NGOs, Turnout, and the Left: A Sub-national Analysis of Brazil

This article is designed to examine the role non-governmental organizations (NGOs) play in politics. Previous evidence suggests that NGOs mobilize communities to challenge existing patterns of authority or that they serve hand in glove with existing elites. We reconcile these two contradictory findings by identifying an important contextual feature that helps determine the extent NGOs mobilize or anesthetize. We argue that a community’s level of education influences not only whether people vote but how they vote. We employ a cross-sectional data set from Brazilian municipalities that allows us to estimate the relationship between NGOs, voting turnout, and electoral results. We find that although there is a significant statistical interaction between literacy and NGOs when explaining voting turnout, the effect is not substantively important. The interaction between literacy and NGOs is, however, an important consideration in determining how people vote: in communities with relatively low literacy rates, a robust NGO presence significantly increases the left’s electoral fortunes. Our findings imply the influence NGOs have on society is more political than social

Balanced scorecard design and performance impacts: some Australian evidence

consideration to the use of performance measurement systems, notably the Balanced Scorecard (BSC). However, there has been limited empirical investigation into the particular benefits that result from the use of the BSC (Ittner and Larcker, 1998). This study empirically examines how the BSC has been applied in practice and whether different BSC designs result in varying performance outcomes. Data is from a cross sectional survey, which provided a sample of 92 Australian firms using BSC. It is hypothesised that the BSC provides greater benefits when 1) cause and effect logic is used between measures 2) nonfinancial measures are tied to compensation and 3) implemented at multiple levels within the organisation. Results support the first proposition, although cause and effect logic appears to be more important if the BSC is tied to compensation. These results are discussed, and implications for practice and future research are presented

Extracting the Groupwise Core Structural Connectivity Network: Bridging Statistical and Graph-Theoretical Approaches

Finding the common structural brain connectivity network for a given population is an open problem, crucial for current neuro-science. Recent evidence suggests there's a tightly connected network shared between humans. Obtaining this network will, among many advantages , allow us to focus cognitive and clinical analyses on common connections, thus increasing their statistical power. In turn, knowledge about the common network will facilitate novel analyses to understand the structure-function relationship in the brain. In this work, we present a new algorithm for computing the core structural connectivity network of a subject sample combining graph theory and statistics. Our algorithm works in accordance with novel evidence on brain topology. We analyze the problem theoretically and prove its complexity. Using 309 subjects, we show its advantages when used as a feature selection for connectivity analysis on populations, outperforming the current approaches

New Massive Gravity Domain Walls

The properties of the asymptotic $AdS_3$ space-times representing flat domain walls (DW's) solutions of the New Massive 3D Gravity with scalar matter are studied. Our analysis is based on $I^{st}$ order BPS-like equations involving an appropriate superpotential. The Brown-York boundary stress-tensor is used for the calculation of DW's tensions as well as of the $CFT_2$'s central charges. The holographic renormalization group flows and the phase transitions in specific deformed $CFT_2$ dual to 3D massive gravity model with quadratic superpotential are discussed.Comment: 12 pages,v2-misprints corrected,comments concerning BPS eqs. for NMG model in d>3 added in Sect.

Biomarkers of chemical exposure: state of the art.

Establishing associations between environmental agents and disease presents challenges to both epidemiologists and toxicologists, particularly in cases of complex gene-environment interactions and when there is a long latency between exposure and disease. Biologic markers, physiological signals that reflect exposure, early cellular response, or inherent or acquired susceptibilities, provide a new strategy for resolving some of these problems. Biomarker research assumes that toxicant-induced diseases are progressive and that injury proceeds from entry of the toxicant into target cells, which induces subcellular biochemical events, to cell- and organ-level events that eventually induce irreversible or persistent organism dysfunction. The epidemiologic value of a biomarker lies in its ability to predict backward toward exposure and forward toward risk of clinical outcome, which is largely unknown. Research in mechanistic toxicology will advance the range of useful biomarkers in epidemiology and clinical medicine

Three Dimensional Electrical Impedance Tomography

The electrical resistivity of mammalian tissues varies widely and is correlated with physiological function. Electrical impedance tomography (EIT) can be used to probe such variations in vivo, and offers a non-invasive means of imaging the internal conductivity distribution of the human body. But the computational complexity of EIT has severe practical limitations, and previous work has been restricted to considering image reconstruction as an essentially two-dimensional problem. This simplification can limit significantly the imaging capabilities of EIT, as the electric currents used to determine the conductivity variations will not in general be confined to a two-dimensional plane. A few studies have attempted three-dimensional EIT image reconstruction, but have not yet succeeded in generating images of a quality suitable for clinical applications. Here we report the development of a three-dimensional EIT system with greatly improved imaging capabilities, which combines our 64-electrode data-collection apparatus with customized matrix inversion techniques. Our results demonstrate the practical potential of EIT for clinical applications, such as lung or brain imaging and diagnostic screening