Safeguarding of intangible cultural heritage – the spirit and the letter of the law

The 2003 UNESCO Convention for the Safeguarding of Intangible Cultural Heritage has been discussed, characterised and criticised for its terms and ideas and for the problems that it is claimed to raise in many ways (Berkaak 2010, Ivey 2004, Kirschenblatt-Gimblet 2004, Keitumetse 2006, Kurin 2004b  Grau 2009). More than a decade has now passed since 2003. The Convention can be seen in the light of how it has functioned so far. Safeguarding of intangible cultural heritage has been an explicit part of the author’s work description at the Foundation for Traditional Music and Dance1 since 1973. He has been member of UNESCO’s evaluating bodies for several years, and has an agenda and opinions to promote on behalf of those for whom and with whom he has worked. For this reason, the article is not meant to be a description of the Convention, nor a distanced, theoretical discussion about broad concepts such as heritage. It is meant to be an article engaging with the interpretations and development of the Convention. It will promote certain points of view and will have a polemic edge. The argumentation seeks to find its support in a close reading of the Convention text itself, in the broad material produced for the implementation of the Convention (i.e. UNESCO 2014a), and in the summary of experiences and recommendations from the evaluation work. (UNESCO 2014b

High stakes classification with multiple unknown classes based on imperfect data

High stakes classification refers to classification problems where erroneously predicting the wrong class is very bad, but assigning "unknown" is acceptable. We make the argument that these problems require us to give multiple unknown classes, to get the most information out of our analysis. With imperfect data we refer to covariates with a large number of missing values, large noise variance, and some errors in the data. The combination of high stakes classification and imperfect data is very common in practice, but it is very difficult to work on using current methods. We present a one-class classifier (OCC) to solve this problem, and we call it NBP. The classifier is based on Naive Bayes, simple to implement, and interpretable. We show that NBP gives both good predictive performance, and works for high stakes classification based on imperfect data. The model we present is quite simple; it is just an OCC based on density estimation. However, we have always felt a big gap between the applied classification problems we have worked on and the theory and models we use for classification, and this model closes that gap. Our main contribution is the motivation for why this model is a good approach, and we hope that this paper will inspire further development down this path.Comment: 8 page

Blood concentrations of lead (Pb), mercury (Hg) and cadmium (Cd) in Scandinavian and Alaskan brown bears (Ursus arctos)

Lead (Pb), mercury (Hg) and cadmium (Cd) are among the most serious environmental heavy metal pollutants with long half-life times that will make them present in both the environment and living organisms over a long period of time. They are found naturally in all ecosystems but the exposure is often due to human activities. Both humans and animals are exposed to these through ingestion or inhalation. With this bakground I will in this study test differences in the bear blood concentrations among brown bears (Ursus arctos) in Scandinavia and Alaskan National Parks. The measurements of the bears in Alaska are taken in Lake Clark, Gates of the Arctic and Katmai. In general, the results shows a trend in differences depending on the heavy metal in relation to the study area. The results revealed elevated blood Pb concentrations among the sampled brown bears from Scandinavia compared to Alaska. On the other hand, they also revealed significantly low blood Hg concentrations among Scandinavian brown bears compared to Alaskan brown bears. For blood Cd concentrations there was less clear differences among the study areas. Bears in Gates of the Arctic and Scandinavia had higher blood Cd concentrations than in Katmai and Lake Clark. Overall, we see that the differences in the heavy metal concentrations of the brown bears in this study are due to location. Studied locations differed in primary food resources and aerial heavy metal depositions mainly human caused, but also natural occurences. The exposure of heavy metals can also be due to both new and old emissions as they have accumulation capacity both in the environment and in animal body tissues. A study like this can provide opportunities for further research where the actual results can be seen in the context of other analyzes of blood concentrations of heavy metals

Bond graph modeling and simulation of wind turbine systems

This paper addresses the problem of bond graph methodology as a graphical approach for the modeling of wind turbine generating systems. The purpose of this paper is to show some of the benefits the bond graph approach has, in contributing a model for wind turbine systems. We will present a nonlinear model of a wind turbine generating system, containing blade pitch, drive train, tower motion and generator. All which will be modeled by means of bond graph. We will especially focus on the drive train, and show the difference between modeling with a classical mechanical method and by using bond graph. The model consists of realistic parameters, but we are not trying to validate a specific wind turbine generating system. Simulations are carried out in the bond graph simulation software 20-sim