An automated identification and analysis of ontological terms in gastrointestinal diseases and nutrition-related literature provides useful insights

With an unprecedented growth in the biomedical literature, keeping up to date with the new developments presents an immense challenge. Publications are often studied in isolation of the established literature, with interpretation being subjective and often introducing human bias. With ontology-driven annotation of biomedical data gaining popularity in recent years and online databases offering metatags with rich textual information, it is now possible to automatically text-mine ontological terms and complement the laborious task of manual management, interpretation, and analysis of the accumulated literature with downstream statistical analysis. In this paper, we have formulated an automated workflow through which we have identified ontological information, including nutrition-related terms in PubMed abstracts (from 1991 to 2016) for two main types of Inflammatory Bowel Diseases: Crohn’s Disease and Ulcerative Colitis; and two other gastrointestinal (GI) diseases, namely, Coeliac Disease and Irritable Bowel Syndrome. Our analysis reveals unique clustering patterns as well as spatial and temporal trends inherent to the considered GI diseases in terms of literature that has been accumulated so far. Although automated interpretation cannot replace human judgement, the developed workflow shows promising results and can be a useful tool in systematic literature reviews. The workflow is available at https://github.com/KociOrges/pytag

Collective Decision Dynamics in the Presence of External Drivers

We develop a sequence of models describing information transmission and decision dynamics for a network of individual agents subject to multiple sources of influence. Our general framework is set in the context of an impending natural disaster, where individuals, represented by nodes on the network, must decide whether or not to evacuate. Sources of influence include a one-to-many externally driven global broadcast as well as pairwise interactions, across links in the network, in which agents transmit either continuous opinions or binary actions. We consider both uniform and variable threshold rules on the individual opinion as baseline models for decision-making. Our results indicate that 1) social networks lead to clustering and cohesive action among individuals, 2) binary information introduces high temporal variability and stagnation, and 3) information transmission over the network can either facilitate or hinder action adoption, depending on the influence of the global broadcast relative to the social network. Our framework highlights the essential role of local interactions between agents in predicting collective behavior of the population as a whole.Comment: 14 pages, 7 figure

Research trends in nanotechnology studies across geo-economic areas

The purpose of this paper is to analyze the current temporal and spatial research trajectories in nanoscience and nanotechnology studies in order to display the worldwide patterns of research fields across main economic players. The results show the leadership of Europe and North America in nanotechnology research, although the role of China has been growing over time. Current nanotechnology studies have been growing in chemistry and medicine because of applications of nanomaterials mainly in Chemical Engineering, Biochemistry, Genetics and Molecular Biology. Results also show a relative higher scientific performance in nanotechnology research production by South Korea in comparison with Japan and other geo-economic areas. This research can provide vital findings to support research and innovation policies aimed at improving the development of this technological system for modern patterns of economic growth.Nanoscience, Nanotechnology, Technological Trajectories, Research Trends, Data Mining, Comparative Innovation Systems, Technological System

Opinion Optimization in Directed Social Networks

Shifting social opinions has far-reaching implications in various aspects, such as public health campaigns, product marketing, and political candidates. In this paper, we study a problem of opinion optimization based on the popular Friedkin-Johnsen (FJ) model for opinion dynamics in an unweighted directed social network with $n$ nodes and $m$ edges. In the FJ model, the internal opinion of every node lies in the closed interval $[0, 1]$, with 0 and 1 being polar opposites of opinions about a certain issue. Concretely, we focus on the problem of selecting a small number of $k\ll n$ nodes and changing their internal opinions to 0, in order to minimize the average opinion at equilibrium. We then design an algorithm that returns the optimal solution to the problem in $O(n^3)$ time. To speed up the computation, we further develop a fast algorithm by sampling spanning forests, the time complexity of which is $O(ln)$, with $l$ being the number of samplings. Finally, we execute extensive experiments on various real directed networks, which show that the effectiveness of our two algorithms is similar to each other, both of which outperform several baseline strategies of node selection. Moreover, our fast algorithm is more efficient than the first one, which is scalable to massive graphs with more than twenty million nodes

Development of a Methodology for the Economic Assessment of Managerial Decisions as a Factor of Increased Economic Security

