ISAL provides this news aggregation facility as a service to the community.
Inclusion of news feeds in this service implies neither a relationship between ISAL and the news provider, nor an endorsement of the content by ISAL.

  • Strong connectivity in real directed networks
    by cxdig (Complexity Digest) on March 18, 2023

    Niall Rodgers, et al. PNAS 120 (12) e2215752120 Many real-world systems are connected in a complex directed network, such as food webs, social, or neural networks. Spreading and synchronization processes often occur in such systems, and understanding the percolation transition (formation of a giant connected component) is key to controlling these dynamics. However, unlike in the undirected case, this had not been understood in directed […]

  • The impact of signal variability on epidemic growth rate estimation from wastewater surveillance data
    by cxdig (Complexity Digest) on March 16, 2023

    Ewan Colman, Rowland R. Kao Background Testing samples of waste water for markers of infectious disease became a widespread method of surveillance during the COVID-19 pandemic. While these data generally correlate well with other indicators of national prevalence, samples that cover localised regions tend to be highly variable over short time scales. Methods We introduce a procedure for estimating the realtime growth rate of pathogen prevalence using time series data from wastewater sampling. […]

  • Stocks and cryptocurrencies: Antifragile or robust? A novel antifragility measure of the stock and cryptocurrency markets
    by cxdig (Complexity Digest) on March 16, 2023

    Darío Alatorre,  Carlos Gershenson, José L. Mateos PLoS ONE 18(3): e0280487 In contrast with robust systems that resist noise or fragile systems that break with noise, antifragility is defined as a property of complex systems that benefit from noise or disorder. Here we define and test a simple measure of antifragility for complex dynamical systems. In this work we use our antifragility measure to analyze real data from return prices in the stock and cryptocurrency markets. Our definition of […]

  • Using Markov chains and temporal alignment to identify clinical patterns in Dementia
    by cxdig (Complexity Digest) on March 16, 2023

    Using Markov chains and temporal alignment to identify clinical patterns in DementiaLuísa Marote e Costa, João Colaç,, Alexandra M. Carvalho,, Susana Vinga, Andreia Sofia Teixeira Journal of Biomedical Informatics In the healthcare sector, resorting to big data and advanced analytics is a great advantage when dealing with complex groups of patients in terms of comorbidities, representing a significant step towards personalized targeting. In this work, we focus on understanding key features […]

  • Structure-based approach can identify driver nodes in ensembles of biologically-inspired Boolean networks
    by cxdig (Complexity Digest) on March 15, 2023

    Eli Newby, Jorge Gómez Tejeda Zañudo, Réka Albert Because the attractors of biological networks reflect stable behaviors (e.g., cell phenotypes), identifying control interventions that can drive a system towards its attractors (attractor control) is of particular relevance when controlling biological systems. Driving a network's feedback vertex set (FVS) by node-state override into a state consistent with a target attractor is proven to force every system trajectory to the target attractor, […]