The aim of this study is to develop a framework for the integration of ancillary topographic information into supervised image classification to improve the accuracy of the classification product. Integration of topographic data into classification is basically through modification of training set in order to provide additional sensitivity to topographical characteristics associated with each land cover class in the study area. Multi-spectral Landsat 7 ETM 30x30 meter bands are the remotely sensed data used in the study. Ancillary topographic data are elevation, slope and aspect derived from 1/25000 scaled topographic map contours. A five-phase methodological framework was proposed for developing procedures for the integration of topographical data into a standard image classification task. Briefly / first phase is the selection of initial class spectral signatures, second phase is analyzing the information content of class spectral signatures and topographical data for a potential relationship, and quantification of the related topographical data. Third phase is the selection of class topographical signatures from the related topographical data. Fourth phase is redefinition of two training sets where one of which includes spectral information only and the other includes both spectral and topographical information. The last phase is classification. Two products were derived where, first product used bands as input and was trained by spectral information only and the second was the product for which bands and topographical data was used as input and it was trained with both spectral and topographical information. Method was applied to image and associated ancillary topographical data covering rural lands mainly composed of agricultural practices and rangelands in Ankara. Method provided an improvement of 10% in overall accuracy for the classification with the integration of topographical data compared to that depended only on spectral data from remotely sensed images.

(in English): Work Starting Reader and Information Literacy in the Municipal Library of Prague - library Smíchov and Barrandov aims to describe and compare the work with starting readers and conduct information education starting readers in accordance with the framework educational programs in the older central city of Prague 5-Smíchov and newer housing estate on the outskirts of Prague 5 in Barrandov. Comparator library Smíchov and Barrandov among the branches of the Municipal Library in Prague. From a comparison of these libraries work is based on methodical instructions for working with starting readers, which rely on the conclusions from interviews conducted by teachers and educators catchment primary schools and kindergartens, as well as with parents beginning readers.

<p>Nuclear engineering hosts diverse domains including, but not limited to, power plant automation, human-machine interfacing, detection and identification of special nuclear materials, modeling of reactor kinetics and dynamics that most frequently are described by systems of differential equations (DEs), either ordinary (ODEs) or partial ones (PDEs). In this work we study multiple problems related to safety and Special Nuclear Material detection, and numerical solutions for partial differential equations using neural networks. More specifically, this work is divided in six chapters. Chapter 1 is the introduction, in Chapter</p> <p>2 we discuss the development of a gamma-ray radionuclide library for the characterization</p> <p>of gamma-spectra. In Chapter 3, we present a new approach, the ”Variance Counterbalancing”, for stochastic</p> <p>large-scale learning. In Chapter 4, we introduce a systematic approach for constructing proper trial solutions to partial differential equations (PDEs) of up to second order, using neural forms that satisfy prescribed initial, boundary and interface conditions. Chapter 5 is about an alternative, less imposing development of neural-form trial solutions for PDEs, inside rectangular and non-rectangular convex boundaries. Chapter 6 presents an ensemble method that avoids the multicollinearity issue and provides</p> <p>enhanced generalization performance that could be suitable for handling ”few-shots”- problems frequently appearing in nuclear engineering.</p>