Статьи

Inflation is one of the most important macroeconomic indicators that have a great impact on the population of any country and region. Inflation is influenced by a range of factors, including inflation expectations. Many central banks take this factor into consideration while implementing monetary policy within the inflation targeting regime. Nowadays, a lot of people are active users of the Internet, especially social networks. It is hypothesised that people search, read, and discuss mainly only those issues that are of particular interest to them. It is logical to assume that the dynamics of prices may also be in the focus of users’ discussions. So, such discussions could be regarded as an alternative source of more rapid information about inflation expectations. This study is based on unstructured data from VKontakte social network used to analyse upward and downward inflationary trends (on the example of the Omsk region). The sample of more than 8.5 million posts was collected between January 2010 and May 2022. The authors used BERT neural networks to solve the problem. These models demonstrated better results than the benchmarks (e. g., logistic regression, decision tree classifier, etc.). It makes possible to define pro-inflationary and disinflationary types of keywords in different contexts and get their visualisation with SHAP method. This analysis provides additional operational information about inflationary processes at the regional level The proposed approach can be scaled for other regions. At the same time, the limitation of the work is the time and power costs for the initial training of similar models for all regions of Russia.

Despite the growing popularity of online shopping, there is a lack of research on regional differences in consumer behaviour and preferences, particularly among women. The study aims to investigate the regional differences in women’s online shopping behaviour in Kazakhstan by conducting an in-depth analysis of the factors influencing female consumers. Using data from a survey of 400 women across different regions of Kazakhstan, logistic regression analysis was utilised to examine the relationship between online shopping frequency and several independent variables. The analysis found that the pandemic significantly affected online shopping behaviour in Kazakhstan, leading to decreased shopping frequency across all regions. Additionally, we found that women living in urban areas were significantly more likely to shop online frequently than those in rural areas, with an odds ratio of 0.504 (p = 0.014). The research also revealed notable differences in Internet penetration rates, with Karaganda, Pavlodar regions and Astana city having the highest rates among women (93.1 %, 93.0 %, and 94.5 %, respectively), while Atyrau and Kyzylorda regions had the lowest (80.7 % and 80.0 %). Therefore, it is recommended that policymakers should focus on expanding Internet infrastructure in remote regions by developing customised online marketplaces that meet the needs of urban areas like Almaty city. The findings of this study underscore the importance of considering regional differences in understanding the factors that drive online shopping behaviour in Kazakhstan. By investing in initiatives that promote e-commerce adoption and cater to consumers’ unique needs and preferences in different regions, policymakers can help foster a more inclusive and dynamic e-commerce ecosystem in Kazakhstan.

Export development is a priority for the Russian economy, as it plays a crucial role in ensuring sustainable economic growth. In this context, understanding the determinants of regional export development is essential. In their export activities, Russian companies face a range of limiting factors, many of which have been thoroughly examined, with corresponding mitigation strategies incorporated into export plans. However, the role of the global climate agenda and the energy transition in shaping export development remains largely unexplored for Russian regions. The shift of focus to fulfilling environmental goals creates a new type of economic risk for exporters - transitional climate risks, which intensified after February 2022. This study investigates the comprehensive impact of the global energy transition on export flows in Russian regions and identifies region-specific factors that influence how the energy shift affects export levels. The hypothesis is that the global energy transition creates both risks and opportunities for Russian regions, with varying effects depending on the specific components of the energy shift and the socio-economic and environmental characteristics of each region. Using the gravity equation with the Poisson Pseudo-Maximum Likelihood (PPML) technique, the study finds that the impact of the global energy transition on Russian regional exports is multidirectional. First, environmental regulations in partner countries reduce exports from many Russian regions by 0.3 %, though regions with favorable socio-economic conditions for innovation and active regional environmental policies see an increase in exports-by 0.3 % and 0.7 %, respectively. Second, the production of alternative energy in partner countries decreases Russian exports by 0.2 %. Finally, exports from mineral-abundant Russian regions benefit from the global energy transition. These findings contribute to the literature on Russian export promotion and offer valuable policy insights for addressing the challenges and opportunities posed by the global energy transition.

