Arsham Ghavasieh and Manlio De Domenico
Phys. Rev. E 107, 044304

The network density matrix formalism allows for describing the dynamics of information on top of complex structures and it has been successfully used to analyze, e.g., a system’s robustness, perturbations, coarse-graining multilayer networks, characterization of emergent network states, and performing multiscale analysis. However, this framework is usually limited to diffusion dynamics on undirected networks. Here, to overcome some limitations, we propose an approach to derive density matrices based on dynamical systems and information theory, which allows for encapsulating a much wider range of linear and nonlinear dynamics and richer classes of structure, such as directed and signed ones. We use our framework to study the response to local stochastic perturbations of synthetic and empirical networks, including neural systems consisting of excitatory and inhibitory links and gene-regulatory interactions. Our findings demonstrate that topological complexity does not necessarily lead to functional diversity, i.e., the complex and heterogeneous response to stimuli or perturbations. Instead, functional diversity is a genuine emergent property which cannot be deduced from the knowledge of topological features such as heterogeneity, modularity, the presence of asymmetries, and dynamical properties of a system.

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Jian Gao, Dashun Wang

The ongoing artificial intelligence (AI) revolution has the potential to change almost every line of work. As AI capabilities continue to improve in accuracy, robustness, and reach, AI may outperform and even replace human experts across many valuable tasks. Despite enormous efforts devoted to understanding AI’s impact on labor and the economy and its recent success in accelerating scientific discovery and progress, we lack a systematic understanding of how advances in AI may benefit scientific research across disciplines and fields. Here we develop a measurement framework to estimate both the direct use of AI and the potential benefit of AI in scientific research by applying natural language processing techniques to 87.6 million publications and 7.1 million patents. We find that the use of AI in research appears widespread throughout the sciences, growing especially rapidly since 2015, and papers that use AI exhibit an impact premium, more likely to be highly cited both within and outside their disciplines. While almost every discipline contains some subfields that benefit substantially from AI, analyzing 4.6 million course syllabi across various educational disciplines, we find a systematic misalignment between the education of AI and its impact on research, suggesting the supply of AI talents in scientific disciplines is not commensurate with AI research demands. Lastly, examining who benefits from AI within the scientific workforce, we find that disciplines with a higher proportion of women or black scientists tend to be associated with less benefit, suggesting that AI’s growing impact on research may further exacerbate existing inequalities in science. As the connection between AI and scientific research deepens, our findings may have an increasing value, with important implications for the equity and sustainability of the research enterprise.

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González, A.; González, S.; Pereira, G.; Blanco, G. and von Lücken, C. (2023). In Proceedings of the 8th International Conference on Complexity, Future Information Systems and Risk – COMPLEXIS, ISBN 978-989-758-644-6; ISSN 2184-5034, SciTePress, pages 109-121. DOI: 10.5220/0012059400003485

International Economic Integration can be described as a process in which a group of countries seeks mutual benefits through mechanisms such as the elimination and/or reduction of trade, social, and political barriers between others. From an economic point of view, the importance of the integration of countries is fundamental for their development simply because most of them are part of some system of international economic integration. In this work, the issue of economic integration will not be discussed in depth but instead will oversee proposing some well-known metrics in the field of economic development that could be very useful as analysis and decision-making tools. in the process of regional economic integration. In this sense, this work proposes using concepts and metrics of Economic Complexity and Economic Fitness to identify combined productive capacities between countries that are part of an economic block, whether real or fictitious. The problem in understanding how econo mically integrate the countries is to identify the combined productive capacities that would exist if two or more countries that make up an economic block are considered as a single country. Experimental analyzes were carried out for a fictitious case, where a world with 10 countries and 15 products is presented; in addition, 3 economic blocks were defined, which were analyzed applying economic complexity and economic fitness metrics. The results obtained reflect the great importance of economic integration since, by establishing economic blocks, it is possible to capture more productive capacities by improving both the diversity of the economic block and the ubiquity of the products produced in it by addressing the productive capacities of the member countries.

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Giulia Menichetti, Babak Ravandi, Dariush Mozaffarian & Albert-László Barabási
Nature Communications volume 14, Article number: 2312 (2023)

Despite the accumulating evidence that increased consumption of ultra-processed food has adverse health implications, it remains difficult to decide what constitutes processed food. Indeed, the current processing-based classification of food has limited coverage and does not differentiate between degrees of processing, hindering consumer choices and slowing research on the health implications of processed food. Here we introduce a machine learning algorithm that accurately predicts the degree of processing for any food, indicating that over 73% of the US food supply is ultra-processed. We show that the increased reliance of an individual’s diet on ultra-processed food correlates with higher risk of metabolic syndrome, diabetes, angina, elevated blood pressure and biological age, and reduces the bio-availability of vitamins. Finally, we find that replacing foods with less processed alternatives can significantly reduce the health implications of ultra-processed food, suggesting that access to information on the degree of processing, currently unavailable to consumers, could improve population health.

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