3,832 research outputs found
Optimizing Associative Information Transfer within Content-addressable Memory
Original article can be found at: http://www.oldcitypublishing.com/IJUC/IJUC.htmlPeer reviewe
Phase-field modeling and simulation of fracture in brittle materials with strongly anisotropic surface energy
Crack propagation in brittle materials with anisotropic surface energy is important in applications involving single crystals, extruded polymers, or geological and organic materials. Furthermore, when this anisotropy is strong, the phenomenology of crack propagation becomes very rich, with forbidden crack propagation directions or complex sawtooth crack patterns. This problem interrogates fundamental issues in fracture mechanics, including the principles behind the selection of crack direction. Here, we propose a variational phase-field model for strongly anisotropic fracture, which resorts to the extended Cahn-Hilliard framework proposed in the context of crystal growth. Previous phase-field models for anisotropic fracture were formulated in a framework only allowing for weak anisotropy. We implement numerically our higher-order phase-field model with smooth local maximum entropy approximants in a direct Galerkin method. The numerical results exhibit all the features of strongly anisotropic fracture and reproduce strikingly well recent experimental observations.Peer ReviewedPostprint (author’s final draft
Proposal of a health care network based on big data analytics for PDs
Health care networks for Parkinson's disease (PD) already exist and have been already proposed in the literature, but most of them are not able to analyse the vast volume of data generated from medical examinations and collected and organised in a pre-defined manner. In this work, the authors propose a novel health care network based on big data analytics for PD. The main goal of the proposed architecture is to support clinicians in the objective assessment of the typical PD motor issues and alterations. The proposed health care network has the ability to retrieve a vast volume of acquired heterogeneous data from a Data warehouse and train an ensemble SVM to classify and rate the motor severity of a PD patient. Once the network is trained, it will be able to analyse the data collected during motor examinations of a PD patient and generate a diagnostic report on the basis of the previously acquired knowledge. Such a diagnostic report represents a tool both to monitor the follow up of the disease for each patient and give robust advice about the severity of the disease to clinicians
Automated computation of materials properties
Materials informatics offers a promising pathway towards rational materials
design, replacing the current trial-and-error approach and accelerating the
development of new functional materials. Through the use of sophisticated data
analysis techniques, underlying property trends can be identified, facilitating
the formulation of new design rules. Such methods require large sets of
consistently generated, programmatically accessible materials data.
Computational materials design frameworks using standardized parameter sets are
the ideal tools for producing such data. This work reviews the state-of-the-art
in computational materials design, with a focus on these automated
frameworks. Features such as structural prototyping and
automated error correction that enable rapid generation of large datasets are
discussed, and the way in which integrated workflows can simplify the
calculation of complex properties, such as thermal conductivity and mechanical
stability, is demonstrated. The organization of large datasets composed of
calculations, and the tools that render them
programmatically accessible for use in statistical learning applications, are
also described. Finally, recent advances in leveraging existing data to predict
novel functional materials, such as entropy stabilized ceramics, bulk metallic
glasses, thermoelectrics, superalloys, and magnets, are surveyed.Comment: 25 pages, 7 figures, chapter in a boo
- …