Controlling distant contacts to reduce disease spreading on disordered complex networks

In real social networks, person-to-person interactions are known to be heterogeneous, which can affect the way a disease spreads through a population, reaches a tipping point in the fraction of infected individuals, and becomes an epidemic. This property, called disorder, is usually associated with contact times between individuals and can be modeled by a weighted network, where the weights are related to normalized contact times ω. In this paper, we study the SIR model for disease spreading when both close and distant types of interactions are present. We develop a mitigation strategy that reduces only the time duration of distant contacts, which are easier to alter in practice. Using branching theory, supported by simulations, we found that the effectiveness of the strategy increases when the density f1 of close contacts decreases. Moreover, we found a threshold f̃1=Tc∕β below which the strategy can bring the system from an epidemic to a non-epidemic phase, even when close contacts have the longest time durations.Fil: Pérez, Ignacio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Físicas de Mar del Plata. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Físicas de Mar del Plata; ArgentinaFil: Trunfio, Paul A.. Boston University; Estados UnidosFil: la Rocca, Cristian Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Físicas de Mar del Plata. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Físicas de Mar del Plata; Argentina. Boston University; Estados UnidosFil: Braunstein, Lidia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Físicas de Mar del Plata. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Físicas de Mar del Plata; Argentina. Boston University; Estados Unido

Cascading Failures in Complex Networks

Cascading failure is a potentially devastating process that spreads on real-world complex networks and can impact the integrity of wide-ranging infrastructures, natural systems, and societal cohesiveness. One of the essential features that create complex network vulnerability to failure propagation is the dependency among their components, exposing entire systems to significant risks from destabilizing hazards such as human attacks, natural disasters or internal breakdowns. Developing realistic models for cascading failures as well as strategies to halt and mitigate the failure propagation can point to new approaches to restoring and strengthening real-world networks. In this review, we summarize recent progress on models developed based on physics and complex network science to understand the mechanisms, dynamics and overall impact of cascading failures. We present models for cascading failures in single networks and interdependent networks and explain how different dynamic propagation mechanisms can lead to an abrupt collapse and a rich dynamic behavior. Finally, we close the review with novel emerging strategies for containing cascades of failures and discuss open questions that remain to be addressed.Comment: This review has been accepted for publication in the Journal of Complex Networks Published by Oxford University Pres

Erratum to: Cascading failures in complex networks

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Charged particles interacting with a mixed supported lipid bilayer as a biomimetic pulmonary surfactant

This study shows the interactions of charged particles with mixed supported lipid bilayers (SLB) as biomimetic pulmonary surfactants. We tested two types of charged particles: positively charged and negatively charged particles. Two parameters were measured: adsorption density of particles on the SLB and the diffusion coefficient of lipids by FRAPP techniques as a measure of interaction strength between particles and lipids. We found that positively charged particles do not adsorb on the bilayer, probably due to the electrostatic repulsion between positively charged parts of the lipid head and the positive groups on the particle surface, therefore no variation in diffusion coefficient of lipid molecules was observed. On the contrary, the negatively charged particles, driven by electrostatic interactions are adsorbed onto the supported bilayer. The adsorption of negatively charged particles increases with the zeta-potential of the particle. Consecutively, the diffusion coefficient of lipids is reduced probably due to binding onto the lipid heads which slows down their Brownian motion. The results are directly relevant for understanding the interactions of particulate matter with pulmonary structures which could lead to pulmonary surfactant inhibition or deficiency causing severe respiratory distress or pathologies

Cascading failures in complex networks

Cascading failure is a potentially devastating process that spreads on real-world complex networks and can impact the integrity of wide-ranging infrastructures, natural systems and societal cohesiveness. One of the essential features that create complex network vulnerability to failure propagation is the dependency among their components, exposing entire systems to significant risks from destabilizing hazards such as human attacks, natural disasters or internal breakdowns. Developing realistic models for cascading failures as well as strategies to halt and mitigate the failure propagation can point to new approaches to restoring and strengthening real-world networks. In this review, we summarize recent progress on models developed based on physics and complex network science to understand the mechanisms, dynamics and overall impact of cascading failures. We present models for cascading failures in single networks and interdependent networks and explain how different dynamic propagation mechanisms can lead to an abrupt collapse and a rich dynamic behaviour. Finally, we close the review with novel emerging strategies for containing cascades of failures and discuss open questions that remain to be addressed.Fil: Valdez, Lucas Daniel. Boston University; Estados UnidosFil: Shekhtman, Louis. Bar Ilan University; IsraelFil: la Rocca, Cristian Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Físicas de Mar del Plata. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Físicas de Mar del Plata; ArgentinaFil: Zhang, Xin. College Of Transport And Communication; ChinaFil: Buldyrev, Sergey V.. Yeshiva University; Estados UnidosFil: Trunfio, Paul A. Boston University; Estados UnidosFil: Braunstein, Lidia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Físicas de Mar del Plata. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Físicas de Mar del Plata; ArgentinaFil: Havlin, Shlomo. Bar Ilan University; Israe