Investigation of finite-volume methods to capture shocks and turbulence spectra in compressible flows

The aim of the present paper is to provide a comparison between several finite-volume methods of different numerical accuracy: second-order Godunov method with PPM interpolation and high-order finite-volume WENO method. The results show that while on a smooth problem the high-order method perform better than the second-order one, when the solution contains a shock all the methods collapse to first-order accuracy. In the context of the decay of compressible homogeneous isotropic turbulence with shocklets, the actual overall order of accuracy of the methods reduces to second-order, despite the use of fifth-order reconstruction schemes at cell interfaces. Most important, results in terms of turbulent spectra are similar regardless of the numerical methods employed, except that the PPM method fails to provide an accurate representation in the high-frequency range of the spectra. It is found that this specific issue comes from the slope-limiting procedure and a novel hybrid PPM/WENO method is developed that has the ability to capture the turbulent spectra with the accuracy of a high-order method, but at the cost of the second-order Godunov method. Overall, it is shown that virtually the same physical solution can be obtained much faster by refining a simulation with the second-order method and carefully chosen numerical procedures, rather than running a coarse high-order simulation. Our results demonstrate the importance of evaluating the accuracy of a numerical method in terms of its actual spectral dissipation and dispersion properties on mixed smooth/shock cases, rather than by the theoretical formal order of convergence rate.Comment: This paper was previously composed of 2 parts, and this submission was part 1. It is now replaced by the combined pape

A Critical Evaluation Of Traditional African Family System And Contemporary Social Welfare

Beyond reasonable doubt, the influence of Western culture and civilizations has enervated traditional African family systems, and their functions as providers of social welfare. Hitherto, traditional African family and clan by extension served as the plausible medium by which Africans proffered solutions to those social, economic and other existential problems found within their communities. However, measuring and evaluating the successes of the various social welfare programs organized by the family and clan was a difficult task to achieve. It seems the services rendered merely controlled the social problems but did not improve the standard of living of the victims. This paper seeks to critically analyse the traditional African family system and its role as a provider of social welfare and argues that it can complement the Western models towards holistic social integration. Thus this work adopts the theory of change as a tool for understanding how Western models of social welfare should be integrated with traditional African approaches to care for the ‘weak’ in the society. Advocating for a paradigm shift from the modern models of providing social welfare to the traditional African family and clan model is consequent upon the claim that the latter is more embracing in approach

A preliminary spatial analysis of diagnosed stroke disease in Osun state, Nigeria

Introduction: There have been a number of clinical studies on diagnosed Stroke disease. However, there have been few studies on the geographical disparities for stroke. This study investigates the spatial pattern of stroke disease reflecting socio-demographic characteristics in the State. Methods: Stroke patients’ admissions for 22 years (from 1990 to 2012) were examined. Their socio-demographic characteristics were extracted from their health records and analyzed. The location of the stroke patients were categorized by Local Governments Areas (LGAs). Spatial maps were generated and produced in a Geographical Information System (GIS) environment. It involves the analysis of the distribution of stroke cases in relation to their underlying population to determine the areas of high and low density of diagnosed cases across the state. Results: The result highlighted the spatial distribution of diagnosed stroke cases and also highlighted the areas of concern regarding their spatial distribution within the state. Social inequalities in stroke were persistent as incidence rates in urban areas (North) were around 3 times higher than in the rural areas (South). However, this could be due to better healthcare access in the urban areas than in the rural areas as there were disparities in the distribution of healthcare facilities involved in administering care to stroke patients in Osun State. Conclusion: The outcome of this study appears to indicate that spatial inequalities in the access to Stroke healthcare is a concern that needs to be addressed in order to manage the disease adequately

Crystal structures of N ′-Aminopyridine-2-carboximidamide and N ′-{[1-(pyridin-2-yl)ethylidene]amino}pyridine-2-carboximidamide

© Eya'ane Meva et al. 2017. The crystal structures of N,-Aminopyridine-2-carboximidamide (C 6 H 8 N 4 ), 1, and N,-{[1-(pyridin-2-yl)ethylidene]amino}pyridine-2-carboximidamide (C 13 H 13 N 5 ), 2, are described. The non-H atoms in compound 1 are nearly planar (r.m.s. deviation from planarity = 0.0108 Å ), while 2 is twisted about the central N-N bond by 17.8 (2)-. Both molecules are linked by intermolecular N-H·N hydrogen-bonding interactions; 1 forms a two-dimensional hydrogen-bonding network and for 2 the network is a one-dimensional chain. The bond lengths of these molecules are similar to those in other literature reports of azine and diimine systems

Robust Principal Component Analysis?

This paper is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individually? We prove that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the L1 norm. This suggests the possibility of a principled approach to robust principal component analysis since our methodology and results assert that one can recover the principal components of a data matrix even though a positive fraction of its entries are arbitrarily corrupted. This extends to the situation where a fraction of the entries are missing as well. We discuss an algorithm for solving this optimization problem, and present applications in the area of video surveillance, where our methodology allows for the detection of objects in a cluttered background, and in the area of face recognition, where it offers a principled way of removing shadows and specularities in images of faces