Category: Papers

Resilience analytics: coverage and robustness in multi-modal transportation networks

A multi-modal transportation system of a city can be modeled as a multiplex network with different layers corresponding to different transportation modes. These layers include, but are not limited to, bus network, metro network, and road network. Formally, a multiplex network is a multilayer graph in which the same set of nodes are connected by different types of relationships. Intra-layer relationships denote the road segments connecting stations of the same transportation mode, whereas inter-layer relationships represent connections between different transportation modes within the same station. Given a multi-modal transportation system of a city, we are interested in assessing its quality or efficiency by estimating the coverage i.e., a portion of the city that can be covered by a random walker who navigates through it within a given time budget, or steps. We are also interested in the robustness of the whole transportation system which denotes the degree to which the system is able to withstand a random or targeted failure affecting one or more parts of it. Previous approaches proposed a mathematical framework to numerically compute the coverage in multiplex networks. However solutions are usually based on eigenvalue decomposition, known to be time consuming and hard to obtain in the case of large systems. In this work, we propose MUME, an efficient algorithm for Multi-modal Urban Mobility Estimation, that takes advantage of the special structure of the supra-Laplacian matrix of the transportation multiplex, to compute the coverage of the system. We conduct a comprehensive series of experiments to demonstrate the effectiveness and efficiency of MUME on both synthetic and real transportation networks of various cities such as Paris, London, New York and Chicago. A future goal is to use this experience to make projections for a fast growing city like Doha.

 

Resilience analytics: coverage and robustness in multi-modal transportation networks

Abdelkader Baggag, Sofiane Abbar, Tahar Zanouda and Jaideep Srivastava
EPJ Data Science 2018 7:14
https://doi.org/10.1140/epjds/s13688-018-0139-7

Source: epjdatascience.springeropen.com

Niche emergence as an autocatalytic process in the evolution of ecosystems

The utilisation of the ecospace and the change in diversity through time has been suggested to be due to the effect of niche partitioning, as a global long-term pattern in the fossil record. However, niche partitioning, as a way to coexist, could be a limited means to share the environmental resources and condition during evolutionary time. In fact, a physical limit impedes a high partitioning without a high restriction of the niche’s variables. Here, we propose that niche emergence, rather than niche partitioning, is what mostly drives ecological diversity. In particular, we view ecosystems in terms of autocatalytic sets: catalytically closed and self-sustaining reaction (or interaction) networks. We provide some examples of such ecological autocatalytic networks, how this can give rise to an expanding process of niche emergence (both in time and space), and how these networks have evolved over time (so-called evoRAFs). Furthermore, we use the autocatalytic set formalism to show that it can be expected to observe a power-law in the size distribution of extinction events in ecosystems. In short, we elaborate on our earlier argument that new species create new niches, and that biodiversity is therefore an autocatalytic process.

 

Niche emergence as an autocatalytic process in the evolution of ecosystems

Roberto Cazzolla Gatti, Brian Fath, Wim Hordijk, Stuart Kauffman, Robert Ulanowicz

Journal of Theoretical Biology

Source: www.sciencedirect.com

Guest Editorial: Special Issue on Approaches to Control Biological and Biologically Inspired Networks

The emerging field at the intersection of quantitative biology, network modeling, and control theory has enjoyed significant progress in recent years. This Special Issue brings together a selection of papers on complementary approaches to observe, identify, and control biological and biologically inspired networks. These approaches advance the state of the art in the field by addressing challenges common to many such networks, including high dimensionality, strong nonlinearity, uncertainty, and limited opportunities for observation and intervention. Because these challenges are not unique to biological systems, it is expected that many of the results presented in these contributions will also find applications in other domains, including physical, social, and technological networks.

 

Guest Editorial: Special Issue on Approaches to Control Biological and Biologically Inspired Networks

Reka Albert ; John Baillieul ; Adilson E. Motter

IEEE Transactions on Control of Network Systems ( Early Access )

Source: ieeexplore.ieee.org

The biology of consciousness from the bottom up

This essay aims to outline a scientific approach to the investigation of consciousness emphasizing achievements and promise of hardcore bottom-up biology. We propose to contemplate what would be the minimal requirements of consciousness in the simplest of life forms. We show that, starting from the molecular nuts and bolts of such life forms, it is the extreme multitudinousness of the moving material components forming consciousness, and their organized swarming, that appears outstanding. This is in stark contrast with the impression obtained from introspection that consciousness is a single, unconstrained, immaterial stream.

 

The biology of consciousness from the bottom up
Claude MJ Braun, Shaun Lovejoy

Adaptive Behavior

Vol 26, Issue 3, 2018

Source: journals.sagepub.com