Improvement of 'winner takes all' neural network training for the purpose of diesel engine fault clustering

© 2016 IEEE.To create a diagnostic system for diesel engines, it is necessary to analyze a huge amount of data obtained from the automated test systems for diesel engines. Therefore, it is worth to implement the analysis with the help of an artificial neural network. The application of the artificial neural network for diesel engine fault clustering allows reducing the amount of stored data by creation of a knowledge database for the weighting factors. Self-training makes it possible to revise this database, improving the accuracy of clustering, and to modify network structure, in case the new types of faults will appear. The modified neural network training algorithm involves the usage of input vector data originally found within each cluster group as the initial weighting factors. This algorithm allows decreasing the load on the computing devices by reducing the number of training cycles in comparison with other existing algorithms. The efficiency of the method can be improved with a larger number of samples and dimensions of input and output parameters

International Cooperation Relations and Supply Chain of the Republic of Korea in the Conditions of the Global Economic Crisis

Abstract- This prime aim of this study is to investigate the relationship between imports, exports, supply chain management and international relations along with mediation effect of import export policies. The Republic of Korea is one of the states whose participation in the system of international economic relations remains noticeable, despite the unfavorable background of external factors. The article sets the task of determining the sustainability of international cooperation relations of the Republic of Korea in the conditions of the global economic crisis

ИСЛАМСКАЯ ГЕРМЕНЕВТИКА И ЭКЗЕГЕТИКА КАК КОНСТИТУИРУЮЩАЯ ОСНОВА ИНСТИТУТА УЛЕМОВ

The article is devoted to the problem of criteria of sociological assessment of the state of the institute of ulama. In solving this problem, the author suggests starting from that fundamental understanding of the nature of the phenomenon of social institution, which is developed within the framework of structural functionalism. In accordance with this, the phenomenon of the institution of the Ulema derives from the Islamic ideas of hermeneutics / exegesis, which constitute and legitimize this institution. Thus, the idea of the sacral component of Islamic hermeneutics / exegetics predetermines for the ulama the basic institutional principle of continuity with the tradition of the Prophet. On this basis, the author formulates some key sociologically measurable criteria for the state of the institution of the Ulema in Russia.Статья посвящена проблеме критериев социологической оценки состояния института улемов. В решении этой проблемы автор, руководствуясь структурно-функционалистским пониманием природы социального института, предлагает видеть в институте улемов не только организационные структуры, но и конституирующие его социокультурные основания, в частности, исламские представления о герменевтике/экзегетике. По мнению автора, представление о сакральной составляющей исламской герменевтики/экзегетики предопределяет необходимость преемственной связи улемов с традицией Пророка как базовый для них институциональный принцип. На этой основе автор формулирует некоторые ключевые социологически измеримые критерии состояния института улемов

The Ancient Persian Form of the First Mongol Dynasties’ Legitimation

Research objectives: To examine the ancient Persian tradition of legitimation of rule by the first Mongol dynasties. Materials: Medieval primary source texts of Abu ʾl-Qasim Ferdowsi, Nizam al-Mulk, Abu Hamid al-Ghazali, Shihab al-Din Suhrawardi, Rashid al-Din, and Abu Bakr Qalandar. Results and novelty of the research: Despite the fact that before the adoption of Islam, the Mongol dynasties used their own forms of legitimization of their rule, based on gaining the right to power and special grace from the heavenly god, Tengri, the Ilkhanids (Hulaguids), ruling in the Near and Middle East (from 1256 to 1353), and the Jochid rulers of various states in the Eastern Europe, Central Asia and Western Siberia (from the 13th to 17th century), who ruled in the Desht-i-Qipchaq, actively used elements of the ancient Persian concept of power, based on special divine light bestowed upon the ruler – Farr. This was accompanied by images of just rulers and the use of traditional titulature of Iranian rulers – the “king” (Shah) and the “king of kings” (Shah-i Shahan). Testimonies to this practice can be found both in the works of philosophers and historians of the period of these Mongol dynasties (for example, in the “Qalandar-name” of Abu Bakr Qalandar) and in the illustrations of the “Shahnameh” from the 14th century that portrayed the Mongolian khans in the image of ancient Persian heroes. This means that, on the one hand, the first Mongol dynasties felt some lack of legitimacy in the conquered territories, on the other hand that there was a desire of the autochthonous elites to link the new rulers to the political culture that had existed before their arrival. As concerns the Jochids, what can be asserted is the spread of an Iranian type of political culture after the migration of urban populations from the area of Persia into the Desht-i-Qipchaq

