The Ethnic 'Other' in Ukrainian History Textbooks: The Case of Russia and the Russians

This paper examines portrayals of Russia and the Russians in two generations of Ukrainian history textbooks. It observes that the textbooks are highly condemning of Ukraine's main ethnic other in the guise of foreign ruler: the tsarist authorities and the Soviet regime are always attributed dubious and malicious intentions even if there is appreciation for some of their policies. By contrast, the books, certainly those of the second generation, refrain from presenting highly biased accounts of the ethnic other as a national group (i.e. Russians). Instances where negative judgements do fall onto Russians are counterbalanced by excerpts criticizing ethnic Ukrainians or highlighting conflicting interests within the Ukrainian ethnic group. The negative appraisal of the ethnic other as foreign ruler is clearly instrumental for the nation-building project as it sustains a discourse legitimating the existence of Ukraine as independent state. However, recent trends in history education, the paper concludes, suggest that the importance of nurturing patriotism as a national policy objective is diminishing

A new approach to mathematical water quality modeling in reservoirs: Neural networks

Neural Networks are becoming more and more valuable tools for system modeling and function approximation as computing power of microcomputers increase. Modeling of complex ecological systems such as reservoir limnology is very difficult since the ecological interactions within a reservoir are difficult to define mathematically and are usually system specific. To illustrate the potential use of Neural Networks in ecological modeling, a software was developed to train the data from Keban Dam Reservoir by backpropogation algorithm. Although the available data was insufficient and irregular, the system was trained successfully to estimate the chlorophyll-a concentration given the time, total suspended solids, total phosphorus, dissolved inorganic nitrogen and secchi depth. The model was quite successful in estimating the output with an average error of 0.01268 to 8.11612x10(-8) percent for the 5 sampling stations

Case studies on the use of neural networks in eutrophication modeling

Artificial neural networks are becoming more and more common to be used in development of prediction models for complex systems as the theory behind them develops and the processing power of computers increase. A three layer Levenberg-Marquardt feedforward learning algorithm was used to model the eutrophication process in three water bodies of Turkey (Keban Dam Reservoir, Mogan and Eymir Lakes). Despite the very complex and peculiar nature of Keban Dam, a relatively good correlation (correlation coefficient between 0.60 and 0.75) was observed between the measured and calculated values. For Mogan and Eymir, which are much smaller and more homogenous lakes compared to Keban Dam Reservoir, correlation values as high as 0.95 were achieved between the measured and calculated values. Neural network models were able to model non-linear behavior in eutrophication process reasonably well and could successfully estimate some extreme values from validation and test data sets which were not used in training the neural network

The Representation and Navigation of Complex Data

In this paper we are attempting to address issues related to perception and consciousness deriving from the management of overwhelming data, utilizing artistic/design and sound production practices in virtual reality/environments. In the ordinary flow of day to day activities the self descriptive, self-reflexive, and recursive processes of data collection reveal themselves. These pairs are not encountered as binary oppositions in conflict, but in a continual management of data transformation. We converge with our own solutions “ and the development of technological tools “ and give birth to new scientific tools as well as intuitively artistically generated tools, literally and figuratively. A system prototype - “Vineta” - has been developed at the IPP allowing navigation through scientific and technical data without typing and revising keyword-based queries. The chosen approach to visualizing documents and terms in navigational retrieval includes the representation of documents and terms as graphical objects, and dynamic positioning of these objects in a 3-dimensional virtual navigation space. Users can navigate through this virtual space examining individual documents and clusters of documents at various levels of detail