Multicriteria problems of regulation when planning building processes

In the rpocess of project construction organization under conditions of limited resources the problem of investor arises, which in onecriteria setting is as follows N invsted projects reindexed i=1,2,...,n are considered, initial parameters of the problem are:- duraion of iproject construction, - expected profit per unit of time from i project after putting it info operation ,-the time fixed , after the expiry of it a fine is paid for each overdued unit of time units in number. The investor resources are limited, i.e., at every moment of time investor can provid delivering of necessary resources only for one project .Any admissible problem decision of investor represents one of n! permutation of of numbers 1,2,..,n. X={x}-is the set of all admissible solutions (SAS) of this problem . In works of different authors the quality of decision is evaluated either by objective function (OF) of the type MINSUM where,by the objective function of the of the type MINMA

Сравнительный анализ прогнозных моделей ARIMA и lSTM на примере акций российских компаний

The article aims to find the best time series predictive model, considering the minimization of errors and high accuracy of the prediction. The authors performed the comparative analysis of the most popular “traditional” econometric model ARIMA and the deep learning model LSTM (Long short-term memory) based on a recurrent neural network. The study provides a mathematical description of these predictive models. The authors developed algorithms for predicting time series based on the “Rolling forecasting origin” approach. These are Python-based algorithms using the Keras, Theano and Statsmodels libraries. Stock quotes of Russian companies Alrosa, Gazprom, KamAZ, NLMK, Kiwi, Rosneft, VTB and Yandex for the period from June 2, 2014 to November 11, 2019, broken down by week, served as input data. The research results confirm the superiority of the LSTM model, where the RMSE error is 65% less than with the ARIMA model. Therefore, an LSTM model-based algorithm is more preferable for the better quality of time series prediction.Цель статьи —  поиск лучшей модели для прогноза временных рядов с учетом минимизации ошибок и высокой точности прогноза. Использован метод сравнительного анализа наиболее популярной «традиционной» эконометрической модели ARIMA и модели глубокого обучения LSTM (Long short-term memory) на основе рекуррентной нейронной сети. Приведено математическое описание этих прогнозных моделей. Авторы разработали алгоритмы для прогноза временных рядов, основанные на подходе “Rolling forecasting origin” («прогнозирование происхождения»). Алгоритмы реализованы в среде программирования Python с подключенными библиотеками Keras, Theano и Statsmodels. В качестве входных наборов данных импортированы значения котировок акций российских компаний: Алроса, Газпром, КамАЗ, НЛМК, Киви, Роснефть, ВТБ и Яндекс за период с 02.06.2014 по 11.11.2019 г. с разбивкой по неделям. Результаты исследования подтверждают превосходство модели LSTM, при которой среднеквадратическая ошибка RMSE на 65% меньше, чем при использовании модели ARIMA. Сделан вывод, что для повышения качества прогноза временных рядов предпочтительно применять алгоритм на основе модели LSTM

Characteristics of the state of bone tissue in genetically modified mice with impaired enzymatic regulation of steroid hormone metabolism

The aim was to evaluate the structural and functional changes of bone tissue in mice with null expression of 11β-HSD2 or both 11β-HSD2 and Apolipoprotein

Arginase Inhibitor in the Pharmacological Correction of Endothelial Dysfunction

This paper is about a way of correction of endothelial dysfunction with the inhibitor of arginase: L-norvaline. There is an imbalance between vasoconstriction and vasodilatation factors of endothelium on the basis of endothelial dysfunction. Among vasodilatation agents, nitrogen oxide plays the basic role. Amino acid L-arginine serves as a source of molecules of nitrogen oxide in an organism. Because of the high activity of arginase enzyme which catalyzes the hydrolysis of L-arginine into ornithine and urea, the bioavailability of nitrogen oxide decreases. The inhibitors of arginase suppress the activity of the given enzyme, raising and production of nitrogen oxide, preventing the development of endothelial dysfunction