131 research outputs found

    Numerical modeling of the thermal contact in metal forming processes

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    Heat flow across the interface of solid bodies in contact is an important aspect in several engineering applications. This work presents a finite element model for the analysis of thermal contact, which takes into account the effect of contact pressure and gap dimension in the heat flow across the interface between two bodies. Additionally, the frictional heat generation is also addressed, which is dictated by the contact forces predicted by the mechanical problem. The frictional contact problem and thermal problem are formulated in the frame of the finite element method. A new law is proposed to define the interfacial heat transfer coefficient (IHTC) as a function of the contact pressure and gap distance, enabling a smooth transition between two contact status (gap and contact). The staggered scheme used as coupling strategy to solve the thermomechanical problem is briefly presented. Four numerical examples are presented to validate the finite element model and highlight the importance of the proposed law on the predicted temperature.The authors gratefully acknowledge the financial support of the Portuguese Foundation for Science and Technology (FCT) under the project PTDC/EMS-TEC/1805/2012 and by FEDER funds through the program COMPETE Programa Operacional Factores de Competitividade, under the project CENTRO-07-0224- FEDER-002001 (MT4MOBI). The second author is also grateful to the FCT for the postdoctoral grant SFRH/BPD/101334/2014. The authors would like to thank Prof. A. Andrade-Campos for helpful contributions on the development of the finite element code presented in this work.info:eu-repo/semantics/publishedVersio

    A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

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    Enhanced protein fold recognition through a novel data integration approach

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    <p>Abstract</p> <p>Background</p> <p>Protein fold recognition is a key step in protein three-dimensional (3D) structure discovery. There are multiple fold discriminatory data sources which use physicochemical and structural properties as well as further data sources derived from local sequence alignments. This raises the issue of finding the most efficient method for combining these different informative data sources and exploring their relative significance for protein fold classification. Kernel methods have been extensively used for biological data analysis. They can incorporate separate fold discriminatory features into kernel matrices which encode the similarity between samples in their respective data sources.</p> <p>Results</p> <p>In this paper we consider the problem of integrating multiple data sources using a kernel-based approach. We propose a novel information-theoretic approach based on a Kullback-Leibler (KL) divergence between the output kernel matrix and the input kernel matrix so as to integrate heterogeneous data sources. One of the most appealing properties of this approach is that it can easily cope with multi-class classification and multi-task learning by an appropriate choice of the output kernel matrix. Based on the position of the output and input kernel matrices in the KL-divergence objective, there are two formulations which we respectively refer to as <it>MKLdiv-dc </it>and <it>MKLdiv-conv</it>. We propose to efficiently solve MKLdiv-dc by a difference of convex (DC) programming method and MKLdiv-conv by a projected gradient descent algorithm. The effectiveness of the proposed approaches is evaluated on a benchmark dataset for protein fold recognition and a yeast protein function prediction problem.</p> <p>Conclusion</p> <p>Our proposed methods MKLdiv-dc and MKLdiv-conv are able to achieve state-of-the-art performance on the SCOP PDB-40D benchmark dataset for protein fold prediction and provide useful insights into the relative significance of informative data sources. In particular, MKLdiv-dc further improves the fold discrimination accuracy to 75.19% which is a more than 5% improvement over competitive Bayesian probabilistic and SVM margin-based kernel learning methods. Furthermore, we report a competitive performance on the yeast protein function prediction problem.</p

    Estrogen receptor transcription and transactivation: Basic aspects of estrogen action

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    Estrogen signaling has turned out to be much more complex and exciting than previously thought; the paradigm shift in our understanding of estrogen action came in 1996, when the presence of a new estrogen receptor (ER), ERβ, was reported. An intricate interplay between the classical ERα and the novel ERβ is of paramount importance for the final biological effect of estrogen in different target cells

    Gender differences in the use of cardiovascular interventions in HIV-positive persons; the D:A:D Study

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    Effect of sonic versus ultrasonic activation on aqueous solution penetration in root canal dentin.

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    Angiopeptin inhibits intimal hyperplasia after angioplasty in porcine coronary arteries.

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