Using bald eagles to indicate the health of the Great Lakes' environment

The bald eagle (Haliaeetus leucocephalus) is one of the most studied birds of North America, and a great amount of natural life‐history information, including the response of various stressors on the eagles' ability to reproduce, are well known. In Michigan, the eagle has been chosen to track the trends of bioaccumulative compounds of concern across watersheds in the state. The state has been divided into major watersheds, and 20% of these are surveyed each year. A control area in northern Minnesota, Voyageurs National Park, is also sampled annually. We report here on the methods used, the preliminary results of the 1999 field season, and how differences in mercury concentrations varied over a 10‐year period. Mercury in feathers of nestling eagles declined over time only in Lakes Michigan and Huron, but have not decreased among other subpopulations in Michigan. Concentrations of polychlorinated biphenyls (PCBs) and 4,4′‐DDE in blood plasma from nestling eagles have declined over time for most subpopulations; however, they remain greater for breeding areas associated with the Great Lakes' food web. Sea eagles of the genus Haliaeetus are a good sentinel species to track trends in bioaccumulative compounds in aquatic systems.     

Summary of discussion on ‘Sciences related to pitch and coke usage in carbon manufacture’

This article is a brief summary of the Discussion session held after the presentation of the preceding papers at the conference organized by the Industrial Carbon and Graphite Group of the Society of Chemical Industry, London, March 1984.     

Introduction of a nonlinearity measure for principal component models

Although principal component analysis (PCA) is an important tool in standard multivariate data analysis, little interest has been devoted to assessing whether the underlying relationship within a given variable set can be described by a linear PCA model or whether nonlinear PCA must be utilized. This paper addresses this deficiency by introducing a nonlinearity measure for principal component models. The measure is based on the following two principles: (i) the range of recorded process operation is divided into smaller regions; and (ii) accuracy bounds are determined for the sum of the discarded eigenvalues. If this sum is within the accuracy bounds for each region, the process is assumed to be linear and vice versa. This procedure is automated through the use of cross-validation. Finally, the paper shows the utility of the new nonlinearity measure using two simulation studies and with data from an industrial melter process.     

Linear demosaicing inspired by the human visual system.

There is an analogy between single-chip color cameras and the human visual system in that these two systems acquire only one limited wavelength sensitivity band per spatial location. We have exploited this analogy, defining a model that characterizes a one-color per spatial position image as a coding into luminance and chrominance of the corresponding three colors per spatial position image. Luminance is defined with full spatial resolution while chrominance contains subsampled opponent colors. Moreover, luminance and chrominance follow a particular arrangement in the Fourier domain, allowing for demosaicing by spatial frequency filtering. This model shows that visual artifacts after demosaicing are due to aliasing between luminance and chrominance and could be solved using a preprocessing filter. This approach also gives new insights for the representation of single-color per spatial location images and enables formal and controllable procedures to design demosaicing algorithms that perform well compared to concurrent approaches, as demonstrated by experiments.