The article notes that the emergence of such a phenomenon as the interdependence of security and development, the so-called security-development nexus, becomes a determinant during the development of strategic documents at all hierarchical levels. It gives relevance to the search for methodological solutions that would on a strategic level take into account any potential threats to economic security, and on a tactical level provide for pragmatic actions that are not in conflict with the strategic development vector of business entities. The authors identify the instability factors that pose a real threat to economic security. They substantiate the expediency of forming a new model of the national economy development with a focal point on new industrialization. The article factors in the most important trends in the development of the global economy that determine the strategic vector of enhancing the economic security in Russia. It is ascertained that in the conditions of new industrialization, the intellectual core of the high-tech economy sector is formed by convergent technologies (NBICS technologies). The authors offer a methodological approach to the economic assessment of managerial decisions in the context of uncertainty. They also identify methodological principles that must be taken into account in developing a modern methodology for the economic assessment of business decisions. The principles include forming a preferred reality, or the so-called “vision of the future,” the priority of network solutions as the basis for the formation of new markets; mass customization and individualization of demands, principal changes in the profile of competences that ensure competitiveness on the labor market, use of the ideology of inclusive development and impact investment that creates common values. The proposed methodology is based on the optimum combination of traditional methods used for the economic assessment of managerial decisions with the method of real options and reflexive assessments with regard to entropy as a measure of uncertainty. The proposed methodological approach has been tested in respect of the Ural mining and metallurgical complex.The article has been prepared with the support of the grant from the Russian Foundation for Basic Research № 16–06–00403 "Modelling the Motivational Potentials of the Multi-subject Industrial Policy in the Context of New Industrialization"

Measuring internet activity: a (selective) review of methods and metrics

Two Decades after the birth of the World Wide Web, more than two billion people around the world are Internet users. The digital landscape is littered with hints that the affordances of digital communications are being leveraged to transform life in profound and important ways. The reach and influence of digitally mediated activity grow by the day and touch upon all aspects of life, from health, education, and commerce to religion and governance. This trend demands that we seek answers to the biggest questions about how digitally mediated communication changes society and the role of different policies in helping or hindering the beneficial aspects of these changes. Yet despite the profusion of data the digital age has brought upon us—we now have access to a flood of information about the movements, relationships, purchasing decisions, interests, and intimate thoughts of people around the world—the distance between the great questions of the digital age and our understanding of the impact of digital communications on society remains large. A number of ongoing policy questions have emerged that beg for better empirical data and analyses upon which to base wider and more insightful perspectives on the mechanics of social, economic, and political life online. This paper seeks to describe the conceptual and practical impediments to measuring and understanding digital activity and highlights a sample of the many efforts to fill the gap between our incomplete understanding of digital life and the formidable policy questions related to developing a vibrant and healthy Internet that serves the public interest and contributes to human wellbeing. Our primary focus is on efforts to measure Internet activity, as we believe obtaining robust, accurate data is a necessary and valuable first step that will lead us closer to answering the vitally important questions of the digital realm. Even this step is challenging: the Internet is difficult to measure and monitor, and there is no simple aggregate measure of Internet activity—no GDP, no HDI. In the following section we present a framework for assessing efforts to document digital activity. The next three sections offer a summary and description of many of the ongoing projects that document digital activity, with two final sections devoted to discussion and conclusions

Hierarchical self-organization of non-cooperating individuals

Hierarchy is one of the most conspicuous features of numerous natural, technological and social systems. The underlying structures are typically complex and their most relevant organizational principle is the ordering of the ties among the units they are made of according to a network displaying hierarchical features. In spite of the abundant presence of hierarchy no quantitative theoretical interpretation of the origins of a multi-level, knowledge-based social network exists. Here we introduce an approach which is capable of reproducing the emergence of a multi-levelled network structure based on the plausible assumption that the individuals (representing the nodes of the network) can make the right estimate about the state of their changing environment to a varying degree. Our model accounts for a fundamental feature of knowledge-based organizations: the less capable individuals tend to follow those who are better at solving the problems they all face. We find that relatively simple rules lead to hierarchical self-organization and the specific structures we obtain possess the two, perhaps most important features of complex systems: a simultaneous presence of adaptability and stability. In addition, the performance (success score) of the emerging networks is significantly higher than the average expected score of the individuals without letting them copy the decisions of the others. The results of our calculations are in agreement with a related experiment and can be useful from the point of designing the optimal conditions for constructing a given complex social structure as well as understanding the hierarchical organization of such biological structures of major importance as the regulatory pathways or the dynamics of neural networks.Comment: Supplementary videos are to be found at http://hal.elte.hu/~nepusz/research/supplementary/hierarchy