In this study, we investigated data spreading in computer networks with scale-free topology under various levels of improved clustering. Starting from a pure Barabási–Albert (BA) network topology, we applied a Poisson-based rewiring procedure with increasing rewiring probability, which promotes local connections. We then performed wired computer network simulations in NS2 simulator for these topologies. We found that for pure BA network, data transfer (throughput) is maximum, where time required for establishing routing scheme, end-to-end delays in data transmission and number of nodes acting in data transfer are at their minimum levels. Improving clustering increases these parameters those are at their minima. A noteworthy finding of this study is that, for moderate levels of clustering, total throughput remains close to its maximum yielding stable transfer rates, although number of infected nodes and end-to-end delay increase. This indicates that clustering promotes spreading phenomena in networks, although it increases average separation. As a result, clustering property emerges as a catalyzer in data spreading with minimal effects on the total amount of transmission.
Spreading in scale-free computer networks with improved clustering
İlker Türker and Zafer Albayrak
International Journal of Modern Physics BVol. 32, No. 28, 1850309 (2018)
Understanding of modern government is limited by a lack of comprehensive, reliable, comparable data on what governments do and how they are organized to execute their diverse responsibilities. We demonstrate that such data can be collected from the extensive footprint that governments leave on the Internet, opening a range of unresolved puzzles and questions about modern government to closer empirical inquiry. The online footprint of the 50 US state governments reflects their close embeddedness with state economies and suggests that other factors widely hypothesized to influence government play more limited roles, including location and income. It also casts doubt on the degree to which state government functional structures systematically reflect voters’ recent ideological preferences.
Functional structures of US state governments
Stephen Kosack, Michele Coscia, Evann Smith, Kim Albrecht, Albert-László Barabási, and Ricardo Hausmann
PNAS November 13, 2018 115 (46) 11748-11753; published ahead of print October 29, 2018 https://doi.org/10.1073/pnas.1803228115
‘Complexity’ is fast becoming a ‘god term’ in medical education, but little is known about how scholars in the field apply complexity science to the exploration of education phenomena. Complexity science presents both opportunities and challenges to those wishing to adopt its approaches in their research, and debates about its application in the field have emerged. However, these debates have tended towards a reductive characterisation of complexity versus simplicity. We argue that a more productive discussion centres on the multiplicity of complexity orientations, with their diverse disciplinary roots, concepts and terminologies. We discuss this multiplicity and use it to explore how medical education researchers have taken up complexity science in prominent journals in the field.
What is the state of complexity science in medical education research?
Sayra Cristancho, Emily Field, Lorelei Lingard
Medical Education https://doi.org/10.1111/medu.13651
We present two distributed methods for the estimation of the kinematic parameters, the dynamic parameters, and the kinematic state of an unknown planar body manipulated by a decentralized multi-agent system. The proposed approaches rely on the rigid body kinematics and dynamics, on nonlinear observation theory, and on consensus algorithms. The only three requirements are that each agent can exert a 2D wrench on the load, it can measure the velocity of its contact point, and that the communication graph is connected. Both theoretical nonlinear observability analysis and convergence proofs are provided. The first method assumes constant parameters while the second one can deal with time-varying parameters and can be applied in parallel to any task-oriented control law. For the cases in which a control law is not provided, we propose a distributed and safe control strategy satisfying the observability condition. The effectiveness and robustness of the estimation strategy is showcased by means of realistic MonteCarlo simulations.
Distributed Estimation of State and Parameters in Multi-Agent Cooperative Manipulation
Antonio Franchi ; Antonio Petitti ; Alessandro Rizzo
IEEE Transactions on Control of Network Systems
The aim of LANET is to provide a forum to join all scientists who are somehow related to the research on Network Science in Latin America. The rapid growth of the field of Network Science in the last two decades has manifested in the form of schools, workshops, and conferences in Latin America. However, the creation of LANET as a stable and periodic forum devoted to Network Science will further spur the formation of research groups interested in the field and help to establish it as a discipline across Latin American Universities and Research Institutions.