Month: May 2023

The role of complexity for digital twins of cities

G. Caldarelli, E. Arcaute, M. Barthelemy, M. Batty, C. Gershenson, D. Helbing, S. Mancuso, Y. Moreno, J. J. Ramasco, C. Rozenblat, A. Sánchez & J. L. Fernández-Villacañas 
Nature Computational Science (2023)

We argue that theories and methods drawn from complexity science are urgently needed to guide the development and use of digital twins for cities. The theoretical framework from complexity science takes into account both the short-term and the long-term dynamics of cities and their interactions. This is the foundation for a new approach that treats cities not as large machines or logistic systems but as mutually interwoven self-organizing phenomena, which evolve, to an extent, like living systems.

Read the full article at: https://rdcu.be/da7wK 

Competitive exclusion principle among synthetic non-biochemical protocells

Sai Krishna Katla, Chenyu Lin, Juan Pérez-Mercader

Cell Reports Physical Science VOLUME 4, ISSUE 4, 101359, APRIL 19, 2023

• One-pot synthesis of simple non-biochemical, but functional, protocell populations
• Two populations emerge, differing only in that one has an advantage for reproduction
• The populations compete for food in a shared environment, with one dying out
• Simple protocells display Darwin’s “struggle for existence” without biochemistry

Read the full article at: www.cell.com

Lyapunov exponents for temporal networks

Annalisa Caligiuri, Victor M. Eguíluz, Leonardo Di Gaetano, Tobias Galla, and Lucas Lacasa

Phys. Rev. E 107, 044305

By interpreting a temporal network as a trajectory of a latent graph dynamical system, we introduce the concept of dynamical instability of a temporal network and construct a measure to estimate the network maximum Lyapunov exponent (nMLE) of a temporal network trajectory. Extending conventional algorithmic methods from nonlinear time-series analysis to networks, we show how to quantify sensitive dependence on initial conditions and estimate the nMLE directly from a single network trajectory. We validate our method for a range of synthetic generative network models displaying low- and high-dimensional chaos and finally discuss potential applications.

Read the full article at: link.aps.org