Modelling the Sustainability of International Economic Relations and Supply Chain of the World States

Abstract- Studying and monitoring the international cooperation zone allows us to take a fresh look at the integration processes in the system of world economic relations and answer the question about the nature and scale of international economic integration and globalization. This paper investigated the relationship between economy, supply chain management, international trade and international cooperation zone with mediation effect of international relations. The empirical basis of this study was the data of the state statistical service of the two largest economies in the Asia-Pacific region — the Republic of Korea and Japan. As part of the study, the task was set to conduct a comparative analysis of the system of international economic relations of the Republic of Korea and Japan

Russian-Chinese trade relations after the signing of the St. Petersburg’s Treaty in 1881

The article is devoted to the characteristics of Russian-Chinese trade relations after the signing of the St. Petersburg Treaty in 1881. The General content and nature of trade contacts between the two countries, the main territorial directions and volumes of Russian-Chinese trade are Considered. The reasons that did not allow Russia to firmly establish itself in the Chinese market in the late XIX — early XX centuries are revealed.Статья посвящена характеристике российско-китайских торговых отношений после подписания Петербургского договора в 1881 г. Рассматриваются общее содержание и характер торговых контактов двух стран, основные территориальные направления и объемы российско-китайской торговли. Раскрываются причины, не позволившие России прочно закрепиться на китайском рынке в конце XIX — начале XX в

Анализ профиля экспрессии длинных некодирующих РНК у больных с идиопатическим и COVID-19-индуцированным легочным фиброзом

Introduction. Idiopathic pulmonary fibrosis (IPF) comprises an interstitial lung disease with unclear pathogenesis, rapid progression, and no effective treatment. Pulmonary fibrosis is reported to be one of the most severe complications induced by a new coronavirus infection COVID-19. The mechanisms triggering pulmonary fibrosis and leading to its rapid progression remain substantially unclear. Evidence suggests that immune and genetic factors contribute to the development of this disease. Among the latter, the role of long non-coding RNAs (dnRNAs) has been actively studied to date. Materials and methods. Considering the role of TP53TG1, LINC00342, H19, MALAT1, DNM3OS, and MEG3 dnRNAs as regulators of signaling pathways associated with fibroblast activation and epithelial-mesenchymal transition, the authors analyzed the expression level of selected dnRNAs in lung tissue and blood mononuclear cells of patients with IPF (N = 12), post-COVID-19 pulmonary fibrosis (N = 14), and in control group (N = 27). Results and discussion. Blood mononuclear cells in patients with IPF and post-COVID-19 PF revealed similar patterns of TP53TG1 and MALAT1 dnRNA expression. The level of relative expression of MALAT1 was significantly higher in patients with IPF (Fold Change=3.207, P = 0.0005) and with post-COVID-19 PF (Fold Change=9.854, P = 0.0003), while the relative expression level of TP53TG1 reduced in patients with IPF (Fold Change=0.4308, P = 0.0313) and with post-COVID-19 PF (Fold Change=0.1888, P = 0.0003 in blood mononuclear cells, Fold Change=0.1791, P = 0.0237 in lung tissue). Increased expression of DNM3OS in blood mononuclear cells (Fold Change=12.899, P = 0.0016) and lung tissue (Fold Change=9.527, P = 0.0001), LINC00342 (Fold Change=2.221, P = 0.0309) in blood mononuclear cells was revealed only in patients with IPF. Conclusion. Evaluation of the dnRNA expression profile of TP53TG1, LINC00342, MALAT1 and DNM3OS in blood mononuclei can be used as an informative and non-invasive biomarker in IPF and post COVID-19 PF.Введение. Идиопатический легочный фиброз (ИЛФ) является интерстициальным заболеванием легких с неясным патогенезом, быстропрогрессирующим и не имеющим эффективного лечения. Одним из самых грозных осложнений после перенесенной новой коронавирусной инфекции COVID-19 является легочный фиброз. Механизмы, которые запускают легочный фиброз и приводят к его быстрому прогрессированию, остаются в значительной степени неопределенными. Имеются данные о вкладе иммунных и генетических факторов в развитие данного заболевания. Среди последних на сегодня активно изучается роль длинных некодирующих РНК (днРНК). Материалы и методы. Учитывая роль днРНК TP53TG1, LINC00342, H19, MALAT1, DNM3OS и MEG3 как регуляторов сигнальных путей, связанных с активацией фибробластов и эпителиально-мезенхимального перехода, мы проанализировали уровень экспрессии выбранных днРНК в легочной ткани и мононуклеарных клетках крови больных с ИЛФ (N = 12), пост-COVID-19 легочным фиброзом (N = 14) и контрольной группе (N = 27). Результаты и обсуждение. Определены сходные паттерны экспрессии днРНК TP53TG1 и MALAT1 в мононуклеарах крови у больных с ИЛФ и пост-COVID-19 ЛФ. Уровень относительной экспрессии MALAT1 был значимо выше у больных: при ИЛФ (Fold Change = 3,207, P = 0,0005) и при пост-COVID-19 ЛФ (Fold Change = 9,854, P = 0,0003). В то время как относительный уровень экспрессии TP53TG1 был снижен: при ИЛФ (Fold Change = 0,4308, P = 0,0313) и при пост-COVID-19 ЛФ (Fold Change = 0,1888, P = 0,0003 в мононуклеарах крови, Fold Change = 0,1791, P = 0,0237 в легочной ткани). Повышение уровня экспрессии DNM3OS в мононуклеарах крови (Fold Change = 12,899, P = 0,0016) и легочной ткани (Fold Change = 9,527, P = 0,0001), LINC00342 (Fold Change = 2,221, P = 0,0309) в мононуклеарах крови было установлено только у больных ИЛФ. Заключение. Таким образом, оценка профиля экспрессии днРНК TP53TG1, LINC00342, MALAT1 и DNM3OS в мононуклеарах крови может быть использована в качестве информативного и неинвазивного биомаркера при ИЛФ и пост COVID-19 ЛФ