R&D&I является важным направлением инновационного роста отечественных субъектов экономики. Замкнутость на внутренней системе управления инновациями может не привести к результатам, запланированным в рамках ключевых стратегических документов государственного уровня. Расширение ее границ и вовлечение внешних участников в решение проблем инновационной деятельности станет опорой для создания мощной интегрированной структуры, обеспечивающей инновационный лифт и научно-технологическое обновление предприятий. Научно обоснованный подход к формированию сбалансированной системы коллаборативного управления инновациям - залог устойчивого функционирования инновационно-активных предприятий. Цель настоящего исследования состоит в формировании научно-практических рекомендаций по созданию R&D&I системы поддержки инновационно-активных предприятий для обеспечения эффективности их функционирования в условиях глобальных экономических вызовов. В процессе достижения поставленной цели использовались следующие методы научного познания: структурный анализ и синтез, системный анализ, обобщение и описание, моделирование. По результатам проведенного исследования разработана структурно-функциональная модель R&D&I системы поддержки инновационно-активных предприятий, в отличие от существующих, создающая платформу для сбалансированного инновационного развития и роста конкурентоспособности посредством когерентной организации интегрированных инновационных цепочек и результативного управления инновациями в единой синергетической среде. Научные выводы и предложения имеют высокую значимость для развития теоретико-методологических положений теории инноватики, управления инновационной деятельностью и решения проблем инновационно-активных предприятий РФ. Исследование опирается на современные труды ведущих отечественных и зарубежных экономистов. R&D&I система поддержки инновационно-активных предприятий создает основу для их устойчивого функционирования в условиях санкционных ограничений и экономических потрясений. Автором определен вектор развития отечественных предприятий для обеспечения эффективной реализации миссии и достижения стратегических целей.

In the context of sanctions pressure on Russia the issues of import substitution in the field of digital technologies are getting even more urgent. Highly qualified specialists with respective competencies are in short demand in the country. The government take some measures to that end, but they can solve all the problems. Engagement of required specialists from abroad - those working on a remote basis - could improve the situation. However, there are certain unresolved issues in the labor legislation of the Russian Federation and some other countries, including members of international economic integration organizations such as the Eurasian Economic Union that hinder this process. Identification of hindrances and restrictions preventing free movement of human resources in EAEU member states and efficient use of remote work in the field of IT is what this paper is dedicated to. To that end the actual situation unfolding in the labor market of the field of IT on the territory of EAEU member states has been analyzed, and so has been labor and other legislation of member states regulating the labor of remote workers and the academic literature and papers published in the periodicals. The system analysis and comprehensive review of sources and comparative legal studies have become the main methods of research. As a result, legal and organizational restrictions preventing efficient application of remote work of IT specialists typical for some or even all EAEU member states have been identified. Also, there have been suggested some ways to overcome the identified restrictions that can be implemented by adopting new legal provisions or by amending the existing ones.

Usage of neural network technologies, namely various learning and self-learning programs that gather, analyze a great volume of information on an employee and manufacturing process, have an access to a worldwide network, are integrated with video-, audio-, and other fixation systems by employers generates new economic and technological and social and legal risks. Inconsistence of labor and information legislation in the field of protection of personal data of employees determines novel conditions for potential violations of rights of employees and growth of the quantity of labor conflicts. Separate problems are caused by gathering and treating personal data with artificial intelligence and far from all of them are technical in nature, even if determined by specific features of neural network technologies. This article discusses main risks for personal data of employees under conditions of usage of specific neural network technologies for staff recruitment and supervision over employees, including monitoring in order to ensure the safety of work performance. With this object in view, labor codes, laws, and bylaws on labor protection, laws on protection of personal data of the countries of the EAEU. Systemic and complex analysis of sources of legal regulation and comparative legal studies were primary investigative techniques. In consequence of this, legal and social risks of usage of neural network technologies for staff recruitment and organization of supervision over compliance with labor discipline by employees, especially in regard to the provision of safe labor conditions, as well as discrepancies between labor and information legislations were revealed. Several methods for overcoming revealed discrepancies, which allow to ensure a proper protection of employees’ rights in the field of protection of personal data before long, were proposed.

Introduction. Coronavirus infection has received much attention over the past three years. In addition to its enormous social significance, the COVID-19 pandemic has highlighted a number of conceptually new clinical aspects of human immunopathology. Due to a higher susceptibility to infectious complications, this problem is particularly relevant for patients suffering from autoimmune inflammatory rheumatic diseases (AIRDs). In the presence of AIRDs, the possibility of developing a wide range of delayed COVID-19 effects becomes most likely. According to the COVID-19 Global Rheumatology Alliance registries, of 600 cases of new coronavirus infection in patients with AIRDs, about 46% required hospitalization.

