Basic principles of building of E-network model of a complex technical system

Methodological bases of building of dynamic models of hybrid systems using principles of a «block» modelling are exposed on the basis of the E-network formalism. The presented methods are based on use of mechanisms of hierarchical interaction of dynamic model elements. Principles of organization of E-network hierarchical circuits with use of rigid and flexible structures are shown. The mechanism of interaction of static and dynamic components is specified

Technique automated of diagram construction in business process management systems

Technique allowing reducing stages of analysis and design of application while implementing Business Process Management System (BPMS) has been suggested. It was possible due to elimination of enterprise activity examination stage and formation of business process models on the basis of structural functional models obtained as a result of reengineering project or developing quality management system. The required steps of model construction in BPMS were revealed. Appropriateness of business process modeling with the help of traditional meansand further use of models for transfer into BPMS by conversion was validated. Algorithm of automated transformation on the basis of processing XML-files of models was suggeste

Forecasting of epizootic Activity of the Central Caucasian natural High-Mountain Plague Focus

Central-Caucasian natural plague focus was permanently epizootically active since its discovering in 1971 till 2007. Inter-epizootic period has been in progress since 2008. It was not possible to isolate agent strains from field material. Therefore a forecast for focus activation is a relevant task, especially against the background of registered plague cases in humans in 2014–2016. Objective of the study was to create a forecasting model for quantitative prediction of possible activation or maintenance of inter-epizootic period. Materials and methods. We used archival data of Kabardino-Balkar Plague Control Station: journals of rodents’ autopsy, annual reports on epizootiological surveillance, meteorological data from meteostation “Kislovodsk” over the period of 1989–2017, and our epidemiological data for the period 2010 to 2017. We applied Spearman nonparametric correlation analysis, regression analysis, including principal component method, quarterly analysis, and inhomogeneous sequential pattern recognition procedures for statistical processing. Results and discussion. We have designed statistical model which provides for forecasting of plague focus epizootic activity proactively, a year in advance and 99 % probability or higher. The model was tested on retrospective data over the course of 7 years. All predictions were correct. The operational forecasts from 2015 to 2017 proved right too. However there is a possibility of fast changes in the ecology system conditions of the Central-Caucasian natural plague focus because of the global warming. Thereby the forecasting model will be annually checked for informative value of the predictors and, if necessary, adjusted accordingly

Constructing Artificial Neural Networks in the E-net Basis

В данной работе предложена методика реализации искусственной нейронной сети, обеспечивающей возможность реконфигурации структуры и параметров сети в процессе ее работы. Результаты работы могут быть использованы в задачах построения многофункциональных компьютерных тренажеров для идентификации объектов управления, что в свою очередь позволит значительно снизить время настройки и калибровки аналитико- имитационной модели компьютерного тренажера, а также отследить и обучить искусственную нейронную сеть действиям оператора.The given article introduces the method of implementation of the artificial neural network. The method gives the possibility of the network structure and parameters reconfiguration during its work. The results can be used in order to construct multifunctional computer simulator identifying control plants. It will significantly reduce the setting and calibration time of the analytical and simulation model. It will also allow monitoring and teaching the artificial neural network to model the operators actions

Constructing Artificial Neural Networks in the E-net Basis

В данной работе предложена методика реализации искусственной нейронной сети, обеспечивающей возможность реконфигурации структуры и параметров сети в процессе ее работы. Результаты работы могут быть использованы в задачах построения многофункциональных компьютерных тренажеров для идентификации объектов управления, что в свою очередь позволит значительно снизить время настройки и калибровки аналитико- имитационной модели компьютерного тренажера, а также отследить и обучить искусственную нейронную сеть действиям оператора.The given article introduces the method of implementation of the artificial neural network. The method gives the possibility of the network structure and parameters reconfiguration during its work. The results can be used in order to construct multifunctional computer simulator identifying control plants. It will significantly reduce the setting and calibration time of the analytical and simulation model. It will also allow monitoring and teaching the artificial neural network to model the operators actions

Epizootiologic Monitoring of Natural Focus of Tularemia in the Stavropol Region in 2010-2017

Objective of the study was an assessment of the current epizootiological situation on tularemia in the Stavropol Region. Materials and methods. Processed were the data of laboratory investigations of the field material over the period of 2010-2017. All field samples were studied in the laboratories of the Stavropol Anti-Plague Institute using PCR and bioassay. Results and discussion. This paper presents the analysis of the epizootiological situation for the period of 2010-2017 in the Stavropol Region. The species composition and the number of the main carriers of tularemia have been established. Epizootic activity of the focus is defined by mice of the genus Sylvaemus. Data on the isolation of strains from ticks, small mammals and environmental objects are presented and processed. According to our studies, over the past seven years, infection with tularemia agent has been detected in seven species of mammals: S. uralensis, Microtus arvalis, M. socialis, Mus musculus, Crocidura suaveolens, Erinaceus roumanicus, Lepus europaeus. For the period of epizootic monitoring between 2010 and 2017 37 strains of the causative agent were isolated from small mammals - 12 (32.4 %), ectoparasites - 9 (24.3 %), and environmental objects - 16 (43.2 %). All isolated strains have been identified as Francisella tularensis holarctica biovar II, eryR