Human interaction networks inferred from country-wide telephone activity recordings were recently used to redraw political maps by projecting their topological partitions into geographical space. The results showed remarkable spatial cohesiveness of the network communities and a significant overlap between the redrawn and the administrative borders. Here we present a similar analysis based on one of the most popular online social networks represented by the ties between more than 5.8 million of its geo-located users. The worldwide coverage of their measured activity allowed us to analyze the large-scale regional subgraphs of entire continents and an extensive set of examples for single countries. We present results for North and South America, Europe and Asia. In our analysis we used the well-established method of modularity clustering after an aggregation of the individual links into a weighted graph connecting equal-area geographical pixels. Our results show fingerprints of both of the opposing forces of dividing local conflicts and of uniting cross-cultural trends of globalization.

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Twitter is a popular public conversation platform with world-wide audience and diverse forms of connections between users. In this paper we introduce the concept of aggregated regional Twitter networks in order to characterize communication between geopolitical regions. We present the study of a follower and a mention graph created from an extensive data set collected during the second half of the year of 2012. With a k-shell decomposition the global core-periphery structure is revealed and by means of a modified Regional-SIR model we also consider basic information spreading properties.adapting a simple information propagation model we can further differentiate the most efficient information sources of the world -as reflected in high volume of individual conversations. Methods Twitter dataWe used a data set collected from the freely available Twitter stream during the second half of the year of 2012 [3]. We refer to Twitter users in this data set allowing public access to their geographical location information as the geousers, and each one is located to a single fixed position [3]. Those that fell into unmapped territories (e.g., oceans) were discarded from further analysis. By means of aggregation of their different forms of connections we create two different communication networks between geopolitical regions: the mention (M) and the follower (F ) networks.For creating the user-level follower graph we used 177, 176, 790 links between 3, 312, 961 geo-users identified as the most active ones. Given their list, the additional follower relations were collected separately [3]. The source of a following link is the followed user while its target is the follower. A network built using the inverse follower relation is also be meaningful, and can be used for showing the direction of interest. To create the user-level mention graph we used 132, 436, 279 mention messages between 5, 381, 565 geo-users. The source of a mention link is the sender while its target is the mentioned user.

The subject of this paper is electrical crosstalk, an interference between the current/voltage characteristics of the two working electrodes in four-electrode (generator/ collector) systems. Cross-talk arises in electrochemical cells of finite resistance due to the superposition of the electrical fields of the working electrodes, and often causes difficulties in the interpretation of measurement results. In this paper we present an algorithm for modelling simple generation/collection experiments with a rotating ring-disk electrode (RRDE) immersed into a finite resistance solution of a redox couple. We show that based on the analysis of the Kirchhoff (Laplace) matrix of the simulation mesh, the effect of electrical cross-talk may be accounted for in such experiments. The intensity of cross-talk is found to be heavily influenced by the selection of the reference point for potential measurements; in practice this is the position of the reference electrode or the tip of We dedicate this work to Prof. György Inzelt on the occasion of his 70 th birthday and in recognition of his great contribution to electrochemistry.

Principal component analysis (PCA) and related techniques have been successfully employed in natural language processing. Text mining applications in the age of the online social media (OSM) face new challenges due to properties specific to these use cases (e.g. spelling issues specific to texts posted by users, the presence of spammers and bots, service announcements, etc.). In this paper, we employ a Robust PCA technique to separate typical outliers and highly localized topics from the low-dimensional structure present in language use in online social networks. Our focus is on identifying geospatial features among the messages posted by the users of the Twitter microblogging service. Using a dataset which consists of over 200 million geolocated tweets collected over the course of a year, we investigate whether the information present in word usage frequencies can be used to identify regional features of language use and topics of interest. Using the PCA pursuit method, we are able to identify important low-dimensional features, which constitute smoothly varying functions of the geographic location.