Improvement of 'winner takes all' neural network training for the purpose of diesel engine fault clustering

© 2016 IEEE.To create a diagnostic system for diesel engines, it is necessary to analyze a huge amount of data obtained from the automated test systems for diesel engines. Therefore, it is worth to implement the analysis with the help of an artificial neural network. The application of the artificial neural network for diesel engine fault clustering allows reducing the amount of stored data by creation of a knowledge database for the weighting factors. Self-training makes it possible to revise this database, improving the accuracy of clustering, and to modify network structure, in case the new types of faults will appear. The modified neural network training algorithm involves the usage of input vector data originally found within each cluster group as the initial weighting factors. This algorithm allows decreasing the load on the computing devices by reducing the number of training cycles in comparison with other existing algorithms. The efficiency of the method can be improved with a larger number of samples and dimensions of input and output parameters

Improvement of 'winner takes all' neural network training for the purpose of diesel engine fault clustering

© 2016 IEEE.To create a diagnostic system for diesel engines, it is necessary to analyze a huge amount of data obtained from the automated test systems for diesel engines. Therefore, it is worth to implement the analysis with the help of an artificial neural network. The application of the artificial neural network for diesel engine fault clustering allows reducing the amount of stored data by creation of a knowledge database for the weighting factors. Self-training makes it possible to revise this database, improving the accuracy of clustering, and to modify network structure, in case the new types of faults will appear. The modified neural network training algorithm involves the usage of input vector data originally found within each cluster group as the initial weighting factors. This algorithm allows decreasing the load on the computing devices by reducing the number of training cycles in comparison with other existing algorithms. The efficiency of the method can be improved with a larger number of samples and dimensions of input and output parameters

Improvement of 'winner takes all' neural network training for the purpose of diesel engine fault clustering

© 2016 IEEE.To create a diagnostic system for diesel engines, it is necessary to analyze a huge amount of data obtained from the automated test systems for diesel engines. Therefore, it is worth to implement the analysis with the help of an artificial neural network. The application of the artificial neural network for diesel engine fault clustering allows reducing the amount of stored data by creation of a knowledge database for the weighting factors. Self-training makes it possible to revise this database, improving the accuracy of clustering, and to modify network structure, in case the new types of faults will appear. The modified neural network training algorithm involves the usage of input vector data originally found within each cluster group as the initial weighting factors. This algorithm allows decreasing the load on the computing devices by reducing the number of training cycles in comparison with other existing algorithms. The efficiency of the method can be improved with a larger number of samples and dimensions of input and output parameters