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Топливо и ÑеоÑÐ¸Ñ Ð³Ð¾ÑениÑ
2014ÐзлагаÑÑÑÑ ÑеоÑеÑиÑеÑкие и пÑакÑиÑеÑкие вопÑоÑÑ, ÑвÑзаннÑе Ñо Ñжиганием оÑганиÑеÑкого Ñоплива. ÐÑедÑÑÐ°Ð²Ð»ÐµÐ½Ñ ÑÑндаменÑалÑнÑе оÑÐ½Ð¾Ð²Ñ ÑеоÑии гоÑениÑ. ÐаеÑÑÑ Ð¾Ð¿Ð¸Ñание Ñизико-Ñ Ð¸Ð¼Ð¸ÑеÑÐºÐ¸Ñ ÑвойÑÑв
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ÐолноводнÑй ÑазовÑаÑаÑелÑ
2021ÐÐ°Ð½Ð½Ð°Ñ ÑабоÑа поÑвÑÑена иÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑи ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ Ð²Ð¾Ð»Ð½Ð¾Ð²Ð¾Ð´Ð½Ð¾Ð³Ð¾ оÑвеÑвлÑÑÑего ÑазовÑаÑаÑÐµÐ»Ñ Ð´Ð»Ñ ÑазиÑованной анÑенной ÑеÑÑÑки Ñ Ð¿Ð¾ÑледоваÑелÑнÑм пиÑанием.
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2020
Рданной ÑабоÑе ÑаÑÑмоÑÑена возможноÑÑÑ Ð¿Ð¾Ð»ÑÑение Ð¿Ð¾Ð»Ñ Ñ Ð¸Ð½Ð´ÑкÑией 100 Тл в неÑазÑÑÑаÑÑемÑÑ ÐºÐ²Ð°Ð·Ð¸Ð±ÐµÑÑиловом магниÑе Ñ Ð¸Ð½ÐµÑÑионнÑм ÑдеÑжанием его ÑоÑÑевой ÑаÑÑи. ÐÑа ÑаÑÑÑ Ð¼Ð°Ð³Ð½Ð¸Ñа ÑÑавновеÑена
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Рданной ÑабоÑе ÑаÑÑмоÑÑена возможноÑÑÑ Ð¿Ð¾Ð»ÑÑение Ð¿Ð¾Ð»Ñ Ñ Ð¸Ð½Ð´ÑкÑией 100 Тл в неÑазÑÑÑаÑÑемÑÑ ÐºÐ²Ð°Ð·Ð¸Ð±ÐµÑÑиловом магниÑе Ñ Ð¸Ð½ÐµÑÑионнÑм ÑдеÑжанием его ÑоÑÑевой ÑаÑÑи. ÐÑа ÑаÑÑÑ Ð¼Ð°Ð³Ð½Ð¸Ñа ÑÑавновеÑена
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2022
The theme of the final qualifying work: Analysis of the impact of covid-19 on china's stock market volatility. The relevance of this work lies in the fact that the Chinese stock market will experience significant volatility during the period of the new corona virus.
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The theme of the final qualifying work: Analysis of the impact of covid-19 on china's stock market volatility. The relevance of this work lies in the fact that the Chinese stock market will experience significant volatility during the period of the new corona virus.
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2021
ÐÐ°Ð½Ð½Ð°Ñ ÑабоÑа поÑвÑÑена ÑаÑÑмоÑÑÐµÐ½Ð¸Ñ Ñакого вида пÑоблем Ð¼ÐµÑ Ð°Ð½Ð¸ÑеÑкого напÑÑжениÑ, как Ñепловое напÑÑжение, и ÑлÑÑÑÐµÐ½Ð¸Ñ Ð¿Ð°ÑамеÑÑов ÑеплонапÑÑженноÑÑи ÑоÑÑиÑованного дизелÑного двигаÑелÑ.
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ÐÐ°Ð½Ð½Ð°Ñ ÑабоÑа поÑвÑÑена ÑаÑÑмоÑÑÐµÐ½Ð¸Ñ Ñакого вида пÑоблем Ð¼ÐµÑ Ð°Ð½Ð¸ÑеÑкого напÑÑжениÑ, как Ñепловое напÑÑжение, и ÑлÑÑÑÐµÐ½Ð¸Ñ Ð¿Ð°ÑамеÑÑов ÑеплонапÑÑженноÑÑи ÑоÑÑиÑованного дизелÑного двигаÑелÑ.
