National Technical University of Athens

About: National Technical University of Athens is a education organization based out in Athens, Attiki, Greece. It is known for research contribution in the topics: Large Hadron Collider & Nonlinear system. The organization has 13445 authors who have published 31259 publications receiving 723504 citations. The organization is also known as: Athens Polytechnic & NTUA.

Fabric evolution within shear bands of granular materials and its relation to critical state theory

Abstract: In an effort to study the relation of fabrics to the critical states of granular aggregates, the discrete element method (DEM) is used to investigate the evolution of fabrics of virtual granular materials consisting of 2D elongated particles. Specimens with a great variety of initial fabrics in terms of void ratios, preferred particle orientations, and intensities of fabric anisotropy were fabricated and tested with direct shear and biaxial compression tests. During loading of a typical specimen, deformation naturally localizes within shear bands while the remaining of the sample stops deforming. Thus, studying the evolution of fabric requires performing continuous local fabric measurements inside these bands, a suitable task for the proposed DEM methodology. It is found that a common ultimate/critical state is eventually reached by all specimens regardless of their initial states. The ultimate/critical state is characterized by a critical void ratio e which depends on the mean stress p, while the other critical state fabric variables related to particle orientations are largely independent of p. These findings confirm the uniqueness of the critical state line in the e − p space, and show that the critical state itself is necessarily anisotropic. Additional findings include the following: (1) shear bands are highly heterogeneous and critical states exist only in a statistical sense; (2) critical states can only be reached at very large local shear deformations, which are not always obtained by biaxial compression tests (both physical and numerical); (3) the fabric evolution processes are very complex and highly dependent on the initial fabrics. Copyright © 2010 John Wiley & Sons, Ltd.

Crowdsourcing Network Inference: The DREAM Predictive Signaling Network Challenge

Abstract: Computational analyses of systematic measurements on the states and activities of signaling proteins (as captured by phosphoproteomic data, for example) have the potential to uncover uncharacterized protein-protein interactions and to identify the subset that are important for cellular response to specific biological stimuli. However, inferring mechanistically plausible protein signaling networks (PSNs) from phosphoproteomics data is a difficult task, owing in part to the lack of sufficiently comprehensive experimental measurements, the inherent limitations of network inference algorithms, and a lack of standards for assessing the accuracy of inferred PSNs. A case study in which 12 research groups inferred PSNs from a phosphoproteomics data set demonstrates an assessment of inferred PSNs on the basis of the accuracy of their predictions. The concurrent prediction of the same previously unreported signaling interactions by different participating teams suggests relevant validation experiments and establishes a framework for combining PSNs inferred by multiple research groups into a composite PSN. We conclude that crowdsourcing the construction of PSNs-that is, outsourcing the task to the interested community-may be an effective strategy for network inference.