Yoshiki Sugitani, Yuanzhao Zhang, and Adilson E. Motter
Phys. Rev. Lett. 126, 164101

Previous research on nonlinear oscillator networks has shown that chaos synchronization is attainable for identical oscillators but deteriorates in the presence of parameter mismatches. Here, we identify regimes for which the opposite occurs and show that oscillator heterogeneity can synchronize chaos for conditions under which identical oscillators cannot. This effect is not limited to small mismatches and is observed for random oscillator heterogeneity on both homogeneous and heterogeneous network structures. The results are demonstrated experimentally using networks of Chua’s oscillators and are further supported by numerical simulations and theoretical analysis. In particular, we propose a general mechanism based on heterogeneity-induced mode mixing that provides insights into the observed phenomenon. Since individual differences are ubiquitous and often unavoidable in real systems, it follows that such imperfections can be an unexpected source of synchronization stability.

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Dietram A. Scheufele, Andrew J. Hoffman, Liz Neeley, and Czerne M. Reid

PNAS April 13, 2021 118 (15) e2104068118

The misinformation crisis exemplified and intensified by the COVID-19 pandemic lays a gauntlet at the door of all science communicators. Scholars, experts, educators, activists, organizers, public servants, and philanthropists share an obligation to engage in “difficult, broad-based negotiation of moral, financial, and other societal trade-offs alongside a collective investigation of scientific potential” (18). In the end, it is our hope that this colloquium issue will stimulate deeper explorations of the causes and cures for misinformation, conducted in closer collaborations among researchers and practitioners.

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Edward D. Lee, Christopher P. Kempes, and Geoffrey B. West

PNAS April 13, 2021 118 (15) e2020424118

Although termite mounds stand out as an example of remarkably regular patterns emerging over long times from local interactions, ecological spatial patterns range from regular to random, and temporal patterns range from transient to stable. We propose a minimal quantitative framework to unify this variety by accounting for how quickly sessile organisms grow and die mediated by competition for fluctuating resources. Building on metabolic scaling theory for forests, we reproduce a wide range of spatial patterns and predict transient features such as population shock waves that align with observations. By connecting diverse ecological dynamics, our work will help apply lessons from model systems more broadly (e.g., by leveraging remote mapping to infer local ecological conditions).

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