From the dawn of development of modern medical science, the aspect of physiological impact and co-relation of climate and cosmological events have been grossly neglected. At, this point of time in human history when stand at the verge of a vehemently aggressive climate change and unprecedented and sudden cosmological events, it’s indeed important to retrospect into the same from the perspective of physiology and pathology. The current article puts forward many aspects of conditioning of human body in relation to surrounding climate and cosmological events. Few attempts by individuals and societies of doctors to study the same have been made from classical to modern times. This review paper discusses and puts forward the co-relation of climate and cosmological events with human physiology in a summary with regards to already conducted scientific studies. The research objective: is to draw attention of scientific community and medical fraternity towards the aspect of co-relation of climate and cosmological events with human physiology.

Materials and methods: a wide scale literature review from classical, neo-classical and modern medical findings. Results and discussion: the article gives a comprehensive overview of how climatic conditions and cosmological events results into adaptation of medical parameters in human beings.

Conclusion: Human beings are just another species from the animal kingdom living on this planet Earth. We are not surrounded by nature, but we are nature. It is important for us as a civilization to investigate and learn the biological and material co-existential relationship with nature.

The article is devoted to Responsibilities of a transport university for the development and dissemination of advanced competencies in the fi elds of transport and transport education in the context of the expanding BRICS space

В рецензируемой книге “Я знаю , что так писать” собраны некоторые дневники из архива “Прожито”, а также анализ и полезные описания каждого дневника. Основу этого тома составляют дневники Александра Бардовского, Анисима Никулина, Софьи Гутшебаш, Нины Обуховой-Духовской, Берты Злотниковой, Александра Гришкевича и Владимира Томилина. Эти дневники содержат ценную информацию, аналогичную тем, которые были получены в ходе этнографических исследований. Они могут помочь в изучении исторически обоснованных социальных наук и способствовать интерпретации, репрезентации и сопереживанию. Однако важно отметить, что рассказы из первых рук имеют ограничения и не должны заменять строгие методологии и теоретические основы. Автор также указывает на необходимость включения блокады в более широкий контекст войны и выживания. Ленинградская блокада - всего лишь один случай из многих других во всем мире. Сравнивая эти истории с опытом разных регионов и войн, исследователи могут сделать вывод.

The genus Raphignathus Dugès (Acari: Raphignathidae) of Asian Russia is reviewed. Raphignathus sakhalinensis sp. n. is described based on female and male specimens collected from spruce bark on Sakhalin Island; R. longipes sp. n. is described based on female and male specimens collected from various regions of Russia (Bashkortostan, Tyumenskaya Oblast, the Respublika Altay and Kamchatka). Raphignathus fani Doğan and Ayyildiz and R. ozkani Doğan are recorded from Russia for the first time and redescribed. Raphignathus gracilis (Rack) is recorded from Asian Russia for the first time. A key to the species of Raphignathus of Russia is also provided.

Одним из способов повышения качества разговорных агентов является персонификация.

Персонификация улучшает качество взаимодействия пользователя с разговорным агентом и повышает удовлетворенность пользователей за счет повышения консистентности и специфичности ответов.

Диалог с агентом становится более последовательным, минимизируется противоречивость ответов, которые оказываются более конкретными и интересными.

Для обучения и тестирования персонифицированных разговорных агентов требуются специфичные наборы данных, содержащие факты о персоне и тексты диалогов персон, в
репликах которых используются факты о персонах.

Существует несколько наборов на английском и китайском языках, содержащие в описании персоны в среднем пять фактов.

Диалоги в наборах данных составлены пользователями краудсорсинга, которые многократно имитировали различные персоны.

Метод:

В данной работе предложена методика сбора оригинального корпуса данных, содержащего расширенный набор фактов о персоне и естественные диалоги между персонами.

Новый корпус данных RuPersonaChat основан на трех различных сценариях записи: интервью, короткая беседа, длинная беседа.

Впервые собран корпус данных для персонификации разговорных агентов, включающий естественные диалоги и расширенное описание персоны.

Предложена дополнительная разметка набора данных, которая ставит в соответствие реплики персоны и факты о персоне, на основе которых она была сформулирована.

Основные результаты:

Разработана методика сбора оригинального корпуса тестовых данных, позволяющего осуществлять тестирование языковых моделей для решения большего количества задач в рамках разработки персонифицированного разговорного агента.

Собранный набор данных включает 139 диалогов и 2608 реплик.

Корпус использован для тестирования моделей генерации ответов и вопросов. Наилучшие результаты получены с использованием модели Gpt3-large (перплексия равна 15,7).

Обсуждение:

Собранный корпус данных RuPersonaChat может быть использован для тестирования п