The unrelaxed dynamical structure of the galaxy cluster Abell 85

For the first time, we explore the dynamics of the central region of a galaxy cluster within $r_{500}\sim 600h^{-1}$~kpc from its center by combining optical and X-ray spectroscopy. We use (1) the caustic technique that identifies the cluster substructures and their galaxy members with optical spectroscopic data, and (2) the X-ray redshift fitting procedure that estimates the redshift distribution of the intracluster medium (ICM). We use the spatial and redshift distributions of the galaxies and of the X-ray emitting gas to associate the optical substructures to the X-ray regions. When we apply this approach to Abell 85 (A85), a complex dynamical structure of A85 emerges from our analysis: a galaxy group, with redshift $z=0.0509 \pm 0.0021$ is passing through the cluster center along the line of sight dragging part of the ICM present in the cluster core; two additional groups, at redshift $z=0.0547 \pm 0.0022$ and $z=0.0570 \pm 0.0020$, are going through the cluster in opposite directions, almost perpendicularly to the line of sight, and have substantially perturbed the dynamics of the ICM. An additional group in the outskirts of A85, at redshift $z=0.0561 \pm 0.0023$, is associated to a secondary peak of the X-ray emission, at redshift $z=0.0583^{+0.0039}_{-0.0047}$. Although our analysis and results on A85 need to be confirmed by high-resolution spectroscopy, they demonstrate how our new approach can be a powerful tool to constrain the formation history of galaxy clusters by unveiling their central and surrounding structures.Comment: 12 pages, 11 figures, accepted by Ap

VB and R codes using Households databases available in the NSI's : A prelude to statistical applied studies

We describe the main features of the households databases we can find in most of our National Statistics Institute. We provide algorithms aimed at extracting a diversity of variables on which different statistical procedures may be applied. Here, we particularly focus on the scaled income, as a beginning. Associated codes (MS Visual Basic and R codes) have been successfully tested and delivered in the text and in a separate fileComment: 42 pages, 3 figure

Historical Macroeconomics and American Macroeconomic History

What can macroeconomic history offer macroeconomic theorists and macroeconometricians? Macroeconomic history offers more than longer time series or special `controlled experiments.' It suggests an historical definition of the economy, which has implications for macroeconometric methods. The defining characteristic of the historical view is its emphasis on `path dependence': ways in which the cumulative past, including the history of shocks and their effects, change the structure of the economy. This essay reviews American macroeconomic history to illustrate its potential uses and draw out methodological implications. `Keynesian' models can account for the most obvious cycle patterns in all historical periods, while `new classical' models cannot. Nominal wage rigidity was important historically and some models of wage rigidity receive more support from history than others.A shortcoming of both Keynesian and new-classical approaches is the assumption that low-frequency change is exogenous to demand. The history of the Kuznets cycle shows how aggregate-demand shocks can produce endogenous changes in aggregate supply. Economies of scale, learning effects, and convergences of expectations-many within the spatial contexts of city building and frontier settlement-seem to have been very important in making the aggregate supply `path-dependent.' Institutional innovation (especially government regulation) has been another source of endogenous change in aggregate supply. The historical view's emphasis on endogenous structural change points in the analysis over short sample periods to identify the sources and consequences of macroeconomic shocks.

A new technique for short-term reliability assessment of transmission and distribution networks

This paper proposes a new methodology for shortterm (24 hours) reliability assessment of transmission and distribution networks, including detailed substations models. Substations are first considered as single electrical nodes to evaluate the reliability of delivery nodes. If nodes (substations) with a high LOLP (Loss of Load Probability) are identified in this preliminary analysis, the critical substations are modeled in detail to obtain the corresponding reliability indices with a higher accuracy, especially the indices corresponding to delivery points (feeders). The proposed methodology includes a topological analysis module similar to the topological processor used in State Estimation, a DC Load Flow, a DC-OPF module to compute remedial actions, and a reliability evaluation module based on state enumeration. The proposed approach is flexible and easy to implement, and special efforts have been made to reduce the computational requirements and to present the results in a way appropriate to both operators and planning engineers.Ministerio de Ciencia y Tecnología (España) ENE 2004-0334

Ensemble Classification of Alzheimer's Disease and Mild Cognitive Impairment Based on Complex Graph Measures from Diffusion Tensor Images

The human brain is a complex network of interacting regions. The gray matter regions of brain are interconnected by white matter tracts, together forming one integrative complex network. In this article, we report our investigation about the potential of applying brain connectivity patterns as an aid in diagnosing Alzheimer's disease and Mild Cognitive Impairment (MCI). We performed pattern analysis of graph theoretical measures derived from Diffusion Tensor Imaging (DTI) data representing structural brain networks of 45 subjects, consisting of 15 patients of Alzheimer's disease (AD), 15 patients of MCI, and 15 healthy subjects (CT). We considered pair-wise class combinations of subjects, defining three separate classification tasks, i.e., AD-CT, AD-MCI, and CT-MCI, and used an ensemble classification module to perform the classification tasks. Our ensemble framework with feature selection shows a promising performance with classification accuracy of 83.3% for AD vs. MCI, 80% for AD vs. CT, and 70% for MCI vs. CT. Moreover, our findings suggest that AD can be related to graph measures abnormalities at Brodmann areas in the sensorimotor cortex and piriform cortex. In this way, node redundancy coefficient and load centrality in the primary motor cortex were recognized as good indicators of AD in contrast to MCI. In general, load centrality, betweenness centrality, and closeness centrality were found to be the most relevant network measures, as they were the top identified features at different nodes. The present study can be regarded as a “proof of concept” about a procedure for the classification of MRI markers between AD dementia, MCI, and normal old individuals, due to the small and not well-defined groups of AD and MCI patients. Future studies with larger samples of subjects and more sophisticated patient exclusion criteria are necessary toward the development of a more precise technique for clinical diagnosis