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Prevention of Computer Worker Health Disturbances Caused by Physical and Physiological Risk

REFERENCES Anonymous (2012). Annual Report of Working Environment. National Labour Inspectorate of Estonia. Available at: http://www.ti.ee/index.php?page=1716& . Accessed 13 January 2013. Brauer, C., Mikkelsen, S. (2010). The influence of individual and contextual psychosocial work factors on the perception of the indoor environment at work: A multilevel analysis. Int. Arch. Occup. Environ. Health, 83 , 639–651. Brewer, S., Van Eerd, D., Amick, B. C., Irvin, E., Daum, K. M., Gerr, F., Moore, J. S. Cullen, K., Rempel, D. (2006). Workplace

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Preferences of Patients for Discussing Sudden Unexpected Death in Epilepsy

perceptions of sudden unexpected death in epilepsy. Chronic Illn ., 11 (3), 230–241. Hesdorffer, D. C., Tomson, T., Benn, E., Sander, J. W., Nilsson, L., Langan, Y., Walczak, T. S., Beghi, E., Brodie, M. J., Hauser, A. (2011). Combined analysis of risk factors for SUDEP. Epilepsia, 52 (6), 1150–1159. Langan, Y., Nashef, L., Sander J. W. (2005). Case-control study of SUDEP. Neurology. 64 (7), 1131–1133. Leppik, I. E. How to get patients with epilepsy to take their medication. (1990). The problem of noncompliance. Postgrad Med., 88 (1), 253

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Concepts of Rurality and Urbanity as Analytical Categories in Multidimensional Research

. Antrop, M. (2000). Changing Patterns in the Urbanized Countryside of Western Europe. Landsc. Ecol. , 15, pp. 257-270. Apsītis, V. (2000). Latvijas arhitektūra astoņdesmit gados (1920-2000) [Eighty Years of Latvian Architecture (1920-2000)]. LZA Vēstis, Section A , 54(1/2), 82-89. (in Latvian). Basten, L. (2004). Perceptions of urban space in the periphery: Potsdam's Kirchsteigfeld. Tijdschrift voor Economische en Sociale Geografie , 95(1), 89-99. Bauls, A., Krišjāne, Z. (2000). Latvian

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Smoothing vs. sharpening of colour images: Together or separated

1 Introduction Several factors impact on colour images and they do not only affect visual perception of the image. They also hinder the identification and distinction of image features that are relevant for different applications such as segmentation or pattern recognition. Noise is one of the most common of these factors and it can significantly affect visual quality of images, as well as the performance of most image processing tasks. It is the result of errors in the image acquisition process. In several cases, images are taken under not suitable

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