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When an individual participates in empirical studies involving the visual arts, they most often are presented with a stream of images, shown on a computer, depicting reproductions of artworks by respected artists but which are often not known to the viewer. While art can of course be shown in presentia actuale -e.g., in the museum-this laboratory paradigm has become our go-to basis for assessing interaction, and, often in conjunction with some means of rating, for assessing evaluative, emotional, cognitive, and even neurophysiological response. However, the question is rarely asked: Do participants actually believe that every image that they are viewing is indeed "Art"? Relatedly, how does this evaluation relate to aesthetic appreciation, and do the answers to these questions vary in accordance with different strategies and interpersonal differences? In this paper, we consider the spontaneous classification of digital reproductions as art or not art. Participants viewed a range of image types-Abstract, Hyperrealistic, Poorly Executed paintings, Readymade sculptures, as well as Renaissance and Baroque paintings. They classified these as "art" or "not art" using both binary and analog scales, and also assessed for liking. Almost universally, individuals did not find all items within a class to be "art," nor did all participants agree on the arthood status for any one item. Art classification in turn showed a significant positive correlation with liking. Whether an object was classified as art moreover correlated with specific personality variables, tastes, and decision strategies. The impact of these findings is discussed for selection/assessment of participants and for better understanding the basis of findings in past and future empirical art research.

Chemical plants are complex large-scale systems which need designing robust fault detection schemes to ensure high product quality, reliability and safety under different operating conditions. The present paper is concerned with a feasibility study of the application of the black-box modeling method and Kullback Leibler divergence (KLD) to the fault detection in a distillation column process. A Nonlinear Auto-Regressive Moving Average with eXogenous input (NARMAX) polynomial model is firstly developed to estimate the nonlinear behavior of the plant. Furthermore, the KLD is applied to detect abnormal modes. The proposed FD method is implemented and validated experimentally using realistic faults of a distillation plant of laboratory scale. The experimental results clearly demonstrate the fact that proposed method is effective and gives early alarm to operators., (Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.)

Objectives: Health costs, which are increasing at a yearly rate of 4 %, represent 11% and thus a large share of Austria's gross domestic product (GDP). High expenditures derive frommental health care costs, including medication. In this article we investigate whether the costs and usage of psychopharmaceutic products in Austria are rising., Method: We did a descriptive analysis of the sales figures and number for packaging units of pharmaceutical products of ATC-classes N05 and N06 in all Austrian hospitals, pharmacies and medicine chests for the years 2006-2013. All data were provided free of charge by IMSHealth., Results: The sales volume and number of prescribed packaging units of pharmaceuticals of ATC-classes N05 and N06 increased over the time period in question. In 2013, about 25% more packaging units were being sold than in 2006. Among the two ATC-classes, however, the indication subgroups developed differently. Expenditures increased a total of about 31%within the period of consideration., Conclusions: The increase in psycho-pharmaceutical sales exceeds the expansion rates of other health expenditures (17.8 %). During the 9 years of observation, 25% more psychopharmaceutical products were sold. This may result from increased prevalence of mental disorders, higher usage or an increment in prescriptions.