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8. Organization of Extracurricular Activities of Children with Severe Intellectual Disabilities in Multi-age Groups

Author

Parfenova, V. V.

Subjects
ОБРАЗОВАНИЕ. ПЕДАГОГИКА, ОБРАЗ ЖИЗНИ, КОНЦЕПТЫ, ЖИЗНЕННЫЙ МИР, ШКОЛЬНИКИ, УМСТВЕННО ОТСТАЛЫЕ ДЕТИ, OLYGOPHRENOPEDAGOGY, ТЕОРИЯ И МЕТОДИКА ВОСПИТАНИЯ, LIFE-WORLD, ДЕТИ С НАРУШЕНИЯМИ ИНТЕЛЛЕКТА, УМСТВЕННАЯ ОТСТАЛОСТЬ, КОРРЕКЦИОННЫЕ ШКОЛЫ, INTELLECTUAL DISORDERS, СОЦИАЛИЗАЦИЯ ДЕТЕЙ, MULTI-AGE GROUPS, ЭКСПЕРИМЕНТАЛЬНАЯ РАБОТА, ИНТЕЛЛЕКТУАЛЬНЫЕ НАРУШЕНИЯ, ОЛИГОФРЕНОПЕДАГОГИКА, EXTRACURRICULAR ACTIVITY, МОДЕЛИ ВНЕУРОЧНОЙ ДЕЯТЕЛЬНОСТИ, ДЕФЕКТОЛОГИЯ. СПЕЦИАЛЬНЫЕ ШКОЛЫ, CHILDREN WITH INTELLECTUAL DISORDERS, РАЗНОВОЗРАСТНЫЕ ГРУППЫ, ВНЕУРОЧНАЯ ДЕЯТЕЛЬНОСТЬ, MODELS OF EXTRACURRICULAR ACTIVITY, СПЕЦИАЛЬНЫЕ ШКОЛЫ, НАРУШЕНИЯ ИНТЕЛЛЕКТА, SOCIALIZATION OF CHILDREN
Abstract

В статье рассматривается модель внеурочной деятельности для детей с выраженными интеллектуальными нарушениями на основе концепта "жизненный мир". The purpose of the study is to develop, theoretically substantiate and experimentally test a model of extracurricular activities for children with severe intellectual disabilities based on the concept of "life-world". The article summarizes and analyzes the data from observations and experimental work, based on which the author concludes that the organization of extracurricular activities in multiage groups intensifies their socialization mechanisms and enhances progress in their social and personal development and can become one of the conditions for the functioning of the model.

Published
2022

9. Methods' development for analysis of agricultural data in the conditions of AIC digitalization.

Author

Parfenova, V. E., Amagaeva, Yu. G., Galanina, O. V., and Olenicheva, S. A.

Subjects
*AGRICULTURAL development, *ARTIFICIAL neural networks, *DATA analysis, *AGRICULTURAL forecasts, *STATISTICS, *DECISION making, *BIG data
Abstract

The article is devoted to modern methods of data analysis in the context of the agricultural industry's transition to the digital economy. It provides an overview of the changes that have occurred in the agricultural data flow. It was shown that a significant part of the collected data required for making management decisions is not a random sample to which classical methods of analysis can be applied. In this regard, it is concluded that the traditional statistical direction of data analysis is limited; it is also concluded that there is a need to develop an alternative direction including intelligent methods of analysis and methods for analyzing big data. The practical implementation of the traditional regression method application and the intelligent method of artificial neural networks for solving a specific problem of forecasting domestic prices for barley in one of the Russian regions is presented. The forecast on neural networks showed the best result [ABSTRACT FROM AUTHOR]

Published
2022
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23. Comparison of the presence of chemical and biomarkers in the surface microlayer in water areas of health resort zones of Lake Baikal and in atmospheric aerosol of this region

Author

Sergeev, A. N., Safatov, A. S., Agafonov, A. P., Andreeva, I. S., Arshinov, M. Yu., Belan, B. D., Buryak, G. A., Generalov, V. M., Zakharova, Yu. R., Lapteva, N. A., Ol’kin, S. E., Panchenko, M. V., Parfenova, V. V., Reznikova, I. K., Simonenkov, D. V., Teplyakova, T. V., and Ternovoi, V. A.

Published
2009
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45. Sterilization of Mail by Means of an Electron Beam Accelerator

Author

Auslender, V. L., Vedernikov, V. A., Grachev, M. A., Drukker, V. V., Korchagin, A. I., Kruglyakov, E. P., Kudryavtsev, A. M., Kulikova, N. S., Netsvetaeva, O. G., Pavlova, O. N., Parfenova, V. V., Semenova, E. A., Serbin, V. I., Terkina, I. A., Tkov, A. V., and Chebykin, E. P.

Published
2002
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