introduced novel entropy-related definitions are monitored. Then we compare the hierarchical structure which emerges for the different cases of association, which can be in terms of citations among papers, classification of papers in categories or key words in abstracts and titles. Our study allowed us to evaluate, to some extent, the utility and versatility of concepts such as entropy to permeate in different areas of science. Furthermore, the use of specific terms (key words) in titles and abstracts provided a useful way to account for the interaction between areas in the category research space.

A technological model that has had great growth is the linking of people through virtual groups created in digital media, also called social networks. This article presents an analysis of a collaborative social network whose design is based on the organizational structure of a university. By means of implementing a computer system that promotes a service of car sharing, and thus improve the transport conditions of its community, it is possible to find symmetrical and asymmetric relationships that they come of common user association rules in the university. Based on this study, the behavior of the network can be predicted thanks to the observed behavior patterns of users. These predictions are of great help in the planning of future activities where the network is expected to have certain collaborative behavior among its individuals when it comes to performing actions with a common benefit and achieve goals planned in the future.