130 resultados para Landscape metrics, Matrix


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The classification of a concrete mixture as self-compacting (SCC) is performed by a series of empirical characterization tests that have been designed to assess not only the flowability of the mixture but also its segregation resistance and filling ability. The objective of the present work is to correlate the rheological parameters of SCC matrix, yield stress and plastic viscosity, to slump flow measurements. The focus of the slump flow test investigation was centered on the fully yielded flow regime and an empirical model relating the yield stress to material and flow parameters is proposed. Our experimental data revealed that the time for a spread of 500 mm which is used in engineering practice as reference for measurement parameters, is an arbitrary choice. Our findings indicate that the non-dimensional final spread is linearly related to the non-dimensional yield-stress. Finally, there are strong indications that the non-dimensional viscosity of the mixture is associated with the non-dimensional final spread as well as the stopping time of the slump flow; this experimental data set suggests an exponential decay of the final spread and stopping time with viscosity. © Appl. Rheol.

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Endothelial filopodia play key roles in guiding the tubular sprouting during angiogenesis. However, their dynamic morphological characteristics, with the associated implications in cell motility, have been subjected to limited investigations. In this work, the interaction between endothelial cells and extracellular matrix fibrils was recapitulated in vitro, where a specific focus was paid to derive the key morphological parameters to define the dynamics of filopodium-like protrusion during cell motility. Based on one-dimensional gelatin fibrils patterned by near-field electrospinning (NFES), we study the response of endothelial cells (EA.hy926) under normal culture or ROCK inhibition. It is shown that the behaviour of temporal protrusion length versus cell motility can be divided into distinct modes. Persistent migration was found to be one of the modes which permitted cell displacement for over 300 μm at a speed of approximately 1 μm min-1. ROCK inhibition resulted in abnormally long protrusions and diminished the persistent migration, but dramatically increased the speeds of protrusion extension and retraction. Finally, we also report the breakage of protrusion during cell motility, and examine its phenotypic behaviours. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

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Performance measurement and management (PMM) is a management and research paradox. On one hand, it provides management with many critical, useful, and needed functions. Yet, there is evidence that it can adversely affect performance. This paper attempts to resolve this paradox by focusing on the issue of "fit". That is, in today's dynamic and turbulent environment, changes in either the business environment or the business strategy can lead to the need for new or revised measures and metrics. Yet, if these measures and metrics are either not revised or incorrectly revised, then we can encounter situations where what the firm wants to achieve (as communicated by its strategy) and what the firm measures and rewards are not synchronised with each other (i.e., there is a lack of "fit"). This situation can adversely affect the ability of the firm to compete. The issue of fit is explored using a three phase Delphi approach. Initially intended to resolve this first paradox, the Delphi study identified another paradox - one in which the researchers found that in a dynamic environment, firms do revise their strategies, yet, often the PMM system is not changed. To resolve this second paradox, the paper proposes a new framework - one that shows that under certain conditions, the observed metrics "lag" is not only explainable but also desirable. The findings suggest a need to recast the accepted relationship between strategy and PMM system and the output included the Performance Alignment Matrix that had utility for managers. © 2013 .

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Motivated by the problem of learning a linear regression model whose parameter is a large fixed-rank non-symmetric matrix, we consider the optimization of a smooth cost function defined on the set of fixed-rank matrices. We adopt the geometric framework of optimization on Riemannian quotient manifolds. We study the underlying geometries of several well-known fixed-rank matrix factorizations and then exploit the Riemannian quotient geometry of the search space in the design of a class of gradient descent and trust-region algorithms. The proposed algorithms generalize our previous results on fixed-rank symmetric positive semidefinite matrices, apply to a broad range of applications, scale to high-dimensional problems, and confer a geometric basis to recent contributions on the learning of fixed-rank non-symmetric matrices. We make connections with existing algorithms in the context of low-rank matrix completion and discuss the usefulness of the proposed framework. Numerical experiments suggest that the proposed algorithms compete with state-of-the-art algorithms and that manifold optimization offers an effective and versatile framework for the design of machine learning algorithms that learn a fixed-rank matrix. © 2013 Springer-Verlag Berlin Heidelberg.