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Explicit and Implicit Trust Within Safety Culture. Risk Analysis. Vol. 26, Is. 5, pp. 1139- 1150. doi:10.1111/j.1539-6924.2006.00821.x. COVELLO, V.T., et al.(1989). Principles and guidelines
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GDMS-R: A mixed SQL to manage raster and vector data Spatial Data Infrastructure 26 Kili�nov�, H., Mi�ijovsk�, J. & Pechanec, V. Vyu�it� metod DPZ p�i prostorov�ch anal�z�ch ekoton� Use of remote sensing methods
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in Czech) MYNARZ, Miroslav, ŠIMONÍK, Zdeněk (2010). Assessment Methods for Explosion Load Parameters of Chosen Building Structure. Spektrum, Ostrava, 2010, vol. 10, no. 2, p. 24-
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Dittmann, M. 2005. Anger on the road. Monitor on Psychology, 36(6): 26. Driskell, J. E., Johnston, J. H. 1998. Stress exposure training. Making decisions under stress:
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Database and Information Systems Teaching database courses at the Department of Computer Science News Credit conditions Real time test Model for the test from PL/SQL
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Zeno is able of speech and is also equipped with multiple sensors. All the above mentioned robots have been used for inter- action with children. For
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25] D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in International Conference on Learning Representations (ICLR), 2015. [26
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Regional Trade Agreements and Trade in Agri-Food Products: Evidence for the European Union from Gravity Modeling Using Disaggregated Data. Agricultural Economics,
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They are the target of Civil Protection, the reason why infrastructures are run and why measures for their protection are taken at all. In addition to being the reason for the other stakeholders’ actions, they bear a great responsibility
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The average values for GPA(1) ratio for all Polish companies illustrated by the GPA(1)_MAPP ratios were fluctuating in the analysed
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Mixture models for the test set images: normalized histogram is shown in blue, and mixture model in green. The parameters for these
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Publications Searching Pilot production, technology E-Monography NANOTECHNOLOGY CENTRE CENTRE FOR ENERGY AND ENVIRONMENTAL TECHNOLOGIES VSB - TECHNICAL UNIVERSITY
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pp. 401-406). [25] Tichý, T., Holčapek, M. (2011). Simulation methodology for financial assets with imprecise data. In Proceedings of Mathematical Methods in Economics, Praha: VŠE Praha, 2011. (pp. 709-714). [
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pp. 401-406). [25] Tichý, T., Holčapek, M. (2011). Simulation methodology for financial assets with imprecise data. In Proceedings of Mathematical Methods in Economics, Praha: VŠE Praha, 2011. (pp. 709-714). [
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pp. 401-406). [25] Tichý, T., Holčapek, M. (2011). Simulation methodology for financial assets with imprecise data. In Proceedings of Mathematical Methods in Economics, Praha: VŠE Praha, 2011. (pp. 709-714). [
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Communications team should be sure to have their side of the story heard. This is not only important for the company’s reputation, but the decision could also have a direct fi nancial impact. Usability of the document
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In particular, we are not able to adopt the polynomial lag distribution for tp and for )( * tt uu − . We use instead only one lag for inflation and
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For a test point x, we identify the k nearest neighbors in our dataset D. The predicted value (e.g. for cost j) for
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Pose Prediction Loss Functions We considered four loss functions and evaluated them on a subset of the synthetic dataset consisting of synthetic blue funnel object images. We used an initial
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POPIELARCZYK, D., MARSCHALKO, M., TEMPLIN, T., NIEMIEC, D., YILMAZ, I., MATUSZKOVÁ, B. Bathymetric Monitoring of Alluvial River Bottom Changes for Purposes of Stability of Water Power Plant Structure with a New Methodology