Low Mach number effect in simulation of high Mach number flow

In this note, we relate the two well-known difficulties of Godunov schemes: the carbuncle phenomena in simulating high Mach number flow, and the inaccurate pressure profile in simulating low Mach number flow. We introduced two simple low-Mach-number modifications for the classical Roe flux to decrease the difference between the acoustic and advection contributions of the numerical dissipation. While the first modification increases the local numerical dissipation, the second decreases it. The numerical tests on the double-Mach reflection problem show that both modifications eliminate the kinked Mach stem suffered by the original flux. These results suggest that, other than insufficient numerical dissipation near the shock front, the carbuncle phenomena is strongly relevant to the non-comparable acoustic and advection contributions of the numerical dissipation produced by Godunov schemes due to the low Mach number effect.Comment: 9 pages, 1 figur

MidA is a putative methyltransferase that is required for mitochondrial complex I function

10 páginas, 6 figuras.-- et al.Dictyostelium and human MidA are homologous proteins that belong to a family of proteins of unknown function called DUF185. Using yeast two-hybrid screening and pull-down experiments, we showed that both proteins interact with the mitochondrial complex I subunit NDUFS2. Consistent with this, Dictyostelium cells lacking MidA showed a specific defect in complex I activity, and knockdown of human MidA in HEK293T cells resulted in reduced levels of assembled complex I. These results indicate a role for MidA in complex I assembly or stability. A structural bioinformatics analysis suggested the presence of a methyltransferase domain; this was further supported by site-directed mutagenesis of specific residues from the putative catalytic site. Interestingly, this complex I deficiency in a Dictyostelium midA- mutant causes a complex phenotypic outcome, which includes phototaxis and thermotaxis defects. We found that these aspects of the phenotype are mediated by a chronic activation of AMPK, revealing a possible role of AMPK signaling in complex I cytopathology.This work was supported by grants BMC2006-00394 and BMC2009-09050 to R.E. from the Spanish Ministerio de Ciencia e Innovación; to P.R.F. from the Thyne Reid Memorial Trusts and the Australian Research Council; to A.V. and O.G. from the Spanish National Bioinformatics Institute (www.inab.org), a platform of Genome Spain; to R.G. from the Fondo de Investigaciones Sanitarias, Instituto de Salud Carlos III, Spain (PI070167) and from the Comunidad de Madrid (GEN-0269/2006). S.C. is supported by a research contract from Consejería de Educación de la Comunidad de Madrid y del Fondo Social Europeo (FSE).Peer Reviewe

Relaxation time of the order parameter in a high-temperature superconductor

We present femtosecond time-resolved measurements on the high-Tc superconductor Tl2Ba2Ca2Cu3O10. At temperatures below Tc, we observe a relaxation process which is distinct from the equilibration of hot carriers in the normal state. Our results demonstrate an increasing relaxation rate as the superconducting gap opens. This is consistent with the behavior of conventional metallic superconductors

Applications of Supervised Machine Learning Algorithms in Additive Manufacturing: A Review

Additive Manufacturing (AM) simplifies the fabrication of complex geometries. Its scope has rapidly expanded from the fabrication of pre-production visualization models to the manufacturing of end use parts driving the need for better part quality assurance in the additively manufactured parts. Machine learning (ML) is one of the promising techniques that can be used to achieve this goal. Current research in this field includes the use of supervised and unsupervised ML algorithms for quality control and prediction of mechanical properties of AM parts. This paper explores the applications of supervised learning algorithms - Support Vector Machines and Random Forests. Support vector machines provide high accuracy in classifying the data and is used to decide whether the final parts have the desired properties. Random Forests consist of an ensemble of decision trees capable of both classification and regression. This paper reviews the implementation of both algorithms and analyzes the research carried out on their applications in AM.Mechanical Engineerin