In the article, development stages of the virtual simulator Duomatic 09-32 hardware-software complex intended for drivers-operators training in driving of the special self-moving Duomatic 09-32 rolling stock are considered. In the presented research the data domain is studied, the concept of the virtual model basis of the Duomatic 09-32 simulator is developed and the system is designed. The bearing – tamping - leveling machine Duomatic 09-32 is the special machine on railway transport for railway track bearing in a longitudinal and cross profile and in respect of and also for tamping of ballast. It is applied at construction, repair and the current maintenance of a railway. The simulator considers many aspects of railway transport management process (structures, people on the ways, railway signs, traffic lights etc.) that allows the operator to gain qualitatively skills of the analysis and information processing and actions execution for management of train structure depending on a situation. Considering responsibility of the engineer not only for himself, but also for other participants of the movement, management of train structure is considered as difficult process. It demands emotional pressure, ability to constantly analyze a surrounding situation and to predict development of the situation. The simulator allows to receive necessary skills and makes drivers training activity more convenient and safe.

Information system was developed in the form of a web application that makes it possible to identify microscopic images of cytological samples, to establish an initial diagnosis and to provide recommendations for its further confirmation based on additional data. Approaches, assumptions and prerequisites adopted in the information system development are described. It is proposed to use neural networks as the information system element in sample identification and making the initial diagnosis. Patient data, affected area images and microscopic images of cytological samples are planned to be collected in the information system database under creation. Cytological sample images serve as the input data for neural networks operation. Cytological picture assessment is based on the use of the following characteristic features: preparation background, number and location of cells, size and shape of cells, nucleus, presence of multinucleated cells and fission entities (atypical mitoses), etc.

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