02220nas a2200145   4500008004100000245010000041210006900141300001100210490000900221520172400230100002101954700002301975700002801998856004802026       2020                        eng d00aForecasting of Population Narcotization under the Implementation of a Drug Use Reduction Policy0 aForecasting of Population Narcotization under the Implementation  a1–140 v20203 aIn this paper, we present an approach to drug addiction simulation and forecasting in the medium and long terms in cities having a high population density and a high rate of social communication. Drug addiction forecasting is one of the basic components of the antidrug policy, giving informational and analytic support both at the regional and at the governmental level. However, views on the drug consumption problem vary in different regions, and as a consequence, several approaches to antidrug policy implementation exist. Thereby, notwithstanding the fact that the phenomenology of the population narcotization process is similar in the different regions, approaches to the modeling of drug addiction may also substantially differ for different kinds of antidrug policies. This paper presents a survey of the available antidrug policies and the corresponding approaches to the simulation of population narcotization. This article considers the approach to the construction of the regression model of anesthesia on the main components formed on the basis of indicators of social and economic development. The substantiation of the chosen method is given, which is associated with a significant correlation of indicators, which characterizes the presence of a small number of superfactors. This allows us to form a conclusion about the general level of development of the region as the main factor determining the drug addiction. A new model is proposed for one of the most widespread antidrug policies, namely, the drug use reduction policy. The model helps determine the significant factors of population narcotization and allows to estimate its damage. The model is tested successfully using St. Petersburg data.1 aMityagin, Sergey1 aGershenson, Carlos1 aBoukhanovsky, Alexander  uhttp://turing.iimas.unam.mx/sos/?q=node/18802151nas a2200193   4500008004100000020001400041245007300055210006900128300001400197490000600211520154100217100002601758700002901784700002301813700002201836700002301858700003001881856004601911       2018                        eng d  a2432-272500aFrom neuroscience to computer science: a topical approach on Twitter0 aFrom neuroscience to computer science a topical approach on Twit  a187–2080 v13 aTwitter is perhaps the most influential microblogging service, with 271 million regular users producing approximately 500 million tweets per day. Previous studies of tweets discussing scientific topics are limited to local surveys or may not be representative geographically. This indicates a need to harvest and analyse tweets with the aim of understanding the level of dissemination of science related topics worldwide. In this study, we use Twitter as case of study and explore the hypothesis of science popularization via the social stream. We present and discuss tweets related to popular science around the world using eleven keywords. We analyze a sample of 306,163 tweets posted by 91,557 users with the aim of identifying tweets and those categories formed around temporally similar topics. We systematically examined the data to track and analyze the online activity around users tweeting about popular science. In addition, we identify locations of high Twitter activity that occur in several places around the world. We also examine which sources (mobile devices, apps, and other social networks) are used to share popular science related links. Furthermore, this study provides insights into the geographic density of popular science tweets worldwide. We show that emergent topics related to popular science are important because they could help to explore how science becomes of public interest. The study also offers some important insights for studying the type of scientific content that users are more likely to tweet.1 aPiña-García, C., A.1 aSiqueiros-García, Mario1 aRobles-Belmont, E.1 aCarreón, Gustavo1 aGershenson, Carlos1 aLópez, Julio, Amador Dí  uhttps://doi.org/10.1007/s42001-017-0002-9