Fire and restoration of the largest urban forest of the world in Rio de Janeiro City,Brazil

National Park of Tijuca in Rio de Janeiro (Brazil) is about 3,300 ha and considered the largest urban forest in the world. Its floristic composition is typical of Atlantic Rain Forest. The reserve is being altered because of fire occurrences and urban expansion. This study identified locations and causes of forest fires, and makes management recommendations to restore damaged areas. From 1991 to 2000, forest firefighters recorded an average of 75-fire occurrences/year. Identified causes included hot air balloons (24%), intentional (24%), rubbish burning (21%) and religious practices (17%). Primary fuels included invasive grasses and ferns. Although hot air balloons destroyed larger areas of forest in each occurrence, a greater number of fires started in the invasive vegetation along roads that bisect the forest. In response to recurrent forests, invasive vegetation has spread gradually into the forest increasing forest degradation. To decrease fire damage, sites with high fire frequencies and density of invasive vegetation were planted with less flammable species. Results indicate that fire frequency decreased and density of invasive vegetation declined. This approach appears to prevent fire incidence, reduce the need for fire fighting, and preserve existing biodiversity.     

Führung durch Selbstführung

This paper outlines the conceptual bases, strategies, and psychological attributes of leadership by self-leadership. In addition, relations to other theories of leadership are drawn. Dimensions and correlates of self-leadership competence are described and discussed. Various approaches for developing self-leadership competence are discussed as well.     

Bayesian modelling of catch in a north-west Atlantic fishery

Summary. We model daily catches of fishing boats in the Grand Bank fishing grounds. We use data on catches per species for a number of vessels collected by the European Union in the context of the Northwest Atlantic Fisheries Organization. Many variables can be thought to influence the amount caught: a number of ship characteristics (such as the size of the ship, the fishing technique used and the mesh size of the nets) are obvious candidates, but one can also consider the season or the actual location of the catch. Our database leads to 28 possible regressors (arising from six continuous variables and four categorical variables, whose 22 levels are treated separately), resulting in a set of 177 million possible linear regression models for the log-catch. Zero observations are modelled separately through a probit model. Inference is based on Bayesian model averaging, using a Markov chain Monte Carlo approach. Particular attention is paid to the prediction of catches for single and aggregated ships.     

Bayesian analysis of mixture modelling using the multivariate t distribution

A finite mixture model using the multivariate t distribution has been shown as a robust extension of normal mixtures. In this paper, we present a Bayesian approach for inference about parameters of t-mixture models. The specifications of prior distributions are weakly informative to avoid causing nonintegrable posterior distributions. We present two efficient EM-type algorithms for computing the joint posterior mode with the observed data and an incomplete future vector as the sample. Markov chain Monte Carlo sampling schemes are also developed to obtain the target posterior distribution of parameters. The advantages of Bayesian approach over the maximum likelihood method are demonstrated via a set of real data.     

A semi-parametric approach to robust parameter design

Parameter design or robust parameter design (RPD) is an engineering methodology intended as a cost-effective approach for improving the quality of products and processes. The goal of parameter design is to choose the levels of the control variables that optimize a defined quality characteristic. An essential component of RPD involves the assumption of well estimated models for the process mean and variance. Traditionally, the modeling of the mean and variance has been done parametrically. It is often the case, particularly when modeling the variance, that nonparametric techniques are more appropriate due to the nature of the curvature in the underlying function. Most response surface experiments involve sparse data. In sparse data situations with unusual curvature in the underlying function, nonparametric techniques often result in estimates with problematic variation whereas their parametric counterparts may result in estimates with problematic bias. We propose the use of semi-parametric modeling within the robust design setting, combining parametric and nonparametric functions to improve the quality of both mean and variance model estimation. The proposed method will be illustrated with an example and simulations.     

Gender differences in social workers’ assessments and help-giving strategies towards single parents

The study investigates Swedish male and female social workers’ assessments and help-giving strategies towards single parents applying for income support. The study was carried out with the help of a vignette distributed as a mail questionnaire. Results show that social workers tend to conform to assessments and help-giving strategies that are consistent with expectations linked to their own gender role.     

Lines through the origin—is NO INT the answer?

The fitting of a straight line that must pass through (0, 0) is a frequently encountered application of linear regression. Many computer packages provide a least-squares option sometimes identified as NO INT, or even NOINT, or as ‘suppressing the intercept’. Often these procedures are invalid or do not correspond to the most suitable model. This paper questions the least-squares-line approach and suggests some alternatives. Two measures are proposed for comparing the fit of alternative models.