Robots did not write this sentence, or any other part of Nature. But that could change. Dramatic shifts in labour are reshaping society, the environment and the political landscape. Consider this disorienting estimate from the World Economic Forum: 65% of children entering primary schools now will grow up to work in jobs that do not yet exist. This week, Nature asks: what light is research shedding on the future of work, and how will the changes affect scientists’ working world?
Even after over a century of active research, the heart continues to reveal new complexities in behavior and remains difficult to understand fully. Multi-scale dynamics ranging from cellular and subcellular behavior to chambers of the heart and the full organ make analysis complicated. In addition, different types of heart functions, including electrical wave signaling, mechanical contraction, and blood flow, present separate challenges. Theory, numerical modeling, and experiments provide different contributions to our understanding of cardiac processes and behavior. This Focus Issue includes papers from across all these spectra and addresses a number of interesting open questions regarding the complex dynamics of the heart.
Introduction to Focus Issue: Complex Cardiac Dynamics featured
Elizabeth M. Cherry, Flavio H. Fenton, Trine Krogh-Madsen, Stefan Luther, and Ulrich Parlitz
Chaos 27, 093701 (2017); doi: http://dx.doi.org/10.1063/1.5003940
We critically analyse the point of view for which laws of nature are just a mean to compress data. Discussing some basic notions of dynamical systems and information theory, we show that the idea that the analysis of large amount of data by means of an algorithm of compression is equivalent to the knowledge one can have from scientific laws, is rather naive. In particular we discuss the subtle conceptual topic of the initial conditions of phenomena which are generally incompressible. Starting from this point, we argue that laws of nature represent more than a pure compression of data, and that the availability of large amount of data, in general, is not particularly useful to understand the behaviour of complex phenomena.
Compressibility, laws of nature, initial conditions and complexity
Sergio Chibbaro, Angelo Vulpiani
Self-organization and adaptability are critical properties of complex adaptive systems (CAS), and their analysis provides insight into the design of these systems, consequently leading to real-world advancements. However, these properties are difficult to analyze in real-world scenarios due to performance constraints, metric design, and limitations in existing modeling tools. Several metrics have been proposed for their identification, but metric effectiveness under the same experimental settings has not been studied before. In this paper we present an observation tool, part of a complex adaptive systems modeling framework, that allows for the analysis of these metrics for large-scale complex models. We compare and contrast a wide range of metrics implemented in our observation tool. Our experimental analysis uses the classic model of Game of Life to provide a baseline for analysis, and a more complex Emergency Department model to further explore the suitability of these metrics and the modeling and analysis challenges faced when using them.
Identifying Self-Organization and Adaptability in Complex Adaptive Systems
Lachlan Birdsey ; Claudia Szabo ; Katrina Falkner
Published in: Self-Adaptive and Self-Organizing Systems (SASO), 2017 IEEE 11th International Conference on
Scholarly productivity impacts nearly every aspect of a researcher’s career, from their initial placement as faculty to funding and tenure decisions. Historically, expectations for individuals rely on 60 years of research on aggregate trends, which suggest that productivity rises rapidly to an early-career peak and then gradually declines. Here we show, using comprehensive data on the publication and employment histories of an entire field of research, that the canonical narrative of “rapid rise, gradual decline” describes only about one-fifth of individual faculty, and the remaining four-fifths exhibit a rich diversity of productivity patterns. This suggests existing models and expectations for faculty productivity require revision, as they capture only one of many ways to have a successful career in science.
The misleading narrative of the canonical faculty productivity trajectory
Samuel F. Way, Allison C. Morgan, Aaron Clauset, and Daniel B. Larremore