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2023
This paper explores the application of machine learning methods for time series forecasting. The options for using ARIMA models, neural networks and the gradient boosting algorithm are considered. As training samples, stock prices were used, as well as data obtained as a result of applying technical indicators.
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This paper explores the application of machine learning methods for time series forecasting. The options for using ARIMA models, neural networks and the gradient boosting algorithm are considered. As training samples, stock prices were used, as well as data obtained as a result of applying technical indicators.
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2016
Designed the satellite monitoring web-system of mobile objects. The system designed for monitoring objects in real time and display routes objects for a specified period with digital maps. Designed an easily modifiable and scalable client-server web-application, in accordance with the architectural pattern MVC. Conducted testing of the system.
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Designed the satellite monitoring web-system of mobile objects. The system designed for monitoring objects in real time and display routes objects for a specified period with digital maps. Designed an easily modifiable and scalable client-server web-application, in accordance with the architectural pattern MVC. Conducted testing of the system.
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2023
The topic of the final qualification work: "Analysis of the stress-strain state of the implant prosthesis using finite element method". The purpose of this work is to study the stress-strain state of an implant prosthesis under various loading configurations, rotation of the implant, and the distance between the extreme part of the prosthesis and the ...
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The topic of the final qualification work: "Analysis of the stress-strain state of the implant prosthesis using finite element method". The purpose of this work is to study the stress-strain state of an implant prosthesis under various loading configurations, rotation of the implant, and the distance between the extreme part of the prosthesis and the ...
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2021
ÐÑÑ Ð¾Ð´Ð½Ñе даннÑе по ÑабоÑе бÑли взÑÑÑ Ð¸Ð· Ð±Ð°Ð·Ñ Ð´Ð°Ð½Ð½ÑÑ ISOLDE Ñ Ð¾Ñенками ÑиÑла пÑоизводимÑÑ Ð² миÑени ÑдеÑ. Рданной ÑабоÑе пÑоизводилÑÑ Ð°Ð½Ð°Ð»Ð¸Ð· вÑÑ Ð¾Ð´Ð¾Ð² изоÑопов Ð¿Ð¾Ð»Ð¾Ð½Ð¸Ñ Ð¸ изомеÑов, полÑÑеннÑÑ Ð² ÑолÑÑ ...
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ÐÑÑ Ð¾Ð´Ð½Ñе даннÑе по ÑабоÑе бÑли взÑÑÑ Ð¸Ð· Ð±Ð°Ð·Ñ Ð´Ð°Ð½Ð½ÑÑ ISOLDE Ñ Ð¾Ñенками ÑиÑла пÑоизводимÑÑ Ð² миÑени ÑдеÑ. Рданной ÑабоÑе пÑоизводилÑÑ Ð°Ð½Ð°Ð»Ð¸Ð· вÑÑ Ð¾Ð´Ð¾Ð² изоÑопов Ð¿Ð¾Ð»Ð¾Ð½Ð¸Ñ Ð¸ изомеÑов, полÑÑеннÑÑ Ð² ÑолÑÑ ...
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2019
Рданной ÑабоÑе ÑаÑÑмаÑÑиваеÑÑÑ Ð¿ÑакÑиÑеÑÐºÐ°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ Ð¸ иÑпÑÑÐ°Ð½Ð¸Ñ ÐºÐ²Ð°Ð·Ð¸Ð±ÐµÑÑилового магниÑа Ñ ÐºÐ¾Ð¼Ð¿Ð°Ñндом повÑÑенной пÑоÑноÑÑи. СÑавнение полÑÑеннÑÑ Ð¿ÑакÑиÑеÑÐºÐ¸Ñ Ð´Ð°Ð½Ð½ÑÑ Ñ ÑеоÑеÑиÑеÑки
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Рданной ÑабоÑе ÑаÑÑмаÑÑиваеÑÑÑ Ð¿ÑакÑиÑеÑÐºÐ°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ Ð¸ иÑпÑÑÐ°Ð½Ð¸Ñ ÐºÐ²Ð°Ð·Ð¸Ð±ÐµÑÑилового магниÑа Ñ ÐºÐ¾Ð¼Ð¿Ð°Ñндом повÑÑенной пÑоÑноÑÑи. СÑавнение полÑÑеннÑÑ Ð¿ÑакÑиÑеÑÐºÐ¸Ñ Ð´Ð°Ð½Ð½ÑÑ Ñ ÑеоÑеÑиÑеÑки
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